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id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Fabio Marcelo Breunig</h3></div><div class="js-work-strip profile--work_container" data-work-id="122115072"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/122115072/Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_November_1_2019_Rio_Grande_do_Sul_Brazil"><img alt="Research paper thumbnail of Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/122115072/Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_November_1_2019_Rio_Grande_do_Sul_Brazil">Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">&lt;strong&gt;Title&lt;/strong&gt;: Unmanned Aerial Vehicle (UAV) data acquired over a subtropica...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">&lt;strong&gt;Title&lt;/strong&gt;: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil &lt;strong&gt;Keywords&lt;/strong&gt; Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. &lt;strong&gt;Data description:&lt;/strong&gt; The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also &lt;em&gt;Drone&lt;/em&gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Please, see the PDF file. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): &lt;strong&gt;Parameters&lt;/strong&gt; Specification/value Date (YYYYMMDD): 20191101 Time of day (BRT = -3) 13:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 250 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High-speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3&quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 301 photos Across track coverage 80% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br" rel="nofollow">breunig@ufsm.br</a> An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Please, see the PDF file. Figure 2. The capture of an orthomosaic in the processing [...]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115072"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115072"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115072; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115072]").text(description); $(".js-view-count[data-work-id=122115072]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115072; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115072']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=122115072]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115072,"title":"Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil","translated_title":"","metadata":{"abstract":"\u0026lt;strong\u0026gt;Title\u0026lt;/strong\u0026gt;: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil \u0026lt;strong\u0026gt;Keywords\u0026lt;/strong\u0026gt; Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. \u0026lt;strong\u0026gt;Data description:\u0026lt;/strong\u0026gt; The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also \u0026lt;em\u0026gt;Drone\u0026lt;/em\u0026gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Please, see the PDF file. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): \u0026lt;strong\u0026gt;Parameters\u0026lt;/strong\u0026gt; Specification/value Date (YYYYMMDD): 20191101 Time of day (BRT = -3) 13:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 250 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High-speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3\u0026quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 301 photos Across track coverage 80% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, breunig@ufsm.br An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Please, see the PDF file. Figure 2. The capture of an orthomosaic in the processing [...]","publication_date":{"day":24,"month":2,"year":2021,"errors":{}}},"translated_abstract":"\u0026lt;strong\u0026gt;Title\u0026lt;/strong\u0026gt;: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil \u0026lt;strong\u0026gt;Keywords\u0026lt;/strong\u0026gt; Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. \u0026lt;strong\u0026gt;Data description:\u0026lt;/strong\u0026gt; The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also \u0026lt;em\u0026gt;Drone\u0026lt;/em\u0026gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Please, see the PDF file. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): \u0026lt;strong\u0026gt;Parameters\u0026lt;/strong\u0026gt; Specification/value Date (YYYYMMDD): 20191101 Time of day (BRT = -3) 13:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 250 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High-speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3\u0026quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 301 photos Across track coverage 80% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, breunig@ufsm.br An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Please, see the PDF file. Figure 2. The capture of an orthomosaic in the processing [...]","internal_url":"https://www.academia.edu/122115072/Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_November_1_2019_Rio_Grande_do_Sul_Brazil","translated_internal_url":"","created_at":"2024-07-16T22:58:48.377-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_November_1_2019_Rio_Grande_do_Sul_Brazil","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"\u0026lt;strong\u0026gt;Title\u0026lt;/strong\u0026gt;: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil \u0026lt;strong\u0026gt;Keywords\u0026lt;/strong\u0026gt; Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. \u0026lt;strong\u0026gt;Data description:\u0026lt;/strong\u0026gt; The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also \u0026lt;em\u0026gt;Drone\u0026lt;/em\u0026gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Please, see the PDF file. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): \u0026lt;strong\u0026gt;Parameters\u0026lt;/strong\u0026gt; Specification/value Date (YYYYMMDD): 20191101 Time of day (BRT = -3) 13:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 250 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High-speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3\u0026quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 301 photos Across track coverage 80% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, breunig@ufsm.br An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Please, see the PDF file. Figure 2. The capture of an orthomosaic in the processing [...]","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":1872418,"name":"Subtropics","url":"https://www.academia.edu/Documents/in/Subtropics"}],"urls":[{"id":43558376,"url":"https://zenodo.org/record/4558265"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115070"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/122115070/An%C3%A1lise_Do_Efeito_Topogr%C3%A1fico_Sobre_%C3%8Dndices_De_Vegeta%C3%A7%C3%A3o_Utilizando_Imagens_Rapideye_Na_Serra_Do_Mar_PR"><img alt="Research paper thumbnail of Análise Do Efeito Topográfico Sobre Índices De Vegetação Utilizando Imagens Rapideye Na Serra Do Mar – PR" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/122115070/An%C3%A1lise_Do_Efeito_Topogr%C3%A1fico_Sobre_%C3%8Dndices_De_Vegeta%C3%A7%C3%A3o_Utilizando_Imagens_Rapideye_Na_Serra_Do_Mar_PR">Análise Do Efeito Topográfico Sobre Índices De Vegetação Utilizando Imagens Rapideye Na Serra Do Mar – PR</a></div><div class="wp-workCard_item"><span>Ra'e Ga</span><span>, Apr 22, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115070"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115070"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115070; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115051"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122115051/Efeito_Topogr%C3%A1fico_sobre_a_Resposta_Espectral_de_Povoamentos_Florestais_de_Pinus_taeda_Linnaeus_no_Sul_do_Brasil"><img alt="Research paper thumbnail of Efeito Topográfico sobre a Resposta Espectral de Povoamentos Florestais de Pinus taeda Linnaeus no Sul do Brasil" class="work-thumbnail" src="https://attachments.academia-assets.com/116843807/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122115051/Efeito_Topogr%C3%A1fico_sobre_a_Resposta_Espectral_de_Povoamentos_Florestais_de_Pinus_taeda_Linnaeus_no_Sul_do_Brasil">Efeito Topográfico sobre a Resposta Espectral de Povoamentos Florestais de Pinus taeda Linnaeus no Sul do Brasil</a></div><div class="wp-workCard_item"><span>Revista Brasileira de Geografia Física</span><span>, Dec 31, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Este estudo tem como objetivo examinar a influência da variação sazonal da geometria da irradiânc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Este estudo tem como objetivo examinar a influência da variação sazonal da geometria da irradiância solar na resposta espectral de povoamentos florestais de Pinus taeda, localizados na Região Centro-Oeste do Estado de Santa Catarina, Brasil. A influência do efeito topográfico foi analisada por meio de medidas de refletância de superfície e índices de vegetação, obtidas a partir de três imagens MSI/Sentinel-2, adquiridas em baixo, médio e alto ângulo de elevação solar, no segundo semestre de 2018. A influência do efeito topográfico foi analisada a partir das oscilações de valor dos dados de sensoriamento remoto em comparação com as condições de iluminação, delineadas a partir das variações angulares de orientação de vertentes e declividade. Foram realizadas análises de correlação e regressão entre os dados espectrais e as condições de iluminação do terreno, definidas pelos valores do cosseno do ângulo de incidência. As medidas de refletância do dossel foram influenciadas diretamente pelo aspecto direcional da exposição a iluminação, e diminuições em seus valores foram identificadas nas porções do dossel com orientação contrária ao azimute solar (sul e sudoeste). Os índices de vegetação NDVI, WDRVI e NDRE, apresentaram valores superestimados nas porções sombreadas do dossel, devido às características espectrais dos dosséis de Pinus. O NDRE foi o índice menos sensível ao efeito topográfico, enquanto o EVI apresentou maior variação a ele associado. A aplicação da normalização topográfica mostrou-se eficaz em mitigar as diferenças entre os valores obtidos em diferentes condições de iluminação. Palavras-chave: sensoriamento remoto, Sentinel-2, silvicultura, geometria solar. Topographic effect on the spectral response of Pinus taeda Linnaeus forest stands in Southern Brazil A B S T R A C T This study aims to examine the influence of seasonal variation of solar irradiance geometry on the spectral response of Pinus taeda forest stands, located in the Midwest Region of Santa Catarina State, Brazil. The influence of the topographic effect was analyzed using surface reflectance and vegetation indices measurements, obtained from three MSI/Sentinel-2 images, acquired at low, medium, and high solar elevation angle, in the second semester of 2018. The influence of the topographic effect was analyzed from the value oscillations of remote sensing data compared to the illumination conditions, delineated from the angular variations of aspect and slope. Correlation and regression analyses were performed between the spectral data and the terrain illumination conditions, defined by the values of the cosine of the incidence angle. The canopy reflectance measurements were directly influenced by the directional aspect of the lighting exposure, and decreases in their values were identified in the portions of the canopy with orientation contrary to the solar azimuth (south). The vegetation indices NDVI, WDRVI, and NDRE, showed overestimated values in the shaded portions of the canopy, due to the spectral characteristics of the pine canopies. The NDRE was the least sensitive index to the topographic effect, while the EVI showed a greater variation associated with it. The application of the topographic effect correction proved to be effective in mitigating the differences between the values obtained under different lighting conditions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1d37a5d501f3ac8727b6539616b7588b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116843807,"asset_id":122115051,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116843807/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115051"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115051"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115051; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115051]").text(description); $(".js-view-count[data-work-id=122115051]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115051; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115051']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "1d37a5d501f3ac8727b6539616b7588b" } } $('.js-work-strip[data-work-id=122115051]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115051,"title":"Efeito Topográfico sobre a Resposta Espectral de Povoamentos Florestais de Pinus taeda Linnaeus no Sul do Brasil","translated_title":"","metadata":{"publisher":"Federal University of Pernambuco","grobid_abstract":"Este estudo tem como objetivo examinar a influência da variação sazonal da geometria da irradiância solar na resposta espectral de povoamentos florestais de Pinus taeda, localizados na Região Centro-Oeste do Estado de Santa Catarina, Brasil. A influência do efeito topográfico foi analisada por meio de medidas de refletância de superfície e índices de vegetação, obtidas a partir de três imagens MSI/Sentinel-2, adquiridas em baixo, médio e alto ângulo de elevação solar, no segundo semestre de 2018. A influência do efeito topográfico foi analisada a partir das oscilações de valor dos dados de sensoriamento remoto em comparação com as condições de iluminação, delineadas a partir das variações angulares de orientação de vertentes e declividade. Foram realizadas análises de correlação e regressão entre os dados espectrais e as condições de iluminação do terreno, definidas pelos valores do cosseno do ângulo de incidência. As medidas de refletância do dossel foram influenciadas diretamente pelo aspecto direcional da exposição a iluminação, e diminuições em seus valores foram identificadas nas porções do dossel com orientação contrária ao azimute solar (sul e sudoeste). Os índices de vegetação NDVI, WDRVI e NDRE, apresentaram valores superestimados nas porções sombreadas do dossel, devido às características espectrais dos dosséis de Pinus. O NDRE foi o índice menos sensível ao efeito topográfico, enquanto o EVI apresentou maior variação a ele associado. A aplicação da normalização topográfica mostrou-se eficaz em mitigar as diferenças entre os valores obtidos em diferentes condições de iluminação. Palavras-chave: sensoriamento remoto, Sentinel-2, silvicultura, geometria solar. Topographic effect on the spectral response of Pinus taeda Linnaeus forest stands in Southern Brazil A B S T R A C T This study aims to examine the influence of seasonal variation of solar irradiance geometry on the spectral response of Pinus taeda forest stands, located in the Midwest Region of Santa Catarina State, Brazil. The influence of the topographic effect was analyzed using surface reflectance and vegetation indices measurements, obtained from three MSI/Sentinel-2 images, acquired at low, medium, and high solar elevation angle, in the second semester of 2018. The influence of the topographic effect was analyzed from the value oscillations of remote sensing data compared to the illumination conditions, delineated from the angular variations of aspect and slope. Correlation and regression analyses were performed between the spectral data and the terrain illumination conditions, defined by the values of the cosine of the incidence angle. The canopy reflectance measurements were directly influenced by the directional aspect of the lighting exposure, and decreases in their values were identified in the portions of the canopy with orientation contrary to the solar azimuth (south). The vegetation indices NDVI, WDRVI, and NDRE, showed overestimated values in the shaded portions of the canopy, due to the spectral characteristics of the pine canopies. The NDRE was the least sensitive index to the topographic effect, while the EVI showed a greater variation associated with it. The application of the topographic effect correction proved to be effective in mitigating the differences between the values obtained under different lighting conditions.","publication_date":{"day":31,"month":12,"year":2021,"errors":{}},"publication_name":"Revista Brasileira de Geografia Física","grobid_abstract_attachment_id":116843807},"translated_abstract":null,"internal_url":"https://www.academia.edu/122115051/Efeito_Topogr%C3%A1fico_sobre_a_Resposta_Espectral_de_Povoamentos_Florestais_de_Pinus_taeda_Linnaeus_no_Sul_do_Brasil","translated_internal_url":"","created_at":"2024-07-16T22:58:20.766-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":116843807,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843807/thumbnails/1.jpg","file_name":"josicleda_2C_250124-202183-2-LE.pdf_filename_UTF-8josicleda_2C_250124-202183-2-LE.pdf","download_url":"https://www.academia.edu/attachments/116843807/download_file","bulk_download_file_name":"Efeito_Topografico_sobre_a_Resposta_Espe.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843807/josicleda_2C_250124-202183-2-LE.pdf_filename_UTF-8josicleda_2C_250124-202183-2-LE-libre.pdf?1721196682=\u0026response-content-disposition=attachment%3B+filename%3DEfeito_Topografico_sobre_a_Resposta_Espe.pdf\u0026Expires=1741801541\u0026Signature=UMHDQSaHwgA1fFfiiBKsFcUhm1ssg5HNZl1xfdsWFOqHIWFP9FiGo38RScYQqu~t5tCkKq6GHKTErg5iqKpjREGWJ2x7AwdtY~BD92nljNzAPMCtQlzrn08442WJQf1kl5HPaktlAlQCaTZnLCLDnL7eEds-qhBm1HYsniBNMUduAlDwuwSue-8IPrNari6ru2dvzQ-qJT1W~Rh6R1DqSGccahmwSPvrtEJmdO2TASSsnenCh9lSiYZ8pQYGpFO2sJu5y~0S~fte~FFYy1JfsKQy06viuoom84dgDG90THIQ0Ex-e7RsM6Qi-ZbA1270TdZVSGDjqXnisBwEMl9ssw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Efeito_Topográfico_sobre_a_Resposta_Espectral_de_Povoamentos_Florestais_de_Pinus_taeda_Linnaeus_no_Sul_do_Brasil","translated_slug":"","page_count":23,"language":"pt","content_type":"Work","summary":"Este estudo tem como objetivo examinar a influência da variação sazonal da geometria da irradiância solar na resposta espectral de povoamentos florestais de Pinus taeda, localizados na Região Centro-Oeste do Estado de Santa Catarina, Brasil. A influência do efeito topográfico foi analisada por meio de medidas de refletância de superfície e índices de vegetação, obtidas a partir de três imagens MSI/Sentinel-2, adquiridas em baixo, médio e alto ângulo de elevação solar, no segundo semestre de 2018. A influência do efeito topográfico foi analisada a partir das oscilações de valor dos dados de sensoriamento remoto em comparação com as condições de iluminação, delineadas a partir das variações angulares de orientação de vertentes e declividade. Foram realizadas análises de correlação e regressão entre os dados espectrais e as condições de iluminação do terreno, definidas pelos valores do cosseno do ângulo de incidência. As medidas de refletância do dossel foram influenciadas diretamente pelo aspecto direcional da exposição a iluminação, e diminuições em seus valores foram identificadas nas porções do dossel com orientação contrária ao azimute solar (sul e sudoeste). Os índices de vegetação NDVI, WDRVI e NDRE, apresentaram valores superestimados nas porções sombreadas do dossel, devido às características espectrais dos dosséis de Pinus. O NDRE foi o índice menos sensível ao efeito topográfico, enquanto o EVI apresentou maior variação a ele associado. A aplicação da normalização topográfica mostrou-se eficaz em mitigar as diferenças entre os valores obtidos em diferentes condições de iluminação. Palavras-chave: sensoriamento remoto, Sentinel-2, silvicultura, geometria solar. Topographic effect on the spectral response of Pinus taeda Linnaeus forest stands in Southern Brazil A B S T R A C T This study aims to examine the influence of seasonal variation of solar irradiance geometry on the spectral response of Pinus taeda forest stands, located in the Midwest Region of Santa Catarina State, Brazil. The influence of the topographic effect was analyzed using surface reflectance and vegetation indices measurements, obtained from three MSI/Sentinel-2 images, acquired at low, medium, and high solar elevation angle, in the second semester of 2018. The influence of the topographic effect was analyzed from the value oscillations of remote sensing data compared to the illumination conditions, delineated from the angular variations of aspect and slope. Correlation and regression analyses were performed between the spectral data and the terrain illumination conditions, defined by the values of the cosine of the incidence angle. The canopy reflectance measurements were directly influenced by the directional aspect of the lighting exposure, and decreases in their values were identified in the portions of the canopy with orientation contrary to the solar azimuth (south). The vegetation indices NDVI, WDRVI, and NDRE, showed overestimated values in the shaded portions of the canopy, due to the spectral characteristics of the pine canopies. The NDRE was the least sensitive index to the topographic effect, while the EVI showed a greater variation associated with it. The application of the topographic effect correction proved to be effective in mitigating the differences between the values obtained under different lighting conditions.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":116843807,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843807/thumbnails/1.jpg","file_name":"josicleda_2C_250124-202183-2-LE.pdf_filename_UTF-8josicleda_2C_250124-202183-2-LE.pdf","download_url":"https://www.academia.edu/attachments/116843807/download_file","bulk_download_file_name":"Efeito_Topografico_sobre_a_Resposta_Espe.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843807/josicleda_2C_250124-202183-2-LE.pdf_filename_UTF-8josicleda_2C_250124-202183-2-LE-libre.pdf?1721196682=\u0026response-content-disposition=attachment%3B+filename%3DEfeito_Topografico_sobre_a_Resposta_Espe.pdf\u0026Expires=1741801541\u0026Signature=UMHDQSaHwgA1fFfiiBKsFcUhm1ssg5HNZl1xfdsWFOqHIWFP9FiGo38RScYQqu~t5tCkKq6GHKTErg5iqKpjREGWJ2x7AwdtY~BD92nljNzAPMCtQlzrn08442WJQf1kl5HPaktlAlQCaTZnLCLDnL7eEds-qhBm1HYsniBNMUduAlDwuwSue-8IPrNari6ru2dvzQ-qJT1W~Rh6R1DqSGccahmwSPvrtEJmdO2TASSsnenCh9lSiYZ8pQYGpFO2sJu5y~0S~fte~FFYy1JfsKQy06viuoom84dgDG90THIQ0Ex-e7RsM6Qi-ZbA1270TdZVSGDjqXnisBwEMl9ssw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":951,"name":"Humanities","url":"https://www.academia.edu/Documents/in/Humanities"}],"urls":[{"id":43558361,"url":"https://periodicos.ufpe.br/revistas/rbgfe/article/download/250124/40526"}]}, dispatcherData: dispatcherData }); 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=122115050]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115050,"title":"Monitoramento interanual do manejo florestal com sensoriamento remoto","translated_title":"","metadata":{"publication_date":{"day":null,"month":null,"year":2019,"errors":{}}},"translated_abstract":null,"internal_url":"https://www.academia.edu/122115050/Monitoramento_interanual_do_manejo_florestal_com_sensoriamento_remoto","translated_internal_url":"","created_at":"2024-07-16T22:58:20.569-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Monitoramento_interanual_do_manejo_florestal_com_sensoriamento_remoto","translated_slug":"","page_count":null,"language":"es","content_type":"Work","summary":null,"owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[],"urls":[{"id":43558360,"url":"https://www.mysciencework.com/publication/show/monitoramento-interanual-do-manejo-florestal-com-sensoriamento-remoto-2b7b48ef"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115049"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122115049/Biomass_of_a_fragment_of_the_montane_seasonal_deciduous_forest_the_south_Brazil"><img alt="Research paper thumbnail of Biomass of a fragment of the montane seasonal deciduous forest the south Brazil" class="work-thumbnail" src="https://attachments.academia-assets.com/116843804/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122115049/Biomass_of_a_fragment_of_the_montane_seasonal_deciduous_forest_the_south_Brazil">Biomass of a fragment of the montane seasonal deciduous forest the south Brazil</a></div><div class="wp-workCard_item"><span>Acta Iguazu</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The objective the work was to quantify the biomass using the destructive method of a forest fragm...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The objective the work was to quantify the biomass using the destructive method of a forest fragment located in the South of Brazil. The study was conducted in fragment of Montane Seasonal Deciduous Forest with approximately 55 ha, centered at the coordinates S 27˚23'44" and W 53˚25'59". For that, three plots of 12x12 m were installed. All plants with diameter at breast height (DBH) greater than 5 cm were identified at the species level. All trees with DBH>10 cm had the following information collected: DBH, total height (h), height at the morphological inversion point (MIP) and the diameter at 0%, 25%, 50%, 75% and 90% of the MIP height. As result, we verified that trees with DBH> 10 cm represent more than 90% of the total biomass (345.2 Mg ha-1). The estimated biomass of leaf litter was 12.4 Mg ha-1. The stock of dry aboveground biomass and litter for the study area was 380.5 Mg ha-1 .</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b02b4578f8d690d750fc6339846d53dc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116843804,"asset_id":122115049,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116843804/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115049"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115049"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115049; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115049]").text(description); $(".js-view-count[data-work-id=122115049]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115049; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115049']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b02b4578f8d690d750fc6339846d53dc" } } $('.js-work-strip[data-work-id=122115049]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115049,"title":"Biomass of a fragment of the montane seasonal deciduous forest the south Brazil","translated_title":"","metadata":{"publisher":"State University of West Paraná","grobid_abstract":"The objective the work was to quantify the biomass using the destructive method of a forest fragment located in the South of Brazil. The study was conducted in fragment of Montane Seasonal Deciduous Forest with approximately 55 ha, centered at the coordinates S 27˚23'44\" and W 53˚25'59\". For that, three plots of 12x12 m were installed. All plants with diameter at breast height (DBH) greater than 5 cm were identified at the species level. All trees with DBH\u003e10 cm had the following information collected: DBH, total height (h), height at the morphological inversion point (MIP) and the diameter at 0%, 25%, 50%, 75% and 90% of the MIP height. As result, we verified that trees with DBH\u003e 10 cm represent more than 90% of the total biomass (345.2 Mg ha-1). The estimated biomass of leaf litter was 12.4 Mg ha-1. The stock of dry aboveground biomass and litter for the study area was 380.5 Mg ha-1 .","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Acta Iguazu","grobid_abstract_attachment_id":116843804},"translated_abstract":null,"internal_url":"https://www.academia.edu/122115049/Biomass_of_a_fragment_of_the_montane_seasonal_deciduous_forest_the_south_Brazil","translated_internal_url":"","created_at":"2024-07-16T22:58:20.295-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":116843804,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843804/thumbnails/1.jpg","file_name":"13811.pdf","download_url":"https://www.academia.edu/attachments/116843804/download_file","bulk_download_file_name":"Biomass_of_a_fragment_of_the_montane_sea.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843804/13811-libre.pdf?1721196670=\u0026response-content-disposition=attachment%3B+filename%3DBiomass_of_a_fragment_of_the_montane_sea.pdf\u0026Expires=1741801541\u0026Signature=Xl9pj-BV1Bi~2gO-hJw4B7LYLAe5QLVlgdC~VdeBTXAhq0usA8SKDrX4SHnrnvFhaufAj8MdyITuj0ThdjUM-2idg8sAy~twH1z4pNiL9Bk7Pji4md8QLZBxDcY9bCtHc9x1FrA6k7DwDUKlhqmyJ1cahmDT6b39ajFhA4SmQCPbvt7Fns~TYbDAFA9-Ansx4qP-AVBE6DvXHa59iNW8VvaDJ6vXZ-O7aXPDGS0MH3vgwlHyNY5XD4SGcehOrnE37wg5X7vW9Q~YigvYez7ayIt5Sp9cAxr-vVmotTc1GfQNh1YAsBWwPyS8yGnnfNPl260o60xBYtBeepoAvnItXQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Biomass_of_a_fragment_of_the_montane_seasonal_deciduous_forest_the_south_Brazil","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":"The objective the work was to quantify the biomass using the destructive method of a forest fragment located in the South of Brazil. The study was conducted in fragment of Montane Seasonal Deciduous Forest with approximately 55 ha, centered at the coordinates S 27˚23'44\" and W 53˚25'59\". For that, three plots of 12x12 m were installed. All plants with diameter at breast height (DBH) greater than 5 cm were identified at the species level. All trees with DBH\u003e10 cm had the following information collected: DBH, total height (h), height at the morphological inversion point (MIP) and the diameter at 0%, 25%, 50%, 75% and 90% of the MIP height. As result, we verified that trees with DBH\u003e 10 cm represent more than 90% of the total biomass (345.2 Mg ha-1). The estimated biomass of leaf litter was 12.4 Mg ha-1. The stock of dry aboveground biomass and litter for the study area was 380.5 Mg ha-1 .","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":116843804,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843804/thumbnails/1.jpg","file_name":"13811.pdf","download_url":"https://www.academia.edu/attachments/116843804/download_file","bulk_download_file_name":"Biomass_of_a_fragment_of_the_montane_sea.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843804/13811-libre.pdf?1721196670=\u0026response-content-disposition=attachment%3B+filename%3DBiomass_of_a_fragment_of_the_montane_sea.pdf\u0026Expires=1741801541\u0026Signature=Xl9pj-BV1Bi~2gO-hJw4B7LYLAe5QLVlgdC~VdeBTXAhq0usA8SKDrX4SHnrnvFhaufAj8MdyITuj0ThdjUM-2idg8sAy~twH1z4pNiL9Bk7Pji4md8QLZBxDcY9bCtHc9x1FrA6k7DwDUKlhqmyJ1cahmDT6b39ajFhA4SmQCPbvt7Fns~TYbDAFA9-Ansx4qP-AVBE6DvXHa59iNW8VvaDJ6vXZ-O7aXPDGS0MH3vgwlHyNY5XD4SGcehOrnE37wg5X7vW9Q~YigvYez7ayIt5Sp9cAxr-vVmotTc1GfQNh1YAsBWwPyS8yGnnfNPl260o60xBYtBeepoAvnItXQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":116843805,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843805/thumbnails/1.jpg","file_name":"13811.pdf","download_url":"https://www.academia.edu/attachments/116843805/download_file","bulk_download_file_name":"Biomass_of_a_fragment_of_the_montane_sea.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843805/13811-libre.pdf?1721196670=\u0026response-content-disposition=attachment%3B+filename%3DBiomass_of_a_fragment_of_the_montane_sea.pdf\u0026Expires=1741801541\u0026Signature=MTEj0TYdx7fwcu~m78cDdtqDX7hyY2cu9F9TS20AkbyByMAtBo24L3HQPvOiC1U22uf87Es8eDi~poIDqRTVl5PYjuKQ7Lz43mSt9oN3N246XhhMDgdM2fkBl5iWeE2dhBRy7EizQNcDb5yXTC3MFRiJRmswsYoaMTcHozPneaT~9xD3opJooZKBT-5ZIZYDjIHifzuJKPGPpQVjsInt3aacDKc7TRqu~iLcoFmUl0pY1jqk9-kfnbQCaS5eAkHUXAq9qJK-WWUl5JemfzsaAyIO98HUgUBJRyFnK1Ho7FN2e9pdqvA8EBxdyFH780sfCU6XpJf7i-7wjOxC1teI7g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1041,"name":"Forestry","url":"https://www.academia.edu/Documents/in/Forestry"},{"id":188993,"name":"Litter","url":"https://www.academia.edu/Documents/in/Litter"},{"id":372637,"name":"Deciduous","url":"https://www.academia.edu/Documents/in/Deciduous"},{"id":2860163,"name":"Montane Ecology","url":"https://www.academia.edu/Documents/in/Montane_Ecology"}],"urls":[{"id":43558359,"url":"http://e-revista.unioeste.br/index.php/actaiguazu/article/download/17120/13811"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115048"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/122115048/Distribui%C3%A7%C3%A3o_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448"><img alt="Research paper thumbnail of Distribuição vertical da biomassa acima do solo em floresta estacional decidual - DOI:10.5039/agraria.v12i3a5448" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/122115048/Distribui%C3%A7%C3%A3o_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448">Distribuição vertical da biomassa acima do solo em floresta estacional decidual - DOI:10.5039/agraria.v12i3a5448</a></div><div class="wp-workCard_item"><span>Revista Brasileira de Ciências Agrárias (Agrária)</span><span>, Sep 27, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implem...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height &lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m was 1.5 Mg ha-1. Average litter mass was 15.6 Mg ha-1. Trees of large diameters must be analyzed very carefully for quantify the biomass and carbon in the forests, because few individuals might represent a large part of the biomass of a forest ecosystem.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115048"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115048"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115048; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115048]").text(description); $(".js-view-count[data-work-id=122115048]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115048; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115048']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=122115048]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115048,"title":"Distribuição vertical da biomassa acima do solo em floresta estacional decidual - DOI:10.5039/agraria.v12i3a5448","translated_title":"","metadata":{"abstract":"Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height \u0026lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m was 1.5 Mg ha-1. Average litter mass was 15.6 Mg ha-1. Trees of large diameters must be analyzed very carefully for quantify the biomass and carbon in the forests, because few individuals might represent a large part of the biomass of a forest ecosystem.","publication_date":{"day":27,"month":9,"year":2017,"errors":{}},"publication_name":"Revista Brasileira de Ciências Agrárias (Agrária)"},"translated_abstract":"Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height \u0026lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m was 1.5 Mg ha-1. Average litter mass was 15.6 Mg ha-1. Trees of large diameters must be analyzed very carefully for quantify the biomass and carbon in the forests, because few individuals might represent a large part of the biomass of a forest ecosystem.","internal_url":"https://www.academia.edu/122115048/Distribui%C3%A7%C3%A3o_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448","translated_internal_url":"","created_at":"2024-07-16T22:58:20.078-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Distribuição_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height \u0026lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m was 1.5 Mg ha-1. Average litter mass was 15.6 Mg ha-1. Trees of large diameters must be analyzed very carefully for quantify the biomass and carbon in the forests, because few individuals might represent a large part of the biomass of a forest ecosystem.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1041,"name":"Forestry","url":"https://www.academia.edu/Documents/in/Forestry"},{"id":146385,"name":"Land Reclamation","url":"https://www.academia.edu/Documents/in/Land_Reclamation"},{"id":372637,"name":"Deciduous","url":"https://www.academia.edu/Documents/in/Deciduous"},{"id":459822,"name":"Carbon Stock","url":"https://www.academia.edu/Documents/in/Carbon_Stock"}],"urls":[{"id":43558358,"url":"http://www.agraria.pro.br/ojs-2.4.6/index.php?journal=agraria\u0026page=article\u0026op=view\u0026path%5B%5D=agraria_v12i3a5448\u0026path%5B%5D=4992"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115047"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/122115047/Relationships_between_MODIS_phenological_metrics_topographic_shade_and_anomalous_temperature_patterns_in_seasonal_deciduous_forests_of_south_Brazil"><img alt="Research paper thumbnail of Relationships between MODIS phenological metrics, topographic shade, and anomalous temperature patterns in seasonal deciduous forests of south Brazil" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/122115047/Relationships_between_MODIS_phenological_metrics_topographic_shade_and_anomalous_temperature_patterns_in_seasonal_deciduous_forests_of_south_Brazil">Relationships between MODIS phenological metrics, topographic shade, and anomalous temperature patterns in seasonal deciduous forests of south Brazil</a></div><div class="wp-workCard_item"><span>International Journal of Remote Sensing</span><span>, Sep 17, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We investigated the relationships between 11 phenological metrics, topographic shade, and anomalo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We investigated the relationships between 11 phenological metrics, topographic shade, and anomalous temperature patterns detected using wavelet analysis in seasonal deciduous forests of south Brazil. To obtain the metrics, we applied the TIMESAT algorithm to the enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra. MODIS acquires data from the study area under a large seasonal amplitude in the solar zenith angle (SZA). We evaluated the effect of topography on phenological metrics by correlating the metrics with shaded relief values. To analyse the inter-annual phenological metric variations with anomalous and regular temperature patterns, we calculated standard anomalies for each metric. Finally, we established relationships between the metrics and the minimum, maximum, and mean temperatures from growing seasons that spanned over 10 seasonal cycles between 2002 and 2012. The correlation results with shaded relief showed that the left (LD) and right derivative (RD), small integral (SInt), seasonal amplitude (SA), base level (BL), and maximum VI value (MV) were sensitive to topographic effects. The seasonal cycles with the highest temperatures in the growing season (2006/2007 and 2009/2010) exhibited a delay at the end of the cycle and a higher interval of duration and productivity, which was indicated by the positive standard anomalies for end of season (EOS), length of season (LOS), large integral (LInt), and SInt. We observed a different result for the lowest temperature cycle (2003/2004). The means for these metrics in anomalous seasons differed significantly from the metrics of other regular cycles at the 0.05 significance level using paired t-tests. Statistically significant correlations were observed between the metrics and minimum and mean temperature values of the 10 seasonal cycles.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115047"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115047"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115047; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115047]").text(description); $(".js-view-count[data-work-id=122115047]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115047; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115047']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=122115047]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115047,"title":"Relationships between MODIS phenological metrics, topographic shade, and anomalous temperature patterns in seasonal deciduous forests of south Brazil","translated_title":"","metadata":{"abstract":"We investigated the relationships between 11 phenological metrics, topographic shade, and anomalous temperature patterns detected using wavelet analysis in seasonal deciduous forests of south Brazil. To obtain the metrics, we applied the TIMESAT algorithm to the enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra. MODIS acquires data from the study area under a large seasonal amplitude in the solar zenith angle (SZA). We evaluated the effect of topography on phenological metrics by correlating the metrics with shaded relief values. To analyse the inter-annual phenological metric variations with anomalous and regular temperature patterns, we calculated standard anomalies for each metric. Finally, we established relationships between the metrics and the minimum, maximum, and mean temperatures from growing seasons that spanned over 10 seasonal cycles between 2002 and 2012. The correlation results with shaded relief showed that the left (LD) and right derivative (RD), small integral (SInt), seasonal amplitude (SA), base level (BL), and maximum VI value (MV) were sensitive to topographic effects. The seasonal cycles with the highest temperatures in the growing season (2006/2007 and 2009/2010) exhibited a delay at the end of the cycle and a higher interval of duration and productivity, which was indicated by the positive standard anomalies for end of season (EOS), length of season (LOS), large integral (LInt), and SInt. We observed a different result for the lowest temperature cycle (2003/2004). The means for these metrics in anomalous seasons differed significantly from the metrics of other regular cycles at the 0.05 significance level using paired t-tests. Statistically significant correlations were observed between the metrics and minimum and mean temperature values of the 10 seasonal cycles.","publisher":"Taylor \u0026 Francis","publication_date":{"day":17,"month":9,"year":2015,"errors":{}},"publication_name":"International Journal of Remote Sensing"},"translated_abstract":"We investigated the relationships between 11 phenological metrics, topographic shade, and anomalous temperature patterns detected using wavelet analysis in seasonal deciduous forests of south Brazil. To obtain the metrics, we applied the TIMESAT algorithm to the enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra. MODIS acquires data from the study area under a large seasonal amplitude in the solar zenith angle (SZA). We evaluated the effect of topography on phenological metrics by correlating the metrics with shaded relief values. To analyse the inter-annual phenological metric variations with anomalous and regular temperature patterns, we calculated standard anomalies for each metric. Finally, we established relationships between the metrics and the minimum, maximum, and mean temperatures from growing seasons that spanned over 10 seasonal cycles between 2002 and 2012. The correlation results with shaded relief showed that the left (LD) and right derivative (RD), small integral (SInt), seasonal amplitude (SA), base level (BL), and maximum VI value (MV) were sensitive to topographic effects. The seasonal cycles with the highest temperatures in the growing season (2006/2007 and 2009/2010) exhibited a delay at the end of the cycle and a higher interval of duration and productivity, which was indicated by the positive standard anomalies for end of season (EOS), length of season (LOS), large integral (LInt), and SInt. We observed a different result for the lowest temperature cycle (2003/2004). The means for these metrics in anomalous seasons differed significantly from the metrics of other regular cycles at the 0.05 significance level using paired t-tests. Statistically significant correlations were observed between the metrics and minimum and mean temperature values of the 10 seasonal cycles.","internal_url":"https://www.academia.edu/122115047/Relationships_between_MODIS_phenological_metrics_topographic_shade_and_anomalous_temperature_patterns_in_seasonal_deciduous_forests_of_south_Brazil","translated_internal_url":"","created_at":"2024-07-16T22:58:19.251-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Relationships_between_MODIS_phenological_metrics_topographic_shade_and_anomalous_temperature_patterns_in_seasonal_deciduous_forests_of_south_Brazil","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"We investigated the relationships between 11 phenological metrics, topographic shade, and anomalous temperature patterns detected using wavelet analysis in seasonal deciduous forests of south Brazil. To obtain the metrics, we applied the TIMESAT algorithm to the enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra. MODIS acquires data from the study area under a large seasonal amplitude in the solar zenith angle (SZA). We evaluated the effect of topography on phenological metrics by correlating the metrics with shaded relief values. To analyse the inter-annual phenological metric variations with anomalous and regular temperature patterns, we calculated standard anomalies for each metric. Finally, we established relationships between the metrics and the minimum, maximum, and mean temperatures from growing seasons that spanned over 10 seasonal cycles between 2002 and 2012. The correlation results with shaded relief showed that the left (LD) and right derivative (RD), small integral (SInt), seasonal amplitude (SA), base level (BL), and maximum VI value (MV) were sensitive to topographic effects. The seasonal cycles with the highest temperatures in the growing season (2006/2007 and 2009/2010) exhibited a delay at the end of the cycle and a higher interval of duration and productivity, which was indicated by the positive standard anomalies for end of season (EOS), length of season (LOS), large integral (LInt), and SInt. We observed a different result for the lowest temperature cycle (2003/2004). The means for these metrics in anomalous seasons differed significantly from the metrics of other regular cycles at the 0.05 significance level using paired t-tests. Statistically significant correlations were observed between the metrics and minimum and mean temperature values of the 10 seasonal cycles.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1252,"name":"Remote Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing"},{"id":17943,"name":"Phenology","url":"https://www.academia.edu/Documents/in/Phenology"},{"id":162010,"name":"Geomatic Engineering","url":"https://www.academia.edu/Documents/in/Geomatic_Engineering"},{"id":372637,"name":"Deciduous","url":"https://www.academia.edu/Documents/in/Deciduous"}],"urls":[{"id":43558357,"url":"https://doi.org/10.1080/01431161.2015.1084437"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115045"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122115045/Assessing_the_Long_Term_Variability_of_TSS_and_Chlorophyll_in_Subtropical_Reservoirs_Using_MODIS_Data"><img alt="Research paper thumbnail of Assessing the Long-Term Variability of TSS and Chlorophyll in Subtropical Reservoirs Using MODIS Data" class="work-thumbnail" src="https://attachments.academia-assets.com/116843837/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122115045/Assessing_the_Long_Term_Variability_of_TSS_and_Chlorophyll_in_Subtropical_Reservoirs_Using_MODIS_Data">Assessing the Long-Term Variability of TSS and Chlorophyll in Subtropical Reservoirs Using MODIS Data</a></div><div class="wp-workCard_item"><span>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</span><span>, Dec 1, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The remote sensing assessment of the long-term variability in optically active constituents of th...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The remote sensing assessment of the long-term variability in optically active constituents of the waters is very important to understand the dynamics of the reservoirs at temporal and spatial scales. We evaluated variations over time (2000-2015) in total suspended solids (TSS) and chlorophyll-a concentrations in the Passo Real reservoir, located in south Brazil. For this purpose, we tested two MODIS composite products: MOD09A1 and MCD43A4. Regression relationships of the red (TSS) and green (chlorophyll-a) reflectance of these products with limnological data of 12 field campaigns were obtained and compared to estimate these constituents on a per-pixel basis. Three major sections of the reservoir (upper, middle, and lower sections) were selected in the analysis. The results showed that the MODIS MCD43A4 (corrected for bidirectional effects) produced better estimates of TSS and chlorophyll-a than the MOD09A1 product. The highest concentrations of TSS were observed from September to October with the concomitant increase in precipitation and the predominance of exposed soils. The peak of TSS concentration between these two months was closely followed by another peak of chlorophyll-a content. It occurs probably because of the higher nutrient availability with sediment loading, the intensive land use with crop development, and the increase in water temperatures. Differences over time between the peaks of TSS and chlorophyll-a varied from the upper (two-month shift) to the lower (one-month shift) sections of the reservoir. They were associated with the different stream inflows and the residence time of the waters.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="64b3328f791ebbe27e4145ffcdb27623" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116843837,"asset_id":122115045,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116843837/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115045"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115045"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115045; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115045]").text(description); $(".js-view-count[data-work-id=122115045]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115045; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115045']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "64b3328f791ebbe27e4145ffcdb27623" } } $('.js-work-strip[data-work-id=122115045]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115045,"title":"Assessing the Long-Term Variability of TSS and Chlorophyll in Subtropical Reservoirs Using MODIS Data","translated_title":"","metadata":{"publisher":"Institute of Electrical and Electronics Engineers","grobid_abstract":"The remote sensing assessment of the long-term variability in optically active constituents of the waters is very important to understand the dynamics of the reservoirs at temporal and spatial scales. We evaluated variations over time (2000-2015) in total suspended solids (TSS) and chlorophyll-a concentrations in the Passo Real reservoir, located in south Brazil. For this purpose, we tested two MODIS composite products: MOD09A1 and MCD43A4. Regression relationships of the red (TSS) and green (chlorophyll-a) reflectance of these products with limnological data of 12 field campaigns were obtained and compared to estimate these constituents on a per-pixel basis. Three major sections of the reservoir (upper, middle, and lower sections) were selected in the analysis. The results showed that the MODIS MCD43A4 (corrected for bidirectional effects) produced better estimates of TSS and chlorophyll-a than the MOD09A1 product. The highest concentrations of TSS were observed from September to October with the concomitant increase in precipitation and the predominance of exposed soils. The peak of TSS concentration between these two months was closely followed by another peak of chlorophyll-a content. It occurs probably because of the higher nutrient availability with sediment loading, the intensive land use with crop development, and the increase in water temperatures. Differences over time between the peaks of TSS and chlorophyll-a varied from the upper (two-month shift) to the lower (one-month shift) sections of the reservoir. They were associated with the different stream inflows and the residence time of the waters.","publication_date":{"day":1,"month":12,"year":2016,"errors":{}},"publication_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","grobid_abstract_attachment_id":116843837},"translated_abstract":null,"internal_url":"https://www.academia.edu/122115045/Assessing_the_Long_Term_Variability_of_TSS_and_Chlorophyll_in_Subtropical_Reservoirs_Using_MODIS_Data","translated_internal_url":"","created_at":"2024-07-16T22:58:18.653-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":116843837,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843837/thumbnails/1.jpg","file_name":"JSTARS.2016.258982020240717-1-ks6h3y.pdf","download_url":"https://www.academia.edu/attachments/116843837/download_file","bulk_download_file_name":"Assessing_the_Long_Term_Variability_of_T.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843837/JSTARS.2016.258982020240717-1-ks6h3y-libre.pdf?1721196660=\u0026response-content-disposition=attachment%3B+filename%3DAssessing_the_Long_Term_Variability_of_T.pdf\u0026Expires=1741801541\u0026Signature=b1DVogDeIN9d3VyOVJdN6i41i6vYJ8cAAKSWFlQwhOo1cBSbIsL3b9aEasmh7d6lL~SC520uTBC38VxnOIzKgkcDlIaKYS82sf~PaqtCD0~cevin6kk9Iup2TWPpeE7s8MqxEo0N1C-9wKajPtMWW4nUuufUgVPxlkebymWuPnvm3jA4iP2tEYa3tt8241Lgj1M8IgnTkDxcvZnch~iyRRBgiIBcROcLrMxDxNeulNE3-U1WUhRVJGL2Nrg8RVYH4Cqw9FWDrUuSIighYB1-2irVU3MuCbeRUBKrcUTrQVZqnpyagSv1miz9Uja2w1BeZPqSHrS-x04wSDmdeRD-Zg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Assessing_the_Long_Term_Variability_of_TSS_and_Chlorophyll_in_Subtropical_Reservoirs_Using_MODIS_Data","translated_slug":"","page_count":7,"language":"en","content_type":"Work","summary":"The remote sensing assessment of the long-term variability in optically active constituents of the waters is very important to understand the dynamics of the reservoirs at temporal and spatial scales. We evaluated variations over time (2000-2015) in total suspended solids (TSS) and chlorophyll-a concentrations in the Passo Real reservoir, located in south Brazil. For this purpose, we tested two MODIS composite products: MOD09A1 and MCD43A4. Regression relationships of the red (TSS) and green (chlorophyll-a) reflectance of these products with limnological data of 12 field campaigns were obtained and compared to estimate these constituents on a per-pixel basis. Three major sections of the reservoir (upper, middle, and lower sections) were selected in the analysis. The results showed that the MODIS MCD43A4 (corrected for bidirectional effects) produced better estimates of TSS and chlorophyll-a than the MOD09A1 product. The highest concentrations of TSS were observed from September to October with the concomitant increase in precipitation and the predominance of exposed soils. The peak of TSS concentration between these two months was closely followed by another peak of chlorophyll-a content. It occurs probably because of the higher nutrient availability with sediment loading, the intensive land use with crop development, and the increase in water temperatures. Differences over time between the peaks of TSS and chlorophyll-a varied from the upper (two-month shift) to the lower (one-month shift) sections of the reservoir. They were associated with the different stream inflows and the residence time of the waters.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":116843837,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843837/thumbnails/1.jpg","file_name":"JSTARS.2016.258982020240717-1-ks6h3y.pdf","download_url":"https://www.academia.edu/attachments/116843837/download_file","bulk_download_file_name":"Assessing_the_Long_Term_Variability_of_T.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843837/JSTARS.2016.258982020240717-1-ks6h3y-libre.pdf?1721196660=\u0026response-content-disposition=attachment%3B+filename%3DAssessing_the_Long_Term_Variability_of_T.pdf\u0026Expires=1741801541\u0026Signature=b1DVogDeIN9d3VyOVJdN6i41i6vYJ8cAAKSWFlQwhOo1cBSbIsL3b9aEasmh7d6lL~SC520uTBC38VxnOIzKgkcDlIaKYS82sf~PaqtCD0~cevin6kk9Iup2TWPpeE7s8MqxEo0N1C-9wKajPtMWW4nUuufUgVPxlkebymWuPnvm3jA4iP2tEYa3tt8241Lgj1M8IgnTkDxcvZnch~iyRRBgiIBcROcLrMxDxNeulNE3-U1WUhRVJGL2Nrg8RVYH4Cqw9FWDrUuSIighYB1-2irVU3MuCbeRUBKrcUTrQVZqnpyagSv1miz9Uja2w1BeZPqSHrS-x04wSDmdeRD-Zg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":85564,"name":"Chlorophyll","url":"https://www.academia.edu/Documents/in/Chlorophyll"},{"id":192294,"name":"Sediment","url":"https://www.academia.edu/Documents/in/Sediment"},{"id":244969,"name":"Nutrient","url":"https://www.academia.edu/Documents/in/Nutrient"},{"id":291658,"name":"Precipitation","url":"https://www.academia.edu/Documents/in/Precipitation"},{"id":1431418,"name":"Chlorophyll a","url":"https://www.academia.edu/Documents/in/Chlorophyll_a"},{"id":1666540,"name":"Total suspended solids.","url":"https://www.academia.edu/Documents/in/Total_suspended_solids"}],"urls":[{"id":43558355,"url":"https://doi.org/10.1109/jstars.2016.2589820"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115043"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122115043/Dynamics_of_limnological_parameters_in_reservoirs_A_case_study_in_South_Brazil_using_remote_sensing_and_meteorological_data"><img alt="Research paper thumbnail of Dynamics of limnological parameters in reservoirs: A case study in South Brazil using remote sensing and meteorological data" class="work-thumbnail" src="https://attachments.academia-assets.com/116843836/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122115043/Dynamics_of_limnological_parameters_in_reservoirs_A_case_study_in_South_Brazil_using_remote_sensing_and_meteorological_data">Dynamics of limnological parameters in reservoirs: A case study in South Brazil using remote sensing and meteorological data</a></div><div class="wp-workCard_item"><span>Science of The Total Environment</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Reservoirs are important in Brazil for the production of hydroelectric power and human water cons...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Reservoirs are important in Brazil for the production of hydroelectric power and human water consumption. The objective was to evaluate the variability of total suspended solids (TSS) and chlorophyll-a as well as the rainfall/ temperature and land use impacts on these optically active constituents (OAC). The study area is the Passo Real reservoir in south Brazil. The methodology was divided in four steps. First, we used wavelet to detect anomalous periods of rainfall and temperature (2002-2014). Second, we carried out 12 field campaigns to obtain in situ measurements for limnological characterization (2009-2010). The third step was the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra and Aqua satellites data corrected and non-corrected for bidirectional effects. Finally, we evaluated potential drivers of OAC changes over time using cross-correlation analysis. The results showed a decrease in the TSS and chlorophyll-a concentrations from the upper to the lower streams of the reservoir. The exponential regression between the MODIS red reflectance and TSS had an adjusted r 2 of 0.63. It decreased to 0.53 for the relationship between the green reflectance and chlorophyll-a. MODIS data corrected for bidirectional effects provided better OAC estimates than non-corrected data. The validation of MODIS TSS and chlorophyll-a estimates using a separate set of measurements showed a RMSE of 2.98 mg/l and 2.33 μg/l, respectively. MODIS estimates indicated a gradual transition in OAC from the upper to the lower streams in</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="926ee8abc41f05ba29b40f47c3509902" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116843836,"asset_id":122115043,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116843836/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115043"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115043"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115043; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115043]").text(description); $(".js-view-count[data-work-id=122115043]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115043; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115043']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "926ee8abc41f05ba29b40f47c3509902" } } $('.js-work-strip[data-work-id=122115043]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115043,"title":"Dynamics of limnological parameters in reservoirs: A case study in South Brazil using remote sensing and meteorological data","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"Reservoirs are important in Brazil for the production of hydroelectric power and human water consumption. 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The exponential regression between the MODIS red reflectance and TSS had an adjusted r 2 of 0.63. It decreased to 0.53 for the relationship between the green reflectance and chlorophyll-a. MODIS data corrected for bidirectional effects provided better OAC estimates than non-corrected data. The validation of MODIS TSS and chlorophyll-a estimates using a separate set of measurements showed a RMSE of 2.98 mg/l and 2.33 μg/l, respectively. MODIS estimates indicated a gradual transition in OAC from the upper to the lower streams in","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Science of The Total Environment","grobid_abstract_attachment_id":116843836},"translated_abstract":null,"internal_url":"https://www.academia.edu/122115043/Dynamics_of_limnological_parameters_in_reservoirs_A_case_study_in_South_Brazil_using_remote_sensing_and_meteorological_data","translated_internal_url":"","created_at":"2024-07-16T22:58:18.344-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":116843836,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843836/thumbnails/1.jpg","file_name":"j.scitotenv.2016.09.05020240717-1-1sh3k9.pdf","download_url":"https://www.academia.edu/attachments/116843836/download_file","bulk_download_file_name":"Dynamics_of_limnological_parameters_in_r.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843836/j.scitotenv.2016.09.05020240717-1-1sh3k9-libre.pdf?1721196672=\u0026response-content-disposition=attachment%3B+filename%3DDynamics_of_limnological_parameters_in_r.pdf\u0026Expires=1741801541\u0026Signature=JsaDbDq1WVe~2cxG6Wo1qCZ2Cf1TxqFHG63sSwAjcTrZXP8XsHBiTQm1M5xhnlUxgh3TMcTfWUSI~~51dqOpt8ujwbGSeYPNctyh8wJpojJox5NDpqe9x1COodrwJwLOWL-gY9nEYKCnCBSZALBrHCLXtUOmg0DtRHvkAMH3O3l33hJXnURZXHklanw5Q~EtJIqBvDULC8N4hdRihAnpDsdYazktdtJvNlgsB2o0usMIG8iTttXKu-FaSq~soYfqKaVITMXTpLD6NP1l1XQWJm5z0Z-FDaKv6hi5cJdtkc2b9yhhmG0v3uQtyDx3lQIl8Y1VZbT8SsRZxiD3GX2pMA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Dynamics_of_limnological_parameters_in_reservoirs_A_case_study_in_South_Brazil_using_remote_sensing_and_meteorological_data","translated_slug":"","page_count":11,"language":"en","content_type":"Work","summary":"Reservoirs are important in Brazil for the production of hydroelectric power and human water consumption. The objective was to evaluate the variability of total suspended solids (TSS) and chlorophyll-a as well as the rainfall/ temperature and land use impacts on these optically active constituents (OAC). The study area is the Passo Real reservoir in south Brazil. The methodology was divided in four steps. First, we used wavelet to detect anomalous periods of rainfall and temperature (2002-2014). Second, we carried out 12 field campaigns to obtain in situ measurements for limnological characterization (2009-2010). The third step was the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra and Aqua satellites data corrected and non-corrected for bidirectional effects. Finally, we evaluated potential drivers of OAC changes over time using cross-correlation analysis. The results showed a decrease in the TSS and chlorophyll-a concentrations from the upper to the lower streams of the reservoir. The exponential regression between the MODIS red reflectance and TSS had an adjusted r 2 of 0.63. It decreased to 0.53 for the relationship between the green reflectance and chlorophyll-a. MODIS data corrected for bidirectional effects provided better OAC estimates than non-corrected data. The validation of MODIS TSS and chlorophyll-a estimates using a separate set of measurements showed a RMSE of 2.98 mg/l and 2.33 μg/l, respectively. 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The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height &lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115042"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115042"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115042; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115042]").text(description); $(".js-view-count[data-work-id=122115042]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115042; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115042']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=122115042]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115042,"title":"Distribuição vertical da biomassa acima do solo em floresta estacional decidual - DOI:10.5039/agraria.v12i3a5448","translated_title":"","metadata":{"abstract":"Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. 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Biomass of plants taller than 1.3 m and with diameter at breast height \u0026lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m ...","publication_date":{"day":null,"month":null,"year":2017,"errors":{}}},"translated_abstract":"Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height \u0026lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m ...","internal_url":"https://www.academia.edu/122115042/Distribui%C3%A7%C3%A3o_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448","translated_internal_url":"","created_at":"2024-07-16T22:58:17.230-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Distribuição_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height \u0026lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m ...","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1041,"name":"Forestry","url":"https://www.academia.edu/Documents/in/Forestry"},{"id":146385,"name":"Land Reclamation","url":"https://www.academia.edu/Documents/in/Land_Reclamation"},{"id":372637,"name":"Deciduous","url":"https://www.academia.edu/Documents/in/Deciduous"},{"id":459822,"name":"Carbon Stock","url":"https://www.academia.edu/Documents/in/Carbon_Stock"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115019"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122115019/Directional_and_angular_effects_on_the_spectral_reflectance_of_waters_with_variable_amounts_of_total_suspended_solids"><img alt="Research paper thumbnail of Directional and angular effects on the spectral reflectance of waters with variable amounts of total suspended solids" class="work-thumbnail" src="https://attachments.academia-assets.com/116843821/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122115019/Directional_and_angular_effects_on_the_spectral_reflectance_of_waters_with_variable_amounts_of_total_suspended_solids">Directional and angular effects on the spectral reflectance of waters with variable amounts of total suspended solids</a></div><div class="wp-workCard_item"><span>Journal of Applied Remote Sensing</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Viewing geometry is one of the most important factors to consider when water bodies are observed ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Viewing geometry is one of the most important factors to consider when water bodies are observed from satellite sensors with large field of view. We examine the directional and angular effects on the reflectance of waters with different concentrations of total suspended solids (TSSs). In the laboratory, we measure the reflectance in five view zenith angles (VZAs) and eight view azimuth angles (VAAs) for optically shallow waters having four concentrations of TSSs. Seven empirical models to estimate TSSs based only on the reflectance of the red band (∼660 nm) are evaluated. In addition, we analyze Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra reflectance measured in 13 consecutive satellite overpasses. The results show that the reflectance of the inland-like water is affected by data acquisition geometry. The best wavelength to estimate TSS is 625 nm for most VZAs and VAAs. The lowest correlations between reflectance and TSS are observed at extreme viewing with the anisotropy decreasing with increasing concentrations of TSSs. Directional and angular effects are also observed for MODIS (acquired and simulated data) with TSS underestimates observed close to the orthogonal plane for all VZAs, and TSS overestimates observed in the principal scattering plane in the forward scattering direction. More anisotropic waters are observed for VZA greater than AE30 deg. Results highlight the need for correcting MODIS data for bidirectional effects in inland water studies.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d9a153bb0f9f810493ff2f754b56d55c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116843821,"asset_id":122115019,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116843821/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115019"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115019"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115019; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115019]").text(description); $(".js-view-count[data-work-id=122115019]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115019; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115019']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d9a153bb0f9f810493ff2f754b56d55c" } } $('.js-work-strip[data-work-id=122115019]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115019,"title":"Directional and angular effects on the spectral reflectance of waters with variable amounts of total suspended solids","translated_title":"","metadata":{"publisher":"SPIE-Intl Soc Optical Eng","grobid_abstract":"Viewing geometry is one of the most important factors to consider when water bodies are observed from satellite sensors with large field of view. We examine the directional and angular effects on the reflectance of waters with different concentrations of total suspended solids (TSSs). In the laboratory, we measure the reflectance in five view zenith angles (VZAs) and eight view azimuth angles (VAAs) for optically shallow waters having four concentrations of TSSs. Seven empirical models to estimate TSSs based only on the reflectance of the red band (∼660 nm) are evaluated. In addition, we analyze Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra reflectance measured in 13 consecutive satellite overpasses. The results show that the reflectance of the inland-like water is affected by data acquisition geometry. The best wavelength to estimate TSS is 625 nm for most VZAs and VAAs. The lowest correlations between reflectance and TSS are observed at extreme viewing with the anisotropy decreasing with increasing concentrations of TSSs. Directional and angular effects are also observed for MODIS (acquired and simulated data) with TSS underestimates observed close to the orthogonal plane for all VZAs, and TSS overestimates observed in the principal scattering plane in the forward scattering direction. More anisotropic waters are observed for VZA greater than AE30 deg. Results highlight the need for correcting MODIS data for bidirectional effects in inland water studies.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Journal of Applied Remote Sensing","grobid_abstract_attachment_id":116843821},"translated_abstract":null,"internal_url":"https://www.academia.edu/122115019/Directional_and_angular_effects_on_the_spectral_reflectance_of_waters_with_variable_amounts_of_total_suspended_solids","translated_internal_url":"","created_at":"2024-07-16T22:57:45.579-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":116843821,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843821/thumbnails/1.jpg","file_name":"3THMBB2.pdf","download_url":"https://www.academia.edu/attachments/116843821/download_file","bulk_download_file_name":"Directional_and_angular_effects_on_the_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843821/3THMBB2-libre.pdf?1721196665=\u0026response-content-disposition=attachment%3B+filename%3DDirectional_and_angular_effects_on_the_s.pdf\u0026Expires=1741801541\u0026Signature=bG0sVctJM5qbHJXNNd9AbPmMnFwcrgRLK0M7zVkAl0B0Twlrkym7rYwHexgKB0k5s3I0CVCB8CtXwEf52zerJ-4YWbU7Qa-9Pff9OEdPjwK8vmvh0zanRVrUoviO4VqGvzkBrcGOROElbvscvB0Y61UkcWsuT4ZzNgsSZys3-R~xsJj3RcwB49RKjAVBMzk~ngizHW-u3X2TSRDkaewV0-wSzPw8HN2WtOCpqA71PGlDfTUA8eY~lnLHeWF4HYP~b4qKiPW0ukayB1~lhCuKgxg80PyLnALZUtWBD5UHJWruGEtWWAmDwXdUazGto4bHzEZhUwvyJy57mu6nPSJ3HA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Directional_and_angular_effects_on_the_spectral_reflectance_of_waters_with_variable_amounts_of_total_suspended_solids","translated_slug":"","page_count":15,"language":"en","content_type":"Work","summary":"Viewing geometry is one of the most important factors to consider when water bodies are observed from satellite sensors with large field of view. We examine the directional and angular effects on the reflectance of waters with different concentrations of total suspended solids (TSSs). In the laboratory, we measure the reflectance in five view zenith angles (VZAs) and eight view azimuth angles (VAAs) for optically shallow waters having four concentrations of TSSs. Seven empirical models to estimate TSSs based only on the reflectance of the red band (∼660 nm) are evaluated. In addition, we analyze Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra reflectance measured in 13 consecutive satellite overpasses. The results show that the reflectance of the inland-like water is affected by data acquisition geometry. The best wavelength to estimate TSS is 625 nm for most VZAs and VAAs. The lowest correlations between reflectance and TSS are observed at extreme viewing with the anisotropy decreasing with increasing concentrations of TSSs. Directional and angular effects are also observed for MODIS (acquired and simulated data) with TSS underestimates observed close to the orthogonal plane for all VZAs, and TSS overestimates observed in the principal scattering plane in the forward scattering direction. More anisotropic waters are observed for VZA greater than AE30 deg. Results highlight the need for correcting MODIS data for bidirectional effects in inland water studies.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":116843821,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843821/thumbnails/1.jpg","file_name":"3THMBB2.pdf","download_url":"https://www.academia.edu/attachments/116843821/download_file","bulk_download_file_name":"Directional_and_angular_effects_on_the_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843821/3THMBB2-libre.pdf?1721196665=\u0026response-content-disposition=attachment%3B+filename%3DDirectional_and_angular_effects_on_the_s.pdf\u0026Expires=1741801541\u0026Signature=bG0sVctJM5qbHJXNNd9AbPmMnFwcrgRLK0M7zVkAl0B0Twlrkym7rYwHexgKB0k5s3I0CVCB8CtXwEf52zerJ-4YWbU7Qa-9Pff9OEdPjwK8vmvh0zanRVrUoviO4VqGvzkBrcGOROElbvscvB0Y61UkcWsuT4ZzNgsSZys3-R~xsJj3RcwB49RKjAVBMzk~ngizHW-u3X2TSRDkaewV0-wSzPw8HN2WtOCpqA71PGlDfTUA8eY~lnLHeWF4HYP~b4qKiPW0ukayB1~lhCuKgxg80PyLnALZUtWBD5UHJWruGEtWWAmDwXdUazGto4bHzEZhUwvyJy57mu6nPSJ3HA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":516,"name":"Optics","url":"https://www.academia.edu/Documents/in/Optics"},{"id":1252,"name":"Remote Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing"},{"id":37333,"name":"Anisotropy","url":"https://www.academia.edu/Documents/in/Anisotropy"},{"id":162010,"name":"Geomatic Engineering","url":"https://www.academia.edu/Documents/in/Geomatic_Engineering"},{"id":441926,"name":"Scattering","url":"https://www.academia.edu/Documents/in/Scattering"},{"id":571931,"name":"Spectroradiometer","url":"https://www.academia.edu/Documents/in/Spectroradiometer"},{"id":800990,"name":"Reflectivity","url":"https://www.academia.edu/Documents/in/Reflectivity"},{"id":1497461,"name":"Applied remote sensing","url":"https://www.academia.edu/Documents/in/Applied_remote_sensing"},{"id":1766044,"name":"Azimuth","url":"https://www.academia.edu/Documents/in/Azimuth"},{"id":2881906,"name":"Zenith","url":"https://www.academia.edu/Documents/in/Zenith"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117737343"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/117737343/Recarga_de_aqu%C3%ADfero_em_Floresta_Estacional_Decidual_nativa_Parque_Estadual_do_Turvo_RS_"><img alt="Research paper thumbnail of Recarga de aquífero em Floresta Estacional Decidual nativa, Parque Estadual do Turvo (RS)" class="work-thumbnail" src="https://attachments.academia-assets.com/113519472/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/117737343/Recarga_de_aqu%C3%ADfero_em_Floresta_Estacional_Decidual_nativa_Parque_Estadual_do_Turvo_RS_">Recarga de aquífero em Floresta Estacional Decidual nativa, Parque Estadual do Turvo (RS)</a></div><div class="wp-workCard_item"><span>Ciência Florestal</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Considerando a importância das florestas na retenção das águas oriundas da precipitação pluviomét...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Considerando a importância das florestas na retenção das águas oriundas da precipitação pluviométrica e a sua função estratégica na recarga de aquíferos, este estudo teve por objetivo estimar a recarga direta e natural do aquífero raso da zona de alteração dos basaltos da Formação Serra Geral, subjacente à Floresta Estacional Decidual nativa, bioma de Mata Atlântica. Para estimar a recarga do aquífero, foi aplicado o método Water Table Fluctuation (WTF), com base na análise de séries temporais do nível freático e da precipitação, coletadas durante o período de 50 dias (de janeiro a março) no Parque Estadual do Turvo - PET (RS). Para a análise sistemática da variação do nível de água subterrânea (N.A.), foram coletados dados sub-horários com medidores de pressão automáticos (CTD), em três piezômetros localizados no PET. Em simultâneo (com igual periodicidade), foram coletados dados de precipitação pluviométrica no interior do PET, com uma estação experimental. A recarga média, direta...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a05b79b153dbdb80ab75b857b3b53e81" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113519472,"asset_id":117737343,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113519472/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="117737343"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="117737343"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117737343; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117737343]").text(description); $(".js-view-count[data-work-id=117737343]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117737343; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117737343']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a05b79b153dbdb80ab75b857b3b53e81" } } $('.js-work-strip[data-work-id=117737343]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117737343,"title":"Recarga de aquífero em Floresta Estacional Decidual nativa, Parque Estadual do Turvo (RS)","translated_title":"","metadata":{"abstract":"Considerando a importância das florestas na retenção das águas oriundas da precipitação pluviométrica e a sua função estratégica na recarga de aquíferos, este estudo teve por objetivo estimar a recarga direta e natural do aquífero raso da zona de alteração dos basaltos da Formação Serra Geral, subjacente à Floresta Estacional Decidual nativa, bioma de Mata Atlântica. Para estimar a recarga do aquífero, foi aplicado o método Water Table Fluctuation (WTF), com base na análise de séries temporais do nível freático e da precipitação, coletadas durante o período de 50 dias (de janeiro a março) no Parque Estadual do Turvo - PET (RS). Para a análise sistemática da variação do nível de água subterrânea (N.A.), foram coletados dados sub-horários com medidores de pressão automáticos (CTD), em três piezômetros localizados no PET. Em simultâneo (com igual periodicidade), foram coletados dados de precipitação pluviométrica no interior do PET, com uma estação experimental. A recarga média, direta...","publisher":"Universidad Federal de Santa Maria","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"Ciência Florestal"},"translated_abstract":"Considerando a importância das florestas na retenção das águas oriundas da precipitação pluviométrica e a sua função estratégica na recarga de aquíferos, este estudo teve por objetivo estimar a recarga direta e natural do aquífero raso da zona de alteração dos basaltos da Formação Serra Geral, subjacente à Floresta Estacional Decidual nativa, bioma de Mata Atlântica. Para estimar a recarga do aquífero, foi aplicado o método Water Table Fluctuation (WTF), com base na análise de séries temporais do nível freático e da precipitação, coletadas durante o período de 50 dias (de janeiro a março) no Parque Estadual do Turvo - PET (RS). Para a análise sistemática da variação do nível de água subterrânea (N.A.), foram coletados dados sub-horários com medidores de pressão automáticos (CTD), em três piezômetros localizados no PET. Em simultâneo (com igual periodicidade), foram coletados dados de precipitação pluviométrica no interior do PET, com uma estação experimental. 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Para estimar a recarga do aquífero, foi aplicado o método Water Table Fluctuation (WTF), com base na análise de séries temporais do nível freático e da precipitação, coletadas durante o período de 50 dias (de janeiro a março) no Parque Estadual do Turvo - PET (RS). Para a análise sistemática da variação do nível de água subterrânea (N.A.), foram coletados dados sub-horários com medidores de pressão automáticos (CTD), em três piezômetros localizados no PET. Em simultâneo (com igual periodicidade), foram coletados dados de precipitação pluviométrica no interior do PET, com uma estação experimental. A recarga média, direta...","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":113519472,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113519472/thumbnails/1.jpg","file_name":"46356.pdf","download_url":"https://www.academia.edu/attachments/113519472/download_file","bulk_download_file_name":"Recarga_de_aquifero_em_Floresta_Estacion.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113519472/46356-libre.pdf?1713544705=\u0026response-content-disposition=attachment%3B+filename%3DRecarga_de_aquifero_em_Floresta_Estacion.pdf\u0026Expires=1741801541\u0026Signature=Jx1j75cw65wK-~cyQKmoBsuEUfT1ZewnFNH5SdHeY~2zU8yEoWppoYkoDWDKvcXdeCa9I4bd8u2xTbeZF2rbIRLSNRr1sMAqgnfqtTq5Tc~JklgeJe0ZUFUCbnpkZUsRGcDsjxfVgo121wRqe7EGCuKBt-jioNZg0f3B80ER8~ZMXQCfxZ5USt2zqKQOfkg1JsadGw2kH5W96dzupBiJ7PrXv7Y5R3dlS0OBcKBR3pfLlrEQpeCWY1IQdB2-t89ghzvMeVqdZR3qw-Bh5hMNyWY4G8u-TOWK2fLaGS7LPngtCiVbFLMS8JnmH66Hl2A1jjl7m9xk-fm7VpUMgE~5BA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":113519473,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113519473/thumbnails/1.jpg","file_name":"46356.pdf","download_url":"https://www.academia.edu/attachments/113519473/download_file","bulk_download_file_name":"Recarga_de_aquifero_em_Floresta_Estacion.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113519473/46356-libre.pdf?1713544700=\u0026response-content-disposition=attachment%3B+filename%3DRecarga_de_aquifero_em_Floresta_Estacion.pdf\u0026Expires=1741801541\u0026Signature=DVWcjSNfKVrnAAmwlfpLxn~Ka2ZnybmF6GuPFJBhrDjyjyUx8rcZPDevuk85ZsK6Dw-bPawrIc~9BAOAYhU3OLfu5Fr1c5FGiAAKr1VmeuVxxBKP5c52-aN0GShETCQirXtq863srse26Xfpc6Pr5CH2IXLdz4Yh4p8wXXJyzTGrVuAfbmlzJEUZLAeLQq3koUkGPMZeSbudpnYfJdiWfnNaEu-EODJPszT56gdzFLxVzBtximRv4DN6FwJqw~AkTWPOhb1HxPcNVzznA8jhOOIyhzc-Mo8vdcUpQqaFeeVN-D~ChPrOsuxde4mbEwiCxGK2o4IIMopJr2MsGMsdSQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":770577,"name":"Ciência florestal","url":"https://www.academia.edu/Documents/in/Ciencia_florestal"}],"urls":[{"id":41230232,"url":"https://periodicos.ufsm.br/cienciaflorestal/article/download/47112/46356"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952936"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/114952936/Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_October_29_2019_in_the_Rio_Grande_do_Sul_State_Brazil"><img alt="Research paper thumbnail of Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on October 29, 2019, in the Rio Grande do Sul State, Brazil" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/114952936/Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_October_29_2019_in_the_Rio_Grande_do_Sul_State_Brazil">Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on October 29, 2019, in the Rio Grande do Sul State, Brazil</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Title: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM cam...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Title: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on October 29, 2019, in the Rio Grande do Sul State, Brazil Keywords Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. Data description: The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also &lt;em&gt;Drone&lt;/em&gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): Parameters Specification/value Date (YYYYMMDD): 20191029 Time of day (BRT = -3) 12:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 200 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3&quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 587 photos Along track coverage 85% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br" rel="nofollow">breunig@ufsm.br</a> An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Figure 2. The capture of an orthomosaic in the processing workflow of the X3 camera. The lowest quality was applied. Figure 3. The capture of a DEM in the processing [...]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952936"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952936"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952936; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952936]").text(description); $(".js-view-count[data-work-id=114952936]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952936; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952936']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=114952936]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952936,"title":"Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on October 29, 2019, in the Rio Grande do Sul State, Brazil","translated_title":"","metadata":{"abstract":"Title: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on October 29, 2019, in the Rio Grande do Sul State, Brazil Keywords Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. Data description: The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also \u0026lt;em\u0026gt;Drone\u0026lt;/em\u0026gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): Parameters Specification/value Date (YYYYMMDD): 20191029 Time of day (BRT = -3) 12:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 200 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3\u0026quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 587 photos Along track coverage 85% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, breunig@ufsm.br An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Figure 2. The capture of an orthomosaic in the processing workflow of the X3 camera. The lowest quality was applied. Figure 3. The capture of a DEM in the processing [...]","publication_date":{"day":27,"month":2,"year":2021,"errors":{}}},"translated_abstract":"Title: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on October 29, 2019, in the Rio Grande do Sul State, Brazil Keywords Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. Data description: The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also \u0026lt;em\u0026gt;Drone\u0026lt;/em\u0026gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): Parameters Specification/value Date (YYYYMMDD): 20191029 Time of day (BRT = -3) 12:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 200 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3\u0026quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 587 photos Along track coverage 85% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, breunig@ufsm.br An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Figure 2. The capture of an orthomosaic in the processing workflow of the X3 camera. The lowest quality was applied. Figure 3. The capture of a DEM in the processing [...]","internal_url":"https://www.academia.edu/114952936/Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_October_29_2019_in_the_Rio_Grande_do_Sul_State_Brazil","translated_internal_url":"","created_at":"2024-02-15T11:02:43.041-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_October_29_2019_in_the_Rio_Grande_do_Sul_State_Brazil","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Title: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on October 29, 2019, in the Rio Grande do Sul State, Brazil Keywords Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. Data description: The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also \u0026lt;em\u0026gt;Drone\u0026lt;/em\u0026gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): Parameters Specification/value Date (YYYYMMDD): 20191029 Time of day (BRT = -3) 12:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 200 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3\u0026quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 587 photos Along track coverage 85% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, breunig@ufsm.br An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Figure 2. The capture of an orthomosaic in the processing workflow of the X3 camera. The lowest quality was applied. Figure 3. The capture of a DEM in the processing [...]","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":1872418,"name":"Subtropics","url":"https://www.academia.edu/Documents/in/Subtropics"}],"urls":[{"id":39493897,"url":"https://zenodo.org/record/4565584"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952935"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/114952935/Assessing_the_effect_of_spatial_resolution_on_the_delineation_of_management_zones_for_smallholder_farming_in_southern_Brazil"><img alt="Research paper thumbnail of Assessing the effect of spatial resolution on the delineation of management zones for smallholder farming in southern Brazil" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/114952935/Assessing_the_effect_of_spatial_resolution_on_the_delineation_of_management_zones_for_smallholder_farming_in_southern_Brazil">Assessing the effect of spatial resolution on the delineation of management zones for smallholder farming in southern Brazil</a></div><div class="wp-workCard_item"><span>Remote Sensing Applications: Society and Environment</span><span>, Aug 1, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delin...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delineate management zones for smallholder farming in southern Brazil. In this study, we investigated the spatial resolution influence on the AGB estimates of rye, calculated from regression relationships with the Normalized Difference Vegetation Index (NDVI), and on the subsequent delineation of management zones using the Management Zone Analyst (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were compared with observations from the PlanetScope (PS) satellite constellation (3 m) and the Operational Land Imager (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green vegetation, soil, and shadow) was applied to surface reflectance data of these instruments for evaluating the cover-crop development at two dates in August 2017. Because of the differences in the technical specifications of the sensors, we resampled the UAV dataset into four levels of spatial resolution (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, we obtained confusion matrices for the original and resampled data. The results showed that the increasing amounts of rye AGB from the beginning to the end of August promoted significant changes in surface reflectance and in soil-green vegetation fractions calculated at variable spatial resolution. The performance of the regression models to estimate cover-crop AGB was approximately similar in the transition from the sub-metric (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV camera to the PS data. For all datasets, the MZA detected two management zones with zone 2 having higher cover-crop AGB than zone 1. When compared to the UAV management zone map (reference), the PS map had a moderate-to-substantial agreement, while the OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial agreements with the reference map were observed at simulated 1 m and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 and overall accuracies of 86.40% and 81.40%, respectively. We conclude that the 3 m spatial resolution data of the PS comprise an alternative to delineate management zones for smallholder farming in southern Brazil when compared to the very-high spatial resolution observations of the UAV cameras.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952935"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952935"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952935; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952935]").text(description); $(".js-view-count[data-work-id=114952935]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952935; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952935']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=114952935]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952935,"title":"Assessing the effect of spatial resolution on the delineation of management zones for smallholder farming in southern Brazil","translated_title":"","metadata":{"abstract":"Abstract Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delineate management zones for smallholder farming in southern Brazil. In this study, we investigated the spatial resolution influence on the AGB estimates of rye, calculated from regression relationships with the Normalized Difference Vegetation Index (NDVI), and on the subsequent delineation of management zones using the Management Zone Analyst (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were compared with observations from the PlanetScope (PS) satellite constellation (3 m) and the Operational Land Imager (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green vegetation, soil, and shadow) was applied to surface reflectance data of these instruments for evaluating the cover-crop development at two dates in August 2017. Because of the differences in the technical specifications of the sensors, we resampled the UAV dataset into four levels of spatial resolution (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, we obtained confusion matrices for the original and resampled data. The results showed that the increasing amounts of rye AGB from the beginning to the end of August promoted significant changes in surface reflectance and in soil-green vegetation fractions calculated at variable spatial resolution. The performance of the regression models to estimate cover-crop AGB was approximately similar in the transition from the sub-metric (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV camera to the PS data. For all datasets, the MZA detected two management zones with zone 2 having higher cover-crop AGB than zone 1. When compared to the UAV management zone map (reference), the PS map had a moderate-to-substantial agreement, while the OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial agreements with the reference map were observed at simulated 1 m and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 and overall accuracies of 86.40% and 81.40%, respectively. We conclude that the 3 m spatial resolution data of the PS comprise an alternative to delineate management zones for smallholder farming in southern Brazil when compared to the very-high spatial resolution observations of the UAV cameras.","publisher":"Elsevier BV","publication_date":{"day":1,"month":8,"year":2020,"errors":{}},"publication_name":"Remote Sensing Applications: Society and Environment"},"translated_abstract":"Abstract Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delineate management zones for smallholder farming in southern Brazil. In this study, we investigated the spatial resolution influence on the AGB estimates of rye, calculated from regression relationships with the Normalized Difference Vegetation Index (NDVI), and on the subsequent delineation of management zones using the Management Zone Analyst (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were compared with observations from the PlanetScope (PS) satellite constellation (3 m) and the Operational Land Imager (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green vegetation, soil, and shadow) was applied to surface reflectance data of these instruments for evaluating the cover-crop development at two dates in August 2017. Because of the differences in the technical specifications of the sensors, we resampled the UAV dataset into four levels of spatial resolution (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, we obtained confusion matrices for the original and resampled data. The results showed that the increasing amounts of rye AGB from the beginning to the end of August promoted significant changes in surface reflectance and in soil-green vegetation fractions calculated at variable spatial resolution. The performance of the regression models to estimate cover-crop AGB was approximately similar in the transition from the sub-metric (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV camera to the PS data. For all datasets, the MZA detected two management zones with zone 2 having higher cover-crop AGB than zone 1. When compared to the UAV management zone map (reference), the PS map had a moderate-to-substantial agreement, while the OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial agreements with the reference map were observed at simulated 1 m and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 and overall accuracies of 86.40% and 81.40%, respectively. We conclude that the 3 m spatial resolution data of the PS comprise an alternative to delineate management zones for smallholder farming in southern Brazil when compared to the very-high spatial resolution observations of the UAV cameras.","internal_url":"https://www.academia.edu/114952935/Assessing_the_effect_of_spatial_resolution_on_the_delineation_of_management_zones_for_smallholder_farming_in_southern_Brazil","translated_internal_url":"","created_at":"2024-02-15T11:02:42.823-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Assessing_the_effect_of_spatial_resolution_on_the_delineation_of_management_zones_for_smallholder_farming_in_southern_Brazil","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Abstract Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delineate management zones for smallholder farming in southern Brazil. In this study, we investigated the spatial resolution influence on the AGB estimates of rye, calculated from regression relationships with the Normalized Difference Vegetation Index (NDVI), and on the subsequent delineation of management zones using the Management Zone Analyst (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were compared with observations from the PlanetScope (PS) satellite constellation (3 m) and the Operational Land Imager (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green vegetation, soil, and shadow) was applied to surface reflectance data of these instruments for evaluating the cover-crop development at two dates in August 2017. Because of the differences in the technical specifications of the sensors, we resampled the UAV dataset into four levels of spatial resolution (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, we obtained confusion matrices for the original and resampled data. The results showed that the increasing amounts of rye AGB from the beginning to the end of August promoted significant changes in surface reflectance and in soil-green vegetation fractions calculated at variable spatial resolution. The performance of the regression models to estimate cover-crop AGB was approximately similar in the transition from the sub-metric (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV camera to the PS data. For all datasets, the MZA detected two management zones with zone 2 having higher cover-crop AGB than zone 1. When compared to the UAV management zone map (reference), the PS map had a moderate-to-substantial agreement, while the OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial agreements with the reference map were observed at simulated 1 m and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 and overall accuracies of 86.40% and 81.40%, respectively. We conclude that the 3 m spatial resolution data of the PS comprise an alternative to delineate management zones for smallholder farming in southern Brazil when compared to the very-high spatial resolution observations of the UAV cameras.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":10225,"name":"Agriculture","url":"https://www.academia.edu/Documents/in/Agriculture"}],"urls":[{"id":39493896,"url":"https://doi.org/10.1016/j.rsase.2020.100325"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952934"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114952934/Avalia%C3%A7%C3%A3o_Das_Rela%C3%A7%C3%B5es_Entre_Os_Par%C3%A2metros_Da_Modelagem_Geomorfol%C3%B3gica_e_a_Cobertura_Florestal_No_Noroeste_Do_Rio_Grande_Do_Sul_Assessment_of_Relations_Between_the_Geomorphological_Modeling_Parameters_and_the_Forest_Cover_in_the_Northwest_of_RS"><img alt="Research paper thumbnail of Avaliação Das Relações Entre Os Parâmetros Da Modelagem Geomorfológica e a Cobertura Florestal No Noroeste Do Rio Grande Do Sul / Assessment of Relations Between the Geomorphological Modeling Parameters and the Forest Cover in the Northwest of RS" class="work-thumbnail" src="https://attachments.academia-assets.com/111503636/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114952934/Avalia%C3%A7%C3%A3o_Das_Rela%C3%A7%C3%B5es_Entre_Os_Par%C3%A2metros_Da_Modelagem_Geomorfol%C3%B3gica_e_a_Cobertura_Florestal_No_Noroeste_Do_Rio_Grande_Do_Sul_Assessment_of_Relations_Between_the_Geomorphological_Modeling_Parameters_and_the_Forest_Cover_in_the_Northwest_of_RS">Avaliação Das Relações Entre Os Parâmetros Da Modelagem Geomorfológica e a Cobertura Florestal No Noroeste Do Rio Grande Do Sul / Assessment of Relations Between the Geomorphological Modeling Parameters and the Forest Cover in the Northwest of RS</a></div><div class="wp-workCard_item"><span>Geo UERJ</span><span>, Dec 30, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">© 2017 Rosa et al. Este é um artigo de acesso aberto distribuído sob os termos da Licença Creativ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">© 2017 Rosa et al. Este é um artigo de acesso aberto distribuído sob os termos da Licença Creative Commons Atribuição-Não Comercial-Compartilha Igual (CC BY-NC-SA 4.0), que permite uso, distribuição e reprodução para fins não comercias, com a citação dos autores e da fonte original e sob a mesma licença..</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2bd7a95bbcb93f56092cb4b300f30516" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111503636,"asset_id":114952934,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111503636/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952934"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952934"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952934; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952934]").text(description); $(".js-view-count[data-work-id=114952934]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952934; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952934']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2bd7a95bbcb93f56092cb4b300f30516" } } $('.js-work-strip[data-work-id=114952934]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952934,"title":"Avaliação Das Relações Entre Os Parâmetros Da Modelagem Geomorfológica e a Cobertura Florestal No Noroeste Do Rio Grande Do Sul / Assessment of Relations Between the Geomorphological Modeling Parameters and the Forest Cover in the Northwest of RS","translated_title":"","metadata":{"publisher":"Universidade do Estado do Rio de Janeiro","grobid_abstract":"© 2017 Rosa et al. Este é um artigo de acesso aberto distribuído sob os termos da Licença Creative Commons Atribuição-Não Comercial-Compartilha Igual (CC BY-NC-SA 4.0), que permite uso, distribuição e reprodução para fins não comercias, com a citação dos autores e da fonte original e sob a mesma licença..","publication_date":{"day":30,"month":12,"year":2017,"errors":{}},"publication_name":"Geo UERJ","grobid_abstract_attachment_id":111503635},"translated_abstract":null,"internal_url":"https://www.academia.edu/114952934/Avalia%C3%A7%C3%A3o_Das_Rela%C3%A7%C3%B5es_Entre_Os_Par%C3%A2metros_Da_Modelagem_Geomorfol%C3%B3gica_e_a_Cobertura_Florestal_No_Noroeste_Do_Rio_Grande_Do_Sul_Assessment_of_Relations_Between_the_Geomorphological_Modeling_Parameters_and_the_Forest_Cover_in_the_Northwest_of_RS","translated_internal_url":"","created_at":"2024-02-15T11:02:42.610-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":111503636,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503636/thumbnails/1.jpg","file_name":"23053.pdf","download_url":"https://www.academia.edu/attachments/111503636/download_file","bulk_download_file_name":"Avaliacao_Das_Relacoes_Entre_Os_Parametr.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503636/23053-libre.pdf?1708023866=\u0026response-content-disposition=attachment%3B+filename%3DAvaliacao_Das_Relacoes_Entre_Os_Parametr.pdf\u0026Expires=1741801542\u0026Signature=AEmXyENqYj2CjqUr5cQ2aWGjtmz46Q7VKBnL~XvyJlmbt8pEG-9eSFv~iethCnGi10nsweAW8GBmMD8Ig94olB7d0u~01PZn-c-ziDAEFxZQyPwKpngAYyP6qrS-2Q45TuAMAzkTxzjl~x-l16FLt2li0wo5aW4tuXrLSUUClNmHKsKyvb5bqFyV5~E7wqM~jxAyoMO9BzVUuPr9yayvzwZnU2dmxoKN7gUpqLgrjiLAV4m4IvpZU1O48Mf~yuSAzG40g2QuKh6kcmloEIm2~4~YeJYKtolq7IB0n1gsKidLBbG0-OSPsGFO7A9p~GO~a~exl~4WI1ZCVq1upYBbMA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Avaliação_Das_Relações_Entre_Os_Parâmetros_Da_Modelagem_Geomorfológica_e_a_Cobertura_Florestal_No_Noroeste_Do_Rio_Grande_Do_Sul_Assessment_of_Relations_Between_the_Geomorphological_Modeling_Parameters_and_the_Forest_Cover_in_the_Northwest_of_RS","translated_slug":"","page_count":22,"language":"pt","content_type":"Work","summary":"© 2017 Rosa et al. Este é um artigo de acesso aberto distribuído sob os termos da Licença Creative Commons Atribuição-Não Comercial-Compartilha Igual (CC BY-NC-SA 4.0), que permite uso, distribuição e reprodução para fins não comercias, com a citação dos autores e da fonte original e sob a mesma licença..","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":111503636,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503636/thumbnails/1.jpg","file_name":"23053.pdf","download_url":"https://www.academia.edu/attachments/111503636/download_file","bulk_download_file_name":"Avaliacao_Das_Relacoes_Entre_Os_Parametr.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503636/23053-libre.pdf?1708023866=\u0026response-content-disposition=attachment%3B+filename%3DAvaliacao_Das_Relacoes_Entre_Os_Parametr.pdf\u0026Expires=1741801542\u0026Signature=AEmXyENqYj2CjqUr5cQ2aWGjtmz46Q7VKBnL~XvyJlmbt8pEG-9eSFv~iethCnGi10nsweAW8GBmMD8Ig94olB7d0u~01PZn-c-ziDAEFxZQyPwKpngAYyP6qrS-2Q45TuAMAzkTxzjl~x-l16FLt2li0wo5aW4tuXrLSUUClNmHKsKyvb5bqFyV5~E7wqM~jxAyoMO9BzVUuPr9yayvzwZnU2dmxoKN7gUpqLgrjiLAV4m4IvpZU1O48Mf~yuSAzG40g2QuKh6kcmloEIm2~4~YeJYKtolq7IB0n1gsKidLBbG0-OSPsGFO7A9p~GO~a~exl~4WI1ZCVq1upYBbMA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":111503635,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503635/thumbnails/1.jpg","file_name":"23053.pdf","download_url":"https://www.academia.edu/attachments/111503635/download_file","bulk_download_file_name":"Avaliacao_Das_Relacoes_Entre_Os_Parametr.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503635/23053-libre.pdf?1708023859=\u0026response-content-disposition=attachment%3B+filename%3DAvaliacao_Das_Relacoes_Entre_Os_Parametr.pdf\u0026Expires=1741801542\u0026Signature=Gc1YbJXcbNdLoXe5SsgQezQjE3CP4tVwnJkqqpnenq5gUOC6KE3iKT3XxRPI4f9U9rCTodLeKdsxbOoxqeATriILB2~wxyRDrygUihx-tvlW2ZMJON~-ltiw9oekQJ~8gnDyJfeYGFrOjlAJeMnVts~zhOSeq~GrkP2VCA3EViDsGE8ju6h5d85Gvw6Kw4X1fY5G9jlFHMPklcCwcI9nQIn91PgJD1IUAehR6jbq3jQUB7JeoFA-lNx338RbtujI2DY2wRazqH1bkeLxZ9Wmmw1tulEZv7DGDZx9PWl7Ov-6Z1uQ7bH5lD1GLqQUWcqnkwVtgka-unNqKE4sN2S6UA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":1041,"name":"Forestry","url":"https://www.academia.edu/Documents/in/Forestry"}],"urls":[{"id":39493895,"url":"https://www.e-publicacoes.uerj.br/index.php/geouerj/article/download/19502/23053"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952933"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114952933/A_hyperspectral_experiment_over_tropical_forests_based_on_the_EO_1_orbit_change_and_PROSAIL_simulation"><img alt="Research paper thumbnail of A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation" class="work-thumbnail" src="https://attachments.academia-assets.com/111503654/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114952933/A_hyperspectral_experiment_over_tropical_forests_based_on_the_EO_1_orbit_change_and_PROSAIL_simulation">A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation</a></div><div class="wp-workCard_item"><span>Giscience & Remote Sensing</span><span>, Sep 24, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004-2008) to the EO-1 drift period (2011-2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14°(2015) and 22°(2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen's r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="16a7c8160e53a3b7f5968d1775d2ac77" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111503654,"asset_id":114952933,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111503654/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952933"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952933"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952933; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952933]").text(description); $(".js-view-count[data-work-id=114952933]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952933; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952933']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "16a7c8160e53a3b7f5968d1775d2ac77" } } $('.js-work-strip[data-work-id=114952933]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952933,"title":"A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation","translated_title":"","metadata":{"publisher":"Taylor \u0026 Francis","grobid_abstract":"We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. 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The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14°(2015) and 22°(2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen's r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. 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Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen's r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":111503654,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503654/thumbnails/1.jpg","file_name":"15481603.2019.166859520240215-1-f46thg.pdf","download_url":"https://www.academia.edu/attachments/111503654/download_file","bulk_download_file_name":"A_hyperspectral_experiment_over_tropical.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503654/15481603.2019.166859520240215-1-f46thg-libre.pdf?1708023850=\u0026response-content-disposition=attachment%3B+filename%3DA_hyperspectral_experiment_over_tropical.pdf\u0026Expires=1741801542\u0026Signature=MyYnjMeNTcwW6bQoX~Pk9T54HceUGk0JG4M6if6N8SlMFHcTELtJB96YuggDmCR0Uo3l~UNtOOKFmGD3m7g7Y6oDnPwc7ERoUGiVAJ0cbos5ua0ytjtCmNmbKBUZ6XkQ0C4CZ75zw31eDPjate8wQaOyotkqvERA0oK9SQqZQ1nvvlfPoJuX7V3opm7DZUV1Yqdf3xX4S4~jBEAdC5-613tI1fM-x~uqLvWK8RDbK6px42oIDHjrCUgViPWfI2EbPZR1dlCEqgf~co~de7Vib1cjJtJum3pAJTuaSSb5bxgSBWEHvCPUXPsyu53LuCRbRUvFl8823YrnUSarAHXKag__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1252,"name":"Remote Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing"},{"id":108112,"name":"Tropical forest","url":"https://www.academia.edu/Documents/in/Tropical_forest"},{"id":285356,"name":"Hyperspectral Imaging","url":"https://www.academia.edu/Documents/in/Hyperspectral_Imaging"}],"urls":[{"id":39493894,"url":"https://doi.org/10.1080/15481603.2019.1668595"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952932"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114952932/Din%C3%A2mica_Espacial_Do_Estoque_De_Biomassa_e_Carbono_Em_Remanescentes_Florestais_No_Rio_Grande_Do_Sul_Brasil"><img alt="Research paper thumbnail of Dinâmica Espacial Do Estoque De Biomassa e Carbono Em Remanescentes Florestais No Rio Grande Do Sul – Brasil" class="work-thumbnail" src="https://attachments.academia-assets.com/111503653/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114952932/Din%C3%A2mica_Espacial_Do_Estoque_De_Biomassa_e_Carbono_Em_Remanescentes_Florestais_No_Rio_Grande_Do_Sul_Brasil">Dinâmica Espacial Do Estoque De Biomassa e Carbono Em Remanescentes Florestais No Rio Grande Do Sul – Brasil</a></div><div class="wp-workCard_item"><span>Biofix Scientific Journal</span><span>, Apr 11, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Entre os diversos serviços ambientais prestados pelos ecossistemas florestais, destaca-se o seque...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Entre os diversos serviços ambientais prestados pelos ecossistemas florestais, destaca-se o sequestro e estocagem do carbono na forma de biomassa. Assim, o objetivo do trabalho foi estimar e investigar a dinâmica do estoque da biomassa e do carbono florestal aéreo em estágios sucessionais de regeneração em florestas nativas na Região Noroeste do Estado do Rio Grande do Sul, Brasil, utilizando dados de Sensoriamento Remoto. O estudo foi realizado em três décadas (1985-2014), abrangendo 9.046,9 km². A classificação das sucessões florestais em estágios de regeneração foi realizada a partir das seguintes classes de tamanho: inicial (< 5 ha), médio (5-10 ha), avançado (> 10 ha). Na sequência, foi estimada a biomassa e o carbono para cada fragmento florestal, conforme sua evolução ao longo do tempo. A biomassa florestal foi estimada para as datas de 1985, 1994, 2005 e 2014, avaliando a dinâmica da biomassa e do carbono florestal presente na área de estudo durante este período. A biomassa total estimada foi de 80.831 Gg, dividida em três estágios de regeneração: inicial (659 Gg), médio (2.549 Gg) e avançado (77.623 Gg). O carbono estimado foi de 296 Gg para o estágio inicial, 1.147 para o estágio médio e 34.930 para o estágio avançado, totalizando 36.373 Gg Os resultados obtidos neste estudo são de suma importância, pois demonstram que as florestas presentes na região noroeste do Rio Grande Do Sul estão contribuindo positivamente com a remoção de dióxido de carbono da atmosfera, consequentemente auxiliando na mitigação das alterações climáticas.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0f018c9f615bac48349d5599adfca0c8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111503653,"asset_id":114952932,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111503653/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952932"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952932"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952932; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952932]").text(description); $(".js-view-count[data-work-id=114952932]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952932; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952932']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0f018c9f615bac48349d5599adfca0c8" } } $('.js-work-strip[data-work-id=114952932]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952932,"title":"Dinâmica Espacial Do Estoque De Biomassa e Carbono Em Remanescentes Florestais No Rio Grande Do Sul – Brasil","translated_title":"","metadata":{"publisher":"Universidade Federal do Paraná","grobid_abstract":"Entre os diversos serviços ambientais prestados pelos ecossistemas florestais, destaca-se o sequestro e estocagem do carbono na forma de biomassa. Assim, o objetivo do trabalho foi estimar e investigar a dinâmica do estoque da biomassa e do carbono florestal aéreo em estágios sucessionais de regeneração em florestas nativas na Região Noroeste do Estado do Rio Grande do Sul, Brasil, utilizando dados de Sensoriamento Remoto. O estudo foi realizado em três décadas (1985-2014), abrangendo 9.046,9 km². A classificação das sucessões florestais em estágios de regeneração foi realizada a partir das seguintes classes de tamanho: inicial (\u003c 5 ha), médio (5-10 ha), avançado (\u003e 10 ha). Na sequência, foi estimada a biomassa e o carbono para cada fragmento florestal, conforme sua evolução ao longo do tempo. A biomassa florestal foi estimada para as datas de 1985, 1994, 2005 e 2014, avaliando a dinâmica da biomassa e do carbono florestal presente na área de estudo durante este período. A biomassa total estimada foi de 80.831 Gg, dividida em três estágios de regeneração: inicial (659 Gg), médio (2.549 Gg) e avançado (77.623 Gg). O carbono estimado foi de 296 Gg para o estágio inicial, 1.147 para o estágio médio e 34.930 para o estágio avançado, totalizando 36.373 Gg Os resultados obtidos neste estudo são de suma importância, pois demonstram que as florestas presentes na região noroeste do Rio Grande Do Sul estão contribuindo positivamente com a remoção de dióxido de carbono da atmosfera, consequentemente auxiliando na mitigação das alterações climáticas.","publication_date":{"day":11,"month":4,"year":2023,"errors":{}},"publication_name":"Biofix Scientific Journal","grobid_abstract_attachment_id":111503653},"translated_abstract":null,"internal_url":"https://www.academia.edu/114952932/Din%C3%A2mica_Espacial_Do_Estoque_De_Biomassa_e_Carbono_Em_Remanescentes_Florestais_No_Rio_Grande_Do_Sul_Brasil","translated_internal_url":"","created_at":"2024-02-15T11:02:42.099-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":111503653,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503653/thumbnails/1.jpg","file_name":"49049.pdf","download_url":"https://www.academia.edu/attachments/111503653/download_file","bulk_download_file_name":"Dinamica_Espacial_Do_Estoque_De_Biomassa.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503653/49049-libre.pdf?1708023850=\u0026response-content-disposition=attachment%3B+filename%3DDinamica_Espacial_Do_Estoque_De_Biomassa.pdf\u0026Expires=1741801542\u0026Signature=ddfOWFyxFIlSKi4A0E7Y24EZX1QNhD7fluPPgUe-SBfgeiQghkhE8tg8YueHQKxACMpz7snuJV4qBb25krlAsFp601tdRF52HOAXv5RbQPp2TUOz~l1fSTTQ2WER1eGPiRrmcabnjyeXcOD52esCZJslsqF3VawtXqOtPWfvh8dk9aJ6Fz3P6Y-6dcU9Wxcy-ZdU22uThbHskDLSDUOs~VWgWhYLl1XdLosP-n5dqPDeV-IAfgQK9snS069xV5JYUIpEFJzcRboDLSCt8lprbuArPCfx2SQ5HrFaC8HYrJ0~af7sBCytJB2ThxhxScZhy9XevmQJKYQ-4oUjLkK6Vw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Dinâmica_Espacial_Do_Estoque_De_Biomassa_e_Carbono_Em_Remanescentes_Florestais_No_Rio_Grande_Do_Sul_Brasil","translated_slug":"","page_count":9,"language":"pt","content_type":"Work","summary":"Entre os diversos serviços ambientais prestados pelos ecossistemas florestais, destaca-se o sequestro e estocagem do carbono na forma de biomassa. Assim, o objetivo do trabalho foi estimar e investigar a dinâmica do estoque da biomassa e do carbono florestal aéreo em estágios sucessionais de regeneração em florestas nativas na Região Noroeste do Estado do Rio Grande do Sul, Brasil, utilizando dados de Sensoriamento Remoto. O estudo foi realizado em três décadas (1985-2014), abrangendo 9.046,9 km². A classificação das sucessões florestais em estágios de regeneração foi realizada a partir das seguintes classes de tamanho: inicial (\u003c 5 ha), médio (5-10 ha), avançado (\u003e 10 ha). Na sequência, foi estimada a biomassa e o carbono para cada fragmento florestal, conforme sua evolução ao longo do tempo. A biomassa florestal foi estimada para as datas de 1985, 1994, 2005 e 2014, avaliando a dinâmica da biomassa e do carbono florestal presente na área de estudo durante este período. A biomassa total estimada foi de 80.831 Gg, dividida em três estágios de regeneração: inicial (659 Gg), médio (2.549 Gg) e avançado (77.623 Gg). O carbono estimado foi de 296 Gg para o estágio inicial, 1.147 para o estágio médio e 34.930 para o estágio avançado, totalizando 36.373 Gg Os resultados obtidos neste estudo são de suma importância, pois demonstram que as florestas presentes na região noroeste do Rio Grande Do Sul estão contribuindo positivamente com a remoção de dióxido de carbono da atmosfera, consequentemente auxiliando na mitigação das alterações climáticas.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":111503653,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503653/thumbnails/1.jpg","file_name":"49049.pdf","download_url":"https://www.academia.edu/attachments/111503653/download_file","bulk_download_file_name":"Dinamica_Espacial_Do_Estoque_De_Biomassa.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503653/49049-libre.pdf?1708023850=\u0026response-content-disposition=attachment%3B+filename%3DDinamica_Espacial_Do_Estoque_De_Biomassa.pdf\u0026Expires=1741801542\u0026Signature=ddfOWFyxFIlSKi4A0E7Y24EZX1QNhD7fluPPgUe-SBfgeiQghkhE8tg8YueHQKxACMpz7snuJV4qBb25krlAsFp601tdRF52HOAXv5RbQPp2TUOz~l1fSTTQ2WER1eGPiRrmcabnjyeXcOD52esCZJslsqF3VawtXqOtPWfvh8dk9aJ6Fz3P6Y-6dcU9Wxcy-ZdU22uThbHskDLSDUOs~VWgWhYLl1XdLosP-n5dqPDeV-IAfgQK9snS069xV5JYUIpEFJzcRboDLSCt8lprbuArPCfx2SQ5HrFaC8HYrJ0~af7sBCytJB2ThxhxScZhy9XevmQJKYQ-4oUjLkK6Vw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1041,"name":"Forestry","url":"https://www.academia.edu/Documents/in/Forestry"},{"id":459822,"name":"Carbon Stock","url":"https://www.academia.edu/Documents/in/Carbon_Stock"}],"urls":[{"id":39493893,"url":"https://doi.org/10.5380/biofix.v8i1.86271"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952931"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114952931/Directional_Effects_on_the_Spectral_Response_of_Pinus_Elliottii_Stands_Cultivated_in_Subtropical_Latitudes"><img alt="Research paper thumbnail of Directional Effects on the Spectral Response of Pinus Elliottii Stands Cultivated in Subtropical Latitudes" class="work-thumbnail" src="https://attachments.academia-assets.com/111503652/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114952931/Directional_Effects_on_the_Spectral_Response_of_Pinus_Elliottii_Stands_Cultivated_in_Subtropical_Latitudes">Directional Effects on the Spectral Response of Pinus Elliottii Stands Cultivated in Subtropical Latitudes</a></div><div class="wp-workCard_item"><span>Ra'e Ga</span><span>, Apr 22, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Remote sensing techniques and related products are an efficient and low-cost alternative of great...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Remote sensing techniques and related products are an efficient and low-cost alternative of great potential for monitoring Pine crops. However, there are no conclusive studies on the magnitude of directional effects caused by variations in the geometry of data acquisition by large field-of-view satellite instruments face to the well-defined canopy architecture of Pine. This paper investigates the viewing geometry influence on the data acquisition of reflectance and vegetation indices used to monitor Pine stands. Two multitemporal series of reflectance measurements of the MOD09GA and MCD43A4 products, without and with the application of nadir normalization, respectively, were acquired and sampled according to the view zenith angle for pairs of consecutive dates with observations made in opposite directions. The influence of directional effects on the response of the Enhanced Vegetation Index, Normalized Difference Vegetation Index, and Wide Dynamic Range Vegetation Index was verified from analysis of variance (ANOVA). The results showed that the reflectance measurements were substantially influenced by the directional effects, showing lower values when calculating from the forward scattering direction, and higher values when obtained from the backscattering direction. From the three studied vegetation indices, the EVI was the most sensitive metric to directional effects. However, when calculating using normalized reflectance measurements to nadir, the directional influence on the EVI was greatly reduced when compared to the other two vegetation indices.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="40fdcbc74f78c49dcd7472eabf0be32c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111503652,"asset_id":114952931,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111503652/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952931"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952931"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952931; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952931]").text(description); $(".js-view-count[data-work-id=114952931]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952931; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952931']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "40fdcbc74f78c49dcd7472eabf0be32c" } } $('.js-work-strip[data-work-id=114952931]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952931,"title":"Directional Effects on the Spectral Response of Pinus Elliottii Stands Cultivated in Subtropical Latitudes","translated_title":"","metadata":{"publisher":"Universidade Federal do Paraná","ai_title_tag":"Directional Effects on Spectral Response in Pine Crops","grobid_abstract":"Remote sensing techniques and related products are an efficient and low-cost alternative of great potential for monitoring Pine crops. However, there are no conclusive studies on the magnitude of directional effects caused by variations in the geometry of data acquisition by large field-of-view satellite instruments face to the well-defined canopy architecture of Pine. This paper investigates the viewing geometry influence on the data acquisition of reflectance and vegetation indices used to monitor Pine stands. Two multitemporal series of reflectance measurements of the MOD09GA and MCD43A4 products, without and with the application of nadir normalization, respectively, were acquired and sampled according to the view zenith angle for pairs of consecutive dates with observations made in opposite directions. The influence of directional effects on the response of the Enhanced Vegetation Index, Normalized Difference Vegetation Index, and Wide Dynamic Range Vegetation Index was verified from analysis of variance (ANOVA). The results showed that the reflectance measurements were substantially influenced by the directional effects, showing lower values when calculating from the forward scattering direction, and higher values when obtained from the backscattering direction. From the three studied vegetation indices, the EVI was the most sensitive metric to directional effects. However, when calculating using normalized reflectance measurements to nadir, the directional influence on the EVI was greatly reduced when compared to the other two vegetation indices.","publication_date":{"day":22,"month":4,"year":2023,"errors":{}},"publication_name":"Ra'e Ga","grobid_abstract_attachment_id":111503652},"translated_abstract":null,"internal_url":"https://www.academia.edu/114952931/Directional_Effects_on_the_Spectral_Response_of_Pinus_Elliottii_Stands_Cultivated_in_Subtropical_Latitudes","translated_internal_url":"","created_at":"2024-02-15T11:02:41.290-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":111503652,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503652/thumbnails/1.jpg","file_name":"49130.pdf","download_url":"https://www.academia.edu/attachments/111503652/download_file","bulk_download_file_name":"Directional_Effects_on_the_Spectral_Resp.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503652/49130-libre.pdf?1708023857=\u0026response-content-disposition=attachment%3B+filename%3DDirectional_Effects_on_the_Spectral_Resp.pdf\u0026Expires=1741801542\u0026Signature=ehT9Nhwbus0XcHn2W6MJeE6hW0oZoeEQR9RmaD8H0rbxSoCR8mGa7TjESfCCC~BKATI6KDG6vHa2hsXaFamX3YPQ8vH-cVr0yGL7vLvFyaz9bPOXMbfGuH19W76UVt~pqt-DdZ0GKmGyG~PQwtP7y4RaLPXs53s6Ybd6lawM~Gf7NxfGItz3M3K5hSONt5T3OyL5tujm0MLOIU8egqTMl924keZdgWv8wr9rtAMnJ-ZmgLNtfwvKn~FCB3zoaOz6R7X1Lua~ysh52OKkW0dODzMQ3sYXGXX1o3cEg2UImj0dpR0GAkud9f26rqQcFpv96VB3MHi4gLIWmONrth7K6Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Directional_Effects_on_the_Spectral_Response_of_Pinus_Elliottii_Stands_Cultivated_in_Subtropical_Latitudes","translated_slug":"","page_count":22,"language":"en","content_type":"Work","summary":"Remote sensing techniques and related products are an efficient and low-cost alternative of great potential for monitoring Pine crops. However, there are no conclusive studies on the magnitude of directional effects caused by variations in the geometry of data acquisition by large field-of-view satellite instruments face to the well-defined canopy architecture of Pine. This paper investigates the viewing geometry influence on the data acquisition of reflectance and vegetation indices used to monitor Pine stands. Two multitemporal series of reflectance measurements of the MOD09GA and MCD43A4 products, without and with the application of nadir normalization, respectively, were acquired and sampled according to the view zenith angle for pairs of consecutive dates with observations made in opposite directions. The influence of directional effects on the response of the Enhanced Vegetation Index, Normalized Difference Vegetation Index, and Wide Dynamic Range Vegetation Index was verified from analysis of variance (ANOVA). The results showed that the reflectance measurements were substantially influenced by the directional effects, showing lower values when calculating from the forward scattering direction, and higher values when obtained from the backscattering direction. From the three studied vegetation indices, the EVI was the most sensitive metric to directional effects. However, when calculating using normalized reflectance measurements to nadir, the directional influence on the EVI was greatly reduced when compared to the other two vegetation indices.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":111503652,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503652/thumbnails/1.jpg","file_name":"49130.pdf","download_url":"https://www.academia.edu/attachments/111503652/download_file","bulk_download_file_name":"Directional_Effects_on_the_Spectral_Resp.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503652/49130-libre.pdf?1708023857=\u0026response-content-disposition=attachment%3B+filename%3DDirectional_Effects_on_the_Spectral_Resp.pdf\u0026Expires=1741801542\u0026Signature=ehT9Nhwbus0XcHn2W6MJeE6hW0oZoeEQR9RmaD8H0rbxSoCR8mGa7TjESfCCC~BKATI6KDG6vHa2hsXaFamX3YPQ8vH-cVr0yGL7vLvFyaz9bPOXMbfGuH19W76UVt~pqt-DdZ0GKmGyG~PQwtP7y4RaLPXs53s6Ybd6lawM~Gf7NxfGItz3M3K5hSONt5T3OyL5tujm0MLOIU8egqTMl924keZdgWv8wr9rtAMnJ-ZmgLNtfwvKn~FCB3zoaOz6R7X1Lua~ysh52OKkW0dODzMQ3sYXGXX1o3cEg2UImj0dpR0GAkud9f26rqQcFpv96VB3MHi4gLIWmONrth7K6Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1041,"name":"Forestry","url":"https://www.academia.edu/Documents/in/Forestry"}],"urls":[{"id":39493892,"url":"https://doi.org/10.5380/raega.v56i0.85857"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952930"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114952930/Forest_Biomass_and_Carbon_Estimation_Using_Vegetation_Indices_and_Leaf_Area_Index"><img alt="Research paper thumbnail of Forest Biomass and Carbon Estimation Using Vegetation Indices and Leaf Area Index" class="work-thumbnail" src="https://attachments.academia-assets.com/111503638/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114952930/Forest_Biomass_and_Carbon_Estimation_Using_Vegetation_Indices_and_Leaf_Area_Index">Forest Biomass and Carbon Estimation Using Vegetation Indices and Leaf Area Index</a></div><div class="wp-workCard_item"><span>Revista Brasileira de Geografia Física</span><span>, Jul 19, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Among the consequences of the current dynamics of manmade land use and cover, forest fragmentatio...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Among the consequences of the current dynamics of manmade land use and cover, forest fragmentation results in a large number of isolated fragments of different sizes. Forest provides many environmental services such as carbon sequestration and storage in the form of biomass, demonstrating the crucial role of native forests and the need to develop new methods that estimate forest biomass and carbon by non-destructive methods. This work aimed to develop a methodology to study and estimate the dynamics of forest biomass and carbon stock in successive stages of regeneration in the northwestern region of Rio Grande do Sul, which was the study area. Yearly images from 1985 to 2014 of the Normalized Difference Vegetation Index-NDVI were calculated, and a global regression was archived to obtain the leaf area index (LAI) and estimate the forest biomass. It was possible to observe the good relationship between the increase in LAI as a function of the NDVI, estimating the forest biomass of 61,156 Gg in 2014, where 602 Gg correspond to the initial regeneration stage, 8,287 Gg for the medium stage, and 52,267 Gg for the advanced stage of regeneration. The total estimated carbon for the study area in 2014 was 27,520 Gg CO2 of which 271 Gg were present in the initial stage, 3,729 Gg in the medium stage, and 23,520 Gg in the advanced stage of regeneration.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="929712dca2d1c5b1c71e09d125b7b4c2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111503638,"asset_id":114952930,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111503638/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952930"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952930"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952930; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952930]").text(description); $(".js-view-count[data-work-id=114952930]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952930; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952930']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "929712dca2d1c5b1c71e09d125b7b4c2" } } $('.js-work-strip[data-work-id=114952930]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952930,"title":"Forest Biomass and Carbon Estimation Using Vegetation Indices and Leaf Area Index","translated_title":"","metadata":{"publisher":"Federal University of Pernambuco","ai_title_tag":"Estimating Forest Biomass and Carbon Using NDVI and LAI","grobid_abstract":"Among the consequences of the current dynamics of manmade land use and cover, forest fragmentation results in a large number of isolated fragments of different sizes. Forest provides many environmental services such as carbon sequestration and storage in the form of biomass, demonstrating the crucial role of native forests and the need to develop new methods that estimate forest biomass and carbon by non-destructive methods. This work aimed to develop a methodology to study and estimate the dynamics of forest biomass and carbon stock in successive stages of regeneration in the northwestern region of Rio Grande do Sul, which was the study area. Yearly images from 1985 to 2014 of the Normalized Difference Vegetation Index-NDVI were calculated, and a global regression was archived to obtain the leaf area index (LAI) and estimate the forest biomass. It was possible to observe the good relationship between the increase in LAI as a function of the NDVI, estimating the forest biomass of 61,156 Gg in 2014, where 602 Gg correspond to the initial regeneration stage, 8,287 Gg for the medium stage, and 52,267 Gg for the advanced stage of regeneration. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="100373" id="papers"><div class="js-work-strip profile--work_container" data-work-id="122115072"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/122115072/Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_November_1_2019_Rio_Grande_do_Sul_Brazil"><img alt="Research paper thumbnail of Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/122115072/Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_November_1_2019_Rio_Grande_do_Sul_Brazil">Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">&lt;strong&gt;Title&lt;/strong&gt;: Unmanned Aerial Vehicle (UAV) data acquired over a subtropica...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">&lt;strong&gt;Title&lt;/strong&gt;: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil &lt;strong&gt;Keywords&lt;/strong&gt; Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. &lt;strong&gt;Data description:&lt;/strong&gt; The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also &lt;em&gt;Drone&lt;/em&gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Please, see the PDF file. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): &lt;strong&gt;Parameters&lt;/strong&gt; Specification/value Date (YYYYMMDD): 20191101 Time of day (BRT = -3) 13:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 250 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High-speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3&quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 301 photos Across track coverage 80% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br" rel="nofollow">breunig@ufsm.br</a> An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Please, see the PDF file. Figure 2. The capture of an orthomosaic in the processing [...]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115072"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115072"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115072; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115072]").text(description); $(".js-view-count[data-work-id=122115072]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115072; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115072']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=122115072]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115072,"title":"Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil","translated_title":"","metadata":{"abstract":"\u0026lt;strong\u0026gt;Title\u0026lt;/strong\u0026gt;: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil \u0026lt;strong\u0026gt;Keywords\u0026lt;/strong\u0026gt; Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. \u0026lt;strong\u0026gt;Data description:\u0026lt;/strong\u0026gt; The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also \u0026lt;em\u0026gt;Drone\u0026lt;/em\u0026gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Please, see the PDF file. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): \u0026lt;strong\u0026gt;Parameters\u0026lt;/strong\u0026gt; Specification/value Date (YYYYMMDD): 20191101 Time of day (BRT = -3) 13:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 250 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High-speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3\u0026quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 301 photos Across track coverage 80% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, breunig@ufsm.br An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Please, see the PDF file. Figure 2. The capture of an orthomosaic in the processing [...]","publication_date":{"day":24,"month":2,"year":2021,"errors":{}}},"translated_abstract":"\u0026lt;strong\u0026gt;Title\u0026lt;/strong\u0026gt;: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil \u0026lt;strong\u0026gt;Keywords\u0026lt;/strong\u0026gt; Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. \u0026lt;strong\u0026gt;Data description:\u0026lt;/strong\u0026gt; The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also \u0026lt;em\u0026gt;Drone\u0026lt;/em\u0026gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Please, see the PDF file. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): \u0026lt;strong\u0026gt;Parameters\u0026lt;/strong\u0026gt; Specification/value Date (YYYYMMDD): 20191101 Time of day (BRT = -3) 13:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 250 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High-speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3\u0026quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 301 photos Across track coverage 80% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, breunig@ufsm.br An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Please, see the PDF file. Figure 2. The capture of an orthomosaic in the processing [...]","internal_url":"https://www.academia.edu/122115072/Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_November_1_2019_Rio_Grande_do_Sul_Brazil","translated_internal_url":"","created_at":"2024-07-16T22:58:48.377-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_November_1_2019_Rio_Grande_do_Sul_Brazil","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"\u0026lt;strong\u0026gt;Title\u0026lt;/strong\u0026gt;: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on November 1, 2019, Rio Grande do Sul, Brazil \u0026lt;strong\u0026gt;Keywords\u0026lt;/strong\u0026gt; Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. \u0026lt;strong\u0026gt;Data description:\u0026lt;/strong\u0026gt; The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also \u0026lt;em\u0026gt;Drone\u0026lt;/em\u0026gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Please, see the PDF file. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): \u0026lt;strong\u0026gt;Parameters\u0026lt;/strong\u0026gt; Specification/value Date (YYYYMMDD): 20191101 Time of day (BRT = -3) 13:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 250 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High-speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3\u0026quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 301 photos Across track coverage 80% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, breunig@ufsm.br An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Please, see the PDF file. Figure 2. The capture of an orthomosaic in the processing [...]","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":1872418,"name":"Subtropics","url":"https://www.academia.edu/Documents/in/Subtropics"}],"urls":[{"id":43558376,"url":"https://zenodo.org/record/4558265"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115070"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/122115070/An%C3%A1lise_Do_Efeito_Topogr%C3%A1fico_Sobre_%C3%8Dndices_De_Vegeta%C3%A7%C3%A3o_Utilizando_Imagens_Rapideye_Na_Serra_Do_Mar_PR"><img alt="Research paper thumbnail of Análise Do Efeito Topográfico Sobre Índices De Vegetação Utilizando Imagens Rapideye Na Serra Do Mar – PR" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/122115070/An%C3%A1lise_Do_Efeito_Topogr%C3%A1fico_Sobre_%C3%8Dndices_De_Vegeta%C3%A7%C3%A3o_Utilizando_Imagens_Rapideye_Na_Serra_Do_Mar_PR">Análise Do Efeito Topográfico Sobre Índices De Vegetação Utilizando Imagens Rapideye Na Serra Do Mar – PR</a></div><div class="wp-workCard_item"><span>Ra'e Ga</span><span>, Apr 22, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115070"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115070"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115070; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115070]").text(description); $(".js-view-count[data-work-id=122115070]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115070; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115070']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115051"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122115051/Efeito_Topogr%C3%A1fico_sobre_a_Resposta_Espectral_de_Povoamentos_Florestais_de_Pinus_taeda_Linnaeus_no_Sul_do_Brasil"><img alt="Research paper thumbnail of Efeito Topográfico sobre a Resposta Espectral de Povoamentos Florestais de Pinus taeda Linnaeus no Sul do Brasil" class="work-thumbnail" src="https://attachments.academia-assets.com/116843807/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122115051/Efeito_Topogr%C3%A1fico_sobre_a_Resposta_Espectral_de_Povoamentos_Florestais_de_Pinus_taeda_Linnaeus_no_Sul_do_Brasil">Efeito Topográfico sobre a Resposta Espectral de Povoamentos Florestais de Pinus taeda Linnaeus no Sul do Brasil</a></div><div class="wp-workCard_item"><span>Revista Brasileira de Geografia Física</span><span>, Dec 31, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Este estudo tem como objetivo examinar a influência da variação sazonal da geometria da irradiânc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Este estudo tem como objetivo examinar a influência da variação sazonal da geometria da irradiância solar na resposta espectral de povoamentos florestais de Pinus taeda, localizados na Região Centro-Oeste do Estado de Santa Catarina, Brasil. A influência do efeito topográfico foi analisada por meio de medidas de refletância de superfície e índices de vegetação, obtidas a partir de três imagens MSI/Sentinel-2, adquiridas em baixo, médio e alto ângulo de elevação solar, no segundo semestre de 2018. A influência do efeito topográfico foi analisada a partir das oscilações de valor dos dados de sensoriamento remoto em comparação com as condições de iluminação, delineadas a partir das variações angulares de orientação de vertentes e declividade. Foram realizadas análises de correlação e regressão entre os dados espectrais e as condições de iluminação do terreno, definidas pelos valores do cosseno do ângulo de incidência. As medidas de refletância do dossel foram influenciadas diretamente pelo aspecto direcional da exposição a iluminação, e diminuições em seus valores foram identificadas nas porções do dossel com orientação contrária ao azimute solar (sul e sudoeste). Os índices de vegetação NDVI, WDRVI e NDRE, apresentaram valores superestimados nas porções sombreadas do dossel, devido às características espectrais dos dosséis de Pinus. O NDRE foi o índice menos sensível ao efeito topográfico, enquanto o EVI apresentou maior variação a ele associado. A aplicação da normalização topográfica mostrou-se eficaz em mitigar as diferenças entre os valores obtidos em diferentes condições de iluminação. Palavras-chave: sensoriamento remoto, Sentinel-2, silvicultura, geometria solar. Topographic effect on the spectral response of Pinus taeda Linnaeus forest stands in Southern Brazil A B S T R A C T This study aims to examine the influence of seasonal variation of solar irradiance geometry on the spectral response of Pinus taeda forest stands, located in the Midwest Region of Santa Catarina State, Brazil. The influence of the topographic effect was analyzed using surface reflectance and vegetation indices measurements, obtained from three MSI/Sentinel-2 images, acquired at low, medium, and high solar elevation angle, in the second semester of 2018. The influence of the topographic effect was analyzed from the value oscillations of remote sensing data compared to the illumination conditions, delineated from the angular variations of aspect and slope. Correlation and regression analyses were performed between the spectral data and the terrain illumination conditions, defined by the values of the cosine of the incidence angle. The canopy reflectance measurements were directly influenced by the directional aspect of the lighting exposure, and decreases in their values were identified in the portions of the canopy with orientation contrary to the solar azimuth (south). The vegetation indices NDVI, WDRVI, and NDRE, showed overestimated values in the shaded portions of the canopy, due to the spectral characteristics of the pine canopies. The NDRE was the least sensitive index to the topographic effect, while the EVI showed a greater variation associated with it. The application of the topographic effect correction proved to be effective in mitigating the differences between the values obtained under different lighting conditions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1d37a5d501f3ac8727b6539616b7588b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116843807,"asset_id":122115051,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116843807/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115051"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115051"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115051; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115051]").text(description); $(".js-view-count[data-work-id=122115051]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115051; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115051']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "1d37a5d501f3ac8727b6539616b7588b" } } $('.js-work-strip[data-work-id=122115051]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115051,"title":"Efeito Topográfico sobre a Resposta Espectral de Povoamentos Florestais de Pinus taeda Linnaeus no Sul do Brasil","translated_title":"","metadata":{"publisher":"Federal University of Pernambuco","grobid_abstract":"Este estudo tem como objetivo examinar a influência da variação sazonal da geometria da irradiância solar na resposta espectral de povoamentos florestais de Pinus taeda, localizados na Região Centro-Oeste do Estado de Santa Catarina, Brasil. A influência do efeito topográfico foi analisada por meio de medidas de refletância de superfície e índices de vegetação, obtidas a partir de três imagens MSI/Sentinel-2, adquiridas em baixo, médio e alto ângulo de elevação solar, no segundo semestre de 2018. A influência do efeito topográfico foi analisada a partir das oscilações de valor dos dados de sensoriamento remoto em comparação com as condições de iluminação, delineadas a partir das variações angulares de orientação de vertentes e declividade. Foram realizadas análises de correlação e regressão entre os dados espectrais e as condições de iluminação do terreno, definidas pelos valores do cosseno do ângulo de incidência. As medidas de refletância do dossel foram influenciadas diretamente pelo aspecto direcional da exposição a iluminação, e diminuições em seus valores foram identificadas nas porções do dossel com orientação contrária ao azimute solar (sul e sudoeste). Os índices de vegetação NDVI, WDRVI e NDRE, apresentaram valores superestimados nas porções sombreadas do dossel, devido às características espectrais dos dosséis de Pinus. O NDRE foi o índice menos sensível ao efeito topográfico, enquanto o EVI apresentou maior variação a ele associado. A aplicação da normalização topográfica mostrou-se eficaz em mitigar as diferenças entre os valores obtidos em diferentes condições de iluminação. Palavras-chave: sensoriamento remoto, Sentinel-2, silvicultura, geometria solar. Topographic effect on the spectral response of Pinus taeda Linnaeus forest stands in Southern Brazil A B S T R A C T This study aims to examine the influence of seasonal variation of solar irradiance geometry on the spectral response of Pinus taeda forest stands, located in the Midwest Region of Santa Catarina State, Brazil. The influence of the topographic effect was analyzed using surface reflectance and vegetation indices measurements, obtained from three MSI/Sentinel-2 images, acquired at low, medium, and high solar elevation angle, in the second semester of 2018. The influence of the topographic effect was analyzed from the value oscillations of remote sensing data compared to the illumination conditions, delineated from the angular variations of aspect and slope. Correlation and regression analyses were performed between the spectral data and the terrain illumination conditions, defined by the values of the cosine of the incidence angle. The canopy reflectance measurements were directly influenced by the directional aspect of the lighting exposure, and decreases in their values were identified in the portions of the canopy with orientation contrary to the solar azimuth (south). 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A influência do efeito topográfico foi analisada por meio de medidas de refletância de superfície e índices de vegetação, obtidas a partir de três imagens MSI/Sentinel-2, adquiridas em baixo, médio e alto ângulo de elevação solar, no segundo semestre de 2018. A influência do efeito topográfico foi analisada a partir das oscilações de valor dos dados de sensoriamento remoto em comparação com as condições de iluminação, delineadas a partir das variações angulares de orientação de vertentes e declividade. Foram realizadas análises de correlação e regressão entre os dados espectrais e as condições de iluminação do terreno, definidas pelos valores do cosseno do ângulo de incidência. As medidas de refletância do dossel foram influenciadas diretamente pelo aspecto direcional da exposição a iluminação, e diminuições em seus valores foram identificadas nas porções do dossel com orientação contrária ao azimute solar (sul e sudoeste). Os índices de vegetação NDVI, WDRVI e NDRE, apresentaram valores superestimados nas porções sombreadas do dossel, devido às características espectrais dos dosséis de Pinus. O NDRE foi o índice menos sensível ao efeito topográfico, enquanto o EVI apresentou maior variação a ele associado. A aplicação da normalização topográfica mostrou-se eficaz em mitigar as diferenças entre os valores obtidos em diferentes condições de iluminação. Palavras-chave: sensoriamento remoto, Sentinel-2, silvicultura, geometria solar. Topographic effect on the spectral response of Pinus taeda Linnaeus forest stands in Southern Brazil A B S T R A C T This study aims to examine the influence of seasonal variation of solar irradiance geometry on the spectral response of Pinus taeda forest stands, located in the Midwest Region of Santa Catarina State, Brazil. The influence of the topographic effect was analyzed using surface reflectance and vegetation indices measurements, obtained from three MSI/Sentinel-2 images, acquired at low, medium, and high solar elevation angle, in the second semester of 2018. The influence of the topographic effect was analyzed from the value oscillations of remote sensing data compared to the illumination conditions, delineated from the angular variations of aspect and slope. Correlation and regression analyses were performed between the spectral data and the terrain illumination conditions, defined by the values of the cosine of the incidence angle. The canopy reflectance measurements were directly influenced by the directional aspect of the lighting exposure, and decreases in their values were identified in the portions of the canopy with orientation contrary to the solar azimuth (south). The vegetation indices NDVI, WDRVI, and NDRE, showed overestimated values in the shaded portions of the canopy, due to the spectral characteristics of the pine canopies. The NDRE was the least sensitive index to the topographic effect, while the EVI showed a greater variation associated with it. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115050"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/122115050/Monitoramento_interanual_do_manejo_florestal_com_sensoriamento_remoto"><img alt="Research paper thumbnail of Monitoramento interanual do manejo florestal com sensoriamento remoto" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/122115050/Monitoramento_interanual_do_manejo_florestal_com_sensoriamento_remoto">Monitoramento interanual do manejo florestal com sensoriamento remoto</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115050"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115050"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115050; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=122115050]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115050,"title":"Monitoramento interanual do manejo florestal com sensoriamento remoto","translated_title":"","metadata":{"publication_date":{"day":null,"month":null,"year":2019,"errors":{}}},"translated_abstract":null,"internal_url":"https://www.academia.edu/122115050/Monitoramento_interanual_do_manejo_florestal_com_sensoriamento_remoto","translated_internal_url":"","created_at":"2024-07-16T22:58:20.569-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Monitoramento_interanual_do_manejo_florestal_com_sensoriamento_remoto","translated_slug":"","page_count":null,"language":"es","content_type":"Work","summary":null,"owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[],"urls":[{"id":43558360,"url":"https://www.mysciencework.com/publication/show/monitoramento-interanual-do-manejo-florestal-com-sensoriamento-remoto-2b7b48ef"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115049"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122115049/Biomass_of_a_fragment_of_the_montane_seasonal_deciduous_forest_the_south_Brazil"><img alt="Research paper thumbnail of Biomass of a fragment of the montane seasonal deciduous forest the south Brazil" class="work-thumbnail" src="https://attachments.academia-assets.com/116843804/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122115049/Biomass_of_a_fragment_of_the_montane_seasonal_deciduous_forest_the_south_Brazil">Biomass of a fragment of the montane seasonal deciduous forest the south Brazil</a></div><div class="wp-workCard_item"><span>Acta Iguazu</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The objective the work was to quantify the biomass using the destructive method of a forest fragm...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The objective the work was to quantify the biomass using the destructive method of a forest fragment located in the South of Brazil. The study was conducted in fragment of Montane Seasonal Deciduous Forest with approximately 55 ha, centered at the coordinates S 27˚23'44" and W 53˚25'59". For that, three plots of 12x12 m were installed. All plants with diameter at breast height (DBH) greater than 5 cm were identified at the species level. All trees with DBH>10 cm had the following information collected: DBH, total height (h), height at the morphological inversion point (MIP) and the diameter at 0%, 25%, 50%, 75% and 90% of the MIP height. As result, we verified that trees with DBH> 10 cm represent more than 90% of the total biomass (345.2 Mg ha-1). The estimated biomass of leaf litter was 12.4 Mg ha-1. The stock of dry aboveground biomass and litter for the study area was 380.5 Mg ha-1 .</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b02b4578f8d690d750fc6339846d53dc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116843804,"asset_id":122115049,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116843804/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115049"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115049"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115049; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115049]").text(description); $(".js-view-count[data-work-id=122115049]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115049; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115049']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b02b4578f8d690d750fc6339846d53dc" } } $('.js-work-strip[data-work-id=122115049]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115049,"title":"Biomass of a fragment of the montane seasonal deciduous forest the south Brazil","translated_title":"","metadata":{"publisher":"State University of West Paraná","grobid_abstract":"The objective the work was to quantify the biomass using the destructive method of a forest fragment located in the South of Brazil. The study was conducted in fragment of Montane Seasonal Deciduous Forest with approximately 55 ha, centered at the coordinates S 27˚23'44\" and W 53˚25'59\". For that, three plots of 12x12 m were installed. All plants with diameter at breast height (DBH) greater than 5 cm were identified at the species level. All trees with DBH\u003e10 cm had the following information collected: DBH, total height (h), height at the morphological inversion point (MIP) and the diameter at 0%, 25%, 50%, 75% and 90% of the MIP height. As result, we verified that trees with DBH\u003e 10 cm represent more than 90% of the total biomass (345.2 Mg ha-1). The estimated biomass of leaf litter was 12.4 Mg ha-1. The stock of dry aboveground biomass and litter for the study area was 380.5 Mg ha-1 .","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Acta Iguazu","grobid_abstract_attachment_id":116843804},"translated_abstract":null,"internal_url":"https://www.academia.edu/122115049/Biomass_of_a_fragment_of_the_montane_seasonal_deciduous_forest_the_south_Brazil","translated_internal_url":"","created_at":"2024-07-16T22:58:20.295-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":116843804,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843804/thumbnails/1.jpg","file_name":"13811.pdf","download_url":"https://www.academia.edu/attachments/116843804/download_file","bulk_download_file_name":"Biomass_of_a_fragment_of_the_montane_sea.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843804/13811-libre.pdf?1721196670=\u0026response-content-disposition=attachment%3B+filename%3DBiomass_of_a_fragment_of_the_montane_sea.pdf\u0026Expires=1741801541\u0026Signature=Xl9pj-BV1Bi~2gO-hJw4B7LYLAe5QLVlgdC~VdeBTXAhq0usA8SKDrX4SHnrnvFhaufAj8MdyITuj0ThdjUM-2idg8sAy~twH1z4pNiL9Bk7Pji4md8QLZBxDcY9bCtHc9x1FrA6k7DwDUKlhqmyJ1cahmDT6b39ajFhA4SmQCPbvt7Fns~TYbDAFA9-Ansx4qP-AVBE6DvXHa59iNW8VvaDJ6vXZ-O7aXPDGS0MH3vgwlHyNY5XD4SGcehOrnE37wg5X7vW9Q~YigvYez7ayIt5Sp9cAxr-vVmotTc1GfQNh1YAsBWwPyS8yGnnfNPl260o60xBYtBeepoAvnItXQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Biomass_of_a_fragment_of_the_montane_seasonal_deciduous_forest_the_south_Brazil","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":"The objective the work was to quantify the biomass using the destructive method of a forest fragment located in the South of Brazil. The study was conducted in fragment of Montane Seasonal Deciduous Forest with approximately 55 ha, centered at the coordinates S 27˚23'44\" and W 53˚25'59\". For that, three plots of 12x12 m were installed. All plants with diameter at breast height (DBH) greater than 5 cm were identified at the species level. All trees with DBH\u003e10 cm had the following information collected: DBH, total height (h), height at the morphological inversion point (MIP) and the diameter at 0%, 25%, 50%, 75% and 90% of the MIP height. As result, we verified that trees with DBH\u003e 10 cm represent more than 90% of the total biomass (345.2 Mg ha-1). The estimated biomass of leaf litter was 12.4 Mg ha-1. The stock of dry aboveground biomass and litter for the study area was 380.5 Mg ha-1 .","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":116843804,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843804/thumbnails/1.jpg","file_name":"13811.pdf","download_url":"https://www.academia.edu/attachments/116843804/download_file","bulk_download_file_name":"Biomass_of_a_fragment_of_the_montane_sea.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843804/13811-libre.pdf?1721196670=\u0026response-content-disposition=attachment%3B+filename%3DBiomass_of_a_fragment_of_the_montane_sea.pdf\u0026Expires=1741801541\u0026Signature=Xl9pj-BV1Bi~2gO-hJw4B7LYLAe5QLVlgdC~VdeBTXAhq0usA8SKDrX4SHnrnvFhaufAj8MdyITuj0ThdjUM-2idg8sAy~twH1z4pNiL9Bk7Pji4md8QLZBxDcY9bCtHc9x1FrA6k7DwDUKlhqmyJ1cahmDT6b39ajFhA4SmQCPbvt7Fns~TYbDAFA9-Ansx4qP-AVBE6DvXHa59iNW8VvaDJ6vXZ-O7aXPDGS0MH3vgwlHyNY5XD4SGcehOrnE37wg5X7vW9Q~YigvYez7ayIt5Sp9cAxr-vVmotTc1GfQNh1YAsBWwPyS8yGnnfNPl260o60xBYtBeepoAvnItXQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":116843805,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843805/thumbnails/1.jpg","file_name":"13811.pdf","download_url":"https://www.academia.edu/attachments/116843805/download_file","bulk_download_file_name":"Biomass_of_a_fragment_of_the_montane_sea.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843805/13811-libre.pdf?1721196670=\u0026response-content-disposition=attachment%3B+filename%3DBiomass_of_a_fragment_of_the_montane_sea.pdf\u0026Expires=1741801541\u0026Signature=MTEj0TYdx7fwcu~m78cDdtqDX7hyY2cu9F9TS20AkbyByMAtBo24L3HQPvOiC1U22uf87Es8eDi~poIDqRTVl5PYjuKQ7Lz43mSt9oN3N246XhhMDgdM2fkBl5iWeE2dhBRy7EizQNcDb5yXTC3MFRiJRmswsYoaMTcHozPneaT~9xD3opJooZKBT-5ZIZYDjIHifzuJKPGPpQVjsInt3aacDKc7TRqu~iLcoFmUl0pY1jqk9-kfnbQCaS5eAkHUXAq9qJK-WWUl5JemfzsaAyIO98HUgUBJRyFnK1Ho7FN2e9pdqvA8EBxdyFH780sfCU6XpJf7i-7wjOxC1teI7g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1041,"name":"Forestry","url":"https://www.academia.edu/Documents/in/Forestry"},{"id":188993,"name":"Litter","url":"https://www.academia.edu/Documents/in/Litter"},{"id":372637,"name":"Deciduous","url":"https://www.academia.edu/Documents/in/Deciduous"},{"id":2860163,"name":"Montane Ecology","url":"https://www.academia.edu/Documents/in/Montane_Ecology"}],"urls":[{"id":43558359,"url":"http://e-revista.unioeste.br/index.php/actaiguazu/article/download/17120/13811"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115048"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/122115048/Distribui%C3%A7%C3%A3o_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448"><img alt="Research paper thumbnail of Distribuição vertical da biomassa acima do solo em floresta estacional decidual - DOI:10.5039/agraria.v12i3a5448" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/122115048/Distribui%C3%A7%C3%A3o_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448">Distribuição vertical da biomassa acima do solo em floresta estacional decidual - DOI:10.5039/agraria.v12i3a5448</a></div><div class="wp-workCard_item"><span>Revista Brasileira de Ciências Agrárias (Agrária)</span><span>, Sep 27, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implem...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height &lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m was 1.5 Mg ha-1. Average litter mass was 15.6 Mg ha-1. Trees of large diameters must be analyzed very carefully for quantify the biomass and carbon in the forests, because few individuals might represent a large part of the biomass of a forest ecosystem.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115048"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115048"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115048; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115048]").text(description); $(".js-view-count[data-work-id=122115048]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115048; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115048']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=122115048]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115048,"title":"Distribuição vertical da biomassa acima do solo em floresta estacional decidual - DOI:10.5039/agraria.v12i3a5448","translated_title":"","metadata":{"abstract":"Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height \u0026lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m was 1.5 Mg ha-1. Average litter mass was 15.6 Mg ha-1. Trees of large diameters must be analyzed very carefully for quantify the biomass and carbon in the forests, because few individuals might represent a large part of the biomass of a forest ecosystem.","publication_date":{"day":27,"month":9,"year":2017,"errors":{}},"publication_name":"Revista Brasileira de Ciências Agrárias (Agrária)"},"translated_abstract":"Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height \u0026lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m was 1.5 Mg ha-1. Average litter mass was 15.6 Mg ha-1. Trees of large diameters must be analyzed very carefully for quantify the biomass and carbon in the forests, because few individuals might represent a large part of the biomass of a forest ecosystem.","internal_url":"https://www.academia.edu/122115048/Distribui%C3%A7%C3%A3o_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448","translated_internal_url":"","created_at":"2024-07-16T22:58:20.078-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Distribuição_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height \u0026lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m was 1.5 Mg ha-1. Average litter mass was 15.6 Mg ha-1. Trees of large diameters must be analyzed very carefully for quantify the biomass and carbon in the forests, because few individuals might represent a large part of the biomass of a forest ecosystem.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1041,"name":"Forestry","url":"https://www.academia.edu/Documents/in/Forestry"},{"id":146385,"name":"Land Reclamation","url":"https://www.academia.edu/Documents/in/Land_Reclamation"},{"id":372637,"name":"Deciduous","url":"https://www.academia.edu/Documents/in/Deciduous"},{"id":459822,"name":"Carbon Stock","url":"https://www.academia.edu/Documents/in/Carbon_Stock"}],"urls":[{"id":43558358,"url":"http://www.agraria.pro.br/ojs-2.4.6/index.php?journal=agraria\u0026page=article\u0026op=view\u0026path%5B%5D=agraria_v12i3a5448\u0026path%5B%5D=4992"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115047"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/122115047/Relationships_between_MODIS_phenological_metrics_topographic_shade_and_anomalous_temperature_patterns_in_seasonal_deciduous_forests_of_south_Brazil"><img alt="Research paper thumbnail of Relationships between MODIS phenological metrics, topographic shade, and anomalous temperature patterns in seasonal deciduous forests of south Brazil" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/122115047/Relationships_between_MODIS_phenological_metrics_topographic_shade_and_anomalous_temperature_patterns_in_seasonal_deciduous_forests_of_south_Brazil">Relationships between MODIS phenological metrics, topographic shade, and anomalous temperature patterns in seasonal deciduous forests of south Brazil</a></div><div class="wp-workCard_item"><span>International Journal of Remote Sensing</span><span>, Sep 17, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We investigated the relationships between 11 phenological metrics, topographic shade, and anomalo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We investigated the relationships between 11 phenological metrics, topographic shade, and anomalous temperature patterns detected using wavelet analysis in seasonal deciduous forests of south Brazil. To obtain the metrics, we applied the TIMESAT algorithm to the enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra. MODIS acquires data from the study area under a large seasonal amplitude in the solar zenith angle (SZA). We evaluated the effect of topography on phenological metrics by correlating the metrics with shaded relief values. To analyse the inter-annual phenological metric variations with anomalous and regular temperature patterns, we calculated standard anomalies for each metric. Finally, we established relationships between the metrics and the minimum, maximum, and mean temperatures from growing seasons that spanned over 10 seasonal cycles between 2002 and 2012. The correlation results with shaded relief showed that the left (LD) and right derivative (RD), small integral (SInt), seasonal amplitude (SA), base level (BL), and maximum VI value (MV) were sensitive to topographic effects. The seasonal cycles with the highest temperatures in the growing season (2006/2007 and 2009/2010) exhibited a delay at the end of the cycle and a higher interval of duration and productivity, which was indicated by the positive standard anomalies for end of season (EOS), length of season (LOS), large integral (LInt), and SInt. We observed a different result for the lowest temperature cycle (2003/2004). The means for these metrics in anomalous seasons differed significantly from the metrics of other regular cycles at the 0.05 significance level using paired t-tests. Statistically significant correlations were observed between the metrics and minimum and mean temperature values of the 10 seasonal cycles.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115047"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115047"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115047; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115047]").text(description); $(".js-view-count[data-work-id=122115047]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115047; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115047']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=122115047]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115047,"title":"Relationships between MODIS phenological metrics, topographic shade, and anomalous temperature patterns in seasonal deciduous forests of south Brazil","translated_title":"","metadata":{"abstract":"We investigated the relationships between 11 phenological metrics, topographic shade, and anomalous temperature patterns detected using wavelet analysis in seasonal deciduous forests of south Brazil. To obtain the metrics, we applied the TIMESAT algorithm to the enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra. MODIS acquires data from the study area under a large seasonal amplitude in the solar zenith angle (SZA). We evaluated the effect of topography on phenological metrics by correlating the metrics with shaded relief values. To analyse the inter-annual phenological metric variations with anomalous and regular temperature patterns, we calculated standard anomalies for each metric. Finally, we established relationships between the metrics and the minimum, maximum, and mean temperatures from growing seasons that spanned over 10 seasonal cycles between 2002 and 2012. The correlation results with shaded relief showed that the left (LD) and right derivative (RD), small integral (SInt), seasonal amplitude (SA), base level (BL), and maximum VI value (MV) were sensitive to topographic effects. The seasonal cycles with the highest temperatures in the growing season (2006/2007 and 2009/2010) exhibited a delay at the end of the cycle and a higher interval of duration and productivity, which was indicated by the positive standard anomalies for end of season (EOS), length of season (LOS), large integral (LInt), and SInt. We observed a different result for the lowest temperature cycle (2003/2004). The means for these metrics in anomalous seasons differed significantly from the metrics of other regular cycles at the 0.05 significance level using paired t-tests. Statistically significant correlations were observed between the metrics and minimum and mean temperature values of the 10 seasonal cycles.","publisher":"Taylor \u0026 Francis","publication_date":{"day":17,"month":9,"year":2015,"errors":{}},"publication_name":"International Journal of Remote Sensing"},"translated_abstract":"We investigated the relationships between 11 phenological metrics, topographic shade, and anomalous temperature patterns detected using wavelet analysis in seasonal deciduous forests of south Brazil. To obtain the metrics, we applied the TIMESAT algorithm to the enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra. MODIS acquires data from the study area under a large seasonal amplitude in the solar zenith angle (SZA). We evaluated the effect of topography on phenological metrics by correlating the metrics with shaded relief values. To analyse the inter-annual phenological metric variations with anomalous and regular temperature patterns, we calculated standard anomalies for each metric. Finally, we established relationships between the metrics and the minimum, maximum, and mean temperatures from growing seasons that spanned over 10 seasonal cycles between 2002 and 2012. The correlation results with shaded relief showed that the left (LD) and right derivative (RD), small integral (SInt), seasonal amplitude (SA), base level (BL), and maximum VI value (MV) were sensitive to topographic effects. The seasonal cycles with the highest temperatures in the growing season (2006/2007 and 2009/2010) exhibited a delay at the end of the cycle and a higher interval of duration and productivity, which was indicated by the positive standard anomalies for end of season (EOS), length of season (LOS), large integral (LInt), and SInt. We observed a different result for the lowest temperature cycle (2003/2004). The means for these metrics in anomalous seasons differed significantly from the metrics of other regular cycles at the 0.05 significance level using paired t-tests. Statistically significant correlations were observed between the metrics and minimum and mean temperature values of the 10 seasonal cycles.","internal_url":"https://www.academia.edu/122115047/Relationships_between_MODIS_phenological_metrics_topographic_shade_and_anomalous_temperature_patterns_in_seasonal_deciduous_forests_of_south_Brazil","translated_internal_url":"","created_at":"2024-07-16T22:58:19.251-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Relationships_between_MODIS_phenological_metrics_topographic_shade_and_anomalous_temperature_patterns_in_seasonal_deciduous_forests_of_south_Brazil","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"We investigated the relationships between 11 phenological metrics, topographic shade, and anomalous temperature patterns detected using wavelet analysis in seasonal deciduous forests of south Brazil. To obtain the metrics, we applied the TIMESAT algorithm to the enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra. MODIS acquires data from the study area under a large seasonal amplitude in the solar zenith angle (SZA). We evaluated the effect of topography on phenological metrics by correlating the metrics with shaded relief values. To analyse the inter-annual phenological metric variations with anomalous and regular temperature patterns, we calculated standard anomalies for each metric. Finally, we established relationships between the metrics and the minimum, maximum, and mean temperatures from growing seasons that spanned over 10 seasonal cycles between 2002 and 2012. The correlation results with shaded relief showed that the left (LD) and right derivative (RD), small integral (SInt), seasonal amplitude (SA), base level (BL), and maximum VI value (MV) were sensitive to topographic effects. The seasonal cycles with the highest temperatures in the growing season (2006/2007 and 2009/2010) exhibited a delay at the end of the cycle and a higher interval of duration and productivity, which was indicated by the positive standard anomalies for end of season (EOS), length of season (LOS), large integral (LInt), and SInt. We observed a different result for the lowest temperature cycle (2003/2004). The means for these metrics in anomalous seasons differed significantly from the metrics of other regular cycles at the 0.05 significance level using paired t-tests. Statistically significant correlations were observed between the metrics and minimum and mean temperature values of the 10 seasonal cycles.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1252,"name":"Remote Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing"},{"id":17943,"name":"Phenology","url":"https://www.academia.edu/Documents/in/Phenology"},{"id":162010,"name":"Geomatic Engineering","url":"https://www.academia.edu/Documents/in/Geomatic_Engineering"},{"id":372637,"name":"Deciduous","url":"https://www.academia.edu/Documents/in/Deciduous"}],"urls":[{"id":43558357,"url":"https://doi.org/10.1080/01431161.2015.1084437"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115045"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122115045/Assessing_the_Long_Term_Variability_of_TSS_and_Chlorophyll_in_Subtropical_Reservoirs_Using_MODIS_Data"><img alt="Research paper thumbnail of Assessing the Long-Term Variability of TSS and Chlorophyll in Subtropical Reservoirs Using MODIS Data" class="work-thumbnail" src="https://attachments.academia-assets.com/116843837/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122115045/Assessing_the_Long_Term_Variability_of_TSS_and_Chlorophyll_in_Subtropical_Reservoirs_Using_MODIS_Data">Assessing the Long-Term Variability of TSS and Chlorophyll in Subtropical Reservoirs Using MODIS Data</a></div><div class="wp-workCard_item"><span>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</span><span>, Dec 1, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The remote sensing assessment of the long-term variability in optically active constituents of th...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The remote sensing assessment of the long-term variability in optically active constituents of the waters is very important to understand the dynamics of the reservoirs at temporal and spatial scales. We evaluated variations over time (2000-2015) in total suspended solids (TSS) and chlorophyll-a concentrations in the Passo Real reservoir, located in south Brazil. For this purpose, we tested two MODIS composite products: MOD09A1 and MCD43A4. Regression relationships of the red (TSS) and green (chlorophyll-a) reflectance of these products with limnological data of 12 field campaigns were obtained and compared to estimate these constituents on a per-pixel basis. Three major sections of the reservoir (upper, middle, and lower sections) were selected in the analysis. The results showed that the MODIS MCD43A4 (corrected for bidirectional effects) produced better estimates of TSS and chlorophyll-a than the MOD09A1 product. The highest concentrations of TSS were observed from September to October with the concomitant increase in precipitation and the predominance of exposed soils. The peak of TSS concentration between these two months was closely followed by another peak of chlorophyll-a content. It occurs probably because of the higher nutrient availability with sediment loading, the intensive land use with crop development, and the increase in water temperatures. Differences over time between the peaks of TSS and chlorophyll-a varied from the upper (two-month shift) to the lower (one-month shift) sections of the reservoir. They were associated with the different stream inflows and the residence time of the waters.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="64b3328f791ebbe27e4145ffcdb27623" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116843837,"asset_id":122115045,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116843837/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115045"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115045"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115045; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115045]").text(description); $(".js-view-count[data-work-id=122115045]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115045; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115045']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "64b3328f791ebbe27e4145ffcdb27623" } } $('.js-work-strip[data-work-id=122115045]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115045,"title":"Assessing the Long-Term Variability of TSS and Chlorophyll in Subtropical Reservoirs Using MODIS Data","translated_title":"","metadata":{"publisher":"Institute of Electrical and Electronics Engineers","grobid_abstract":"The remote sensing assessment of the long-term variability in optically active constituents of the waters is very important to understand the dynamics of the reservoirs at temporal and spatial scales. 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The peak of TSS concentration between these two months was closely followed by another peak of chlorophyll-a content. It occurs probably because of the higher nutrient availability with sediment loading, the intensive land use with crop development, and the increase in water temperatures. Differences over time between the peaks of TSS and chlorophyll-a varied from the upper (two-month shift) to the lower (one-month shift) sections of the reservoir. They were associated with the different stream inflows and the residence time of the waters.","publication_date":{"day":1,"month":12,"year":2016,"errors":{}},"publication_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","grobid_abstract_attachment_id":116843837},"translated_abstract":null,"internal_url":"https://www.academia.edu/122115045/Assessing_the_Long_Term_Variability_of_TSS_and_Chlorophyll_in_Subtropical_Reservoirs_Using_MODIS_Data","translated_internal_url":"","created_at":"2024-07-16T22:58:18.653-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":116843837,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843837/thumbnails/1.jpg","file_name":"JSTARS.2016.258982020240717-1-ks6h3y.pdf","download_url":"https://www.academia.edu/attachments/116843837/download_file","bulk_download_file_name":"Assessing_the_Long_Term_Variability_of_T.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843837/JSTARS.2016.258982020240717-1-ks6h3y-libre.pdf?1721196660=\u0026response-content-disposition=attachment%3B+filename%3DAssessing_the_Long_Term_Variability_of_T.pdf\u0026Expires=1741801541\u0026Signature=b1DVogDeIN9d3VyOVJdN6i41i6vYJ8cAAKSWFlQwhOo1cBSbIsL3b9aEasmh7d6lL~SC520uTBC38VxnOIzKgkcDlIaKYS82sf~PaqtCD0~cevin6kk9Iup2TWPpeE7s8MqxEo0N1C-9wKajPtMWW4nUuufUgVPxlkebymWuPnvm3jA4iP2tEYa3tt8241Lgj1M8IgnTkDxcvZnch~iyRRBgiIBcROcLrMxDxNeulNE3-U1WUhRVJGL2Nrg8RVYH4Cqw9FWDrUuSIighYB1-2irVU3MuCbeRUBKrcUTrQVZqnpyagSv1miz9Uja2w1BeZPqSHrS-x04wSDmdeRD-Zg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Assessing_the_Long_Term_Variability_of_TSS_and_Chlorophyll_in_Subtropical_Reservoirs_Using_MODIS_Data","translated_slug":"","page_count":7,"language":"en","content_type":"Work","summary":"The remote sensing assessment of the long-term variability in optically active constituents of the waters is very important to understand the dynamics of the reservoirs at temporal and spatial scales. We evaluated variations over time (2000-2015) in total suspended solids (TSS) and chlorophyll-a concentrations in the Passo Real reservoir, located in south Brazil. For this purpose, we tested two MODIS composite products: MOD09A1 and MCD43A4. Regression relationships of the red (TSS) and green (chlorophyll-a) reflectance of these products with limnological data of 12 field campaigns were obtained and compared to estimate these constituents on a per-pixel basis. Three major sections of the reservoir (upper, middle, and lower sections) were selected in the analysis. The results showed that the MODIS MCD43A4 (corrected for bidirectional effects) produced better estimates of TSS and chlorophyll-a than the MOD09A1 product. The highest concentrations of TSS were observed from September to October with the concomitant increase in precipitation and the predominance of exposed soils. The peak of TSS concentration between these two months was closely followed by another peak of chlorophyll-a content. It occurs probably because of the higher nutrient availability with sediment loading, the intensive land use with crop development, and the increase in water temperatures. Differences over time between the peaks of TSS and chlorophyll-a varied from the upper (two-month shift) to the lower (one-month shift) sections of the reservoir. They were associated with the different stream inflows and the residence time of the waters.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":116843837,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843837/thumbnails/1.jpg","file_name":"JSTARS.2016.258982020240717-1-ks6h3y.pdf","download_url":"https://www.academia.edu/attachments/116843837/download_file","bulk_download_file_name":"Assessing_the_Long_Term_Variability_of_T.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843837/JSTARS.2016.258982020240717-1-ks6h3y-libre.pdf?1721196660=\u0026response-content-disposition=attachment%3B+filename%3DAssessing_the_Long_Term_Variability_of_T.pdf\u0026Expires=1741801541\u0026Signature=b1DVogDeIN9d3VyOVJdN6i41i6vYJ8cAAKSWFlQwhOo1cBSbIsL3b9aEasmh7d6lL~SC520uTBC38VxnOIzKgkcDlIaKYS82sf~PaqtCD0~cevin6kk9Iup2TWPpeE7s8MqxEo0N1C-9wKajPtMWW4nUuufUgVPxlkebymWuPnvm3jA4iP2tEYa3tt8241Lgj1M8IgnTkDxcvZnch~iyRRBgiIBcROcLrMxDxNeulNE3-U1WUhRVJGL2Nrg8RVYH4Cqw9FWDrUuSIighYB1-2irVU3MuCbeRUBKrcUTrQVZqnpyagSv1miz9Uja2w1BeZPqSHrS-x04wSDmdeRD-Zg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":85564,"name":"Chlorophyll","url":"https://www.academia.edu/Documents/in/Chlorophyll"},{"id":192294,"name":"Sediment","url":"https://www.academia.edu/Documents/in/Sediment"},{"id":244969,"name":"Nutrient","url":"https://www.academia.edu/Documents/in/Nutrient"},{"id":291658,"name":"Precipitation","url":"https://www.academia.edu/Documents/in/Precipitation"},{"id":1431418,"name":"Chlorophyll a","url":"https://www.academia.edu/Documents/in/Chlorophyll_a"},{"id":1666540,"name":"Total suspended solids.","url":"https://www.academia.edu/Documents/in/Total_suspended_solids"}],"urls":[{"id":43558355,"url":"https://doi.org/10.1109/jstars.2016.2589820"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115043"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122115043/Dynamics_of_limnological_parameters_in_reservoirs_A_case_study_in_South_Brazil_using_remote_sensing_and_meteorological_data"><img alt="Research paper thumbnail of Dynamics of limnological parameters in reservoirs: A case study in South Brazil using remote sensing and meteorological data" class="work-thumbnail" src="https://attachments.academia-assets.com/116843836/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122115043/Dynamics_of_limnological_parameters_in_reservoirs_A_case_study_in_South_Brazil_using_remote_sensing_and_meteorological_data">Dynamics of limnological parameters in reservoirs: A case study in South Brazil using remote sensing and meteorological data</a></div><div class="wp-workCard_item"><span>Science of The Total Environment</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Reservoirs are important in Brazil for the production of hydroelectric power and human water cons...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Reservoirs are important in Brazil for the production of hydroelectric power and human water consumption. The objective was to evaluate the variability of total suspended solids (TSS) and chlorophyll-a as well as the rainfall/ temperature and land use impacts on these optically active constituents (OAC). The study area is the Passo Real reservoir in south Brazil. The methodology was divided in four steps. First, we used wavelet to detect anomalous periods of rainfall and temperature (2002-2014). Second, we carried out 12 field campaigns to obtain in situ measurements for limnological characterization (2009-2010). The third step was the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra and Aqua satellites data corrected and non-corrected for bidirectional effects. Finally, we evaluated potential drivers of OAC changes over time using cross-correlation analysis. The results showed a decrease in the TSS and chlorophyll-a concentrations from the upper to the lower streams of the reservoir. The exponential regression between the MODIS red reflectance and TSS had an adjusted r 2 of 0.63. It decreased to 0.53 for the relationship between the green reflectance and chlorophyll-a. MODIS data corrected for bidirectional effects provided better OAC estimates than non-corrected data. The validation of MODIS TSS and chlorophyll-a estimates using a separate set of measurements showed a RMSE of 2.98 mg/l and 2.33 μg/l, respectively. MODIS estimates indicated a gradual transition in OAC from the upper to the lower streams in</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="926ee8abc41f05ba29b40f47c3509902" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116843836,"asset_id":122115043,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116843836/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115043"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115043"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115043; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115043]").text(description); $(".js-view-count[data-work-id=122115043]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115043; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115043']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "926ee8abc41f05ba29b40f47c3509902" } } $('.js-work-strip[data-work-id=122115043]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115043,"title":"Dynamics of limnological parameters in reservoirs: A case study in South Brazil using remote sensing and meteorological data","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"Reservoirs are important in Brazil for the production of hydroelectric power and human water consumption. The objective was to evaluate the variability of total suspended solids (TSS) and chlorophyll-a as well as the rainfall/ temperature and land use impacts on these optically active constituents (OAC). The study area is the Passo Real reservoir in south Brazil. The methodology was divided in four steps. First, we used wavelet to detect anomalous periods of rainfall and temperature (2002-2014). Second, we carried out 12 field campaigns to obtain in situ measurements for limnological characterization (2009-2010). The third step was the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra and Aqua satellites data corrected and non-corrected for bidirectional effects. Finally, we evaluated potential drivers of OAC changes over time using cross-correlation analysis. The results showed a decrease in the TSS and chlorophyll-a concentrations from the upper to the lower streams of the reservoir. The exponential regression between the MODIS red reflectance and TSS had an adjusted r 2 of 0.63. It decreased to 0.53 for the relationship between the green reflectance and chlorophyll-a. MODIS data corrected for bidirectional effects provided better OAC estimates than non-corrected data. The validation of MODIS TSS and chlorophyll-a estimates using a separate set of measurements showed a RMSE of 2.98 mg/l and 2.33 μg/l, respectively. MODIS estimates indicated a gradual transition in OAC from the upper to the lower streams in","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Science of The Total Environment","grobid_abstract_attachment_id":116843836},"translated_abstract":null,"internal_url":"https://www.academia.edu/122115043/Dynamics_of_limnological_parameters_in_reservoirs_A_case_study_in_South_Brazil_using_remote_sensing_and_meteorological_data","translated_internal_url":"","created_at":"2024-07-16T22:58:18.344-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":116843836,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843836/thumbnails/1.jpg","file_name":"j.scitotenv.2016.09.05020240717-1-1sh3k9.pdf","download_url":"https://www.academia.edu/attachments/116843836/download_file","bulk_download_file_name":"Dynamics_of_limnological_parameters_in_r.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843836/j.scitotenv.2016.09.05020240717-1-1sh3k9-libre.pdf?1721196672=\u0026response-content-disposition=attachment%3B+filename%3DDynamics_of_limnological_parameters_in_r.pdf\u0026Expires=1741801541\u0026Signature=JsaDbDq1WVe~2cxG6Wo1qCZ2Cf1TxqFHG63sSwAjcTrZXP8XsHBiTQm1M5xhnlUxgh3TMcTfWUSI~~51dqOpt8ujwbGSeYPNctyh8wJpojJox5NDpqe9x1COodrwJwLOWL-gY9nEYKCnCBSZALBrHCLXtUOmg0DtRHvkAMH3O3l33hJXnURZXHklanw5Q~EtJIqBvDULC8N4hdRihAnpDsdYazktdtJvNlgsB2o0usMIG8iTttXKu-FaSq~soYfqKaVITMXTpLD6NP1l1XQWJm5z0Z-FDaKv6hi5cJdtkc2b9yhhmG0v3uQtyDx3lQIl8Y1VZbT8SsRZxiD3GX2pMA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Dynamics_of_limnological_parameters_in_reservoirs_A_case_study_in_South_Brazil_using_remote_sensing_and_meteorological_data","translated_slug":"","page_count":11,"language":"en","content_type":"Work","summary":"Reservoirs are important in Brazil for the production of hydroelectric power and human water consumption. The objective was to evaluate the variability of total suspended solids (TSS) and chlorophyll-a as well as the rainfall/ temperature and land use impacts on these optically active constituents (OAC). The study area is the Passo Real reservoir in south Brazil. The methodology was divided in four steps. First, we used wavelet to detect anomalous periods of rainfall and temperature (2002-2014). Second, we carried out 12 field campaigns to obtain in situ measurements for limnological characterization (2009-2010). The third step was the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra and Aqua satellites data corrected and non-corrected for bidirectional effects. Finally, we evaluated potential drivers of OAC changes over time using cross-correlation analysis. The results showed a decrease in the TSS and chlorophyll-a concentrations from the upper to the lower streams of the reservoir. The exponential regression between the MODIS red reflectance and TSS had an adjusted r 2 of 0.63. It decreased to 0.53 for the relationship between the green reflectance and chlorophyll-a. MODIS data corrected for bidirectional effects provided better OAC estimates than non-corrected data. The validation of MODIS TSS and chlorophyll-a estimates using a separate set of measurements showed a RMSE of 2.98 mg/l and 2.33 μg/l, respectively. MODIS estimates indicated a gradual transition in OAC from the upper to the lower streams in","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":116843836,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843836/thumbnails/1.jpg","file_name":"j.scitotenv.2016.09.05020240717-1-1sh3k9.pdf","download_url":"https://www.academia.edu/attachments/116843836/download_file","bulk_download_file_name":"Dynamics_of_limnological_parameters_in_r.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843836/j.scitotenv.2016.09.05020240717-1-1sh3k9-libre.pdf?1721196672=\u0026response-content-disposition=attachment%3B+filename%3DDynamics_of_limnological_parameters_in_r.pdf\u0026Expires=1741801541\u0026Signature=JsaDbDq1WVe~2cxG6Wo1qCZ2Cf1TxqFHG63sSwAjcTrZXP8XsHBiTQm1M5xhnlUxgh3TMcTfWUSI~~51dqOpt8ujwbGSeYPNctyh8wJpojJox5NDpqe9x1COodrwJwLOWL-gY9nEYKCnCBSZALBrHCLXtUOmg0DtRHvkAMH3O3l33hJXnURZXHklanw5Q~EtJIqBvDULC8N4hdRihAnpDsdYazktdtJvNlgsB2o0usMIG8iTttXKu-FaSq~soYfqKaVITMXTpLD6NP1l1XQWJm5z0Z-FDaKv6hi5cJdtkc2b9yhhmG0v3uQtyDx3lQIl8Y1VZbT8SsRZxiD3GX2pMA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary"},{"id":571931,"name":"Spectroradiometer","url":"https://www.academia.edu/Documents/in/Spectroradiometer"},{"id":1431418,"name":"Chlorophyll a","url":"https://www.academia.edu/Documents/in/Chlorophyll_a"}],"urls":[{"id":43558354,"url":"https://doi.org/10.1016/j.scitotenv.2016.09.050"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115042"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/122115042/Distribui%C3%A7%C3%A3o_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448"><img alt="Research paper thumbnail of Distribuição vertical da biomassa acima do solo em floresta estacional decidual - DOI:10.5039/agraria.v12i3a5448" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/122115042/Distribui%C3%A7%C3%A3o_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448">Distribuição vertical da biomassa acima do solo em floresta estacional decidual - DOI:10.5039/agraria.v12i3a5448</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implem...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height &lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115042"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115042"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115042; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115042]").text(description); $(".js-view-count[data-work-id=122115042]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115042; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115042']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=122115042]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115042,"title":"Distribuição vertical da biomassa acima do solo em floresta estacional decidual - DOI:10.5039/agraria.v12i3a5448","translated_title":"","metadata":{"abstract":"Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height \u0026lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m ...","publication_date":{"day":null,"month":null,"year":2017,"errors":{}}},"translated_abstract":"Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height \u0026lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m ...","internal_url":"https://www.academia.edu/122115042/Distribui%C3%A7%C3%A3o_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448","translated_internal_url":"","created_at":"2024-07-16T22:58:17.230-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Distribuição_vertical_da_biomassa_acima_do_solo_em_floresta_estacional_decidual_DOI_10_5039_agraria_v12i3a5448","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation, and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height \u0026lt; 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m ...","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1041,"name":"Forestry","url":"https://www.academia.edu/Documents/in/Forestry"},{"id":146385,"name":"Land Reclamation","url":"https://www.academia.edu/Documents/in/Land_Reclamation"},{"id":372637,"name":"Deciduous","url":"https://www.academia.edu/Documents/in/Deciduous"},{"id":459822,"name":"Carbon Stock","url":"https://www.academia.edu/Documents/in/Carbon_Stock"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122115019"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122115019/Directional_and_angular_effects_on_the_spectral_reflectance_of_waters_with_variable_amounts_of_total_suspended_solids"><img alt="Research paper thumbnail of Directional and angular effects on the spectral reflectance of waters with variable amounts of total suspended solids" class="work-thumbnail" src="https://attachments.academia-assets.com/116843821/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122115019/Directional_and_angular_effects_on_the_spectral_reflectance_of_waters_with_variable_amounts_of_total_suspended_solids">Directional and angular effects on the spectral reflectance of waters with variable amounts of total suspended solids</a></div><div class="wp-workCard_item"><span>Journal of Applied Remote Sensing</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Viewing geometry is one of the most important factors to consider when water bodies are observed ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Viewing geometry is one of the most important factors to consider when water bodies are observed from satellite sensors with large field of view. We examine the directional and angular effects on the reflectance of waters with different concentrations of total suspended solids (TSSs). In the laboratory, we measure the reflectance in five view zenith angles (VZAs) and eight view azimuth angles (VAAs) for optically shallow waters having four concentrations of TSSs. Seven empirical models to estimate TSSs based only on the reflectance of the red band (∼660 nm) are evaluated. In addition, we analyze Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra reflectance measured in 13 consecutive satellite overpasses. The results show that the reflectance of the inland-like water is affected by data acquisition geometry. The best wavelength to estimate TSS is 625 nm for most VZAs and VAAs. The lowest correlations between reflectance and TSS are observed at extreme viewing with the anisotropy decreasing with increasing concentrations of TSSs. Directional and angular effects are also observed for MODIS (acquired and simulated data) with TSS underestimates observed close to the orthogonal plane for all VZAs, and TSS overestimates observed in the principal scattering plane in the forward scattering direction. More anisotropic waters are observed for VZA greater than AE30 deg. Results highlight the need for correcting MODIS data for bidirectional effects in inland water studies.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d9a153bb0f9f810493ff2f754b56d55c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116843821,"asset_id":122115019,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116843821/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122115019"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122115019"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122115019; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122115019]").text(description); $(".js-view-count[data-work-id=122115019]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122115019; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122115019']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d9a153bb0f9f810493ff2f754b56d55c" } } $('.js-work-strip[data-work-id=122115019]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122115019,"title":"Directional and angular effects on the spectral reflectance of waters with variable amounts of total suspended solids","translated_title":"","metadata":{"publisher":"SPIE-Intl Soc Optical Eng","grobid_abstract":"Viewing geometry is one of the most important factors to consider when water bodies are observed from satellite sensors with large field of view. We examine the directional and angular effects on the reflectance of waters with different concentrations of total suspended solids (TSSs). In the laboratory, we measure the reflectance in five view zenith angles (VZAs) and eight view azimuth angles (VAAs) for optically shallow waters having four concentrations of TSSs. Seven empirical models to estimate TSSs based only on the reflectance of the red band (∼660 nm) are evaluated. In addition, we analyze Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra reflectance measured in 13 consecutive satellite overpasses. The results show that the reflectance of the inland-like water is affected by data acquisition geometry. The best wavelength to estimate TSS is 625 nm for most VZAs and VAAs. The lowest correlations between reflectance and TSS are observed at extreme viewing with the anisotropy decreasing with increasing concentrations of TSSs. Directional and angular effects are also observed for MODIS (acquired and simulated data) with TSS underestimates observed close to the orthogonal plane for all VZAs, and TSS overestimates observed in the principal scattering plane in the forward scattering direction. More anisotropic waters are observed for VZA greater than AE30 deg. Results highlight the need for correcting MODIS data for bidirectional effects in inland water studies.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Journal of Applied Remote Sensing","grobid_abstract_attachment_id":116843821},"translated_abstract":null,"internal_url":"https://www.academia.edu/122115019/Directional_and_angular_effects_on_the_spectral_reflectance_of_waters_with_variable_amounts_of_total_suspended_solids","translated_internal_url":"","created_at":"2024-07-16T22:57:45.579-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":116843821,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843821/thumbnails/1.jpg","file_name":"3THMBB2.pdf","download_url":"https://www.academia.edu/attachments/116843821/download_file","bulk_download_file_name":"Directional_and_angular_effects_on_the_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843821/3THMBB2-libre.pdf?1721196665=\u0026response-content-disposition=attachment%3B+filename%3DDirectional_and_angular_effects_on_the_s.pdf\u0026Expires=1741801541\u0026Signature=bG0sVctJM5qbHJXNNd9AbPmMnFwcrgRLK0M7zVkAl0B0Twlrkym7rYwHexgKB0k5s3I0CVCB8CtXwEf52zerJ-4YWbU7Qa-9Pff9OEdPjwK8vmvh0zanRVrUoviO4VqGvzkBrcGOROElbvscvB0Y61UkcWsuT4ZzNgsSZys3-R~xsJj3RcwB49RKjAVBMzk~ngizHW-u3X2TSRDkaewV0-wSzPw8HN2WtOCpqA71PGlDfTUA8eY~lnLHeWF4HYP~b4qKiPW0ukayB1~lhCuKgxg80PyLnALZUtWBD5UHJWruGEtWWAmDwXdUazGto4bHzEZhUwvyJy57mu6nPSJ3HA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Directional_and_angular_effects_on_the_spectral_reflectance_of_waters_with_variable_amounts_of_total_suspended_solids","translated_slug":"","page_count":15,"language":"en","content_type":"Work","summary":"Viewing geometry is one of the most important factors to consider when water bodies are observed from satellite sensors with large field of view. We examine the directional and angular effects on the reflectance of waters with different concentrations of total suspended solids (TSSs). In the laboratory, we measure the reflectance in five view zenith angles (VZAs) and eight view azimuth angles (VAAs) for optically shallow waters having four concentrations of TSSs. Seven empirical models to estimate TSSs based only on the reflectance of the red band (∼660 nm) are evaluated. In addition, we analyze Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra reflectance measured in 13 consecutive satellite overpasses. The results show that the reflectance of the inland-like water is affected by data acquisition geometry. The best wavelength to estimate TSS is 625 nm for most VZAs and VAAs. The lowest correlations between reflectance and TSS are observed at extreme viewing with the anisotropy decreasing with increasing concentrations of TSSs. Directional and angular effects are also observed for MODIS (acquired and simulated data) with TSS underestimates observed close to the orthogonal plane for all VZAs, and TSS overestimates observed in the principal scattering plane in the forward scattering direction. More anisotropic waters are observed for VZA greater than AE30 deg. Results highlight the need for correcting MODIS data for bidirectional effects in inland water studies.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":116843821,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116843821/thumbnails/1.jpg","file_name":"3THMBB2.pdf","download_url":"https://www.academia.edu/attachments/116843821/download_file","bulk_download_file_name":"Directional_and_angular_effects_on_the_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116843821/3THMBB2-libre.pdf?1721196665=\u0026response-content-disposition=attachment%3B+filename%3DDirectional_and_angular_effects_on_the_s.pdf\u0026Expires=1741801541\u0026Signature=bG0sVctJM5qbHJXNNd9AbPmMnFwcrgRLK0M7zVkAl0B0Twlrkym7rYwHexgKB0k5s3I0CVCB8CtXwEf52zerJ-4YWbU7Qa-9Pff9OEdPjwK8vmvh0zanRVrUoviO4VqGvzkBrcGOROElbvscvB0Y61UkcWsuT4ZzNgsSZys3-R~xsJj3RcwB49RKjAVBMzk~ngizHW-u3X2TSRDkaewV0-wSzPw8HN2WtOCpqA71PGlDfTUA8eY~lnLHeWF4HYP~b4qKiPW0ukayB1~lhCuKgxg80PyLnALZUtWBD5UHJWruGEtWWAmDwXdUazGto4bHzEZhUwvyJy57mu6nPSJ3HA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":516,"name":"Optics","url":"https://www.academia.edu/Documents/in/Optics"},{"id":1252,"name":"Remote Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing"},{"id":37333,"name":"Anisotropy","url":"https://www.academia.edu/Documents/in/Anisotropy"},{"id":162010,"name":"Geomatic Engineering","url":"https://www.academia.edu/Documents/in/Geomatic_Engineering"},{"id":441926,"name":"Scattering","url":"https://www.academia.edu/Documents/in/Scattering"},{"id":571931,"name":"Spectroradiometer","url":"https://www.academia.edu/Documents/in/Spectroradiometer"},{"id":800990,"name":"Reflectivity","url":"https://www.academia.edu/Documents/in/Reflectivity"},{"id":1497461,"name":"Applied remote sensing","url":"https://www.academia.edu/Documents/in/Applied_remote_sensing"},{"id":1766044,"name":"Azimuth","url":"https://www.academia.edu/Documents/in/Azimuth"},{"id":2881906,"name":"Zenith","url":"https://www.academia.edu/Documents/in/Zenith"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117737343"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/117737343/Recarga_de_aqu%C3%ADfero_em_Floresta_Estacional_Decidual_nativa_Parque_Estadual_do_Turvo_RS_"><img alt="Research paper thumbnail of Recarga de aquífero em Floresta Estacional Decidual nativa, Parque Estadual do Turvo (RS)" class="work-thumbnail" src="https://attachments.academia-assets.com/113519472/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/117737343/Recarga_de_aqu%C3%ADfero_em_Floresta_Estacional_Decidual_nativa_Parque_Estadual_do_Turvo_RS_">Recarga de aquífero em Floresta Estacional Decidual nativa, Parque Estadual do Turvo (RS)</a></div><div class="wp-workCard_item"><span>Ciência Florestal</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Considerando a importância das florestas na retenção das águas oriundas da precipitação pluviomét...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Considerando a importância das florestas na retenção das águas oriundas da precipitação pluviométrica e a sua função estratégica na recarga de aquíferos, este estudo teve por objetivo estimar a recarga direta e natural do aquífero raso da zona de alteração dos basaltos da Formação Serra Geral, subjacente à Floresta Estacional Decidual nativa, bioma de Mata Atlântica. Para estimar a recarga do aquífero, foi aplicado o método Water Table Fluctuation (WTF), com base na análise de séries temporais do nível freático e da precipitação, coletadas durante o período de 50 dias (de janeiro a março) no Parque Estadual do Turvo - PET (RS). Para a análise sistemática da variação do nível de água subterrânea (N.A.), foram coletados dados sub-horários com medidores de pressão automáticos (CTD), em três piezômetros localizados no PET. Em simultâneo (com igual periodicidade), foram coletados dados de precipitação pluviométrica no interior do PET, com uma estação experimental. 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Data description: The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also &lt;em&gt;Drone&lt;/em&gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): Parameters Specification/value Date (YYYYMMDD): 20191029 Time of day (BRT = -3) 12:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 200 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3&quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 587 photos Along track coverage 85% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br" rel="nofollow">breunig@ufsm.br</a> An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Figure 2. The capture of an orthomosaic in the processing workflow of the X3 camera. The lowest quality was applied. Figure 3. The capture of a DEM in the processing [...]</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952936"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952936"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952936; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952936]").text(description); $(".js-view-count[data-work-id=114952936]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952936; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952936']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=114952936]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952936,"title":"Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on October 29, 2019, in the Rio Grande do Sul State, Brazil","translated_title":"","metadata":{"abstract":"Title: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on October 29, 2019, in the Rio Grande do Sul State, Brazil Keywords Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. Data description: The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also \u0026lt;em\u0026gt;Drone\u0026lt;/em\u0026gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): Parameters Specification/value Date (YYYYMMDD): 20191029 Time of day (BRT = -3) 12:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 200 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3\u0026quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 587 photos Along track coverage 85% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, breunig@ufsm.br An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Figure 2. The capture of an orthomosaic in the processing workflow of the X3 camera. The lowest quality was applied. Figure 3. The capture of a DEM in the processing [...]","publication_date":{"day":27,"month":2,"year":2021,"errors":{}}},"translated_abstract":"Title: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on October 29, 2019, in the Rio Grande do Sul State, Brazil Keywords Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. Data description: The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also \u0026lt;em\u0026gt;Drone\u0026lt;/em\u0026gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): Parameters Specification/value Date (YYYYMMDD): 20191029 Time of day (BRT = -3) 12:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 200 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3\u0026quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 587 photos Along track coverage 85% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, breunig@ufsm.br An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Figure 2. The capture of an orthomosaic in the processing workflow of the X3 camera. The lowest quality was applied. Figure 3. The capture of a DEM in the processing [...]","internal_url":"https://www.academia.edu/114952936/Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_October_29_2019_in_the_Rio_Grande_do_Sul_State_Brazil","translated_internal_url":"","created_at":"2024-02-15T11:02:43.041-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Unmanned_Aerial_Vehicle_UAV_data_acquired_over_a_subtropical_forest_area_of_the_UFSM_campus_Frederico_Westphalen_on_October_29_2019_in_the_Rio_Grande_do_Sul_State_Brazil","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Title: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on October 29, 2019, in the Rio Grande do Sul State, Brazil Keywords Images, Drone, UAV, Forest, UFSM, Remote Sensing, GIS. Data description: The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also \u0026lt;em\u0026gt;Drone\u0026lt;/em\u0026gt;) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (Alvares et al., 2013). The rainfall is well distributed throughout the year. Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table): Parameters Specification/value Date (YYYYMMDD): 20191029 Time of day (BRT = -3) 12:00 h UAV – Drone - Camera Phantom 4 Fly high (meters above ground) 200 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky ( ) Low cloud coverage (some clouds) ( ) Completely cloudy Wind condition ( x ) no wind ( ) Low speed ( ) High speed wind Approximate data acquisition duration 30 minutes Total of photographs acquired Sensor 1/2.3\u0026quot; CMOS Effective pixels:12.4 M Lens FOV 94° 20 mm (35 mm format equivalent) f/2.8 focus at ∞ 587 photos Along track coverage 85% Cross-track coverage 80% Fly planning software Drone Deploy For more information contact: Fábio Marcelo Breunig, breunig@ufsm.br An example of the mosaic and DEM is showed below (Figure 2 e Figura 3), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted. Figure 2. The capture of an orthomosaic in the processing workflow of the X3 camera. The lowest quality was applied. Figure 3. The capture of a DEM in the processing [...]","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":1872418,"name":"Subtropics","url":"https://www.academia.edu/Documents/in/Subtropics"}],"urls":[{"id":39493897,"url":"https://zenodo.org/record/4565584"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952935"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/114952935/Assessing_the_effect_of_spatial_resolution_on_the_delineation_of_management_zones_for_smallholder_farming_in_southern_Brazil"><img alt="Research paper thumbnail of Assessing the effect of spatial resolution on the delineation of management zones for smallholder farming in southern Brazil" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/114952935/Assessing_the_effect_of_spatial_resolution_on_the_delineation_of_management_zones_for_smallholder_farming_in_southern_Brazil">Assessing the effect of spatial resolution on the delineation of management zones for smallholder farming in southern Brazil</a></div><div class="wp-workCard_item"><span>Remote Sensing Applications: Society and Environment</span><span>, Aug 1, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delin...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delineate management zones for smallholder farming in southern Brazil. In this study, we investigated the spatial resolution influence on the AGB estimates of rye, calculated from regression relationships with the Normalized Difference Vegetation Index (NDVI), and on the subsequent delineation of management zones using the Management Zone Analyst (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were compared with observations from the PlanetScope (PS) satellite constellation (3 m) and the Operational Land Imager (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green vegetation, soil, and shadow) was applied to surface reflectance data of these instruments for evaluating the cover-crop development at two dates in August 2017. Because of the differences in the technical specifications of the sensors, we resampled the UAV dataset into four levels of spatial resolution (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, we obtained confusion matrices for the original and resampled data. The results showed that the increasing amounts of rye AGB from the beginning to the end of August promoted significant changes in surface reflectance and in soil-green vegetation fractions calculated at variable spatial resolution. The performance of the regression models to estimate cover-crop AGB was approximately similar in the transition from the sub-metric (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV camera to the PS data. For all datasets, the MZA detected two management zones with zone 2 having higher cover-crop AGB than zone 1. When compared to the UAV management zone map (reference), the PS map had a moderate-to-substantial agreement, while the OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial agreements with the reference map were observed at simulated 1 m and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 and overall accuracies of 86.40% and 81.40%, respectively. We conclude that the 3 m spatial resolution data of the PS comprise an alternative to delineate management zones for smallholder farming in southern Brazil when compared to the very-high spatial resolution observations of the UAV cameras.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952935"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952935"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952935; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952935]").text(description); $(".js-view-count[data-work-id=114952935]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952935; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952935']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=114952935]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952935,"title":"Assessing the effect of spatial resolution on the delineation of management zones for smallholder farming in southern Brazil","translated_title":"","metadata":{"abstract":"Abstract Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delineate management zones for smallholder farming in southern Brazil. In this study, we investigated the spatial resolution influence on the AGB estimates of rye, calculated from regression relationships with the Normalized Difference Vegetation Index (NDVI), and on the subsequent delineation of management zones using the Management Zone Analyst (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were compared with observations from the PlanetScope (PS) satellite constellation (3 m) and the Operational Land Imager (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green vegetation, soil, and shadow) was applied to surface reflectance data of these instruments for evaluating the cover-crop development at two dates in August 2017. Because of the differences in the technical specifications of the sensors, we resampled the UAV dataset into four levels of spatial resolution (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, we obtained confusion matrices for the original and resampled data. The results showed that the increasing amounts of rye AGB from the beginning to the end of August promoted significant changes in surface reflectance and in soil-green vegetation fractions calculated at variable spatial resolution. The performance of the regression models to estimate cover-crop AGB was approximately similar in the transition from the sub-metric (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV camera to the PS data. For all datasets, the MZA detected two management zones with zone 2 having higher cover-crop AGB than zone 1. When compared to the UAV management zone map (reference), the PS map had a moderate-to-substantial agreement, while the OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial agreements with the reference map were observed at simulated 1 m and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 and overall accuracies of 86.40% and 81.40%, respectively. We conclude that the 3 m spatial resolution data of the PS comprise an alternative to delineate management zones for smallholder farming in southern Brazil when compared to the very-high spatial resolution observations of the UAV cameras.","publisher":"Elsevier BV","publication_date":{"day":1,"month":8,"year":2020,"errors":{}},"publication_name":"Remote Sensing Applications: Society and Environment"},"translated_abstract":"Abstract Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delineate management zones for smallholder farming in southern Brazil. In this study, we investigated the spatial resolution influence on the AGB estimates of rye, calculated from regression relationships with the Normalized Difference Vegetation Index (NDVI), and on the subsequent delineation of management zones using the Management Zone Analyst (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were compared with observations from the PlanetScope (PS) satellite constellation (3 m) and the Operational Land Imager (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green vegetation, soil, and shadow) was applied to surface reflectance data of these instruments for evaluating the cover-crop development at two dates in August 2017. Because of the differences in the technical specifications of the sensors, we resampled the UAV dataset into four levels of spatial resolution (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, we obtained confusion matrices for the original and resampled data. The results showed that the increasing amounts of rye AGB from the beginning to the end of August promoted significant changes in surface reflectance and in soil-green vegetation fractions calculated at variable spatial resolution. The performance of the regression models to estimate cover-crop AGB was approximately similar in the transition from the sub-metric (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV camera to the PS data. For all datasets, the MZA detected two management zones with zone 2 having higher cover-crop AGB than zone 1. When compared to the UAV management zone map (reference), the PS map had a moderate-to-substantial agreement, while the OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial agreements with the reference map were observed at simulated 1 m and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 and overall accuracies of 86.40% and 81.40%, respectively. We conclude that the 3 m spatial resolution data of the PS comprise an alternative to delineate management zones for smallholder farming in southern Brazil when compared to the very-high spatial resolution observations of the UAV cameras.","internal_url":"https://www.academia.edu/114952935/Assessing_the_effect_of_spatial_resolution_on_the_delineation_of_management_zones_for_smallholder_farming_in_southern_Brazil","translated_internal_url":"","created_at":"2024-02-15T11:02:42.823-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Assessing_the_effect_of_spatial_resolution_on_the_delineation_of_management_zones_for_smallholder_farming_in_southern_Brazil","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Abstract Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delineate management zones for smallholder farming in southern Brazil. In this study, we investigated the spatial resolution influence on the AGB estimates of rye, calculated from regression relationships with the Normalized Difference Vegetation Index (NDVI), and on the subsequent delineation of management zones using the Management Zone Analyst (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were compared with observations from the PlanetScope (PS) satellite constellation (3 m) and the Operational Land Imager (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green vegetation, soil, and shadow) was applied to surface reflectance data of these instruments for evaluating the cover-crop development at two dates in August 2017. Because of the differences in the technical specifications of the sensors, we resampled the UAV dataset into four levels of spatial resolution (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, we obtained confusion matrices for the original and resampled data. The results showed that the increasing amounts of rye AGB from the beginning to the end of August promoted significant changes in surface reflectance and in soil-green vegetation fractions calculated at variable spatial resolution. The performance of the regression models to estimate cover-crop AGB was approximately similar in the transition from the sub-metric (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV camera to the PS data. For all datasets, the MZA detected two management zones with zone 2 having higher cover-crop AGB than zone 1. When compared to the UAV management zone map (reference), the PS map had a moderate-to-substantial agreement, while the OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial agreements with the reference map were observed at simulated 1 m and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 and overall accuracies of 86.40% and 81.40%, respectively. We conclude that the 3 m spatial resolution data of the PS comprise an alternative to delineate management zones for smallholder farming in southern Brazil when compared to the very-high spatial resolution observations of the UAV cameras.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":10225,"name":"Agriculture","url":"https://www.academia.edu/Documents/in/Agriculture"}],"urls":[{"id":39493896,"url":"https://doi.org/10.1016/j.rsase.2020.100325"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952934"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114952934/Avalia%C3%A7%C3%A3o_Das_Rela%C3%A7%C3%B5es_Entre_Os_Par%C3%A2metros_Da_Modelagem_Geomorfol%C3%B3gica_e_a_Cobertura_Florestal_No_Noroeste_Do_Rio_Grande_Do_Sul_Assessment_of_Relations_Between_the_Geomorphological_Modeling_Parameters_and_the_Forest_Cover_in_the_Northwest_of_RS"><img alt="Research paper thumbnail of Avaliação Das Relações Entre Os Parâmetros Da Modelagem Geomorfológica e a Cobertura Florestal No Noroeste Do Rio Grande Do Sul / Assessment of Relations Between the Geomorphological Modeling Parameters and the Forest Cover in the Northwest of RS" class="work-thumbnail" src="https://attachments.academia-assets.com/111503636/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114952934/Avalia%C3%A7%C3%A3o_Das_Rela%C3%A7%C3%B5es_Entre_Os_Par%C3%A2metros_Da_Modelagem_Geomorfol%C3%B3gica_e_a_Cobertura_Florestal_No_Noroeste_Do_Rio_Grande_Do_Sul_Assessment_of_Relations_Between_the_Geomorphological_Modeling_Parameters_and_the_Forest_Cover_in_the_Northwest_of_RS">Avaliação Das Relações Entre Os Parâmetros Da Modelagem Geomorfológica e a Cobertura Florestal No Noroeste Do Rio Grande Do Sul / Assessment of Relations Between the Geomorphological Modeling Parameters and the Forest Cover in the Northwest of RS</a></div><div class="wp-workCard_item"><span>Geo UERJ</span><span>, Dec 30, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">© 2017 Rosa et al. Este é um artigo de acesso aberto distribuído sob os termos da Licença Creativ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">© 2017 Rosa et al. Este é um artigo de acesso aberto distribuído sob os termos da Licença Creative Commons Atribuição-Não Comercial-Compartilha Igual (CC BY-NC-SA 4.0), que permite uso, distribuição e reprodução para fins não comercias, com a citação dos autores e da fonte original e sob a mesma licença..</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2bd7a95bbcb93f56092cb4b300f30516" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111503636,"asset_id":114952934,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111503636/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952934"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952934"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952934; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952934]").text(description); $(".js-view-count[data-work-id=114952934]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952934; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952934']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2bd7a95bbcb93f56092cb4b300f30516" } } $('.js-work-strip[data-work-id=114952934]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952934,"title":"Avaliação Das Relações Entre Os Parâmetros Da Modelagem Geomorfológica e a Cobertura Florestal No Noroeste Do Rio Grande Do Sul / Assessment of Relations Between the Geomorphological Modeling Parameters and the Forest Cover in the Northwest of RS","translated_title":"","metadata":{"publisher":"Universidade do Estado do Rio de Janeiro","grobid_abstract":"© 2017 Rosa et al. Este é um artigo de acesso aberto distribuído sob os termos da Licença Creative Commons Atribuição-Não Comercial-Compartilha Igual (CC BY-NC-SA 4.0), que permite uso, distribuição e reprodução para fins não comercias, com a citação dos autores e da fonte original e sob a mesma licença..","publication_date":{"day":30,"month":12,"year":2017,"errors":{}},"publication_name":"Geo UERJ","grobid_abstract_attachment_id":111503635},"translated_abstract":null,"internal_url":"https://www.academia.edu/114952934/Avalia%C3%A7%C3%A3o_Das_Rela%C3%A7%C3%B5es_Entre_Os_Par%C3%A2metros_Da_Modelagem_Geomorfol%C3%B3gica_e_a_Cobertura_Florestal_No_Noroeste_Do_Rio_Grande_Do_Sul_Assessment_of_Relations_Between_the_Geomorphological_Modeling_Parameters_and_the_Forest_Cover_in_the_Northwest_of_RS","translated_internal_url":"","created_at":"2024-02-15T11:02:42.610-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":111503636,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503636/thumbnails/1.jpg","file_name":"23053.pdf","download_url":"https://www.academia.edu/attachments/111503636/download_file","bulk_download_file_name":"Avaliacao_Das_Relacoes_Entre_Os_Parametr.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503636/23053-libre.pdf?1708023866=\u0026response-content-disposition=attachment%3B+filename%3DAvaliacao_Das_Relacoes_Entre_Os_Parametr.pdf\u0026Expires=1741801542\u0026Signature=AEmXyENqYj2CjqUr5cQ2aWGjtmz46Q7VKBnL~XvyJlmbt8pEG-9eSFv~iethCnGi10nsweAW8GBmMD8Ig94olB7d0u~01PZn-c-ziDAEFxZQyPwKpngAYyP6qrS-2Q45TuAMAzkTxzjl~x-l16FLt2li0wo5aW4tuXrLSUUClNmHKsKyvb5bqFyV5~E7wqM~jxAyoMO9BzVUuPr9yayvzwZnU2dmxoKN7gUpqLgrjiLAV4m4IvpZU1O48Mf~yuSAzG40g2QuKh6kcmloEIm2~4~YeJYKtolq7IB0n1gsKidLBbG0-OSPsGFO7A9p~GO~a~exl~4WI1ZCVq1upYBbMA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Avaliação_Das_Relações_Entre_Os_Parâmetros_Da_Modelagem_Geomorfológica_e_a_Cobertura_Florestal_No_Noroeste_Do_Rio_Grande_Do_Sul_Assessment_of_Relations_Between_the_Geomorphological_Modeling_Parameters_and_the_Forest_Cover_in_the_Northwest_of_RS","translated_slug":"","page_count":22,"language":"pt","content_type":"Work","summary":"© 2017 Rosa et al. Este é um artigo de acesso aberto distribuído sob os termos da Licença Creative Commons Atribuição-Não Comercial-Compartilha Igual (CC BY-NC-SA 4.0), que permite uso, distribuição e reprodução para fins não comercias, com a citação dos autores e da fonte original e sob a mesma licença..","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":111503636,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503636/thumbnails/1.jpg","file_name":"23053.pdf","download_url":"https://www.academia.edu/attachments/111503636/download_file","bulk_download_file_name":"Avaliacao_Das_Relacoes_Entre_Os_Parametr.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503636/23053-libre.pdf?1708023866=\u0026response-content-disposition=attachment%3B+filename%3DAvaliacao_Das_Relacoes_Entre_Os_Parametr.pdf\u0026Expires=1741801542\u0026Signature=AEmXyENqYj2CjqUr5cQ2aWGjtmz46Q7VKBnL~XvyJlmbt8pEG-9eSFv~iethCnGi10nsweAW8GBmMD8Ig94olB7d0u~01PZn-c-ziDAEFxZQyPwKpngAYyP6qrS-2Q45TuAMAzkTxzjl~x-l16FLt2li0wo5aW4tuXrLSUUClNmHKsKyvb5bqFyV5~E7wqM~jxAyoMO9BzVUuPr9yayvzwZnU2dmxoKN7gUpqLgrjiLAV4m4IvpZU1O48Mf~yuSAzG40g2QuKh6kcmloEIm2~4~YeJYKtolq7IB0n1gsKidLBbG0-OSPsGFO7A9p~GO~a~exl~4WI1ZCVq1upYBbMA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":111503635,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503635/thumbnails/1.jpg","file_name":"23053.pdf","download_url":"https://www.academia.edu/attachments/111503635/download_file","bulk_download_file_name":"Avaliacao_Das_Relacoes_Entre_Os_Parametr.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503635/23053-libre.pdf?1708023859=\u0026response-content-disposition=attachment%3B+filename%3DAvaliacao_Das_Relacoes_Entre_Os_Parametr.pdf\u0026Expires=1741801542\u0026Signature=Gc1YbJXcbNdLoXe5SsgQezQjE3CP4tVwnJkqqpnenq5gUOC6KE3iKT3XxRPI4f9U9rCTodLeKdsxbOoxqeATriILB2~wxyRDrygUihx-tvlW2ZMJON~-ltiw9oekQJ~8gnDyJfeYGFrOjlAJeMnVts~zhOSeq~GrkP2VCA3EViDsGE8ju6h5d85Gvw6Kw4X1fY5G9jlFHMPklcCwcI9nQIn91PgJD1IUAehR6jbq3jQUB7JeoFA-lNx338RbtujI2DY2wRazqH1bkeLxZ9Wmmw1tulEZv7DGDZx9PWl7Ov-6Z1uQ7bH5lD1GLqQUWcqnkwVtgka-unNqKE4sN2S6UA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":1041,"name":"Forestry","url":"https://www.academia.edu/Documents/in/Forestry"}],"urls":[{"id":39493895,"url":"https://www.e-publicacoes.uerj.br/index.php/geouerj/article/download/19502/23053"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952933"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114952933/A_hyperspectral_experiment_over_tropical_forests_based_on_the_EO_1_orbit_change_and_PROSAIL_simulation"><img alt="Research paper thumbnail of A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation" class="work-thumbnail" src="https://attachments.academia-assets.com/111503654/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114952933/A_hyperspectral_experiment_over_tropical_forests_based_on_the_EO_1_orbit_change_and_PROSAIL_simulation">A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation</a></div><div class="wp-workCard_item"><span>Giscience & Remote Sensing</span><span>, Sep 24, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004-2008) to the EO-1 drift period (2011-2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14°(2015) and 22°(2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen's r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="16a7c8160e53a3b7f5968d1775d2ac77" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111503654,"asset_id":114952933,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111503654/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952933"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952933"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952933; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952933]").text(description); $(".js-view-count[data-work-id=114952933]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952933; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952933']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "16a7c8160e53a3b7f5968d1775d2ac77" } } $('.js-work-strip[data-work-id=114952933]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952933,"title":"A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation","translated_title":"","metadata":{"publisher":"Taylor \u0026 Francis","grobid_abstract":"We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004-2008) to the EO-1 drift period (2011-2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14°(2015) and 22°(2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen's r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.","publication_date":{"day":24,"month":9,"year":2019,"errors":{}},"publication_name":"Giscience \u0026 Remote Sensing","grobid_abstract_attachment_id":111503654},"translated_abstract":null,"internal_url":"https://www.academia.edu/114952933/A_hyperspectral_experiment_over_tropical_forests_based_on_the_EO_1_orbit_change_and_PROSAIL_simulation","translated_internal_url":"","created_at":"2024-02-15T11:02:42.376-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":111503654,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503654/thumbnails/1.jpg","file_name":"15481603.2019.166859520240215-1-f46thg.pdf","download_url":"https://www.academia.edu/attachments/111503654/download_file","bulk_download_file_name":"A_hyperspectral_experiment_over_tropical.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503654/15481603.2019.166859520240215-1-f46thg-libre.pdf?1708023850=\u0026response-content-disposition=attachment%3B+filename%3DA_hyperspectral_experiment_over_tropical.pdf\u0026Expires=1741801542\u0026Signature=MyYnjMeNTcwW6bQoX~Pk9T54HceUGk0JG4M6if6N8SlMFHcTELtJB96YuggDmCR0Uo3l~UNtOOKFmGD3m7g7Y6oDnPwc7ERoUGiVAJ0cbos5ua0ytjtCmNmbKBUZ6XkQ0C4CZ75zw31eDPjate8wQaOyotkqvERA0oK9SQqZQ1nvvlfPoJuX7V3opm7DZUV1Yqdf3xX4S4~jBEAdC5-613tI1fM-x~uqLvWK8RDbK6px42oIDHjrCUgViPWfI2EbPZR1dlCEqgf~co~de7Vib1cjJtJum3pAJTuaSSb5bxgSBWEHvCPUXPsyu53LuCRbRUvFl8823YrnUSarAHXKag__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_hyperspectral_experiment_over_tropical_forests_based_on_the_EO_1_orbit_change_and_PROSAIL_simulation","translated_slug":"","page_count":18,"language":"en","content_type":"Work","summary":"We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004-2008) to the EO-1 drift period (2011-2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14°(2015) and 22°(2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen's r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":111503654,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503654/thumbnails/1.jpg","file_name":"15481603.2019.166859520240215-1-f46thg.pdf","download_url":"https://www.academia.edu/attachments/111503654/download_file","bulk_download_file_name":"A_hyperspectral_experiment_over_tropical.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503654/15481603.2019.166859520240215-1-f46thg-libre.pdf?1708023850=\u0026response-content-disposition=attachment%3B+filename%3DA_hyperspectral_experiment_over_tropical.pdf\u0026Expires=1741801542\u0026Signature=MyYnjMeNTcwW6bQoX~Pk9T54HceUGk0JG4M6if6N8SlMFHcTELtJB96YuggDmCR0Uo3l~UNtOOKFmGD3m7g7Y6oDnPwc7ERoUGiVAJ0cbos5ua0ytjtCmNmbKBUZ6XkQ0C4CZ75zw31eDPjate8wQaOyotkqvERA0oK9SQqZQ1nvvlfPoJuX7V3opm7DZUV1Yqdf3xX4S4~jBEAdC5-613tI1fM-x~uqLvWK8RDbK6px42oIDHjrCUgViPWfI2EbPZR1dlCEqgf~co~de7Vib1cjJtJum3pAJTuaSSb5bxgSBWEHvCPUXPsyu53LuCRbRUvFl8823YrnUSarAHXKag__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1252,"name":"Remote Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing"},{"id":108112,"name":"Tropical forest","url":"https://www.academia.edu/Documents/in/Tropical_forest"},{"id":285356,"name":"Hyperspectral Imaging","url":"https://www.academia.edu/Documents/in/Hyperspectral_Imaging"}],"urls":[{"id":39493894,"url":"https://doi.org/10.1080/15481603.2019.1668595"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952932"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114952932/Din%C3%A2mica_Espacial_Do_Estoque_De_Biomassa_e_Carbono_Em_Remanescentes_Florestais_No_Rio_Grande_Do_Sul_Brasil"><img alt="Research paper thumbnail of Dinâmica Espacial Do Estoque De Biomassa e Carbono Em Remanescentes Florestais No Rio Grande Do Sul – Brasil" class="work-thumbnail" src="https://attachments.academia-assets.com/111503653/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114952932/Din%C3%A2mica_Espacial_Do_Estoque_De_Biomassa_e_Carbono_Em_Remanescentes_Florestais_No_Rio_Grande_Do_Sul_Brasil">Dinâmica Espacial Do Estoque De Biomassa e Carbono Em Remanescentes Florestais No Rio Grande Do Sul – Brasil</a></div><div class="wp-workCard_item"><span>Biofix Scientific Journal</span><span>, Apr 11, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Entre os diversos serviços ambientais prestados pelos ecossistemas florestais, destaca-se o seque...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Entre os diversos serviços ambientais prestados pelos ecossistemas florestais, destaca-se o sequestro e estocagem do carbono na forma de biomassa. Assim, o objetivo do trabalho foi estimar e investigar a dinâmica do estoque da biomassa e do carbono florestal aéreo em estágios sucessionais de regeneração em florestas nativas na Região Noroeste do Estado do Rio Grande do Sul, Brasil, utilizando dados de Sensoriamento Remoto. O estudo foi realizado em três décadas (1985-2014), abrangendo 9.046,9 km². A classificação das sucessões florestais em estágios de regeneração foi realizada a partir das seguintes classes de tamanho: inicial (< 5 ha), médio (5-10 ha), avançado (> 10 ha). Na sequência, foi estimada a biomassa e o carbono para cada fragmento florestal, conforme sua evolução ao longo do tempo. A biomassa florestal foi estimada para as datas de 1985, 1994, 2005 e 2014, avaliando a dinâmica da biomassa e do carbono florestal presente na área de estudo durante este período. A biomassa total estimada foi de 80.831 Gg, dividida em três estágios de regeneração: inicial (659 Gg), médio (2.549 Gg) e avançado (77.623 Gg). O carbono estimado foi de 296 Gg para o estágio inicial, 1.147 para o estágio médio e 34.930 para o estágio avançado, totalizando 36.373 Gg Os resultados obtidos neste estudo são de suma importância, pois demonstram que as florestas presentes na região noroeste do Rio Grande Do Sul estão contribuindo positivamente com a remoção de dióxido de carbono da atmosfera, consequentemente auxiliando na mitigação das alterações climáticas.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0f018c9f615bac48349d5599adfca0c8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111503653,"asset_id":114952932,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111503653/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952932"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952932"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952932; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952932]").text(description); $(".js-view-count[data-work-id=114952932]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952932; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952932']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0f018c9f615bac48349d5599adfca0c8" } } $('.js-work-strip[data-work-id=114952932]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952932,"title":"Dinâmica Espacial Do Estoque De Biomassa e Carbono Em Remanescentes Florestais No Rio Grande Do Sul – Brasil","translated_title":"","metadata":{"publisher":"Universidade Federal do Paraná","grobid_abstract":"Entre os diversos serviços ambientais prestados pelos ecossistemas florestais, destaca-se o sequestro e estocagem do carbono na forma de biomassa. Assim, o objetivo do trabalho foi estimar e investigar a dinâmica do estoque da biomassa e do carbono florestal aéreo em estágios sucessionais de regeneração em florestas nativas na Região Noroeste do Estado do Rio Grande do Sul, Brasil, utilizando dados de Sensoriamento Remoto. O estudo foi realizado em três décadas (1985-2014), abrangendo 9.046,9 km². A classificação das sucessões florestais em estágios de regeneração foi realizada a partir das seguintes classes de tamanho: inicial (\u003c 5 ha), médio (5-10 ha), avançado (\u003e 10 ha). Na sequência, foi estimada a biomassa e o carbono para cada fragmento florestal, conforme sua evolução ao longo do tempo. A biomassa florestal foi estimada para as datas de 1985, 1994, 2005 e 2014, avaliando a dinâmica da biomassa e do carbono florestal presente na área de estudo durante este período. A biomassa total estimada foi de 80.831 Gg, dividida em três estágios de regeneração: inicial (659 Gg), médio (2.549 Gg) e avançado (77.623 Gg). O carbono estimado foi de 296 Gg para o estágio inicial, 1.147 para o estágio médio e 34.930 para o estágio avançado, totalizando 36.373 Gg Os resultados obtidos neste estudo são de suma importância, pois demonstram que as florestas presentes na região noroeste do Rio Grande Do Sul estão contribuindo positivamente com a remoção de dióxido de carbono da atmosfera, consequentemente auxiliando na mitigação das alterações climáticas.","publication_date":{"day":11,"month":4,"year":2023,"errors":{}},"publication_name":"Biofix Scientific Journal","grobid_abstract_attachment_id":111503653},"translated_abstract":null,"internal_url":"https://www.academia.edu/114952932/Din%C3%A2mica_Espacial_Do_Estoque_De_Biomassa_e_Carbono_Em_Remanescentes_Florestais_No_Rio_Grande_Do_Sul_Brasil","translated_internal_url":"","created_at":"2024-02-15T11:02:42.099-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":159361,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":111503653,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503653/thumbnails/1.jpg","file_name":"49049.pdf","download_url":"https://www.academia.edu/attachments/111503653/download_file","bulk_download_file_name":"Dinamica_Espacial_Do_Estoque_De_Biomassa.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503653/49049-libre.pdf?1708023850=\u0026response-content-disposition=attachment%3B+filename%3DDinamica_Espacial_Do_Estoque_De_Biomassa.pdf\u0026Expires=1741801542\u0026Signature=ddfOWFyxFIlSKi4A0E7Y24EZX1QNhD7fluPPgUe-SBfgeiQghkhE8tg8YueHQKxACMpz7snuJV4qBb25krlAsFp601tdRF52HOAXv5RbQPp2TUOz~l1fSTTQ2WER1eGPiRrmcabnjyeXcOD52esCZJslsqF3VawtXqOtPWfvh8dk9aJ6Fz3P6Y-6dcU9Wxcy-ZdU22uThbHskDLSDUOs~VWgWhYLl1XdLosP-n5dqPDeV-IAfgQK9snS069xV5JYUIpEFJzcRboDLSCt8lprbuArPCfx2SQ5HrFaC8HYrJ0~af7sBCytJB2ThxhxScZhy9XevmQJKYQ-4oUjLkK6Vw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Dinâmica_Espacial_Do_Estoque_De_Biomassa_e_Carbono_Em_Remanescentes_Florestais_No_Rio_Grande_Do_Sul_Brasil","translated_slug":"","page_count":9,"language":"pt","content_type":"Work","summary":"Entre os diversos serviços ambientais prestados pelos ecossistemas florestais, destaca-se o sequestro e estocagem do carbono na forma de biomassa. Assim, o objetivo do trabalho foi estimar e investigar a dinâmica do estoque da biomassa e do carbono florestal aéreo em estágios sucessionais de regeneração em florestas nativas na Região Noroeste do Estado do Rio Grande do Sul, Brasil, utilizando dados de Sensoriamento Remoto. O estudo foi realizado em três décadas (1985-2014), abrangendo 9.046,9 km². A classificação das sucessões florestais em estágios de regeneração foi realizada a partir das seguintes classes de tamanho: inicial (\u003c 5 ha), médio (5-10 ha), avançado (\u003e 10 ha). Na sequência, foi estimada a biomassa e o carbono para cada fragmento florestal, conforme sua evolução ao longo do tempo. A biomassa florestal foi estimada para as datas de 1985, 1994, 2005 e 2014, avaliando a dinâmica da biomassa e do carbono florestal presente na área de estudo durante este período. A biomassa total estimada foi de 80.831 Gg, dividida em três estágios de regeneração: inicial (659 Gg), médio (2.549 Gg) e avançado (77.623 Gg). O carbono estimado foi de 296 Gg para o estágio inicial, 1.147 para o estágio médio e 34.930 para o estágio avançado, totalizando 36.373 Gg Os resultados obtidos neste estudo são de suma importância, pois demonstram que as florestas presentes na região noroeste do Rio Grande Do Sul estão contribuindo positivamente com a remoção de dióxido de carbono da atmosfera, consequentemente auxiliando na mitigação das alterações climáticas.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":111503653,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503653/thumbnails/1.jpg","file_name":"49049.pdf","download_url":"https://www.academia.edu/attachments/111503653/download_file","bulk_download_file_name":"Dinamica_Espacial_Do_Estoque_De_Biomassa.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503653/49049-libre.pdf?1708023850=\u0026response-content-disposition=attachment%3B+filename%3DDinamica_Espacial_Do_Estoque_De_Biomassa.pdf\u0026Expires=1741801542\u0026Signature=ddfOWFyxFIlSKi4A0E7Y24EZX1QNhD7fluPPgUe-SBfgeiQghkhE8tg8YueHQKxACMpz7snuJV4qBb25krlAsFp601tdRF52HOAXv5RbQPp2TUOz~l1fSTTQ2WER1eGPiRrmcabnjyeXcOD52esCZJslsqF3VawtXqOtPWfvh8dk9aJ6Fz3P6Y-6dcU9Wxcy-ZdU22uThbHskDLSDUOs~VWgWhYLl1XdLosP-n5dqPDeV-IAfgQK9snS069xV5JYUIpEFJzcRboDLSCt8lprbuArPCfx2SQ5HrFaC8HYrJ0~af7sBCytJB2ThxhxScZhy9XevmQJKYQ-4oUjLkK6Vw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1041,"name":"Forestry","url":"https://www.academia.edu/Documents/in/Forestry"},{"id":459822,"name":"Carbon Stock","url":"https://www.academia.edu/Documents/in/Carbon_Stock"}],"urls":[{"id":39493893,"url":"https://doi.org/10.5380/biofix.v8i1.86271"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952931"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114952931/Directional_Effects_on_the_Spectral_Response_of_Pinus_Elliottii_Stands_Cultivated_in_Subtropical_Latitudes"><img alt="Research paper thumbnail of Directional Effects on the Spectral Response of Pinus Elliottii Stands Cultivated in Subtropical Latitudes" class="work-thumbnail" src="https://attachments.academia-assets.com/111503652/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114952931/Directional_Effects_on_the_Spectral_Response_of_Pinus_Elliottii_Stands_Cultivated_in_Subtropical_Latitudes">Directional Effects on the Spectral Response of Pinus Elliottii Stands Cultivated in Subtropical Latitudes</a></div><div class="wp-workCard_item"><span>Ra'e Ga</span><span>, Apr 22, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Remote sensing techniques and related products are an efficient and low-cost alternative of great...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Remote sensing techniques and related products are an efficient and low-cost alternative of great potential for monitoring Pine crops. However, there are no conclusive studies on the magnitude of directional effects caused by variations in the geometry of data acquisition by large field-of-view satellite instruments face to the well-defined canopy architecture of Pine. This paper investigates the viewing geometry influence on the data acquisition of reflectance and vegetation indices used to monitor Pine stands. Two multitemporal series of reflectance measurements of the MOD09GA and MCD43A4 products, without and with the application of nadir normalization, respectively, were acquired and sampled according to the view zenith angle for pairs of consecutive dates with observations made in opposite directions. The influence of directional effects on the response of the Enhanced Vegetation Index, Normalized Difference Vegetation Index, and Wide Dynamic Range Vegetation Index was verified from analysis of variance (ANOVA). The results showed that the reflectance measurements were substantially influenced by the directional effects, showing lower values when calculating from the forward scattering direction, and higher values when obtained from the backscattering direction. From the three studied vegetation indices, the EVI was the most sensitive metric to directional effects. However, when calculating using normalized reflectance measurements to nadir, the directional influence on the EVI was greatly reduced when compared to the other two vegetation indices.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="40fdcbc74f78c49dcd7472eabf0be32c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111503652,"asset_id":114952931,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111503652/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952931"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952931"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952931; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952931]").text(description); $(".js-view-count[data-work-id=114952931]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952931; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952931']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "40fdcbc74f78c49dcd7472eabf0be32c" } } $('.js-work-strip[data-work-id=114952931]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952931,"title":"Directional Effects on the Spectral Response of Pinus Elliottii Stands Cultivated in Subtropical Latitudes","translated_title":"","metadata":{"publisher":"Universidade Federal do Paraná","ai_title_tag":"Directional Effects on Spectral Response in Pine Crops","grobid_abstract":"Remote sensing techniques and related products are an efficient and low-cost alternative of great potential for monitoring Pine crops. However, there are no conclusive studies on the magnitude of directional effects caused by variations in the geometry of data acquisition by large field-of-view satellite instruments face to the well-defined canopy architecture of Pine. This paper investigates the viewing geometry influence on the data acquisition of reflectance and vegetation indices used to monitor Pine stands. Two multitemporal series of reflectance measurements of the MOD09GA and MCD43A4 products, without and with the application of nadir normalization, respectively, were acquired and sampled according to the view zenith angle for pairs of consecutive dates with observations made in opposite directions. The influence of directional effects on the response of the Enhanced Vegetation Index, Normalized Difference Vegetation Index, and Wide Dynamic Range Vegetation Index was verified from analysis of variance (ANOVA). The results showed that the reflectance measurements were substantially influenced by the directional effects, showing lower values when calculating from the forward scattering direction, and higher values when obtained from the backscattering direction. From the three studied vegetation indices, the EVI was the most sensitive metric to directional effects. 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However, there are no conclusive studies on the magnitude of directional effects caused by variations in the geometry of data acquisition by large field-of-view satellite instruments face to the well-defined canopy architecture of Pine. This paper investigates the viewing geometry influence on the data acquisition of reflectance and vegetation indices used to monitor Pine stands. Two multitemporal series of reflectance measurements of the MOD09GA and MCD43A4 products, without and with the application of nadir normalization, respectively, were acquired and sampled according to the view zenith angle for pairs of consecutive dates with observations made in opposite directions. The influence of directional effects on the response of the Enhanced Vegetation Index, Normalized Difference Vegetation Index, and Wide Dynamic Range Vegetation Index was verified from analysis of variance (ANOVA). The results showed that the reflectance measurements were substantially influenced by the directional effects, showing lower values when calculating from the forward scattering direction, and higher values when obtained from the backscattering direction. From the three studied vegetation indices, the EVI was the most sensitive metric to directional effects. However, when calculating using normalized reflectance measurements to nadir, the directional influence on the EVI was greatly reduced when compared to the other two vegetation indices.","owner":{"id":159361,"first_name":"Fabio Marcelo","middle_initials":null,"last_name":"Breunig","page_name":"FabioMarceloBreunig","domain_name":"ufpr-br","created_at":"2010-04-02T04:12:06.482-07:00","display_name":"Fabio Marcelo Breunig","url":"https://ufpr-br.academia.edu/FabioMarceloBreunig"},"attachments":[{"id":111503652,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/111503652/thumbnails/1.jpg","file_name":"49130.pdf","download_url":"https://www.academia.edu/attachments/111503652/download_file","bulk_download_file_name":"Directional_Effects_on_the_Spectral_Resp.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/111503652/49130-libre.pdf?1708023857=\u0026response-content-disposition=attachment%3B+filename%3DDirectional_Effects_on_the_Spectral_Resp.pdf\u0026Expires=1741801542\u0026Signature=ehT9Nhwbus0XcHn2W6MJeE6hW0oZoeEQR9RmaD8H0rbxSoCR8mGa7TjESfCCC~BKATI6KDG6vHa2hsXaFamX3YPQ8vH-cVr0yGL7vLvFyaz9bPOXMbfGuH19W76UVt~pqt-DdZ0GKmGyG~PQwtP7y4RaLPXs53s6Ybd6lawM~Gf7NxfGItz3M3K5hSONt5T3OyL5tujm0MLOIU8egqTMl924keZdgWv8wr9rtAMnJ-ZmgLNtfwvKn~FCB3zoaOz6R7X1Lua~ysh52OKkW0dODzMQ3sYXGXX1o3cEg2UImj0dpR0GAkud9f26rqQcFpv96VB3MHi4gLIWmONrth7K6Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1041,"name":"Forestry","url":"https://www.academia.edu/Documents/in/Forestry"}],"urls":[{"id":39493892,"url":"https://doi.org/10.5380/raega.v56i0.85857"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114952930"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114952930/Forest_Biomass_and_Carbon_Estimation_Using_Vegetation_Indices_and_Leaf_Area_Index"><img alt="Research paper thumbnail of Forest Biomass and Carbon Estimation Using Vegetation Indices and Leaf Area Index" class="work-thumbnail" src="https://attachments.academia-assets.com/111503638/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114952930/Forest_Biomass_and_Carbon_Estimation_Using_Vegetation_Indices_and_Leaf_Area_Index">Forest Biomass and Carbon Estimation Using Vegetation Indices and Leaf Area Index</a></div><div class="wp-workCard_item"><span>Revista Brasileira de Geografia Física</span><span>, Jul 19, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Among the consequences of the current dynamics of manmade land use and cover, forest fragmentatio...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Among the consequences of the current dynamics of manmade land use and cover, forest fragmentation results in a large number of isolated fragments of different sizes. Forest provides many environmental services such as carbon sequestration and storage in the form of biomass, demonstrating the crucial role of native forests and the need to develop new methods that estimate forest biomass and carbon by non-destructive methods. This work aimed to develop a methodology to study and estimate the dynamics of forest biomass and carbon stock in successive stages of regeneration in the northwestern region of Rio Grande do Sul, which was the study area. Yearly images from 1985 to 2014 of the Normalized Difference Vegetation Index-NDVI were calculated, and a global regression was archived to obtain the leaf area index (LAI) and estimate the forest biomass. It was possible to observe the good relationship between the increase in LAI as a function of the NDVI, estimating the forest biomass of 61,156 Gg in 2014, where 602 Gg correspond to the initial regeneration stage, 8,287 Gg for the medium stage, and 52,267 Gg for the advanced stage of regeneration. The total estimated carbon for the study area in 2014 was 27,520 Gg CO2 of which 271 Gg were present in the initial stage, 3,729 Gg in the medium stage, and 23,520 Gg in the advanced stage of regeneration.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="929712dca2d1c5b1c71e09d125b7b4c2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111503638,"asset_id":114952930,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111503638/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114952930"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114952930"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114952930; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114952930]").text(description); $(".js-view-count[data-work-id=114952930]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114952930; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114952930']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "929712dca2d1c5b1c71e09d125b7b4c2" } } $('.js-work-strip[data-work-id=114952930]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114952930,"title":"Forest Biomass and Carbon Estimation Using Vegetation Indices and Leaf Area Index","translated_title":"","metadata":{"publisher":"Federal University of Pernambuco","ai_title_tag":"Estimating Forest Biomass and Carbon Using NDVI and LAI","grobid_abstract":"Among the consequences of the current dynamics of manmade land use and cover, forest fragmentation results in a large number of isolated fragments of different sizes. Forest provides many environmental services such as carbon sequestration and storage in the form of biomass, demonstrating the crucial role of native forests and the need to develop new methods that estimate forest biomass and carbon by non-destructive methods. This work aimed to develop a methodology to study and estimate the dynamics of forest biomass and carbon stock in successive stages of regeneration in the northwestern region of Rio Grande do Sul, which was the study area. Yearly images from 1985 to 2014 of the Normalized Difference Vegetation Index-NDVI were calculated, and a global regression was archived to obtain the leaf area index (LAI) and estimate the forest biomass. It was possible to observe the good relationship between the increase in LAI as a function of the NDVI, estimating the forest biomass of 61,156 Gg in 2014, where 602 Gg correspond to the initial regeneration stage, 8,287 Gg for the medium stage, and 52,267 Gg for the advanced stage of regeneration. 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Forest provides many environmental services such as carbon sequestration and storage in the form of biomass, demonstrating the crucial role of native forests and the need to develop new methods that estimate forest biomass and carbon by non-destructive methods. This work aimed to develop a methodology to study and estimate the dynamics of forest biomass and carbon stock in successive stages of regeneration in the northwestern region of Rio Grande do Sul, which was the study area. Yearly images from 1985 to 2014 of the Normalized Difference Vegetation Index-NDVI were calculated, and a global regression was archived to obtain the leaf area index (LAI) and estimate the forest biomass. It was possible to observe the good relationship between the increase in LAI as a function of the NDVI, estimating the forest biomass of 61,156 Gg in 2014, where 602 Gg correspond to the initial regeneration stage, 8,287 Gg for the medium stage, and 52,267 Gg for the advanced stage of regeneration. 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