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ds2-5-body-sm-bold ds2-5-body-link" href="https://ufrj.academia.edu/ArianeEvald">Ariane Evald</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Universidade Federal do Rio de Janeiro (UFRJ)</p></div></div></ul></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="45518824" href="https://www.academia.edu/Documents/in/Perceptions_of_the_Past"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/PetersonFiorio","location":"/PetersonFiorio","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/PetersonFiorio","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" 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class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Biological Sciences"]}" data-trace="false" data-dom-id="Pill-react-component-167f9cf2-95de-4cc9-b654-d189d170a0f2"></div> <div id="Pill-react-component-167f9cf2-95de-4cc9-b654-d189d170a0f2"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Peterson Fiorio</h3></div><div class="js-work-strip profile--work_container" data-work-id="120463941"><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/120463941/EFFECT_OF_DIFFERENT_NITROGEN_FERTILIZATION_RATES_ON_THE_SPECTRAL_RESPONSE_OF_Brachiaria_brizantha_cv_MARAND%C3%9A_LEAVES"><img alt="Research paper thumbnail of EFFECT OF DIFFERENT NITROGEN FERTILIZATION RATES ON THE SPECTRAL RESPONSE OF Brachiaria brizantha cv. MARANDÚ LEAVES" class="work-thumbnail" src="https://attachments.academia-assets.com/115604660/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/120463941/EFFECT_OF_DIFFERENT_NITROGEN_FERTILIZATION_RATES_ON_THE_SPECTRAL_RESPONSE_OF_Brachiaria_brizantha_cv_MARAND%C3%9A_LEAVES">EFFECT OF DIFFERENT NITROGEN FERTILIZATION RATES ON THE SPECTRAL RESPONSE OF Brachiaria brizantha cv. MARANDÚ LEAVES</a></div><div class="wp-workCard_item"><span>Engenharia Agricola</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Hyperspectral sensors and regression analysis have been used to analyze the most important spectr...</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">Hyperspectral sensors and regression analysis have been used to analyze the most important spectral ranges for biophysical parameters of target crops, aiding in management decision-making. This study aimed to analyze the spectral response of Brachiaria brizantha cv. Marandú leaves to increasing rates of urea fertilization and predict leaf nitrogen content (LNC). Four rates of urea fertilization (0, 25, 50, and 75 kg of N ha-1) were applied. Eight leaves were collected per plot seven times at monthly intervals and subjected to hyperspectral analysis. Leaf spectral responses differed statistically within the visible region, particularly at 550 nm (green). The regression models achieved moderate to good R² values (0.53 to 0.83) for predicting LNC and identified important wavelengths in the red edge region (715 to 720 nm). These findings demonstrate the potential of spectral analysis to detect changes and forecast leaf nitrogen content in B. brizantha cv. Marandú crops at different fertilization levels.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="934136ac90267d18fc2cedc6a9b01153" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115604660,"asset_id":120463941,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115604660/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="120463941"><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="120463941"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120463941; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120463941]").text(description); $(".js-view-count[data-work-id=120463941]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120463941; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120463941']"); 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: "934136ac90267d18fc2cedc6a9b01153" } } $('.js-work-strip[data-work-id=120463941]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120463941,"title":"EFFECT OF DIFFERENT NITROGEN FERTILIZATION RATES ON THE SPECTRAL RESPONSE OF Brachiaria brizantha cv. 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These findings demonstrate the potential of spectral analysis to detect changes and forecast leaf nitrogen content in B. brizantha cv. 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This study aimed to analyze the spectral response of Brachiaria brizantha cv. Marandú leaves to increasing rates of urea fertilization and predict leaf nitrogen content (LNC). Four rates of urea fertilization (0, 25, 50, and 75 kg of N ha-1) were applied. Eight leaves were collected per plot seven times at monthly intervals and subjected to hyperspectral analysis. Leaf spectral responses differed statistically within the visible region, particularly at 550 nm (green). The regression models achieved moderate to good R² values (0.53 to 0.83) for predicting LNC and identified important wavelengths in the red edge region (715 to 720 nm). These findings demonstrate the potential of spectral analysis to detect changes and forecast leaf nitrogen content in B. brizantha cv. Marandú crops at different fertilization levels.","owner":{"id":45518824,"first_name":"Peterson","middle_initials":null,"last_name":"Fiorio","page_name":"PetersonFiorio","domain_name":"independent","created_at":"2016-03-21T04:17:07.025-07:00","display_name":"Peterson Fiorio","url":"https://independent.academia.edu/PetersonFiorio"},"attachments":[{"id":115604660,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115604660/thumbnails/1.jpg","file_name":"scielo.pdf","download_url":"https://www.academia.edu/attachments/115604660/download_file","bulk_download_file_name":"EFFECT_OF_DIFFERENT_NITROGEN_FERTILIZATI.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115604660/scielo-libre.pdf?1717417425=\u0026response-content-disposition=attachment%3B+filename%3DEFFECT_OF_DIFFERENT_NITROGEN_FERTILIZATI.pdf\u0026Expires=1741749272\u0026Signature=LZH4jRhfxxHQW7iqO4oNXoeTfyFIPm~pkpeylX8xJH1nC5dtHEbVFE5-tkg5rOrwP8v986O52Mn~zoBOUz6xdYnHHDP~8YbMYWze4GY0ysZsinSeXVmwqnTXVsAped5-ivwYlWbEsUfZOVFhZ~gPzkRLqzZ75ji9j0KaayWqLvE2-ECxjG0LEJZfvfHG-0O3qE~4uIxMMj-dkrz7NEtAhPLsMhdJfNaZYxyG4PouwemE8pHnhMBfYbHfSzt7q2RpG~GVQekPO5zRorQc3mp6M8bx1f5PQTCW3finrPyYxAy5y6aA03E6pllcjsSGlf7GWe1yhPdgNrg7sWfRJBmZPQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1037,"name":"Agronomy","url":"https://www.academia.edu/Documents/in/Agronomy"},{"id":651937,"name":"Engenharia Agrícola","url":"https://www.academia.edu/Documents/in/Engenharia_Agr%C3%ADcola"},{"id":963742,"name":"Brachiaria","url":"https://www.academia.edu/Documents/in/Brachiaria"},{"id":1439535,"name":"Engenharia Agricola","url":"https://www.academia.edu/Documents/in/Engenharia_Agricola"}],"urls":[{"id":42598383,"url":"https://doi.org/10.1590/1809-4430-eng.agric.v43n3e20220008/2023"}]}, 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="120463940"><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/120463940/Detection_of_nutritional_stress_in_sugarcane_by_VIS_NIR_SWIR_reflectance_spectroscopy"><img alt="Research paper thumbnail of Detection of nutritional stress in sugarcane by VIS-NIR-SWIR reflectance spectroscopy" class="work-thumbnail" src="https://attachments.academia-assets.com/115604639/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/120463940/Detection_of_nutritional_stress_in_sugarcane_by_VIS_NIR_SWIR_reflectance_spectroscopy">Detection of nutritional stress in sugarcane by VIS-NIR-SWIR reflectance spectroscopy</a></div><div class="wp-workCard_item"><span>Ciencia Rural</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study applied spectroradiometry techniques with hyperspectral data to identify the correlati...</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">This study applied spectroradiometry techniques with hyperspectral data to identify the correlations between sugarcane leaf reflectance and the contents of Nitrogen (N), phosphorus (P), Potassium (K), Sulfur (S), Calcium (Ca) and Magnesium (Mg). During the harvests 2019/20 and 2020/21, sugarcane was introduced to nutritional stress by the application of limestone doses. Liming was applied in a fractional way and, at the end of five years, the amounts corresponded to 0, 9, 15 and 21 t ha-1 of dolomitic limestone. The leaf hyperspectral reflectance data and the state of nutrients in the exponential growth phase of the culture were registered. The wavelengths correlated with N, P, K, S, Ca and Mg were identified using the Spearman's correlation analysis. The test of similarity (ANOSIM) and the Principal Component Analysis (PCA) were applied to evaluate data variability, as well as the Partial Least Squares Regression (PLSR) for the prediction of the nutritional contents. The order of the degree of correlation in the region of visible was: P > K > N > Ca > S > Mg and for the region of the near infrared: P > K > Ca > N > S > Mg. P presented peaks with high correlations in the wavelengths 706-717 nm (-0.78) and 522-543 nm (-0.76). The values of the PLSR registered the best spectral responses in the region of VIS and red-edge, regions that are more sensitive to the deficiency of sulfur, potassium and phosphorus.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="24c8d6ee68c662009efeed1d7bfe6aca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115604639,"asset_id":120463940,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115604639/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="120463940"><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="120463940"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120463940; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120463940]").text(description); $(".js-view-count[data-work-id=120463940]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120463940; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120463940']"); 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: "24c8d6ee68c662009efeed1d7bfe6aca" } } $('.js-work-strip[data-work-id=120463940]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120463940,"title":"Detection of nutritional stress in sugarcane by VIS-NIR-SWIR reflectance spectroscopy","translated_title":"","metadata":{"publisher":"Universidade Federal de Santa Maria","ai_title_tag":"Nutritional Stress Detection in Sugarcane Using Spectroscopy","grobid_abstract":"This study applied spectroradiometry techniques with hyperspectral data to identify the correlations between sugarcane leaf reflectance and the contents of Nitrogen (N), phosphorus (P), Potassium (K), Sulfur (S), Calcium (Ca) and Magnesium (Mg). During the harvests 2019/20 and 2020/21, sugarcane was introduced to nutritional stress by the application of limestone doses. Liming was applied in a fractional way and, at the end of five years, the amounts corresponded to 0, 9, 15 and 21 t ha-1 of dolomitic limestone. The leaf hyperspectral reflectance data and the state of nutrients in the exponential growth phase of the culture were registered. The wavelengths correlated with N, P, K, S, Ca and Mg were identified using the Spearman's correlation analysis. The test of similarity (ANOSIM) and the Principal Component Analysis (PCA) were applied to evaluate data variability, as well as the Partial Least Squares Regression (PLSR) for the prediction of the nutritional contents. The order of the degree of correlation in the region of visible was: P \u003e K \u003e N \u003e Ca \u003e S \u003e Mg and for the region of the near infrared: P \u003e K \u003e Ca \u003e N \u003e S \u003e Mg. P presented peaks with high correlations in the wavelengths 706-717 nm (-0.78) and 522-543 nm (-0.76). The values of the PLSR registered the best spectral responses in the region of VIS and red-edge, regions that are more sensitive to the deficiency of sulfur, potassium and phosphorus.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Ciencia Rural","grobid_abstract_attachment_id":115604636},"translated_abstract":null,"internal_url":"https://www.academia.edu/120463940/Detection_of_nutritional_stress_in_sugarcane_by_VIS_NIR_SWIR_reflectance_spectroscopy","translated_internal_url":"","created_at":"2024-06-03T04:18:09.189-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":45518824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":115604639,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115604639/thumbnails/1.jpg","file_name":"YjpkL9dLFKsCdNyzkVM4Vvw.pdf","download_url":"https://www.academia.edu/attachments/115604639/download_file","bulk_download_file_name":"Detection_of_nutritional_stress_in_sugar.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115604639/YjpkL9dLFKsCdNyzkVM4Vvw-libre.pdf?1717417453=\u0026response-content-disposition=attachment%3B+filename%3DDetection_of_nutritional_stress_in_sugar.pdf\u0026Expires=1741749272\u0026Signature=Xma0CHxXW87DnqaBbO2TM8UQ02WDAznQENRyRPZV0zjGExIf2npR~CrUXAVFTXhciOXxxNya-8Up2g3n88CbQDz8ahkPxQMyoAIwcNdIvcIYziRaX2az04gqxcRNpnvv2GX6vd9E-kcceID1fbdTGROJo30n9NJEnV-Dn8YI8O1FB5xPz4RhQiGTb4YY4jigN~KlLypnOyNR~uAkBqRGRLXMz6czrGg0SSlo-bkGLdgygAi9J4zScP83ZRyabO9MklCfAYeZpBGj4X6ZMY4XnlWUgG0s7zZBHQFNWYl~f4mZ8BJaNYYamCVSSCr17S9Y-WwErIW5KffXlIYtCiBlQw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Detection_of_nutritional_stress_in_sugarcane_by_VIS_NIR_SWIR_reflectance_spectroscopy","translated_slug":"","page_count":15,"language":"en","content_type":"Work","summary":"This study applied spectroradiometry techniques with hyperspectral data to identify the correlations between sugarcane leaf reflectance and the contents of Nitrogen (N), phosphorus (P), Potassium (K), Sulfur (S), Calcium (Ca) and Magnesium (Mg). During the harvests 2019/20 and 2020/21, sugarcane was introduced to nutritional stress by the application of limestone doses. Liming was applied in a fractional way and, at the end of five years, the amounts corresponded to 0, 9, 15 and 21 t ha-1 of dolomitic limestone. The leaf hyperspectral reflectance data and the state of nutrients in the exponential growth phase of the culture were registered. The wavelengths correlated with N, P, K, S, Ca and Mg were identified using the Spearman's correlation analysis. The test of similarity (ANOSIM) and the Principal Component Analysis (PCA) were applied to evaluate data variability, as well as the Partial Least Squares Regression (PLSR) for the prediction of the nutritional contents. The order of the degree of correlation in the region of visible was: P \u003e K \u003e N \u003e Ca \u003e S \u003e Mg and for the region of the near infrared: P \u003e K \u003e Ca \u003e N \u003e S \u003e Mg. P presented peaks with high correlations in the wavelengths 706-717 nm (-0.78) and 522-543 nm (-0.76). The values of the PLSR registered the best spectral responses in the region of VIS and red-edge, regions that are more sensitive to the deficiency of sulfur, potassium and phosphorus.","owner":{"id":45518824,"first_name":"Peterson","middle_initials":null,"last_name":"Fiorio","page_name":"PetersonFiorio","domain_name":"independent","created_at":"2016-03-21T04:17:07.025-07:00","display_name":"Peterson Fiorio","url":"https://independent.academia.edu/PetersonFiorio"},"attachments":[{"id":115604639,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115604639/thumbnails/1.jpg","file_name":"YjpkL9dLFKsCdNyzkVM4Vvw.pdf","download_url":"https://www.academia.edu/attachments/115604639/download_file","bulk_download_file_name":"Detection_of_nutritional_stress_in_sugar.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115604639/YjpkL9dLFKsCdNyzkVM4Vvw-libre.pdf?1717417453=\u0026response-content-disposition=attachment%3B+filename%3DDetection_of_nutritional_stress_in_sugar.pdf\u0026Expires=1741749272\u0026Signature=Xma0CHxXW87DnqaBbO2TM8UQ02WDAznQENRyRPZV0zjGExIf2npR~CrUXAVFTXhciOXxxNya-8Up2g3n88CbQDz8ahkPxQMyoAIwcNdIvcIYziRaX2az04gqxcRNpnvv2GX6vd9E-kcceID1fbdTGROJo30n9NJEnV-Dn8YI8O1FB5xPz4RhQiGTb4YY4jigN~KlLypnOyNR~uAkBqRGRLXMz6czrGg0SSlo-bkGLdgygAi9J4zScP83ZRyabO9MklCfAYeZpBGj4X6ZMY4XnlWUgG0s7zZBHQFNWYl~f4mZ8BJaNYYamCVSSCr17S9Y-WwErIW5KffXlIYtCiBlQw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":115604636,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115604636/thumbnails/1.jpg","file_name":"YjpkL9dLFKsCdNyzkVM4Vvw.pdf","download_url":"https://www.academia.edu/attachments/115604636/download_file","bulk_download_file_name":"Detection_of_nutritional_stress_in_sugar.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115604636/YjpkL9dLFKsCdNyzkVM4Vvw-libre.pdf?1717417517=\u0026response-content-disposition=attachment%3B+filename%3DDetection_of_nutritional_stress_in_sugar.pdf\u0026Expires=1741749272\u0026Signature=TA5V5frjM55tiXL4R~1-3Xp-m6wuvDY9TLNbuHgpjVRM7YB78ImppFjM9p9-8xDh9iL7KtgYSBrW8helvIDSpfWVRKSjWb5gmECe6gjxAfsSCGN0-MYhOJMqfP3de98ms7HP68KM01bWikKDYJLqis05nA96NSpL6baN-aXgJ7dou3N301A1ieJvVArm0Yx3lWeBjz~CRy~TuYmgIgpDi6ZZNXT8DfNpTXDKwx5P0xJt0dIhUWIO4bnYbZBeOs3NHg4Gbu7qZjL4aAopyQbDJ5bbHE~UCM5Lwh73msR~dYJuGIe0GGm~2lIHtOeSl63b2ptiCTc3MtMzAgLewU3-yw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":53108,"name":"Phosphorus","url":"https://www.academia.edu/Documents/in/Phosphorus"},{"id":160656,"name":"Potassium","url":"https://www.academia.edu/Documents/in/Potassium"},{"id":244969,"name":"Nutrient","url":"https://www.academia.edu/Documents/in/Nutrient"},{"id":285012,"name":"Sulfur","url":"https://www.academia.edu/Documents/in/Sulfur"},{"id":358917,"name":"Partial Least Squares Regression","url":"https://www.academia.edu/Documents/in/Partial_Least_Squares_Regression"}],"urls":[{"id":42598382,"url":"https://www.scielo.br/j/cr/a/YjpkL9dLFKsCdNyzkVM4Vvw/?lang=en\u0026format=pdf"}]}, 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="120463937"><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/120463937/Mapping_Productivity_and_Essential_Biophysical_Parameters_of_Cultivated_Tropical_Grasslands_from_Sentinel_2_Imagery"><img alt="Research paper thumbnail of Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery" class="work-thumbnail" src="https://attachments.academia-assets.com/115604633/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/120463937/Mapping_Productivity_and_Essential_Biophysical_Parameters_of_Cultivated_Tropical_Grasslands_from_Sentinel_2_Imagery">Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery</a></div><div class="wp-workCard_item"><span>Agronomy</span><span>, May 15, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextrica...</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">Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)'s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species' cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall'Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)-near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R 2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg −1. The estimation of grassland productivity based on red-edge and NIR bands showed a R 2 = 0.54 and a RMSE = 1800 kg ha −1. For the LAI, the best index was the NAOC (R 2 = 0.57 and RMSE = 1.4 m 2 m −2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2a75ac46350e2216991b19b70303cfbe" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115604633,"asset_id":120463937,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115604633/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="120463937"><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="120463937"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120463937; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120463937]").text(description); $(".js-view-count[data-work-id=120463937]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120463937; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120463937']"); 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: "2a75ac46350e2216991b19b70303cfbe" } } $('.js-work-strip[data-work-id=120463937]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120463937,"title":"Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery","translated_title":"","metadata":{"publisher":"Multidisciplinary Digital Publishing Institute","ai_title_tag":"Estimating Grassland Productivity via Sentinel-2","grobid_abstract":"Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)'s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species' cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall'Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)-near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R 2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg −1. The estimation of grassland productivity based on red-edge and NIR bands showed a R 2 = 0.54 and a RMSE = 1800 kg ha −1. For the LAI, the best index was the NAOC (R 2 = 0.57 and RMSE = 1.4 m 2 m −2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization.","publication_date":{"day":15,"month":5,"year":2020,"errors":{}},"publication_name":"Agronomy","grobid_abstract_attachment_id":115604633},"translated_abstract":null,"internal_url":"https://www.academia.edu/120463937/Mapping_Productivity_and_Essential_Biophysical_Parameters_of_Cultivated_Tropical_Grasslands_from_Sentinel_2_Imagery","translated_internal_url":"","created_at":"2024-06-03T04:18:07.768-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":45518824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":115604633,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115604633/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/115604633/download_file","bulk_download_file_name":"Mapping_Productivity_and_Essential_Bioph.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115604633/pdf-libre.pdf?1717417420=\u0026response-content-disposition=attachment%3B+filename%3DMapping_Productivity_and_Essential_Bioph.pdf\u0026Expires=1741749272\u0026Signature=RkHxqIIIzdpDKFfFQzEbekyzqBOsxjFe1OEaoa7K8~rbgb0hyUN7SWWsrfexDk6KEUkmyUK~3XBmI48NhPga28y6RS6DgfThh5Rw88qPBvINPJ8IsVxsNK1IkZgEvot01~tY3guDJmEgBaJWIILhPSENXc6lBfxMc0teR9MatnWTwRGbUW7A63zDpQoekLDuLS27OIidDX91Sesx~qIchvVUYz6UduBZju-m0cAp-OOOZfEDMYmlg0Drmv1kEwnLeob5NWLMdRDcGYlwvdwg1nRe0swt8TTu4TH2jgg1HanJtk5zKmk8V6DlP7a2eMyfMCB-lBpXdCKlpsn2glW4Kg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Mapping_Productivity_and_Essential_Biophysical_Parameters_of_Cultivated_Tropical_Grasslands_from_Sentinel_2_Imagery","translated_slug":"","page_count":23,"language":"en","content_type":"Work","summary":"Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)'s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species' cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall'Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)-near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R 2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg −1. The estimation of grassland productivity based on red-edge and NIR bands showed a R 2 = 0.54 and a RMSE = 1800 kg ha −1. For the LAI, the best index was the NAOC (R 2 = 0.57 and RMSE = 1.4 m 2 m −2). 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lineares múltiplas utilizando-se dados espectrais obtidos por sensores em laborátorio e orbital, na estimativa de atributos do solo" 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/117902932/Regress%C3%B5es_lineares_m%C3%BAltiplas_utilizando_se_dados_espectrais_obtidos_por_sensores_em_labor%C3%A1torio_e_orbital_na_estimativa_de_atributos_do_solo">Regressões lineares múltiplas utilizando-se dados espectrais obtidos por sensores em laborátorio e orbital, na estimativa de atributos do solo</a></div><div class="wp-workCard_item"><span>Anais</span><span>, 2003</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span 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de perda de solo em ambiente SIG utilizando diferentes fontes de dados topográficos Estimates of soil loss in a GIS environment using different sources of topographic data</a></div><div class="wp-workCard_item"><span>AMBIÊNCIA</span><span>, Apr 15, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Estimativas de perda de solo em ambiente SIG utilizando diferentes fontes de dados topográficos E...</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">Estimativas de perda de solo em ambiente SIG utilizando diferentes fontes de dados topográficos Estimates of soil loss in a GIS environment using different sources of topographic data</span></div><div class="wp-workCard_item wp-workCard--actions"><span 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alt="Research paper thumbnail of Potencialidade do uso da terra na microbacia hidrográfica do córrego do ceveiro na região de Piracicaba" class="work-thumbnail" src="https://attachments.academia-assets.com/113650108/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/117902930/Potencialidade_do_uso_da_terra_na_microbacia_hidrogr%C3%A1fica_do_c%C3%B3rrego_do_ceveiro_na_regi%C3%A3o_de_Piracicaba">Potencialidade do uso da terra na microbacia hidrográfica do córrego do ceveiro na região de Piracicaba</a></div><div class="wp-workCard_item"><span>Scientia Agricola</span><span>, 1999</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">RESUMO: O objetivo do presente trabalho foi avaliar a ocupação agrícola da Microbacia Hidrográfic...</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">RESUMO: O objetivo do presente trabalho foi avaliar a ocupação agrícola da Microbacia Hidrográfica do Ceveiro para o ano de 1995 e estabelecer a sua potencialidade. Para tanto, foi utilizado o Sistema de Análise Ambiental para Planejamento Agrícola. A microbacia localiza-se na região de Piracicaba, SP, com aproximadamente 1990 ha. Predominam os Podzólicos e os solos Litólicos de textura média e argilosa. O uso da terra foi determinado por fotointerpretação, cujos os limites foram digitalizados e cruzados com os dados de declividade e solos, gerando mapas de intensidade de uso e aptidão agrícola. Os dados indicaram que apenas 27% da área da bacia estava sendo utilizada adequadamente, 48% estava sendo sub-utilizada e 23% excessivamente utilizada, com sérios riscos de degradação dos solos. As principais distorções observadas quanto ao uso da terra foram em relação a cultura da canade-açúcar, que ocupa áreas destinadas a cultura anual e pastagens. Palavras-chave: aptidão agrícola, uso da terra, sensoriamento remoto LAND USE POTENTIAL IN THE CEVEIRO STREAM WATERSHED OF PIRACICABA ABSTRACT: The objective of this work was to evaluate the agricultural potential of the Ceveiro watershed Piracicaba, SP, Brazil, which contains 1990 ha considering available environmental conditions. Medium and clayey textured Ultisols and Entisols predominate. Land use was determined by remote sensing, the limits of the were digitalized and crossed with soils and shape data, using the Environmental Analysis System for Agricultural Planning. These results were then compared with the current land usage patterns that occurred in 1995, to determine if differences existed between current and prescribed land use. In 1995, 48% of the area was being used below its potential use, 27% used appropriately, and 23% used with excessive intensity. The main difference in land use between the appropriately used and excessively used areas was due to sugarcane production.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="27b1d11a4f0c2ad7caf70d6fd0a4458b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113650108,"asset_id":117902930,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113650108/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="117902930"><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="117902930"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117902930; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117902930]").text(description); $(".js-view-count[data-work-id=117902930]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117902930; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117902930']"); 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: "27b1d11a4f0c2ad7caf70d6fd0a4458b" } } $('.js-work-strip[data-work-id=117902930]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117902930,"title":"Potencialidade do uso da terra na microbacia hidrográfica do córrego do ceveiro na região de Piracicaba","translated_title":"","metadata":{"publisher":"University of São Paulo","grobid_abstract":"RESUMO: O objetivo do presente trabalho foi avaliar a ocupação agrícola da Microbacia Hidrográfica do Ceveiro para o ano de 1995 e estabelecer a sua potencialidade. 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Palavras-chave: aptidão agrícola, uso da terra, sensoriamento remoto LAND USE POTENTIAL IN THE CEVEIRO STREAM WATERSHED OF PIRACICABA ABSTRACT: The objective of this work was to evaluate the agricultural potential of the Ceveiro watershed Piracicaba, SP, Brazil, which contains 1990 ha considering available environmental conditions. Medium and clayey textured Ultisols and Entisols predominate. Land use was determined by remote sensing, the limits of the were digitalized and crossed with soils and shape data, using the Environmental Analysis System for Agricultural Planning. These results were then compared with the current land usage patterns that occurred in 1995, to determine if differences existed between current and prescribed land use. In 1995, 48% of the area was being used below its potential use, 27% used appropriately, and 23% used with excessive intensity. 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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="117902927"><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/117902927/Internal_soil_standard_method_for_the_Brazilian_soil_spectral_library_Performance_and_proximate_analysis"><img alt="Research paper thumbnail of Internal soil standard method for the Brazilian soil spectral library: Performance and proximate analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/113650139/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/117902927/Internal_soil_standard_method_for_the_Brazilian_soil_spectral_library_Performance_and_proximate_analysis">Internal soil standard method for the Brazilian soil spectral library: Performance and proximate analysis</a></div><div class="wp-workCard_item"><span>Geoderma</span><span>, Feb 1, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">There are several methods to extract soil information by spectral sensing. For this reason, the d...</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">There are several methods to extract soil information by spectral sensing. For this reason, the database should be built including standards and protocols both in the lab and in field acquisitions. If we does not align the measurement one to each other, the models will have no merit in term of large-scale application and stay as an academic exercise only. The use of standard samples with known spectra is expected to allow parameterizing mathematically soil spectra collected by different equipment and with distinct geometries. This study was proposed for the reason that to date, the new methodology proposed has not been evaluated in Brazilian tropical soils (Oxisols) in visible, near and short infrared (VIS-NIR-SWIR-350 to 2500 nm) spectral regions from three spectrometers (FieldSpec 3) in three distinct protocols (Long Light, Near Light and Contact Probe). The current study compared the spectral intensity of each combination between spectrometers and protocols by ANOVA module and the clay prediction capacity by PLSR with cross-validation, before and after the internal soil standard (ISS) method application. The results showed that visual spectral variation is minimized by the ISS method and its correction enables better proxy modeling of clay content especially if the data are mixed from different protocols. The model for clay prediction was improved showing a favorable case to use the ISS technique in any soil spectral measurement in a better way to merge spectral libraries from different sources.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="33add4a9908cb450895096682f469e95" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113650139,"asset_id":117902927,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113650139/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="117902927"><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="117902927"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117902927; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117902927]").text(description); $(".js-view-count[data-work-id=117902927]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117902927; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117902927']"); 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: "33add4a9908cb450895096682f469e95" } } $('.js-work-strip[data-work-id=117902927]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117902927,"title":"Internal soil standard method for the Brazilian soil spectral library: Performance and proximate analysis","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"There are several methods to extract soil information by spectral sensing. For this reason, the database should be built including standards and protocols both in the lab and in field acquisitions. If we does not align the measurement one to each other, the models will have no merit in term of large-scale application and stay as an academic exercise only. The use of standard samples with known spectra is expected to allow parameterizing mathematically soil spectra collected by different equipment and with distinct geometries. This study was proposed for the reason that to date, the new methodology proposed has not been evaluated in Brazilian tropical soils (Oxisols) in visible, near and short infrared (VIS-NIR-SWIR-350 to 2500 nm) spectral regions from three spectrometers (FieldSpec 3) in three distinct protocols (Long Light, Near Light and Contact Probe). The current study compared the spectral intensity of each combination between spectrometers and protocols by ANOVA module and the clay prediction capacity by PLSR with cross-validation, before and after the internal soil standard (ISS) method application. The results showed that visual spectral variation is minimized by the ISS method and its correction enables better proxy modeling of clay content especially if the data are mixed from different protocols. The model for clay prediction was improved showing a favorable case to use the ISS technique in any soil spectral measurement in a better way to merge spectral libraries from different sources.","publication_date":{"day":1,"month":2,"year":2018,"errors":{}},"publication_name":"Geoderma","grobid_abstract_attachment_id":113650139},"translated_abstract":null,"internal_url":"https://www.academia.edu/117902927/Internal_soil_standard_method_for_the_Brazilian_soil_spectral_library_Performance_and_proximate_analysis","translated_internal_url":"","created_at":"2024-04-22T15:01:07.475-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":45518824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":113650139,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113650139/thumbnails/1.jpg","file_name":"j.geoderma.2017.09.01420240422-1-ixbzqh.pdf","download_url":"https://www.academia.edu/attachments/113650139/download_file","bulk_download_file_name":"Internal_soil_standard_method_for_the_Br.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113650139/j.geoderma.2017.09.01420240422-1-ixbzqh-libre.pdf?1713823542=\u0026response-content-disposition=attachment%3B+filename%3DInternal_soil_standard_method_for_the_Br.pdf\u0026Expires=1741749272\u0026Signature=WydcD0AQNouvMzD5RU0m73QP5eLhTQuM4-WN4LiI9L4NDADglBfiW5A4PmgaPRjyo5uAEvX50m1Q-8-6Emlf3HPJFqrkk~7i0dVe7EfhILsntw6ca7MYDHNYy5SBlmaqvzp0iv6WEGx5KIlzifeWHkJaSoirnkIWUoIrzMdQ~KsflgUDqoJt2eacOL~GlodbCOhKDHroCrRlbqk58-3CmTMvJ6VcvHylaUVbekKFbiizfnViNutICmdmW5pQEuEk3cvoPEmvkVk-N-2liXxcN158nlMEKBcHRkQv1Sdi3u21gRsYWGbTLMvDW2c8oYm~MiM~QayIQkAOWQBc5vd-vg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Internal_soil_standard_method_for_the_Brazilian_soil_spectral_library_Performance_and_proximate_analysis","translated_slug":"","page_count":9,"language":"en","content_type":"Work","summary":"There are several methods to extract soil information by spectral sensing. 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The current study compared the spectral intensity of each combination between spectrometers and protocols by ANOVA module and the clay prediction capacity by PLSR with cross-validation, before and after the internal soil standard (ISS) method application. The results showed that visual spectral variation is minimized by the ISS method and its correction enables better proxy modeling of clay content especially if the data are mixed from different protocols. The model for clay prediction was improved showing a favorable case to use the ISS technique in any soil spectral measurement in a better way to merge spectral libraries from different sources.","owner":{"id":45518824,"first_name":"Peterson","middle_initials":null,"last_name":"Fiorio","page_name":"PetersonFiorio","domain_name":"independent","created_at":"2016-03-21T04:17:07.025-07:00","display_name":"Peterson Fiorio","url":"https://independent.academia.edu/PetersonFiorio"},"attachments":[{"id":113650139,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113650139/thumbnails/1.jpg","file_name":"j.geoderma.2017.09.01420240422-1-ixbzqh.pdf","download_url":"https://www.academia.edu/attachments/113650139/download_file","bulk_download_file_name":"Internal_soil_standard_method_for_the_Br.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113650139/j.geoderma.2017.09.01420240422-1-ixbzqh-libre.pdf?1713823542=\u0026response-content-disposition=attachment%3B+filename%3DInternal_soil_standard_method_for_the_Br.pdf\u0026Expires=1741749272\u0026Signature=WydcD0AQNouvMzD5RU0m73QP5eLhTQuM4-WN4LiI9L4NDADglBfiW5A4PmgaPRjyo5uAEvX50m1Q-8-6Emlf3HPJFqrkk~7i0dVe7EfhILsntw6ca7MYDHNYy5SBlmaqvzp0iv6WEGx5KIlzifeWHkJaSoirnkIWUoIrzMdQ~KsflgUDqoJt2eacOL~GlodbCOhKDHroCrRlbqk58-3CmTMvJ6VcvHylaUVbekKFbiizfnViNutICmdmW5pQEuEk3cvoPEmvkVk-N-2liXxcN158nlMEKBcHRkQv1Sdi3u21gRsYWGbTLMvDW2c8oYm~MiM~QayIQkAOWQBc5vd-vg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"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":47884,"name":"Biological Sciences","url":"https://www.academia.edu/Documents/in/Biological_Sciences"},{"id":58054,"name":"Environmental Sciences","url":"https://www.academia.edu/Documents/in/Environmental_Sciences"},{"id":95360,"name":"Spectral analysis","url":"https://www.academia.edu/Documents/in/Spectral_analysis"},{"id":1222799,"name":"Soil Water","url":"https://www.academia.edu/Documents/in/Soil_Water"},{"id":1442002,"name":"Spectrometer","url":"https://www.academia.edu/Documents/in/Spectrometer"},{"id":1941070,"name":"GEODERMA","url":"https://www.academia.edu/Documents/in/GEODERMA"}],"urls":[{"id":41314689,"url":"https://doi.org/10.1016/j.geoderma.2017.09.014"}]}, 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="117902926"><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/117902926/Visible_NIR_reflectance_a_new_approach_on_soil_evaluation"><img alt="Research paper thumbnail of Visible–NIR reflectance: a new approach on soil evaluation" class="work-thumbnail" src="https://attachments.academia-assets.com/113650146/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/117902926/Visible_NIR_reflectance_a_new_approach_on_soil_evaluation">Visible–NIR reflectance: a new approach on soil evaluation</a></div><div class="wp-workCard_item"><span>Geoderma</span><span>, Jul 1, 2004</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The aim of this work was to develop a spectral reflectance (SR)-based methodology to evaluate soi...</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 aim of this work was to develop a spectral reflectance (SR)-based methodology to evaluate soil types and soil tillage systems. Soil samples, located along nine toposequences with basalt and shale, were collected in São Paulo State, Brazil. SR of soil samples was measured with a laboratory spectroradiometer between 450 and 2500 nm. SR curves were evaluated at different depths to determine soil classes, as for conventional methods of soil classification. Soil survey maps were developed by descriptive interpretation of the spectral curves and statistical analysis as well. These maps were compared with detailed and semidetailed soil maps based on chemical, physical and mineralogical soil attributes (conventional method). Organic matter, total iron, silt, sand and mineralogy (quartz, magnetite, kaolinite and smectite) were the most important attributes influencing reflectance intensity and spectral features and allowed characterization and discrimination of soils. Strong absorption features centered at 1900 nm were related to OH molecules in free water of 2:1 minerals. Identification of soil classes was most precise when the entire spectral curve of different depths was analysed simultaneously, including features and intensities. Parent material variation along toposequences and weathering variability modified soil characteristics and its respective SR data. The detection of these alterations was essential to determine soil line demarcation. Soil line demarcation and number of soil classes detected by SR analyses were similar to the detailed soil map determined by conventional methods. SR laboratory measurements can be used as a methodology to assist soil surveys. Spectral data can also group soils under similar tillage systems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e0c43a414ecb4f3aeae96b7fdc11a721" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113650146,"asset_id":117902926,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113650146/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="117902926"><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="117902926"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117902926; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117902926]").text(description); $(".js-view-count[data-work-id=117902926]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117902926; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117902926']"); 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: "e0c43a414ecb4f3aeae96b7fdc11a721" } } $('.js-work-strip[data-work-id=117902926]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117902926,"title":"Visible–NIR reflectance: a new approach on soil evaluation","translated_title":"","metadata":{"publisher":"Elsevier BV","ai_title_tag":"Spectral Reflectance Method for Soil Evaluation in Brazil","grobid_abstract":"The aim of this work was to develop a spectral reflectance (SR)-based methodology to evaluate soil types and soil tillage systems. Soil samples, located along nine toposequences with basalt and shale, were collected in São Paulo State, Brazil. SR of soil samples was measured with a laboratory spectroradiometer between 450 and 2500 nm. SR curves were evaluated at different depths to determine soil classes, as for conventional methods of soil classification. Soil survey maps were developed by descriptive interpretation of the spectral curves and statistical analysis as well. These maps were compared with detailed and semidetailed soil maps based on chemical, physical and mineralogical soil attributes (conventional method). Organic matter, total iron, silt, sand and mineralogy (quartz, magnetite, kaolinite and smectite) were the most important attributes influencing reflectance intensity and spectral features and allowed characterization and discrimination of soils. Strong absorption features centered at 1900 nm were related to OH molecules in free water of 2:1 minerals. Identification of soil classes was most precise when the entire spectral curve of different depths was analysed simultaneously, including features and intensities. Parent material variation along toposequences and weathering variability modified soil characteristics and its respective SR data. The detection of these alterations was essential to determine soil line demarcation. Soil line demarcation and number of soil classes detected by SR analyses were similar to the detailed soil map determined by conventional methods. SR laboratory measurements can be used as a methodology to assist soil surveys. 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Soil samples, located along nine toposequences with basalt and shale, were collected in São Paulo State, Brazil. SR of soil samples was measured with a laboratory spectroradiometer between 450 and 2500 nm. SR curves were evaluated at different depths to determine soil classes, as for conventional methods of soil classification. Soil survey maps were developed by descriptive interpretation of the spectral curves and statistical analysis as well. These maps were compared with detailed and semidetailed soil maps based on chemical, physical and mineralogical soil attributes (conventional method). Organic matter, total iron, silt, sand and mineralogy (quartz, magnetite, kaolinite and smectite) were the most important attributes influencing reflectance intensity and spectral features and allowed characterization and discrimination of soils. Strong absorption features centered at 1900 nm were related to OH molecules in free water of 2:1 minerals. 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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="117902925"><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/117902925/Diferentes_im%C3%A1genes_de_sat%C3%A9lite_en_el_mapeamento_visual_de_drenajes_y_nacientes_en_muestras_circulares"><img alt="Research paper thumbnail of Diferentes imágenes de satélite en el mapeamento visual de drenajes y nacientes en muestras circulares" 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/117902925/Diferentes_im%C3%A1genes_de_sat%C3%A9lite_en_el_mapeamento_visual_de_drenajes_y_nacientes_en_muestras_circulares">Diferentes imágenes de satélite en el mapeamento visual de drenajes y nacientes en muestras circulares</a></div><div class="wp-workCard_item"><span>Interciencia: Revista de ciencia y tecnología de América</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">espanolEl uso de imagenes de satelite viene siendo cada vez mas comun en la obtencion de datos am...</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">espanolEl uso de imagenes de satelite viene siendo cada vez mas comun en la obtencion de datos ambientales, como los cursos del agua y las nacientes. El presente trabajo tuvo por objetivo comparar tres diferentes productos utilizados en el mapeo manual (visual) de drenajes y nacientes: un mosaico de ima- genes del satelite SPOT, imagenes del programa Google Earth® y una imagen del satelite Landsat 8; cada producto con resolucion espacial diferente. Se utilizo la rede de drenaje presente en cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacion. Para una mejor comparacion entre los productos, fueron analizados dos factores: longitud de drenaje (LD) y numero de nacientes (NN), los analisis se realizaron apenas en caracter cuantitativo. Para tanto, fueron dispuestas tres muestras circulares de 2,75km de radio en diferentes areas del municipio de Piracicaba donde serian observadas densidad de drenajes (DD) diferentes y, por lo tanto, relieves diferentes. Para el parametro LD, la imagen del satelite SPOT presento mayor semejanza con las cartas 1:10.000 en las muestras con mayor DD. Para la muestra con menor DD, la imagen del Google Earth® se mostro mas proxima. Para a cuantificacion del NN hubo mayor semejanza entre los productos estudiados, siendo ese resultado dependiente de la resolucion espacial (nivel de detalle) del producto estudiado. Se concluye que es posible utilizar cualquiera de los productos analizados en la elaboracion de mapas hidrologicos y de nacientes para los mas variados trabajos, incluso para aceptacion de organos publicos que utilizan cartas topograficas como referencia. EnglishThe use of satellite imagery is becoming increasingly common in order to obtain environmental data, such as streams and springs. Thus, this study aimed to compare three different products used in manual (visual) mapping of drainage and springs: a SPOT mosaic of satellite images, Google Earth® images and, one Landsat 8 satellite image; each product with a different spatial resolution. Topographic maps of drainage network for Piracicaba, Sao Paulo, Brazil, on a 1:10,000 scale were used as a basis for comparison. Two factors have been analyzed for better comparison between products: drainage length (DL) and number of springs (SN). The analyses carried out were only of a quantitative character. Three circular samples with 2.75 km radius were arranged in different areas of the municipality of Piracicaba, where different drainage densities (DD) were observed and therefore different reliefs. For the DL parameter, the SPOT satellite image showed greater similarity to the 1:10,000 scale maps in the samples with higher DD, while for the sample with the lowest DD, the Google Earth® image was more similar. For SN quantification there was a greater similarity between the studied products, a result entirely dependent upon the studied product’s spatial resolution (detail level). It was concluded that it is possible to use any of the analyzed products to map hydrology and springs for various tasks, including for public agencies using topographic maps as a reference. portuguesO uso de imagens de satelite vem se tornando cada vez mais comum na obtencao de dados ambientais, como os cursos d’agua e as nascentes. O presente trabalho teve por objetivo comparar tres diferentes produtos utilizados no mapeamento manual (visual) de drenagens e nascentes: um mosaico de imagens do satelite SPOT, imagens do programa Google Earth® e uma imagem do satelite Landsat 8; cada produto com resolu- cao espacial diferente. Utilizou-se a rede de drenagem presente em cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacao. Para uma melhor comparacao entre os produtos, foram analisados dois fatores: comprimento de drenagem (CD) e numero de nascentes (NN), sendo as analises realizadas apenas em carater quantitativo. Para tanto, foram dispostas tres amostras circulares de 2,75km de raio em diferentes areas do municipio de Piracicaba aonde fossem observadas densidades de drenagem (DD) diferentes e, portanto, relevos diferentes. Para o parâmetro CD, a imagem do satelite SPOT apresentou maior semelhanca com as cartas 1:10.000 nas amostras com maior DD. Para a amostra com a menor DD, a imagem do Google Earth® mostrou-se mais pro- xima. Para a quantificacao do NN houve maior semelhanca entre os produtos estudados, sendo esse resultado dependente da resolucao espacial (nivel de detalhamento) do produto estudado. Concluiu-se que e possivel utilizar qualquer um dos produtos analisados na elaboracao de mapas hidrologicos e de nascentes, para os mais variados trabalhos, inclusive para aceitacao de orgaos publicos que utilizam cartas topograficas como referencia.</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="117902925"><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="117902925"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117902925; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117902925]").text(description); $(".js-view-count[data-work-id=117902925]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117902925; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117902925']"); 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" } } $('.js-work-strip[data-work-id=117902925]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117902925,"title":"Diferentes imágenes de satélite en el mapeamento visual de drenajes y nacientes en muestras circulares","translated_title":"","metadata":{"abstract":"espanolEl uso de imagenes de satelite viene siendo cada vez mas comun en la obtencion de datos ambientales, como los cursos del agua y las nacientes. El presente trabajo tuvo por objetivo comparar tres diferentes productos utilizados en el mapeo manual (visual) de drenajes y nacientes: un mosaico de ima- genes del satelite SPOT, imagenes del programa Google Earth® y una imagen del satelite Landsat 8; cada producto con resolucion espacial diferente. Se utilizo la rede de drenaje presente en cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacion. Para una mejor comparacion entre los productos, fueron analizados dos factores: longitud de drenaje (LD) y numero de nacientes (NN), los analisis se realizaron apenas en caracter cuantitativo. Para tanto, fueron dispuestas tres muestras circulares de 2,75km de radio en diferentes areas del municipio de Piracicaba donde serian observadas densidad de drenajes (DD) diferentes y, por lo tanto, relieves diferentes. Para el parametro LD, la imagen del satelite SPOT presento mayor semejanza con las cartas 1:10.000 en las muestras con mayor DD. Para la muestra con menor DD, la imagen del Google Earth® se mostro mas proxima. Para a cuantificacion del NN hubo mayor semejanza entre los productos estudiados, siendo ese resultado dependiente de la resolucion espacial (nivel de detalle) del producto estudiado. Se concluye que es posible utilizar cualquiera de los productos analizados en la elaboracion de mapas hidrologicos y de nacientes para los mas variados trabajos, incluso para aceptacion de organos publicos que utilizan cartas topograficas como referencia. EnglishThe use of satellite imagery is becoming increasingly common in order to obtain environmental data, such as streams and springs. Thus, this study aimed to compare three different products used in manual (visual) mapping of drainage and springs: a SPOT mosaic of satellite images, Google Earth® images and, one Landsat 8 satellite image; each product with a different spatial resolution. Topographic maps of drainage network for Piracicaba, Sao Paulo, Brazil, on a 1:10,000 scale were used as a basis for comparison. Two factors have been analyzed for better comparison between products: drainage length (DL) and number of springs (SN). The analyses carried out were only of a quantitative character. Three circular samples with 2.75 km radius were arranged in different areas of the municipality of Piracicaba, where different drainage densities (DD) were observed and therefore different reliefs. For the DL parameter, the SPOT satellite image showed greater similarity to the 1:10,000 scale maps in the samples with higher DD, while for the sample with the lowest DD, the Google Earth® image was more similar. For SN quantification there was a greater similarity between the studied products, a result entirely dependent upon the studied product’s spatial resolution (detail level). It was concluded that it is possible to use any of the analyzed products to map hydrology and springs for various tasks, including for public agencies using topographic maps as a reference. portuguesO uso de imagens de satelite vem se tornando cada vez mais comum na obtencao de dados ambientais, como os cursos d’agua e as nascentes. O presente trabalho teve por objetivo comparar tres diferentes produtos utilizados no mapeamento manual (visual) de drenagens e nascentes: um mosaico de imagens do satelite SPOT, imagens do programa Google Earth® e uma imagem do satelite Landsat 8; cada produto com resolu- cao espacial diferente. Utilizou-se a rede de drenagem presente em cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacao. Para uma melhor comparacao entre os produtos, foram analisados dois fatores: comprimento de drenagem (CD) e numero de nascentes (NN), sendo as analises realizadas apenas em carater quantitativo. Para tanto, foram dispostas tres amostras circulares de 2,75km de raio em diferentes areas do municipio de Piracicaba aonde fossem observadas densidades de drenagem (DD) diferentes e, portanto, relevos diferentes. Para o parâmetro CD, a imagem do satelite SPOT apresentou maior semelhanca com as cartas 1:10.000 nas amostras com maior DD. Para a amostra com a menor DD, a imagem do Google Earth® mostrou-se mais pro- xima. Para a quantificacao do NN houve maior semelhanca entre os produtos estudados, sendo esse resultado dependente da resolucao espacial (nivel de detalhamento) do produto estudado. Concluiu-se que e possivel utilizar qualquer um dos produtos analisados na elaboracao de mapas hidrologicos e de nascentes, para os mais variados trabalhos, inclusive para aceitacao de orgaos publicos que utilizam cartas topograficas como referencia.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Interciencia: Revista de ciencia y tecnología de América"},"translated_abstract":"espanolEl uso de imagenes de satelite viene siendo cada vez mas comun en la obtencion de datos ambientales, como los cursos del agua y las nacientes. El presente trabajo tuvo por objetivo comparar tres diferentes productos utilizados en el mapeo manual (visual) de drenajes y nacientes: un mosaico de ima- genes del satelite SPOT, imagenes del programa Google Earth® y una imagen del satelite Landsat 8; cada producto con resolucion espacial diferente. Se utilizo la rede de drenaje presente en cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacion. Para una mejor comparacion entre los productos, fueron analizados dos factores: longitud de drenaje (LD) y numero de nacientes (NN), los analisis se realizaron apenas en caracter cuantitativo. Para tanto, fueron dispuestas tres muestras circulares de 2,75km de radio en diferentes areas del municipio de Piracicaba donde serian observadas densidad de drenajes (DD) diferentes y, por lo tanto, relieves diferentes. Para el parametro LD, la imagen del satelite SPOT presento mayor semejanza con las cartas 1:10.000 en las muestras con mayor DD. Para la muestra con menor DD, la imagen del Google Earth® se mostro mas proxima. Para a cuantificacion del NN hubo mayor semejanza entre los productos estudiados, siendo ese resultado dependiente de la resolucion espacial (nivel de detalle) del producto estudiado. Se concluye que es posible utilizar cualquiera de los productos analizados en la elaboracion de mapas hidrologicos y de nacientes para los mas variados trabajos, incluso para aceptacion de organos publicos que utilizan cartas topograficas como referencia. EnglishThe use of satellite imagery is becoming increasingly common in order to obtain environmental data, such as streams and springs. Thus, this study aimed to compare three different products used in manual (visual) mapping of drainage and springs: a SPOT mosaic of satellite images, Google Earth® images and, one Landsat 8 satellite image; each product with a different spatial resolution. Topographic maps of drainage network for Piracicaba, Sao Paulo, Brazil, on a 1:10,000 scale were used as a basis for comparison. Two factors have been analyzed for better comparison between products: drainage length (DL) and number of springs (SN). The analyses carried out were only of a quantitative character. Three circular samples with 2.75 km radius were arranged in different areas of the municipality of Piracicaba, where different drainage densities (DD) were observed and therefore different reliefs. For the DL parameter, the SPOT satellite image showed greater similarity to the 1:10,000 scale maps in the samples with higher DD, while for the sample with the lowest DD, the Google Earth® image was more similar. For SN quantification there was a greater similarity between the studied products, a result entirely dependent upon the studied product’s spatial resolution (detail level). It was concluded that it is possible to use any of the analyzed products to map hydrology and springs for various tasks, including for public agencies using topographic maps as a reference. portuguesO uso de imagens de satelite vem se tornando cada vez mais comum na obtencao de dados ambientais, como os cursos d’agua e as nascentes. O presente trabalho teve por objetivo comparar tres diferentes produtos utilizados no mapeamento manual (visual) de drenagens e nascentes: um mosaico de imagens do satelite SPOT, imagens do programa Google Earth® e uma imagem do satelite Landsat 8; cada produto com resolu- cao espacial diferente. Utilizou-se a rede de drenagem presente em cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacao. Para uma melhor comparacao entre os produtos, foram analisados dois fatores: comprimento de drenagem (CD) e numero de nascentes (NN), sendo as analises realizadas apenas em carater quantitativo. Para tanto, foram dispostas tres amostras circulares de 2,75km de raio em diferentes areas do municipio de Piracicaba aonde fossem observadas densidades de drenagem (DD) diferentes e, portanto, relevos diferentes. Para o parâmetro CD, a imagem do satelite SPOT apresentou maior semelhanca com as cartas 1:10.000 nas amostras com maior DD. Para a amostra com a menor DD, a imagem do Google Earth® mostrou-se mais pro- xima. Para a quantificacao do NN houve maior semelhanca entre os produtos estudados, sendo esse resultado dependente da resolucao espacial (nivel de detalhamento) do produto estudado. Concluiu-se que e possivel utilizar qualquer um dos produtos analisados na elaboracao de mapas hidrologicos e de nascentes, para os mais variados trabalhos, inclusive para aceitacao de orgaos publicos que utilizam cartas topograficas como referencia.","internal_url":"https://www.academia.edu/117902925/Diferentes_im%C3%A1genes_de_sat%C3%A9lite_en_el_mapeamento_visual_de_drenajes_y_nacientes_en_muestras_circulares","translated_internal_url":"","created_at":"2024-04-22T15:01:06.357-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":45518824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Diferentes_imágenes_de_satélite_en_el_mapeamento_visual_de_drenajes_y_nacientes_en_muestras_circulares","translated_slug":"","page_count":null,"language":"es","content_type":"Work","summary":"espanolEl uso de imagenes de satelite viene siendo cada vez mas comun en la obtencion de datos ambientales, como los cursos del agua y las nacientes. El presente trabajo tuvo por objetivo comparar tres diferentes productos utilizados en el mapeo manual (visual) de drenajes y nacientes: un mosaico de ima- genes del satelite SPOT, imagenes del programa Google Earth® y una imagen del satelite Landsat 8; cada producto con resolucion espacial diferente. Se utilizo la rede de drenaje presente en cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacion. Para una mejor comparacion entre los productos, fueron analizados dos factores: longitud de drenaje (LD) y numero de nacientes (NN), los analisis se realizaron apenas en caracter cuantitativo. Para tanto, fueron dispuestas tres muestras circulares de 2,75km de radio en diferentes areas del municipio de Piracicaba donde serian observadas densidad de drenajes (DD) diferentes y, por lo tanto, relieves diferentes. Para el parametro LD, la imagen del satelite SPOT presento mayor semejanza con las cartas 1:10.000 en las muestras con mayor DD. Para la muestra con menor DD, la imagen del Google Earth® se mostro mas proxima. Para a cuantificacion del NN hubo mayor semejanza entre los productos estudiados, siendo ese resultado dependiente de la resolucion espacial (nivel de detalle) del producto estudiado. Se concluye que es posible utilizar cualquiera de los productos analizados en la elaboracion de mapas hidrologicos y de nacientes para los mas variados trabajos, incluso para aceptacion de organos publicos que utilizan cartas topograficas como referencia. EnglishThe use of satellite imagery is becoming increasingly common in order to obtain environmental data, such as streams and springs. Thus, this study aimed to compare three different products used in manual (visual) mapping of drainage and springs: a SPOT mosaic of satellite images, Google Earth® images and, one Landsat 8 satellite image; each product with a different spatial resolution. Topographic maps of drainage network for Piracicaba, Sao Paulo, Brazil, on a 1:10,000 scale were used as a basis for comparison. Two factors have been analyzed for better comparison between products: drainage length (DL) and number of springs (SN). The analyses carried out were only of a quantitative character. Three circular samples with 2.75 km radius were arranged in different areas of the municipality of Piracicaba, where different drainage densities (DD) were observed and therefore different reliefs. For the DL parameter, the SPOT satellite image showed greater similarity to the 1:10,000 scale maps in the samples with higher DD, while for the sample with the lowest DD, the Google Earth® image was more similar. For SN quantification there was a greater similarity between the studied products, a result entirely dependent upon the studied product’s spatial resolution (detail level). It was concluded that it is possible to use any of the analyzed products to map hydrology and springs for various tasks, including for public agencies using topographic maps as a reference. portuguesO uso de imagens de satelite vem se tornando cada vez mais comum na obtencao de dados ambientais, como os cursos d’agua e as nascentes. O presente trabalho teve por objetivo comparar tres diferentes produtos utilizados no mapeamento manual (visual) de drenagens e nascentes: um mosaico de imagens do satelite SPOT, imagens do programa Google Earth® e uma imagem do satelite Landsat 8; cada produto com resolu- cao espacial diferente. Utilizou-se a rede de drenagem presente em cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacao. Para uma melhor comparacao entre os produtos, foram analisados dois fatores: comprimento de drenagem (CD) e numero de nascentes (NN), sendo as analises realizadas apenas em carater quantitativo. Para tanto, foram dispostas tres amostras circulares de 2,75km de raio em diferentes areas do municipio de Piracicaba aonde fossem observadas densidades de drenagem (DD) diferentes e, portanto, relevos diferentes. Para o parâmetro CD, a imagem do satelite SPOT apresentou maior semelhanca com as cartas 1:10.000 nas amostras com maior DD. Para a amostra com a menor DD, a imagem do Google Earth® mostrou-se mais pro- xima. Para a quantificacao do NN houve maior semelhanca entre os produtos estudados, sendo esse resultado dependente da resolucao espacial (nivel de detalhamento) do produto estudado. Concluiu-se que e possivel utilizar qualquer um dos produtos analisados na elaboracao de mapas hidrologicos e de nascentes, para os mais variados trabalhos, inclusive para aceitacao de orgaos publicos que utilizam cartas topograficas como referencia.","owner":{"id":45518824,"first_name":"Peterson","middle_initials":null,"last_name":"Fiorio","page_name":"PetersonFiorio","domain_name":"independent","created_at":"2016-03-21T04:17:07.025-07:00","display_name":"Peterson Fiorio","url":"https://independent.academia.edu/PetersonFiorio"},"attachments":[],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics"},{"id":951,"name":"Humanities","url":"https://www.academia.edu/Documents/in/Humanities"}],"urls":[{"id":41314687,"url":"https://dialnet.unirioja.es/servlet/articulo?codigo=5413423"}]}, 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="117902924"><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/117902924/Analysis_of_Total_Soil_Nutrient_Content_with_X_ray_Fluorescence_Spectroscopy_XRF_Assessing_Different_Predictive_Modeling_Strategies_and_Auxiliary_Variables"><img alt="Research paper thumbnail of Analysis of Total Soil Nutrient Content with X-ray Fluorescence Spectroscopy (XRF): Assessing Different Predictive Modeling Strategies and Auxiliary Variables" class="work-thumbnail" src="https://attachments.academia-assets.com/113650098/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" 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Forest","url":"https://www.academia.edu/Documents/in/Random_Forest"},{"id":81034,"name":"Predictive Modelling","url":"https://www.academia.edu/Documents/in/Predictive_Modelling"},{"id":123230,"name":"Regression Analysis","url":"https://www.academia.edu/Documents/in/Regression_Analysis"},{"id":358917,"name":"Partial Least Squares Regression","url":"https://www.academia.edu/Documents/in/Partial_Least_Squares_Regression"},{"id":795003,"name":"Linear Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression"}],"urls":[{"id":41314686,"url":"https://www.mdpi.com/2624-7402/5/2/43/pdf?version=1680538413"}]}, 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="117902923"><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/117902923/Orbital_and_laboratory_spectral_data_to_optimize_soil_analysis"><img alt="Research paper thumbnail of Orbital and laboratory spectral data to optimize soil analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/113650096/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/117902923/Orbital_and_laboratory_spectral_data_to_optimize_soil_analysis">Orbital and laboratory spectral data to optimize soil analysis</a></div><div class="wp-workCard_item"><span>Scientia Agricola</span><span>, Apr 1, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Traditional soil analyses are time-consuming with high cost and environmental risks, thus the use...</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">Traditional soil analyses are time-consuming with high cost and environmental risks, thus the use of new technologies such as remote sensing have to be estimulated. The purpose of this work was to quantify soil attributes by laboratory and orbital sensors as a non-destructive and a nonpollutant method. The study area was in the region of Barra Bonita, state of São Paulo, Brazil, in a 473 ha bare soil area. A sampling grid was established (100 × 100 m), with a total of 474 locations and a total of 948 soil samples. Each location was georeferenced and soil samples were collected for analysis. Reflectance data for each soil sample was measured with a laboratory sensor (450 to 2,500 nm). For the same locations, reflectance data was obtained from a TM-Landsat-5 image. Multiple linear regression equations were developed for 50% of the samples. Two models were developed: one for spectroradiometric laboratory data and the second for TM-Landsat-5 orbital data. The remaining 50% of the samples were used to validate the models. The test compared the attribute content quantified by the spectral models and that determined in the laboratory (conventional methods). The highest coefficients of determination for the laboratory data were for clay content (R 2 = 0.86) and sand (R 2 = 0.82) and for the orbital data (R 2 = 0.61 and 0.63, respectively). By using the present methodology, it was possible to estimate CEC (R 2 = 0.64) by the laboratory sensor. Laboratory and orbital sensors can optimize time, costs and environment pollutants when associated with traditional soil analysis.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c29f12c0929fba479776419b02c61c17" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113650096,"asset_id":117902923,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113650096/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="117902923"><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="117902923"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117902923; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117902923]").text(description); $(".js-view-count[data-work-id=117902923]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117902923; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117902923']"); 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: "c29f12c0929fba479776419b02c61c17" } } $('.js-work-strip[data-work-id=117902923]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117902923,"title":"Orbital and laboratory spectral data to optimize soil analysis","translated_title":"","metadata":{"publisher":"University of São Paulo","grobid_abstract":"Traditional soil analyses are time-consuming with high cost and environmental risks, thus the use of new technologies such as remote sensing have to be estimulated. The purpose of this work was to quantify soil attributes by laboratory and orbital sensors as a non-destructive and a nonpollutant method. The study area was in the region of Barra Bonita, state of São Paulo, Brazil, in a 473 ha bare soil area. A sampling grid was established (100 × 100 m), with a total of 474 locations and a total of 948 soil samples. Each location was georeferenced and soil samples were collected for analysis. Reflectance data for each soil sample was measured with a laboratory sensor (450 to 2,500 nm). For the same locations, reflectance data was obtained from a TM-Landsat-5 image. Multiple linear regression equations were developed for 50% of the samples. Two models were developed: one for spectroradiometric laboratory data and the second for TM-Landsat-5 orbital data. The remaining 50% of the samples were used to validate the models. The test compared the attribute content quantified by the spectral models and that determined in the laboratory (conventional methods). The highest coefficients of determination for the laboratory data were for clay content (R 2 = 0.86) and sand (R 2 = 0.82) and for the orbital data (R 2 = 0.61 and 0.63, respectively). By using the present methodology, it was possible to estimate CEC (R 2 = 0.64) by the laboratory sensor. Laboratory and orbital sensors can optimize time, costs and environment pollutants when associated with traditional soil analysis.","publication_date":{"day":1,"month":4,"year":2009,"errors":{}},"publication_name":"Scientia Agricola","grobid_abstract_attachment_id":113650096},"translated_abstract":null,"internal_url":"https://www.academia.edu/117902923/Orbital_and_laboratory_spectral_data_to_optimize_soil_analysis","translated_internal_url":"","created_at":"2024-04-22T15:01:05.953-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":45518824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":113650096,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113650096/thumbnails/1.jpg","file_name":"15.pdf","download_url":"https://www.academia.edu/attachments/113650096/download_file","bulk_download_file_name":"Orbital_and_laboratory_spectral_data_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113650096/15-libre.pdf?1713823547=\u0026response-content-disposition=attachment%3B+filename%3DOrbital_and_laboratory_spectral_data_to.pdf\u0026Expires=1741749273\u0026Signature=D5Ua-GiNmVpEitOAawT8yktIjMCBGZ4AGTlTvDzE8-met~vgZLU6vzs7hnlhqgIXSASzYbwunn91H1YR9-nL33UVeJmdfg~D3oalxmkHFRxDGITN7u5nQ50kApieTJtlHHJwvTEkS5CBcNJsq~Z3xtITioWON8VT-HfS~yzpkxrGSBEfIylwg9-TV7yMZGq7P202MoA1GzAw0uIQVSci1J-mho~RnT3B3tSrQJCk8YDYbajRtImuAox1vssrvhzM65kUrdRFclwROB5-NLcC95LbawLR9F1tiEV~vZ2deijCV9tfY9C3RFzB0tmiLz8hdhMmVlNgbPHoYukADO6ADw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Orbital_and_laboratory_spectral_data_to_optimize_soil_analysis","translated_slug":"","page_count":8,"language":"en","content_type":"Work","summary":"Traditional soil analyses are time-consuming with high cost and environmental risks, thus the use of new technologies such as remote sensing have to be estimulated. The purpose of this work was to quantify soil attributes by laboratory and orbital sensors as a non-destructive and a nonpollutant method. The study area was in the region of Barra Bonita, state of São Paulo, Brazil, in a 473 ha bare soil area. A sampling grid was established (100 × 100 m), with a total of 474 locations and a total of 948 soil samples. Each location was georeferenced and soil samples were collected for analysis. Reflectance data for each soil sample was measured with a laboratory sensor (450 to 2,500 nm). For the same locations, reflectance data was obtained from a TM-Landsat-5 image. Multiple linear regression equations were developed for 50% of the samples. Two models were developed: one for spectroradiometric laboratory data and the second for TM-Landsat-5 orbital data. The remaining 50% of the samples were used to validate the models. The test compared the attribute content quantified by the spectral models and that determined in the laboratory (conventional methods). The highest coefficients of determination for the laboratory data were for clay content (R 2 = 0.86) and sand (R 2 = 0.82) and for the orbital data (R 2 = 0.61 and 0.63, respectively). By using the present methodology, it was possible to estimate CEC (R 2 = 0.64) by the laboratory sensor. 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And nowadays, all ...</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">Urban sprawl is the primary responsible for the current disposition of cities. And nowadays, all the importance that the conservation of the vegetal cover has is acknowledged, since it interferes with the quality of life, retains pollutant particles and the lack of it causes thermal discomfort. In this way the present work aims at analyzing the relationship between the presence of vegetation and the average temperature of the districts of the city of Piracicaba, in the state of São Paulo. RapidEye sensor image was used for mapping and quantification of vegetation and Landsat 8 for temperature quantification at 4 different times of the year. When it comes to vegetation, the city of Piracicaba has it in 40.44% of its territory. With the spatialization of the temperature, it is possible to observe its variation, presenting a thermal amplitude of 6.4° C in autumn. The areas that present the lowest rates of green areas stand with the highest temperatures, where the vertical constructions prevail, coupled with reduced tree cover.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="31ac01cd10420b9d513ebca8eb445fa0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113650140,"asset_id":117902922,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113650140/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="117902922"><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="117902922"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117902922; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117902922]").text(description); $(".js-view-count[data-work-id=117902922]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117902922; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117902922']"); 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: "31ac01cd10420b9d513ebca8eb445fa0" } } $('.js-work-strip[data-work-id=117902922]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117902922,"title":"Study of the Existing Relationship Between Temperature and Distribution of Vegetation Through the Use of Geoprocessment, in the City of Piracicaba, State of São Paulo","translated_title":"","metadata":{"publisher":"Brazilian Journal of Agriculture","ai_title_tag":"Temperature and Vegetation Distribution in Piracicaba, SP","grobid_abstract":"Urban sprawl is the primary responsible for the current disposition of cities. 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And nowadays, all the importance that the conservation of the vegetal cover has is acknowledged, since it interferes with the quality of life, retains pollutant particles and the lack of it causes thermal discomfort. In this way the present work aims at analyzing the relationship between the presence of vegetation and the average temperature of the districts of the city of Piracicaba, in the state of São Paulo. RapidEye sensor image was used for mapping and quantification of vegetation and Landsat 8 for temperature quantification at 4 different times of the year. When it comes to vegetation, the city of Piracicaba has it in 40.44% of its territory. With the spatialization of the temperature, it is possible to observe its variation, presenting a thermal amplitude of 6.4° C in autumn. 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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="4874479" id="papers"><div class="js-work-strip profile--work_container" data-work-id="120463941"><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/120463941/EFFECT_OF_DIFFERENT_NITROGEN_FERTILIZATION_RATES_ON_THE_SPECTRAL_RESPONSE_OF_Brachiaria_brizantha_cv_MARAND%C3%9A_LEAVES"><img alt="Research paper thumbnail of EFFECT OF DIFFERENT NITROGEN FERTILIZATION RATES ON THE SPECTRAL RESPONSE OF Brachiaria brizantha cv. MARANDÚ LEAVES" class="work-thumbnail" src="https://attachments.academia-assets.com/115604660/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/120463941/EFFECT_OF_DIFFERENT_NITROGEN_FERTILIZATION_RATES_ON_THE_SPECTRAL_RESPONSE_OF_Brachiaria_brizantha_cv_MARAND%C3%9A_LEAVES">EFFECT OF DIFFERENT NITROGEN FERTILIZATION RATES ON THE SPECTRAL RESPONSE OF Brachiaria brizantha cv. MARANDÚ LEAVES</a></div><div class="wp-workCard_item"><span>Engenharia Agricola</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Hyperspectral sensors and regression analysis have been used to analyze the most important spectr...</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">Hyperspectral sensors and regression analysis have been used to analyze the most important spectral ranges for biophysical parameters of target crops, aiding in management decision-making. This study aimed to analyze the spectral response of Brachiaria brizantha cv. Marandú leaves to increasing rates of urea fertilization and predict leaf nitrogen content (LNC). Four rates of urea fertilization (0, 25, 50, and 75 kg of N ha-1) were applied. Eight leaves were collected per plot seven times at monthly intervals and subjected to hyperspectral analysis. Leaf spectral responses differed statistically within the visible region, particularly at 550 nm (green). The regression models achieved moderate to good R² values (0.53 to 0.83) for predicting LNC and identified important wavelengths in the red edge region (715 to 720 nm). These findings demonstrate the potential of spectral analysis to detect changes and forecast leaf nitrogen content in B. brizantha cv. Marandú crops at different fertilization levels.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="934136ac90267d18fc2cedc6a9b01153" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115604660,"asset_id":120463941,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115604660/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="120463941"><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="120463941"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120463941; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120463941]").text(description); $(".js-view-count[data-work-id=120463941]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120463941; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120463941']"); 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: "934136ac90267d18fc2cedc6a9b01153" } } $('.js-work-strip[data-work-id=120463941]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120463941,"title":"EFFECT OF DIFFERENT NITROGEN FERTILIZATION RATES ON THE SPECTRAL RESPONSE OF Brachiaria brizantha cv. MARANDÚ LEAVES","translated_title":"","metadata":{"publisher":"Sociedade Brasileira de Engenharia Agrícola","grobid_abstract":"Hyperspectral sensors and regression analysis have been used to analyze the most important spectral ranges for biophysical parameters of target crops, aiding in management decision-making. This study aimed to analyze the spectral response of Brachiaria brizantha cv. Marandú leaves to increasing rates of urea fertilization and predict leaf nitrogen content (LNC). Four rates of urea fertilization (0, 25, 50, and 75 kg of N ha-1) were applied. Eight leaves were collected per plot seven times at monthly intervals and subjected to hyperspectral analysis. Leaf spectral responses differed statistically within the visible region, particularly at 550 nm (green). The regression models achieved moderate to good R² values (0.53 to 0.83) for predicting LNC and identified important wavelengths in the red edge region (715 to 720 nm). These findings demonstrate the potential of spectral analysis to detect changes and forecast leaf nitrogen content in B. brizantha cv. Marandú crops at different fertilization levels.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Engenharia Agricola","grobid_abstract_attachment_id":115604660},"translated_abstract":null,"internal_url":"https://www.academia.edu/120463941/EFFECT_OF_DIFFERENT_NITROGEN_FERTILIZATION_RATES_ON_THE_SPECTRAL_RESPONSE_OF_Brachiaria_brizantha_cv_MARAND%C3%9A_LEAVES","translated_internal_url":"","created_at":"2024-06-03T04:18:09.801-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":45518824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":115604660,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115604660/thumbnails/1.jpg","file_name":"scielo.pdf","download_url":"https://www.academia.edu/attachments/115604660/download_file","bulk_download_file_name":"EFFECT_OF_DIFFERENT_NITROGEN_FERTILIZATI.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115604660/scielo-libre.pdf?1717417425=\u0026response-content-disposition=attachment%3B+filename%3DEFFECT_OF_DIFFERENT_NITROGEN_FERTILIZATI.pdf\u0026Expires=1741749272\u0026Signature=LZH4jRhfxxHQW7iqO4oNXoeTfyFIPm~pkpeylX8xJH1nC5dtHEbVFE5-tkg5rOrwP8v986O52Mn~zoBOUz6xdYnHHDP~8YbMYWze4GY0ysZsinSeXVmwqnTXVsAped5-ivwYlWbEsUfZOVFhZ~gPzkRLqzZ75ji9j0KaayWqLvE2-ECxjG0LEJZfvfHG-0O3qE~4uIxMMj-dkrz7NEtAhPLsMhdJfNaZYxyG4PouwemE8pHnhMBfYbHfSzt7q2RpG~GVQekPO5zRorQc3mp6M8bx1f5PQTCW3finrPyYxAy5y6aA03E6pllcjsSGlf7GWe1yhPdgNrg7sWfRJBmZPQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"EFFECT_OF_DIFFERENT_NITROGEN_FERTILIZATION_RATES_ON_THE_SPECTRAL_RESPONSE_OF_Brachiaria_brizantha_cv_MARANDÚ_LEAVES","translated_slug":"","page_count":9,"language":"en","content_type":"Work","summary":"Hyperspectral sensors and regression analysis have been used to analyze the most important spectral ranges for biophysical parameters of target crops, aiding in management decision-making. This study aimed to analyze the spectral response of Brachiaria brizantha cv. Marandú leaves to increasing rates of urea fertilization and predict leaf nitrogen content (LNC). Four rates of urea fertilization (0, 25, 50, and 75 kg of N ha-1) were applied. Eight leaves were collected per plot seven times at monthly intervals and subjected to hyperspectral analysis. Leaf spectral responses differed statistically within the visible region, particularly at 550 nm (green). The regression models achieved moderate to good R² values (0.53 to 0.83) for predicting LNC and identified important wavelengths in the red edge region (715 to 720 nm). These findings demonstrate the potential of spectral analysis to detect changes and forecast leaf nitrogen content in B. brizantha cv. Marandú crops at different fertilization levels.","owner":{"id":45518824,"first_name":"Peterson","middle_initials":null,"last_name":"Fiorio","page_name":"PetersonFiorio","domain_name":"independent","created_at":"2016-03-21T04:17:07.025-07:00","display_name":"Peterson Fiorio","url":"https://independent.academia.edu/PetersonFiorio"},"attachments":[{"id":115604660,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115604660/thumbnails/1.jpg","file_name":"scielo.pdf","download_url":"https://www.academia.edu/attachments/115604660/download_file","bulk_download_file_name":"EFFECT_OF_DIFFERENT_NITROGEN_FERTILIZATI.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115604660/scielo-libre.pdf?1717417425=\u0026response-content-disposition=attachment%3B+filename%3DEFFECT_OF_DIFFERENT_NITROGEN_FERTILIZATI.pdf\u0026Expires=1741749272\u0026Signature=LZH4jRhfxxHQW7iqO4oNXoeTfyFIPm~pkpeylX8xJH1nC5dtHEbVFE5-tkg5rOrwP8v986O52Mn~zoBOUz6xdYnHHDP~8YbMYWze4GY0ysZsinSeXVmwqnTXVsAped5-ivwYlWbEsUfZOVFhZ~gPzkRLqzZ75ji9j0KaayWqLvE2-ECxjG0LEJZfvfHG-0O3qE~4uIxMMj-dkrz7NEtAhPLsMhdJfNaZYxyG4PouwemE8pHnhMBfYbHfSzt7q2RpG~GVQekPO5zRorQc3mp6M8bx1f5PQTCW3finrPyYxAy5y6aA03E6pllcjsSGlf7GWe1yhPdgNrg7sWfRJBmZPQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1037,"name":"Agronomy","url":"https://www.academia.edu/Documents/in/Agronomy"},{"id":651937,"name":"Engenharia Agrícola","url":"https://www.academia.edu/Documents/in/Engenharia_Agr%C3%ADcola"},{"id":963742,"name":"Brachiaria","url":"https://www.academia.edu/Documents/in/Brachiaria"},{"id":1439535,"name":"Engenharia Agricola","url":"https://www.academia.edu/Documents/in/Engenharia_Agricola"}],"urls":[{"id":42598383,"url":"https://doi.org/10.1590/1809-4430-eng.agric.v43n3e20220008/2023"}]}, 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="120463940"><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/120463940/Detection_of_nutritional_stress_in_sugarcane_by_VIS_NIR_SWIR_reflectance_spectroscopy"><img alt="Research paper thumbnail of Detection of nutritional stress in sugarcane by VIS-NIR-SWIR reflectance spectroscopy" class="work-thumbnail" src="https://attachments.academia-assets.com/115604639/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/120463940/Detection_of_nutritional_stress_in_sugarcane_by_VIS_NIR_SWIR_reflectance_spectroscopy">Detection of nutritional stress in sugarcane by VIS-NIR-SWIR reflectance spectroscopy</a></div><div class="wp-workCard_item"><span>Ciencia Rural</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study applied spectroradiometry techniques with hyperspectral data to identify the correlati...</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">This study applied spectroradiometry techniques with hyperspectral data to identify the correlations between sugarcane leaf reflectance and the contents of Nitrogen (N), phosphorus (P), Potassium (K), Sulfur (S), Calcium (Ca) and Magnesium (Mg). During the harvests 2019/20 and 2020/21, sugarcane was introduced to nutritional stress by the application of limestone doses. Liming was applied in a fractional way and, at the end of five years, the amounts corresponded to 0, 9, 15 and 21 t ha-1 of dolomitic limestone. The leaf hyperspectral reflectance data and the state of nutrients in the exponential growth phase of the culture were registered. The wavelengths correlated with N, P, K, S, Ca and Mg were identified using the Spearman's correlation analysis. The test of similarity (ANOSIM) and the Principal Component Analysis (PCA) were applied to evaluate data variability, as well as the Partial Least Squares Regression (PLSR) for the prediction of the nutritional contents. The order of the degree of correlation in the region of visible was: P > K > N > Ca > S > Mg and for the region of the near infrared: P > K > Ca > N > S > Mg. P presented peaks with high correlations in the wavelengths 706-717 nm (-0.78) and 522-543 nm (-0.76). The values of the PLSR registered the best spectral responses in the region of VIS and red-edge, regions that are more sensitive to the deficiency of sulfur, potassium and phosphorus.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="24c8d6ee68c662009efeed1d7bfe6aca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115604639,"asset_id":120463940,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115604639/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="120463940"><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="120463940"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120463940; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120463940]").text(description); $(".js-view-count[data-work-id=120463940]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120463940; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120463940']"); 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: "24c8d6ee68c662009efeed1d7bfe6aca" } } $('.js-work-strip[data-work-id=120463940]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120463940,"title":"Detection of nutritional stress in sugarcane by VIS-NIR-SWIR reflectance spectroscopy","translated_title":"","metadata":{"publisher":"Universidade Federal de Santa Maria","ai_title_tag":"Nutritional Stress Detection in Sugarcane Using Spectroscopy","grobid_abstract":"This study applied spectroradiometry techniques with hyperspectral data to identify the correlations between sugarcane leaf reflectance and the contents of Nitrogen (N), phosphorus (P), Potassium (K), Sulfur (S), Calcium (Ca) and Magnesium (Mg). During the harvests 2019/20 and 2020/21, sugarcane was introduced to nutritional stress by the application of limestone doses. Liming was applied in a fractional way and, at the end of five years, the amounts corresponded to 0, 9, 15 and 21 t ha-1 of dolomitic limestone. The leaf hyperspectral reflectance data and the state of nutrients in the exponential growth phase of the culture were registered. The wavelengths correlated with N, P, K, S, Ca and Mg were identified using the Spearman's correlation analysis. The test of similarity (ANOSIM) and the Principal Component Analysis (PCA) were applied to evaluate data variability, as well as the Partial Least Squares Regression (PLSR) for the prediction of the nutritional contents. The order of the degree of correlation in the region of visible was: P \u003e K \u003e N \u003e Ca \u003e S \u003e Mg and for the region of the near infrared: P \u003e K \u003e Ca \u003e N \u003e S \u003e Mg. P presented peaks with high correlations in the wavelengths 706-717 nm (-0.78) and 522-543 nm (-0.76). The values of the PLSR registered the best spectral responses in the region of VIS and red-edge, regions that are more sensitive to the deficiency of sulfur, potassium and phosphorus.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Ciencia Rural","grobid_abstract_attachment_id":115604636},"translated_abstract":null,"internal_url":"https://www.academia.edu/120463940/Detection_of_nutritional_stress_in_sugarcane_by_VIS_NIR_SWIR_reflectance_spectroscopy","translated_internal_url":"","created_at":"2024-06-03T04:18:09.189-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":45518824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":115604639,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115604639/thumbnails/1.jpg","file_name":"YjpkL9dLFKsCdNyzkVM4Vvw.pdf","download_url":"https://www.academia.edu/attachments/115604639/download_file","bulk_download_file_name":"Detection_of_nutritional_stress_in_sugar.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115604639/YjpkL9dLFKsCdNyzkVM4Vvw-libre.pdf?1717417453=\u0026response-content-disposition=attachment%3B+filename%3DDetection_of_nutritional_stress_in_sugar.pdf\u0026Expires=1741749272\u0026Signature=Xma0CHxXW87DnqaBbO2TM8UQ02WDAznQENRyRPZV0zjGExIf2npR~CrUXAVFTXhciOXxxNya-8Up2g3n88CbQDz8ahkPxQMyoAIwcNdIvcIYziRaX2az04gqxcRNpnvv2GX6vd9E-kcceID1fbdTGROJo30n9NJEnV-Dn8YI8O1FB5xPz4RhQiGTb4YY4jigN~KlLypnOyNR~uAkBqRGRLXMz6czrGg0SSlo-bkGLdgygAi9J4zScP83ZRyabO9MklCfAYeZpBGj4X6ZMY4XnlWUgG0s7zZBHQFNWYl~f4mZ8BJaNYYamCVSSCr17S9Y-WwErIW5KffXlIYtCiBlQw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Detection_of_nutritional_stress_in_sugarcane_by_VIS_NIR_SWIR_reflectance_spectroscopy","translated_slug":"","page_count":15,"language":"en","content_type":"Work","summary":"This study applied spectroradiometry techniques with hyperspectral data to identify the correlations between sugarcane leaf reflectance and the contents of Nitrogen (N), phosphorus (P), Potassium (K), Sulfur (S), Calcium (Ca) and Magnesium (Mg). During the harvests 2019/20 and 2020/21, sugarcane was introduced to nutritional stress by the application of limestone doses. Liming was applied in a fractional way and, at the end of five years, the amounts corresponded to 0, 9, 15 and 21 t ha-1 of dolomitic limestone. The leaf hyperspectral reflectance data and the state of nutrients in the exponential growth phase of the culture were registered. The wavelengths correlated with N, P, K, S, Ca and Mg were identified using the Spearman's correlation analysis. The test of similarity (ANOSIM) and the Principal Component Analysis (PCA) were applied to evaluate data variability, as well as the Partial Least Squares Regression (PLSR) for the prediction of the nutritional contents. The order of the degree of correlation in the region of visible was: P \u003e K \u003e N \u003e Ca \u003e S \u003e Mg and for the region of the near infrared: P \u003e K \u003e Ca \u003e N \u003e S \u003e Mg. P presented peaks with high correlations in the wavelengths 706-717 nm (-0.78) and 522-543 nm (-0.76). The values of the PLSR registered the best spectral responses in the region of VIS and red-edge, regions that are more sensitive to the deficiency of sulfur, potassium and phosphorus.","owner":{"id":45518824,"first_name":"Peterson","middle_initials":null,"last_name":"Fiorio","page_name":"PetersonFiorio","domain_name":"independent","created_at":"2016-03-21T04:17:07.025-07:00","display_name":"Peterson Fiorio","url":"https://independent.academia.edu/PetersonFiorio"},"attachments":[{"id":115604639,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115604639/thumbnails/1.jpg","file_name":"YjpkL9dLFKsCdNyzkVM4Vvw.pdf","download_url":"https://www.academia.edu/attachments/115604639/download_file","bulk_download_file_name":"Detection_of_nutritional_stress_in_sugar.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115604639/YjpkL9dLFKsCdNyzkVM4Vvw-libre.pdf?1717417453=\u0026response-content-disposition=attachment%3B+filename%3DDetection_of_nutritional_stress_in_sugar.pdf\u0026Expires=1741749272\u0026Signature=Xma0CHxXW87DnqaBbO2TM8UQ02WDAznQENRyRPZV0zjGExIf2npR~CrUXAVFTXhciOXxxNya-8Up2g3n88CbQDz8ahkPxQMyoAIwcNdIvcIYziRaX2az04gqxcRNpnvv2GX6vd9E-kcceID1fbdTGROJo30n9NJEnV-Dn8YI8O1FB5xPz4RhQiGTb4YY4jigN~KlLypnOyNR~uAkBqRGRLXMz6czrGg0SSlo-bkGLdgygAi9J4zScP83ZRyabO9MklCfAYeZpBGj4X6ZMY4XnlWUgG0s7zZBHQFNWYl~f4mZ8BJaNYYamCVSSCr17S9Y-WwErIW5KffXlIYtCiBlQw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":115604636,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115604636/thumbnails/1.jpg","file_name":"YjpkL9dLFKsCdNyzkVM4Vvw.pdf","download_url":"https://www.academia.edu/attachments/115604636/download_file","bulk_download_file_name":"Detection_of_nutritional_stress_in_sugar.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115604636/YjpkL9dLFKsCdNyzkVM4Vvw-libre.pdf?1717417517=\u0026response-content-disposition=attachment%3B+filename%3DDetection_of_nutritional_stress_in_sugar.pdf\u0026Expires=1741749272\u0026Signature=TA5V5frjM55tiXL4R~1-3Xp-m6wuvDY9TLNbuHgpjVRM7YB78ImppFjM9p9-8xDh9iL7KtgYSBrW8helvIDSpfWVRKSjWb5gmECe6gjxAfsSCGN0-MYhOJMqfP3de98ms7HP68KM01bWikKDYJLqis05nA96NSpL6baN-aXgJ7dou3N301A1ieJvVArm0Yx3lWeBjz~CRy~TuYmgIgpDi6ZZNXT8DfNpTXDKwx5P0xJt0dIhUWIO4bnYbZBeOs3NHg4Gbu7qZjL4aAopyQbDJ5bbHE~UCM5Lwh73msR~dYJuGIe0GGm~2lIHtOeSl63b2ptiCTc3MtMzAgLewU3-yw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":53108,"name":"Phosphorus","url":"https://www.academia.edu/Documents/in/Phosphorus"},{"id":160656,"name":"Potassium","url":"https://www.academia.edu/Documents/in/Potassium"},{"id":244969,"name":"Nutrient","url":"https://www.academia.edu/Documents/in/Nutrient"},{"id":285012,"name":"Sulfur","url":"https://www.academia.edu/Documents/in/Sulfur"},{"id":358917,"name":"Partial Least Squares Regression","url":"https://www.academia.edu/Documents/in/Partial_Least_Squares_Regression"}],"urls":[{"id":42598382,"url":"https://www.scielo.br/j/cr/a/YjpkL9dLFKsCdNyzkVM4Vvw/?lang=en\u0026format=pdf"}]}, 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="120463937"><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/120463937/Mapping_Productivity_and_Essential_Biophysical_Parameters_of_Cultivated_Tropical_Grasslands_from_Sentinel_2_Imagery"><img alt="Research paper thumbnail of Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery" class="work-thumbnail" src="https://attachments.academia-assets.com/115604633/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/120463937/Mapping_Productivity_and_Essential_Biophysical_Parameters_of_Cultivated_Tropical_Grasslands_from_Sentinel_2_Imagery">Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery</a></div><div class="wp-workCard_item"><span>Agronomy</span><span>, May 15, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextrica...</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">Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)'s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species' cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall'Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)-near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R 2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg −1. The estimation of grassland productivity based on red-edge and NIR bands showed a R 2 = 0.54 and a RMSE = 1800 kg ha −1. For the LAI, the best index was the NAOC (R 2 = 0.57 and RMSE = 1.4 m 2 m −2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2a75ac46350e2216991b19b70303cfbe" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115604633,"asset_id":120463937,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115604633/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="120463937"><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="120463937"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120463937; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120463937]").text(description); $(".js-view-count[data-work-id=120463937]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120463937; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120463937']"); 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: "2a75ac46350e2216991b19b70303cfbe" } } $('.js-work-strip[data-work-id=120463937]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120463937,"title":"Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery","translated_title":"","metadata":{"publisher":"Multidisciplinary Digital Publishing Institute","ai_title_tag":"Estimating Grassland Productivity via Sentinel-2","grobid_abstract":"Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)'s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species' cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall'Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)-near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R 2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg −1. The estimation of grassland productivity based on red-edge and NIR bands showed a R 2 = 0.54 and a RMSE = 1800 kg ha −1. For the LAI, the best index was the NAOC (R 2 = 0.57 and RMSE = 1.4 m 2 m −2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization.","publication_date":{"day":15,"month":5,"year":2020,"errors":{}},"publication_name":"Agronomy","grobid_abstract_attachment_id":115604633},"translated_abstract":null,"internal_url":"https://www.academia.edu/120463937/Mapping_Productivity_and_Essential_Biophysical_Parameters_of_Cultivated_Tropical_Grasslands_from_Sentinel_2_Imagery","translated_internal_url":"","created_at":"2024-06-03T04:18:07.768-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":45518824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":115604633,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115604633/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/115604633/download_file","bulk_download_file_name":"Mapping_Productivity_and_Essential_Bioph.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115604633/pdf-libre.pdf?1717417420=\u0026response-content-disposition=attachment%3B+filename%3DMapping_Productivity_and_Essential_Bioph.pdf\u0026Expires=1741749272\u0026Signature=RkHxqIIIzdpDKFfFQzEbekyzqBOsxjFe1OEaoa7K8~rbgb0hyUN7SWWsrfexDk6KEUkmyUK~3XBmI48NhPga28y6RS6DgfThh5Rw88qPBvINPJ8IsVxsNK1IkZgEvot01~tY3guDJmEgBaJWIILhPSENXc6lBfxMc0teR9MatnWTwRGbUW7A63zDpQoekLDuLS27OIidDX91Sesx~qIchvVUYz6UduBZju-m0cAp-OOOZfEDMYmlg0Drmv1kEwnLeob5NWLMdRDcGYlwvdwg1nRe0swt8TTu4TH2jgg1HanJtk5zKmk8V6DlP7a2eMyfMCB-lBpXdCKlpsn2glW4Kg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Mapping_Productivity_and_Essential_Biophysical_Parameters_of_Cultivated_Tropical_Grasslands_from_Sentinel_2_Imagery","translated_slug":"","page_count":23,"language":"en","content_type":"Work","summary":"Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)'s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species' cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall'Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)-near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R 2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg −1. The estimation of grassland productivity based on red-edge and NIR bands showed a R 2 = 0.54 and a RMSE = 1800 kg ha −1. For the LAI, the best index was the NAOC (R 2 = 0.57 and RMSE = 1.4 m 2 m −2). 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lineares múltiplas utilizando-se dados espectrais obtidos por sensores em laborátorio e orbital, na estimativa de atributos do solo" 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/117902932/Regress%C3%B5es_lineares_m%C3%BAltiplas_utilizando_se_dados_espectrais_obtidos_por_sensores_em_labor%C3%A1torio_e_orbital_na_estimativa_de_atributos_do_solo">Regressões lineares múltiplas utilizando-se dados espectrais obtidos por sensores em laborátorio e orbital, na estimativa de atributos do solo</a></div><div class="wp-workCard_item"><span>Anais</span><span>, 2003</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span 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de perda de solo em ambiente SIG utilizando diferentes fontes de dados topográficos Estimates of soil loss in a GIS environment using different sources of topographic data</a></div><div class="wp-workCard_item"><span>AMBIÊNCIA</span><span>, Apr 15, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Estimativas de perda de solo em ambiente SIG utilizando diferentes fontes de dados topográficos E...</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">Estimativas de perda de solo em ambiente SIG utilizando diferentes fontes de dados topográficos Estimates of soil loss in a GIS environment using different sources of topographic data</span></div><div class="wp-workCard_item wp-workCard--actions"><span 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alt="Research paper thumbnail of Potencialidade do uso da terra na microbacia hidrográfica do córrego do ceveiro na região de Piracicaba" class="work-thumbnail" src="https://attachments.academia-assets.com/113650108/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/117902930/Potencialidade_do_uso_da_terra_na_microbacia_hidrogr%C3%A1fica_do_c%C3%B3rrego_do_ceveiro_na_regi%C3%A3o_de_Piracicaba">Potencialidade do uso da terra na microbacia hidrográfica do córrego do ceveiro na região de Piracicaba</a></div><div class="wp-workCard_item"><span>Scientia Agricola</span><span>, 1999</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">RESUMO: O objetivo do presente trabalho foi avaliar a ocupação agrícola da Microbacia Hidrográfic...</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">RESUMO: O objetivo do presente trabalho foi avaliar a ocupação agrícola da Microbacia Hidrográfica do Ceveiro para o ano de 1995 e estabelecer a sua potencialidade. Para tanto, foi utilizado o Sistema de Análise Ambiental para Planejamento Agrícola. A microbacia localiza-se na região de Piracicaba, SP, com aproximadamente 1990 ha. Predominam os Podzólicos e os solos Litólicos de textura média e argilosa. O uso da terra foi determinado por fotointerpretação, cujos os limites foram digitalizados e cruzados com os dados de declividade e solos, gerando mapas de intensidade de uso e aptidão agrícola. Os dados indicaram que apenas 27% da área da bacia estava sendo utilizada adequadamente, 48% estava sendo sub-utilizada e 23% excessivamente utilizada, com sérios riscos de degradação dos solos. As principais distorções observadas quanto ao uso da terra foram em relação a cultura da canade-açúcar, que ocupa áreas destinadas a cultura anual e pastagens. Palavras-chave: aptidão agrícola, uso da terra, sensoriamento remoto LAND USE POTENTIAL IN THE CEVEIRO STREAM WATERSHED OF PIRACICABA ABSTRACT: The objective of this work was to evaluate the agricultural potential of the Ceveiro watershed Piracicaba, SP, Brazil, which contains 1990 ha considering available environmental conditions. Medium and clayey textured Ultisols and Entisols predominate. Land use was determined by remote sensing, the limits of the were digitalized and crossed with soils and shape data, using the Environmental Analysis System for Agricultural Planning. These results were then compared with the current land usage patterns that occurred in 1995, to determine if differences existed between current and prescribed land use. In 1995, 48% of the area was being used below its potential use, 27% used appropriately, and 23% used with excessive intensity. The main difference in land use between the appropriately used and excessively used areas was due to sugarcane production.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="27b1d11a4f0c2ad7caf70d6fd0a4458b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113650108,"asset_id":117902930,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113650108/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="117902930"><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="117902930"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117902930; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117902930]").text(description); $(".js-view-count[data-work-id=117902930]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117902930; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117902930']"); 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: "27b1d11a4f0c2ad7caf70d6fd0a4458b" } } $('.js-work-strip[data-work-id=117902930]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117902930,"title":"Potencialidade do uso da terra na microbacia hidrográfica do córrego do ceveiro na região de Piracicaba","translated_title":"","metadata":{"publisher":"University of São Paulo","grobid_abstract":"RESUMO: O objetivo do presente trabalho foi avaliar a ocupação agrícola da Microbacia Hidrográfica do Ceveiro para o ano de 1995 e estabelecer a sua potencialidade. 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Palavras-chave: aptidão agrícola, uso da terra, sensoriamento remoto LAND USE POTENTIAL IN THE CEVEIRO STREAM WATERSHED OF PIRACICABA ABSTRACT: The objective of this work was to evaluate the agricultural potential of the Ceveiro watershed Piracicaba, SP, Brazil, which contains 1990 ha considering available environmental conditions. Medium and clayey textured Ultisols and Entisols predominate. Land use was determined by remote sensing, the limits of the were digitalized and crossed with soils and shape data, using the Environmental Analysis System for Agricultural Planning. These results were then compared with the current land usage patterns that occurred in 1995, to determine if differences existed between current and prescribed land use. In 1995, 48% of the area was being used below its potential use, 27% used appropriately, and 23% used with excessive intensity. 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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="117902927"><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/117902927/Internal_soil_standard_method_for_the_Brazilian_soil_spectral_library_Performance_and_proximate_analysis"><img alt="Research paper thumbnail of Internal soil standard method for the Brazilian soil spectral library: Performance and proximate analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/113650139/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/117902927/Internal_soil_standard_method_for_the_Brazilian_soil_spectral_library_Performance_and_proximate_analysis">Internal soil standard method for the Brazilian soil spectral library: Performance and proximate analysis</a></div><div class="wp-workCard_item"><span>Geoderma</span><span>, Feb 1, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">There are several methods to extract soil information by spectral sensing. For this reason, the d...</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">There are several methods to extract soil information by spectral sensing. For this reason, the database should be built including standards and protocols both in the lab and in field acquisitions. If we does not align the measurement one to each other, the models will have no merit in term of large-scale application and stay as an academic exercise only. The use of standard samples with known spectra is expected to allow parameterizing mathematically soil spectra collected by different equipment and with distinct geometries. This study was proposed for the reason that to date, the new methodology proposed has not been evaluated in Brazilian tropical soils (Oxisols) in visible, near and short infrared (VIS-NIR-SWIR-350 to 2500 nm) spectral regions from three spectrometers (FieldSpec 3) in three distinct protocols (Long Light, Near Light and Contact Probe). The current study compared the spectral intensity of each combination between spectrometers and protocols by ANOVA module and the clay prediction capacity by PLSR with cross-validation, before and after the internal soil standard (ISS) method application. The results showed that visual spectral variation is minimized by the ISS method and its correction enables better proxy modeling of clay content especially if the data are mixed from different protocols. The model for clay prediction was improved showing a favorable case to use the ISS technique in any soil spectral measurement in a better way to merge spectral libraries from different sources.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="33add4a9908cb450895096682f469e95" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113650139,"asset_id":117902927,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113650139/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="117902927"><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="117902927"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117902927; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117902927]").text(description); $(".js-view-count[data-work-id=117902927]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117902927; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117902927']"); 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: "33add4a9908cb450895096682f469e95" } } $('.js-work-strip[data-work-id=117902927]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117902927,"title":"Internal soil standard method for the Brazilian soil spectral library: Performance and proximate analysis","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"There are several methods to extract soil information by spectral sensing. For this reason, the database should be built including standards and protocols both in the lab and in field acquisitions. If we does not align the measurement one to each other, the models will have no merit in term of large-scale application and stay as an academic exercise only. The use of standard samples with known spectra is expected to allow parameterizing mathematically soil spectra collected by different equipment and with distinct geometries. This study was proposed for the reason that to date, the new methodology proposed has not been evaluated in Brazilian tropical soils (Oxisols) in visible, near and short infrared (VIS-NIR-SWIR-350 to 2500 nm) spectral regions from three spectrometers (FieldSpec 3) in three distinct protocols (Long Light, Near Light and Contact Probe). The current study compared the spectral intensity of each combination between spectrometers and protocols by ANOVA module and the clay prediction capacity by PLSR with cross-validation, before and after the internal soil standard (ISS) method application. The results showed that visual spectral variation is minimized by the ISS method and its correction enables better proxy modeling of clay content especially if the data are mixed from different protocols. The model for clay prediction was improved showing a favorable case to use the ISS technique in any soil spectral measurement in a better way to merge spectral libraries from different sources.","publication_date":{"day":1,"month":2,"year":2018,"errors":{}},"publication_name":"Geoderma","grobid_abstract_attachment_id":113650139},"translated_abstract":null,"internal_url":"https://www.academia.edu/117902927/Internal_soil_standard_method_for_the_Brazilian_soil_spectral_library_Performance_and_proximate_analysis","translated_internal_url":"","created_at":"2024-04-22T15:01:07.475-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":45518824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":113650139,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113650139/thumbnails/1.jpg","file_name":"j.geoderma.2017.09.01420240422-1-ixbzqh.pdf","download_url":"https://www.academia.edu/attachments/113650139/download_file","bulk_download_file_name":"Internal_soil_standard_method_for_the_Br.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113650139/j.geoderma.2017.09.01420240422-1-ixbzqh-libre.pdf?1713823542=\u0026response-content-disposition=attachment%3B+filename%3DInternal_soil_standard_method_for_the_Br.pdf\u0026Expires=1741749272\u0026Signature=WydcD0AQNouvMzD5RU0m73QP5eLhTQuM4-WN4LiI9L4NDADglBfiW5A4PmgaPRjyo5uAEvX50m1Q-8-6Emlf3HPJFqrkk~7i0dVe7EfhILsntw6ca7MYDHNYy5SBlmaqvzp0iv6WEGx5KIlzifeWHkJaSoirnkIWUoIrzMdQ~KsflgUDqoJt2eacOL~GlodbCOhKDHroCrRlbqk58-3CmTMvJ6VcvHylaUVbekKFbiizfnViNutICmdmW5pQEuEk3cvoPEmvkVk-N-2liXxcN158nlMEKBcHRkQv1Sdi3u21gRsYWGbTLMvDW2c8oYm~MiM~QayIQkAOWQBc5vd-vg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Internal_soil_standard_method_for_the_Brazilian_soil_spectral_library_Performance_and_proximate_analysis","translated_slug":"","page_count":9,"language":"en","content_type":"Work","summary":"There are several methods to extract soil information by spectral sensing. 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The current study compared the spectral intensity of each combination between spectrometers and protocols by ANOVA module and the clay prediction capacity by PLSR with cross-validation, before and after the internal soil standard (ISS) method application. The results showed that visual spectral variation is minimized by the ISS method and its correction enables better proxy modeling of clay content especially if the data are mixed from different protocols. The model for clay prediction was improved showing a favorable case to use the ISS technique in any soil spectral measurement in a better way to merge spectral libraries from different sources.","owner":{"id":45518824,"first_name":"Peterson","middle_initials":null,"last_name":"Fiorio","page_name":"PetersonFiorio","domain_name":"independent","created_at":"2016-03-21T04:17:07.025-07:00","display_name":"Peterson Fiorio","url":"https://independent.academia.edu/PetersonFiorio"},"attachments":[{"id":113650139,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113650139/thumbnails/1.jpg","file_name":"j.geoderma.2017.09.01420240422-1-ixbzqh.pdf","download_url":"https://www.academia.edu/attachments/113650139/download_file","bulk_download_file_name":"Internal_soil_standard_method_for_the_Br.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113650139/j.geoderma.2017.09.01420240422-1-ixbzqh-libre.pdf?1713823542=\u0026response-content-disposition=attachment%3B+filename%3DInternal_soil_standard_method_for_the_Br.pdf\u0026Expires=1741749272\u0026Signature=WydcD0AQNouvMzD5RU0m73QP5eLhTQuM4-WN4LiI9L4NDADglBfiW5A4PmgaPRjyo5uAEvX50m1Q-8-6Emlf3HPJFqrkk~7i0dVe7EfhILsntw6ca7MYDHNYy5SBlmaqvzp0iv6WEGx5KIlzifeWHkJaSoirnkIWUoIrzMdQ~KsflgUDqoJt2eacOL~GlodbCOhKDHroCrRlbqk58-3CmTMvJ6VcvHylaUVbekKFbiizfnViNutICmdmW5pQEuEk3cvoPEmvkVk-N-2liXxcN158nlMEKBcHRkQv1Sdi3u21gRsYWGbTLMvDW2c8oYm~MiM~QayIQkAOWQBc5vd-vg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"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":47884,"name":"Biological Sciences","url":"https://www.academia.edu/Documents/in/Biological_Sciences"},{"id":58054,"name":"Environmental Sciences","url":"https://www.academia.edu/Documents/in/Environmental_Sciences"},{"id":95360,"name":"Spectral analysis","url":"https://www.academia.edu/Documents/in/Spectral_analysis"},{"id":1222799,"name":"Soil Water","url":"https://www.academia.edu/Documents/in/Soil_Water"},{"id":1442002,"name":"Spectrometer","url":"https://www.academia.edu/Documents/in/Spectrometer"},{"id":1941070,"name":"GEODERMA","url":"https://www.academia.edu/Documents/in/GEODERMA"}],"urls":[{"id":41314689,"url":"https://doi.org/10.1016/j.geoderma.2017.09.014"}]}, 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="117902926"><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/117902926/Visible_NIR_reflectance_a_new_approach_on_soil_evaluation"><img alt="Research paper thumbnail of Visible–NIR reflectance: a new approach on soil evaluation" class="work-thumbnail" src="https://attachments.academia-assets.com/113650146/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/117902926/Visible_NIR_reflectance_a_new_approach_on_soil_evaluation">Visible–NIR reflectance: a new approach on soil evaluation</a></div><div class="wp-workCard_item"><span>Geoderma</span><span>, Jul 1, 2004</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The aim of this work was to develop a spectral reflectance (SR)-based methodology to evaluate soi...</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 aim of this work was to develop a spectral reflectance (SR)-based methodology to evaluate soil types and soil tillage systems. Soil samples, located along nine toposequences with basalt and shale, were collected in São Paulo State, Brazil. SR of soil samples was measured with a laboratory spectroradiometer between 450 and 2500 nm. SR curves were evaluated at different depths to determine soil classes, as for conventional methods of soil classification. Soil survey maps were developed by descriptive interpretation of the spectral curves and statistical analysis as well. These maps were compared with detailed and semidetailed soil maps based on chemical, physical and mineralogical soil attributes (conventional method). Organic matter, total iron, silt, sand and mineralogy (quartz, magnetite, kaolinite and smectite) were the most important attributes influencing reflectance intensity and spectral features and allowed characterization and discrimination of soils. Strong absorption features centered at 1900 nm were related to OH molecules in free water of 2:1 minerals. Identification of soil classes was most precise when the entire spectral curve of different depths was analysed simultaneously, including features and intensities. Parent material variation along toposequences and weathering variability modified soil characteristics and its respective SR data. The detection of these alterations was essential to determine soil line demarcation. Soil line demarcation and number of soil classes detected by SR analyses were similar to the detailed soil map determined by conventional methods. SR laboratory measurements can be used as a methodology to assist soil surveys. Spectral data can also group soils under similar tillage systems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e0c43a414ecb4f3aeae96b7fdc11a721" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113650146,"asset_id":117902926,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113650146/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="117902926"><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="117902926"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117902926; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117902926]").text(description); $(".js-view-count[data-work-id=117902926]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117902926; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117902926']"); 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: "e0c43a414ecb4f3aeae96b7fdc11a721" } } $('.js-work-strip[data-work-id=117902926]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117902926,"title":"Visible–NIR reflectance: a new approach on soil evaluation","translated_title":"","metadata":{"publisher":"Elsevier BV","ai_title_tag":"Spectral Reflectance Method for Soil Evaluation in Brazil","grobid_abstract":"The aim of this work was to develop a spectral reflectance (SR)-based methodology to evaluate soil types and soil tillage systems. Soil samples, located along nine toposequences with basalt and shale, were collected in São Paulo State, Brazil. SR of soil samples was measured with a laboratory spectroradiometer between 450 and 2500 nm. SR curves were evaluated at different depths to determine soil classes, as for conventional methods of soil classification. Soil survey maps were developed by descriptive interpretation of the spectral curves and statistical analysis as well. These maps were compared with detailed and semidetailed soil maps based on chemical, physical and mineralogical soil attributes (conventional method). Organic matter, total iron, silt, sand and mineralogy (quartz, magnetite, kaolinite and smectite) were the most important attributes influencing reflectance intensity and spectral features and allowed characterization and discrimination of soils. Strong absorption features centered at 1900 nm were related to OH molecules in free water of 2:1 minerals. Identification of soil classes was most precise when the entire spectral curve of different depths was analysed simultaneously, including features and intensities. Parent material variation along toposequences and weathering variability modified soil characteristics and its respective SR data. The detection of these alterations was essential to determine soil line demarcation. Soil line demarcation and number of soil classes detected by SR analyses were similar to the detailed soil map determined by conventional methods. SR laboratory measurements can be used as a methodology to assist soil surveys. 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Soil samples, located along nine toposequences with basalt and shale, were collected in São Paulo State, Brazil. SR of soil samples was measured with a laboratory spectroradiometer between 450 and 2500 nm. SR curves were evaluated at different depths to determine soil classes, as for conventional methods of soil classification. Soil survey maps were developed by descriptive interpretation of the spectral curves and statistical analysis as well. These maps were compared with detailed and semidetailed soil maps based on chemical, physical and mineralogical soil attributes (conventional method). Organic matter, total iron, silt, sand and mineralogy (quartz, magnetite, kaolinite and smectite) were the most important attributes influencing reflectance intensity and spectral features and allowed characterization and discrimination of soils. Strong absorption features centered at 1900 nm were related to OH molecules in free water of 2:1 minerals. 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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="117902925"><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/117902925/Diferentes_im%C3%A1genes_de_sat%C3%A9lite_en_el_mapeamento_visual_de_drenajes_y_nacientes_en_muestras_circulares"><img alt="Research paper thumbnail of Diferentes imágenes de satélite en el mapeamento visual de drenajes y nacientes en muestras circulares" 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/117902925/Diferentes_im%C3%A1genes_de_sat%C3%A9lite_en_el_mapeamento_visual_de_drenajes_y_nacientes_en_muestras_circulares">Diferentes imágenes de satélite en el mapeamento visual de drenajes y nacientes en muestras circulares</a></div><div class="wp-workCard_item"><span>Interciencia: Revista de ciencia y tecnología de América</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">espanolEl uso de imagenes de satelite viene siendo cada vez mas comun en la obtencion de datos am...</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">espanolEl uso de imagenes de satelite viene siendo cada vez mas comun en la obtencion de datos ambientales, como los cursos del agua y las nacientes. El presente trabajo tuvo por objetivo comparar tres diferentes productos utilizados en el mapeo manual (visual) de drenajes y nacientes: un mosaico de ima- genes del satelite SPOT, imagenes del programa Google Earth® y una imagen del satelite Landsat 8; cada producto con resolucion espacial diferente. Se utilizo la rede de drenaje presente en cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacion. Para una mejor comparacion entre los productos, fueron analizados dos factores: longitud de drenaje (LD) y numero de nacientes (NN), los analisis se realizaron apenas en caracter cuantitativo. Para tanto, fueron dispuestas tres muestras circulares de 2,75km de radio en diferentes areas del municipio de Piracicaba donde serian observadas densidad de drenajes (DD) diferentes y, por lo tanto, relieves diferentes. Para el parametro LD, la imagen del satelite SPOT presento mayor semejanza con las cartas 1:10.000 en las muestras con mayor DD. Para la muestra con menor DD, la imagen del Google Earth® se mostro mas proxima. Para a cuantificacion del NN hubo mayor semejanza entre los productos estudiados, siendo ese resultado dependiente de la resolucion espacial (nivel de detalle) del producto estudiado. Se concluye que es posible utilizar cualquiera de los productos analizados en la elaboracion de mapas hidrologicos y de nacientes para los mas variados trabajos, incluso para aceptacion de organos publicos que utilizan cartas topograficas como referencia. EnglishThe use of satellite imagery is becoming increasingly common in order to obtain environmental data, such as streams and springs. Thus, this study aimed to compare three different products used in manual (visual) mapping of drainage and springs: a SPOT mosaic of satellite images, Google Earth® images and, one Landsat 8 satellite image; each product with a different spatial resolution. Topographic maps of drainage network for Piracicaba, Sao Paulo, Brazil, on a 1:10,000 scale were used as a basis for comparison. Two factors have been analyzed for better comparison between products: drainage length (DL) and number of springs (SN). The analyses carried out were only of a quantitative character. Three circular samples with 2.75 km radius were arranged in different areas of the municipality of Piracicaba, where different drainage densities (DD) were observed and therefore different reliefs. For the DL parameter, the SPOT satellite image showed greater similarity to the 1:10,000 scale maps in the samples with higher DD, while for the sample with the lowest DD, the Google Earth® image was more similar. For SN quantification there was a greater similarity between the studied products, a result entirely dependent upon the studied product’s spatial resolution (detail level). It was concluded that it is possible to use any of the analyzed products to map hydrology and springs for various tasks, including for public agencies using topographic maps as a reference. portuguesO uso de imagens de satelite vem se tornando cada vez mais comum na obtencao de dados ambientais, como os cursos d’agua e as nascentes. O presente trabalho teve por objetivo comparar tres diferentes produtos utilizados no mapeamento manual (visual) de drenagens e nascentes: um mosaico de imagens do satelite SPOT, imagens do programa Google Earth® e uma imagem do satelite Landsat 8; cada produto com resolu- cao espacial diferente. Utilizou-se a rede de drenagem presente em cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacao. Para uma melhor comparacao entre os produtos, foram analisados dois fatores: comprimento de drenagem (CD) e numero de nascentes (NN), sendo as analises realizadas apenas em carater quantitativo. Para tanto, foram dispostas tres amostras circulares de 2,75km de raio em diferentes areas do municipio de Piracicaba aonde fossem observadas densidades de drenagem (DD) diferentes e, portanto, relevos diferentes. Para o parâmetro CD, a imagem do satelite SPOT apresentou maior semelhanca com as cartas 1:10.000 nas amostras com maior DD. Para a amostra com a menor DD, a imagem do Google Earth® mostrou-se mais pro- xima. Para a quantificacao do NN houve maior semelhanca entre os produtos estudados, sendo esse resultado dependente da resolucao espacial (nivel de detalhamento) do produto estudado. Concluiu-se que e possivel utilizar qualquer um dos produtos analisados na elaboracao de mapas hidrologicos e de nascentes, para os mais variados trabalhos, inclusive para aceitacao de orgaos publicos que utilizam cartas topograficas como referencia.</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="117902925"><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="117902925"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117902925; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117902925]").text(description); $(".js-view-count[data-work-id=117902925]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117902925; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117902925']"); 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" } } $('.js-work-strip[data-work-id=117902925]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117902925,"title":"Diferentes imágenes de satélite en el mapeamento visual de drenajes y nacientes en muestras circulares","translated_title":"","metadata":{"abstract":"espanolEl uso de imagenes de satelite viene siendo cada vez mas comun en la obtencion de datos ambientales, como los cursos del agua y las nacientes. El presente trabajo tuvo por objetivo comparar tres diferentes productos utilizados en el mapeo manual (visual) de drenajes y nacientes: un mosaico de ima- genes del satelite SPOT, imagenes del programa Google Earth® y una imagen del satelite Landsat 8; cada producto con resolucion espacial diferente. Se utilizo la rede de drenaje presente en cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacion. Para una mejor comparacion entre los productos, fueron analizados dos factores: longitud de drenaje (LD) y numero de nacientes (NN), los analisis se realizaron apenas en caracter cuantitativo. Para tanto, fueron dispuestas tres muestras circulares de 2,75km de radio en diferentes areas del municipio de Piracicaba donde serian observadas densidad de drenajes (DD) diferentes y, por lo tanto, relieves diferentes. Para el parametro LD, la imagen del satelite SPOT presento mayor semejanza con las cartas 1:10.000 en las muestras con mayor DD. Para la muestra con menor DD, la imagen del Google Earth® se mostro mas proxima. Para a cuantificacion del NN hubo mayor semejanza entre los productos estudiados, siendo ese resultado dependiente de la resolucion espacial (nivel de detalle) del producto estudiado. Se concluye que es posible utilizar cualquiera de los productos analizados en la elaboracion de mapas hidrologicos y de nacientes para los mas variados trabajos, incluso para aceptacion de organos publicos que utilizan cartas topograficas como referencia. EnglishThe use of satellite imagery is becoming increasingly common in order to obtain environmental data, such as streams and springs. Thus, this study aimed to compare three different products used in manual (visual) mapping of drainage and springs: a SPOT mosaic of satellite images, Google Earth® images and, one Landsat 8 satellite image; each product with a different spatial resolution. Topographic maps of drainage network for Piracicaba, Sao Paulo, Brazil, on a 1:10,000 scale were used as a basis for comparison. Two factors have been analyzed for better comparison between products: drainage length (DL) and number of springs (SN). The analyses carried out were only of a quantitative character. Three circular samples with 2.75 km radius were arranged in different areas of the municipality of Piracicaba, where different drainage densities (DD) were observed and therefore different reliefs. For the DL parameter, the SPOT satellite image showed greater similarity to the 1:10,000 scale maps in the samples with higher DD, while for the sample with the lowest DD, the Google Earth® image was more similar. For SN quantification there was a greater similarity between the studied products, a result entirely dependent upon the studied product’s spatial resolution (detail level). It was concluded that it is possible to use any of the analyzed products to map hydrology and springs for various tasks, including for public agencies using topographic maps as a reference. portuguesO uso de imagens de satelite vem se tornando cada vez mais comum na obtencao de dados ambientais, como os cursos d’agua e as nascentes. O presente trabalho teve por objetivo comparar tres diferentes produtos utilizados no mapeamento manual (visual) de drenagens e nascentes: um mosaico de imagens do satelite SPOT, imagens do programa Google Earth® e uma imagem do satelite Landsat 8; cada produto com resolu- cao espacial diferente. Utilizou-se a rede de drenagem presente em cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacao. Para uma melhor comparacao entre os produtos, foram analisados dois fatores: comprimento de drenagem (CD) e numero de nascentes (NN), sendo as analises realizadas apenas em carater quantitativo. Para tanto, foram dispostas tres amostras circulares de 2,75km de raio em diferentes areas do municipio de Piracicaba aonde fossem observadas densidades de drenagem (DD) diferentes e, portanto, relevos diferentes. Para o parâmetro CD, a imagem do satelite SPOT apresentou maior semelhanca com as cartas 1:10.000 nas amostras com maior DD. Para a amostra com a menor DD, a imagem do Google Earth® mostrou-se mais pro- xima. Para a quantificacao do NN houve maior semelhanca entre os produtos estudados, sendo esse resultado dependente da resolucao espacial (nivel de detalhamento) do produto estudado. Concluiu-se que e possivel utilizar qualquer um dos produtos analisados na elaboracao de mapas hidrologicos e de nascentes, para os mais variados trabalhos, inclusive para aceitacao de orgaos publicos que utilizam cartas topograficas como referencia.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Interciencia: Revista de ciencia y tecnología de América"},"translated_abstract":"espanolEl uso de imagenes de satelite viene siendo cada vez mas comun en la obtencion de datos ambientales, como los cursos del agua y las nacientes. El presente trabajo tuvo por objetivo comparar tres diferentes productos utilizados en el mapeo manual (visual) de drenajes y nacientes: un mosaico de ima- genes del satelite SPOT, imagenes del programa Google Earth® y una imagen del satelite Landsat 8; cada producto con resolucion espacial diferente. Se utilizo la rede de drenaje presente en cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacion. Para una mejor comparacion entre los productos, fueron analizados dos factores: longitud de drenaje (LD) y numero de nacientes (NN), los analisis se realizaron apenas en caracter cuantitativo. Para tanto, fueron dispuestas tres muestras circulares de 2,75km de radio en diferentes areas del municipio de Piracicaba donde serian observadas densidad de drenajes (DD) diferentes y, por lo tanto, relieves diferentes. Para el parametro LD, la imagen del satelite SPOT presento mayor semejanza con las cartas 1:10.000 en las muestras con mayor DD. Para la muestra con menor DD, la imagen del Google Earth® se mostro mas proxima. Para a cuantificacion del NN hubo mayor semejanza entre los productos estudiados, siendo ese resultado dependiente de la resolucion espacial (nivel de detalle) del producto estudiado. Se concluye que es posible utilizar cualquiera de los productos analizados en la elaboracion de mapas hidrologicos y de nacientes para los mas variados trabajos, incluso para aceptacion de organos publicos que utilizan cartas topograficas como referencia. EnglishThe use of satellite imagery is becoming increasingly common in order to obtain environmental data, such as streams and springs. Thus, this study aimed to compare three different products used in manual (visual) mapping of drainage and springs: a SPOT mosaic of satellite images, Google Earth® images and, one Landsat 8 satellite image; each product with a different spatial resolution. Topographic maps of drainage network for Piracicaba, Sao Paulo, Brazil, on a 1:10,000 scale were used as a basis for comparison. Two factors have been analyzed for better comparison between products: drainage length (DL) and number of springs (SN). The analyses carried out were only of a quantitative character. Three circular samples with 2.75 km radius were arranged in different areas of the municipality of Piracicaba, where different drainage densities (DD) were observed and therefore different reliefs. For the DL parameter, the SPOT satellite image showed greater similarity to the 1:10,000 scale maps in the samples with higher DD, while for the sample with the lowest DD, the Google Earth® image was more similar. For SN quantification there was a greater similarity between the studied products, a result entirely dependent upon the studied product’s spatial resolution (detail level). It was concluded that it is possible to use any of the analyzed products to map hydrology and springs for various tasks, including for public agencies using topographic maps as a reference. portuguesO uso de imagens de satelite vem se tornando cada vez mais comum na obtencao de dados ambientais, como os cursos d’agua e as nascentes. O presente trabalho teve por objetivo comparar tres diferentes produtos utilizados no mapeamento manual (visual) de drenagens e nascentes: um mosaico de imagens do satelite SPOT, imagens do programa Google Earth® e uma imagem do satelite Landsat 8; cada produto com resolu- cao espacial diferente. Utilizou-se a rede de drenagem presente em cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacao. Para uma melhor comparacao entre os produtos, foram analisados dois fatores: comprimento de drenagem (CD) e numero de nascentes (NN), sendo as analises realizadas apenas em carater quantitativo. Para tanto, foram dispostas tres amostras circulares de 2,75km de raio em diferentes areas do municipio de Piracicaba aonde fossem observadas densidades de drenagem (DD) diferentes e, portanto, relevos diferentes. Para o parâmetro CD, a imagem do satelite SPOT apresentou maior semelhanca com as cartas 1:10.000 nas amostras com maior DD. Para a amostra com a menor DD, a imagem do Google Earth® mostrou-se mais pro- xima. Para a quantificacao do NN houve maior semelhanca entre os produtos estudados, sendo esse resultado dependente da resolucao espacial (nivel de detalhamento) do produto estudado. Concluiu-se que e possivel utilizar qualquer um dos produtos analisados na elaboracao de mapas hidrologicos e de nascentes, para os mais variados trabalhos, inclusive para aceitacao de orgaos publicos que utilizam cartas topograficas como referencia.","internal_url":"https://www.academia.edu/117902925/Diferentes_im%C3%A1genes_de_sat%C3%A9lite_en_el_mapeamento_visual_de_drenajes_y_nacientes_en_muestras_circulares","translated_internal_url":"","created_at":"2024-04-22T15:01:06.357-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":45518824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Diferentes_imágenes_de_satélite_en_el_mapeamento_visual_de_drenajes_y_nacientes_en_muestras_circulares","translated_slug":"","page_count":null,"language":"es","content_type":"Work","summary":"espanolEl uso de imagenes de satelite viene siendo cada vez mas comun en la obtencion de datos ambientales, como los cursos del agua y las nacientes. El presente trabajo tuvo por objetivo comparar tres diferentes productos utilizados en el mapeo manual (visual) de drenajes y nacientes: un mosaico de ima- genes del satelite SPOT, imagenes del programa Google Earth® y una imagen del satelite Landsat 8; cada producto con resolucion espacial diferente. Se utilizo la rede de drenaje presente en cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacion. Para una mejor comparacion entre los productos, fueron analizados dos factores: longitud de drenaje (LD) y numero de nacientes (NN), los analisis se realizaron apenas en caracter cuantitativo. Para tanto, fueron dispuestas tres muestras circulares de 2,75km de radio en diferentes areas del municipio de Piracicaba donde serian observadas densidad de drenajes (DD) diferentes y, por lo tanto, relieves diferentes. Para el parametro LD, la imagen del satelite SPOT presento mayor semejanza con las cartas 1:10.000 en las muestras con mayor DD. Para la muestra con menor DD, la imagen del Google Earth® se mostro mas proxima. Para a cuantificacion del NN hubo mayor semejanza entre los productos estudiados, siendo ese resultado dependiente de la resolucion espacial (nivel de detalle) del producto estudiado. Se concluye que es posible utilizar cualquiera de los productos analizados en la elaboracion de mapas hidrologicos y de nacientes para los mas variados trabajos, incluso para aceptacion de organos publicos que utilizan cartas topograficas como referencia. EnglishThe use of satellite imagery is becoming increasingly common in order to obtain environmental data, such as streams and springs. Thus, this study aimed to compare three different products used in manual (visual) mapping of drainage and springs: a SPOT mosaic of satellite images, Google Earth® images and, one Landsat 8 satellite image; each product with a different spatial resolution. Topographic maps of drainage network for Piracicaba, Sao Paulo, Brazil, on a 1:10,000 scale were used as a basis for comparison. Two factors have been analyzed for better comparison between products: drainage length (DL) and number of springs (SN). The analyses carried out were only of a quantitative character. Three circular samples with 2.75 km radius were arranged in different areas of the municipality of Piracicaba, where different drainage densities (DD) were observed and therefore different reliefs. For the DL parameter, the SPOT satellite image showed greater similarity to the 1:10,000 scale maps in the samples with higher DD, while for the sample with the lowest DD, the Google Earth® image was more similar. For SN quantification there was a greater similarity between the studied products, a result entirely dependent upon the studied product’s spatial resolution (detail level). It was concluded that it is possible to use any of the analyzed products to map hydrology and springs for various tasks, including for public agencies using topographic maps as a reference. portuguesO uso de imagens de satelite vem se tornando cada vez mais comum na obtencao de dados ambientais, como os cursos d’agua e as nascentes. O presente trabalho teve por objetivo comparar tres diferentes produtos utilizados no mapeamento manual (visual) de drenagens e nascentes: um mosaico de imagens do satelite SPOT, imagens do programa Google Earth® e uma imagem do satelite Landsat 8; cada produto com resolu- cao espacial diferente. Utilizou-se a rede de drenagem presente em cartas topograficas de Piracicaba, Sao Paulo, Brasil, escala 1:10.000, como base para comparacao. Para uma melhor comparacao entre os produtos, foram analisados dois fatores: comprimento de drenagem (CD) e numero de nascentes (NN), sendo as analises realizadas apenas em carater quantitativo. Para tanto, foram dispostas tres amostras circulares de 2,75km de raio em diferentes areas do municipio de Piracicaba aonde fossem observadas densidades de drenagem (DD) diferentes e, portanto, relevos diferentes. Para o parâmetro CD, a imagem do satelite SPOT apresentou maior semelhanca com as cartas 1:10.000 nas amostras com maior DD. Para a amostra com a menor DD, a imagem do Google Earth® mostrou-se mais pro- xima. Para a quantificacao do NN houve maior semelhanca entre os produtos estudados, sendo esse resultado dependente da resolucao espacial (nivel de detalhamento) do produto estudado. Concluiu-se que e possivel utilizar qualquer um dos produtos analisados na elaboracao de mapas hidrologicos e de nascentes, para os mais variados trabalhos, inclusive para aceitacao de orgaos publicos que utilizam cartas topograficas como referencia.","owner":{"id":45518824,"first_name":"Peterson","middle_initials":null,"last_name":"Fiorio","page_name":"PetersonFiorio","domain_name":"independent","created_at":"2016-03-21T04:17:07.025-07:00","display_name":"Peterson Fiorio","url":"https://independent.academia.edu/PetersonFiorio"},"attachments":[],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics"},{"id":951,"name":"Humanities","url":"https://www.academia.edu/Documents/in/Humanities"}],"urls":[{"id":41314687,"url":"https://dialnet.unirioja.es/servlet/articulo?codigo=5413423"}]}, 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="117902924"><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/117902924/Analysis_of_Total_Soil_Nutrient_Content_with_X_ray_Fluorescence_Spectroscopy_XRF_Assessing_Different_Predictive_Modeling_Strategies_and_Auxiliary_Variables"><img alt="Research paper thumbnail of Analysis of Total Soil Nutrient Content with X-ray Fluorescence Spectroscopy (XRF): Assessing Different Predictive Modeling Strategies and Auxiliary Variables" class="work-thumbnail" src="https://attachments.academia-assets.com/113650098/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" 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Forest","url":"https://www.academia.edu/Documents/in/Random_Forest"},{"id":81034,"name":"Predictive Modelling","url":"https://www.academia.edu/Documents/in/Predictive_Modelling"},{"id":123230,"name":"Regression Analysis","url":"https://www.academia.edu/Documents/in/Regression_Analysis"},{"id":358917,"name":"Partial Least Squares Regression","url":"https://www.academia.edu/Documents/in/Partial_Least_Squares_Regression"},{"id":795003,"name":"Linear Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression"}],"urls":[{"id":41314686,"url":"https://www.mdpi.com/2624-7402/5/2/43/pdf?version=1680538413"}]}, 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="117902923"><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/117902923/Orbital_and_laboratory_spectral_data_to_optimize_soil_analysis"><img alt="Research paper thumbnail of Orbital and laboratory spectral data to optimize soil analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/113650096/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/117902923/Orbital_and_laboratory_spectral_data_to_optimize_soil_analysis">Orbital and laboratory spectral data to optimize soil analysis</a></div><div class="wp-workCard_item"><span>Scientia Agricola</span><span>, Apr 1, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Traditional soil analyses are time-consuming with high cost and environmental risks, thus the use...</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">Traditional soil analyses are time-consuming with high cost and environmental risks, thus the use of new technologies such as remote sensing have to be estimulated. The purpose of this work was to quantify soil attributes by laboratory and orbital sensors as a non-destructive and a nonpollutant method. The study area was in the region of Barra Bonita, state of São Paulo, Brazil, in a 473 ha bare soil area. A sampling grid was established (100 × 100 m), with a total of 474 locations and a total of 948 soil samples. Each location was georeferenced and soil samples were collected for analysis. Reflectance data for each soil sample was measured with a laboratory sensor (450 to 2,500 nm). For the same locations, reflectance data was obtained from a TM-Landsat-5 image. Multiple linear regression equations were developed for 50% of the samples. Two models were developed: one for spectroradiometric laboratory data and the second for TM-Landsat-5 orbital data. The remaining 50% of the samples were used to validate the models. The test compared the attribute content quantified by the spectral models and that determined in the laboratory (conventional methods). The highest coefficients of determination for the laboratory data were for clay content (R 2 = 0.86) and sand (R 2 = 0.82) and for the orbital data (R 2 = 0.61 and 0.63, respectively). By using the present methodology, it was possible to estimate CEC (R 2 = 0.64) by the laboratory sensor. Laboratory and orbital sensors can optimize time, costs and environment pollutants when associated with traditional soil analysis.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c29f12c0929fba479776419b02c61c17" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113650096,"asset_id":117902923,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113650096/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="117902923"><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="117902923"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117902923; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117902923]").text(description); $(".js-view-count[data-work-id=117902923]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117902923; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117902923']"); 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: "c29f12c0929fba479776419b02c61c17" } } $('.js-work-strip[data-work-id=117902923]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117902923,"title":"Orbital and laboratory spectral data to optimize soil analysis","translated_title":"","metadata":{"publisher":"University of São Paulo","grobid_abstract":"Traditional soil analyses are time-consuming with high cost and environmental risks, thus the use of new technologies such as remote sensing have to be estimulated. The purpose of this work was to quantify soil attributes by laboratory and orbital sensors as a non-destructive and a nonpollutant method. The study area was in the region of Barra Bonita, state of São Paulo, Brazil, in a 473 ha bare soil area. A sampling grid was established (100 × 100 m), with a total of 474 locations and a total of 948 soil samples. Each location was georeferenced and soil samples were collected for analysis. Reflectance data for each soil sample was measured with a laboratory sensor (450 to 2,500 nm). For the same locations, reflectance data was obtained from a TM-Landsat-5 image. Multiple linear regression equations were developed for 50% of the samples. Two models were developed: one for spectroradiometric laboratory data and the second for TM-Landsat-5 orbital data. The remaining 50% of the samples were used to validate the models. The test compared the attribute content quantified by the spectral models and that determined in the laboratory (conventional methods). The highest coefficients of determination for the laboratory data were for clay content (R 2 = 0.86) and sand (R 2 = 0.82) and for the orbital data (R 2 = 0.61 and 0.63, respectively). By using the present methodology, it was possible to estimate CEC (R 2 = 0.64) by the laboratory sensor. Laboratory and orbital sensors can optimize time, costs and environment pollutants when associated with traditional soil analysis.","publication_date":{"day":1,"month":4,"year":2009,"errors":{}},"publication_name":"Scientia Agricola","grobid_abstract_attachment_id":113650096},"translated_abstract":null,"internal_url":"https://www.academia.edu/117902923/Orbital_and_laboratory_spectral_data_to_optimize_soil_analysis","translated_internal_url":"","created_at":"2024-04-22T15:01:05.953-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":45518824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":113650096,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113650096/thumbnails/1.jpg","file_name":"15.pdf","download_url":"https://www.academia.edu/attachments/113650096/download_file","bulk_download_file_name":"Orbital_and_laboratory_spectral_data_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113650096/15-libre.pdf?1713823547=\u0026response-content-disposition=attachment%3B+filename%3DOrbital_and_laboratory_spectral_data_to.pdf\u0026Expires=1741749273\u0026Signature=D5Ua-GiNmVpEitOAawT8yktIjMCBGZ4AGTlTvDzE8-met~vgZLU6vzs7hnlhqgIXSASzYbwunn91H1YR9-nL33UVeJmdfg~D3oalxmkHFRxDGITN7u5nQ50kApieTJtlHHJwvTEkS5CBcNJsq~Z3xtITioWON8VT-HfS~yzpkxrGSBEfIylwg9-TV7yMZGq7P202MoA1GzAw0uIQVSci1J-mho~RnT3B3tSrQJCk8YDYbajRtImuAox1vssrvhzM65kUrdRFclwROB5-NLcC95LbawLR9F1tiEV~vZ2deijCV9tfY9C3RFzB0tmiLz8hdhMmVlNgbPHoYukADO6ADw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Orbital_and_laboratory_spectral_data_to_optimize_soil_analysis","translated_slug":"","page_count":8,"language":"en","content_type":"Work","summary":"Traditional soil analyses are time-consuming with high cost and environmental risks, thus the use of new technologies such as remote sensing have to be estimulated. The purpose of this work was to quantify soil attributes by laboratory and orbital sensors as a non-destructive and a nonpollutant method. The study area was in the region of Barra Bonita, state of São Paulo, Brazil, in a 473 ha bare soil area. A sampling grid was established (100 × 100 m), with a total of 474 locations and a total of 948 soil samples. Each location was georeferenced and soil samples were collected for analysis. Reflectance data for each soil sample was measured with a laboratory sensor (450 to 2,500 nm). For the same locations, reflectance data was obtained from a TM-Landsat-5 image. Multiple linear regression equations were developed for 50% of the samples. Two models were developed: one for spectroradiometric laboratory data and the second for TM-Landsat-5 orbital data. The remaining 50% of the samples were used to validate the models. The test compared the attribute content quantified by the spectral models and that determined in the laboratory (conventional methods). The highest coefficients of determination for the laboratory data were for clay content (R 2 = 0.86) and sand (R 2 = 0.82) and for the orbital data (R 2 = 0.61 and 0.63, respectively). By using the present methodology, it was possible to estimate CEC (R 2 = 0.64) by the laboratory sensor. 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And nowadays, all ...</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">Urban sprawl is the primary responsible for the current disposition of cities. And nowadays, all the importance that the conservation of the vegetal cover has is acknowledged, since it interferes with the quality of life, retains pollutant particles and the lack of it causes thermal discomfort. In this way the present work aims at analyzing the relationship between the presence of vegetation and the average temperature of the districts of the city of Piracicaba, in the state of São Paulo. RapidEye sensor image was used for mapping and quantification of vegetation and Landsat 8 for temperature quantification at 4 different times of the year. When it comes to vegetation, the city of Piracicaba has it in 40.44% of its territory. With the spatialization of the temperature, it is possible to observe its variation, presenting a thermal amplitude of 6.4° C in autumn. The areas that present the lowest rates of green areas stand with the highest temperatures, where the vertical constructions prevail, coupled with reduced tree cover.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="31ac01cd10420b9d513ebca8eb445fa0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113650140,"asset_id":117902922,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113650140/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="117902922"><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="117902922"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117902922; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117902922]").text(description); $(".js-view-count[data-work-id=117902922]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117902922; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117902922']"); 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: "31ac01cd10420b9d513ebca8eb445fa0" } } $('.js-work-strip[data-work-id=117902922]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117902922,"title":"Study of the Existing Relationship Between Temperature and Distribution of Vegetation Through the Use of Geoprocessment, in the City of Piracicaba, State of São Paulo","translated_title":"","metadata":{"publisher":"Brazilian Journal of Agriculture","ai_title_tag":"Temperature and Vegetation Distribution in Piracicaba, SP","grobid_abstract":"Urban sprawl is the primary responsible for the current disposition of cities. 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And nowadays, all the importance that the conservation of the vegetal cover has is acknowledged, since it interferes with the quality of life, retains pollutant particles and the lack of it causes thermal discomfort. In this way the present work aims at analyzing the relationship between the presence of vegetation and the average temperature of the districts of the city of Piracicaba, in the state of São Paulo. RapidEye sensor image was used for mapping and quantification of vegetation and Landsat 8 for temperature quantification at 4 different times of the year. When it comes to vegetation, the city of Piracicaba has it in 40.44% of its territory. With the spatialization of the temperature, it is possible to observe its variation, presenting a thermal amplitude of 6.4° C in autumn. 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