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A. Shaheen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gaza Strip faces economic and political issues such as conflict, siege and urbanization; all these have led to an increase in the air pollution over Gaza Strip. In this study, Particulate matter 10 (PM10) concentration over Gaza Strip has been estimated by Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper Plus (ETM+) data, based on a multispectral algorithm. Simultaneously, in-situ measurements for the corresponding particulate are acquired for selected time period. Landsat and ground data for eleven years are used to develop the algorithm while four years data (2002, 2006, 2010 and 2014) have been used to validate the results of algorithm. The developed algorithm gives highest regression, R coefficient value i.e. 0.86; RMSE value as 9.71 µg/m³; P values as 0. Average validation of algorithm show that calculated PM10 strongly correlates with measured PM10, indicating high efficiency of algorithm for the mapping of PM10 concentration during the years 2000 to 2014. Overall results show increase in minimum, maximum and average yearly PM10 concentrations, also presents similar trend over urban area. The rate of urbanization has been evaluated by supervised classification of the Landsat image. Urban sprawl from year 2000 to 2014 results in a high concentration of PM10 in the study area. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PM10" title="PM10">PM10</a>, <a href="https://publications.waset.org/abstracts/search?q=landsat" title=" landsat"> landsat</a>, <a href="https://publications.waset.org/abstracts/search?q=atmospheric%20reflectance" title=" atmospheric reflectance"> atmospheric reflectance</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaza%20strip" title=" Gaza strip"> Gaza strip</a>, <a href="https://publications.waset.org/abstracts/search?q=urbanization" title=" urbanization "> urbanization </a> </p> <a href="https://publications.waset.org/abstracts/69422/estimating-air-particulate-matter-10-using-satellite-data-and-analyzing-its-annual-temporal-pattern-over-gaza-strip-palestine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69422.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">260</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">215</span> Estimation of PM10 Concentration Using Ground Measurements and Landsat 8 OLI Satellite Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salah%20Abdul%20Hameed%20Saleh">Salah Abdul Hameed Saleh</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghada%20Hasan"> Ghada Hasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this work is to produce an empirical model for the determination of particulate matter (PM10) concentration in the atmosphere using visible bands of Landsat 8 OLI satellite image over Kirkuk city- IRAQ. The suggested algorithm is established on the aerosol optical reflectance model. The reflectance model is a function of the optical properties of the atmosphere, which can be related to its concentrations. The concentration of PM10 measurements was collected using Particle Mass Profiler and Counter in a Single Handheld Unit (Aerocet 531) meter simultaneously by the Landsat 8 OLI satellite image date. The PM10 measurement locations were defined by a handheld global positioning system (GPS). The obtained reflectance values for visible bands (Coastal aerosol, Blue, Green and blue bands) of landsat 8 OLI image were correlated with in-suite measured PM10. The feasibility of the proposed algorithms was investigated based on the correlation coefficient (R) and root-mean-square error (RMSE) compared with the PM10 ground measurement data. A choice of our proposed multispectral model was founded on the highest value correlation coefficient (R) and lowest value of the root mean square error (RMSE) with PM10 ground data. The outcomes of this research showed that visible bands of Landsat 8 OLI were capable of calculating PM10 concentration with an acceptable level of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20pollution" title="air pollution">air pollution</a>, <a href="https://publications.waset.org/abstracts/search?q=PM10%20concentration" title=" PM10 concentration"> PM10 concentration</a>, <a href="https://publications.waset.org/abstracts/search?q=Lansat8%20OLI%20image" title=" Lansat8 OLI image"> Lansat8 OLI image</a>, <a href="https://publications.waset.org/abstracts/search?q=reflectance" title=" reflectance"> reflectance</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral%20algorithms" title=" multispectral algorithms"> multispectral algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Kirkuk%20area" title=" Kirkuk area"> Kirkuk area</a> </p> <a href="https://publications.waset.org/abstracts/19705/estimation-of-pm10-concentration-using-ground-measurements-and-landsat-8-oli-satellite-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19705.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">214</span> The Use of Thermal Infrared Wavelengths to Determine the Volcanic Soils</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Levent%20Basayigit">Levent Basayigit</a>, <a href="https://publications.waset.org/abstracts/search?q=Mert%20Dedeoglu"> Mert Dedeoglu</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadime%20Ozogul"> Fadime Ozogul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, an application was carried out to determine the Volcanic Soils by using remote sensing.  The study area was located on the Golcuk formation in Isparta-Turkey. The thermal bands of Landsat 7 image were used for processing. The implementation of the climate model that was based on the water index was used in ERDAS Imagine software together with pixel based image classification. Soil Moisture Index (SMI) was modeled by using the surface temperature (Ts) which was obtained from thermal bands and vegetation index (NDVI) derived from Landsat 7. Surface moisture values were grouped and classified by using scoring system. Thematic layers were compared together with the field studies. Consequently, different moisture levels for volcanic soils were indicator for determination and separation. Those thermal wavelengths are preferable bands for separation of volcanic soils using moisture and temperature models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Landsat%207" title="Landsat 7">Landsat 7</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20moisture%20index" title=" soil moisture index"> soil moisture index</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature%20models" title=" temperature models"> temperature models</a>, <a href="https://publications.waset.org/abstracts/search?q=volcanic%20soils" title=" volcanic soils"> volcanic soils</a> </p> <a href="https://publications.waset.org/abstracts/68582/the-use-of-thermal-infrared-wavelengths-to-determine-the-volcanic-soils" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68582.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">311</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">213</span> Research Analysis of Urban Area Expansion Based on Remote Sensing </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sheheryar%20Khan">Sheheryar Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Weidong%20Li"> Weidong Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Fanqian%20Meng"> Fanqian Meng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Urban Heat Island (UHI) effect is one of the foremost problems out of other ecological and socioeconomic issues in urbanization. Due to this phenomenon that human-made urban areas have replaced the rural landscape with the surface that increases thermal conductivity and urban warmth; as a result, the temperature in the city is higher than in the surrounding rural areas. To affect the evidence of this phenomenon in the Zhengzhou city area, an observation of the temperature variations in the urban area is done through a scientific method that has been followed. Landsat 8 satellite images were taken from 2013 to 2015 to calculate the effect of Urban Heat Island (UHI) along with the NPP-VRRIS night-time remote sensing data to analyze the result for a better understanding of the center of the built-up area. To further support the evidence, the correlation between land surface temperatures and the normalized difference vegetation index (NDVI) was calculated using the Red band 4 and Near-infrared band 5 of the Landsat 8 data. Mono-window algorithm was applied to retrieve the land surface temperature (LST) distribution from the Landsat 8 data using Band 10 and 11 accordingly to convert the top-of-atmosphere radiance (TOA) and to convert the satellite brightness temperature. Along with Landsat 8 data, NPP-VIIRS night-light data is preprocessed to get the research area data. The analysis between Landsat 8 data and NPP night-light data was taken to compare the output center of the Built-up area of Zhengzhou city. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=built-up%20area" title="built-up area">built-up area</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20surface%20temperature" title=" land surface temperature"> land surface temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=mono-window%20algorithm" title=" mono-window algorithm"> mono-window algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=NDVI" title=" NDVI"> NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold%20method" title=" threshold method"> threshold method</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhengzhou" title=" Zhengzhou"> Zhengzhou</a> </p> <a href="https://publications.waset.org/abstracts/133463/research-analysis-of-urban-area-expansion-based-on-remote-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133463.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">212</span> Remote Sensing and GIS-Based Environmental Monitoring by Extracting Land Surface Temperature of Abbottabad, Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malik%20Abid%20Hussain%20Khokhar">Malik Abid Hussain Khokhar</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Adnan%20Tahir"> Muhammad Adnan Tahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Hisham%20Bin%20Hafeez%20Awan"> Hisham Bin Hafeez Awan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Continuous environmental determinism and climatic change in the entire globe due to increasing land surface temperature (LST) has become a vital phenomenon nowadays. LST is accelerating because of increasing greenhouse gases in the environment which results of melting down ice caps, ice sheets and glaciers. It has not only worse effects on vegetation and water bodies of the region but has also severe impacts on monsoon areas in the form of capricious rainfall and monsoon failure extensive precipitation. Environment can be monitored with the help of various geographic information systems (GIS) based algorithms i.e. SC (Single), DA (Dual Angle), Mao, Sobrino and SW (Split Window). Estimation of LST is very much possible from digital image processing of satellite imagery. This paper will encompass extraction of LST of Abbottabad using SW technique of GIS and Remote Sensing over last ten years by means of Landsat 7 ETM+ (Environmental Thematic Mapper) and Landsat 8 vide their Thermal Infrared (TIR Sensor) and Optical Land Imager (OLI sensor less Landsat 7 ETM+) having 100 m TIR resolution and 30 m Spectral Resolutions. These sensors have two TIR bands each; their emissivity and spectral radiance will be used as input statistics in SW algorithm for LST extraction. Emissivity will be derived from Normalized Difference Vegetation Index (NDVI) threshold methods using 2-5 bands of OLI with the help of e-cognition software, and spectral radiance will be extracted TIR Bands (Band 10-11 and Band 6 of Landsat 7 ETM+). Accuracy of results will be evaluated by weather data as well. The successive research will have a significant role for all tires of governing bodies related to climate change departments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=environment" title="environment">environment</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat%208" title=" Landsat 8"> Landsat 8</a>, <a href="https://publications.waset.org/abstracts/search?q=SW%20Algorithm" title=" SW Algorithm"> SW Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=TIR" title=" TIR"> TIR</a> </p> <a href="https://publications.waset.org/abstracts/55617/remote-sensing-and-gis-based-environmental-monitoring-by-extracting-land-surface-temperature-of-abbottabad-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55617.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">359</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">211</span> Land Cover Change Analysis Using Remote Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tahir%20Ali%20Akbar">Tahir Ali Akbar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hirra%20Jabbar"> Hirra Jabbar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Land cover change analysis plays a significant role in understanding the trends of urban sprawl and land use transformation due to anthropogenic activities. In this study, the spatio-temporal dynamics of major land covers were analyzed in the last twenty years (1988-2016) for District Lahore located in the Punjab Province of Pakistan. The Landsat satellite imageries were downloaded from USGS Global Visualization Viewer of Earth Resources Observation and Science Center located in Sioux Falls, South Dakota USA. The imageries included: (i) Landsat TM-5 for 1988 and 2001; and (ii) Landsat-8 OLI for 2016. The raw digital numbers of Landsat-5 images were converted into spectral radiance and then planetary reflectance. The digital numbers of Landsat-8 image were directly converted into planetary reflectance. The normalized difference vegetation index (NDVI) was used to classify the processed images into six major classes of water, buit-up, barren land, shrub and grassland, sparse vegetation and dense vegetation. The NDVI output results were improved by visual interpretation using high-resolution satellite imageries. The results indicated that the built-up areas were increased to 21% in 2016 from 10% in 1988. The decrease in % areas was found in case of water, barren land and shrub & grassland. There were improvements in percentage of areas for the vegetation. The increasing trend of urban sprawl for Lahore requires implementation of GIS based spatial planning, monitoring and management system for its sustainable development. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20cover%20changes" title="land cover changes">land cover changes</a>, <a href="https://publications.waset.org/abstracts/search?q=NDVI" title=" NDVI"> NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20sprawl" title=" urban sprawl"> urban sprawl</a> </p> <a href="https://publications.waset.org/abstracts/67795/land-cover-change-analysis-using-remote-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67795.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">324</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">210</span> Land Use/Land Cover Mapping Using Landsat 8 and Sentinel-2 in a Mediterranean Landscape</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moschos%20Vogiatzis">Moschos Vogiatzis</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Perakis"> K. Perakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial-explicit and up-to-date land use/land cover information is fundamental for spatial planning, land management, sustainable development, and sound decision-making. In the last decade, many satellite-derived land cover products at different spatial, spectral, and temporal resolutions have been developed, such as the European Copernicus Land Cover product. However, more efficient and detailed information for land use/land cover is required at the regional or local scale. A typical Mediterranean basin with a complex landscape comprised of various forest types, crops, artificial surfaces, and wetlands was selected to test and develop our approach. In this study, we investigate the improvement of Copernicus Land Cover product (CLC2018) using Landsat 8 and Sentinel-2 pixel-based classification based on all available existing geospatial data (Forest Maps, LPIS, Natura2000 habitats, cadastral parcels, etc.). We examined and compared the performance of the Random Forest classifier for land use/land cover mapping. In total, 10 land use/land cover categories were recognized in Landsat 8 and 11 in Sentinel-2A. A comparison of the overall classification accuracies for 2018 shows that Landsat 8 classification accuracy was slightly higher than Sentinel-2A (82,99% vs. 80,30%). We concluded that the main land use/land cover types of CLC2018, even within a heterogeneous area, can be successfully mapped and updated according to CLC nomenclature. Future research should be oriented toward integrating spatiotemporal information from seasonal bands and spectral indexes in the classification process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use%2Fland%20cover" title=" land use/land cover"> land use/land cover</a>, <a href="https://publications.waset.org/abstracts/search?q=mapping" title=" mapping"> mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/152892/land-useland-cover-mapping-using-landsat-8-and-sentinel-2-in-a-mediterranean-landscape" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152892.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">133</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">209</span> Analysis on the Feasibility of Landsat 8 Imagery for Water Quality Parameters Assessment in an Oligotrophic Mediterranean Lake </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Markogianni">V. Markogianni</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Kalivas"> D. Kalivas</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Petropoulos"> G. Petropoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Dimitriou"> E. Dimitriou </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lake water quality monitoring in combination with the use of earth observation products constitutes a major component in many water quality monitoring programs. Landsat 8 images of Trichonis Lake (Greece) acquired on 30/10/2013 and 30/08/2014 were used in order to explore the possibility of Landsat 8 to estimate water quality parameters and particularly CDOM absorption at specific wavelengths, chlorophyll-<em>a</em> and nutrient concentrations in this oligotrophic freshwater body, characterized by inexistent quantitative, temporal and spatial variability. Water samples have been collected at 22 different stations, on late August of 2014 and the satellite image of the same date was used to statistically correlate the <em>in-situ</em> measurements with various combinations of Landsat 8 bands in order to develop algorithms that best describe those relationships and calculate accurately the aforementioned water quality components. Optimal models were applied to the image of late October of 2013 and the validation of the results was conducted through their comparison with the respective available <em>in-situ</em> data of 2013. Initial results indicated the limited ability of the Landsat 8 sensor to accurately estimate water quality components in an oligotrophic waterbody. As resulted by the validation process, ammonium concentrations were proved to be the most accurately estimated component (R = 0.7), followed by chl-<em>a </em>concentration (R = 0.5) and the CDOM absorption at 420 nm (R = 0.3). <em>In-situ</em> nitrate, nitrite, phosphate and total nitrogen concentrations of 2014 were measured as lower than the detection limit of the instrument used, hence no statistical elaboration was conducted. On the other hand, multiple linear regression among reflectance measures and total phosphorus concentrations resulted in low and statistical insignificant correlations. Our results were concurrent with other studies in international literature, indicating that estimations for eutrophic and mesotrophic lakes are more accurate than oligotrophic, owing to the lack of suspended particles that are detectable by satellite sensors. Nevertheless, although those predictive models, developed and applied to Trichonis oligotrophic lake are less accurate, may still be useful indicators of its water quality deterioration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landsat%208" title="landsat 8">landsat 8</a>, <a href="https://publications.waset.org/abstracts/search?q=oligotrophic%20lake" title=" oligotrophic lake"> oligotrophic lake</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality" title=" water quality"> water quality</a> </p> <a href="https://publications.waset.org/abstracts/79190/analysis-on-the-feasibility-of-landsat-8-imagery-for-water-quality-parameters-assessment-in-an-oligotrophic-mediterranean-lake" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79190.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">400</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">208</span> A Method to Estimate Wheat Yield Using Landsat Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zama%20Mahmood">Zama Mahmood</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increasing demand of food management, monitoring of the crop growth and forecasting its yield well before harvest is very important. These days, yield assessment together with monitoring of crop development and its growth are being identified with the help of satellite and remote sensing images. Studies using remote sensing data along with field survey validation reported high correlation between vegetation indices and yield. With the development of remote sensing technique, the detection of crop and its mechanism using remote sensing data on regional or global scales have become popular topics in remote sensing applications. Punjab, specially the southern Punjab region is extremely favourable for wheat production. But measuring the exact amount of wheat production is a tedious job for the farmers and workers using traditional ground based measurements. However, remote sensing can provide the most real time information. In this study, using the Normalized Differentiate Vegetation Index (NDVI) indicator developed from Landsat satellite images, the yield of wheat has been estimated during the season of 2013-2014 for the agricultural area around Bahawalpur. The average yield of the wheat was found 35 kg/acre by analysing field survey data. The field survey data is in fair agreement with the NDVI values extracted from Landsat images. A correlation between wheat production (ton) and number of wheat pixels has also been calculated which is in proportional pattern with each other. Also a strong correlation between the NDVI and wheat area was found (R2=0.71) which represents the effectiveness of the remote sensing tools for crop monitoring and production estimation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landsat" title="landsat">landsat</a>, <a href="https://publications.waset.org/abstracts/search?q=NDVI" title=" NDVI"> NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=yield" title=" yield"> yield</a> </p> <a href="https://publications.waset.org/abstracts/31728/a-method-to-estimate-wheat-yield-using-landsat-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31728.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">337</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">207</span> Estimating PM2.5 Concentrations Based on Landsat 8 Imagery and Historical Field Data over the Metropolitan Area of Mexico City</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20T.%20Sepulveda-Hirose">Rodrigo T. Sepulveda-Hirose</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20B.%20Carrera-Aguilar"> Ana B. Carrera-Aguilar</a>, <a href="https://publications.waset.org/abstracts/search?q=Francisco%20Andree%20Ramirez-Casas"> Francisco Andree Ramirez-Casas</a>, <a href="https://publications.waset.org/abstracts/search?q=Alondra%20Orozco-Gomez"> Alondra Orozco-Gomez</a>, <a href="https://publications.waset.org/abstracts/search?q=Miguel%20Angel%20Sanchez-Caro"> Miguel Angel Sanchez-Caro</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Herrera-Ventosa"> Carlos Herrera-Ventosa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> High concentrations of particulate matter in the atmosphere pose a threat to human health, especially over areas with high concentrations of population; however, field air pollution monitoring is expensive and time-consuming. In order to achieve reduced costs and global coverage of the whole urban area, remote sensing can be used. This study evaluates PM2.5 concentrations, over the Mexico City´s metropolitan area, are estimated using atmospheric reflectance from LANDSAT 8, satellite imagery and historical PM2.5 measurements of the Automatic Environmental Monitoring Network of Mexico City (RAMA). Through the processing of the available satellite images, a preliminary model was generated to evaluate the optimal bands for the generation of the final model for Mexico City. Work on the final model continues with the results of the preliminary model. It was found that infrared bands have helped to model in other cities, but the effectiveness that these bands could provide for the geographic and climatic conditions of Mexico City is still being evaluated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20pollution%20modeling" title="air pollution modeling">air pollution modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat%208" title=" Landsat 8"> Landsat 8</a>, <a href="https://publications.waset.org/abstracts/search?q=PM2.5" title=" PM2.5"> PM2.5</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/108009/estimating-pm25-concentrations-based-on-landsat-8-imagery-and-historical-field-data-over-the-metropolitan-area-of-mexico-city" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108009.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">207</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">206</span> Geographical Data Visualization Using Video Games Technologies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nizar%20Karim%20Uribe-Orihuela">Nizar Karim Uribe-Orihuela</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernando%20Brambila-Paz"> Fernando Brambila-Paz</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivette%20Caldelas"> Ivette Caldelas</a>, <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20Montufar-Chaveznava"> Rodrigo Montufar-Chaveznava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present the advances corresponding to the implementation of a strategy to visualize geographical data using a Software Development Kit (SDK) for video games. We use multispectral images from Landsat 7 platform and Laser Imaging Detection and Ranging (LIDAR) data from The National Institute of Geography and Statistics of Mexican (INEGI). We select a place of interest to visualize from Landsat platform and make some processing to the image (rotations, atmospheric correction and enhancement). The resulting image will be our gray scale color-map to fusion with the LIDAR data, which was selected using the same coordinates than in Landsat. The LIDAR data is translated to 8-bit raw data. Both images are fused in a software developed using Unity (an SDK employed for video games). The resulting image is then displayed and can be explored moving around. The idea is the software could be used for students of geology and geophysics at the Engineering School of the National University of Mexico. They will download the software and images corresponding to a geological place of interest to a smartphone and could virtually visit and explore the site with a virtual reality visor such as Google cardboard. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=virtual%20reality" title="virtual reality">virtual reality</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20technologies" title=" interactive technologies"> interactive technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=geographical%20data%20visualization" title=" geographical data visualization"> geographical data visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20games%20technologies" title=" video games technologies"> video games technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20material" title=" educational material"> educational material</a> </p> <a href="https://publications.waset.org/abstracts/79894/geographical-data-visualization-using-video-games-technologies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79894.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">250</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">205</span> Greenland Monitoring Using Vegetation Index: A Case Study of Lal Suhanra National Park</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rabia%20Munsaf%20Khan">Rabia Munsaf Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Eshrat%20Fatima"> Eshrat Fatima</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The analysis of the spatial extent and temporal change of vegetation cover using remotely sensed data is of critical importance to agricultural sciences. Pakistan, being an agricultural country depends on this resource as it makes 70% of the GDP. The case study is of Lal Suhanra National Park, which is not only the biggest forest reserve of Pakistan but also of Asia. The study is performed using different temporal images of Landsat. Also, the results of Landsat are cross-checked by using Sentinel-2 imagery as it has both higher spectral and spatial resolution. Vegetation can easily be detected using NDVI which is a common and widely used index. It is an important vegetation index, widely applied in research on global environmental and climatic change. The images are then classified to observe the change occurred over 15 years. Vegetation cover maps of 2000 and 2016 are used to generate the map of vegetation change detection for the respective years and to find out the changing pattern of vegetation cover. Also, the NDVI values aided in the detection of percentage decrease in vegetation cover. The study reveals that vegetation cover of the area has decreased significantly during the year 2000 and 2016. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Landsat" title="Landsat">Landsat</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20difference%20vegetation%20index%20%28NDVI%29" title=" normalized difference vegetation index (NDVI)"> normalized difference vegetation index (NDVI)</a>, <a href="https://publications.waset.org/abstracts/search?q=sentinel%202" title=" sentinel 2"> sentinel 2</a>, <a href="https://publications.waset.org/abstracts/search?q=Greenland%20monitoring" title=" Greenland monitoring"> Greenland monitoring</a> </p> <a href="https://publications.waset.org/abstracts/73688/greenland-monitoring-using-vegetation-index-a-case-study-of-lal-suhanra-national-park" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73688.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">317</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">204</span> Comparison of MODIS-Based Rice Extent Map and Landsat-Based Rice Classification Map in Determining Biomass Energy Potential of Rice Hull in Nueva Ecija, Philippines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Klathea%20Sevilla">Klathea Sevilla</a>, <a href="https://publications.waset.org/abstracts/search?q=Marjorie%20Remolador"> Marjorie Remolador</a>, <a href="https://publications.waset.org/abstracts/search?q=Bryan%20Baltazar"> Bryan Baltazar</a>, <a href="https://publications.waset.org/abstracts/search?q=Imee%20Saladaga"> Imee Saladaga</a>, <a href="https://publications.waset.org/abstracts/search?q=Loureal%20Camille%20Inocencio"> Loureal Camille Inocencio</a>, <a href="https://publications.waset.org/abstracts/search?q=Ma.%20Rosario%20Concepcion%20Ang"> Ma. Rosario Concepcion Ang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The underutilization of biomass resources in the Philippines, combined with its growing population and the rise in fossil fuel prices confirms demand for alternative energy sources. The goal of this paper is to provide a comparison of MODIS-based and Landsat-based agricultural land cover maps when used in the estimation of rice hull’s available energy potential. Biomass resource assessment was done using mathematical models and remote sensing techniques employed in a GIS platform. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomass" title="biomass">biomass</a>, <a href="https://publications.waset.org/abstracts/search?q=geographic%20information%20system%20%28GIS%29" title=" geographic information system (GIS)"> geographic information system (GIS)</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=renewable%20energy" title=" renewable energy"> renewable energy</a> </p> <a href="https://publications.waset.org/abstracts/40771/comparison-of-modis-based-rice-extent-map-and-landsat-based-rice-classification-map-in-determining-biomass-energy-potential-of-rice-hull-in-nueva-ecija-philippines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40771.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">483</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">203</span> Landsat 8-TIRS NEΔT at Kīlauea Volcano and the Active East Rift Zone, Hawaii</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Flora%20Paganelli">Flora Paganelli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The radiometric performance of remotely sensed images is important for volcanic monitoring. The Thermal Infrared Sensor (TIRS) on-board Landsat 8 was designed with specific requirements in regard to the noise-equivalent change in temperature (NEΔT) at ≤ 0.4 K at 300 K for the two thermal infrared bands B10 and B11. This study investigated the on-orbit NEΔT of the TIRS two bands from a scene-based method using clear-sky images over the volcanic activity of Kīlauea Volcano and the active East Rift Zone (Hawaii), in order to optimize the use of TIRS data. Results showed that the NEΔTs of the two bands exceeded the design specification by an order of magnitude at 300 K. Both separate bands and split window algorithm were examined to estimate the effect of NEΔT on the land surface temperature (LST) retrieval, and NEΔT contribution to the final LST error. These results were also useful in the current efforts to assess the requirements for volcanology research campaign using the Hyperspectral Infrared Imager (HyspIRI) whose airborne prototype MODIS/ASTER instruments is plan to be flown by NASA as a single campaign to the Hawaiian Islands in support of volcanology and coastal area monitoring in 2016. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landsat%208" title="landsat 8">landsat 8</a>, <a href="https://publications.waset.org/abstracts/search?q=radiometric%20performance" title=" radiometric performance"> radiometric performance</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20infrared%20sensor%20%28TIRS%29" title=" thermal infrared sensor (TIRS)"> thermal infrared sensor (TIRS)</a>, <a href="https://publications.waset.org/abstracts/search?q=volcanology" title=" volcanology "> volcanology </a> </p> <a href="https://publications.waset.org/abstracts/27723/landsat-8-tirs-nedt-at-kilauea-volcano-and-the-active-east-rift-zone-hawaii" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27723.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">247</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">202</span> Comparing Remote Sensing and in Situ Analyses of Test Wheat Plants as Means for Optimizing Data Collection in Precision Agriculture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Endalkachew%20Abebe%20Kebede">Endalkachew Abebe Kebede</a>, <a href="https://publications.waset.org/abstracts/search?q=Bojin%20Bojinov"> Bojin Bojinov</a>, <a href="https://publications.waset.org/abstracts/search?q=Andon%20Vasilev%20Andonov"> Andon Vasilev Andonov</a>, <a href="https://publications.waset.org/abstracts/search?q=Orhan%20Dengiz"> Orhan Dengiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remote sensing has a potential application in assessing and monitoring the plants' biophysical properties using the spectral responses of plants and soils within the electromagnetic spectrum. However, only a few reports compare the performance of different remote sensing sensors against in-situ field spectral measurement. The current study assessed the potential applications of open data source satellite images (Sentinel 2 and Landsat 9) in estimating the biophysical properties of the wheat crop on a study farm found in the village of OvchaMogila. A Landsat 9 (30 m resolution) and Sentinel-2 (10 m resolution) satellite images with less than 10% cloud cover have been extracted from the open data sources for the period of December 2021 to April 2022. An Unmanned Aerial Vehicle (UAV) has been used to capture the spectral response of plant leaves. In addition, SpectraVue 710s Leaf Spectrometer was used to measure the spectral response of the crop in April at five different locations within the same field. The ten most common vegetation indices have been selected and calculated based on the reflectance wavelength range of remote sensing tools used. The soil samples have been collected in eight different locations within the farm plot. The different physicochemical properties of the soil (pH, texture, N, P₂O₅, and K₂O) have been analyzed in the laboratory. The finer resolution images from the UAV and the Leaf Spectrometer have been used to validate the satellite images. The performance of different sensors has been compared based on the measured leaf spectral response and the extracted vegetation indices using the five sampling points. A scatter plot with the coefficient of determination (R2) and Root Mean Square Error (RMSE) and the correlation (r) matrix prepared using the corr and heatmap python libraries have been used for comparing the performance of Sentinel 2 and Landsat 9 VIs compared to the drone and SpectraVue 710s spectrophotometer. The soil analysis revealed the study farm plot is slightly alkaline (8.4 to 8.52). The soil texture of the study farm is dominantly Clay and Clay Loam.The vegetation indices (VIs) increased linearly with the growth of the plant. Both the scatter plot and the correlation matrix showed that Sentinel 2 vegetation indices have a relatively better correlation with the vegetation indices of the Buteo dronecompared to the Landsat 9. The Landsat 9 vegetation indices somewhat align better with the leaf spectrometer. Generally, the Sentinel 2 showed a better performance than the Landsat 9. Further study with enough field spectral sampling and repeated UAV imaging is required to improve the quality of the current study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landsat%209" title="landsat 9">landsat 9</a>, <a href="https://publications.waset.org/abstracts/search?q=leaf%20spectrometer" title=" leaf spectrometer"> leaf spectrometer</a>, <a href="https://publications.waset.org/abstracts/search?q=sentinel%202" title=" sentinel 2"> sentinel 2</a>, <a href="https://publications.waset.org/abstracts/search?q=UAV" title=" UAV"> UAV</a> </p> <a href="https://publications.waset.org/abstracts/152005/comparing-remote-sensing-and-in-situ-analyses-of-test-wheat-plants-as-means-for-optimizing-data-collection-in-precision-agriculture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152005.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">113</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">201</span> Integrating Time-Series and High-Spatial Remote Sensing Data Based on Multilevel Decision Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xudong%20Guan">Xudong Guan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ainong%20Li"> Ainong Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaohuan%20Liu"> Gaohuan Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chong%20Huang"> Chong Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20Zhao"> Wei Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with a high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that is the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title="image classification">image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20fusion" title=" decision fusion"> decision fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-temporal" title=" multi-temporal"> multi-temporal</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/112195/integrating-time-series-and-high-spatial-remote-sensing-data-based-on-multilevel-decision-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112195.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">130</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">200</span> Impacts of Urbanization on Forest and Agriculture Areas in Savannakhet Province, Lao People's Democratic Republic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chittana%20Phompila">Chittana Phompila</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The current increased population pushes increasing demands for natural resources and living space. In Laos, urban areas have been expanding rapidly in recent years. The rapid urbanization can have negative impacts on landscapes, including forest and agriculture lands. The primary objective of this research were to map current urban areas in a large city in Savannakhet province, in Laos, 2) to compare changes in urbanization between 1990 and 2018, and 3) to estimate forest and agriculture areas lost due to expansions of urban areas during the last over twenty years within study area. Landsat 8 data was used and existing GIS data was collected including spatial data on rivers, lakes, roads, vegetated areas and other land use/land covers). GIS data was obtained from the government sectors. Object based classification (OBC) approach was applied in ECognition for image processing and analysis of urban area using. Historical data from other Landsat instruments (Landsat 5 and 7) were used to allow us comparing changes in urbanization in 1990, 2000, 2010 and 2018 in this study area. Only three main land cover classes were focused and classified, namely forest, agriculture and urban areas. Change detection approach was applied to illustrate changes in built-up areas in these periods. Our study shows that the overall accuracy of map was 95% assessed, kappa~ 0.8. It is found that that there is an ineffective control over forest and land-use conversions from forests and agriculture to urban areas in many main cities across the province. A large area of agriculture and forest has been decreased due to this conversion. Uncontrolled urban expansion and inappropriate land use planning can lead to creating a pressure in our resource utilisation. As consequence, it can lead to food insecurity and national economic downturn in a long term. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=urbanisation" title="urbanisation">urbanisation</a>, <a href="https://publications.waset.org/abstracts/search?q=forest%20cover" title=" forest cover"> forest cover</a>, <a href="https://publications.waset.org/abstracts/search?q=agriculture%20areas" title=" agriculture areas"> agriculture areas</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat%208%20imagery" title=" Landsat 8 imagery"> Landsat 8 imagery</a> </p> <a href="https://publications.waset.org/abstracts/91584/impacts-of-urbanization-on-forest-and-agriculture-areas-in-savannakhet-province-lao-peoples-democratic-republic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91584.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">199</span> Geostatistical Models to Correct Salinity of Soils from Landsat Satellite Sensor: Application to the Oran Region, Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dehni%20Abdellatif">Dehni Abdellatif</a>, <a href="https://publications.waset.org/abstracts/search?q=Lounis%20Mourad"> Lounis Mourad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The new approach of applied spatial geostatistics in materials sciences, agriculture accuracy, agricultural statistics, permitted an apprehension of managing and monitoring the water and groundwater qualities in a relationship with salt-affected soil. The anterior experiences concerning data acquisition, spatial-preparation studies on optical and multispectral data has facilitated the integration of correction models of electrical conductivity related with soils temperature (horizons of soils). For tomography apprehension, this physical parameter has been extracted from calibration of the thermal band (LANDSAT ETM+6) with a radiometric correction. Our study area is Oran region (Northern West of Algeria). Different spectral indices are determined such as salinity and sodicity index, the Combined Spectral Reflectance Index (CSRI), Normalized Difference Vegetation Index (NDVI), emissivity, Albedo, and Sodium Adsorption Ratio (SAR). The approach of geostatistical modeling of electrical conductivity (salinity), appears to be a useful decision support system for estimating corrected electrical resistivity related to the temperature of surface soils, according to the conversion models by substitution, the reference temperature at 25°C (where hydrochemical data are collected with this constraint). The Brightness temperatures extracted from satellite reflectance (LANDSAT ETM+) are used in consistency models to estimate electrical resistivity. The confusions that arise from the effects of salt stress and water stress removed followed by seasonal application of the geostatistical analysis in Geographic Information System (GIS) techniques investigation and monitoring the variation of the electrical conductivity in the alluvial aquifer of Es-Sénia for the salt-affected soil. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geostatistical%20modelling" title="geostatistical modelling">geostatistical modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=landsat" title=" landsat"> landsat</a>, <a href="https://publications.waset.org/abstracts/search?q=brightness%20temperature" title=" brightness temperature"> brightness temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=conductivity" title=" conductivity"> conductivity</a> </p> <a href="https://publications.waset.org/abstracts/24098/geostatistical-models-to-correct-salinity-of-soils-from-landsat-satellite-sensor-application-to-the-oran-region-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24098.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">198</span> Study of Land Use Land Cover Change of Bhimbetka with Temporal Satellite Data and Information Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pranita%20Shivankar">Pranita Shivankar</a>, <a href="https://publications.waset.org/abstracts/search?q=Devashree%20Hardas"> Devashree Hardas</a>, <a href="https://publications.waset.org/abstracts/search?q=Prabodhachandra%20Deshmukh"> Prabodhachandra Deshmukh</a>, <a href="https://publications.waset.org/abstracts/search?q=Arun%20Suryavanshi"> Arun Suryavanshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bhimbetka Rock Shelters is the UNESCO World Heritage Site located about 45 kilometers south of Bhopal in the state of Madhya Pradesh, India. Rapid changes in land use land cover (LULC) adversely affect the environment. In recent past, significant changes are found in the cultural landscape over a period of time. The objective of the paper was to study the changes in land use land cover (LULC) of Bhimbetka and its peripheral region. For this purpose, the supervised classification was carried out by using satellite images of Landsat and IRS LISS III for the year 2000 and 2013. Use of remote sensing in combination with geographic information system is one of the effective information technology tools to generate land use land cover (LULC) change information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IRS%20LISS%20III" title="IRS LISS III">IRS LISS III</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat" title=" Landsat"> Landsat</a>, <a href="https://publications.waset.org/abstracts/search?q=LULC" title=" LULC"> LULC</a>, <a href="https://publications.waset.org/abstracts/search?q=UNESCO" title=" UNESCO"> UNESCO</a>, <a href="https://publications.waset.org/abstracts/search?q=World%20Heritage%20Site" title=" World Heritage Site"> World Heritage Site</a> </p> <a href="https://publications.waset.org/abstracts/58616/study-of-land-use-land-cover-change-of-bhimbetka-with-temporal-satellite-data-and-information-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58616.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">353</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">197</span> Efficacy of Remote Sensing Application in Monitoring the Effectiveness of Afforestation Project in Northern Nigeria </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Garba">T. Garba</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Y.%20Babanyara"> Y. Y. Babanyara</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20G.%20Ilellah"> K. G. Ilellah</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Modibbo"> M. A. Modibbo</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20O.%20Quddus"> T. O. Quddus</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20J.%20Sani"> M. J. Sani </a> </p> <p class="card-text"><strong>Abstract:</strong></p> After the United Nation Convention on Desertification (UNCD) in 1977 which was preceded by extensive, regional, and local studies, and consultations with numerous scientists, decision-makers, and relevant institutions. Global Plan of Action to Combat Desertification (PACD) was formulated, endorsed by member Countries. The role of implementing PACD was vested with Governments of countries affected by desertification. The Federal Government of Nigeria as a signatory and World Bank funded and implement afforestation project aimed at combating desertification between 1988 and 1999. This research, therefore, applied remote sensing techniques to assess the effectiveness of the project. To achieve that a small portion of about 143,609 hectares was curved out from the project area. Normalized Difference of the Vegetative Index (NDVI) and Land Use Land Cover were derived from Landsat TM 1986, Landsat ETM 1999 and Nigeria Sat 1, 2007 of the project area. The findings show that there was an increase in cultivated area due to the project from 1986 through 1999 and 2007. This is further buttressed by the three NDVI imageries due to their high positive pixel value from 0.04 in 1986 to 0.22 in 1999 and to 0.32 in 2007 These signifies the gradual physical development of Afforestation project in the area. In addition, it was also verified by histograms of changes in vegetation which indicated an increased vegetative cover from 60,192 in 1986, to 102,476 in 1999 and then to 88,343 in 2007. The study concluded that Remote Sensing approach has actually confirmed that the project was indeed successful and effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=afforestation" title="afforestation">afforestation</a>, <a href="https://publications.waset.org/abstracts/search?q=desertification" title=" desertification"> desertification</a>, <a href="https://publications.waset.org/abstracts/search?q=landsat" title=" landsat"> landsat</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetative%20index" title=" vegetative index"> vegetative index</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/8290/efficacy-of-remote-sensing-application-in-monitoring-the-effectiveness-of-afforestation-project-in-northern-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8290.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">318</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">196</span> Assessment of Spectral Indices for Soil Salinity Estimation in Irrigated Land</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Lhissou">R. Lhissou </a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20El%20Harti"> A. El Harti </a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Chokmani"> K. Chokmani</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Bachaoui"> E. Bachaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20El%20Ghmari"> A. El Ghmari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Soil salinity is a serious environmental hazard in many countries around the world especially the arid and semi-arid countries like Morocco. Salinization causes negative effects on the ground; it affects agricultural production, infrastructure, water resources and biodiversity. Remote sensing can provide soil salinity information for large areas, and in a relatively short time. In addition, remote sensing is not limited by extremes in terrain or hazardous condition. Contrariwise, experimental methods for monitoring soil salinity by direct measurements in situ are very demanding of time and resources, and also very limited in spatial coverage. In the irrigated perimeter of Tadla plain in central Morocco, the increased use of saline groundwater and surface water, coupled with agricultural intensification leads to the deterioration of soil quality especially by salinization. In this study, we assessed several spectral indices of soil salinity cited in the literature using Landsat TM satellite images and field measurements of electrical conductivity (EC). Three Landsat TM satellite images were taken during 3 months in the dry season (September, October and November 2011). Based on field measurement data of EC collected in three field campaigns over the three dates simultaneously with acquisition dates of Landsat TM satellite images, a two assessment techniques are used to validate a soil salinity spectral indices. Firstly, the spectral indices are validated locally by pixel. The second validation technique is made using a window of size 3x3 pixels. The results of the study indicated that the second technique provides getting a more accurate validation and the assessment has shown its limits when it comes to assess across the pixel. In addition, the EC values measured from field have a good correlation with some spectral indices derived from Landsat TM data and the best results show an r² of 0.88, 0.79 and 0.65 for Salinity Index (SI) in the three dates respectively. The results have shown the usefulness of spectral indices as an auxiliary variable in the spatial estimation and mapping salinity in irrigated land. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title="remote sensing">remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20indices" title=" spectral indices"> spectral indices</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20salinity" title=" soil salinity"> soil salinity</a>, <a href="https://publications.waset.org/abstracts/search?q=irrigated%20land" title=" irrigated land "> irrigated land </a> </p> <a href="https://publications.waset.org/abstracts/9321/assessment-of-spectral-indices-for-soil-salinity-estimation-in-irrigated-land" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9321.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">395</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">195</span> Use of Landsat OLI Images in the Mapping of Landslides: Case of the Taounate Province in Northern Morocco</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Benchelha">S. Benchelha</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Chennaoui"> H. Chennaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Hakdaoui"> M. Hakdaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Baidder"> L. Baidder</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Mansouri"> H. Mansouri</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Ejjaaouani"> H. Ejjaaouani</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Benchelha"> T. Benchelha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Northern Morocco is characterized by relatively young mountains experiencing a very important dynamic compared to other areas of Morocco. The dynamics associated with the formation of the Rif chain (Alpine tectonics), is accompanied by instabilities essentially related to tectonic movements. The realization of important infrastructures (Roads, Highways,...) represents a triggering factor and favoring landslides. This paper is part of the establishment of landslides susceptibility map and concerns the mapping of unstable areas in the province of Taounate. The landslide was identified using the components of the false color (FCC) of images Landsat OLI: i) the first independent component (IC1), ii) The main component (PC), iii) Normalized difference index (NDI). This mapping for landslides class is validated by in-situ surveys. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landslides" title="landslides">landslides</a>, <a href="https://publications.waset.org/abstracts/search?q=False%20Color%20Composite%20%28FCC%29" title=" False Color Composite (FCC)"> False Color Composite (FCC)</a>, <a href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29" title=" Independent Component Analysis (ICA)"> Independent Component Analysis (ICA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Principal%20Component%20Analysis%20%28PCA%29" title=" Principal Component Analysis (PCA)"> Principal Component Analysis (PCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Normalized%20Difference%20Index%20%28NDI%29" title=" Normalized Difference Index (NDI)"> Normalized Difference Index (NDI)</a>, <a href="https://publications.waset.org/abstracts/search?q=Normalized%20Difference%20Mid%20Red%20Index%20%28NDMIDR%29" title=" Normalized Difference Mid Red Index (NDMIDR)"> Normalized Difference Mid Red Index (NDMIDR)</a> </p> <a href="https://publications.waset.org/abstracts/73841/use-of-landsat-oli-images-in-the-mapping-of-landslides-case-of-the-taounate-province-in-northern-morocco" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73841.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">295</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">194</span> Determination of Potential Agricultural Lands Using Landsat 8 OLI Images and GIS: Case Study of Gokceada (Imroz) Turkey</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rahmi%20Kafadar">Rahmi Kafadar</a>, <a href="https://publications.waset.org/abstracts/search?q=Levent%20Genc"> Levent Genc</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In present study, it was aimed to determine potential agricultural lands (PALs) in Gokceada (Imroz) Island of Canakkale province, Turkey. Seven-band Landsat 8 OLI images acquired on July 12 and August 13, 2013, and their 14-band combination image were used to identify current Land Use Land Cover (LULC) status. Principal Component Analysis (PCA) was applied to three Landsat datasets in order to reduce the correlation between the bands. A total of six Original and PCA images were classified using supervised classification method to obtain the LULC maps including 6 main classes (“Forest”, “Agriculture”, “Water Surface”, “Residential Area-Bare Soil”, “Reforestation” and “Other”). Accuracy assessment was performed by checking the accuracy of 120 randomized points for each LULC maps. The best overall accuracy and Kappa statistic values (90.83%, 0.8791% respectively) were found for PCA images which were generated from 14-bands combined images called 3-B/JA. Digital Elevation Model (DEM) with 15 m spatial resolution (ASTER) was used to consider topographical characteristics. Soil properties were obtained by digitizing 1:25000 scaled soil maps of rural services directorate general. Potential Agricultural Lands (PALs) were determined using Geographic information Systems (GIS). Procedure was applied considering that “Other” class of LULC map may be used for agricultural purposes in the future properties. Overlaying analysis was conducted using Slope (S), Land Use Capability Class (LUCC), Other Soil Properties (OSP) and Land Use Capability Sub-Class (SUBC) properties. A total of 901.62 ha areas within “Other” class (15798.2 ha) of LULC map were determined as PALs. These lands were ranked as “Very Suitable”, “Suitable”, “Moderate Suitable” and “Low Suitable”. It was determined that the 8.03 ha were classified as “Very Suitable” while 18.59 ha as suitable and 11.44 ha as “Moderate Suitable” for PALs. In addition, 756.56 ha were found to be “Low Suitable”. The results obtained from this preliminary study can serve as basis for further studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20elevation%20model%20%28DEM%29" title="digital elevation model (DEM)">digital elevation model (DEM)</a>, <a href="https://publications.waset.org/abstracts/search?q=geographic%20information%20systems%20%28GIS%29" title=" geographic information systems (GIS)"> geographic information systems (GIS)</a>, <a href="https://publications.waset.org/abstracts/search?q=gokceada%20%28Imroz%29" title=" gokceada (Imroz)"> gokceada (Imroz)</a>, <a href="https://publications.waset.org/abstracts/search?q=lANDSAT%208%20OLI-TIRS" title=" lANDSAT 8 OLI-TIRS"> lANDSAT 8 OLI-TIRS</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use%20land%20cover%20%28LULC%29" title=" land use land cover (LULC)"> land use land cover (LULC)</a> </p> <a href="https://publications.waset.org/abstracts/26949/determination-of-potential-agricultural-lands-using-landsat-8-oli-images-and-gis-case-study-of-gokceada-imroz-turkey" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26949.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">358</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">193</span> Application of Advanced Remote Sensing Data in Mineral Exploration in the Vicinity of Heavy Dense Forest Cover Area of Jharkhand and Odisha State Mining Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hemant%20Kumar">Hemant Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20N.%20K.%20Sharma"> R. N. K. Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20P.%20Krishna"> A. P. Krishna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study has been carried out on the Saranda in Jharkhand and a part of Odisha state. Geospatial data of Hyperion, a remote sensing satellite, have been used. This study has used a wide variety of patterns related to image processing to enhance and extract the mining class of Fe and Mn ores.Landsat-8, OLI sensor data have also been used to correctly explore related minerals. In this way, various processes have been applied to increase the mineralogy class and comparative evaluation with related frequency done. The Hyperion dataset for hyperspectral remote sensing has been specifically verified as an effective tool for mineral or rock information extraction within the band range of shortwave infrared used. The abundant spatial and spectral information contained in hyperspectral images enables the differentiation of different objects of any object into targeted applications for exploration such as exploration detection, mining. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyperion" title="Hyperion">Hyperion</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral" title=" hyperspectral"> hyperspectral</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor" title=" sensor"> sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat-8" title=" Landsat-8"> Landsat-8</a> </p> <a href="https://publications.waset.org/abstracts/128008/application-of-advanced-remote-sensing-data-in-mineral-exploration-in-the-vicinity-of-heavy-dense-forest-cover-area-of-jharkhand-and-odisha-state-mining-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128008.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">129</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">192</span> Analysis of Enhanced Built-up and Bare Land Index in the Urban Area of Yangon, Myanmar</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Su%20Nandar%20Tin">Su Nandar Tin</a>, <a href="https://publications.waset.org/abstracts/search?q=Wutjanun%20Muttitanon"> Wutjanun Muttitanon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The availability of free global and historical satellite imagery provides a valuable opportunity for mapping and monitoring the year by year for the built-up area, constantly and effectively. Land distribution guidelines and identification of changes are important in preparing and reviewing changes in the ground overview data. This study utilizes Landsat images for thirty years of information to acquire significant, and land spread data that are extremely valuable for urban arranging. This paper is mainly introducing to focus the basic of extracting built-up area for the city development area from the satellite images of LANDSAT 5,7,8 and Sentinel 2A from USGS in every five years. The purpose analyses the changing of the urban built-up area according to the year by year and to get the accuracy of mapping built-up and bare land areas in studying the trend of urban built-up changes the periods from 1990 to 2020. The GIS tools such as raster calculator and built-up area modelling are using in this study and then calculating the indices, which include enhanced built-up and bareness index (EBBI), Normalized difference Built-up index (NDBI), Urban index (UI), Built-up index (BUI) and Normalized difference bareness index (NDBAI) are used to get the high accuracy urban built-up area. Therefore, this study will point out a variable approach to automatically mapping typical enhanced built-up and bare land changes (EBBI) with simple indices and according to the outputs of indexes. Therefore, the percentage of the outputs of enhanced built-up and bareness index (EBBI) of the sentinel-2A can be realized with 48.4% of accuracy than the other index of Landsat images which are 15.6% in 1990 where there is increasing urban expansion area from 43.6% in 1990 to 92.5% in 2020 on the study area for last thirty years. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=built-up%20area" title="built-up area">built-up area</a>, <a href="https://publications.waset.org/abstracts/search?q=EBBI" title=" EBBI"> EBBI</a>, <a href="https://publications.waset.org/abstracts/search?q=NDBI" title=" NDBI"> NDBI</a>, <a href="https://publications.waset.org/abstracts/search?q=NDBAI" title=" NDBAI"> NDBAI</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20index" title=" urban index"> urban index</a> </p> <a href="https://publications.waset.org/abstracts/133052/analysis-of-enhanced-built-up-and-bare-land-index-in-the-urban-area-of-yangon-myanmar" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133052.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">185</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">191</span> Monitoring the Rate of Expansion of Agricultural Fields in Mwekera Forest Reserve Using Remote Sensing and Geographic Information Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Kanja">K. Kanja</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Mweemba"> M. Mweemba</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Malungwa"> K. Malungwa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the rampant population growth coupled with retrenchments currently going on in the Copper mines in Zambia, a number of people are resorting to land clearing for agriculture, illegal settlements as well as charcoal production among other vices. This study aims at assessing the rate of expansion of agricultural fields and illegal settlements in protected areas using remote sensing and Geographic Information System. Zambia’s Mwekera National Forest Reserve was used as a case study. Iterative Self-Organizing Data Analysis Technique (ISODATA), as well as maximum likelihood, supervised classification on four Landsat images as well as an accuracy assessment of the classifications was performed. Over the period under observation, results indicate annual percentage changes to be -0.03, -0.49 and 1.26 for agriculture, forests and settlement respectively indicating a higher conversion of forests into human settlements and agriculture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geographic%20information%20system" title="geographic information system">geographic information system</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20cover%20change" title=" land cover change"> land cover change</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat%20TM%20and%20ETM%2B" title=" Landsat TM and ETM+"> Landsat TM and ETM+</a>, <a href="https://publications.waset.org/abstracts/search?q=Mwekera%20forest%20reserve" title=" Mwekera forest reserve"> Mwekera forest reserve</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/106594/monitoring-the-rate-of-expansion-of-agricultural-fields-in-mwekera-forest-reserve-using-remote-sensing-and-geographic-information-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106594.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">190</span> Landsat Data from Pre Crop Season to Estimate the Area to Be Planted with Summer Crops</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Valdir%20Moura">Valdir Moura</a>, <a href="https://publications.waset.org/abstracts/search?q=Raniele%20dos%20Anjos%20de%20Souza"> Raniele dos Anjos de Souza</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernando%20Gomes%20de%20Souza"> Fernando Gomes de Souza</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20Vagner%20da%20Silva"> Jose Vagner da Silva</a>, <a href="https://publications.waset.org/abstracts/search?q=Jerry%20Adriani%20Johann"> Jerry Adriani Johann</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The estimate of the Area of Land to be planted with annual crops and its stratification by the municipality are important variables in crop forecast. Nowadays in Brazil, these information’s are obtained by the Brazilian Institute of Geography and Statistics (IBGE) and published under the report Assessment of the Agricultural Production. Due to the high cloud cover in the main crop growing season (October to March) it is difficult to acquire good orbital images. Thus, one alternative is to work with remote sensing data from dates before the crop growing season. This work presents the use of multitemporal Landsat data gathered on July and September (before the summer growing season) in order to estimate the area of land to be planted with summer crops in an area of São Paulo State, Brazil. Geographic Information Systems (GIS) and digital image processing techniques were applied for the treatment of the available data. Supervised and non-supervised classifications were used for data in digital number and reflectance formats and the multitemporal Normalized Difference Vegetation Index (NDVI) images. The objective was to discriminate the tracts with higher probability to become planted with summer crops. Classification accuracies were evaluated using a sampling system developed basically for this study region. The estimated areas were corrected using the error matrix derived from these evaluations. The classification techniques presented an excellent level according to the kappa index. The proportion of crops stratified by municipalities was derived by a field work during the crop growing season. These proportion coefficients were applied onto the area of land to be planted with summer crops (derived from Landsat data). Thus, it was possible to derive the area of each summer crop by the municipality. The discrepancies between official statistics and our results were attributed to the sampling and the stratification procedures. Nevertheless, this methodology can be improved in order to provide good crop area estimates using remote sensing data, despite the cloud cover during the growing season. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=area%20intended%20for%20summer%20culture" title="area intended for summer culture">area intended for summer culture</a>, <a href="https://publications.waset.org/abstracts/search?q=estimated%20area%20planted" title=" estimated area planted"> estimated area planted</a>, <a href="https://publications.waset.org/abstracts/search?q=agriculture" title=" agriculture"> agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat" title=" Landsat"> Landsat</a>, <a href="https://publications.waset.org/abstracts/search?q=planting%20schedule" title=" planting schedule"> planting schedule</a> </p> <a href="https://publications.waset.org/abstracts/107898/landsat-data-from-pre-crop-season-to-estimate-the-area-to-be-planted-with-summer-crops" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107898.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">189</span> Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Study Case of the Beterou Catchment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ella%20S%C3%A8d%C3%A9%20Maforikan">Ella Sèdé Maforikan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate land cover mapping is essential for effective environmental monitoring and natural resources management. This study focuses on assessing the classification performance of two satellite datasets and evaluating the impact of different input feature combinations on classification accuracy in the Beterou catchment, situated in the northern part of Benin. Landsat-8 and Sentinel-2 images from June 1, 2020, to March 31, 2021, were utilized. Employing the Random Forest (RF) algorithm on Google Earth Engine (GEE), a supervised classification categorized the land into five classes: forest, savannas, cropland, settlement, and water bodies. GEE was chosen due to its high-performance computing capabilities, mitigating computational burdens associated with traditional land cover classification methods. By eliminating the need for individual satellite image downloads and providing access to an extensive archive of remote sensing data, GEE facilitated efficient model training on remote sensing data. The study achieved commendable overall accuracy (OA), ranging from 84% to 85%, even without incorporating spectral indices and terrain metrics into the model. Notably, the inclusion of additional input sources, specifically terrain features like slope and elevation, enhanced classification accuracy. The highest accuracy was achieved with Sentinel-2 (OA = 91%, Kappa = 0.88), slightly surpassing Landsat-8 (OA = 90%, Kappa = 0.87). This underscores the significance of combining diverse input sources for optimal accuracy in land cover mapping. The methodology presented herein not only enables the creation of precise, expeditious land cover maps but also demonstrates the prowess of cloud computing through GEE for large-scale land cover mapping with remarkable accuracy. The study emphasizes the synergy of different input sources to achieve superior accuracy. As a future recommendation, the application of Light Detection and Ranging (LiDAR) technology is proposed to enhance vegetation type differentiation in the Beterou catchment. Additionally, a cross-comparison between Sentinel-2 and Landsat-8 for assessing long-term land cover changes is suggested. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20cover%20mapping" title="land cover mapping">land cover mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=Google%20Earth%20Engine" title=" Google Earth Engine"> Google Earth Engine</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=Beterou%20catchment" title=" Beterou catchment"> Beterou catchment</a> </p> <a href="https://publications.waset.org/abstracts/177701/land-cover-mapping-using-sentinel-2-landsat-8-satellite-images-and-google-earth-engine-a-study-case-of-the-beterou-catchment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177701.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">72</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">188</span> Assessment of Soil Salinity through Remote Sensing Technique in the Coastal Region of Bangladesh</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Hossen">B. Hossen</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Helmut"> Y. Helmut</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Soil salinity is a major problem for the coastal region of Bangladesh, which has been increasing for the last four decades. Determination of soil salinity is essential for proper land use planning for agricultural crop production. The aim of the research is to estimate and monitor the soil salinity in the study area. Remote sensing can be an effective tool for detecting soil salinity in data-scarce conditions. In the research, Landsat 8 is used, which required atmospheric and radiometric correction, and nine soil salinity indices are applied to develop a soil salinity map. Ground soil salinity data, i.e., EC value, is collected as a printed map which is then scanned and digitized to develop a point shapefile. Linear regression is made between satellite-based generated map and ground soil salinity data, i.e., EC value. The results show that maximum R² value is found for salinity index SI 7 = G*R/B representing 0.022. This minimal R² value refers that there is a negligible relationship between ground EC value and salinity index generated value. Hence, these indices are not appropriate to assess soil salinity though many studies used those soil salinity indices successfully. Therefore, further research is necessary to formulate a model for determining the soil salinity in the coastal of Bangladesh. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=soil%20salinity" title="soil salinity">soil salinity</a>, <a href="https://publications.waset.org/abstracts/search?q=EC" title=" EC"> EC</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat%208" title=" Landsat 8"> Landsat 8</a>, <a href="https://publications.waset.org/abstracts/search?q=salinity%20indices" title=" salinity indices"> salinity indices</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing "> remote sensing </a> </p> <a href="https://publications.waset.org/abstracts/139666/assessment-of-soil-salinity-through-remote-sensing-technique-in-the-coastal-region-of-bangladesh" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139666.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">187</span> Selection of Appropriate Classification Technique for Lithological Mapping of Gali Jagir Area, Pakistan </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khunsa%20Fatima">Khunsa Fatima</a>, <a href="https://publications.waset.org/abstracts/search?q=Umar%20K.%20Khattak"> Umar K. Khattak</a>, <a href="https://publications.waset.org/abstracts/search?q=Allah%20Bakhsh%20Kausar"> Allah Bakhsh Kausar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Satellite images interpretation and analysis assist geologists by providing valuable information about geology and minerals of an area to be surveyed. A test site in Fatejang of district Attock has been studied using Landsat ETM+ and ASTER satellite images for lithological mapping. Five different supervised image classification techniques namely maximum likelihood, parallelepiped, minimum distance to mean, mahalanobis distance and spectral angle mapper have been performed on both satellite data images to find out the suitable classification technique for lithological mapping in the study area. Results of these five image classification techniques were compared with the geological map produced by Geological Survey of Pakistan. The result of maximum likelihood classification technique applied on ASTER satellite image has the highest correlation of 0.66 with the geological map. Field observations and XRD spectra of field samples also verified the results. A lithological map was then prepared based on the maximum likelihood classification of ASTER satellite image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ASTER" title="ASTER">ASTER</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat-ETM%2B" title=" Landsat-ETM+"> Landsat-ETM+</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite" title=" satellite"> satellite</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a> </p> <a href="https://publications.waset.org/abstracts/3823/selection-of-appropriate-classification-technique-for-lithological-mapping-of-gali-jagir-area-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3823.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">402</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=landsat&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=landsat&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=landsat&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=landsat&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=landsat&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=landsat&page=7">7</a></li> 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