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difference vegetation index</title> <meta name="description" content="Search results for: normalized difference vegetation index"> <meta name="keywords" content="normalized difference vegetation index"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research 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method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="normalized difference vegetation index"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 8430</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: normalized difference vegetation index</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8430</span> Image Processing and Calculation of NGRDI Embedded System in Raspberry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Efren%20Lopez%20Jimenez">Efren Lopez Jimenez</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Isabel%20Cajero"> Maria Isabel Cajero</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Irving-Vasqueza"> J. Irving-Vasqueza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use and processing of digital images have opened up new opportunities for the resolution of problems of various kinds, such as the calculation of different vegetation indexes, among other things, differentiating healthy vegetation from humid vegetation. However, obtaining images from which these indexes are calculated is still the exclusive subject of active research. In the present work, we propose to obtain these images using a low cost embedded system (Raspberry Pi) and its processing, using a set of libraries of open code called OpenCV, in order to obtain the Normalized Red-Green Difference Index (NGRDI). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raspberry%20Pi" title="Raspberry Pi">Raspberry Pi</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation%20index" title=" vegetation index"> vegetation index</a>, <a href="https://publications.waset.org/abstracts/search?q=Normalized%20Red-Green%20Difference%20Index%20%28NGRDI%29" title=" Normalized Red-Green Difference Index (NGRDI)"> Normalized Red-Green Difference Index (NGRDI)</a>, <a href="https://publications.waset.org/abstracts/search?q=OpenCV" title=" OpenCV"> OpenCV</a> </p> <a href="https://publications.waset.org/abstracts/72145/image-processing-and-calculation-of-ngrdi-embedded-system-in-raspberry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72145.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">291</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">8429</span> Normalized Difference Vegetation Index and Hyperspectral: Plant Health Assessment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srushti%20R.%20Joshi">Srushti R. Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ujjwal%20Rakesh"> Ujjwal Rakesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Spoorthi%20Sripad"> Spoorthi Sripad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid advancement of remote sensing technologies has revolutionized plant health monitoring, offering valuable insights for precision agriculture and environmental management. This paper presents a comprehensive comparative analysis between the widely employed normalized difference vegetation index (NDVI) and state-of-the-art hyperspectral sensors in the context of plant health assessment. The study aims to elucidate the weigh ups of spectral resolution. Employing a diverse range of vegetative environments, the research utilizes simulated datasets to evaluate the performance of NDVI and hyperspectral sensors in detecting subtle variations indicative of plant stress, disease, and overall vitality. Through meticulous data analysis and statistical validation, this study highlights the superior performance of hyperspectral sensors across the parameters used. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=normalized%20difference%20vegetation%20index" title="normalized difference vegetation index">normalized difference vegetation index</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20sensor" title=" hyperspectral sensor"> hyperspectral sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20resolution" title=" spectral resolution"> spectral resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=infrared" title=" infrared"> infrared</a> </p> <a href="https://publications.waset.org/abstracts/178987/normalized-difference-vegetation-index-and-hyperspectral-plant-health-assessment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178987.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">65</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">8428</span> Assessment of Land Surface Temperature Using Satellite Remote Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Vidhya">R. Vidhya</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Navamuniyammal%20M.%20Sivakumar"> M. Navamuniyammal M. Sivakumar</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Reeta"> S. Reeta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The unplanned urbanization affects the environment due to pollution, conditions of the atmosphere, decreased vegetation and the pervious and impervious soil surface. Considered to be a cumulative effect of all these impacts is the Urban Heat Island. In this paper, the urban heat island effect is studied for the Chennai city, TamilNadu, South India using satellite remote sensing data. LANDSAT 8 OLI and TIRS DATA acquired on 9th September 2014 were used to Land Surface Temperature (LST) map, vegetation fraction map, Impervious surface fraction, Normalized Difference Water Index (NDWI), Normalized Difference Building Index (NDBI) and Normalized Difference Vegetation Index (NDVI) map. The relationship among LST, Vegetation fraction, NDBI, NDWI, and NDVI was calculated. The Chennai city’s Urban Heat Island effect is significant, and the results indicate LST has strong negative correlation with the vegetation present and positive correlation with NDBI. The vegetation is the main factor to control urban heat island effect issues in urban area like Chennai City. This study will help in developing measures to land use planning to reduce the heat effects in urban area based on remote sensing derivatives. <p class="card-text"><strong>Keywords:</strong> <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=brightness%20temperature" title=" brightness temperature"> brightness temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=emissivity" title=" emissivity"> emissivity</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation%20index" title=" vegetation index"> vegetation index</a> </p> <a href="https://publications.waset.org/abstracts/82927/assessment-of-land-surface-temperature-using-satellite-remote-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82927.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">274</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">8427</span> Application of Rapid Eye Imagery in Crop Type Classification Using Vegetation Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sunita%20Singh">Sunita Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajani%20Srivastava"> Rajani Srivastava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For natural resource management and in other applications about earth observation revolutionary remote sensing technology plays a significant role. One of such application in monitoring and classification of crop types at spatial and temporal scale, as it provides latest, most precise and cost-effective information. Present study emphasizes the use of three different vegetation indices of Rapid Eye imagery on crop type classification. It also analyzed the effect of each indices on classification accuracy. Rapid Eye imagery is highly demanded and preferred for agricultural and forestry sectors as it has red-edge and NIR bands. The three indices used in this study were: the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) and all of these incorporated the Red Edge band. The study area is Varanasi district of Uttar Pradesh, India and Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Classification was performed with these three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 85% was obtained using three vegetation indices. The study concluded that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the Rapid Eye imagery can get satisfactory results of classification accuracy without original bands. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GNDVI" title="GNDVI">GNDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=NDRE" title=" NDRE"> NDRE</a>, <a href="https://publications.waset.org/abstracts/search?q=NDVI" title=" NDVI"> NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=rapid%20eye" title=" rapid eye"> rapid eye</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation%20indices" title=" vegetation indices"> vegetation indices</a> </p> <a href="https://publications.waset.org/abstracts/79921/application-of-rapid-eye-imagery-in-crop-type-classification-using-vegetation-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79921.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">362</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">8426</span> Vegetation Index-Deduced Crop Coefficient of Wheat (Triticum aestivum) Using Remote Sensing: Case Study on Four Basins of Golestan Province, Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hoda%20Zolfagharnejad">Hoda Zolfagharnejad</a>, <a href="https://publications.waset.org/abstracts/search?q=Behnam%20Kamkar"> Behnam Kamkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Omid%20Abdi"> Omid Abdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Crop coefficient (Kc) is an important factor contributing to estimation of evapotranspiration, and is also used to determine the irrigation schedule. This study investigated and determined the monthly Kc of winter wheat (<em>Triticum aestivum</em> L.) using five vegetation indices (VIs): Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Soil Adjusted Vegetation Index (SAVI), Infrared Percentage Vegetation Index (IPVI), and Ratio Vegetation Index (RVI) of four basins in Golestan province, Iran. 14 Landsat-8 images according to crop growth stage were used to estimate monthly Kc of wheat. VIs were calculated based on infrared and near infrared bands of Landsat 8 images using Geographical Information System (GIS) software. The best VIs were chosen after establishing a regression relationship among these VIs with FAO Kc and Kc that was modified for the study area by the previous research based on R² and Root Mean Square Error (RMSE). The result showed that local modified SAVI with R²= 0.767 and RMSE= 0.174 was the best index to produce monthly wheat Kc maps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crop%20coefficient" title="crop coefficient">crop coefficient</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=vegetation%20indices" title=" vegetation indices"> vegetation indices</a>, <a href="https://publications.waset.org/abstracts/search?q=wheat" title=" wheat"> wheat</a> </p> <a href="https://publications.waset.org/abstracts/63180/vegetation-index-deduced-crop-coefficient-of-wheat-triticum-aestivum-using-remote-sensing-case-study-on-four-basins-of-golestan-province-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63180.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">412</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">8425</span> Urban Energy Demand Modelling: Spatial Analysis Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hung-Chu%20Chen">Hung-Chu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Han%20Qi"> Han Qi</a>, <a href="https://publications.waset.org/abstracts/search?q=Bauke%20de%20Vries"> Bauke de Vries</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Energy consumption in the urban environment has attracted numerous researches in recent decades. However, it is comparatively rare to find literary works which investigated 3D spatial analysis of urban energy demand modelling. In order to analyze the spatial correlation between urban morphology and energy demand comprehensively, this paper investigates their relation by using the spatial regression tool. In addition, the spatial regression tool which is applied in this paper is ordinary least squares regression (OLS) and geographically weighted regression (GWR) model. Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and building volume are explainers of urban morphology, which act as independent variables of Energy-land use (E-L) model. NDBI and NDVI are used as the index to describe five types of land use: urban area (U), open space (O), artificial green area (G), natural green area (V), and water body (W). Accordingly, annual electricity, gas demand and energy demand are dependent variables of the E-L model. Based on the analytical result of E-L model relation, it revealed that energy demand and urban morphology are closely connected and the possible causes and practical use are discussed. Besides, the spatial analysis methods of OLS and GWR are compared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20demand%20model" title="energy demand model">energy demand model</a>, <a href="https://publications.waset.org/abstracts/search?q=geographically%20weighted%20regression" title=" geographically weighted regression"> geographically weighted regression</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20difference%20built-up%20index" title=" normalized difference built-up index"> normalized difference built-up index</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20difference%20vegetation%20index" title=" normalized difference vegetation index"> normalized difference vegetation index</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20statistics" title=" spatial statistics"> spatial statistics</a> </p> <a href="https://publications.waset.org/abstracts/101697/urban-energy-demand-modelling-spatial-analysis-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101697.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">148</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">8424</span> Normalized Difference Vegetation Index and Normalize Difference Chlorophyll Changes with Different Irrigation Levels on Sillage Corn</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cenk%20Aksit">Cenk Aksit</a>, <a href="https://publications.waset.org/abstracts/search?q=Suleyman%20Kodal"> Suleyman Kodal</a>, <a href="https://publications.waset.org/abstracts/search?q=Yusuf%20Ersoy%20Yildirim"> Yusuf Ersoy Yildirim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Normalized Difference Vegetation Index (NDVI) is a widely used index in the world that provides reference information, such as the health status of the plant, and the density of the vegetation in a certain area, by making use of the electromagnetic radiation reflected from the plant surface. On the other hand, the chlorophyll index provides reference information about the chlorophyll density in the plant by making use of electromagnetic reflections at certain wavelengths. Chlorophyll concentration is higher in healthy plants and decreases as plant health decreases. This study, it was aimed to determine the changes in Normalize Difference Vegetation Index (NDVI) and Normalize Difference Chlorophyll (NDCI) of silage corn irrigated with subsurface drip irrigation systems under different irrigation levels. In 5 days irrigation interval, the daily potential plant water consumption values were collected, and the calculated amount was applied to the full irrigation and 3 irrigation water levels as irrigation water. The changes in NDVI and NDCI of silage corn irrigated with subsurface drip irrigation systems under different irrigation levels were determined. NDVI values have changed according to the amount of irrigation water applied, and the highest NDVI value has been reached in the subject where the most water is applied. Likewise, it was observed that the chlorophyll value decreased in direct proportion to the amount of irrigation water as the plant approached the harvest. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NDVI" title="NDVI">NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=NDCI" title=" NDCI"> NDCI</a>, <a href="https://publications.waset.org/abstracts/search?q=sub-surface%20drip%20irrigation" title=" sub-surface drip irrigation"> sub-surface drip irrigation</a>, <a href="https://publications.waset.org/abstracts/search?q=silage%20corn" title=" silage corn"> silage corn</a>, <a href="https://publications.waset.org/abstracts/search?q=deficit%20irrigation" title=" deficit irrigation"> deficit irrigation</a> </p> <a href="https://publications.waset.org/abstracts/163400/normalized-difference-vegetation-index-and-normalize-difference-chlorophyll-changes-with-different-irrigation-levels-on-sillage-corn" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163400.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">97</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">8423</span> Remote Assessment and Change Detection of GreenLAI of Cotton Crop Using Different Vegetation Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ganesh%20B.%20Shinde">Ganesh B. Shinde</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijaya%20B.%20Musande"> Vijaya B. Musande</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cotton crop identification based on the timely information has significant advantage to the different implications of food, economic and environment. Due to the significant advantages, the accurate detection of cotton crop regions using supervised learning procedure is challenging problem in remote sensing. Here, classifiers on the direct image are played a major role but the results are not much satisfactorily. In order to further improve the effectiveness, variety of vegetation indices are proposed in the literature. But, recently, the major challenge is to find the better vegetation indices for the cotton crop identification through the proposed methodology. Accordingly, fuzzy c-means clustering is combined with neural network algorithm, trained by Levenberg-Marquardt for cotton crop classification. To experiment the proposed method, five LISS-III satellite images was taken and the experimentation was done with six vegetation indices such as Simple Ratio, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Green Atmospherically Resistant Vegetation Index, Wide-Dynamic Range Vegetation Index, Green Chlorophyll Index. Along with these indices, Green Leaf Area Index is also considered for investigation. From the research outcome, Green Atmospherically Resistant Vegetation Index outperformed with all other indices by reaching the average accuracy value of 95.21%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuzzy%20C-Means%20clustering%20%28FCM%29" title="Fuzzy C-Means clustering (FCM)">Fuzzy C-Means clustering (FCM)</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=Levenberg-Marquardt%20%28LM%29%20algorithm" title=" Levenberg-Marquardt (LM) algorithm"> Levenberg-Marquardt (LM) algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation%20indices" title=" vegetation indices"> vegetation indices</a> </p> <a href="https://publications.waset.org/abstracts/18426/remote-assessment-and-change-detection-of-greenlai-of-cotton-crop-using-different-vegetation-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18426.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">8422</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">309</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">8421</span> Analyzing Land use change and its impacts on the Urban Environment in a Fast Growing Metropolitan City of Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Nasar-u-Minallah">Muhammad Nasar-u-Minallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Dagmar%20Haase"> Dagmar Haase</a>, <a href="https://publications.waset.org/abstracts/search?q=Salman%20Qureshi"> Salman Qureshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a rapidly growing developing country cities are becoming more urbanized leading to modifications in urban climate. Rapid urbanization, especially unplanned urban land expansion, together with climate change has a profound impact on the urban settlement and urban thermal environment. Cities, particularly Pakistan are facing remarkably environmental issues and uneven development, and thus it is important to strengthen the investigation of urban environmental pressure brought by land-use changes and urbanization. The present study investigated the long term modification of the urban environment by urbanization utilizing Spatio-temporal dynamics of land-use change, urban population data, urban heat islands, monthly maximum, and minimum temperature of thirty years, multi remote sensing imageries, and spectral indices such as Normalized Difference Built-up Index and Normalized Difference Vegetation Index. The results indicate rapid growth in an urban built-up area and a reduction in vegetation cover in the last three decades (1990-2020). A positive correlation between urban heat islands and Normalized Difference Built-up Index, whereas a negative correlation between urban heat islands and the Normalized Difference Vegetation Index clearly shows how urbanization is affecting the local environment. The increase in air and land surface temperature temperatures is dangerous to human comfort. Practical approaches, such as increasing the urban green spaces and proper planning of the cities, have been suggested to help prevent further modification of the urban thermal environment by urbanization. The findings of this work are thus important for multi-sectorial use in the cities of Pakistan. By taking into consideration these results, the urban planners, decision-makers, and local government can make different policies to mitigate the urban land use impacts on the urban thermal environment in Pakistan. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20use" title="land use">land use</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20environment" title=" urban environment"> urban environment</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20climate" title=" local climate"> local climate</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahore" title=" Lahore"> Lahore</a> </p> <a href="https://publications.waset.org/abstracts/148915/analyzing-land-use-change-and-its-impacts-on-the-urban-environment-in-a-fast-growing-metropolitan-city-of-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148915.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">110</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">8420</span> Construction of Submerged Aquatic Vegetation Index through Global Sensitivity Analysis of Radiative Transfer Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guanhua%20Zhou">Guanhua Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhongqi%20Ma"> Zhongqi Ma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Submerged aquatic vegetation (SAV) in wetlands can absorb nitrogen and phosphorus effectively to prevent the eutrophication of water. It is feasible to monitor the distribution of SAV through remote sensing, but for the reason of weak vegetation signals affected by water body, traditional terrestrial vegetation indices are not applicable. This paper aims at constructing SAV index to enhance the vegetation signals and distinguish SAV from water body. The methodology is as follows: (1) select the bands sensitive to the vegetation parameters based on global sensitivity analysis of SAV canopy radiative transfer model; (2) take the soil line concept as reference, analyze the distribution of SAV and water reflectance simulated by SAV canopy model and semi-analytical water model in the two-dimensional space built by different sensitive bands; (3)select the band combinations which have better separation performance between SAV and water, and use them to build the SAVI indices in the form of normalized difference vegetation index(NDVI); (4)analyze the sensitivity of indices to the water and vegetation parameters, choose the one more sensitive to vegetation parameters. It is proved that index formed of the bands with central wavelengths in 705nm and 842nm has high sensitivity to chlorophyll content in leaves while it is less affected by water constituents. The model simulation shows a general negative, little correlation of SAV index with increasing water depth. Moreover, the index enhances capabilities in separating SAV from water compared to NDVI. The SAV index is expected to have potential in parameter inversion of wetland remote sensing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=global%20sensitivity%20analysis" title="global sensitivity analysis">global sensitivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=radiative%20transfer%20model" title=" radiative transfer model"> radiative transfer model</a>, <a href="https://publications.waset.org/abstracts/search?q=submerged%20aquatic%20vegetation" title=" submerged aquatic vegetation"> submerged aquatic vegetation</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation%20indices" title=" vegetation indices"> vegetation indices</a> </p> <a href="https://publications.waset.org/abstracts/75775/construction-of-submerged-aquatic-vegetation-index-through-global-sensitivity-analysis-of-radiative-transfer-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75775.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">262</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">8419</span> Urban Heat Island Intensity Assessment through Comparative Study on Land Surface Temperature and Normalized Difference Vegetation Index: A Case Study of Chittagong, Bangladesh</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tausif%20A.%20Ishtiaque">Tausif A. Ishtiaque</a>, <a href="https://publications.waset.org/abstracts/search?q=Zarrin%20T.%20Tasin"> Zarrin T. Tasin</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazi%20S.%20Akter"> Kazi S. Akter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Current trend of urban expansion, especially in the developing countries has caused significant changes in land cover, which is generating great concern due to its widespread environmental degradation. Energy consumption of the cities is also increasing with the aggravated heat island effect. Distribution of land surface temperature (LST) is one of the most significant climatic parameters affected by urban land cover change. Recent increasing trend of LST is causing elevated temperature profile of the built up area with less vegetative cover. Gradual change in land cover, especially decrease in vegetative cover is enhancing the Urban Heat Island (UHI) effect in the developing cities around the world. Increase in the amount of urban vegetation cover can be a useful solution for the reduction of UHI intensity. LST and Normalized Difference Vegetation Index (NDVI) have widely been accepted as reliable indicators of UHI and vegetation abundance respectively. Chittagong, the second largest city of Bangladesh, has been a growth center due to rapid urbanization over the last several decades. This study assesses the intensity of UHI in Chittagong city by analyzing the relationship between LST and NDVI based on the type of land use/land cover (LULC) in the study area applying an integrated approach of Geographic Information System (GIS), remote sensing (RS), and regression analysis. Land cover map is prepared through an interactive supervised classification using remotely sensed data from Landsat ETM+ image along with NDVI differencing using ArcGIS. LST and NDVI values are extracted from the same image. The regression analysis between LST and NDVI indicates that within the study area, UHI is directly correlated with LST while negatively correlated with NDVI. It interprets that surface temperature reduces with increase in vegetation cover along with reduction in UHI intensity. Moreover, there are noticeable differences in the relationship between LST and NDVI based on the type of LULC. In other words, depending on the type of land usage, increase in vegetation cover has a varying impact on the UHI intensity. This analysis will contribute to the formulation of sustainable urban land use planning decisions as well as suggesting suitable actions for mitigation of UHI intensity within the study area. <p class="card-text"><strong>Keywords:</strong> <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=land%20surface%20temperature" title=" land surface temperature"> land surface temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20difference%20vegetation%20index" title=" normalized difference vegetation index"> normalized difference vegetation index</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20heat%20island" title=" urban heat island"> urban heat island</a> </p> <a href="https://publications.waset.org/abstracts/60627/urban-heat-island-intensity-assessment-through-comparative-study-on-land-surface-temperature-and-normalized-difference-vegetation-index-a-case-study-of-chittagong-bangladesh" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60627.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">272</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">8418</span> Assessing the Effect of Urban Growth on Land Surface Temperature: A Case Study of Conakry Guinea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arafan%20Traore">Arafan Traore</a>, <a href="https://publications.waset.org/abstracts/search?q=Teiji%20Watanabe"> Teiji Watanabe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conakry, the capital city of the Republic of Guinea, has experienced a rapid urban expansion and population increased in the last two decades, which has resulted in remarkable local weather and climate change, raise energy demand and pollution and treating social, economic and environmental development. In this study, the spatiotemporal variation of the land surface temperature (LST) is retrieved to characterize the effect of urban growth on the thermal environment and quantify its relationship with biophysical indices, a normalized difference vegetation index (NDVI) and a normalized difference built up Index (NDBI). Landsat data TM and OLI/TIRS acquired respectively in 1986, 2000 and 2016 were used for LST retrieval and Land use/cover change analysis. A quantitative analysis based on the integration of a remote sensing and a geography information system (GIS) has revealed an important increased in the LST pattern in the average from 25.21°C in 1986 to 27.06°C in 2000 and 29.34°C in 2016, which was quite eminent with an average gain in surface temperature of 4.13°C over 30 years study period. Additionally, an analysis using a Pearson correlation (r) between (LST) and the biophysical indices, normalized difference vegetation index (NDVI) and a normalized difference built-up Index (NDBI) has revealed a negative relationship between LST and NDVI and a strong positive relationship between LST and NDBI. Which implies that an increase in the NDVI value can reduce the LST intensity; conversely increase in NDBI value may strengthen LST intensity in the study area. Although Landsat data were found efficient in assessing the thermal environment in Conakry, however, the method needs to be refined with in situ measurements of LST in the future studies. The results of this study may assist urban planners, scientists and policies makers concerned about climate variability to make decisions that will enhance sustainable environmental practices in Conakry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Conakry" title="Conakry">Conakry</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=urban%20heat%20island" title=" urban heat island"> urban heat island</a>, <a href="https://publications.waset.org/abstracts/search?q=geography%20information%20system" title=" geography information system"> geography information system</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=land%20use%2Fcover%20change" title=" land use/cover change"> land use/cover change</a> </p> <a href="https://publications.waset.org/abstracts/72248/assessing-the-effect-of-urban-growth-on-land-surface-temperature-a-case-study-of-conakry-guinea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72248.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">246</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">8417</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">290</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">8416</span> Evaluation of Environmental Impact Assessment of Dam Using GIS/Remote Sensing-Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ntungamili%20Kenosi">Ntungamili Kenosi</a>, <a href="https://publications.waset.org/abstracts/search?q=Moatlhodi%20W.%20Letshwenyo"> Moatlhodi W. Letshwenyo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Negative environmental impacts due to construction of large projects such as dams have become an important aspect of land degradation. This paper will review the previous literature on the previous researches or study in the same area of study in the other parts of the world. After dam has been constructed, the actual environmental impacts are investigated and compared to the predicted results of the carried out Environmental Impact Assessment. GIS and Remote Sensing, play an important role in generating automated spatial data sets and in establishing spatial relationships. Results from other sources shows that the normalized vegetation index (NDVI) analysis was used to detect the spatial and temporal change of vegetation biomass in the study area. The result indicated that the natural vegetation biomass is declining. This is mainly due to the expansion of agricultural land and escalating human made structures in the area. Urgent environmental conservation is necessary when adjoining projects site. Less study on the evaluation of EIA on dam has been conducted in Botswana hence there is a need for the same study to be conducted and then it will be easy to be compared to other studies around the world. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Botswana" title="Botswana">Botswana</a>, <a href="https://publications.waset.org/abstracts/search?q=dam" title=" dam"> dam</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20impact%20assessment" title=" environmental impact assessment"> environmental impact assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20vegetation%20index%20%28NDVI%29" title=" normalized vegetation index (NDVI)"> normalized vegetation index (NDVI)</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/35626/evaluation-of-environmental-impact-assessment-of-dam-using-gisremote-sensing-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35626.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">405</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">8415</span> Investigating the Effect of Urban Expansion on the Urban Heat Island and Land Use Land Cover Changes: The Case Study of Lahore, Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shah%20Fahad">Shah Fahad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Managing the Urban Heat Island (UHI) effects is a pressing concern for achieving sustainable urban development and ensuring thermal comfort in major cities of developing nations, such as Lahore, Pakistan. The current UHI effect is mostly triggered by climate change and rapid urbanization. This study explored UHI over the Lahore district and its adjoining urban and rural-urban fringe areas. Landsat satellite data was utilized to investigate spatiotemporal patterns of Land Use and Land Cover changes (LULC), Land Surface Temperature (LST), UHI, Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and Urban Thermal Field Variance Index (UTFVI). The built-up area increased very fast, with a coverage of 22.99% in 2000, 36.06% in 2010, and 47.17% in 2020, while vegetation covered 53.21 % in 2000 and 46.16 % in 2020. It also revealed a significant increase in the mean LST, from 33°C in 2000 to 34.8°C in 2020. The results indicated a significantly positive correlation between LST and NDBI, a weak correlation was also observed between LST and NDVI. The study used scatterplots to show the correlation between NDBI and NDVI with LST, results revealed that the NDBI and LST had an R² value of 0.6831 in 2000 and 0.06541 in 2022, while NDVI and LST had an R² value of 0.0235 in 1998 and 0.0295 in 2022. Proper environmental planning is vital in specific locations to enhance quality of life, protect the ecosystem, and mitigate climate change impacts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20use%20land%20cover" title="land use land cover">land use land cover</a>, <a href="https://publications.waset.org/abstracts/search?q=spatio-temporal%20analysis" title=" spatio-temporal analysis"> spatio-temporal analysis</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=land%20surface%20temperature" title=" land surface temperature"> land surface temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20heat%20island" title=" urban heat island"> urban heat island</a>, <a href="https://publications.waset.org/abstracts/search?q=lahore%20pakistan" title=" lahore pakistan"> lahore pakistan</a> </p> <a href="https://publications.waset.org/abstracts/169005/investigating-the-effect-of-urban-expansion-on-the-urban-heat-island-and-land-use-land-cover-changes-the-case-study-of-lahore-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169005.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">77</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">8414</span> Change Detection of Vegetative Areas Using Land Use Land Cover Derived from NDVI of Desert Encroached Areas</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=T.%20O.%20Quddus"> T. O. Quddus</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=M.%20A.%20Modibbo"> M. A. Modibbo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Desertification is define as the changing of productive land into a desert as the result of ruination of land by man-induced soil erosion, which forces famers in the affected areas to move migrate or encourage into reserved areas in search of a fertile land for their farming activities. This study therefore used remote sensing imageries to determine the level of changes in the vegetative areas. To achieve that Normalized Difference of the Vegetative Index (NDVI), classified imageries and image slicing derived from landsat TM 1986, land sat ETM 1999 and Nigeria sat 1 2007 were used to determine changes in vegetations. From the Classified imageries it was discovered that there a more natural vegetation in classified images of 1986 than that of 1999 and 2007. This finding is also future in the three NDVI imageries, it was discovered that there is increased in high positive pixel value from 0.04 in 1986 to 0.22 in 1999 and to 0.32 in 2007. The figures in the three histogram also indicted that there is increased in vegetative areas from 29.15 Km2 in 1986, to 60.58 Km2 in 1999 and then to 109 Km2 in 2007. The study recommends among other things that there is need to restore natural vegetation through discouraging of farming activities in and around the natural vegetation in the study area. <p class="card-text"><strong>Keywords:</strong> <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=classified%20imageries" title=" classified imageries"> classified imageries</a>, <a href="https://publications.waset.org/abstracts/search?q=change%20detection" title=" change detection"> change detection</a>, <a href="https://publications.waset.org/abstracts/search?q=landsat" title=" landsat"> landsat</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation" title=" vegetation"> vegetation</a> </p> <a href="https://publications.waset.org/abstracts/4155/change-detection-of-vegetative-areas-using-land-use-land-cover-derived-from-ndvi-of-desert-encroached-areas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4155.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">360</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">8413</span> The Use of Remote Sensing in the Study of Vegetation Jebel Boutaleb, Setif, Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Missaoui">Khaled Missaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Amina%20Beldjazia"> Amina Beldjazia</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachid%20Gharzouli"> Rachid Gharzouli</a>, <a href="https://publications.waset.org/abstracts/search?q=Yamna%20Djellouli"> Yamna Djellouli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optical remote sensing makes use of visible, near infrared and short-wave infrared sensors to form images of the earth's surface by detecting the solar radiation reflected from targets on the ground. Different materials reflect and absorb differently at different wavelengths. Thus, the targets can be differentiated by their spectral reflectance signatures in the remotely sensed images. In this work, we are interested to study the distribution of vegetation in the massif forest of Boutaleb (North East of Algeria) which suffered between 1998 and 1999 very large fires. In this case, we use remote sensing with Landsat images from two dates (1984 and 2000) to see the results of these fires. Vegetation has a unique spectral signature which enables it to be distinguished readily from other types of land cover in an optical/near-infrared image. Normalized Difference Vegetation Index (NDVI) is calculated with ENVI 4.7 from Band 3 and 4. The results showed a very important floristic diversity in this forest. The comparison of NDVI from the two dates confirms that there is a decrease of the density of vegetation in this area due to repeated fires. <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=boutaleb" title=" boutaleb"> boutaleb</a>, <a href="https://publications.waset.org/abstracts/search?q=diversity" title=" diversity"> diversity</a>, <a href="https://publications.waset.org/abstracts/search?q=forest" title=" forest"> forest</a> </p> <a href="https://publications.waset.org/abstracts/23426/the-use-of-remote-sensing-in-the-study-of-vegetation-jebel-boutaleb-setif-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23426.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">560</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">8412</span> Multi-Temporal Urban Land Cover Mapping Using Spectral Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mst%20Ilme%20Faridatul">Mst Ilme Faridatul</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Wu"> Bo Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multi-temporal urban land cover mapping is of paramount importance for monitoring urban sprawl and managing the ecological environment. For diversified urban activities, it is challenging to map land covers in a complex urban environment. Spectral indices have proved to be effective for mapping urban land covers. To improve multi-temporal urban land cover classification and mapping, we evaluate the performance of three spectral indices, e.g. modified normalized difference bare-land index (MNDBI), tasseled cap water and vegetation index (TCWVI) and shadow index (ShDI). The MNDBI is developed to evaluate its performance of enhancing urban impervious areas by separating bare lands. A tasseled cap index, TCWVI is developed to evaluate its competence to detect vegetation and water simultaneously. The ShDI is developed to maximize the spectral difference between shadows of skyscrapers and water and enhance water detection. First, this paper presents a comparative analysis of three spectral indices using Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM) and Operational Land Imager (OLI) data. Second, optimized thresholds of the spectral indices are imputed to classify land covers, and finally, their performance of enhancing multi-temporal urban land cover mapping is assessed. The results indicate that the spectral indices are competent to enhance multi-temporal urban land cover mapping and achieves an overall classification accuracy of 93-96%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20cover" title="land cover">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=multi-temporal" title=" multi-temporal"> multi-temporal</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20indices" title=" spectral indices"> spectral indices</a> </p> <a href="https://publications.waset.org/abstracts/103491/multi-temporal-urban-land-cover-mapping-using-spectral-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103491.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">153</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">8411</span> Hydrological Revival Possibilities for River Assi: A Tributary of the River Ganga in the Middle Ganga Basin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anurag%20Mishra">Anurag Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Prabhat%20Kumar%20Singh"> Prabhat Kumar Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Anurag%20Ohri"> Anurag Ohri</a>, <a href="https://publications.waset.org/abstracts/search?q=Shishir%20Gaur"> Shishir Gaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Streams and rivulets are crucial in maintaining river networks and their hydrology, influencing downstream ecosystems, and connecting different watersheds of urban and rural areas. The river Assi, an urban river, once a lifeline for the locals, has degraded over time. Evidence, such as the presence of paleochannels and patterns of water bodies and settlements, suggests that the river Assi was initially an alluvial stream or rivulet that originated near Rishi Durvasha Ashram near Prayagraj, flowing approximately 120 km before joining the river Ganga at Assi ghat in Varanasi. Presently, a major challenge is that nearly 90% of its original channel has been silted and disappeared, with only the last 8 km retaining some semblance of a river. It is possible that initially, the river Assi branched off from the river Ganga and functioned as a Yazoo stream. In this study, paleochannels of the river Assi were identified using Landsat 5 imageries and SRTM DEM. The study employed the Normalized Difference Vegetation Seasonality Index (NDVSI) and Principal Component Analysis (PCA) of the Normalized Difference Vegetation Index (NDVI) to detect these paleochannels. The average elevation of the sub-basin at the Durvasha Rishi Ashram of river Assi is 96 meters, while it reduces to 80 meters near its confluence with the Ganga in Varanasi, resulting in a 16-meter elevation drop along its course. There are 81 subbasins covering an area of 83,241 square kilometers. It is possible that due to the increased resistance in the flow of river Assi near urban areas of Varanasi, a new channel, Morwa, has originated at an elevation of 87 meters, meeting river Varuna at an elevation of 79 meters. The difference in elevation is 8 meters. Furthermore, the study explored the possibility of restoring the paleochannel of the river Assi and nearby ponds and water bodies to improve the river's base flow and overall hydrological conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=River%20Assi" title="River Assi">River Assi</a>, <a href="https://publications.waset.org/abstracts/search?q=small%20river%20restoration" title=" small river restoration"> small river restoration</a>, <a href="https://publications.waset.org/abstracts/search?q=paleochannel%20identification" title=" paleochannel identification"> paleochannel identification</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=GIS" title=" GIS"> GIS</a> </p> <a href="https://publications.waset.org/abstracts/182248/hydrological-revival-possibilities-for-river-assi-a-tributary-of-the-river-ganga-in-the-middle-ganga-basin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182248.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">71</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">8410</span> Impact of Short-Term Drought on Vegetation Health Condition in the Kingdom of Saudi Arabia Using Space Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Ghoneim">E. Ghoneim</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Narron"> C. Narron</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Iqbal"> I. Iqbal</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Hassan"> I. Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Hammam"> E. Hammam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The scarcity of water is becoming a more prominent threat, especially in areas that are already arid in nature. Although the Kingdom of Saudi Arabia (KSA) is an arid country, its southwestern region offers a high variety of botanical landscapes, many of which are wooded forests, while the eastern and northern regions offer large areas of groundwater irrigated farmlands. At present, some parts of KSA, including forests and farmlands, have witnessed protracted and severe drought due to change in rainfall pattern as a result of global climate change. Such prolonged drought that last for several consecutive years is expected to cause deterioration of forested and pastured lands as well as cause crop failure in the KSA (e.g., wheat yield). An analysis to determine vegetation drought vulnerability and severity during the growing season (September-April) over a fourteen year period (2000-2014) in KSA was conducted using MODIS Terra imagery. The Vegetation Condition Index (VCI), derived from the Normalized Difference Vegetation Index (NDVI), and the Temperature Condition Index (TCI), derived from the Land Surface Temperature (LST) data was extracted from MODIS Terra Images. The VCI and TCI were then combined to compute the Vegetation Health Index (VHI). The VHI revealed the overall vegetation health for the area under investigation. A preliminary outcome of the modeled VHI over KSA, using averaged monthly vegetation data over a 14-year period, revealed that the vegetation health condition is deteriorating over time in both naturally vegetated areas and irrigated farmlands. The derived drought map for KSA indicates that both extreme and severe drought occurrences have considerably increased over the same study period. Moreover, based on the cumulative average of drought frequency in each governorate of KSA it was determined that Makkah and Jizan governorates to the east and southwest, witness the most frequency of extreme drought, whereas Tabuk to the northwest, exhibits the less extreme drought frequency. Areas where drought is extreme or severe would most likely have negative influences on agriculture, ecosystems, tourism, and even human welfare. With the drought risk map the kingdom could make informed land management decisions including were to continue with agricultural endeavors and protect forested areas and even where to develop new settlements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drought" title="drought">drought</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation%20health%20condition" title=" vegetation health condition"> vegetation health condition</a>, <a href="https://publications.waset.org/abstracts/search?q=TCI" title=" TCI"> TCI</a>, <a href="https://publications.waset.org/abstracts/search?q=Saudi%20Arabia" title=" Saudi Arabia"> Saudi Arabia</a> </p> <a href="https://publications.waset.org/abstracts/18057/impact-of-short-term-drought-on-vegetation-health-condition-in-the-kingdom-of-saudi-arabia-using-space-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18057.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">386</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">8409</span> Identification of Healthy and BSR-Infected Oil Palm Trees Using Color Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siti%20Khairunniza-Bejo">Siti Khairunniza-Bejo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yusnida%20Yusoff"> Yusnida Yusoff</a>, <a href="https://publications.waset.org/abstracts/search?q=Nik%20Salwani%20Nik%20Yusoff"> Nik Salwani Nik Yusoff</a>, <a href="https://publications.waset.org/abstracts/search?q=Idris%20Abu%20Seman"> Idris Abu Seman</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamad%20Izzuddin%20Anuar"> Mohamad Izzuddin Anuar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of the oil palm plantations have been threatened by Basal Stem Rot (BSR) disease which causes serious economic impact. This study was conducted to identify the healthy and BSR-infected oil palm tree using thirteen color indices. Multispectral and thermal camera was used to capture 216 images of the leaves taken from frond number 1, 9 and 17. Indices of normalized difference vegetation index (NDVI), red (R), green (G), blue (B), near infrared (NIR), green – blue (GB), green/blue (G/B), green – red (GR), green/red (G/R), hue (H), saturation (S), intensity (I) and thermal index (T) were used. From this study, it can be concluded that G index taken from frond number 9 is the best index to differentiate between the healthy and BSR-infected oil palm trees. It not only gave high value of correlation coefficient (R=-0.962), but also high value of separation between healthy and BSR-infected oil palm tree. Furthermore, power and S model developed using G index gave the highest R2 value which is 0.985. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=oil%20palm" title="oil palm">oil palm</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=disease" title=" disease"> disease</a>, <a href="https://publications.waset.org/abstracts/search?q=leaves" title=" leaves"> leaves</a> </p> <a href="https://publications.waset.org/abstracts/28605/identification-of-healthy-and-bsr-infected-oil-palm-trees-using-color-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28605.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">498</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">8408</span> Change Detection of Vegetative Areas Using Land Use Land Cover of Desertification Vulnerable Areas in 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.%20Sabo%20A.%20Babanyara"> Y. Y. Sabo A. 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=A.%20K.%20Mutari"> A. K. Mutari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study used the Normalized Difference Vegetation Index (NDVI) and maps compiled from the classification of Landsat TM and Landsat ETM images of 1986 and 1999 respectively and Nigeria sat 1 images of 2007 to quantify changes in land use and land cover in selected areas of Nigeria covering 143,609 hectares that are threatened by the encroaching Sahara desert. The results of this investigation revealed a decrease in natural vegetation over the three time slices (1986, 1999 and 2007) which was characterised by an increase in high positive pixel values from 0.04 in 1986 to 0.22 and 0.32 in 1999 and 2007 respectively and, a decrease in natural vegetation from 74,411.60ha in 1986 to 28,591.93ha and 21,819.19ha in 1999 and 2007 respectively. The same results also revealed a periodic trend in which there was progressive increase in the cultivated area from 60,191.87ha in 1986 to 104,376.07ha in 1999 and a terminal decrease to 88,868.31ha in 2007. These findings point to expansion of vegetated and cultivated areas in in the initial period between 1988 and 1996 and reversal of these increases in the terminal period between 1988 and 1996. The study also revealed progressive expansion of built-up areas from 1, 681.68ha in 1986 to 2,661.82ha in 1999 and to 3,765.35ha in 2007. These results argue for the urgent need to protect and conserve the depleting natural vegetation by adopting sustainable human resource use practices i.e. intensive farming in order to minimize persistent depletion of natural vegetation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=changes" title="changes">changes</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=desertification" title=" desertification"> desertification</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation%20changes" title=" vegetation changes"> vegetation changes</a> </p> <a href="https://publications.waset.org/abstracts/8292/change-detection-of-vegetative-areas-using-land-use-land-cover-of-desertification-vulnerable-areas-in-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8292.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">387</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">8407</span> Enhanced Image Representation for Deep Belief Network Classification of Hyperspectral Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khitem%20Amiri">Khitem Amiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Farah"> Mohamed Farah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20images" title="hyperspectral images">hyperspectral images</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20belief%20network" title=" deep belief network"> deep belief network</a>, <a href="https://publications.waset.org/abstracts/search?q=radiometric%20indices" title=" radiometric indices"> radiometric indices</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/93458/enhanced-image-representation-for-deep-belief-network-classification-of-hyperspectral-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93458.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">280</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">8406</span> Utility of Geospatial Techniques in Delineating Groundwater-Dependent Ecosystems in Arid Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mangana%20B.%20Rampheri">Mangana B. Rampheri</a>, <a href="https://publications.waset.org/abstracts/search?q=Timothy%20Dube"> Timothy Dube</a>, <a href="https://publications.waset.org/abstracts/search?q=Farai%20Dondofema"> Farai Dondofema</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatenda%20Dalu"> Tatenda Dalu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identifying and delineating groundwater-dependent ecosystems (GDEs) is critical to the well understanding of the GDEs spatial distribution as well as groundwater allocation. However, this information is inadequately understood due to limited available data for the most area of concerns. Thus, this study aims to address this gap using remotely sensed, analytical hierarchy process (AHP) and in-situ data to identify and delineate GDEs in Khakea-Bray Transboundary Aquifer. Our study developed GDEs index, which integrates seven explanatory variables, namely, Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Land-use and landcover (LULC), slope, Topographic Wetness Index (TWI), flow accumulation and curvature. The GDEs map was delineated using the weighted overlay tool in ArcGIS environments. The map was spatially classified into two classes, namely, GDEs and Non-GDEs. The results showed that only 1,34 % (721,91 km2) of the area is characterised by GDEs. Finally, groundwater level (GWL) data was used for validation through correlation analysis. Our results indicated that: 1) GDEs are concentrated at the northern, central, and south-western part of our study area, and 2) the validation results showed that GDEs classes do not overlap with GWL located in the 22 boreholes found in the given area. However, the results show a possible delineation of GDEs in the study area using remote sensing and GIS techniques along with AHP. The results of this study further contribute to identifying and delineating priority areas where appropriate water conservation programs, as well as strategies for sustainable groundwater development, can be implemented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytical%20hierarchy%20process%20%28AHP%29" title="analytical hierarchy process (AHP)">analytical hierarchy process (AHP)</a>, <a href="https://publications.waset.org/abstracts/search?q=explanatory%20variables" title=" explanatory variables"> explanatory variables</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater-dependent%20ecosystems%20%28GDEs%29" title=" groundwater-dependent ecosystems (GDEs)"> groundwater-dependent ecosystems (GDEs)</a>, <a href="https://publications.waset.org/abstracts/search?q=khakea-bray%20transboundary%20aquifer" title=" khakea-bray transboundary aquifer"> khakea-bray transboundary aquifer</a>, <a href="https://publications.waset.org/abstracts/search?q=sentinel-2" title=" sentinel-2"> sentinel-2</a> </p> <a href="https://publications.waset.org/abstracts/153593/utility-of-geospatial-techniques-in-delineating-groundwater-dependent-ecosystems-in-arid-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153593.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">108</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">8405</span> Cotton Crops Vegetative Indices Based Assessment Using Multispectral Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Shahzad%20Shifa">Muhammad Shahzad Shifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Amna%20Shifa"> Amna Shifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Omar"> Muhammad Omar</a>, <a href="https://publications.waset.org/abstracts/search?q=Aamir%20Shahzad"> Aamir Shahzad</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahmat%20Ali%20Khan"> Rahmat Ali Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many applications of remote sensing to vegetation and crop response depend on spectral properties of individual leaves and plants. Vegetation indices are usually determined to estimate crop biophysical parameters like crop canopies and crop leaf area indices with the help of remote sensing. Cotton crops assessment is performed with the help of vegetative indices. Remotely sensed images from an optical multispectral radiometer MSR5 are used in this study. The interpretation is based on the fact that different materials reflect and absorb light differently at different wavelengths. Non-normalized and normalized forms of these datasets are analyzed using two complementary data mining algorithms; K-means and K-nearest neighbor (KNN). Our analysis shows that the use of normalized reflectance data and vegetative indices are suitable for an automated assessment and decision making. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cotton" title="cotton">cotton</a>, <a href="https://publications.waset.org/abstracts/search?q=condition%20assessment" title=" condition assessment"> condition assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=KNN%20algorithm" title=" KNN algorithm"> KNN algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=MSR5" title=" MSR5"> MSR5</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation%20indices" title=" vegetation indices"> vegetation indices</a> </p> <a href="https://publications.waset.org/abstracts/103787/cotton-crops-vegetative-indices-based-assessment-using-multispectral-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103787.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">333</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">8404</span> Some New Bounds for a Real Power of the Normalized Laplacian Eigenvalues</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ay%C5%9Fe%20Dilek%20Maden">Ayşe Dilek Maden</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For a given a simple connected graph, we present some new bounds via a new approach for a special topological index given by the sum of the real number power of the non-zero normalized Laplacian eigenvalues. To use this approach presents an advantage not only to derive old and new bounds on this topic but also gives an idea how some previous results in similar area can be developed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=degree%20Kirchhoff%20index" title="degree Kirchhoff index">degree Kirchhoff index</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20Laplacian%20eigenvalue" title=" normalized Laplacian eigenvalue"> normalized Laplacian eigenvalue</a>, <a href="https://publications.waset.org/abstracts/search?q=spanning%20tree" title=" spanning tree"> spanning tree</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20connected%20graph" title=" simple connected graph"> simple connected graph</a> </p> <a href="https://publications.waset.org/abstracts/13999/some-new-bounds-for-a-real-power-of-the-normalized-laplacian-eigenvalues" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13999.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">366</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">8403</span> Artificial Neural Network and Satellite Derived Chlorophyll Indices for Estimation of Wheat Chlorophyll Content under Rainfed Condition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Naveed%20Tahir">Muhammad Naveed Tahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Yingkuan"> Wang Yingkuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Huang%20Wenjiang"> Huang Wenjiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Raheel%20Osman"> Raheel Osman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Numerous models used in prediction and decision-making process but most of them are linear in natural environment, and linear models reach their limitations with non-linearity in data. Therefore accurate estimation is difficult. Artificial Neural Networks (ANN) found extensive acceptance to address the modeling of the complex real world for the non-linear environment. ANN’s have more general and flexible functional forms than traditional statistical methods can effectively deal with. The link between information technology and agriculture will become more firm in the near future. Monitoring crop biophysical properties non-destructively can provide a rapid and accurate understanding of its response to various environmental influences. Crop chlorophyll content is an important indicator of crop health and therefore the estimation of crop yield. In recent years, remote sensing has been accepted as a robust tool for site-specific management by detecting crop parameters at both local and large scales. The present research combined the ANN model with satellite-derived chlorophyll indices from LANDSAT 8 imagery for predicting real-time wheat chlorophyll estimation. The cloud-free scenes of LANDSAT 8 were acquired (Feb-March 2016-17) at the same time when ground-truthing campaign was performed for chlorophyll estimation by using SPAD-502. Different vegetation indices were derived from LANDSAT 8 imagery using ERADAS Imagine (v.2014) software for chlorophyll determination. The vegetation indices were including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Absorbed Ratio Index (CARI), Modified Chlorophyll Absorbed Ratio Index (MCARI) and Transformed Chlorophyll Absorbed Ratio index (TCARI). For ANN modeling, MATLAB and SPSS (ANN) tools were used. Multilayer Perceptron (MLP) in MATLAB provided very satisfactory results. For training purpose of MLP 61.7% of the data, for validation purpose 28.3% of data and rest 10% of data were used to evaluate and validate the ANN model results. For error evaluation, sum of squares error and relative error were used. ANN model summery showed that sum of squares error of 10.786, the average overall relative error was .099. The MCARI and NDVI were revealed to be more sensitive indices for assessing wheat chlorophyll content with the highest coefficient of determination R²=0.93 and 0.90 respectively. The results suggested that use of high spatial resolution satellite imagery for the retrieval of crop chlorophyll content by using ANN model provides accurate, reliable assessment of crop health status at a larger scale which can help in managing crop nutrition requirement in real time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANN" title="ANN">ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=chlorophyll%20content" title=" chlorophyll content"> chlorophyll content</a>, <a href="https://publications.waset.org/abstracts/search?q=chlorophyll%20indices" title=" chlorophyll indices"> chlorophyll indices</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=wheat" title=" wheat"> wheat</a> </p> <a href="https://publications.waset.org/abstracts/95399/artificial-neural-network-and-satellite-derived-chlorophyll-indices-for-estimation-of-wheat-chlorophyll-content-under-rainfed-condition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95399.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">8402</span> Multi-Temporal Analysis of Vegetation Change within High Contaminated Watersheds by Superfund Sites in Wisconsin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Punwath%20Prum">Punwath Prum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Superfund site is recognized publicly to be a severe environmental problem to surrounding communities and biodiversity due to its hazardous chemical waste from industrial activities. It contaminates the soil and water but also is a leading potential point-source pollution affecting ecosystem in watershed areas from chemical substances. The risks of Superfund site on watershed can be effectively measured by utilizing publicly available data and geospatial analysis by free and open source application. This study analyzed the vegetation change within high risked contaminated watersheds in Wisconsin. The high risk watersheds were measured by which watershed contained high number Superfund sites. The study identified two potential risk watersheds in Lafayette and analyzed the temporal changes of vegetation within the areas based on Normalized difference vegetation index (NDVI) analysis. The raster statistic was used to compare the change of NDVI value over the period. The analysis results showed that the NDVI value within the Superfund sites’ boundary has a significant lower value than nearby surrounding and provides an analogy for environmental hazard affect by the chemical contamination in Superfund site. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=soil%20contamination" title="soil contamination">soil contamination</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20analysis" title=" spatial analysis"> spatial analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=watershed" title=" watershed"> watershed</a> </p> <a href="https://publications.waset.org/abstracts/119900/multi-temporal-analysis-of-vegetation-change-within-high-contaminated-watersheds-by-superfund-sites-in-wisconsin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119900.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">140</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">8401</span> Contribution of Remote Sensing and GIS to the Study of the Impact of the Salinity of Sebkhas on the Quality of Groundwater: Case of Sebkhet Halk El Menjel (Sousse)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gannouni%20Sonia">Gannouni Sonia</a>, <a href="https://publications.waset.org/abstracts/search?q=Hammami%20Asma"> Hammami Asma</a>, <a href="https://publications.waset.org/abstracts/search?q=Saidi%20Salwa"> Saidi Salwa</a>, <a href="https://publications.waset.org/abstracts/search?q=Rebai%20Noamen"> Rebai Noamen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Water resources in Tunisia have experienced quantitative and qualitative degradation, especially when talking about wetlands and Sbekhas. Indeed, the objective of this work is to study the spatio-temporal evolution of salinity for 29 years (from 1987 to 2016). A study of the connection between surface water and groundwater is necessary to know the degree of influence of the Sebkha brines on the water table. The evolution of surface salinity is determined by remote sensing based on Landsat TM and OLI/TIRS satellite images of the years 1987, 2007, 2010, and 2016. The processing of these images allowed us to determine the NDVI(Normalized Difference Vegetation Index), the salinity index, and the surface temperature around Sebkha. In addition, through a geographic information system(GIS), we could establish a map of the distribution of salinity in the subsurface of the water table of Chott Mariem and Hergla/SidiBouAli/Kondar. The results of image processing and the calculation of the index and surface temperature show an increase in salinity downstream of in addition to the sebkha and the development of vegetation cover upstream and the western part of the sebkha. This richness may be due both to contamination by seawater infiltration from the barrier beach of Hergla as well as the passage of groundwater to the sebkha. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatio-temporal%20monitoring" title="spatio-temporal monitoring">spatio-temporal monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=salinity" title=" salinity"> salinity</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=NDVI" title=" NDVI"> NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=sebkha" title=" sebkha"> sebkha</a> </p> <a href="https://publications.waset.org/abstracts/150699/contribution-of-remote-sensing-and-gis-to-the-study-of-the-impact-of-the-salinity-of-sebkhas-on-the-quality-of-groundwater-case-of-sebkhet-halk-el-menjel-sousse" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150699.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">132</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=normalized%20difference%20vegetation%20index&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=normalized%20difference%20vegetation%20index&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=normalized%20difference%20vegetation%20index&page=4">4</a></li> <li class="page-item"><a class="page-link" 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