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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="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> 3998</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: 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">3998</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">3997</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">3996</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">3995</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">3994</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">3993</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">3992</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">3991</span> Rapid Assessment the Ability of Forest Vegetation in Kulonprogo to Store Carbon Using Multispectral Satellite Imagery and Vegetation Index</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ima%20Rahmawati">Ima Rahmawati</a>, <a href="https://publications.waset.org/abstracts/search?q=Nur%20Hafizul%20Kalam"> Nur Hafizul Kalam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Development of industrial and economic sectors in various countries very rapidly caused raising the greenhouse gas (GHG) emissions. Greenhouse gases are dominated by carbon dioxide (CO2) and methane (CH4) in the atmosphere that make the surface temperature of the earth always increase. The increasing gases caused by incomplete combustion of fossil fuels such as petroleum and coals and also high rate of deforestation. Yogyakarta Special Province which every year always become tourist destination, has a great potency in increasing of greenhouse gas emissions mainly from the incomplete combustion. One of effort to reduce the concentration of gases in the atmosphere is keeping and empowering the existing forests in the Province of Yogyakarta, especially forest in Kulonprogro is to be maintained the greenness so that it can absorb and store carbon maximally. Remote sensing technology can be used to determine the ability of forests to absorb carbon and it is connected to the density of vegetation. The purpose of this study is to determine the density of the biomass of forest vegetation and determine the ability of forests to store carbon through Photo-interpretation and Geographic Information System approach. Remote sensing imagery that used in this study is LANDSAT 8 OLI year 2015 recording. LANDSAT 8 OLI imagery has 30 meters spatial resolution for multispectral bands and it can give general overview the condition of the carbon stored from every density of existing vegetation. The method is the transformation of vegetation index combined with allometric calculation of field data then doing regression analysis. The results are model maps of density and capability level of forest vegetation in Kulonprogro, Yogyakarta in storing carbon. <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=carbon" title=" carbon"> carbon</a>, <a href="https://publications.waset.org/abstracts/search?q=kulonprogo" title=" kulonprogo"> kulonprogo</a>, <a href="https://publications.waset.org/abstracts/search?q=forest%20vegetation" title=" forest vegetation"> forest vegetation</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/41473/rapid-assessment-the-ability-of-forest-vegetation-in-kulonprogo-to-store-carbon-using-multispectral-satellite-imagery-and-vegetation-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41473.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">397</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">3990</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">3989</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">3988</span> Estimation of Reservoir Capacity and Sediment Deposition Using Remote Sensing Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Odai%20Ibrahim%20Mohammed%20Al%20Balasmeh">Odai Ibrahim Mohammed Al Balasmeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Tapas%20Karmaker"> Tapas Karmaker</a>, <a href="https://publications.waset.org/abstracts/search?q=Richa%20Babbar"> Richa Babbar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the reservoir capacity and sediment deposition were estimated using remote sensing data. The satellite images were synchronized with water level and storage capacity to find out the change in sediment deposition due to soil erosion and transport by streamflow. The water bodies spread area was estimated using vegetation indices, e.g., normalize differences vegetation index (NDVI) and normalize differences water index (NDWI). The 3D reservoir bathymetry was modeled by integrated water level, storage capacity, and area. From the models of different time span, the change in reservoir storage capacity was estimated. Another reservoir with known water level, storage capacity, area, and sediment deposition was used to validate the estimation technique. The t-test was used to assess the results between observed and estimated reservoir capacity and sediment deposition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellite%20data" title="satellite data">satellite data</a>, <a href="https://publications.waset.org/abstracts/search?q=normalize%20differences%20vegetation%20index" title=" normalize differences vegetation index"> normalize differences vegetation index</a>, <a href="https://publications.waset.org/abstracts/search?q=NDVI" title=" NDVI"> NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=normalize%20differences%20water%20index" title=" normalize differences water index"> normalize differences water index</a>, <a href="https://publications.waset.org/abstracts/search?q=NDWI" title=" NDWI"> NDWI</a>, <a href="https://publications.waset.org/abstracts/search?q=reservoir%20capacity" title=" reservoir capacity"> reservoir capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=sedimentation" title=" sedimentation"> sedimentation</a>, <a href="https://publications.waset.org/abstracts/search?q=t-test%20hypothesis" title=" t-test hypothesis"> t-test hypothesis</a> </p> <a href="https://publications.waset.org/abstracts/125321/estimation-of-reservoir-capacity-and-sediment-deposition-using-remote-sensing-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125321.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">167</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">3987</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">3986</span> Modeling Floodplain Vegetation Response to Groundwater Variability Using ArcSWAT Hydrological Model, Moderate Resolution Imaging Spectroradiometer - Normalised Difference Vegetation Index Data, and Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Newton%20Muhury">Newton Muhury</a>, <a href="https://publications.waset.org/abstracts/search?q=Armando%20A.%20Apan"> Armando A. Apan</a>, <a href="https://publications.waset.org/abstracts/search?q=Tek%20Maraseni"> Tek Maraseni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study modelled the relationships between vegetation response and available water below the soil surface using the Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS) generated Normalised Difference Vegetation Index (NDVI) and soil water content (SWC) data. The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT was used in ArcGIS for the groundwater analysis. The SWAT model was calibrated and validated in SWAT-CUP software using 10 years (2001-2010) of monthly streamflow data. The average Nash-Sutcliffe Efficiency during the calibration and validation was 0.54 and 0.51, respectively, indicating that the model performances were good. Twenty years (2001-2020) of monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) and soil water content for 43 sub-basins were analysed using the WEKA, machine learning tool with a selection of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The modelling results show that different types of vegetation response and soil water content vary in the dry and wet season. For example, the model generated high positive relationships (r=0.76, 0.73, and 0.81) between the measured and predicted NDVI values of all vegetation in the study area against the groundwater flow (GW), soil water content (SWC), and the combination of these two variables, respectively, during the dry season. However, these relationships were reduced by 36.8% (r=0.48) and 13.6% (r=0.63) against GW and SWC, respectively, in the wet season. On the other hand, the model predicted a moderate positive relationship (r=0.63) between shrub vegetation type and soil water content during the dry season, which was reduced by 31.7% (r=0.43) during the wet season. Our models also predicted that vegetation in the top location (upper part) of the sub-basin is highly responsive to GW and SWC (r=0.78, and 0.70) during the dry season. The results of this study indicate the study region is suitable for seasonal crop production in dry season. Moreover, the results predicted that the growth of vegetation in the top-point location is highly dependent on groundwater flow in both dry and wet seasons, and any instability or long-term drought can negatively affect these floodplain vegetation communities. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ArcSWAT" title="ArcSWAT">ArcSWAT</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=floodplain%20vegetation" title=" floodplain vegetation"> floodplain vegetation</a>, <a href="https://publications.waset.org/abstracts/search?q=MODIS%20NDVI" title=" MODIS NDVI"> MODIS NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater" title=" groundwater"> groundwater</a> </p> <a href="https://publications.waset.org/abstracts/156659/modeling-floodplain-vegetation-response-to-groundwater-variability-using-arcswat-hydrological-model-moderate-resolution-imaging-spectroradiometer-normalised-difference-vegetation-index-data-and-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156659.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">119</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">3985</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">3984</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">3983</span> Estimation of Carbon Dioxide Absorption in DKI Jakarta Green Space</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mario%20Belseran">Mario Belseran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The issue of climate change become world attention where one of them increase in air temperature due to greenhouse gas emissions. This climate change is caused by gases in the atmosphere, one of which is CO2. DKI Jakarta as the capital has a dense population with a variety of existing land use. Land use that is dominated by settlements resulting in fewer green space, which functions to absorb atmospheric CO2. Image interpretation SPOT-7 is used to determine the greenness level of vegetation on a green space using the vegetation index NDVI, EVI, GNDVI and OSAVI. Measuring the diameter and height of trees were also performed to obtain the value of biomass that will be used as the CO2 absorption value. The CO2 absorption value that spread in Jakarta are classified into three classes: high, medium, and low. The distribution pattern of CO2 absorption value at green space in Jakarta dominance in the medium class with the distribution pattern is located in South Jakarta, East Jakarta, North Jakarta and West Jakarta. The distribution pattern of green space in Jakarta scattered randomly and more dominate in East Jakarta and South Jakarta <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=carbon%20dioxide" title="carbon dioxide">carbon dioxide</a>, <a href="https://publications.waset.org/abstracts/search?q=DKI%20Jakarta" title=" DKI Jakarta"> DKI Jakarta</a>, <a href="https://publications.waset.org/abstracts/search?q=green%20space" title=" green space"> green space</a>, <a href="https://publications.waset.org/abstracts/search?q=SPOT-7" title=" SPOT-7"> SPOT-7</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/59940/estimation-of-carbon-dioxide-absorption-in-dki-jakarta-green-space" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59940.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">3982</span> An Assessment of Floodplain Vegetation Response to Groundwater Changes Using the Soil & Water Assessment Tool Hydrological Model, Geographic Information System, and Machine Learning in the Southeast Australian River Basin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Newton%20Muhury">Newton Muhury</a>, <a href="https://publications.waset.org/abstracts/search?q=Armando%20A.%20Apan"> Armando A. Apan</a>, <a href="https://publications.waset.org/abstracts/search?q=Tek%20N.%20Marasani"> Tek N. Marasani</a>, <a href="https://publications.waset.org/abstracts/search?q=Gebiaw%20T.%20Ayele"> Gebiaw T. Ayele</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The changing climate has degraded freshwater availability in Australia that influencing vegetation growth to a great extent. This study assessed the vegetation responses to groundwater using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). A hydrological model, SWAT, has been set up in a southeast Australian river catchment for groundwater analysis. The model was calibrated and validated against monthly streamflow from 2001 to 2006 and 2007 to 2010, respectively. The SWAT simulated soil water content for 43 sub-basins and monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) were applied in the machine learning tool, Waikato Environment for Knowledge Analysis (WEKA), using two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The assessment shows that different types of vegetation response and soil water content vary in the dry and wet seasons. The WEKA model generated high positive relationships (r = 0.76, 0.73, and 0.81) between NDVI values of all vegetation in the sub-basins against soil water content (SWC), the groundwater flow (GW), and the combination of these two variables, respectively, during the dry season. However, these responses were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ArcSWAT" title="ArcSWAT">ArcSWAT</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=floodplain%20vegetation" title=" floodplain vegetation"> floodplain vegetation</a>, <a href="https://publications.waset.org/abstracts/search?q=MODIS%20NDVI" title=" MODIS NDVI"> MODIS NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater" title=" groundwater"> groundwater</a> </p> <a href="https://publications.waset.org/abstracts/166605/an-assessment-of-floodplain-vegetation-response-to-groundwater-changes-using-the-soil-water-assessment-tool-hydrological-model-geographic-information-system-and-machine-learning-in-the-southeast-australian-river-basin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166605.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">101</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">3981</span> Extraction of Urban Land Features from TM Landsat Image Using the Land Features Index and Tasseled Cap Transformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Bouhennache">R. Bouhennache</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Bouden"> T. Bouden</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20Taleb"> A. A. Taleb</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Chaddad"> A. Chaddad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose a method to map the urban areas. The method uses an arithmetic calculation processed from the land features indexes and Tasseled cap transformation TC of multi spectral Thematic Mapper Landsat TM image. For this purpose the derived indexes image from the original image such SAVI the soil adjusted vegetation index, UI the urban Index, and EBBI the enhanced built up and bareness index were staked to form a new image and the bands were uncorrelated, also the Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) supervised classification approaches were first applied on the new image TM data using the reference spectra of the spectral library and subsequently the four urban, vegetation, water and soil land cover categories were extracted with their accuracy assessment.The urban features were represented using a logic calculation applied to the brightness, UI-SAVI, NDBI-greenness and EBBI- brightness data sets. The study applied to Blida and mentioned that the urban features can be mapped with an accuracy ranging from 92 % to 95%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EBBI" title="EBBI">EBBI</a>, <a href="https://publications.waset.org/abstracts/search?q=SAVI" title=" SAVI"> SAVI</a>, <a href="https://publications.waset.org/abstracts/search?q=Tasseled%20Cap%20Transformation" title=" Tasseled Cap Transformation"> Tasseled Cap Transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=UI" title=" UI"> UI</a> </p> <a href="https://publications.waset.org/abstracts/33037/extraction-of-urban-land-features-from-tm-landsat-image-using-the-land-features-index-and-tasseled-cap-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33037.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">482</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">3980</span> Relationship between Chlorophyl Content and Calculated Index Values of Citrus Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Namik%20Kemal%20Sonmez">Namik Kemal Sonmez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based passive remote sensing technologies have been widely used in many plant species. However, use of these techniques in orange trees is limited. In this study, the relationships between chlorophyll content (Chl) and calculated red edge (RE) and vegetation index values of the citrus leave at different growth stages were formed the basis for the analysis. Canopy reflectance by hand-held spectroradiometer and total Chl analysis at the lab were measured simultaneously, from the random samples taken from four different parts of an orange orchard. Plant materials consisted of four different age groups of 15, 20, 25, and 30 years old orange trees. Reflectance measurements were conducted between 450 and 900 nanometer (nm) wavelength at four different bands (3 visible bands and 1 near-infrared band) at the four basic physiological periods (flowering, fruit setting, fruit maturity, and dormancy) of orange trees. According to the statistical analysis conducted, there was a strong relationship between the chlorophyll content and calculated indexes (p ≤ 0.01; R²= 0.925 at red edge and R²= 0.986 at vegetation index) at the fruit setting stage of 20 years old trees. Again at this stage, fruit setting, total Chl content values among all orange trees were significantly correlated at the RE and VI with the R² values of 0.672 and 0.635 at the 0.001 level, respectively. This indicated that the relationships between Chl content and index values were very strong at this stage, in comparison to the other stages. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spectroradiometer" title="spectroradiometer">spectroradiometer</a>, <a href="https://publications.waset.org/abstracts/search?q=citrus" title=" citrus"> citrus</a>, <a href="https://publications.waset.org/abstracts/search?q=chlorophyll" title=" chlorophyll"> chlorophyll</a>, <a href="https://publications.waset.org/abstracts/search?q=reflectance" title=" reflectance"> reflectance</a>, <a href="https://publications.waset.org/abstracts/search?q=index" title=" index"> index</a> </p> <a href="https://publications.waset.org/abstracts/29015/relationship-between-chlorophyl-content-and-calculated-index-values-of-citrus-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29015.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">373</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">3979</span> Effects of Soil Erosion on Vegetation Development</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Josephine%20Wanja%20Nyatia">Josephine Wanja Nyatia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The relationship between vegetation and soil erosion deserves attention due to its scientific importance and practical applications. A great deal of information is available about the mechanisms and benefits of vegetation in the control of soil erosion, but the effects of soil erosion on vegetation development and succession is poorly documented. Research shows that soil erosion is the most important driving force for the degradation of upland and mountain ecosystems. Soil erosion interferes with the process of plant community development and vegetation succession, commencing with seed formation and impacting throughout the whole growth phase and affecting seed availability, dispersal, germination and establishment, plant community structure and spatial distribution. There have been almost no studies on the effects of soil erosion on seed development and availability, of surface flows on seed movement and redistribution, and their influences on soil seed bank and on vegetation establishment and distribution. However, these effects may be the main cause of low vegetation cover in regions of high soil erosion activity, and these issues need to be investigated. Moreover, soil erosion is not only a negative influence on vegetation succession and restoration but also a driving force of plant adaptation and evolution. Consequently, we need to study the effects of soil erosion on ecological processes and on development and regulation of vegetation succession from the points of view of pedology and vegetation, plant and seed ecology, and to establish an integrated theory and technology for deriving practical solutions to soil erosion problems <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=soil%20erosion" title="soil erosion">soil erosion</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation" title=" vegetation"> vegetation</a>, <a href="https://publications.waset.org/abstracts/search?q=development" title=" development"> development</a>, <a href="https://publications.waset.org/abstracts/search?q=seed%20availability" title=" seed availability"> seed availability</a> </p> <a href="https://publications.waset.org/abstracts/167892/effects-of-soil-erosion-on-vegetation-development" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167892.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">85</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">3978</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">3977</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">3976</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">3975</span> Automatic Change Detection for High-Resolution Satellite Images of Urban and Suburban Areas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Antigoni%20Panagiotopoulou">Antigoni Panagiotopoulou</a>, <a href="https://publications.waset.org/abstracts/search?q=Lemonia%20Ragia"> Lemonia Ragia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> High-resolution satellite images can provide detailed information about change detection on the earth. In the present work, QuickBird images of spatial resolution 60 cm/pixel and WorldView images of resolution 30 cm/pixel are utilized to perform automatic change detection in urban and suburban areas of Crete, Greece. There is a relative time difference of 13 years among the satellite images. Multiindex scene representation is applied on the images to classify the scene into buildings, vegetation, water and ground. Then, automatic change detection is made possible by pixel-per-pixel comparison of the classified multi-temporal images. The vegetation index and the water index which have been developed in this study prove effective. Furthermore, the proposed change detection approach not only indicates whether changes have taken place or not but also provides specific information relative to the types of changes. Experimentations with other different scenes in the future could help optimize the proposed spectral indices as well as the entire change detection methodology. <p class="card-text"><strong>Keywords:</strong> <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=multiindex%20scene%20representation" title=" multiindex scene representation"> multiindex scene representation</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20index" title=" spectral index"> spectral index</a>, <a href="https://publications.waset.org/abstracts/search?q=QuickBird" title=" QuickBird"> QuickBird</a>, <a href="https://publications.waset.org/abstracts/search?q=WorldView" title=" WorldView"> WorldView</a> </p> <a href="https://publications.waset.org/abstracts/132460/automatic-change-detection-for-high-resolution-satellite-images-of-urban-and-suburban-areas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132460.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">136</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">3974</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">3973</span> Mean Velocity Modeling of Open-Channel Flow with Submerged Vegetation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mabrouka%20Morri">Mabrouka Morri</a>, <a href="https://publications.waset.org/abstracts/search?q=Amel%20Soualmia"> Amel Soualmia</a>, <a href="https://publications.waset.org/abstracts/search?q=Philippe%20Belleudy"> Philippe Belleudy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vegetation affects the mean and turbulent flow structure. It may increase flood risks and sediment transport. Therefore, it is important to develop analytical approaches for the bed shear stress on vegetated bed, to predict resistance caused by vegetation. In the recent years, experimental and numerical models have both been developed to model the effects of submerged vegetation on open-channel flow. In this paper, different analytic models are compared and tested using the criteria of deviation, to explore their capacity for predicting the mean velocity and select the suitable one that will be applied in real case of rivers. The comparison between the measured data in vegetated flume and simulated mean velocities indicated, a good performance, in the case of rigid vegetation, whereas, Huthoff model shows the best agreement with a high coefficient of determination (R2=80%) and the smallest error in the prediction of the average velocities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytic%20models" title="analytic models">analytic models</a>, <a href="https://publications.waset.org/abstracts/search?q=comparison" title=" comparison"> comparison</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20velocity" title=" mean velocity"> mean velocity</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation" title=" vegetation"> vegetation</a> </p> <a href="https://publications.waset.org/abstracts/21381/mean-velocity-modeling-of-open-channel-flow-with-submerged-vegetation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21381.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">276</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">3972</span> Geomatic Techniques to Filter Vegetation from Point Clouds</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Amparo%20N%C3%BA%C3%B1ez-Andr%C3%A9s">M. Amparo Núñez-Andrés</a>, <a href="https://publications.waset.org/abstracts/search?q=Felipe%20Buill"> Felipe Buill</a>, <a href="https://publications.waset.org/abstracts/search?q=Albert%20Prades"> Albert Prades</a> </p> <p class="card-text"><strong>Abstract:</strong></p> More and more frequently, geomatics techniques such as terrestrial laser scanning or digital photogrammetry, either terrestrial or from drones, are being used to obtain digital terrain models (DTM) used for the monitoring of geological phenomena that cause natural disasters, such as landslides, rockfalls, debris-flow. One of the main multitemporal analyses developed from these models is the quantification of volume changes in the slopes and hillsides, either caused by erosion, fall, or land movement in the source area or sedimentation in the deposition zone. To carry out this task, it is necessary to filter the point clouds of all those elements that do not belong to the slopes. Among these elements, vegetation stands out as it is the one we find with the greatest presence and its constant change, both seasonal and daily, as it is affected by factors such as wind. One of the best-known indexes to detect vegetation on the image is the NVDI (Normalized Difference Vegetation Index), which is obtained from the combination of the infrared and red channels. Therefore it is necessary to have a multispectral camera. These cameras are generally of lower resolution than conventional RGB cameras, while their cost is much higher. Therefore we have to look for alternative indices based on RGB. In this communication, we present the results obtained in Georisk project (PID2019‐103974RB‐I00/MCIN/AEI/10.13039/501100011033) by using the GLI (Green Leaf Index) and ExG (Excessive Greenness), as well as the change to the Hue-Saturation-Value (HSV) color space being the H coordinate the one that gives us the most information for vegetation filtering. These filters are applied both to the images, creating binary masks to be used when applying the SfM algorithms, and to the point cloud obtained directly by the photogrammetric process without any previous filter or the one obtained by TLS (Terrestrial Laser Scanning). In this last case, we have also tried to work with a Riegl VZ400i sensor that allows the reception, as in the aerial LiDAR, of several returns of the signal. Information to be used for the classification on the point cloud. After applying all the techniques in different locations, the results show that the color-based filters allow correct filtering in those areas where the presence of shadows is not excessive and there is a contrast between the color of the slope lithology and the vegetation. As we have advanced in the case of using the HSV color space, it is the H coordinate that responds best for this filtering. Finally, the use of the various returns of the TLS signal allows filtering with some limitations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=RGB%20index" title="RGB index">RGB index</a>, <a href="https://publications.waset.org/abstracts/search?q=TLS" title=" TLS"> TLS</a>, <a href="https://publications.waset.org/abstracts/search?q=photogrammetry" title=" photogrammetry"> photogrammetry</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral%20camera" title=" multispectral camera"> multispectral camera</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20cloud" title=" point cloud"> point cloud</a> </p> <a href="https://publications.waset.org/abstracts/162506/geomatic-techniques-to-filter-vegetation-from-point-clouds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162506.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3971</span> Analyzing Impacts of Road Network on Vegetation Using Geographic Information System and Remote Sensing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elizabeth%20Malebogo%20Mosepele"> Elizabeth Malebogo Mosepele</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Road transport has become increasingly common in the world; people rely on road networks for transportation purpose on a daily basis. However, environmental impact of roads on surrounding landscapes extends their potential effects even further. This study investigates the impact of road network on natural vegetation. The study will provide baseline knowledge regarding roadside vegetation and would be helpful in future for conservation of biodiversity along the road verges and improvements of road verges. The general hypothesis of this study is that the amount and condition of road side vegetation could be explained by road network conditions. Remote sensing techniques were used to analyze vegetation conditions. Landsat 8 OLI image was used to assess vegetation cover condition. NDVI image was generated and used as a base from which land cover classes were extracted, comprising four categories viz. healthy vegetation, degraded vegetation, bare surface, and water. The classification of the image was achieved using the supervised classification technique. Road networks were digitized from Google Earth. For observed data, transect based quadrats of 50*50 m were conducted next to road segments for vegetation assessment. Vegetation condition was related to road network, with the multinomial logistic regression confirming a significant relationship between vegetation condition and road network. The null hypothesis formulated was that 'there is no variation in vegetation condition as we move away from the road.' Analysis of vegetation condition revealed degraded vegetation within close proximity of a road segment and healthy vegetation as the distance increase away from the road. The Chi Squared value was compared with critical value of 3.84, at the significance level of 0.05 to determine the significance of relationship. Given that the Chi squared value was 395, 5004, the null hypothesis was therefore rejected; there is significant variation in vegetation the distance increases away from the road. The conclusion is that the road network plays an important role in the condition of vegetation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chi%20squared" title="Chi squared">Chi squared</a>, <a href="https://publications.waset.org/abstracts/search?q=geographic%20information%20system" title=" geographic information system"> geographic information system</a>, <a href="https://publications.waset.org/abstracts/search?q=multinomial%20logistic%20regression" title=" multinomial logistic regression"> multinomial logistic regression</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=road%20side%20vegetation" title=" road side vegetation"> road side vegetation</a> </p> <a href="https://publications.waset.org/abstracts/79182/analyzing-impacts-of-road-network-on-vegetation-using-geographic-information-system-and-remote-sensing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79182.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">432</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">3970</span> A Monitoring System to Detect Vegetation Growth along the Route of Power Overhead Lines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eugene%20Eduful">Eugene Eduful</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces an approach that utilizes a Wireless Sensor Network (WSN) to detect vegetation encroachment between segments of distribution lines. The WSN was designed and implemented, involving the seamless integration of Arduino Uno and Mega systems. This integration demonstrates a method for addressing the challenges posed by vegetation interference. The primary aim of the study is to improve the reliability of power supply in areas characterized by forested terrain, specifically targeting overhead powerlines. The experimental results validate the effectiveness of the proposed system, revealing its ability to accurately identify and locate instances of vegetation encroachment with a remarkably high degree of precision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20network" title="wireless sensor network">wireless sensor network</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation%20encroachment" title=" vegetation encroachment"> vegetation encroachment</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20of%20sight" title=" line of sight"> line of sight</a>, <a href="https://publications.waset.org/abstracts/search?q=Arduino%20Uno" title=" Arduino Uno"> Arduino Uno</a>, <a href="https://publications.waset.org/abstracts/search?q=XBEE" title=" XBEE"> XBEE</a> </p> <a href="https://publications.waset.org/abstracts/176409/a-monitoring-system-to-detect-vegetation-growth-along-the-route-of-power-overhead-lines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176409.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">72</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3969</span> The Relationship between Ruins and Vegetation: Different Approaches during the Centuries and within the Various Disciplinary Fields, Investigation of Writings and Projects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rossana%20Mancini">Rossana Mancini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The charm of a ruin colonised by wild plants and flowers is part of Western culture. The relationship between ruins and vegetation involves a wide range of different fields of research. During the first phase of the research the most important writings and projects about this argument were investigated, to understand how the perception of the co-existence of ruins and vegetation has changed over time and to investigate the various different approaches that these different fields have adopted when tackling this issue. The paper presents some practical examples of projects carried out from the early 1900s on. The major result is that specifically regards conservation, the best attitude is the management of change, an inevitable process when it comes to the co-existence of ruins and nature and, particularly, ruins and vegetation. Limiting ourselves to adopting measures designed to stop, or rather slow down, the increasing level of entropy (and therefore disorder) may not be enough. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ruins" title="ruins">ruins</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation" title=" vegetation"> vegetation</a>, <a href="https://publications.waset.org/abstracts/search?q=conservation" title=" conservation"> conservation</a>, <a href="https://publications.waset.org/abstracts/search?q=archaeology" title=" archaeology"> archaeology</a>, <a href="https://publications.waset.org/abstracts/search?q=architecture" title=" architecture"> architecture</a> </p> <a href="https://publications.waset.org/abstracts/99037/the-relationship-between-ruins-and-vegetation-different-approaches-during-the-centuries-and-within-the-various-disciplinary-fields-investigation-of-writings-and-projects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99037.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">329</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=vegetation%20index&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=vegetation%20index&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=vegetation%20index&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=vegetation%20index&page=5">5</a></li> <li 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href="https://publications.waset.org/abstracts/search?q=vegetation%20index&page=134">134</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=vegetation%20index&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> 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