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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 indices"> <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> 1435</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: vegetation indices</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1435</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">1434</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">1433</span> Cotton Crops Vegetative Indices Based Assessment Using Multispectral Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Shahzad%20Shifa">Muhammad Shahzad Shifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Amna%20Shifa"> Amna Shifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Omar"> Muhammad Omar</a>, <a href="https://publications.waset.org/abstracts/search?q=Aamir%20Shahzad"> Aamir Shahzad</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahmat%20Ali%20Khan"> Rahmat Ali Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many applications of remote sensing to vegetation and crop response depend on spectral properties of individual leaves and plants. Vegetation indices are usually determined to estimate crop biophysical parameters like crop canopies and crop leaf area indices with the help of remote sensing. Cotton crops assessment is performed with the help of vegetative indices. Remotely sensed images from an optical multispectral radiometer MSR5 are used in this study. The interpretation is based on the fact that different materials reflect and absorb light differently at different wavelengths. Non-normalized and normalized forms of these datasets are analyzed using two complementary data mining algorithms; K-means and K-nearest neighbor (KNN). Our analysis shows that the use of normalized reflectance data and vegetative indices are suitable for an automated assessment and decision making. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cotton" title="cotton">cotton</a>, <a href="https://publications.waset.org/abstracts/search?q=condition%20assessment" title=" condition assessment"> condition assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=KNN%20algorithm" title=" KNN algorithm"> KNN algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=MSR5" title=" MSR5"> MSR5</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation%20indices" title=" vegetation indices"> vegetation indices</a> </p> <a href="https://publications.waset.org/abstracts/103787/cotton-crops-vegetative-indices-based-assessment-using-multispectral-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103787.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">333</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1432</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">1431</span> Robust Method for Evaluation of Catchment Response to Rainfall Variations Using Vegetation Indices and Surface Temperature</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Revalin%20Herdianto">Revalin Herdianto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent climate changes increase uncertainties in vegetation conditions such as health and biomass globally and locally. The detection is, however, difficult due to the spatial and temporal scale of vegetation coverage. Due to unique vegetation response to its environmental conditions such as water availability, the interplay between vegetation dynamics and hydrologic conditions leave a signature in their feedback relationship. Vegetation indices (VI) depict vegetation biomass and photosynthetic capacity that indicate vegetation dynamics as a response to variables including hydrologic conditions and microclimate factors such as rainfall characteristics and land surface temperature (LST). It is hypothesized that the signature may be depicted by VI in its relationship with other variables. To study this signature, several catchments in Asia, Australia, and Indonesia were analysed to assess the variations in hydrologic characteristics with vegetation types. Methods used in this study includes geographic identification and pixel marking for studied catchments, analysing time series of VI and LST of the marked pixels, smoothing technique using Savitzky-Golay filter, which is effective for large area and extensive data. Time series of VI, LST, and rainfall from satellite and ground stations coupled with digital elevation models were analysed and presented. This study found that the hydrologic response of vegetation to rainfall variations may be shown in one hydrologic year, in which a drought event can be detected a year later as a suppressed growth. However, an annual rainfall of above average do not promote growth above average as shown by VI. This technique is found to be a robust and tractable approach for assessing catchment dynamics in changing climates. <p class="card-text"><strong>Keywords:</strong> <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=land%20surface%20temperature" title=" land surface temperature"> land surface temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation%20dynamics" title=" vegetation dynamics"> vegetation dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=catchment" title=" catchment"> catchment</a> </p> <a href="https://publications.waset.org/abstracts/69141/robust-method-for-evaluation-of-catchment-response-to-rainfall-variations-using-vegetation-indices-and-surface-temperature" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69141.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">287</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">1430</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">1429</span> Calculation of the Normalized Difference Vegetation Index and the Spectral Signature of Coffee Crops: Benefits of Image Filtering on Mixed Crops</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Catalina%20Albornoz">Catalina Albornoz</a>, <a href="https://publications.waset.org/abstracts/search?q=Giacomo%20Barbieri"> Giacomo Barbieri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Crop monitoring has shown to reduce vulnerability to spreading plagues and pathologies in crops. Remote sensing with Unmanned Aerial Vehicles (UAVs) has made crop monitoring more precise, cost-efficient and accessible. Nowadays, remote monitoring involves calculating maps of vegetation indices by using different software that takes either Truecolor (RGB) or multispectral images as an input. These maps are then used to segment the crop into management zones. Finally, knowing the spectral signature of a crop (the reflected radiation as a function of wavelength) can be used as an input for decision-making and crop characterization. The calculation of vegetation indices using software such as Pix4D has high precision for monoculture plantations. However, this paper shows that using this software on mixed crops may lead to errors resulting in an incorrect segmentation of the field. Within this work, authors propose to filter all the elements different from the main crop before the calculation of vegetation indices and the spectral signature. A filter based on the Sobel method for border detection is used for filtering a coffee crop. Results show that segmentation into management zones changes with respect to the traditional situation in which a filter is not applied. In particular, it is shown how the values of the spectral signature change in up to 17% per spectral band. Future work will quantify the benefits of filtering through the comparison between in situ measurements and the calculated vegetation indices obtained through remote sensing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coffee" title="coffee">coffee</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering" title=" filtering"> filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20crop" title=" mixed crop"> mixed crop</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20agriculture" title=" precision agriculture"> precision agriculture</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=spectral%20signature" title=" spectral signature"> spectral signature</a> </p> <a href="https://publications.waset.org/abstracts/76629/calculation-of-the-normalized-difference-vegetation-index-and-the-spectral-signature-of-coffee-crops-benefits-of-image-filtering-on-mixed-crops" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76629.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">388</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">1428</span> Multi-Temporal Urban Land Cover Mapping Using Spectral Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mst%20Ilme%20Faridatul">Mst Ilme Faridatul</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Wu"> Bo Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multi-temporal urban land cover mapping is of paramount importance for monitoring urban sprawl and managing the ecological environment. For diversified urban activities, it is challenging to map land covers in a complex urban environment. Spectral indices have proved to be effective for mapping urban land covers. To improve multi-temporal urban land cover classification and mapping, we evaluate the performance of three spectral indices, e.g. modified normalized difference bare-land index (MNDBI), tasseled cap water and vegetation index (TCWVI) and shadow index (ShDI). The MNDBI is developed to evaluate its performance of enhancing urban impervious areas by separating bare lands. A tasseled cap index, TCWVI is developed to evaluate its competence to detect vegetation and water simultaneously. The ShDI is developed to maximize the spectral difference between shadows of skyscrapers and water and enhance water detection. First, this paper presents a comparative analysis of three spectral indices using Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM) and Operational Land Imager (OLI) data. Second, optimized thresholds of the spectral indices are imputed to classify land covers, and finally, their performance of enhancing multi-temporal urban land cover mapping is assessed. The results indicate that the spectral indices are competent to enhance multi-temporal urban land cover mapping and achieves an overall classification accuracy of 93-96%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20cover" title="land cover">land cover</a>, <a href="https://publications.waset.org/abstracts/search?q=mapping" title=" mapping"> mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-temporal" title=" multi-temporal"> multi-temporal</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20indices" title=" spectral indices"> spectral indices</a> </p> <a href="https://publications.waset.org/abstracts/103491/multi-temporal-urban-land-cover-mapping-using-spectral-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103491.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1427</span> Reduction of Plants Biodiversity in Hyrcanian Forest by Coal Mining Activities </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahsa%20Tavakoli">Mahsa Tavakoli</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Mohammad%20Hojjati"> Seyed Mohammad Hojjati</a>, <a href="https://publications.waset.org/abstracts/search?q=Yahya%20Kooch"> Yahya Kooch</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Considering that coal mining is one of the important industrial activities, it may cause damages to environment. According to the author’s best knowledge, the effect of traditional coal mining activities on plant biodiversity has not been investigated in the Hyrcanian forests. Therefore, in this study, the effect of coal mining activities on vegetation and tree diversity was investigated in Hyrcanian forest, North Iran. After filed visiting and determining the mine, 16 plots (20×20 m<sup>2</sup>) were established by systematic-randomly (60×60 m<sup>2</sup>) in an area of 4 ha (200×200 m<sup>2</sup>-mine entrance placed at center). An area adjacent to the mine was not affected by the mining activity, and it is considered as the control area. In each plot, the data about trees such as number and type of species were recorded. The biodiversity of vegetation cover was considered 5 square sub-plots (1 m<sup>2</sup>) in each plot. PAST software and Ecological Methodology were used to calculate Biodiversity indices. The value of Shannon Wiener and Simpson diversity indices for tree cover in control area (1.04<span dir="RTL">±</span>0.34 and 0.62<span dir="RTL">±</span>0.20) was significantly higher than mining area (0.78<span dir="RTL">±</span>0.27 and 0.45<span dir="RTL">±</span>0.14). The value of evenness indices for tree cover in the mining area was significantly lower than that of the control area. The value of Shannon Wiener and Simpson diversity indices for vegetation cover in the control area (1.37<span dir="RTL">±</span>0.06 and 0.69<span dir="RTL">±</span>0.02) was significantly higher than the mining area (1.02<span dir="RTL">±</span>0.13 and 0.50<span dir="RTL">±</span>0.07). The value of evenness index in the control area was significantly higher than the mining area. Plant communities are a good indicator of the changes in the site. Study about changes in vegetation biodiversity and plant dynamics in the degraded land can provide necessary information for forest management and reforestation of these areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vegetation%20biodiversity" title="vegetation biodiversity">vegetation biodiversity</a>, <a href="https://publications.waset.org/abstracts/search?q=species%20composition" title=" species composition"> species composition</a>, <a href="https://publications.waset.org/abstracts/search?q=traditional%20coal%20mining" title=" traditional coal mining"> traditional coal mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Caspian%20forest" title=" Caspian forest"> Caspian forest</a> </p> <a href="https://publications.waset.org/abstracts/100549/reduction-of-plants-biodiversity-in-hyrcanian-forest-by-coal-mining-activities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100549.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">183</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">1426</span> Evaluation of Agricultural Drought Impact in the Crop Productivity of East Gojjam Zone</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Walelgn%20Dilnesa%20Cherie">Walelgn Dilnesa Cherie</a>, <a href="https://publications.waset.org/abstracts/search?q=Fasikaw%20Atanaw%20Zimale"> Fasikaw Atanaw Zimale</a>, <a href="https://publications.waset.org/abstracts/search?q=Bekalu%20W.%20Asres"> Bekalu W. Asres</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most catastrophic condition for agricultural production is a drought event, which is also one of the most hydro-metrological-related hazards. According to the combined susceptibility of plants to meteorological and hydrological conditions, agricultural drought is defined as the magnitude, severity, and duration of a drought that affects crop production. The accurate and timely assessment of agricultural drought can lead to the development of risk management strategies, appropriate proactive mechanisms for the protection of farmers, and the improvement of food security. The evaluation of agricultural drought in the East Gojjam zone was the primary subject of this study. To identify the agricultural drought, soil moisture anomalies, soil moisture deficit indices, and Normalized Difference Vegetation Indices (NDVI) are used. The measured welting point, field capacity, and soil moisture were utilized to validate the soil water deficit indices computed from the satellite data. The soil moisture and soil water deficit indices in 2013 in all woredas were minimum; this makes vegetation stress also in all woredas. The soil moisture content decreased in 2013/2014/2019, and 2021 in Dejen, 2014, and 2019 in Awobel Woreda. The max/ min values of NDVI in 2013 are minimum; it dominantly shows vegetation stress and an observed agricultural drought that happened in all woredas. The validation process of satellite and in-situ soil moisture and soil water deficit indices shows a good agreement with a value of R²=0.87 and 0.56, respectively. The study area becomes drought detected region, so government officials, policymakers, and environmentalists pay attention to the protection of drought effects. <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=agricultural%20drought" title=" agricultural drought"> agricultural drought</a>, <a href="https://publications.waset.org/abstracts/search?q=SWDI" title=" SWDI"> SWDI</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20moisture" title=" soil moisture"> soil moisture</a> </p> <a href="https://publications.waset.org/abstracts/168643/evaluation-of-agricultural-drought-impact-in-the-crop-productivity-of-east-gojjam-zone" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168643.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">1425</span> Interference of Mild Drought Stress on Estimation of Nitrogen Status in Winter Wheat by Some Vegetation Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Tavakoli">H. Tavakoli</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20S.%20Mohtasebi"> S. S. Mohtasebi</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Alimardani"> R. Alimardani</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Gebbers"> R. Gebbers</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nitrogen (N) is one of the most important agricultural inputs affecting crop growth, yield and quality in rain-fed cereal production. N demand of crops varies spatially across fields due to spatial differences in soil conditions. In addition, the response of a crop to the fertilizer applications is heavily reliant on plant available water. Matching N supply to water availability is thus essential to achieve an optimal crop response. The objective of this study was to determine effect of drought stress on estimation of nitrogen status of winter wheat by some vegetation indices. During the 2012 growing season, a field experiment was conducted at the Bundessortenamt (German Plant Variety Office) Marquardt experimental station which is located in the village of Marquardt about 5 km northwest of Potsdam, Germany (52°27' N, 12°57' E). The experiment was designed as a randomized split block design with two replications. Treatments consisted of four N fertilization rates (0, 60, 120 and 240 kg N ha-1, in total) and two water regimes (irrigated (Irr) and non-irrigated (NIrr)) in total of 16 plots with dimension of 4.5 × 9.0 m. The indices were calculated using readings of a spectroradiometer made of tec5 components. The main parts were two “Zeiss MMS1 nir enh” diode-array sensors with a nominal rage of 300 to 1150 nm with less than 10 nm resolutions and an effective range of 400 to 1000 nm. The following vegetation indices were calculated: NDVI, GNDVI, SR, MSR, NDRE, RDVI, REIP, SAVI, OSAVI, MSAVI, and PRI. All the experiments were conducted during the growing season in different plant growth stages including: stem elongation (BBCH=32-41), booting stage (BBCH=43), inflorescence emergence, heading (BBCH=56-58), flowering (BBCH=65-69), and development of fruit (BBCH=71). According to the results obtained, among the indices, NDRE and REIP were less affected by drought stress and can provide reliable wheat nitrogen status information, regardless of water status of the plant. They also showed strong relations with nitrogen status of winter wheat. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nitrogen%20status" title="nitrogen status">nitrogen status</a>, <a href="https://publications.waset.org/abstracts/search?q=drought%20stress" title=" drought stress"> drought stress</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=precision%20agriculture" title=" precision agriculture"> precision agriculture</a> </p> <a href="https://publications.waset.org/abstracts/9334/interference-of-mild-drought-stress-on-estimation-of-nitrogen-status-in-winter-wheat-by-some-vegetation-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9334.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">319</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">1424</span> Estimation of Foliar Nitrogen in Selected Vegetation Communities of Uttrakhand Himalayas Using Hyperspectral Satellite Remote Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yogita%20Mishra">Yogita Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Arijit%20Roy"> Arijit Roy</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhruval%20Bhavsar"> Dhruval Bhavsar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study estimates the nitrogen concentration in selected vegetation community’s i.e. chir pine (pinusroxburghii) by using hyperspectral satellite data and also identified the appropriate spectral bands and nitrogen indices. The Short Wave InfraRed reflectance spectrum at 1790 nm and 1680 nm shows the maximum possible absorption by nitrogen in selected species. Among the nitrogen indices, log normalized nitrogen index performed positively and negatively too. The strong positive correlation is taken out from 1510 nm and 760 nm for the pinusroxburghii for leaf nitrogen concentration and leaf nitrogen mass while using NDNI. The regression value of R² developed by using linear equation achieved maximum at 0.7525 for the analysis of satellite image data and R² is maximum at 0.547 for ground truth data for pinusroxburghii respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hyperspectral" title="hyperspectral">hyperspectral</a>, <a href="https://publications.waset.org/abstracts/search?q=NDNI" title=" NDNI"> NDNI</a>, <a href="https://publications.waset.org/abstracts/search?q=nitrogen%20concentration" title=" nitrogen concentration"> nitrogen concentration</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20value" title=" regression value"> regression value</a> </p> <a href="https://publications.waset.org/abstracts/74753/estimation-of-foliar-nitrogen-in-selected-vegetation-communities-of-uttrakhand-himalayas-using-hyperspectral-satellite-remote-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74753.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">295</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1423</span> Inferring the Ecological Quality of Seagrass Beds from Using Composition and Configuration Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fabrice%20Houngnandan">Fabrice Houngnandan</a>, <a href="https://publications.waset.org/abstracts/search?q=Celia%20Fery"> Celia Fery</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Bockel"> Thomas Bockel</a>, <a href="https://publications.waset.org/abstracts/search?q=Julie%20Deter"> Julie Deter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Getting water cleaner and stopping global biodiversity loss requires indices to measure changes and evaluate the achievement of objectives. The endemic and protected seagrass species Posidonia oceanica is a biological indicator used to monitor the ecological quality of marine Mediterranean waters. One ecosystem index (EBQI), two biotic indices (PREI, Bipo), and several landscape indices, which measure the composition and configuration of the P. oceanica seagrass at the population scale have been developed. While the formers are measured at monitoring sites, the landscape indices can be calculated for the entire seabed covered by this ecosystem. This present work aims to search on the link between these indices and the best scale to be used in order to maximize this link. We used data collected between 2014 to 2019 along the French Mediterranean coastline to calculate EBQI, PREI, and Bipo at 100 sites. From the P. oceanica seagrass distribution map, configuration and composition indices around these different sites in 6 different grid sizes (100 m x 100 to 1000 m x 1000 m) were determined. Correlation analyses were first used to find out the grid size presenting the strongest and most significant link between the different types of indices. Finally, several models were compared basis on various metrics to identify the one that best explains the nature of the link between these indices. Our results showed a strong and significant link between biotic indices and the best correlations between biotic and landscape indices within the 600 m x 600 m grid cells. These results showed that the use of landscape indices is possible to monitor the health of seagrass beds at a large scale. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ecological%20indicators" title="ecological indicators">ecological indicators</a>, <a href="https://publications.waset.org/abstracts/search?q=decline" title=" decline"> decline</a>, <a href="https://publications.waset.org/abstracts/search?q=conservation" title=" conservation"> conservation</a>, <a href="https://publications.waset.org/abstracts/search?q=submerged%20aquatic%20vegetation" title=" submerged aquatic vegetation"> submerged aquatic vegetation</a> </p> <a href="https://publications.waset.org/abstracts/125961/inferring-the-ecological-quality-of-seagrass-beds-from-using-composition-and-configuration-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125961.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">131</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">1422</span> Comparing Remote Sensing and in Situ Analyses of Test Wheat Plants as Means for Optimizing Data Collection in Precision Agriculture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Endalkachew%20Abebe%20Kebede">Endalkachew Abebe Kebede</a>, <a href="https://publications.waset.org/abstracts/search?q=Bojin%20Bojinov"> Bojin Bojinov</a>, <a href="https://publications.waset.org/abstracts/search?q=Andon%20Vasilev%20Andonov"> Andon Vasilev Andonov</a>, <a href="https://publications.waset.org/abstracts/search?q=Orhan%20Dengiz"> Orhan Dengiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remote sensing has a potential application in assessing and monitoring the plants' biophysical properties using the spectral responses of plants and soils within the electromagnetic spectrum. However, only a few reports compare the performance of different remote sensing sensors against in-situ field spectral measurement. The current study assessed the potential applications of open data source satellite images (Sentinel 2 and Landsat 9) in estimating the biophysical properties of the wheat crop on a study farm found in the village of OvchaMogila. A Landsat 9 (30 m resolution) and Sentinel-2 (10 m resolution) satellite images with less than 10% cloud cover have been extracted from the open data sources for the period of December 2021 to April 2022. An Unmanned Aerial Vehicle (UAV) has been used to capture the spectral response of plant leaves. In addition, SpectraVue 710s Leaf Spectrometer was used to measure the spectral response of the crop in April at five different locations within the same field. The ten most common vegetation indices have been selected and calculated based on the reflectance wavelength range of remote sensing tools used. The soil samples have been collected in eight different locations within the farm plot. The different physicochemical properties of the soil (pH, texture, N, P₂O₅, and K₂O) have been analyzed in the laboratory. The finer resolution images from the UAV and the Leaf Spectrometer have been used to validate the satellite images. The performance of different sensors has been compared based on the measured leaf spectral response and the extracted vegetation indices using the five sampling points. A scatter plot with the coefficient of determination (R2) and Root Mean Square Error (RMSE) and the correlation (r) matrix prepared using the corr and heatmap python libraries have been used for comparing the performance of Sentinel 2 and Landsat 9 VIs compared to the drone and SpectraVue 710s spectrophotometer. The soil analysis revealed the study farm plot is slightly alkaline (8.4 to 8.52). The soil texture of the study farm is dominantly Clay and Clay Loam.The vegetation indices (VIs) increased linearly with the growth of the plant. Both the scatter plot and the correlation matrix showed that Sentinel 2 vegetation indices have a relatively better correlation with the vegetation indices of the Buteo dronecompared to the Landsat 9. The Landsat 9 vegetation indices somewhat align better with the leaf spectrometer. Generally, the Sentinel 2 showed a better performance than the Landsat 9. Further study with enough field spectral sampling and repeated UAV imaging is required to improve the quality of the current study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landsat%209" title="landsat 9">landsat 9</a>, <a href="https://publications.waset.org/abstracts/search?q=leaf%20spectrometer" title=" leaf spectrometer"> leaf spectrometer</a>, <a href="https://publications.waset.org/abstracts/search?q=sentinel%202" title=" sentinel 2"> sentinel 2</a>, <a href="https://publications.waset.org/abstracts/search?q=UAV" title=" UAV"> UAV</a> </p> <a href="https://publications.waset.org/abstracts/152005/comparing-remote-sensing-and-in-situ-analyses-of-test-wheat-plants-as-means-for-optimizing-data-collection-in-precision-agriculture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152005.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">107</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">1421</span> Unveiling Vegetation Composition and Dynamics Along Urbanization Gradient in Ranchi, Eastern India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Purabi%20Saikia">Purabi Saikia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study was carried out across 84 vegetated grids (>10% vegetation cover) along an urbanization gradient, ranging from the urban core to peri-urban and natural vegetation in and around Ranchi, Eastern India, aiming to examine the phytosociological attributes by belt transect (167 transects each of 0.5 ha) method. Overall, plant species richness was highest in natural vegetation (242 spp.), followed by peri-urban (198 spp.) and urban (182 spp.). Similarly, H’, CD, E, Dmg, Dmn, and ENS showed significant differences in the tree layer (H’: 0.45-3.36; CD: 0.04-1.00; E: 0.25-0.96; Dmg: 0.18-7.15; Dmn: 0.03-4.24, and ENS: 1-29) in the entire urbanization gradient. Various α-diversity indices of the adult trees (H’: 3.98, Dmg: 14.32, Dmn: 2.38, ENS: 54) were comparatively better in urban vegetation compared to peri-urban (H’: 2.49, Dmg: 10.37, Dmn: 0.81, ENS: 12) and natural vegetation (H’: 2.89, Dmg: 13.46, Dmn: 0.85, ENS: 18). Tree communities have shown better response and adaptability in urban vegetation than shrubs and herbs. The prevalence of rare (41%), very rare (29%), and exotic species (39%) in urban vegetation may be due to the intentional introduction of a number of fast-growing exotic tree species in different social forestry plantations that have created a diverse and heterogeneous habitat. Despite contagious distribution, the majority of trees (36.14%) have shown no regeneration in the entire urbanization gradient. Additionally, a quite high percentage of IUCN red-listed plant species (51% and 178 spp.), including endangered (01 sp.), vulnerable (03 spp.), near threatened (04 spp.), least concern (163 spp.), and data deficient (07 spp.), warrant immediate policy interventions. Overall, the study witnessed subsequent transformations in floristic composition and structure from urban to natural vegetation in Eastern India. The outcomes are crucial for fostering resilient ecosystems, biodiversity conservation, and sustainable development in the region that supports diverse plant communities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=floristic%20communities" title="floristic communities">floristic communities</a>, <a href="https://publications.waset.org/abstracts/search?q=urbanization%20gradients" title=" urbanization gradients"> urbanization gradients</a>, <a href="https://publications.waset.org/abstracts/search?q=exotic%20species" title=" exotic species"> exotic species</a>, <a href="https://publications.waset.org/abstracts/search?q=regeneration" title=" regeneration"> regeneration</a> </p> <a href="https://publications.waset.org/abstracts/191906/unveiling-vegetation-composition-and-dynamics-along-urbanization-gradient-in-ranchi-eastern-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191906.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">21</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">1420</span> Employing Remotely Sensed Soil and Vegetation Indices and Predicting ‎by Long ‎Short-Term Memory to Irrigation Scheduling Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elham%20Koohikerade">Elham Koohikerade</a>, <a href="https://publications.waset.org/abstracts/search?q=Silvio%20Jose%20Gumiere"> Silvio Jose Gumiere</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this research, irrigation is highlighted as crucial for improving both the yield and quality of ‎potatoes due to their high sensitivity to soil moisture changes. The study presents a hybrid Long ‎Short-Term Memory (LSTM) model aimed at optimizing irrigation scheduling in potato fields in ‎Quebec City, Canada. This model integrates model-based and satellite-derived datasets to simulate ‎soil moisture content, addressing the limitations of field data. Developed under the guidance of the ‎Food and Agriculture Organization (FAO), the simulation approach compensates for the lack of direct ‎soil sensor data, enhancing the LSTM model's predictions. The model was calibrated using indices ‎like Surface Soil Moisture (SSM), Normalized Vegetation Difference Index (NDVI), Enhanced ‎Vegetation Index (EVI), and Normalized Multi-band Drought Index (NMDI) to effectively forecast ‎soil moisture reductions. Understanding soil moisture and plant development is crucial for assessing ‎drought conditions and determining irrigation needs. This study validated the spectral characteristics ‎of vegetation and soil using ECMWF Reanalysis v5 (ERA5) and Moderate Resolution Imaging ‎Spectrometer (MODIS) data from 2019 to 2023, collected from agricultural areas in Dolbeau and ‎Peribonka, Quebec. Parameters such as surface volumetric soil moisture (0-7 cm), NDVI, EVI, and ‎NMDI were extracted from these images. A regional four-year dataset of soil and vegetation moisture ‎was developed using a machine learning approach combining model-based and satellite-based ‎datasets. The LSTM model predicts soil moisture dynamics hourly across different locations and ‎times, with its accuracy verified through cross-validation and comparison with existing soil moisture ‎datasets. The model effectively captures temporal dynamics, making it valuable for applications ‎requiring soil moisture monitoring over time, such as anomaly detection and memory analysis. By ‎identifying typical peak soil moisture values and observing distribution shapes, irrigation can be ‎scheduled to maintain soil moisture within Volumetric Soil Moisture (VSM) values of 0.25 to 0.30 ‎m²/m², avoiding under and over-watering. The strong correlations between parcels suggest that a ‎uniform irrigation strategy might be effective across multiple parcels, with adjustments based on ‎specific parcel characteristics and historical data trends. The application of the LSTM model to ‎predict soil moisture and vegetation indices yielded mixed results. While the model effectively ‎captures the central tendency and temporal dynamics of soil moisture, it struggles with accurately ‎predicting EVI, NDVI, and NMDI.‎ <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=irrigation%20scheduling" title="irrigation scheduling">irrigation scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM%20neural%20network" title=" LSTM neural network"> LSTM neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=remotely%20sensed%20indices" title=" remotely sensed indices"> remotely sensed indices</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20and%20vegetation%20%E2%80%8Emonitoring" title=" soil and vegetation ‎monitoring"> soil and vegetation ‎monitoring</a> </p> <a href="https://publications.waset.org/abstracts/186495/employing-remotely-sensed-soil-and-vegetation-indices-and-predicting-by-long-short-term-memory-to-irrigation-scheduling-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186495.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">41</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">1419</span> Winter Wheat Yield Forecasting Using Sentinel-2 Imagery at the Early Stages</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chunhua%20Liao">Chunhua Liao</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinfei%20Wang"> Jinfei Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Shan"> Bo Shan</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20Song"> Yang Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongjun%20He"> Yongjun He</a>, <a href="https://publications.waset.org/abstracts/search?q=Taifeng%20Dong"> Taifeng Dong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Winter wheat is one of the main crops in Canada. Forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. It was found that the four-band reflectance (blue, green, red, near-infrared) performed better than their vegetation indices (NDVI, EVI, WDRVI and OSAVI) in wheat yield prediction. The optimum phenological stage for wheat yield prediction with highest accuracy was at the growing stages from the end of the flowering to the beginning of the filling stage. The best MLR model was therefore built to predict wheat yield before harvest using Sentinel-2 data acquired at the end of the flowering stage. Further, to improve the ability of the yield prediction at the early stages, three simple unsupervised domain adaptation (DA) methods were adopted to transform the reflectance data at the early stages to the optimum phenological stage. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. The average testing RMSE of the MLR model at the end of the flowering stage was 604.48 kg/ha. Near the booting stage, the average testing RMSE of yield prediction using the best MLR was reduced to 799.18 kg/ha when applying the mean matching domain adaptation approach to transform the data to the target domain (at the end of the flowering) compared to that using the original data based on the models developed at the booting stage directly (“MLR at the early stage”) (RMSE =1140.64 kg/ha). This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. This study indicated that the simple domain adaptation methods had a great potential in crop yield prediction at the early stages using remote sensing data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wheat%20yield%20prediction" title="wheat yield prediction">wheat yield prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=domain%20adaptation" title=" domain adaptation"> domain adaptation</a>, <a href="https://publications.waset.org/abstracts/search?q=Sentinel-2" title=" Sentinel-2"> Sentinel-2</a>, <a href="https://publications.waset.org/abstracts/search?q=within-field%20scale" title=" within-field scale"> within-field scale</a> </p> <a href="https://publications.waset.org/abstracts/165642/winter-wheat-yield-forecasting-using-sentinel-2-imagery-at-the-early-stages" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165642.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">64</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">1418</span> Band Characterization and Development of Hyperspectral Indices for Retrieving Chlorophyll Content</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramandeep%20Kaur%20M.%20Malhi">Ramandeep Kaur M. Malhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Prashant%20K.%20Srivastava"> Prashant K. Srivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=G.Sandhya%20Kiran"> G.Sandhya Kiran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quantitative estimates of foliar biochemicals, namely chlorophyll content (CC), serve as key information for the assessment of plant productivity, stress, and the availability of nutrients. This also plays a critical role in predicting the dynamic response of any vegetation to altering climate conditions. The advent of hyperspectral data with an enhanced number of available wavelengths has increased the possibility of acquiring improved information on CC. Retrieval of CC is extensively carried through well known spectral indices derived from hyperspectral data. In the present study, an attempt is made to develop hyperspectral indices by identifying optimum bands for CC estimation in Butea monosperma (Lam.) Taub growing in forests of Shoolpaneshwar Wildlife Sanctuary, Narmada district, Gujarat State, India. 196 narrow bands of EO-1 Hyperion images were screened, and the best optimum wavelength from blue, green, red, and near infrared (NIR) regions were identified based on the coefficient of determination (R²) between band reflectance and laboratory estimated CC. The identified optimum wavelengths were then employed for developing 12 hyperspectral indices. These spectral index values and CC values were then correlated to investigate the relation between laboratory measured CC and spectral indices. Band 15 of blue range and Band 22 of green range, Band 40 of the red region, and Band 79 of NIR region were found to be optimum bands for estimating CC. The optimum band based combinations on hyperspectral data proved to be the most effective indices for quantifying Butea CC with NDVI and TVI identified as the best (R² > 0.7, p < 0.01). The study demonstrated the significance of band characterization in the development of the best hyperspectral indices for the chlorophyll estimation, which can aid in monitoring the vitality of forests. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=band" title="band">band</a>, <a href="https://publications.waset.org/abstracts/search?q=characterization" title=" characterization"> characterization</a>, <a href="https://publications.waset.org/abstracts/search?q=chlorophyll" title=" chlorophyll"> chlorophyll</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral" title=" hyperspectral"> hyperspectral</a>, <a href="https://publications.waset.org/abstracts/search?q=indices" title=" indices"> indices</a> </p> <a href="https://publications.waset.org/abstracts/112849/band-characterization-and-development-of-hyperspectral-indices-for-retrieving-chlorophyll-content" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112849.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">155</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">1417</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">1416</span> Artificial Neural Network and Satellite Derived Chlorophyll Indices for Estimation of Wheat Chlorophyll Content under Rainfed Condition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Naveed%20Tahir">Muhammad Naveed Tahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Yingkuan"> Wang Yingkuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Huang%20Wenjiang"> Huang Wenjiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Raheel%20Osman"> Raheel Osman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Numerous models used in prediction and decision-making process but most of them are linear in natural environment, and linear models reach their limitations with non-linearity in data. Therefore accurate estimation is difficult. Artificial Neural Networks (ANN) found extensive acceptance to address the modeling of the complex real world for the non-linear environment. ANN’s have more general and flexible functional forms than traditional statistical methods can effectively deal with. The link between information technology and agriculture will become more firm in the near future. Monitoring crop biophysical properties non-destructively can provide a rapid and accurate understanding of its response to various environmental influences. Crop chlorophyll content is an important indicator of crop health and therefore the estimation of crop yield. In recent years, remote sensing has been accepted as a robust tool for site-specific management by detecting crop parameters at both local and large scales. The present research combined the ANN model with satellite-derived chlorophyll indices from LANDSAT 8 imagery for predicting real-time wheat chlorophyll estimation. The cloud-free scenes of LANDSAT 8 were acquired (Feb-March 2016-17) at the same time when ground-truthing campaign was performed for chlorophyll estimation by using SPAD-502. Different vegetation indices were derived from LANDSAT 8 imagery using ERADAS Imagine (v.2014) software for chlorophyll determination. The vegetation indices were including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Absorbed Ratio Index (CARI), Modified Chlorophyll Absorbed Ratio Index (MCARI) and Transformed Chlorophyll Absorbed Ratio index (TCARI). For ANN modeling, MATLAB and SPSS (ANN) tools were used. Multilayer Perceptron (MLP) in MATLAB provided very satisfactory results. For training purpose of MLP 61.7% of the data, for validation purpose 28.3% of data and rest 10% of data were used to evaluate and validate the ANN model results. For error evaluation, sum of squares error and relative error were used. ANN model summery showed that sum of squares error of 10.786, the average overall relative error was .099. The MCARI and NDVI were revealed to be more sensitive indices for assessing wheat chlorophyll content with the highest coefficient of determination R²=0.93 and 0.90 respectively. The results suggested that use of high spatial resolution satellite imagery for the retrieval of crop chlorophyll content by using ANN model provides accurate, reliable assessment of crop health status at a larger scale which can help in managing crop nutrition requirement in real time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANN" title="ANN">ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=chlorophyll%20content" title=" chlorophyll content"> chlorophyll content</a>, <a href="https://publications.waset.org/abstracts/search?q=chlorophyll%20indices" title=" chlorophyll indices"> chlorophyll indices</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=wheat" title=" wheat"> wheat</a> </p> <a href="https://publications.waset.org/abstracts/95399/artificial-neural-network-and-satellite-derived-chlorophyll-indices-for-estimation-of-wheat-chlorophyll-content-under-rainfed-condition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95399.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1415</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">1414</span> Identification of Healthy and BSR-Infected Oil Palm Trees Using Color Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siti%20Khairunniza-Bejo">Siti Khairunniza-Bejo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yusnida%20Yusoff"> Yusnida Yusoff</a>, <a href="https://publications.waset.org/abstracts/search?q=Nik%20Salwani%20Nik%20Yusoff"> Nik Salwani Nik Yusoff</a>, <a href="https://publications.waset.org/abstracts/search?q=Idris%20Abu%20Seman"> Idris Abu Seman</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamad%20Izzuddin%20Anuar"> Mohamad Izzuddin Anuar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of the oil palm plantations have been threatened by Basal Stem Rot (BSR) disease which causes serious economic impact. This study was conducted to identify the healthy and BSR-infected oil palm tree using thirteen color indices. Multispectral and thermal camera was used to capture 216 images of the leaves taken from frond number 1, 9 and 17. Indices of normalized difference vegetation index (NDVI), red (R), green (G), blue (B), near infrared (NIR), green – blue (GB), green/blue (G/B), green – red (GR), green/red (G/R), hue (H), saturation (S), intensity (I) and thermal index (T) were used. From this study, it can be concluded that G index taken from frond number 9 is the best index to differentiate between the healthy and BSR-infected oil palm trees. It not only gave high value of correlation coefficient (R=-0.962), but also high value of separation between healthy and BSR-infected oil palm tree. Furthermore, power and S model developed using G index gave the highest R2 value which is 0.985. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=oil%20palm" title="oil palm">oil palm</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=disease" title=" disease"> disease</a>, <a href="https://publications.waset.org/abstracts/search?q=leaves" title=" leaves"> leaves</a> </p> <a href="https://publications.waset.org/abstracts/28605/identification-of-healthy-and-bsr-infected-oil-palm-trees-using-color-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28605.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">498</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1413</span> A Study on the Computation of Gourava Indices for Poly-L Lysine Dendrimer and Its Biomedical Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Helen">M. Helen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Chemical graph serves as a convenient model for any real or abstract chemical system. Dendrimers are novel three dimensional hyper branched globular nanopolymeric architectures. Drug delivery scientists are especially enthusiastic about possible utility of dendrimers as drug delivery tool. Dendrimers like poly L lysine (PLL), poly-propylene imine (PPI) and poly-amidoamine (PAMAM), etc., are used as gene carrier in drug delivery system because of their chemical characteristics. These characteristics of chemical compounds are analysed using topological indices (invariants under graph isomorphism) such as Wiener index, Zagreb index, etc., Prof. V. R. Kulli motivated by the application of Zagreb indices in finding the total π energy and derived Gourava indices which is an improved version over Zagreb indices. In this paper, we study the structure of PLL-Dendrimer that has the following applications: reduction in toxicity, colon delivery, and topical delivery. Also, we determine first and second Gourava indices, first and second hyper Gourava indices, product and sum connectivity Gourava indices for PLL-Dendrimer. Gourava Indices have found applications in Quantitative Structure-Property Relationship (QSPR)/ Quantitative Structure-Activity Relationship (QSAR) studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=connectivity%20Gourava%20indices" title="connectivity Gourava indices">connectivity Gourava indices</a>, <a href="https://publications.waset.org/abstracts/search?q=dendrimer" title=" dendrimer"> dendrimer</a>, <a href="https://publications.waset.org/abstracts/search?q=Gourava%20indices" title=" Gourava indices"> Gourava indices</a>, <a href="https://publications.waset.org/abstracts/search?q=hyper%20GouravaG%20indices" title=" hyper GouravaG indices"> hyper GouravaG indices</a> </p> <a href="https://publications.waset.org/abstracts/110831/a-study-on-the-computation-of-gourava-indices-for-poly-l-lysine-dendrimer-and-its-biomedical-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110831.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">138</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">1412</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">1411</span> Analysis of Creative City Indicators in Isfahan City, Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Mokhtari%20Malek%20Abadi">Reza Mokhtari Malek Abadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Saghaei"> Mohsen Saghaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatemeh%20Iman"> Fatemeh Iman </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates the indices of a creative city in Isfahan. Its main aim is to evaluate quantitative status of the creative city indices in Isfahan city, analyze the dispersion and distribution of these indices in Isfahan city. Concerning these, this study tries to analyze the creative city indices in fifteen area of Isfahan through secondary data, questionnaire, TOPSIS model, Shannon entropy and SPSS. Based on this, the fifteen areas of Isfahan city have been ranked with 12 factors of creative city indices. The results of studies show that fifteen areas of Isfahan city are not equally benefiting from creative indices and there is much difference between the areas of Isfahan city. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=grading" title="grading">grading</a>, <a href="https://publications.waset.org/abstracts/search?q=creative%20city" title=" creative city"> creative city</a>, <a href="https://publications.waset.org/abstracts/search?q=creative%20city%20evaluation%20indicators" title=" creative city evaluation indicators"> creative city evaluation indicators</a>, <a href="https://publications.waset.org/abstracts/search?q=regional%20planning%20model" title=" regional planning model"> regional planning model</a> </p> <a href="https://publications.waset.org/abstracts/9914/analysis-of-creative-city-indicators-in-isfahan-city-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9914.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">470</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">1410</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">1409</span> Remotely Sensed Data Fusion to Extract Vegetation Cover in the Cultural Park of Tassili, South of Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Fekir">Y. Fekir</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Mederbal"> K. Mederbal</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Hammadouche"> M. A. Hammadouche</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Anteur"> D. Anteur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cultural park of the Tassili, occupying a large area of Algeria, is characterized by a rich vegetative biodiversity to be preserved and managed both in time and space. The management of a large area (case of Tassili), by its complexity, needs large amounts of data, which for the most part, are spatially localized (DEM, satellite images and socio-economic information etc.), where the use of conventional and traditional methods is quite difficult. The remote sensing, by its efficiency in environmental applications, became an indispensable solution for this kind of studies. Multispectral imaging sensors have been very useful in the last decade in very interesting applications of remote sensing. They can aid in several domains such as the de¬tection and identification of diverse surface targets, topographical details, and geological features. In this work, we try to extract vegetative areas using fusion techniques between data acquired from sensor on-board the Earth Observing 1 (EO-1) satellite and Landsat ETM+ and TM sensors. We have used images acquired over the Oasis of Djanet in the National Park of Tassili in the south of Algeria. Fusion technqiues were applied on the obtained image to extract the vegetative fraction of the different classes of land use. We compare the obtained results in vegetation end member extraction with vegetation indices calculated from both Hyperion and other multispectral sensors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Landsat%20ETM%2B" title="Landsat ETM+">Landsat ETM+</a>, <a href="https://publications.waset.org/abstracts/search?q=EO1" title=" EO1"> EO1</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20fusion" title=" data fusion"> data fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation" title=" vegetation"> vegetation</a>, <a href="https://publications.waset.org/abstracts/search?q=Tassili" title=" Tassili"> Tassili</a>, <a href="https://publications.waset.org/abstracts/search?q=Algeria" title=" Algeria"> Algeria</a> </p> <a href="https://publications.waset.org/abstracts/9997/remotely-sensed-data-fusion-to-extract-vegetation-cover-in-the-cultural-park-of-tassili-south-of-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9997.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">433</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">1408</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">1407</span> Applying Semi-Automatic Digital Aerial Survey Technology and Canopy Characters Classification for Surface Vegetation Interpretation of Archaeological Sites</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yung-Chung%20Chuang">Yung-Chung Chuang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cultural layers of archaeological sites are mainly affected by surface land use, land cover, and root system of surface vegetation. For this reason, continuous monitoring of land use and land cover change is important for archaeological sites protection and management. However, in actual operation, on-site investigation and orthogonal photograph interpretation require a lot of time and manpower. For this reason, it is necessary to perform a good alternative for surface vegetation survey in an automated or semi-automated manner. In this study, we applied semi-automatic digital aerial survey technology and canopy characters classification with very high-resolution aerial photographs for surface vegetation interpretation of archaeological sites. The main idea is based on different landscape or forest type can easily be distinguished with canopy characters (e.g., specific texture distribution, shadow effects and gap characters) extracted by semi-automatic image classification. A novel methodology to classify the shape of canopy characters using landscape indices and multivariate statistics was also proposed. Non-hierarchical cluster analysis was used to assess the optimal number of canopy character clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy character classification (seven categories). Therefore, people could easily predict the forest type and vegetation land cover by corresponding to the specific canopy character category. The results showed that the semi-automatic classification could effectively extract the canopy characters of forest and vegetation land cover. As for forest type and vegetation type prediction, the average prediction accuracy reached 80.3%~91.7% with different sizes of test frame. It represented this technology is useful for archaeological site survey, and can improve the classification efficiency and data update rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20aerial%20survey" title="digital aerial survey">digital aerial survey</a>, <a href="https://publications.waset.org/abstracts/search?q=canopy%20characters%20classification" title=" canopy characters classification"> canopy characters classification</a>, <a href="https://publications.waset.org/abstracts/search?q=archaeological%20sites" title=" archaeological sites"> archaeological sites</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20statistics" title=" multivariate statistics"> multivariate statistics</a> </p> <a href="https://publications.waset.org/abstracts/81393/applying-semi-automatic-digital-aerial-survey-technology-and-canopy-characters-classification-for-surface-vegetation-interpretation-of-archaeological-sites" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81393.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">142</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">1406</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%20indices&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=vegetation%20indices&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=vegetation%20indices&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=vegetation%20indices&page=5">5</a></li> 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href="https://publications.waset.org/abstracts/search?q=vegetation%20indices&page=48">48</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=vegetation%20indices&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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