Titlebook: Application of Remote Sensing and GIS in Natural Resources and Built Infrastructure Management; Vijay P. Singh,Shalini Yadav,Ram Narayan Y

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Comparison of Maximum Likelihood, Neural Networks, and Random Forests Algorithms in Classifying Urbresolution satellite dataset. The comparative results indicate that random forests have outperformed ML and NN in classifying the urban land cover using a high-resolution image. The user and producer accuracies of LULC are found to show no particular trend with any classification algorithm.

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Management of Environmentally Stressed Areas in Watershed Using Multi-criteria Decision Tool in GIStreatment plan using multilayer information in GIS can control soil erosion up to the maximum possible extent and provide sustainable development of the area. A case study has been presented in the chapter to demonstrate the application of the suggested framework in a catchment of water resource projects.

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Crowd-Assisted Flood Disaster Management,verity of these events appear to be increasing. Floods, in particular, cause more devastation, death, and economic impact than any other natural disaster. Disaster reporting has now progressed from official media reporting sources to real-time on-site citizen reporters. Crowd-generated content relat

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A Comparative Analysis of Spatiotemporal Drought Events from Remote Sensing and Standardized Precipecades, detecting these events through remote sensing allowed us to improve the conventional analysis toward an integrated space–time analysis. This chapter proposes a spatiotemporal exploratory analysis of the information from SPI, SPEI and links its results into remote sensing information of NDVI

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