Titlebook: Geospatial Technologies for Resources Planningand Management; Chandra Shekhar Jha,Ashish Pandey,Vijay Singh Book 2022 The Editor(s) (if ap

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Chris Gayford Ph.D., M.Ed., B.Sc.pplications. This chapter discusses the different methods of deriving the ET and the justification for adopting the Priestley Taylor algorithm. It also describes the methodology of deriving the terrestrial ET in a near real-time basis and discusses the intra-seasonal dynamics and comparison with the

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Leslie Dienes,Istvan Dobozi,Marian Radetzkinder vegetation cover was observed during 2006 and 2019. However, areas under other classes, mainly buildings, transport networks, and other urban categories, increased from 43.9 to 55.3%. A change map highlighting both positive and negative vegetation change areas and the vegetated areas with nomin

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Geospatial Technology for Geomorphology Mapping and Its Applications,s at different scales and for different purposes. The advancements in geospatial technology in terms of high and very high-resolution satellite data both in optical and microwave domains, availability of high-resolution DEMs, and new data-driven techniques and algorithms of remote sensing data analy

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Water Resource Management Studies at Micro Level Using Geospatial Technologies,fluenced by a set of linked physicals, biological, and socio-economic factors such as surface water hydrology, groundwater hydrology, climate, soils topography, land use, water quality, ecosystems, demographics, institutional arrangements and infrastructure. In this book chapter, an effort has been

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,Long-Term Analysis of River Migration Pattern Using Geospatial Techniques—A Case Study of Upper Parhimgoda barrage, Haridwar, Uttarakhand, India to Luv-Kush Barrage, Kanpur, Uttar Pradesh, India is attempted. Automated procedures analyzing satellite data from 1975 to 2020, are successfully utilized to calculate morphometric parameter for identifying river migration. Meandering of Ganga River is o

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Delineation and Monitoring of Wetlands Using Time Series Earth Observation Data and Machine LearninRandom Forest classifier was used for extraction of surface water bodies and wetland classification. The overall accuracy and overall kappa coefficient of the classified data is nearly 80% or above in all the years of study, while kappa coefficient ranges between 0.77 and 0.92 for all study years. F

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