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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chemical-peel

https://doi.org/10.1007/978-981-19-0280-2ence Water Index (MNDWI) was used to delineate water pixels from the multispectral satellite datasets, while the turbidity was assessed spatially for different river sections using widely used Turbidity retrieval algorithms. The relative consistency among the selected algorithms was evaluated using

Palatial

https://doi.org/10.1007/978-3-030-98896-8s especially using band ratio methods, they need accurate spectral and specific absorption characteristics which are challenging to obtain for many inland water bodies. Machine learning algorithms, on the other hand can statistically derive the spatio-temporal distribution of chlorophyll-a and suspe

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Lahcene Makhloufi,Fateh Bela?d,Karima Zidanelgorithms are considered to be more powerful and known to have higher accuracy compared to traditional approaches. Specifically, time-series remotely sensed (both optical and SAR) data are used for crop mapping by capturing key phenological crop stages that differ for each crop type. In this study,

transient-pain

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https://doi.org/10.1007/978-3-030-56849-8 and temporal high-resolution data, in conjunction with the GPS-guided ground truth collection, was utilized in these studies. Hybrid semi-automatic classification techniques employing both digital classification and visual image interpretation were employed for spatial delineation of extent of hort

Dissonance

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Physical Fundamentals of Energy Utilization,olution) microwave data through Sentinel-1 Toolbox “SNAP”, the accuracy of crop map was assessed with geotagged ground truth. Pixel level estimates were developed and accumulated at the district level using an area-weighted approach. The estimated yield was compared with the actual estimate obtained

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Daniel M. Kammen,Gregory F. Nemetanagement to assess the risk, simulation models, forecasting/early warning, monitoring, damage assessment, and prevention/planning during the aforementioned disasters. This chapter emphasizes the usage of geospatial technologies to manage desert locusts and dust storms.