Remote Sensing and Artificial Intelligence in Inland Waters Monitoring

Water, vital for life, confronts unprecedented challenges in aquatic ecosystems due to factors like scarcity and pollution. Monitoring at local to global scales is vital for effective management, aligning with sustainable development goals. To address these challenges, the integration of remote sens...

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書誌詳細
フォーマット:	Online
言語:	英語
出版事項:	MDPI - Multidisciplinary Digital Publishing Institute 2024
主題:	remote sensing water quality harmonize RS data machine learning global modeling model development linear regression LASSO regularization L1 coincident data Google Earth Engine cyanobacteria unmanned aerial systems multispectral imagery machine learning classification inland water multi-source satellite observation technology scientometrics CiteSpace few-shot learning underwater aquatic vegetation submerged vegetation foundation model Sentinel-2 VHR WorldView-2 UAV Segment Anything model water quality parameters spatiotemporal distribution Dianshan Lake Sentinel-1 water extraction flood disaster decision tree random forest improved U-Net deep learning hyperspectral imagery PRISMA satellite Chlorophyll-a lakes eutrophication machine learning algorithms in situ water quality data lakes Landsat-8 CMIP6 BCSD PLUS MOP flood risk assessment multi-scenario simulation climate change water quality monitoring Artificial Neural Network (ANN) artificial intelligence WISE sustainable water management genetic algorithm Extreme Gradient Boosting (XGBoost) water monitoring thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
オンライン･アクセス:	ONIX_20240514_9783725805730_357
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Remote Sensing and Artificial Intelligence in Inland Waters Monitoring

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