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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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Formato: Online
Lenguaje:inglés
Publicado: MDPI - Multidisciplinary Digital Publishing Institute 2024
Materias:
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
Acceso en línea:ONIX_20240514_9783725805730_357
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ONIX_20240514_9783725805730_357

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