Google Earth Engine Applications

• Hoofdauteur: Mutanga, Onisimo (auth)
• Andere auteurs: Kumar, Lalit (auth)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: MDPI - Multidisciplinary Digital Publishing Institute 2019
• Onderwerpen: Environmental science, engineering & technology; global monitoring service; suspended sediment concentration; image classification; empirical; Soil Moisture Active Passive; data archival; water resources; GlobCover; dNBR; satellite imagery; SDG; cloud-based geo-processing; spatial resolution; land use change; MTBS; global scale; landsat collection; Geo Big Data; trends; FAPAR; vegetation index; pseudo-invariant features; emergency response; RBR; BULC-U; Africa; Brazilian pasturelands dynamics; Enhanced Vegetation Index; geo-big data; multitemporal analysis; flood; early warning systems; low cost in situ; web portal; composite burn index (CBI); small-scale mining; snow hydrology; RdNBR; seasonal vegetation; burn severity; random forests; land-use cover change; random forest; Support Vector Machines; lower mekong basin; CWC; Random Forest; crop yield; Landsat-8; sun glint correction; protected area; cropland areas; disaster prevention; gross primary productivity (GPP); segmentation; high spatial resolution; satellite-derived bathymetry; Aegean; Brazilian Amazon; image composition; pasture mapping; carbon cycle; machine learning; earth observation; ecosystem assessment; Mato Grosso; FVC; image time series; LAI; semi-arid; google engine; spatial error; Ionian; forest and land use mapping; snow cover; long term monitoring; RHSeg; online application; land cover; PROSAIL; support vector machines; seagrass; wetland; Sentinel-1; Sentinel-2; surface reflectance; user assessment; remote sensing; multi-classifier; time series; machine learning classification; deforestation; Google Earth Engine; decision making; cropland mapping; change detection; google earth engine; industrial mining; data fusion; cloud masking; Google Earth Engine (GEE); NDVI; Bayesian statistics; China; cloud computing; plant traits; Soil Moisture Ocean Salinity; soil moisture; big data analytics; Landsat; phenology; 30-m; MODIS; habitat mapping; Mediterranean; temporal compositing; drought; surface urban heat island; BACI; crop classification
• Online toegang: DOAB: download the publication; DOAB: description of the publication

In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-b...