Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/138782
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dc.contributor.authorKumar, Lalit
dc.contributor.authorMutanga, Onisimo
dc.date.accessioned2019-09-01T09:11:10Z-
dc.date.available2019-09-01T09:11:10Z-
dc.date.issued2019
dc.identifier.isbn9783038978848 9783038978855
dc.identifier.urihttp://oers.taiwanmooc.org/jspui/handle/123456789/138782-
dc.language.isoeng
dc.publisherMDPI - Multidisciplinary Digital Publishing Institute
dc.relation.urihttps://mdpi.com/books/pdfview/book/1262
dc.rights.uriCC BY-NC-ND (姓名標示-非商業性-禁止改作)
dc.sourceDOAB
dc.subject.classificationTechnology (General) --- Environmental Technology
dc.subject.otherGoogle Earth Engine; NDVI; vegetation index; Landsat; remote sensing; phenology; surface reflectance; cropland mapping; cropland areas; 30-m; Landsat-8; Sentinel-2; Random Forest; Support Vector Machines; segmentation; RHSeg; Google Earth Engine; Africa; remote sensing; semi-arid; ecosystem assessment; land use change; image classification; seasonal vegetation; carbon cycle; Google Earth Engine; crop yield; gross primary productivity (GPP); data fusion; Landsat; MODIS; MODIS; Random Forest; pasture mapping; Brazilian pasturelands dynamics; Google Earth Engine; crop classification; multi-classifier; cloud computing; time series; high spatial resolution; BACI; Enhanced Vegetation Index; Google Earth Engine; cloud-based geo-processing; satellite-derived bathymetry; image composition; pseudo-invariant features; sun glint correction; empirical; spatial error; Google Earth Engine; low cost in situ; Sentinel-2; Mediterranean; burn severity; change detection; Landsat; dNBR; RdNBR; RBR; composite burn index (CBI); MTBS; lower mekong basin; landsat collection; suspended sediment concentration; online application; google earth engine; Landsat; Google Earth Engine; protected area; forest and land use mapping; machine learning classification; China; temporal compositing; image time series; multitemporal analysis; change detection; cloud masking; Landsat-8; Google Earth Engine (GEE); Google Earth Engine; LAI; FVC; FAPAR; CWC; plant traits; random forests; PROSAIL; small-scale mining; industrial mining; google engine; image classification; land-use cover change; seagrass; habitat mapping; image composition; machine learning; support vector machines; Google Earth Engine; Sentinel-2; Aegean; Ionian; global scale; soil moisture; Soil Moisture Ocean Salinity; Soil Moisture Active Passive; Google Earth Engine; drought; cloud computing; remote sensing; snow hydrology; water resources; Google Earth Engine; user assessment; MODIS; snow cover; flood; disaster prevention; emergency response; decision making; Google Earth Engine; land cover; deforestation; Brazilian Amazon; Bayesian statistics; BULC-U; Mato Grosso; spatial resolution; Landsat; GlobCover; SDG; surface urban heat island; Geo Big Data; Google Earth Engine; global monitoring service; Google Earth Engine; web portal; satellite imagery; trends; earth observation; wetland; Google Earth Engine; Sentinel-1; Sentinel-2; random forest; cloud computing; geo-big data; cloud computing; big data analytics; long term monitoring; data archival; early warning systems
dc.titleGoogle Earth Engine Applications
dc.type電子教科書
dc.classification自然科學類
Theme:教科書-自然科學類

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