TY - BOOK ID - 33240 TI - Google Earth Engine Applications AU - Kumar, Lalit AU - Mutanga, Onisimo PY - 2019 SN - 9783038978848 9783038978855 DB - DOAB KW - Google Earth Engine KW - NDVI KW - vegetation index KW - Landsat KW - remote sensing KW - phenology KW - surface reflectance KW - cropland mapping KW - cropland areas KW - 30-m KW - Landsat-8 KW - Sentinel-2 KW - Random Forest KW - Support Vector Machines KW - segmentation KW - RHSeg KW - Google Earth Engine KW - Africa KW - remote sensing KW - semi-arid KW - ecosystem assessment KW - land use change KW - image classification KW - seasonal vegetation KW - carbon cycle KW - Google Earth Engine KW - crop yield KW - gross primary productivity (GPP) KW - data fusion KW - Landsat KW - MODIS KW - MODIS KW - Random Forest KW - pasture mapping KW - Brazilian pasturelands dynamics KW - Google Earth Engine KW - crop classification KW - multi-classifier KW - cloud computing KW - time series KW - high spatial resolution KW - BACI KW - Enhanced Vegetation Index KW - Google Earth Engine KW - cloud-based geo-processing KW - satellite-derived bathymetry KW - image composition KW - pseudo-invariant features KW - sun glint correction KW - empirical KW - spatial error KW - Google Earth Engine KW - low cost in situ KW - Sentinel-2 KW - Mediterranean KW - burn severity KW - change detection KW - Landsat KW - dNBR KW - RdNBR KW - RBR KW - composite burn index (CBI) KW - MTBS KW - lower mekong basin KW - landsat collection KW - suspended sediment concentration KW - online application KW - google earth engine KW - Landsat KW - Google Earth Engine KW - protected area KW - forest and land use mapping KW - machine learning classification KW - China KW - temporal compositing KW - image time series KW - multitemporal analysis KW - change detection KW - cloud masking KW - Landsat-8 KW - Google Earth Engine (GEE) KW - Google Earth Engine KW - LAI KW - FVC KW - FAPAR KW - CWC KW - plant traits KW - random forests KW - PROSAIL KW - small-scale mining KW - industrial mining KW - google engine KW - image classification KW - land-use cover change KW - seagrass KW - habitat mapping KW - image composition KW - machine learning KW - support vector machines KW - Google Earth Engine KW - Sentinel-2 KW - Aegean KW - Ionian KW - global scale KW - soil moisture KW - Soil Moisture Ocean Salinity KW - Soil Moisture Active Passive KW - Google Earth Engine KW - drought KW - cloud computing KW - remote sensing KW - snow hydrology KW - water resources KW - Google Earth Engine KW - user assessment KW - MODIS KW - snow cover KW - flood KW - disaster prevention KW - emergency response KW - decision making KW - Google Earth Engine KW - land cover KW - deforestation KW - Brazilian Amazon KW - Bayesian statistics KW - BULC-U KW - Mato Grosso KW - spatial resolution KW - Landsat KW - GlobCover KW - SDG KW - surface urban heat island KW - Geo Big Data KW - Google Earth Engine KW - global monitoring service KW - Google Earth Engine KW - web portal KW - satellite imagery KW - trends KW - earth observation KW - wetland KW - Google Earth Engine KW - Sentinel-1 KW - Sentinel-2 KW - random forest KW - cloud computing KW - geo-big data KW - cloud computing KW - big data analytics KW - long term monitoring KW - data archival KW - early warning systems UR - https://www.doabooks.org/doab?func=search&query=rid:33240 AB - 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-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales. ER -