TY  - BOOK
AU  - Mutanga, Onisimo
AU  - Kumar, Lalit
AB  - In a rapidly changing world, there is an ever-increasing need to monitor the Earth&rsquo;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&rsquo;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.
DO  - 10.3390/books978-3-03897-885-5
ID  - OAPEN ID: 33240
KW  - global monitoring service
KW  - suspended sediment concentration
KW  - image classification
KW  - empirical
KW  - Soil Moisture Active Passive
KW  - data archival
KW  - water resources
KW  - GlobCover
KW  - dNBR
KW  - satellite imagery
KW  - SDG
KW  - cloud-based geo-processing
KW  - spatial resolution
KW  - land use change
KW  - MTBS
KW  - global scale
KW  - landsat collection
KW  - Geo Big Data
KW  - trends
KW  - FAPAR
KW  - vegetation index
KW  - pseudo-invariant features
KW  - emergency response
KW  - RBR
KW  - BULC-U
KW  - Africa
KW  - Brazilian pasturelands dynamics
KW  - Enhanced Vegetation Index
KW  - geo-big data
KW  - multitemporal analysis
KW  - flood
KW  - early warning systems
KW  - low cost in situ
KW  - web portal
KW  - composite burn index (CBI)
KW  - small-scale mining
KW  - snow hydrology
KW  - RdNBR
KW  - seasonal vegetation
KW  - burn severity
KW  - random forests
KW  - land-use cover change
KW  - random forest
KW  - Support Vector Machines
KW  - lower mekong basin
KW  - CWC
KW  - Random Forest
KW  - crop yield
KW  - Landsat-8
KW  - sun glint correction
KW  - protected area
KW  - cropland areas
KW  - disaster prevention
KW  - gross primary productivity (GPP)
KW  - segmentation
KW  - high spatial resolution
KW  - satellite-derived bathymetry
KW  - Aegean
KW  - Brazilian Amazon
KW  - image composition
KW  - pasture mapping
KW  - carbon cycle
KW  - machine learning
KW  - earth observation
KW  - ecosystem assessment
KW  - Mato Grosso
KW  - FVC
KW  - image time series
KW  - LAI
KW  - semi-arid
KW  - google engine
KW  - spatial error
KW  - Ionian
KW  - forest and land use mapping
KW  - snow cover
KW  - long term monitoring
KW  - RHSeg
KW  - online application
KW  - land cover
KW  - PROSAIL
KW  - support vector machines
KW  - seagrass
KW  - wetland
KW  - Sentinel-1
KW  - Sentinel-2
KW  - surface reflectance
KW  - user assessment
KW  - remote sensing
KW  - multi-classifier
KW  - time series
KW  - machine learning classification
KW  - deforestation
KW  - Google Earth Engine
KW  - decision making
KW  - cropland mapping
KW  - change detection
KW  - google earth engine
KW  - industrial mining
KW  - data fusion
KW  - cloud masking
KW  - Google Earth Engine (GEE)
KW  - NDVI
KW  - Bayesian statistics
KW  - China
KW  - cloud computing
KW  - plant traits
KW  - Soil Moisture Ocean Salinity
KW  - soil moisture
KW  - big data analytics
KW  - Landsat
KW  - phenology
KW  - 30-m
KW  - MODIS
KW  - habitat mapping
KW  - Mediterranean
KW  - temporal compositing
KW  - drought
KW  - surface urban heat island
KW  - BACI
KW  - crop classification
L1  - https://mdpi.com/books/pdfview/book/1262
LA  - English
LK  - https://directory.doabooks.org/handle/20.500.12854/48756
PB  - MDPI - Multidisciplinary Digital Publishing Institute
PY  - 2019
SN  - 9783038978855
SN  - 9783038978848
TI  - Google Earth Engine Applicationsnull
ER  - 
