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210512 ||| eng |
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|a 9783038978848
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|a 9783038978855
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|a books978-3-03897-885-5
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100 |
1 |
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|a Mutanga, Onisimo
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245 |
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|a Google Earth Engine Applications
|h Elektronische Ressource
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260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2019
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300 |
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|a 1 electronic resource (420 p.)
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653 |
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|a Africa
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653 |
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|a machine learning
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653 |
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|a Landsat
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653 |
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|a Support Vector Machines
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653 |
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|a land cover
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653 |
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|a FAPAR
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653 |
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|a soil moisture
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653 |
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|a cropland mapping
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653 |
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|a online application
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653 |
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|a ecosystem assessment
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653 |
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|a vegetation index
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653 |
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|a user assessment
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653 |
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|a spatial error
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653 |
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|a data fusion
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653 |
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|a cloud-based geo-processing
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653 |
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|a Landsat-8
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653 |
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|a random forests
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653 |
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|a satellite-derived bathymetry
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653 |
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|a emergency response
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653 |
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|a Mato Grosso
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653 |
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|a Random Forest
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653 |
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|a Bayesian statistics
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653 |
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|a drought
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653 |
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|a RBR
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653 |
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|a protected area
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653 |
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|a SDG
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653 |
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|a land use change
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653 |
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|a pasture mapping
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653 |
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|a surface reflectance
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653 |
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|a earth observation
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653 |
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|a CWC
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653 |
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|a GlobCover
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653 |
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|a global scale
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653 |
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|a land-use cover change
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653 |
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|a Google Earth Engine (GEE)
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653 |
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|a industrial mining
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|a early warning systems
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653 |
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|a snow cover
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653 |
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|a Mediterranean
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653 |
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|a small-scale mining
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653 |
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|a data archival
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653 |
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|a Brazilian pasturelands dynamics
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653 |
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|a BULC-U
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653 |
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|a Google Earth Engine
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|a segmentation
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653 |
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|a China
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653 |
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|a Soil Moisture Active Passive
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653 |
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|a pseudo-invariant features
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653 |
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|a habitat mapping
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653 |
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|a image classification
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653 |
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|a water resources
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653 |
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|a google engine
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653 |
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|a plant traits
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653 |
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|a RdNBR
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653 |
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|a dNBR
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653 |
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|a crop yield
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653 |
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|a multitemporal analysis
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653 |
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|a spatial resolution
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653 |
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|a decision making
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653 |
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|a google earth engine
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|a low cost in situ
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653 |
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|a change detection
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653 |
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|a FVC
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653 |
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|a MODIS
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653 |
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|a image composition
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653 |
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|a gross primary productivity (GPP)
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653 |
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|a NDVI
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653 |
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|a Soil Moisture Ocean Salinity
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653 |
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|a Sentinel-2
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653 |
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|a landsat collection
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653 |
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|a carbon cycle
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653 |
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|a Brazilian Amazon
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653 |
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|a semi-arid
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653 |
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|a random forest
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653 |
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|a global monitoring service
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653 |
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|a support vector machines
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653 |
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|a image time series
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653 |
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|a snow hydrology
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653 |
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|a forest and land use mapping
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653 |
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|a time series
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653 |
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|a surface urban heat island
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653 |
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|a suspended sediment concentration
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653 |
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|a trends
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653 |
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|a machine learning classification
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653 |
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|a multi-classifier
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653 |
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|a Geo Big Data
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653 |
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|a Enhanced Vegetation Index
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653 |
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|a satellite imagery
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653 |
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|a RHSeg
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653 |
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|a cloud masking
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653 |
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|a geo-big data
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653 |
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|a BACI
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|a disaster prevention
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653 |
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|a lower mekong basin
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|a wetland
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653 |
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|a empirical
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653 |
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|a PROSAIL
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|a long term monitoring
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653 |
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|a Environmental science, engineering and technology / bicssc
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653 |
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|a web portal
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653 |
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|a high spatial resolution
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653 |
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|a cropland areas
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653 |
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|a LAI
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653 |
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|a Aegean
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653 |
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|a MTBS
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653 |
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|a flood
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653 |
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|a remote sensing
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653 |
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|a 30-m
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653 |
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|a burn severity
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653 |
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|a cloud computing
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653 |
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|a phenology
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653 |
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|a Sentinel-1
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653 |
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|a sun glint correction
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653 |
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|a seasonal vegetation
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653 |
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|a big data analytics
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653 |
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|a composite burn index (CBI)
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653 |
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|a seagrass
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653 |
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|a Ionian
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653 |
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|a crop classification
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653 |
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|a deforestation
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653 |
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|a temporal compositing
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1 |
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|a Kumar, Lalit
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7 |
|a eng
|2 ISO 639-2
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|b DOAB
|a Directory of Open Access Books
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
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5 |
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|a 10.3390/books978-3-03897-885-5
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856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/1262
|7 0
|x Verlag
|3 Volltext
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856 |
4 |
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|u https://directory.doabooks.org/handle/20.500.12854/48756
|z DOAB: description of the publication
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|a 551
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|a 363
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|a 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.
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