Google Earth Engine Applications

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...

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Main Author: Kumar, Lalit
Other Authors: Mutanga, Onisimo
Format: eBook
Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
Subjects:
Rbr
Sdg
Cwc
Fvc
Lai
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
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653 |a Africa 
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653 |a Landsat 
653 |a Support Vector Machines 
653 |a land cover 
653 |a soil moisture 
653 |a FAPAR 
653 |a cropland mapping 
653 |a online application 
653 |a ecosystem assessment 
653 |a user assessment 
653 |a vegetation index 
653 |a spatial error 
653 |a data fusion 
653 |a cloud-based geo-processing 
653 |a Landsat-8 
653 |a random forests 
653 |a satellite-derived bathymetry 
653 |a emergency response 
653 |a Mato Grosso 
653 |a Random Forest 
653 |a Bayesian statistics 
653 |a drought 
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653 |a land use change 
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653 |a early warning systems 
653 |a snow cover 
653 |a Mediterranean 
653 |a small-scale mining 
653 |a data archival 
653 |a BULC-U 
653 |a Brazilian pasturelands dynamics 
653 |a Google Earth Engine 
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653 |a Soil Moisture Active Passive 
653 |a habitat mapping 
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653 |a change detection 
653 |a low cost in situ 
653 |a FVC 
653 |a MODIS 
653 |a image composition 
653 |a NDVI 
653 |a gross primary productivity (GPP) 
653 |a Sentinel-2 
653 |a Soil Moisture Ocean Salinity 
653 |a landsat collection 
653 |a carbon cycle 
653 |a semi-arid 
653 |a Brazilian Amazon 
653 |a random forest 
653 |a global monitoring service 
653 |a support vector machines 
653 |a image time series 
653 |a snow hydrology 
653 |a forest and land use mapping 
653 |a time series 
653 |a surface urban heat island 
653 |a suspended sediment concentration 
653 |a trends 
653 |a machine learning classification 
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653 |a disaster prevention 
653 |a lower mekong basin 
653 |a wetland 
653 |a empirical 
653 |a PROSAIL 
653 |a long term monitoring 
653 |a web portal 
653 |a high spatial resolution 
653 |a cropland areas 
653 |a LAI 
653 |a Aegean 
653 |a MTBS 
653 |a flood 
653 |a 30-m 
653 |a remote sensing 
653 |a burn severity 
653 |a cloud computing 
653 |a phenology 
653 |a Sentinel-1 
653 |a big data analytics 
653 |a sun glint correction 
653 |a seasonal vegetation 
653 |a Environmental technology. Sanitary engineering 
653 |a composite burn index (CBI) 
653 |a seagrass 
653 |a Ionian 
653 |a crop classification 
653 |a temporal compositing 
653 |a deforestation 
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520 |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.