Google Earth Engine Applications
Google Earth Engine Applications
- MDPI - Multidisciplinary Digital Publishing Institute 2019
- 1 online resource (420 p.)
Open Access
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.
Creative Commons
English
9783038978848 9783038978855 books978-3-03897-885-5
10.3390/books978-3-03897-885-5 doi
Environmental science, engineering and technology
30-m Aegean Africa BACI Bayesian statistics big data analytics Brazilian Amazon Brazilian pasturelands dynamics BULC-U burn severity carbon cycle change detection China cloud computing cloud masking cloud-based geo-processing composite burn index (CBI) crop classification crop yield cropland areas cropland mapping CWC data archival data fusion decision making deforestation disaster prevention dNBR drought early warning systems earth observation ecosystem assessment emergency response empirical Enhanced Vegetation Index FAPAR flood forest and land use mapping FVC Geo Big Data geo-big data global monitoring service global scale GlobCover google earth engine Google Earth Engine Google Earth Engine (GEE) google engine gross primary productivity (GPP) habitat mapping high spatial resolution image classification image composition image time series industrial mining Ionian LAI land cover land use change land-use cover change Landsat landsat collection Landsat-8 long term monitoring low cost in situ lower mekong basin machine learning machine learning classification Mato Grosso Mediterranean MODIS MTBS multi-classifier multitemporal analysis NDVI online application pasture mapping phenology plant traits PROSAIL protected area pseudo-invariant features random forest Random Forest random forests RBR RdNBR remote sensing RHSeg satellite imagery satellite-derived bathymetry SDG seagrass seasonal vegetation segmentation semi-arid Sentinel-1 Sentinel-2 small-scale mining snow cover snow hydrology soil moisture Soil Moisture Active Passive Soil Moisture Ocean Salinity spatial error spatial resolution sun glint correction support vector machines Support Vector Machines surface reflectance surface urban heat island suspended sediment concentration temporal compositing time series trends user assessment vegetation index water resources web portal wetland
Open Access
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.
Creative Commons
English
9783038978848 9783038978855 books978-3-03897-885-5
10.3390/books978-3-03897-885-5 doi
Environmental science, engineering and technology
30-m Aegean Africa BACI Bayesian statistics big data analytics Brazilian Amazon Brazilian pasturelands dynamics BULC-U burn severity carbon cycle change detection China cloud computing cloud masking cloud-based geo-processing composite burn index (CBI) crop classification crop yield cropland areas cropland mapping CWC data archival data fusion decision making deforestation disaster prevention dNBR drought early warning systems earth observation ecosystem assessment emergency response empirical Enhanced Vegetation Index FAPAR flood forest and land use mapping FVC Geo Big Data geo-big data global monitoring service global scale GlobCover google earth engine Google Earth Engine Google Earth Engine (GEE) google engine gross primary productivity (GPP) habitat mapping high spatial resolution image classification image composition image time series industrial mining Ionian LAI land cover land use change land-use cover change Landsat landsat collection Landsat-8 long term monitoring low cost in situ lower mekong basin machine learning machine learning classification Mato Grosso Mediterranean MODIS MTBS multi-classifier multitemporal analysis NDVI online application pasture mapping phenology plant traits PROSAIL protected area pseudo-invariant features random forest Random Forest random forests RBR RdNBR remote sensing RHSeg satellite imagery satellite-derived bathymetry SDG seagrass seasonal vegetation segmentation semi-arid Sentinel-1 Sentinel-2 small-scale mining snow cover snow hydrology soil moisture Soil Moisture Active Passive Soil Moisture Ocean Salinity spatial error spatial resolution sun glint correction support vector machines Support Vector Machines surface reflectance surface urban heat island suspended sediment concentration temporal compositing time series trends user assessment vegetation index water resources web portal wetland