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-based...
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| Materiálatiipa: | Online |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Fáttát: | |
| Liŋkkat: | 33240 |
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| _version_ | 1869526082052423680 |
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| author | Mutanga, Onisimo Kumar, Lalit |
| author_browse | Kumar, Lalit Mutanga, Onisimo |
| author_facet | Mutanga, Onisimo Kumar, Lalit |
| author_sort | Mutanga, Onisimo |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-48756 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-487562024-04-11T20:34:21Z Google Earth Engine Applications Mutanga, Onisimo Kumar, Lalit TD1-1066 T1-995 global monitoring service suspended sediment concentration image classification empirical Soil Moisture Active Passive data archival water resources GlobCover dNBR satellite imagery SDG cloud-based geo-processing spatial resolution land use change MTBS global scale landsat collection Geo Big Data trends FAPAR vegetation index pseudo-invariant features emergency response RBR BULC-U Africa Brazilian pasturelands dynamics Enhanced Vegetation Index geo-big data multitemporal analysis flood early warning systems low cost in situ web portal composite burn index (CBI) small-scale mining snow hydrology RdNBR seasonal vegetation burn severity random forests land-use cover change random forest Support Vector Machines lower mekong basin CWC Random Forest crop yield Landsat-8 sun glint correction protected area cropland areas disaster prevention gross primary productivity (GPP) segmentation high spatial resolution satellite-derived bathymetry Aegean Brazilian Amazon image composition pasture mapping carbon cycle machine learning earth observation ecosystem assessment Mato Grosso FVC image time series LAI semi-arid google engine spatial error Ionian forest and land use mapping snow cover long term monitoring RHSeg online application land cover PROSAIL support vector machines seagrass wetland Sentinel-1 Sentinel-2 surface reflectance user assessment remote sensing multi-classifier time series machine learning classification deforestation Google Earth Engine decision making cropland mapping change detection google earth engine industrial mining data fusion cloud masking Google Earth Engine (GEE) NDVI Bayesian statistics China cloud computing plant traits Soil Moisture Ocean Salinity soil moisture big data analytics Landsat phenology 30-m MODIS habitat mapping Mediterranean temporal compositing drought surface urban heat island BACI crop classification thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology 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. 2021-02-11T14:44:48Z 2021-02-11T14:44:48Z 2019-04-25 16:37:17 2019 book 33240 9783038978855 9783038978848 https://directory.doabooks.org/handle/20.500.12854/48756 eng image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://play.google.com/books/publish/a/14935057684283403269#details/ISBN:9783038978848 https://mdpi.com/books/pdfview/book/1262 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03897-885-5 10.3390/books978-3-03897-885-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783038978855 9783038978848 420 open access |
| spellingShingle | TD1-1066 T1-995 global monitoring service suspended sediment concentration image classification empirical Soil Moisture Active Passive data archival water resources GlobCover dNBR satellite imagery SDG cloud-based geo-processing spatial resolution land use change MTBS global scale landsat collection Geo Big Data trends FAPAR vegetation index pseudo-invariant features emergency response RBR BULC-U Africa Brazilian pasturelands dynamics Enhanced Vegetation Index geo-big data multitemporal analysis flood early warning systems low cost in situ web portal composite burn index (CBI) small-scale mining snow hydrology RdNBR seasonal vegetation burn severity random forests land-use cover change random forest Support Vector Machines lower mekong basin CWC Random Forest crop yield Landsat-8 sun glint correction protected area cropland areas disaster prevention gross primary productivity (GPP) segmentation high spatial resolution satellite-derived bathymetry Aegean Brazilian Amazon image composition pasture mapping carbon cycle machine learning earth observation ecosystem assessment Mato Grosso FVC image time series LAI semi-arid google engine spatial error Ionian forest and land use mapping snow cover long term monitoring RHSeg online application land cover PROSAIL support vector machines seagrass wetland Sentinel-1 Sentinel-2 surface reflectance user assessment remote sensing multi-classifier time series machine learning classification deforestation Google Earth Engine decision making cropland mapping change detection google earth engine industrial mining data fusion cloud masking Google Earth Engine (GEE) NDVI Bayesian statistics China cloud computing plant traits Soil Moisture Ocean Salinity soil moisture big data analytics Landsat phenology 30-m MODIS habitat mapping Mediterranean temporal compositing drought surface urban heat island BACI crop classification thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Mutanga, Onisimo Kumar, Lalit Google Earth Engine Applications |
| title | Google Earth Engine Applications |
| title_full | Google Earth Engine Applications |
| title_fullStr | Google Earth Engine Applications |
| title_full_unstemmed | Google Earth Engine Applications |
| title_short | Google Earth Engine Applications |
| title_sort | google earth engine applications |
| topic | TD1-1066 T1-995 global monitoring service suspended sediment concentration image classification empirical Soil Moisture Active Passive data archival water resources GlobCover dNBR satellite imagery SDG cloud-based geo-processing spatial resolution land use change MTBS global scale landsat collection Geo Big Data trends FAPAR vegetation index pseudo-invariant features emergency response RBR BULC-U Africa Brazilian pasturelands dynamics Enhanced Vegetation Index geo-big data multitemporal analysis flood early warning systems low cost in situ web portal composite burn index (CBI) small-scale mining snow hydrology RdNBR seasonal vegetation burn severity random forests land-use cover change random forest Support Vector Machines lower mekong basin CWC Random Forest crop yield Landsat-8 sun glint correction protected area cropland areas disaster prevention gross primary productivity (GPP) segmentation high spatial resolution satellite-derived bathymetry Aegean Brazilian Amazon image composition pasture mapping carbon cycle machine learning earth observation ecosystem assessment Mato Grosso FVC image time series LAI semi-arid google engine spatial error Ionian forest and land use mapping snow cover long term monitoring RHSeg online application land cover PROSAIL support vector machines seagrass wetland Sentinel-1 Sentinel-2 surface reflectance user assessment remote sensing multi-classifier time series machine learning classification deforestation Google Earth Engine decision making cropland mapping change detection google earth engine industrial mining data fusion cloud masking Google Earth Engine (GEE) NDVI Bayesian statistics China cloud computing plant traits Soil Moisture Ocean Salinity soil moisture big data analytics Landsat phenology 30-m MODIS habitat mapping Mediterranean temporal compositing drought surface urban heat island BACI crop classification thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| topic_facet | TD1-1066 T1-995 global monitoring service suspended sediment concentration image classification empirical Soil Moisture Active Passive data archival water resources GlobCover dNBR satellite imagery SDG cloud-based geo-processing spatial resolution land use change MTBS global scale landsat collection Geo Big Data trends FAPAR vegetation index pseudo-invariant features emergency response RBR BULC-U Africa Brazilian pasturelands dynamics Enhanced Vegetation Index geo-big data multitemporal analysis flood early warning systems low cost in situ web portal composite burn index (CBI) small-scale mining snow hydrology RdNBR seasonal vegetation burn severity random forests land-use cover change random forest Support Vector Machines lower mekong basin CWC Random Forest crop yield Landsat-8 sun glint correction protected area cropland areas disaster prevention gross primary productivity (GPP) segmentation high spatial resolution satellite-derived bathymetry Aegean Brazilian Amazon image composition pasture mapping carbon cycle machine learning earth observation ecosystem assessment Mato Grosso FVC image time series LAI semi-arid google engine spatial error Ionian forest and land use mapping snow cover long term monitoring RHSeg online application land cover PROSAIL support vector machines seagrass wetland Sentinel-1 Sentinel-2 surface reflectance user assessment remote sensing multi-classifier time series machine learning classification deforestation Google Earth Engine decision making cropland mapping change detection google earth engine industrial mining data fusion cloud masking Google Earth Engine (GEE) NDVI Bayesian statistics China cloud computing plant traits Soil Moisture Ocean Salinity soil moisture big data analytics Landsat phenology 30-m MODIS habitat mapping Mediterranean temporal compositing drought surface urban heat island BACI crop classification thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| url | 33240 |
| work_keys_str_mv | AT mutangaonisimo googleearthengineapplications AT kumarlalit googleearthengineapplications |