Explore
Google Earth Engine Applications
Lalit Kumar and Onisimo Mutanga
2019
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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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Keywords
- 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 (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 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
- surface reflectance
- surface urban heat island
- suspended sediment concentration
- temporal compositing
- time series
- Trends
- user assessment
- vegetation index
- water resources
- web portal
- wetland
Links
DOI: 10.3390/books978-3-03897-885-5Editions
