We aimed to develop a remote sensing classification system that avoided the difficult technical details typical of satellite remote sensing. We wanted users to have access to cutting-edge remote sensing methods to allow effective monitoring of the environment at a range of spatial scales. Remap relies on the incredible data storage and geospatial analysis capacities of the Google Earth Engine.

To map the landscape, remap requires only georeferenced training points that identify different map classes, which most often represents ecosystems or land cover classes. Each training point then samples a range of satellite datasets (predictors) to train a random forest classifier. Once the random forest is trained, remap classifies all of the pixels present in a focal region into the map classes defined by the training set.

Remap uses freely available, high resolution, global-scale, and publicly available biophysical ( slope, elevation), spectral ( Landsat Archive imagery) and climatic ( precipitation, temperature) datasets to inform the classification. Each dataset is automatically processed into relevant indices, such as the Normalized Difference Vegetation Index, before training and running the classifier.

Type Short Name Long Name Earth Engine ID
Index NDVI Normalised Difference Vegetation index LANDSAT/LC8_SR
Index NDWI Normalised Difference Water index LANDSAT/LC8_SR
Index WBI Water Band Index LANDSAT/LC8_SR
Index BR Blue band minus Red band LANDSAT/LC8_SR
Index BG Normalised Difference Blue Green LANDSAT/LC8_SR
Band Value Blue Blue band LANDSAT/LC8_SR
Band Value Green Green band LANDSAT/LC8_SR
Band Value Red Red band LANDSAT/LC8_SR
Band Value NIR Near Infrared band LANDSAT/LC8_SR
Elevation Elevation SRTM Digital Elevation Data 30m USGS/SRTMGL1_003
Elevation Slope SRTM Slope USGS/SRTMGL1_003
BIOCLIM Mean Annual Temperature Mean Annual Temperature WORLDCLIM/V1/BIO
BIOCLIM Annual Precipitation Annual Precipitation WORLDCLIM/V1/BIO
See the About page or read our paper for more information.
Developed with support from: