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|
|BIOCLIM||Mean Annual Temperature||Mean Annual Temperature||WORLDCLIM/V1/BIO|
|BIOCLIM||Annual Precipitation||Annual Precipitation||WORLDCLIM/V1/BIO|