Our design brief was to develop an accurate remote sensing classification system that avoided the difficult technical details typical of satellite remote sensing. Here you can learn about how remap works. Remap relies on the incredible data storage and geospatial analysis capacities of the Google Earth Engine.
Remap requires only georeferenced training points that identify the different classes of interest, which most often representative ecosystems or land cover classes. Each training point is then intersected with 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 study area into one of the classes present in 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|