Methods

Overview

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.

See the About page or read our paper for more information.