Existing cover data development is a key data set for natural resource characterization. There are standard methods that can easily utilize the many different models available. Existing natural resource data (e.g., landcover data, digital soil data, land use data, etc.) initially involves the derivation of a map unit classification (the data labels). Further, using training sites (field and in situ derived) as reference data, apply supervised classification methods (i.e., Random Forests) with remotely sensed satellite (e.g., Landsat, sentinel), aerial imagery and other landscape variables (e.g., digital elevation derivations, soils data, geology data, etc.) for labelling segments or pixels with land attributes for a landscape/study area (applied for local and National applications). Random forest is the main tool used a supervised algorithm consisting of many decision trees for labeling the landscape.