Increasing the Thematic Resolution for Trees and Built Area in a Global Land Cover Dataset Using Class Probabilities

Land cover-based models that rely on purpose-specific thematic details are common in environmental fields such as hydrology, water quality, and ecology. Global remotely sensed land cover from the Dynamic World dataset on Google Earth Engine has trees and built area classes, but enables modelers to create more thematically detailed classifications based on pixel class probabilities from their convolutional neural network (CNN) classifier. However, more information is needed about how these probabilities relate to actual heterogeneity within a land cover class. We used Dynamic World CNN class probabilities to subclassify temperate and tropical forest types from the trees class in the Eastern United States and Costa Rica, as well as developed area intensities from the built class. Subclassifications were evaluated against reference data and in a watershed they were not trained for. The results on dominant temperate forest type user’s accuracy (i.e., of all the pixels classified as a specific land cover type, how many are actually that type) ranged from 43% to 76%, while producer’s accuracy (i.e., of all the actual pixels of a specific land cover type, how many were correctly classified) ranged from 50% to 70%. In the untrained watershed, the overall accuracy was 85% for temperate forest types and 52% for developed areas, demonstrating reliability in classifying forest and developed land cover types. This approach creates opportunities to access up-to-date land cover information with greater thematic detail.