Multi-Source Land Cover Classification with an Integrated Feature Set in Mekong Basin

Large-area medium-resolution land cover classification is a key information for monitoring land use, human activity influence on ecological environments, etc. However, the existing land cover classification products lack insights of the utilization of multi-source data and regional characteristics, thus facing accuracy shackles. In this work, we supplemented SAR and DEM data into classification procedure, and gathered a comprehensive set of features, including global ecological zones (GEZs) announced by FAO as an indicator. With a multi-source land cover point label dataset for Mekong basin (LanCoMe) with a revised classification system, a random forest model was trained to produce Mekong land cover classification mappings. The model achieves validation accuracy of 91.3%, and the product is well correlated with other published global land cover products. The result also indicates that GEZs can be a prior feature for large-area land cover tasks.