Data from the moderate-resolution imaging spectroradiometer (MODIS) sensor, in combination with new mapping techniques, has the potential to improve regional research on tropical forest resources and land use dynamics. In this study, a supervised regression tree model was used to map fractions of (1) mature forest, (2) secondary forest, and (3) non-forest, using multi-temporal MODIS 250-m data as explanatory variables, and land cover information derived from high-spatial resolution image data as the response variables. From independent validation data, the overall mean absolute deviation of the resulting maps are estimated at 14.6% for mature forest, 21.6% for secondary forest, and 17.1% for non-forest cover. This study shows the increased potential of this new mapping technique to infer human imprints on forest cover across the highlands of mainland Southeast Asia, compared to other existing map sources.