This paper describes an improved concept for the mapping of tropical forest classes with ALOS AVNIR and ALOS PALSAR data. The improvement comes from a combination of a sample of very high resolution (VHR) satellite images with medium resolution wall-to-wall mapping in a statistical sampling framework. The approach developed makes it possible to obtain reliable information on mapping accuracy over the whole area of interest. A simulation study indicated that the sample of VHR images should be collected in a stratified manner using small (25 km) images. The VHR images should cover approximately one percent of the total area of interest, depending on the accuracy requirement. The recommended size of the reference plots (population units) that are selected within the VHR imagery is in the order of 50 m by 50 m. In a systematic selection the plots should be located at a distance of several hundred meters from each other. The forest variables were predicted with an unsupervised fuzzy classification method. The ALOS AVNIR-based forest/non-forest mapping accuracies varied between 68% and 97% of the areas of the VHR images. The corresponding ALOS PALSAR mapping accuracies were poorer. At AVNIR resolution, the area of natural forest was over-estimated, and the degree of disturbance underestimated in humid, heavily disturbed parts of the study area in Laos. The three predictions for the total forest fraction from VHR, AVNIR and PALSAR data over the area that was covered by the VHR images were 55.1%, 53.6%, and 52.8%, respectively.