The forest snow damage is the disaster which is caused by load of the snow that adheres to a tree canopy, and results in fallen trees. When snow damages occur, an administration needs to identify damaged areas. However the current investigation method relies on a ground survey, which is difficult to grasp the conditions of wide areas. In this study, we developed a forest snow damaged area detection method using high resolution satellite optical sensor imagery and LiDAR data. The method consists of following procedures, (1) detection of damaged areas using satellite optical sensor imagery by a discreet choice model, (2) detection of gap areas using DSM and DEM generated by the LiDAR data, and (3) assimilation of (1) and (2) . The assimilation of (1) and (2) enables the mutual complementation of each other's defects. The method was examined on the IKONOS multispectral imagery and the LiDAR data in the test area. Accuracy assessment was conducted from the aspect of omission and commission. From the aspect of omission, accuracy was evaluated by comparing the 50 randomly selected pixels of the result with aerial photograph interpretation. 47 pixels of 50 (94%) were correctly detected. From the aspect of commission, accuracy was evaluated by examining the result of (3) detection in 56 randomly selected pixels which damage was observed in aerial photography. 46 pixels of 56 (82.1%) were correctly detected. From these results, the method achieved high accuracy, and the effectiveness of the combination was demonstrated.