Fusion of LIDAR data and high resolution images for forest canopy modeling

Three-dimensional forest model is important to forest ecosystem management. Traditional ground investigation requires vast amount of manpower, resources, costs, and time. Hence, it is difficult to promptly obtain accurate information by using ground investigation. Nowadays, Light Detection And Ranging (LIDAR) technology provides high density 3-D point clouds. It can rapidly obtain 3-D information of forest structure. On the other hand, the high resolution images provide plentiful spectral information of forest coverage. Thus, we propose here a scheme to merge LIDAR data and high resolution images for the reconstruction of forest canopies. The objective of this investigation is to perform 3-D forest canopy modeling using LIDAR data and high resolutions image. The proposed scheme comprises three major steps: (1) data preprocessing, (2) vegetation detection, and (3) tree crown extraction. In the vegetation detection, a region-based segmentation and knowledge-based classification are integrated to detect tree regions. Then, a watershed segmentation is performed to extract each individual tree. The LIDAR data used in this research are obtained by Optech ALTM and Leica ALS40 systems. The average density of LIDAR data is about 1.7 points per square meter. The aerial photos with 1:5,000 scale are used in this investigation. Preliminary results indicate that the proposed scheme may reach reliable results.