| dc.description.abstract | Three-dimensional forest model is important to ecosystem management. Traditional ground investigation requires vast amount of manpower, resources, costs, and time. The objective of this investigation is the modeling for 3-D forest canopy using LIDAR data and high resolution images. The proposed scheme comprises three major steps: (1) data preprocessing, (2) vegetation detection, and (3) tree crown extraction, and(4) Parameter modeling. The data preprocessing includes spatial registration of LIDAR and high resolution images, derivation of above ground surface from LIDAR data, and generation of spectral index from high resolution images. In the vegetation detection, a region-based segmentation followed by the knowledge-based classification is employed to detect tree regions. In the next step, we perform the tree crown extraction in vegetation regions. We use watershed segmentation and local maximum search to extract tree crowns. In last step, we use tree height, and terrain information to build the model. The validation data include two test sites in Taiwan and one site in Finland. Taiwan data are in Hsin-Chu and Tai-Chung respecting an urban area and orchard place, respectively. The Finland data was released by the International Society for Photogrammetry and Remote sensing (ISPRS), and Euro Spatial Data Research Organisation (EuroSDR) as a sample test site. The experimental results in Taiwan indicate that the accuracy of extracted individual tree is better than 90%. The accuracy of determined tree heights is better than 0.62m. The experimental results in Finland indicate that the accuracy of extracted individual tree is 80%. The accuracy of tree heights reaches 1.12m. | en_US |