SilviLaser 2008, Sept. 17-19, 2008 – Edinburgh, UK 625 Potential and limits of extraction of forest attributes by fusion of medium point density LiDAR data with ADS40 and RC30 images Lars T. Waser 1 , Christian Ginzler 2 , Meinrad Kuechler 3 , Emanuel Baltsavias 4 1, 2, 3 Swiss Federal Research Institute WSL, Land Resources Assessment, 8903 Birmensdorf, Switzerland, email: (waser, ginzler, kuechler)@wsl.ch 4 Institute of Geodesy and Photogrammetry, ETH Zurich, 8093 Zurich, Switzerland, email: manos@geod.baug.ethz.ch Abstract This study presents an approach for semi-automated derivation of forest attributes (area, composition, stands) by fusion of medium point density LiDAR data with ADS40 and RC30 images to support tasks of the National Forest Inventory (NFI). In a first step, two different canopy height models (CHMs) are generated using a LiDAR DTM with two DSMs derived from the LiDAR data and RC30 images. In a second step, forest area was obtained using a logistic regression approach and explanatory variables from both CHMs. Based on the forest area, tree composition and main tree species are modelled again using logistic regression models and explanatory variables derived from both the ADS40 and RC30 aerial images. In a third step, forest stands are extracted by combining homogenous parts of the CHM with tree species information. Generally, results based on LiDAR CHM produced less satisfactory results due to lower quality. High accuracy for the extraction of forest area, main tree species (kappa = 0.7 to 0.9) is obtained. Further research is needed for the extraction of forest stands. The present study reveals the potential and limits to derive forest attributes and highlights possibilities of their usage for tasks of the Swiss National Forest Inventory. Keywords: Canopy height model, DSM/DTM, Forestry, high-resolution, multisensor 1. Introduction Extraction of forest attributes from airborne remote sensing data have grown over time and will continue to do so in the future since exact information on forest extend, structure and composition is needed for many environmental, monitoring or protection tasks. The present study focuses on the extraction of these attributes and was carried out in the framework of the Swiss National Forest Inventory (NFI) and the Swiss Mire Monitoring Program (Brassel and Lischke, 2001). Recent progress in three-dimensional remote sensing mainly includes digital stereo-photogrammetry, radar interferometry and LiDAR (Watt and Donoghue 2005, Baltsavias et al. 2007). E.g. by subtracting a digital terrain model (DTM) from the corresponding digital surface model (DSM), canopy height models (CHMs) can be calculated that serve as basis for other forest attributes. Using digital photogrammetry, DSMs are generated via image matching, often using cross-correlation (Hyyppä et al. 2000) or less frequently multi image-matching approaches (Zhang and Gruen 2004). Meanwhile several LiDAR systems are commercially available (Naesset and Gobakken 2005), enabling the derivation of DTMs from such data as well (Baltsavias 1999). Several studies have integrated LiDAR with optical remotely sensed data to estimate forest attributes such as stand composition, tree height, crown diameter, basal area, and stem volume (e.g. Straub 2003; St-Onge et al. 2004, Hollaus et al. 2006, Baltsavias et