DETERMINATION OF LARGE-SCALE DIGITAL ELEVATION MODEL IN WOODED AREA WITH AIRBORNE LIDAR DATA BY APPLYING ADAPTIVE QUADTREE-BASED ITERATIVE FILTERING METHOD

Generation of large-scale digital elevation (or terrain) models of wooded areas is fundamental to LIDAR data's application in forest management to a large extent. In this paper, a new method is proposed and implemented, which is characterized by its combination of adaptive quadtree-based processing with common iterative interpolation and filtering methods. Digital elevation models (DEM) of wooded and undulating hill areas are derived from airborne LIDAR data, and they have proved to be in good accordance with field survey data of terrain surfaces, collected by high-precision total-station. Also, good efficiency of adaptive quadtree-based iterative filtering algorithm was verified as well as its adaptation for processing high density LIDAR data over a large extent.