DVIWindo pfeifer_et_al_new.dvi

Airborne laser scanning is an applicable method to derive digital terrain models (DTMs) in wooded terrain. A considerable amount of the laser points are re°ections on the tree tops (vegetation points). Thus, special ¯ltering algorithms are required to obtain the ground surface. Earlier, we proposed to use iterative linear prediction. We review existing methods and compare them to our approach. A list of advantages and disadvantages of our method is presented, but this list has also validity for laser scanner data processing in general. The quality of the DTMs derived from laser scanner data and accuracy investigations are presented for two examples. 1 Introduction The applications of DTMs are well known. Also for forested areas it can be of interest to have a DTM of high quality. Until now, this was not possible. Terrestrial tacheometry on one hand, is too expensive and takes too much time for recording the ground surface in a forest. Photogrammetry on the other hand, can (depending on visibility) only provide a surface through the tree tops. With the emerging of airborne laser scanning systems, the chance is given to obtain high quality DTMs in forested areas. The laser beam from an airborne laser scanner system can penetrate the tree tops and therefore a signal can be received, which originates from the ground surface. Of course not all laser points originate from re°ections on the ground. Depending on the forest structure, time of °ying (season) and tree type the penetration rate (i.e. the portion of ground points) can range from almost 100% to 0% [Rieger et al., 1999b]. Thus, laser scanner systems provide a point cloud, some of the points are ground points, others are so-called vegetation points. The aim of this paper is to demonstrate the applicability of laser scanner data for the derivation of high quality DTMs in forested areas. It shall also be made clear, that it is worth using a sophisticated ¯ltering method for this end. In this paper we will ¯rst present a review of the methods for laser scanner data evaluation, including the classi¯cation and interpolation algorithm we developed. A short comparison of the algorithms will be given. Following is a section on the performance of our algorithm. This section also includes passages valid for many di®erent laser scanner ¯ltering algorithms. In section 4 two examples will be presented in more detail. Results of accuracy investigations will be mentioned too. …