Tree filtering for high density airborne LiDAR data

A high resolution Airborne LiDAR data creates better opportunity for an individual tree measurement and provides valuable results for more precise forest inventory. This paper presents tree filtering approach that able to separate dominant tree and undergrowth vegetation. The results can be used for a detailed individual tree measurement. This process is one of the main steps for a single tree extraction from the high resolution Airborne LiDAR data. The filtering technique lies on the fact that a dominant tree has distinct parts in the histogram that represent tree crown, tree trunk, and ground surface with or without undergrowth vegetation. The shape of the histogram was used to identify points that belong to the tree crown and the tree trunk. More points were assigned to the tree trunk based on an iterative analysis of the histogram at certain height above the ground surface. This step was coupled with the RG segmentation. It was found that the filtering routine failed to remove very close undergrowth vegetation. It was also observed that in order to get a good result, the tree filtering method needs at least small area of the tree trunk.