Single tree detection in forest areas with high-density LiDAR data

The study presents a novel method for delineation of tree crowns anddetection of stem positions of single trees from dense airborne LIDAR data. The core module of the method is a surface reconstructionthat robustly interpolates the canopy height model (CHM) from the LIDAR data. Tree segments are found by applying the watershedalgorithm to the CHM. Possible stem positions of the tallest trees in the segments are subsequently derived from the localmaxima of the CHM. Additional stem positions in the segments are found in a 3-step algorithm. First, all the points between theground and the crown base height are separated. Second, possiblestem points are found by hierarchical clustering these points usingtheir horizontal distances. Third, the stem position is estimatedwith a robust RANSAC-based adjustment of the stem points. We appliedthe method to small-footprint full waveform data that havebeen acquired in the Bavarian Forest National Park with a point densityof approximately 25 points per m². The results indicate that the detection rate for coniferous trees is 61 % and for deciduoustrees 44 %, respectively. 7 % of the detected trees are falsepositives. The mean positioning error is 0.92 cm, whereas the additionalstem detection improves the position on average by 22 cm.     «

The study presents a novel method for delineation of tree crowns anddetection of stem positions of single trees from dense airborne LIDAR data. The core module of the method is a surface reconstructionthat robustly interpolates the canopy height model (CHM) from the LIDAR data. Tree segments are found by applying the watershedalgorithm to the CHM. Possible stem positions of the tallest trees in the segments are subsequently derived from the localmaxima of the CHM. Additional stem positions in th...     »