Individual Tree Stem Detection Using the Vertical Complexity Index on Structure-from-Motion Point Clouds

Abstract

Automated Individual Tree Stem Detection (ITSD) is an important technique for forest management and conservation. In this study, a new method is tested, using unoccupied aerial vehicle (UAV)-derived point clouds from RGB imagery depicting deciduous dense forests in Germany. For the ITSD, the Vertical Complexity Index (VCI) was applied on five different datasets, including four Structure-from-Motion point clouds of a site in the Hainich National Park and one UAV LiDAR point cloud located at the Research Centre Jülich in Germany for comparison. First, four parameters were calibrated on one dataset and then used on the remaining point clouds to test the robustness of the respective method. The results showed that, for the SfM datasets, up to 82% of overstory trees could be detected with a Precision of up to 0.82 and F1-Score of up to 0.78. The LiDAR point cloud achieved an F1-Score of 0.69 using the same parameter set, but performance could be improved up to an F1-Score of 0.86 when adjusting one parameter. False negatives of the SfM datasets can be traced back to leaning trees and a low diameter at breast height (DBH). The application of this new method proved to be robust across different datasets and study sites, showing promising results for RGB-derived point clouds, and considering the low computational power, holding great potential for future analysis. Especially in forest management, the application of this method on low-cost RGB point clouds would be beneficial compared to the more expensive LiDAR imaging.