MORPHOLOGICAL APPROACH FOR FOREST WOODY DEBRIS DETECTION USING MULTI-PLATFORM, MULTI-RESOLUTION LIDAR DATA
Woody debris (WD) is an important element in forest ecosystems. It provides critical habitat for plants, animals, and insects; but it is also a source of fuel contributing to fire propagation and sometimes leads to catastrophic wildfire. Usually, WD inventory is conducted through field surveys using transects and sample plots. Light Detection and Ranging (LiDAR) point clouds are emerging as a valuable source for the development of comprehensive WD detection strategies. Although results from previous LiDAR-based WD detection approaches have been promising, there is still a lack of a general strategy for handling acquired point clouds by different platforms with varying characteristics (e.g., point density) in a complex environment, especially in natural forests. Here, we propose a general morphological WD detection strategy which requires a few intuitive thresholds, making it applicable to multi-platform LiDAR datasets in both plantation and natural forests. The conceptual basis of the strategy is that WD LiDAR points exhibit non-planar characteristics, distinct intensity, and comprise clusters that exceed a minimum size. The developed strategy is tested using leaf-off point clouds acquired by Geiger-mode, uncrewed aerial vehicle (UAV), and backpack LiDAR systems. The developed approach achieved an average recall of 0.83 indicating a low rate of omission errors. Datasets with higher point density (i.e., from UAV and backpack LiDAR) showed better performance. As for the precision evaluation metric, it ranges from 0.40 to 0.85, indicating a higher level of commission errors at the lower range. The commission errors depend on the presence of bushes and undergrowth, with a lower percentage in forest plantations.
- Master of Science in Civil Engineering
- Civil Engineering
- West Lafayette