Morphological Approach for Forest Woody Debris Detection Using Multi-Platform, Multi-Resolution Lidar Data

Abstract

Woody Debris (WD) plays an important role 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. Traditional field surveys for WD assessments are usually restricted to transects and sample plots. Light Detection and Ranging (LiDAR) point clouds emerge as a valuable source for the development of comprehensive WD detection strategies. Although results from previous studies on LiDAR-based WD detection approaches have been promising, there is still a lack of general strategy for handling point clouds acquired by different platforms with varying characteristics (e.g., point density) in different forest types. In this study, we propose a general morphological WD detection strategy which requires few intuitive thresholds, making it applicable to multi-platform LiDAR datasets in both plantation and natural forests.