Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds - Einari Heinaro; Topi Tanhuanpää; Mikko Vastaranta; Tuomas Yrttimaa; Antero Kukko; Teemu Hakala; Teppo Mattsson; Markus Holopainen (Remote Sensing, 2023)
Fallen tree mapping provides valuable information regarding the ecological value of boreal forests. Airborne laser scanning (ALS) enables mapping fallen trees on a large scale. We compared the performance of line-detection-based individual fallen tree detection when using moderate point density ALS data (15 points/m2) and high-point-density unmanned aerial vehicle-based laser scanning (ULS) data (285 points/m2). Furthermore, we inspected the dataset and detection methodology-related factors impacting performance in each case. The results of this study showed that increasing the point density of the laser scanning dataset enables the detection of a larger proportion of fallen trees. However, based on our experiment, a line-detection-based fallen tree detection approach is sensitive to noise, thus generating a large number of false detections, especially with high-point-density data. Different types of filters, such as a simple height-based filter and machine-learning-based filters, can be used for reducing noise. However, using such filters is always a compromise, as in addition to reducing noise and thus false detections, they also reduce the number of true detections. Hence, a less noise-sensitive fallen tree detection method utilizing the finer details visible in high-density point clouds could be more suitable for high-point-density laser scanning data.