A Comparative Evaluation of Point Clouds Data Acquired Using Drone LiDAR in Various Terrains Koo S.1, Jung Y.H.2, Park J.W.1, and Kim S.S.3*
1Researcher, Disaster Scientific Investigation Division, National Disaster Management Research Insititute, Republic of Korea
2Senior Researcher, Disaster Scientific Investigation Division, National Disaster Management Research Insititute, Republic of Korea
3Senior Researcher Officer, Disaster Scientific Investigation Division, National Disaster Management Research Insititute, Republic of Korea
*sskim73[at]korea.kr
Abstract
LiDAR technology collects precise 3D spatial information by integrating lasers with high-precision IMUs and GNSS. Advancements in sensor technology have enabled the use of LiDAR on drones for the efficient acquisition of high-resolution point cloud data. Specifically, LiDAR^s multi-return signal processing technology records multiple reflections from a single laser pulse, allowing the sensor to penetrate obstacles like dense foliage and effectively acquire point data from the ground surface.
In this study, data was acquired and performance was compared in a forested area, flat terrain using a DJI Matrice 350 RTK drone equipped with Zenmuse L1 and L2 scanners. The scanning altitude was varied from 50 to 150 meters.
The study found that the L2 demonstrated higher point density and superior vertical (Z-axis) precision compared to the L1. Notably, it effectively acquired ground point cloud data even within complex, densely vegetated forest environments. Additionally, it was confirmed that while multi-return signals can be useful in complex terrains like forests, they may not always provide reliable point cloud data depending on the specific scanning environment. For simple flat terrain or sparsely forested areas, the study found that using only the first, second, and third returns is sufficient to acquire reliable data.
In conclusion, the L2 strength in collecting reliable point cloud data and performing accurate 3D terrain modeling in complex terrain makes it a suitable instrument for disaster cause investigation and precision terrain analysis.
Keywords: Drone LiDAR, Point Cloud, Multiple return, Point density, Ground point
