Performance Evaluation of Drone LiDAR Sensors for Field Operations in Mountainous Areas

This study evaluated the performance of LiDAR sensors by mounting the DJI Zenmuse L1 and L2 on a DJI Matrice 350 RTK drone to capture ground point cloud data over flat terrain and forested areas.Scans were conducted at altitudes of 50 m and 80 m, with 70 cm 70 cm aerial targets placed on open ground and under forest vegetation to measure point density.We assessed the multiple reflections in the captured LiDAR data and reviewed absolute accuracy using ground control points.Results showed that the L2 sensor achieved higher point density than the L1 sensor in open areas and performed well at low altitudes (50 m).For simple terrain like flat ground, adequate data was obtained with only the first or second reflections.In forested areas, the L2 sensor effectively captured ground data beneath dense vegetation, whereas the L1 sensor struggled to accurately detect ground under these conditions.In terms of absolute accuracy, the L2 sensor showed less noise and higher precision on flat, stable terrain than the L1.On more complex terrain, the L2 enabled accurate 3D modeling at higher point density.Overall, the flexible use of both L1 and L2 sensors is expected to improve disaster site analysis and response across varied landscapes.

Authors

Institutions

Publication Details

Journal
Korean Journal of Remote Sensing
Published
2024-12-31
DOI
https://doi.org/10.7780/kjrs.2024.40.6.2.7
Citations
1
Primary Topic
UAV Applications and Optimization
Type
article
Field-Weighted Citation Impact
1.30

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Performance Evaluation of Drone LiDAR Sensors for Field Operations in Mountainous Areas

Seongsam Kim, Seul Koo, Eontaek Lim, Yonghan Jung
1 citations
Korean Journal of Remote Sensing
UAV Applications and Optimization
1.30
article

Performance Evaluation of Drone LiDAR Sensors for Field Operations in Mountainous Areas

Seongsam Kim, Seul Koo, Eontaek Lim, Yonghan Jung
article en
1 citations

Abstract

This study evaluated the performance of LiDAR sensors by mounting the DJI Zenmuse L1 and L2 on a DJI Matrice 350 RTK drone to capture ground point cloud data over flat terrain and forested areas.Scans were conducted at altitudes of 50 m and 80 m, with 70 cm 70 cm aerial targets placed on open ground and under forest vegetation to measure point density.We assessed the multiple reflections in the captured LiDAR data and reviewed absolute accuracy using ground control points.Results showed that the L2 sensor achieved higher point density than the L1 sensor in open areas and performed well at low altitudes (50 m).For simple terrain like flat ground, adequate data was obtained with only the first or second reflections.In forested areas, the L2 sensor effectively captured ground data beneath dense vegetation, whereas the L1 sensor struggled to accurately detect ground under these conditions.In terms of absolute accuracy, the L2 sensor showed less noise and higher precision on flat, stable terrain than the L1.On more complex terrain, the L2 enabled accurate 3D modeling at higher point density.Overall, the flexible use of both L1 and L2 sensors is expected to improve disaster site analysis and response across varied landscapes.

Korean Journal of Remote SensingVol. 40(6)
National Disaster Management Research Institute (KR)
Ministry of the Interior and Safety
Openalex Percentile: Top 11%
UAV Applications and Optimization
1.30
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Performance Evaluation of Drone LiDAR Sensors for Field Operations in Mountainous Areas — Seongsam Kim, Seul Koo, et al. · Korean Journal of Remote Sensing (2024) | TGRS Research Map | TGRS