Accuracy Assessment of Advanced Laser Scanner Technologies for Forest Survey Based on Three-Dimensional Point Cloud Data

Jin-Soo Kim, Sang-Min Sung (), Ki-Suk Back and Yong-Su Lee
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Jin-Soo Kim: Department of Construction Information, Andong Science College, Andong 36729, Republic of Korea
Sang-Min Sung: CCZ Forest Land Management Office, Korea Forest Conservation Association, Daejeon 35262, Republic of Korea
Ki-Suk Back: School of Civil and Environmental Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
Yong-Su Lee: Department of Civil Engineering, Changwon National University, Changwon 51140, Republic of Korea

Sustainability, 2024, vol. 16, issue 23, 1-15

Abstract: Forests play a crucial role in carbon sequestration and climate change mitigation, offering ecosystem services, biodiversity conservation, and water resource management. As global efforts to reduce greenhouse gas emissions intensify, the demand for accurate spatial information to monitor forest conditions and assess carbon absorption capacity has grown. LiDAR (Light Detection and Ranging) has emerged as a transformative tool, providing high-resolution 3D spatial data for detailed analysis of forest attributes, including tree height, canopy structure, and biomass distribution. Unlike traditional manpower-intensive forest surveys, which are time-consuming and often limited in accuracy, LiDAR offers a more efficient and reliable solution. This study evaluates the accuracy and applicability of advanced LiDAR technologies—drone-mounted, terrestrial, and mobile scanners—for generating 3D forest spatial data. The results show that the terrestrial LiDAR achieved the highest precision for diameter at breast height (DBH) and tree height measurements, with RMSE values of 0.66 cm and 0.91 m, respectively. Drone-mounted LiDAR demonstrated excellent efficiency for large-scale surveys, while mobile LiDAR offered portability and speed but required further improvement in accuracy (e.g., RMSE: DBH 0.76 cm, tree height 1.83 m). By comparing these technologies, this study identifies their strengths, limitations, and optimal application scenarios, contributing to more accurate forest management practices and carbon absorption assessments.

Keywords: forest monitoring; 3D point cloud data; Light Detection and Ranging (LiDAR); drone; carbon absorption (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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