FORMEC 2026

Extraction of Tree Diameter at Breast Height and Height from SLAM LiDAR Data Using an Open-Source Workflow: A Case Study in Eucalyptus Plantations.

15 Sept 2026, 12:24

18m

DP 106 - DP106 (CZU Prague, Czechia)

Oral presentation Paralel session 1

Speaker

Jirawat Yingdee (Faculty of Forestry, Kasetsart University)

Description

Tree diameter at breast height (DBH) and tree height (H) are essential parameters for monitoring forest growth and estimating carbon sequestration. Conventional field-based measurements are labor-intensive, time-consuming, and costly. In recent years, LiDAR technology has increasingly been applied in forestry due to its efficiency and long-term cost-effectiveness. However, extracting DBH and H from LiDAR-derived point cloud data remains complex, and standardized methodologies are still lacking in Thailand. Furthermore, many existing workflows rely on commercial software, which increases operational costs.
This study proposes an open-source workflow for extracting DBH and H to reduce both cost and processing time. Data were collected in a eucalyptus plantation using a SLAM-based laser scanning system. Two alternative processing approaches were evaluated. The first approach integrates CSF and TreeISO plugins in CloudCompare and the AdQSM software to estimate DBH and H. The second approach employs the 3DFin plugin within CloudCompare. Field measurements were conducted to validate the results obtained from both approaches.
A total of 225 trees were analyzed. The results show that the first approach achieved higher accuracy in estimating DBH, with an R² of 0.788, RMSE of 0.726, and MAE of 0.583, compared to the second approach, which yielded an R² of 0.541, RMSE of 1.457, and MAE of 1.325. A similar trend was observed for tree height estimation. The first approach produced estimates closer to field measurements, with an R² of 0.826, RMSE of 0.824, and MAE of 0.689, whereas the second approach resulted in an R² of 0.701, RMSE of 1.055, and MAE of 0.848.
The findings demonstrate that the first approach provides estimates closer to field measurements than the second approach. Additionally, both approaches showed better performance in estimating tree height than DBH. Despite its lower accuracy, the second approach offers advantages in terms of simplicity and reduced processing time. Therefore, each approach presents distinct trade-offs, and the choice of method should depend on user objectives, required accuracy, and resource constraints.