Bibliographic Details
| Title: |
Forest Stem Extraction and Modeling (FoSEM): A LiDAR-Based Framework for Accurate Tree Stem Extraction and Modeling in Radiata Pine Plantations. |
| Authors: |
Ibrahim, Muhammad1 (AUTHOR) muhammad.ibrahim@uwa.edu.au, Wang, Haitian1 (AUTHOR) 23815631@student.uwa.edu.au, A. Iqbal, Irfan2 (AUTHOR) irfan.iqbal@fpc.wa.gov.au, Miao, Yumeng1 (AUTHOR) 23953617@student.uwa.edu.au, Albaqami, Hezam3 (AUTHOR) haalbaqamii@uj.edu.sa, Blom, Hans2 (AUTHOR) hans.blom@fpc.wa.gov.au, Mian, Ajmal1 (AUTHOR) ajmal.mian@uwa.edu.au |
| Source: |
Remote Sensing. Feb2025, Vol. 17 Issue 3, p445. 24p. |
| Subject Terms: |
*K-means clustering, *FOREST management, *PINUS radiata, *FOREST surveys, *FOREST health |
| Abstract: |
Accurate characterization of tree stems is critical for assessing commercial forest health, estimating merchantable timber volume, and informing sustainable value management strategies. Conventional ground-based manual measurements, although precise, are labor-intensive and impractical at large scales, while remote sensing approaches using satellite or UAV imagery often lack the spatial resolution needed to capture individual tree attributes in complex forest environments. To address these challenges, this study provides a significant contribution by introducing a large-scale dataset encompassing 40 plots in Western Australia (WA) with varying tree densities, derived from Hovermap LiDAR acquisitions and destructive sampling. The dataset includes parameters such as plot and tree identifiers, DBH, tree height, stem length, section lengths, and detailed diameter measurements (e.g., DiaMin, DiaMax, DiaMean) across various heights, enabling precise ground-truth calibration and validation. Based on this dataset, we present the Forest Stem Extraction and Modeling (FoSEM) framework, a LiDAR-driven methodology that efficiently and reliably models individual tree stems from dense 3D point clouds. FoSEM integrates ground segmentation, height normalization, and K-means clustering at a predefined elevation to isolate stem cores. It then applies circle fitting to capture cross-sectional geometry and employs MLESAC-based cylinder fitting for robust stem delineation. Experimental evaluations conducted across various radiata pine plots of varying complexity demonstrate that FoSEM consistently achieves high accuracy, with a DBH RMSE of 1.19 cm (rRMSE = 4.67%) and a height RMSE of 1.00 m (rRMSE = 4.24%). These results surpass those of existing methods and highlight FoSEM's adaptability to heterogeneous stand conditions. By providing both a robust method and an extensive dataset, this work advances the state of the art in LiDAR-based forest inventory, enabling more efficient and accurate tree-level assessments in support of sustainable forest management. [ABSTRACT FROM AUTHOR] |
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