Key message <p>We propose a lightweight LiDAR-based simultaneous localization and mapping system that integrates semantic segmentation. The system was evaluated on forest point clouds collected in real-world forest environments and on multiple publicly available datasets. Results demonstrate that the proposed system is both efficient and accurate for individual-tree clustering and diameter at breast height estimation.</p> Context <p>Efficient and automated acquisition of individual-tree parameters is essential for intelligent forest resource inventories. Conventional approaches rely heavily on manual measurements, which limits scalability and makes them unsuitable for large-scale and high-frequency surveys.</p> Aims <p>This study aims to develop a lightweight LiDAR-SLAM system with integrated semantic segmentation to improve the accuracy and real-time performance of individual-tree extraction and DBH measurement in forest environment.</p> Methods <p>The proposed system is built on an enhanced LIO-SAM framework and incorporates an incremental k-d tree (Ikd-Tree) to improve computational efficiency. For semantic segmentation, we adopted SqueezeSegV3 augmented with an Efficient Layer Attention (ELA) mechanism to improve semantic-category recognition. The segmented point clouds were then processed using clustering and cylinder fitting to extract individual trees and estimate DBH.</p> Results <p>The system was tested on public datasets and field-collected forest data, achieving semantic segmentation accuracies of 0.85 and 0.89, with mean Intersection over Union values of 0.55 and 0.67, respectively. The average DBH prediction accuracy reached 97.6%, indicating strong performance in real forest environments.</p> Conclusion <p>By combining a lightweight semantic network with an efficient point-cloud data structure, the proposed system achieved high accuracy and real-time performance, meeting the requirements of large-scale and high-efficiency measurements for forest resource inventory tasks.</p>