A Novel Approach for Individual Tree Structural Parameter Extraction from ALS Data

Abstract. Estimating tree-level structural parameters from airborne laser scanning (ALS) point cloud is essential for sustainable and efficient forest management. This task is currently carried out through labour-intensive and time-consuming manual efforts, particularly in complex forest with overlapping canopies. This paper presents a novel approach for individual tree extraction utilizing an improved YOLO-based model, along with advanced algorithms for structural parameter calculation. The enhanced model, named CCD-YOLO, introduces several key improvements for individual tree segmentation (ITS) by leveraging a newly created ITS dataset. It replaces the C2F module with a CReToNeXt module to enhance feature extraction. A convolutional block attention module (CBAM) is added to highlight crown features and reduce background noise, while a Dynamic Head enables adaptive multi-layer fusion, boosting segmentation accuracy. Additionally, a denoising process is applied, ensuring more accurate and reliable measurements for vertical parameters. Lastly, an improved convex hull algorithm is employed to better accommodate the irregular shapes of tree crowns. The experimental results were evaluated in a dense forest through both internal and external consistency assessments, demonstrating significant performance enhancements. For the individual tree segmentation, the proposed approach achieved a precision of 82.4%, a recall of 72.8%, and an F1 score of 77.9%. In terms of parameter estimation, the accuracy for tree height, crown width, and crown area was 0.82, 0.60, and 0.99, respectively, with corresponding RMSE values of 1.25, 1.01, and 1.16. These results highlight the effectiveness of the proposed method in improving both segmentation and parameter estimation accuracy.