Stem Detection from Terrestrial Laser Scanning Data with Features Selected via Stem-Based Evaluation

• Published in: Forests 2023-10, Vol.14 (10), p.2035
• Main Authors: Chen, Maolin, Liu, Xiangjiang, Pan, Jianping, Mu, Fengyun, Zhao, Lidu
• Format: Article
• Language: English
• Subjects: Accuracy; Classification; Datasets; Ecological studies; Environmental monitoring; feature extraction and selection; Feature selection; Forest management; forestry; Forests; Identification and classification; Laser applications; Lasers; Leaves; Methods; Physiological aspects; stem detection; Stems; Stems (Botany); terrestrial laser scanning (TLS); Vegetation; voxel-based feature
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f14102035

Terrestrial laser scanning (TLS) is an effective tool for extracting stem distribution, providing essential information for forest inventory and ecological studies while also assisting forest managers in monitoring and controlling forest stand density. A feature-based method is commonly integrated into the pipelines of stem detection, facilitating the transition from stem point to stem instance, but most studies focus on feature effectiveness from the point level, neglecting the relationship between stem point extraction and stem detection. In this paper, a feature-based method is proposed to identify stems from TLS data, with features selected from stem levels. Firstly, we propose a series of voxel-based features considering the stem characteristics under the forest. Then, based on the evaluation of some commonly used and proposed features, a stem-based feature selection method is proposed to select a suitable feature combination for stem detection by constructing and evaluating different combinations. Experiments are carried out on three plots with different terrain slopes and tree characteristics, each having a sample plot size of about 8000 m2. The results show that the voxel-based features can supplement the basic features, which improve the average accuracy of stem point extraction and stem detection by 9.5% and 1.2%, respectively. The feature set obtained by the proposed feature selection method achieves a better balance between accuracy and feature number compared with the point-based feature selection method and the features used in previous studies. Moreover, the accuracies of the proposed stem detection methods are also comparable to the three methods evaluated in the international TLS benchmarking project.