Tree Species Classification Using Airborne LiDAR Data Based on Individual Tree Segmentation and Shape Fitting
发布时间：2023-07-09
点击次数：
- DOI码：
- 10.3390/rs15020406
- 发表刊物：
- REMOTE SENSING
- 关键字：
- LiDAR; CHM; Tree Crown Points Group; tree species classification; shape fitting
- 摘要：
- Individual tree species classification is of strategic importance for forest monitoring, analysis, and management, which are critical for sustainable forestry development. In this regard, the paper proposes a method based on the profile of segmented individual tree laser scanning points to identify tree species. The proposed methodology mainly takes advantage of three-dimensional geometric features of a tree crown captured by a laser point cloud to identify tree species. Firstly, the Digital Terrain Model (DTM) and Digital Surface Model (DSM) are used for Crown Height Model (CHM) generation. Then, local maximum algorithms and improved rotating profile-based delineations are used to segment individual trees from the profile CHM point data. In the next step, parallel-line shape fitting is used to fit the tree crown shape. In particular, three basic geometric shapes, namely, triangle, rectangle, and arc are used to fit the tree crown shapes of different tree species. If the crown belongs to the same crown shape or shape combination, parameter classification is used, such as the ratio of crown width and crown height or the apex angle range of the triangles. The proposed method was tested by two real datasets which were acquired from two different sites located at Tiger and Leopard National Park in Northeast China. The experimental results indicate that the average tree classification accuracy is 90.9% and the optimal classification accuracy reached 95.9%, which meets the accuracy requirements for rapid forestry surveying.
- 论文类型：
- 期刊论文
- 学科门类：
- 工学
- 文献类型：
- J
- 是否译文：
- 否
- 发表时间：
- 2023-01-09
- 收录刊物：
- SCI