文献类型：期刊文献

英文题名：Tree Canopy Volume Extraction Fusing ALS and TLS Based on Improved PointNeXt

作者：Sun, Hao[1,2] Ye, Qiaolin[1,2] Chen, Qiao[3] Fu, Liyong[3,4] Xu, Zhongqi[4] Hu, Chunhua[1,2]

第一作者：Sun, Hao

通信作者：Hu, CH[1];Hu, CH[2]

机构：[1]Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Peoples R China;[2]Nanjing Forestry Univ, Coll Artificial Intelligence, Nanjing 210037, Peoples R China;[3]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[4]Hebei Agr Univ, Coll Forestry, Baoding 071001, Peoples R China

年份：2024

卷号：16

期号：14

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20243116783299);Scopus(收录号：2-s2.0-85199770471);WOS:【SCI-EXPANDED(收录号:WOS:001277303700001)】；

基金：This work was supported by the Fundamental Research Funds for the Central Nonprofit Research Institution of CAF (CAFYBB2022ZB002) and the National Key Research and Development Program of China under Grant 2022YFD2201005-03 and the Institute of Resource Information, Chinese Academy of Forestry.

语种：英文

外文关键词：3D point cloud; deep learning; canopy volume; segmentation; PointNeXt; gaps

摘要：Canopy volume is a crucial biological parameter for assessing tree growth, accurately estimating forest Above-Ground Biomass (AGB), and evaluating ecosystem stability. Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) are advanced precision mapping technologies that capture highly accurate point clouds for forest digitization studies. Despite advances in calculating canopy volume, challenges remain in accurately extracting the canopy and removing gaps. This study proposes a canopy volume extraction method based on an improved PointNeXt model, fusing ALS and TLS point cloud data. In this work, improved PointNeXt is first utilized to extract the canopy, enhancing extraction accuracy and mitigating under-segmentation and over-segmentation issues. To effectively calculate canopy volume, the canopy is divided into multiple levels, each projected into the xOy plane. Then, an improved Mean Shift algorithm, combined with KdTree, is employed to remove gaps and obtain parts of the real canopy. Subsequently, a convex hull algorithm is utilized to calculate the area of each part, and the sum of the areas of all parts multiplied by their heights yields the canopy volume. The proposed method's performance is tested on a dataset comprising poplar, willow, and cherry trees. As a result, the improved PointNeXt model achieves a mean intersection over union (mIoU) of 98.19% on the test set, outperforming the original PointNeXt by 1%. Regarding canopy volume, the algorithm's Root Mean Square Error (RMSE) is 0.18 m3, and a high correlation is observed between predicted canopy volumes, with an R-Square (R2) value of 0.92. Therefore, the proposed method effectively and efficiently acquires canopy volume, providing a stable and accurate technical reference for forest biomass statistics.