文献类型：期刊文献

英文题名：A novel UAV lidar-derived shrub structural index for estimating above-ground biomass

作者：Wu, Jiaming[1,2,3] Wang, Yaxin[1,2] Hong, Liang[3] Sun, Bin[1,2] He, Zhenping[4] Li, Zejiang[5] Ma, Zhijie[5]

第一作者：Wu, Jiaming

通信作者：Sun, B[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[3]Yunnan Normal Univ, Fac Geog, Kunming 650050, Peoples R China;[4]Ordos Gen Afforestat Field, Dalate Banner 014300, Peoples R China;[5]Ordos Forestry & Grassland Bur, Ordos 017010, Peoples R China

年份：2026

卷号：334

外文期刊名：REMOTE SENSING OF ENVIRONMENT

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001641088400001)】；

基金：This work was supported by the National Key Research and Development Program of China (Grant No. 2024YFF1308104) and the National Natural Science Foundation of China (Grant No. 42271407) . It also received support from the Technological Innovation Project for Assessing Ecosystem Carbon Sink Potential in Ordos. We thank the Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, and the Ordos Forestry and Grassland Bureau for their support in field data collection. We also sincerely thank the anonymous reviewers for their insightful comments and valuable suggestions on this manuscript.

语种：英文

外文关键词：Caragana korshinskii; Above-ground biomass (AGB); Shrub structure index (SSI); LiDAR; Object-based image analysis (OBIA); Height percentiles

摘要：Precise estimation of shrub above-ground biomass (AGB) in arid regions is crucial for carbon cycle research and ecosystem assessment. Unmanned aerial vehicle (UAV) -borne light detection and ranging (LiDAR) has become a key tool for quantifying three-dimensional vegetation structure and estimating AGB. However, the short stature of arid zone vegetation, combined with sparse and low-quality point clouds acquired by UAV, limits high-accuracy shrub AGB estimation. To address this issue, this study selected Caragana korshinskii, a typical psammophytic shrub in Ordos City, as the research object. By integrating UAV-based multispectral and LiDAR data, a biomass estimation method based on a novel Shrub Structure Index (SSI) was proposed. The SSI workflow reconstructs the three-dimensional shrub structure under sparse point cloud conditions and improves AGB estimation accuracy. This workflow comprises Object-based image analysis (OBIA) classification for individual shrub extraction, Delaunay linear up-sampling, voxel-based partitioning, and dynamic stratification by height percentiles. Experimental results demonstrate that: (1) The individual shrub extraction method utilizing the large-scale mean shift (LSMS) segmentation algorithm and support vector machine (SVM) classification achieved a total quadrat segmentation accuracy of over 90.61 %, an overall classification accuracy of 91.51 % (Kappa = 0.86). (2) In SSI construction, the height-percentile stratification thickness, point-cloud sampling, and voxel edge length together set Caragana korshinskii stratification accuracy and density scale; the 5 % height percentile interval, a sampling size of 100 points, and 0.04 m voxel edge length proved optimal. (3) Comparative experiments showed that the three-dimensional feature integrated SSI significantly outperformed single-feature, two-feature, traditional allometric equation, and random forest (RF) models, with the SSI-based model achieving R-2, RMSE, MAE, and rRMSE of 0.90, 529.01 g, 432.58 g, and 26.54 %, respectively. These results indicate that SSI more effectively captures shrub spatial structure and improves AGB prediction under sparse UAV-LiDAR conditions.