文献类型：期刊文献

英文题名：Remote Sensing Estimation of Biomass of Caragana korshinskii with UAV

作者：Jiamin, Wu[1,2,3] Yaxin, Wang[2,3] Bin, Sun[2,3] Zhijie, Ma[4] Weina, Sun[5] Liang, Hong[1]

第一作者：Jiamin, Wu

机构：[1] Faculty of Geography, Yunnan Normal University, Kunming, 650050, China; [2] Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China; [3] Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing, 100091, China; [4] Ordos Forestry and Grassland Bureau, Ordos, 017010, China; [5] Ordos International Desertification Control Technology Innovation Center, Ordos, 017010, China

年份：2025

卷号：61

期号：6

起止页码：13-21

外文期刊名：Linye Kexue/Scientia Silvae Sinicae

收录：EI(收录号：20253018847876);Scopus(收录号：2-s2.0-105011378917)

语种：英文

外文关键词：Antennas - Classification (of information) - Data mining - Decision trees - Digital storage - Environmental protection - Extraction - Image segmentation - Learning systems - Object oriented programming - Random forests - Remote sensing - Support vector machines - Vegetation

摘要：【Objective】With unmanned aerial vehicle data, an object-oriented method was used to identify individual Caragana korshinskii in Ordos City. RF, SVR, and XGBoost machine learning algorithms were compared to achieve high-precision extraction of individual C. korshinskii and accurate estimation of the biomass, providing a reference for environmental protection and carbon storage research in arid areas.【Method】By comprehensively utilizing UAV-borne multispectral and lidar data, and integrating spectral and vertical structure information, an object-oriented method was used to conduct high-precision extraction of individual C. korshinskii. On this basis, three machine learning algorithms of random forest (RF), support vector regression (SVR) and extreme gradient boosting decision tree (XGBoost) were compared to conduct remote sensing accurate estimation of biomass. 【Result】 1) The ultra-high-resolution image data was obtained by UAV, and the LSMS segmentation algorithm and SVM classifier were able to achieve high-precision identification of individual C. korshinskii. The segmentation accuracy of C. korshinskii in each sample plot was above 86%, the accuracy of the total sample plot was above 90%, the under-segmentation and over-segmentation errors were below 6%, and the overall classification accuracy reached 91.51%. 2) The Recursive Feature Elimination (SVM-RFE) method based on support vector machines identified 17 variables with high contributions to biomass modeling, including 2 planar features and 15 height variables. The cumulative contribution of height variables to biomass was significantly more than that of planar variables (8.7 vs. 1.39). 3) Compared to the RF and SVR models, the XGBoost model provided higher biomass estimation accuracy for C. korshinskii in the study area (R2 = 0.95, RMSE = 259.57 g, MAE = 157.51 g), especially when biomass was below 2 000 g. 4) The multiple vegetation vertical structure information extracted from UAV-LiDAR reflected the diversity and vertical complexity of internal vegetation growth, which was beneficial for improving biomass estimation accuracy. Additionally, integrating multidimensional height variables, such as mean absolute deviation, coefficient of variation, variance, and percentile height, for biomass prediction showed advantages over using a single maximum height variable. 【Conclusion】The combination of LSMS segmentation and SVM classification for individual shrub extraction offers a technical reference for identifying individual vegetation. The introduction of multi-dimensional point cloud height metrics for biomass estimation compensates for the lack of vertical structure information in C. korshinskii provided by single multispectral data, improving the accuracy of biomass estimation. The XGBoost model provides a new perspective and tool for small-scale shrub biomass estimation in arid regions. Additionally, the high-resolution imagery and point cloud data obtained from UAVs avoid damage to the local ecological environment, which is particularly important in the fragile sandy areas. ? 2025, Chinese Society of Forestry. All rights reserved.