文献类型：期刊文献

英文题名：Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods

作者：Xie, Dongbo[1,2] Huang, Hongchao[1,2] Feng, Linyan[1,2] Sharma, Ram P.[3] Chen, Qiao[1] Liu, Qingwang[1] Fu, Liyong[1,2]

第一作者：Xie, Dongbo

通信作者：Fu, LY[1];Fu, LY[2]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forest Management & Growth Modelling, Beijing 100091, Peoples R China;[3]Tribhuwan Univeristy, Inst Forestry, Kritipur 44600, Nepal

年份：2023

卷号：15

期号：13

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20232914419994);Scopus(收录号：2-s2.0-85164981466);WOS:【SCI-EXPANDED(收录号:WOS:001028150200001)】；

基金：This research was funded by the 14th Five-Year Plan Pioneering Project of High Technology Plan of the National Department of Technology (Grant No. 2021YFD2200405).

语种：英文

外文关键词：aboveground biomass; LiDAR; shrub community; desert; nonparametric methods

摘要：Aboveground biomass (AGB) of shrub communities in the desert is a basic quantitative characteristic of the desert ecosystem and an important index to measure ecosystem productivity and monitor desertification. An accurate and efficient method of predicting the AGB of a shrub community is essential for studying the spatial patterns and ecological functions of the desert region. Even though there are several entries in the literature on the AGB prediction of desert shrub communities using remote sensing data, the applicability and accuracy of airborne LiDAR data and prediction methods have not been well studied. We first extracted the elevation, density and intensity variables based on the airborne LiDAR, and then sample plot-level AGB prediction models were constructed using the parametric regression (nonlinear regression) and nonparametric methods (Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting Machine, and Multivariate adaptive regression splines). We evaluated accuracies of all the AGB prediction models we developed based on the fit statistics. Results showed that: (1) the elevation, density and intensity variables obtained from LiDAR point cloud data effectively predicted the AGB of the desert shrub community at a sample plot level, (2) the kappa coefficient of nonlinear mixed-effects (NLME) model obtained was 0.6977 with an improvement by 13% due to the random effects included into the model, and (3) the nonparametric model, such as Support Vector Machine showed the best fit statistics (R-2 = 0.8992), which is 28% higher than the NLME-model, and effectively reduced the heteroscedasticity. The AGB prediction model presented in this paper, which is based on the airborne LiDAR data and machine learning algorithm, will provide a valuable tool to the managers and researchers for evaluating desert ecosystem productivity and monitoring desertification.