干旱区生态环境知识资源中心(Arid): Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods

Arid

DOI	10.3390/rs15133344
	Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods
	Xie, Dongbo; Huang, Hongchao; Feng, Linyan; Sharma, Ram P.; Chen, Qiao; Liu, Qingwang; Fu, Liyong
通讯作者	Fu, LY
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:13
英文摘要	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.
英文关键词	aboveground biomass LiDAR shrub community desert nonparametric methods
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001028150200001
WOS关键词	SEMIARID ECOSYSTEMS ; CANOPY COVER ; LEAF-AREA ; HEIGHT ; CLASSIFICATION ; PLANTATIONS ; VARIABILITY ; ALGORITHMS ; GRASSLAND ; NORTHERN
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398298
推荐引用方式 GB/T 7714	Xie, Dongbo,Huang, Hongchao,Feng, Linyan,et al. Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods[J],2023,15(13).
APA	Xie, Dongbo.,Huang, Hongchao.,Feng, Linyan.,Sharma, Ram P..,Chen, Qiao.,...&Fu, Liyong.(2023).Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods.REMOTE SENSING,15(13).
MLA	Xie, Dongbo,et al."Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods".REMOTE SENSING 15.13(2023).