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Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data

文献类型：期刊论文

作者Xu, Kexin2; Su, Yanjun2; Liu, Jin2; Hu, Tianyu2; Jin, Shichao2; Ma, Qin2; Zhai, Qiuping4; Wang, Rui2; Zhang, Jing2; Li, Yumei2
刊名ECOLOGICAL INDICATORS
出版日期2020
卷号108
关键词Aboveground biomass (AGB) Degraded grassland Machine learning Northern agro-pastoral ecotone Terrestrial laser scanning (TLS)
ISSN号1470-160X
DOI10.1016/j.ecolind.2019.105747
文献子类Article
英文摘要Aboveground biomass (AGB) is an important indicator for grassland ecosystem assessment, management and utilization. Remote sensing technologies have driven the development of grassland AGB estimation from labor-intensive to highly-efficient. However, optical image-based remote sensing methods are fraught with uncertainty issues due to the saturation effects. In this study, we evaluated the capability of the emerging terrestrial laser scanning (TLS) technique in estimating grassland AGB in the northern agro-pastoral ecotone of China. Seven variables (i.e., canopy cover, canopy volume, mean height, maximum height, minimum height, range of height, and standard deviation of height) were extracted from the TLS data of 30 plots across the northern agro-pastoral ecotone of China, and were used to build regression models with field measured AGB using four regression methods, which are simple regression (SR) model, stepwise multiple regression (SMR) model, random forest (RF) model and artificial neural network (ANN) model. The results demonstrate that TLS is a feasible technique for extracting grassland structural parameters. Mean grass height and canopy cover obtained from TLS data have good correspondence with field measurements (R-2 > 0.7, p-values < 0.001). Among the four regression models, the SMR model yields the highest prediction accuracy (R-2 = 0.84, RMSE = 48.89 g/m(2)), followed by the RF model (R-2 = 0.78, RMSE = 68.72 g/m(2)), the SR model (R-2 = 0.80, RMSE = 86.4 g/m(2)), and the ANN model (R-2 = 0.73, RMSE = 101.40 g/m(2)). Minimum grass height and canopy coverage are the two most important variables influencing the prediction accuracy of the SMR model, and the prediction accuracy of the SMR model increases with the increase of point density. The results of this study can provide guidance for choosing the optimal model and data collection method for estimating degraded grassland AGB using TLS in agro-pastoral ecotone.
学科主题Biodiversity Conservation ; Environmental Sciences
出版地AMSTERDAM
电子版国际标准刊号1872-7034
WOS关键词ARTIFICIAL NEURAL-NETWORK ; AIRBORNE LIDAR DATA ; RANDOM FOREST ; CANOPY COVER ; PREDICTION ; CARBON ; SHRUB ; ANN
WOS研究方向Biodiversity & Conservation ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000493902400066
出版者ELSEVIER
资助机构National Key R&D Program of China [2016YFC0500202] ; Frontier Science Key Programs of the Chinese Academy of Sciences [QYZDY-SSW-SMC011] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41871332] ; CAS Pioneer Hundred Talents Program
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/21968]  
专题植被与环境变化国家重点实验室
作者单位1.Linyi Univ, Shandong Prov Key Lab Soil Conservat & Environm P, Coll Resources & Environm, Linyi 276000, Shandong, Peoples R China
2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
3.Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Kexin,Su, Yanjun,Liu, Jin,et al. Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data[J]. ECOLOGICAL INDICATORS,2020,108.
APA Xu, Kexin.,Su, Yanjun.,Liu, Jin.,Hu, Tianyu.,Jin, Shichao.,...&Guo, Qinghua.(2020).Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data.ECOLOGICAL INDICATORS,108.
MLA Xu, Kexin,et al."Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data".ECOLOGICAL INDICATORS 108(2020).

入库方式： OAI收割

来源：植物研究所

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