IB-CAS  > 植被与环境变化国家重点实验室
Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data
Xu, Kexin1; Su, Yanjun1; Liu, Jin1; Hu, Tianyu1; Jin, Shichao1; Ma, Qin1; Zhai, Qiuping2; Wang, Rui1; Zhang, Jing1; Li, Yumei1; Liu, Hon An3; Guo, Qinghua1
2020
发表期刊ECOLOGICAL INDICATORS
ISSN1470-160X
卷号108
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.
关键词Aboveground biomass (AGB) Degraded grassland Machine learning Northern agro-pastoral ecotone Terrestrial laser scanning (TLS)
学科领域Biodiversity Conservation ; Environmental Sciences
DOI10.1016/j.ecolind.2019.105747
收录类别SCI
语种英语
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
WOS记录号WOS:000493902400066
出版者ELSEVIER
文献子类Article
出版地AMSTERDAM
EISSN1872-7034
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
作者邮箱guo.qinghua@gmail.com
引用统计
被引频次:48[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符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.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Linyi Univ, Shandong Prov Key Lab Soil Conservat & Environm P, Coll Resources & Environm, Linyi 276000, Shandong, Peoples R China
4.Peking Univ, Coll Urban & Environm Sci, Beijing 100871, 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).
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
1-s2.0-S1470160X1930(4794KB)期刊论文出版稿开放获取CC BY-NC-SA浏览 请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Xu, Kexin]的文章
[Su, Yanjun]的文章
[Liu, Jin]的文章
百度学术
百度学术中相似的文章
[Xu, Kexin]的文章
[Su, Yanjun]的文章
[Liu, Jin]的文章
必应学术
必应学术中相似的文章
[Xu, Kexin]的文章
[Su, Yanjun]的文章
[Liu, Jin]的文章
相关权益政策
暂无数据
收藏/分享
文件名: 1-s2.0-S1470160X1930740X-main.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。