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A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling

文献类型：期刊论文

作者Zhao, Yapeng1,2; Yin, Xiaozhe3; Fu, Yan1,2; Yue, Tianxiang1,2
刊名ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
出版日期2021-10-21
页码14
关键词Plant species diversity (PSD) Remote sensing Ensemble learning High-accuracy surface modeling (HASM)
ISSN号0944-1344
DOI10.1007/s11356-021-16973-x
通讯作者Zhao, Yapeng(zhaoyp@lreis.ac.cn)
英文摘要Plant species diversity (PSD) has always been an essential component of biodiversity and plays an important role in ecosystem functions and services. However, it is still a huge challenge to simulate the spatial distribution of PSD due to the difficulties of data acquisition and unsatisfactory performance of predicting algorithms over large areas. A surge in the number of remote sensing imagery, along with the great success of machine learning, opens new opportunities for the mapping of PSD. Therefore, different machine learning algorithms combined with high-accuracy surface modeling (HASM) were firstly proposed to predict the PSD in the Xinghai, northeastern Qinghai-Tibetan Plateau, China. Spectral reflectance and vegetation indices, generated from Landsat 8 images, and environmental variables were taken as the potential explanatory factors of machine learning models including least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The prediction generated from these machine learning methods and in situ observation data were integrated by using HASM for the high-accuracy mapping of PSD including three species diversity indices. The results showed that PSD was closely associated with vegetation indices, followed by spectral reflectance and environmental factors. XGBoost combined with HASM (HASM-XGBoost) showed the best performance with the lowest MAE and RMSE. Our results suggested that the fusion of heterogeneous data and the ensemble of heterogeneous models may revolutionize our ability to predict the PSD over large areas, especially in some places limited by sparse field samples.
WOS关键词VEGETATION INDEXES ; RICHNESS ; FOREST ; BIODIVERSITY ; LANDSCAPE ; MODIS ; VARIABILITY ; GRASSLANDS ; ELEVATION ; CLIMATE
资助项目Ministry of Ecology and Environment of China ; National Natural Science Foundation of China[41930647] ; National Natural Science Foundation of China[41590844] ; National Natural Science Foundation of China[41421001] ; National Natural Science Foundation of China[41971358] ; Strategic Priority Research Program (A) of the Chinese Academy of Sciences[XDA20030203] ; Innovation Project of LREIS[O88RA600YA]
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000709739400011
出版者SPRINGER HEIDELBERG
资助机构Ministry of Ecology and Environment of China ; National Natural Science Foundation of China ; Strategic Priority Research Program (A) of the Chinese Academy of Sciences ; Innovation Project of LREIS
源URL[http://ir.igsnrr.ac.cn/handle/311030/167233]  
专题中国科学院地理科学与资源研究所
通讯作者Zhao, Yapeng
作者单位1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Southern Calif, Keck Sch Med, Dept Prevent Med, Los Angeles, CA 90032 USA
推荐引用方式
GB/T 7714		 Zhao, Yapeng,Yin, Xiaozhe,Fu, Yan,et al. A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling[J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,2021:14.
APA Zhao, Yapeng,Yin, Xiaozhe,Fu, Yan,&Yue, Tianxiang.(2021).A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,14.
MLA Zhao, Yapeng,et al."A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2021):14.

入库方式： OAI收割

来源：地理科学与资源研究所

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