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Uncertainty of Partial Dependence Relationship between Climate and Vegetation Growth Calculated by Machine Learning Models

文献类型：期刊论文

作者Liang, Boyi6; Liu, Hongyan3; Cressey, Elizabeth L.4; Xu, Chongyang5; Shi, Liang1,2; Wang, Lu3; Dai, Jingyu3; Wang, Zong6; Wang, Jia6
刊名REMOTE SENSING
出版日期2023-06-03
卷号15期号:11页码:12
关键词machine learning uncertainty variation partial dependence vegetation growth climate
DOI10.3390/rs15112920
通讯作者Liu, Hongyan(lhy@urban.pku.edu.cn)
英文摘要As more machine learning and deep learning models are applied in studying the quantitative relationship between the climate and terrestrial vegetation growth, the uncertainty of these advanced models requires clarification. Partial dependence plots (PDPs) are one of the most widely used methods to estimate the marginal effect of independent variables on the predicted outcome of a machine learning model, and it is regarded as the main basis for conclusions in relevant research. As more controversies regarding the reliability of the results of the PDPs emerge, the uncertainty of the PDPs remains unclear. In this paper, we experiment with real, remote sensing data to systematically analyze the uncertainty of partial dependence relationships between four climate variables (temperature, rainfall, radiation, and windspeed) and vegetation growth, with one conventional linear model and six machine learning models. We tested the uncertainty of the PDP curves across different machine learning models from three aspects: variation, whole linear trends, and the trait of change points. Results show that the PDP of the dominant climate factor (mean air temperature) and vegetation growth parameter (indicated by the normalized difference vegetation index, NDVI) has the smallest relative variation and the whole linear trend of the PDP was comparatively stable across the different models. The mean relative variation of change points across the partial dependence curves of the non-dominant climate factors (i.e., radiation, windspeed, and rainfall) and vegetation growth ranged from 8.96% to 23.8%, respectively, which was much higher than those of the dominant climate factor and vegetation growth. Lastly, the model used for creating the PDP, rather than the relative importance of these climate factors, determines the fluctuation of the PDP output of these climate variables and vegetation growth. These findings have significant implications for using remote sensing data and machine learning models to investigate the quantitative relationships between the climate and terrestrial vegetation.
WOS关键词VARIABLE IMPORTANCE ; NEURAL-NETWORKS ; ALGORITHMS
资助项目National Key Research and Development Program[2022YFF0801803] ; Fundamental Research Funds for the Central Universities[BLX202105] ; Fundamental Research Funds for the Central Universities[BLX202107]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:001002910400001
资助机构National Key Research and Development Program ; Fundamental Research Funds for the Central Universities
源URL[http://ir.igsnrr.ac.cn/handle/311030/197806]  
专题中国科学院地理科学与资源研究所
通讯作者Liu, Hongyan
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
2.Natl Ecosyst Sci Data Ctr, Beijing 100101, Peoples R China
3.Peking Univ, Coll Urban & Environm Sci, MOE Lab Earth Surface Proc, Beijing 100871, Peoples R China
4.Univ Exeter, Fac Environm Sci & Econ, Geog, Exeter EX4 4RJ, England
5.Hebrew Univ Jerusalem, Fac Agr Food & Environm, IL-7610001 Rehovot, Israel
6.Beijing Forestry Univ, Coll Forestry, Precis Forestry Key Lab Beijing, Beijing 100083, Peoples R China
推荐引用方式
GB/T 7714		 Liang, Boyi,Liu, Hongyan,Cressey, Elizabeth L.,et al. Uncertainty of Partial Dependence Relationship between Climate and Vegetation Growth Calculated by Machine Learning Models[J]. REMOTE SENSING,2023,15(11):12.
APA Liang, Boyi.,Liu, Hongyan.,Cressey, Elizabeth L..,Xu, Chongyang.,Shi, Liang.,...&Wang, Jia.(2023).Uncertainty of Partial Dependence Relationship between Climate and Vegetation Growth Calculated by Machine Learning Models.REMOTE SENSING,15(11),12.
MLA Liang, Boyi,et al."Uncertainty of Partial Dependence Relationship between Climate and Vegetation Growth Calculated by Machine Learning Models".REMOTE SENSING 15.11(2023):12.

入库方式： OAI收割

来源：地理科学与资源研究所

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