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Towards interpreting machine learning models for understanding the relationship between vegetation growth and climate factors: A case study of the Anhui Province, China

文献类型：期刊论文

作者Bao, Nana1,2,3,4; Peng, Kai1,4; Yan, Xingting3,5; Lu, Yanxi1,4; Liu, Mingyu1,4; Li, Chenyang1,4; Zhao, Boyuan1,4
刊名ECOLOGICAL INDICATORS
出版日期2024-10-01
卷号167
关键词Vegetation index Climate Machine learning Interpretable methods Anhui Province
ISSN号1470-160X
DOI10.1016/j.ecolind.2024.112636
通讯作者Peng, Kai(y02114024@stu.ahu.edu.cn) ; Yan, Xingting(xingting.yan@ipp.ac.cn)
英文摘要The prediction of vegetation evolution and the understanding of its relationship with climate factors are essential for environmental protection, land use management, and policy planning. It is crucial to investigate accurate prediction methods for vegetation evolution and explore the impacts of climate factors. In this study, we developed machine learning (ML) based vegetation prediction methods by denoting vegetation status using normalized difference vegetation index (NDVI) and quantified the impacts of climate factors using interpretable methods. For the study region in this paper, i.e. the Anhui province in China, the proportions of areas with improved, stable and degraded vegetation status are 86.03 %, 8.13 % and 5.84 % respectively, and the NDVI evolution for the whole study region exhibits annual growth rate of 0.0031y(-1). ML-based NDVI predictors exhibit R-2 value exceeding 0.89 and MAE value below 0.1 for all lead times, which indicates the effectiveness of the ML-based prediction approaches. SHapley Additive exPlanation (SHAP) and Permutation Importance (PI) methods were utilized to provide insights into the black-box ML-based predictors. The results reveal that three temperature variables (minimum, maximum, and mean temperature) and precipitation are the key factors influencing vegetation growth. The increase of precipitation corresponds to an increase in vegetation, while higher minimum temperatures lead to a decrease in vegetation. When considering the combined contribution of minimum temperature and precipitation, it is shown that higher minimum temperature and larger amount of precipitation result in vegetation growth. On the contrary, lower minimum temperature and insufficient precipitation have negative impacts on vegetation. This work promotes the development of ML-based NDVI prediction approaches with transparency by taking advantages of interpretable methods. It provides understandings on how climate change influences vegetation growth in the Anhui Province.
WOS关键词NDVI ; SENSITIVITY ; ECOSYSTEMS ; DYNAMICS ; PATTERNS
资助项目National Natural Science Foundation of China[62273001]
WOS研究方向Biodiversity & Conservation ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001325260200001
出版者ELSEVIER
资助机构National Natural Science Foundation of China
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/135631]  
专题中国科学院合肥物质科学研究院
通讯作者Peng, Kai; Yan, Xingting
作者单位1.Anhui Univ, Sch Internet, Hefei 230039, Peoples R China
2.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applica, Hefei 230601, Peoples R China
3.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
4.Anhui Jinhaidier Informat Technol Co Ltd, Hefei 230088, Peoples R China
5.Forschungszentrum Julich, Inst Energy & Climate Res, D-52425 Julich, Germany
推荐引用方式
GB/T 7714		 Bao, Nana,Peng, Kai,Yan, Xingting,et al. Towards interpreting machine learning models for understanding the relationship between vegetation growth and climate factors: A case study of the Anhui Province, China[J]. ECOLOGICAL INDICATORS,2024,167.
APA Bao, Nana.,Peng, Kai.,Yan, Xingting.,Lu, Yanxi.,Liu, Mingyu.,...&Zhao, Boyuan.(2024).Towards interpreting machine learning models for understanding the relationship between vegetation growth and climate factors: A case study of the Anhui Province, China.ECOLOGICAL INDICATORS,167.
MLA Bao, Nana,et al."Towards interpreting machine learning models for understanding the relationship between vegetation growth and climate factors: A case study of the Anhui Province, China".ECOLOGICAL INDICATORS 167(2024).

入库方式： OAI收割

来源：合肥物质科学研究院

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