ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Functional Plant Ecology
This article is part of the Research TopicPlant Responses to Environmental ChangeView all 10 articles
Mapping Stability and Instability Hotspots in Jiangsu's Vegetation: An Explainable Machine Learning Approach to Climatic and Anthropogenic Drivers
Provisionally accepted
Jiao Fusheng1,2
Xiaojuan Xu1*Haibo Gong3Chuanzhuang Liang2Jing Liu1Kun Zhang1Yue Yang1Dayi Lin1Naifeng Lin1Changxin Zou1Jie Qiu1- 1MEE Nanjing Institute of Environmental Sciences, Nanjing, China
- 2Nanjing Normal University, Nanjing, China
- 3Peking University College of Urban and Environmental Sciences, Beijing, China
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Understanding vegetation stability is essential for evaluating ecosystem resilience and informing adaptive land management under changing climatic conditions. This study investigated the spatiotemporal patterns and climatic drivers of vegetation stability across Jiangsu Province, China. Vegetation productivity was assessed using the annual maximum kernel normalized difference vegetation index, while stability was quantified through two indicators: proportional variability (PV) and lag-one autocorrelation (AR). Results revealed that 15.77% of the province experienced increases in PV and AR, indicating growing vegetation instability, particularly in the south-central and southeastern regions. In contrast, 84.23% of the area showed declining PV and AR trends, suggesting enhanced stability, mainly in the southwestern, northern, and central regions. Spatially, high AR values were observed in western and southern Jiangsu, while high PV values were concentrated along the eastern coast and near Lake Taihu. More stable areas—characterized by low PV and AR—were primarily located in the central and northwestern regions. An interpretable machine learning model identified background solar radiation and its temporal variability as the dominant drivers of vegetation stability, followed by vapor pressure deficit (VPD). Precipitation variability had minimal influence. SHAP dependence plots revealed nonlinear responses: moderate radiation and higher soil moisture promoted stability, while elevated VPD and radiation variability reduced it. Most regions were in favorable ecological condition, although ~20% were classified as poor and another ~20% remained uncertain. These findings highlight the key roles of radiation and moisture in regulating vegetation stability and offer insights for climate-resilient land management in intensively cultivated landscapes.
Keywords: Vegetation stability, Explainable Machine Learning (XAI), Climate–VegetationInteraction, Jiangsu Province, resilience, Temporal variability, Extreme Gradient Boosting(XGBoost)
Received: 02 Aug 2025; Accepted: 10 Nov 2025.
Copyright: © 2025 Fusheng, Xu, Gong, Liang, Liu, Zhang, Yang, Lin, Lin, Zou and Qiu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiaojuan Xu, 929297044@qq.com
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