Vegetation Dynamics and Driving Mechanisms in the Western Sichuan Basin Revealed by An Integrated Analytical Framework Based on Trend Analysis and Machine Learning Algorithms

Vegetation Dynamics and Driving Mechanisms in the Western Sichuan Basin Revealed by An Integrated Analytical Framework Based on Trend Analysis and Machine Learning Algorithms | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 10 October 2025 V1 Latest version Share on Vegetation Dynamics and Driving Mechanisms in the Western Sichuan Basin Revealed by An Integrated Analytical Framework Based on Trend Analysis and Machine Learning Algorithms Authors : Zilin Ye 0009-0004-4465-9445 , Yugang Wang 0009-0000-3006-2681 , Xun Gong , Zhixiao Zhang , Fanqiang Meng , Liuhui Zhao , Dongmei Wang , and Zhengjun Gong [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176008345.56709081/v1 163 views 136 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Identifying vegetation trends and their driving mechanisms under the combined influence of natural and anthropogenic factors is essential for understanding ecosystem dynamics, particularly in ecologically sensitive mountainous regions. This study developed an integrated framework combining trend analysis and interpretable machine learning to evaluate vegetation dynamics in the Western Sichuan Basin from 2000 to 2020. The results revealed a significant improvement in regional vegetation cover, with a growth rate of 0.00152 yr -1 and 81.00% of the area showing positive trends. The selected environmental and anthropogenic variables exhibited distinct nonlinear response patterns and threshold effects. Elevation and temperature were identified as the dominant drivers of vegetation change, while the impact of grazing intensity became increasingly prominent over time. SHAP interaction analysis further revealed that favorable topographic and climatic conditions can amplify positive vegetation responses to human activities. These findings highlight the value of interpretable machine learning for uncovering complex ecosystem processes and provide a scientific basis for guiding ecological restoration and sustainable land-use policies in mountainous landscapes. Supplementary Material File (manuscript.docx) Download 6.86 MB Information & Authors Information Version history V1 Version 1 10 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords driving factors interaction effects machine learning tmrfs research framework vegetation change Authors Affiliations Zilin Ye 0009-0004-4465-9445 Southwest Jiaotong University View all articles by this author Yugang Wang 0009-0000-3006-2681 Southwest Jiaotong University View all articles by this author Xun Gong Southwest Jiaotong University View all articles by this author Zhixiao Zhang Southwest Jiaotong University View all articles by this author Fanqiang Meng Southwest Jiaotong University View all articles by this author Liuhui Zhao Southwest Jiaotong University View all articles by this author Dongmei Wang Southwest Jiaotong University View all articles by this author Zhengjun Gong [email protected] Southwest Jiaotong University View all articles by this author Metrics & Citations Metrics Article Usage 163 views 136 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Zilin Ye, Yugang Wang, Xun Gong, et al. Vegetation Dynamics and Driving Mechanisms in the Western Sichuan Basin Revealed by An Integrated Analytical Framework Based on Trend Analysis and Machine Learning Algorithms. Authorea . 10 October 2025. DOI: https://doi.org/10.22541/au.176008345.56709081/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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