Spatio-temporal changes and driving mechanisms of vegetation in Yunnan Province based on MODIS-KNDVI in recent 20 years

Spatio-temporal changes and driving mechanisms of vegetation in Yunnan Province based on MODIS-KNDVI in recent 20 years | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spatio-temporal changes and driving mechanisms of vegetation in Yunnan Province based on MODIS-KNDVI in recent 20 years Xin Shao, Xue Ding, Jinliang Wang, Shangyun Peng, Zhaoqian Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4247776/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Vegetation cover serves as a pivotal indicator for evaluating key ecosystem attributes, signifi-cantly elucidating the intricate dynamics between global climate shifts and ecosystem equilibrium. The employment of remote sensing for extensive, high-fidelity vegetation cover surveillance is critical in appraising regional environmental transformations and devising targeted vegetation conservation strategies. Implementing the Kernel Normalized Difference Vegetation Index (KNVDI) enhances the precision of vegetation change detection. Leveraging the Google Earth Engine (GEE) for remote sensing data analysis, this investigation harnesses MODIS imagery spanning 2000 to 2020 to construct the KNVDI for meticulous observation of vegetation altera-tions in Yunnan Province, China. Employing GIS methodologies, including Theil-Sen trend analysis, Mann-Kendall tests, and centroid shift models, this study intricately examines the temporal and spatial evolution of vegetation cover over two decades. Incorporating Hurst index projections for future trends and utilizing an optimized geographic detector model, it probes into the underlying drivers of vegetation cover modifications in the region. Findings indicate:(1) a pronounced increase in KNVDI from 2000 to 2020, with a growth rate of 0.028 per decade and an average value of 0.3304, showcasing a west-high, east-low spatial distribution. (2)Areas of vege-tation increase substantially outweigh those of decrease, predominantly located in the northeast and southwest, contrasted with sporadic reductions in central and northwest Yunnan and near significant inland lakes. The average H value for KNVDI suggests a reversal in future vegetation trends compared to the past. (3)Precipitation and aridity emerge as the primary influencers on KNVDI, significantly affecting vegetation dynamics, with their interactions demonstrating en-hanced and nonlinear influence, particularly between precipitation and aridity/elevation. These insights offer valuable implications for the sustainable development and strategic planning of vegetation cover in Yunnan Province. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences MODIS KNVDI (Kernel Normalized Difference Vegetation Index) Spatiotemporal Variation Hurst Exponent GIS Optimal Parameter Geographic Detector Yunnan Province lakes Full Text Additional Declarations No competing interests reported. Supplementary Files kndvi.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4247776","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":358232085,"identity":"cd5935a9-1550-46a2-858c-3859856f6735","order_by":0,"name":"Xin Shao","email":"","orcid":"","institution":"Yunnan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Shao","suffix":""},{"id":358232086,"identity":"0842f58c-e475-4977-a4c7-2c83ac91ccc1","order_by":1,"name":"Xue 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