Detection of smallholder agroforestry management disturbances in Sri Lanka using Sentinel-2 time series, Landsat CCDC, and Climate Normalization

Detection of smallholder agroforestry management disturbances in Sri Lanka using Sentinel-2 time series, Landsat CCDC, and Climate Normalization | 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 Research Article Detection of smallholder agroforestry management disturbances in Sri Lanka using Sentinel-2 time series, Landsat CCDC, and Climate Normalization Wathsala Lakpriya Gunawardena Garu Muni, Erandathie Pathiraja, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9195111/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Detecting smallholder agroforestry management practices (thinning, pruning, and final harvesting) from satellite time series remains challenging in Sri Lanka’s Dry and Intermediate zone environments, where hydroclimatic variability can induce strong vegetation-index fluctuations. We developed a patch-based, multi-sensor framework to assess management detectability across 227 smallholder agroforestry patches in five districts using an “inside patch vs outside reference” (patch–buffer contrast) design. Sentinel-2 monthly contrast series (NDVI, EVI, NDMI, NBR) were used to quantify index sensitivity to disturbance and early recovery and to detect breakpoints within management-informed windows using BFASTmonitor. Landsat CCDC provided pixel-resolved change timing to characterize within-patch spatial heterogeneity and to evaluate consistency with patch-scale breakpoint dates. Climatic influence was screened using CHIRPS rainfall anomalies to distinguish breakpoints occurring under near-normal conditions from climate-sensitive candidates. Results show that management-related disturbances can be detected for a defensible subset of patches, but robust attribution benefits from multi-index agreement, pixel-level spatial diagnostics, and climate-aware interpretation. The proposed framework supports operational monitoring by prioritizing high-confidence patch candidates for targeted validation. Smallholder agroforestry Satellite time series Disturbance detection Patch-buffer contrast Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryTablesGunawardena.docx SupplementaryFiguresGunawardena.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers invited by journal 30 Mar, 2026 Editor assigned by journal 25 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 22 Mar, 2026 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. 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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-9195111","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614833412,"identity":"9002d6bb-bd3f-468b-8ed3-3c51637c46ed","order_by":0,"name":"Wathsala Lakpriya Gunawardena Garu Muni","email":"data:image/png;base64,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","orcid":"","institution":"Yokohama National University","correspondingAuthor":true,"prefix":"","firstName":"Wathsala","middleName":"Lakpriya Gunawardena Garu","lastName":"Muni","suffix":""},{"id":614833413,"identity":"310b8ee5-e84d-412c-9c61-232cb4952b0e","order_by":1,"name":"Erandathie Pathiraja","email":"","orcid":"","institution":"Institute of Policy Studies of Sri Lanka","correspondingAuthor":false,"prefix":"","firstName":"Erandathie","middleName":"","lastName":"Pathiraja","suffix":""},{"id":614833414,"identity":"0fe67121-b3f5-4adf-aa66-c99ea642f5df","order_by":2,"name":"Jeffrey R. 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We developed a patch-based, multi-sensor framework to assess management detectability across 227 smallholder agroforestry patches in five districts using an \u0026ldquo;inside patch vs outside reference\u0026rdquo; (patch\u0026ndash;buffer contrast) design. Sentinel-2 monthly contrast series (NDVI, EVI, NDMI, NBR) were used to quantify index sensitivity to disturbance and early recovery and to detect breakpoints within management-informed windows using BFASTmonitor. Landsat CCDC provided pixel-resolved change timing to characterize within-patch spatial heterogeneity and to evaluate consistency with patch-scale breakpoint dates. Climatic influence was screened using CHIRPS rainfall anomalies to distinguish breakpoints occurring under near-normal conditions from climate-sensitive candidates. Results show that management-related disturbances can be detected for a defensible subset of patches, but robust attribution benefits from multi-index agreement, pixel-level spatial diagnostics, and climate-aware interpretation. 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