Large-scale characterization of forest structure and complexity from remote sensing optical images

Large-scale characterization of forest structure and complexity from remote sensing optical images | 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 Large-scale characterization of forest structure and complexity from remote sensing optical images Xin Xu, Xiaowei Tong, Martin Brandt, Yuemin Yue, Maurice Mugabowindekwe, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4960015/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 Forest structure complexity is an essential variable in forest management and conservation, as it has a direct impact on ecosystem processes and functions. Previous studies have primarily focused on tree cover as a proxy, which often falls short in providing comprehensive information on the structural complexity of forests. Sub-meter resolution remote sensing data and tree crown segmentation techniques hold promise in offering detailed information that can support the characterization of forest structure and complexity. In this study, we generated a dataset with over 5 billion tree crowns, and developed an Overstory Complexity Index (OCI) to characterize forest structure complexity from a horizontal perspective, by analyzing spatial relationships among neighboring trees from remote sensing optical images. We first extracted the location and crown size of overstory trees from optical satellite and aerial imagery at sub-meter resolution. We subsequently calculated the distance between tree crown centers, their angles, the crown size and crown spacing and linked this information with individual trees. We then used Principal Component Analysis (PCA) to condense the structural information into the OCI and tested it in China’s Guangxi province, Rwanda, and Denmark. In addition, we conducted a comparative analysis of OCI between protected and unprotected areas and among different forest types across these regions. Finally, we explored the relationships of terrain slope, distance to settlement and aboveground biomass with the OCI. Our result showed that the distribution of OCI values varies across the different bioclimatic regions, closely related to their respective forest characteristics. Higher OCI values were observed in protected areas as compared to unprotected areas, and OCI showed a positive correlation with terrain slope, distance to settlement and aboveground biomass. The proposed OCI is derived directly from standard tree-level attributes and supports a deeper understanding on forest structure and complexity in diverse ecosystems as compared to existing proxies. forest structure complexity overstory complexity index (OCI) spatial relationship optical images Full Text Additional Declarations The authors declare no competing interests. Supplementary Files supplementary.docx 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-4960015","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":343855460,"identity":"dae454ea-bd3a-461e-9684-982153de65ca","order_by":0,"name":"Xin Xu","email":"","orcid":"","institution":"Department of Geosciences and Natural Resource Management, University of Copenhagen; Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of 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We then used Principal Component Analysis (PCA) to condense the structural information into the OCI and tested it in China\u0026rsquo;s Guangxi province, Rwanda, and Denmark. In addition, we conducted a comparative analysis of OCI between protected and unprotected areas and among different forest types across these regions. Finally, we explored the relationships of terrain slope, distance to settlement and aboveground biomass with the OCI. Our result showed that the distribution of OCI values varies across the different bioclimatic regions, closely related to their respective forest characteristics. Higher OCI values were observed in protected areas as compared to unprotected areas, and OCI showed a positive correlation with terrain slope, distance to settlement and aboveground biomass. 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