A Unified Approach for Dynamic Tree Segmentation using LiDAR Point Cloud Data

A Unified Approach for Dynamic Tree Segmentation using LiDAR Point Cloud Data | 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 A Unified Approach for Dynamic Tree Segmentation using LiDAR Point Cloud Data Nakshi Milan Shah, Sanid Chirakkal, Deepak Putrevu, Shruti Raval This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6525540/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 Accurate segmentation of individual trees from LiDAR point clouds is essential for forest resource management and ecological studies. This research introduces a novel methodology that combines a dynamic Euclidean clustering approach for extracting tree coordinates and Gaussian kernel-based dynamic thresholding to enhance segmentation precision. The method was demonstrated using LiDAR data from the Shivamogga forest region (Shimoga), Karnataka, India, and validated using extensive field inventory data. The performance of the tree height estimation was analyzed by box plot analysis, with a comparison of observed and estimated heights showing a strong alignment in most cases. However, overestimation was observed in denser vegetation areas. The approach demonstrated robustness in handling variable forest conditions, including dense canopy structures and diverse tree sizes. This work contributes to LiDAR-based forest analysis, offering scalable solutions for large-scale ecological assessments and resource management. LiDAR point clouds Tree segmentation Euclidean clustering Gaussian kernel Dynamic thresholding Tree height estimation Remote sensing Large-scale analysis Full Text Additional Declarations The authors declare no competing interests. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6525540","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447802090,"identity":"c015f659-ee19-4689-9d52-209fd66dc1fc","order_by":0,"name":"Nakshi Milan Shah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYFACNgZmBgMGIOJhfADk8vCRooXZAKSFjTgtDGAtbBIQPgFgzn4s+XNBwTZ7c/azxyq/5tjJAI14+OgGHi2WPWnHpGcY3E7c2ZOXdlt2WzLQYWzGxjl4tBgcSG9j5jG4nWBwIMfstuQ2ZqAWHjZpvFrOP2/+DNRib3D+jVmx5LZ6IrTcSDsgDdTCuOFGjhnjx22HidHyLA2kJXHDjTfG0ozbjvOwMRPyy/k04888f0AOyzH8+HNbtT0/e/PDx/i0oABmHjBJrHIQYPxBiupRMApGwSgYMQAAC9dGPKk6IUgAAAAASUVORK5CYII=","orcid":"","institution":"L.J. 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