Stratified AGB Inversion Driven by DGTHI: Quantifying Topographic Controls on Biomass Prediction Across Tree Species

Stratified AGB Inversion Driven by DGTHI: Quantifying Topographic Controls on Biomass Prediction Across Tree Species | 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 Stratified AGB Inversion Driven by DGTHI: Quantifying Topographic Controls on Biomass Prediction Across Tree Species Yihan Zhu, Jiangping Chen, Jianhua Yin, Zilong Qin, Jizhou Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7185026/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Accurate forest aboveground biomass (AGB) estimation is crucial for global carbon cycle research. While existing studies have utilized topographic factors in remote sensing, they often fail to systematically quantify multi-dimensional heterogeneity or address species-specific responses. This study pioneers the application of the Digital Elevation Model (DEM) Grid Topographic Heterogeneity Index (DGTHI)—a composite metric integrating elevation variability, relief, surface roughness, and mean slope—to enhance AGB inversion models by explicitly accounting for terrain-vegetation interactions. Using airborne Light detection and ranging (LiDAR) and 8,804 field-measured trees in Mengyin County, Linyi City, Shandong Province, China, we developed a DGTHI-stratified modeling framework to dissect how topographic heterogeneity governs species-level AGB estimation accuracy at the county scale.Results demonstrate: (1) DGTHI outperformed conventional single-factor topographic corrections, with heterogeneity effects on feature selection following a species hierarchy: acacia > pine > cypress > poplar; (2) DGTHI-driven stratification significantly improved model accuracy, increasing R² by 0.08–0.17 versus unstratified models; (3) Spatial AGB patterns (27–217 t/ha in May 2023) revealed southwest–northeast highs and northwest–southeast lows, directly modulated by DGTHI-mapped heterogeneity. As the first integration of DGTHI into species-specific AGB inversion, this work provides a transferable paradigm for precision carbon mapping in topographically complex forests. Aboveground Biomass Topographic Heterogeneity DGTHI Species-Specific Modeling LiDAR Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Sep, 2025 Reviews received at journal 20 Sep, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviews received at journal 14 Sep, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers invited by journal 12 Aug, 2025 Editor assigned by journal 12 Aug, 2025 Submission checks completed at journal 24 Jul, 2025 First submitted to journal 22 Jul, 2025 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. 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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-7185026","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500691703,"identity":"2d29b6f7-2e86-4a1d-a994-15d692a29688","order_by":0,"name":"Yihan Zhu","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Yihan","middleName":"","lastName":"Zhu","suffix":""},{"id":500691704,"identity":"d3fcd806-2c4f-4db9-8b35-e7fe9b737336","order_by":1,"name":"Jiangping 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While existing studies have utilized topographic factors in remote sensing, they often fail to systematically quantify multi-dimensional heterogeneity or address species-specific responses. This study pioneers the application of the Digital Elevation Model (DEM) Grid Topographic Heterogeneity Index (DGTHI)\u0026mdash;a composite metric integrating elevation variability, relief, surface roughness, and mean slope\u0026mdash;to enhance AGB inversion models by explicitly accounting for terrain-vegetation interactions. Using airborne Light detection and ranging (LiDAR) and 8,804 field-measured trees in Mengyin County, Linyi City, Shandong Province, China, we developed a DGTHI-stratified modeling framework to dissect how topographic heterogeneity governs species-level AGB estimation accuracy at the county scale.Results demonstrate: (1) DGTHI outperformed conventional single-factor topographic corrections, with heterogeneity effects on feature selection following a species hierarchy: acacia\u0026thinsp;\u0026gt;\u0026thinsp;pine\u0026thinsp;\u0026gt;\u0026thinsp;cypress\u0026thinsp;\u0026gt;\u0026thinsp;poplar; (2) DGTHI-driven stratification significantly improved model accuracy, increasing R\u0026sup2; by 0.08\u0026ndash;0.17 versus unstratified models; (3) Spatial AGB patterns (27\u0026ndash;217 t/ha in May 2023) revealed southwest\u0026ndash;northeast highs and northwest\u0026ndash;southeast lows, directly modulated by DGTHI-mapped heterogeneity. 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