Integration of UAV-LiDAR and Sentinel-2A Data for Modeling Robinia pseudoacacia Aboveground Biomass via Artificial Neural Networks

Integration of UAV-LiDAR and Sentinel-2A Data for Modeling Robinia pseudoacacia Aboveground Biomass via Artificial Neural Networks | 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 Integration of UAV-LiDAR and Sentinel-2A Data for Modeling Robinia pseudoacacia Aboveground Biomass via Artificial Neural Networks Jiaqi Cheng, Xuexia Zhang, Jianjun Zhang, Yawei Hu, Jiongchang Zhao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7432796/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 Aboveground forest biomass (AGB), a critical ecosystem component, plays essential roles in maintaining ecological balance, conserving biodiversity, mitigating climate change, and supporting sustainable development. Optical remote sensing characterizes horizontal forest structures through multispectral and textural data, whereas UAV-LiDAR captures vertical structural parameters. Integrating these complementary technologies improves biomass estimation by enhancing model fit and accuracy. This multimodal approach significantly advances aboveground biomass quantification. This study investigated aboveground biomass (AGB) estimation in artificial Robinia pseudoacacia forests within the Caijiachuan watershed of Jixian County, Shanxi Province, utilizing multisource remote sensing data. Field survey data served as ground truths, whereas Sentinel-2A multispectral imagery and UAV-LiDAR point clouds provided optical and radar remote sensing data, respectively. After extracting feature variables from both datasets, we developed and compared AGB estimation models via four machine learning approaches—partial least squares regression (PLS), random forest regression (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)—to evaluate each method's performance with single-source and combined data inputs. The optimal model was subsequently applied to generate a spatial distribution map of Robinia pseudoacacia AGB density across the watershed, followed by analysis of biomass distribution patterns relative to topographic factors (elevation, slope, and aspect). Our results provide important insights as follows:(1) Compared with the single-variable model, the model incorporating both variables demonstrates superior performance (MAE = 2.53, MBE = -0.31, RMSE = 3.96, NMSE = 0.06) (MAE = 4.57, MBE = -2.92, RMSE = 5.61, NMSE = 0.11).(2) Among the four modeling approaches evaluated—partial least squares (PLS) regression, random forest (RF) regression, backpropagation neural network (BPNN), and convolutional neural network (CNN)—the CNN algorithm demonstrated superior performance in reducing estimation errors for Robinia pseudoacacia aboveground biomass.(3) The mean aboveground biomass (AGB) density of Robinia pseudoacacia in the Caijiachuan plantation watershed was 61.49 Mg·ha⁻¹. Approximately 87% of the total biomass occurred within the 1,050–1,200 m elevation range. Slope gradient analysis revealed maximum AGB accumulation on moderately steep and steep slopes, whereas minimum values were observed on gentle slopes. The aspect-dependent distribution revealed peak biomass on semii-shaded northeast-facing slopes, whereas the lowest biomass levels occurred on north-facing shaded slopes. These findings provide an effective method for assessing the aboveground biomass of Robinia pseudoacacia forests in the region, as well as valuable data for evaluating the carbon sequestration capacity of soil and water conservation forests. Robinia pseudoacacia Sentinel-2A image UAV-LiDAR artificial neural networks aboveground biomass remote sensing Full Text Additional Declarations No competing interests reported. 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-7432796","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531809807,"identity":"ece27f3b-000f-4cbd-aa6e-c29499e337c3","order_by":0,"name":"Jiaqi Cheng","email":"","orcid":"","institution":"School of Soil and Water Conservation, Beijing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Cheng","suffix":""},{"id":531809808,"identity":"a25f335f-fa5d-424c-b4fd-af17a8e43f05","order_by":1,"name":"Xuexia 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Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Robinia pseudoacacia, Sentinel-2A image, UAV-LiDAR, artificial neural networks, aboveground biomass, remote sensing","lastPublishedDoi":"10.21203/rs.3.rs-7432796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7432796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAboveground forest biomass (AGB), a critical ecosystem component, plays essential roles in maintaining ecological balance, conserving biodiversity, mitigating climate change, and supporting sustainable development. Optical remote sensing characterizes horizontal forest structures through multispectral and textural data, whereas UAV-LiDAR captures vertical structural parameters. Integrating these complementary technologies improves biomass estimation by enhancing model fit and accuracy. This multimodal approach significantly advances aboveground biomass quantification.\u003c/p\u003e\u003cp\u003eThis study investigated aboveground biomass (AGB) estimation in artificial Robinia pseudoacacia forests within the Caijiachuan watershed of Jixian County, Shanxi Province, utilizing multisource remote sensing data. Field survey data served as ground truths, whereas Sentinel-2A multispectral imagery and UAV-LiDAR point clouds provided optical and radar remote sensing data, respectively. After extracting feature variables from both datasets, we developed and compared AGB estimation models via four machine learning approaches\u0026mdash;partial least squares regression (PLS), random forest regression (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)\u0026mdash;to evaluate each method's performance with single-source and combined data inputs. The optimal model was subsequently applied to generate a spatial distribution map of Robinia pseudoacacia AGB density across the watershed, followed by analysis of biomass distribution patterns relative to topographic factors (elevation, slope, and aspect).\u003c/p\u003e\u003cp\u003eOur results provide important insights as follows:(1) Compared with the single-variable model, the model incorporating both variables demonstrates superior performance (MAE\u0026thinsp;=\u0026thinsp;2.53, MBE = -0.31, RMSE\u0026thinsp;=\u0026thinsp;3.96, NMSE\u0026thinsp;=\u0026thinsp;0.06) (MAE\u0026thinsp;=\u0026thinsp;4.57, MBE = -2.92, RMSE\u0026thinsp;=\u0026thinsp;5.61, NMSE\u0026thinsp;=\u0026thinsp;0.11).(2) Among the four modeling approaches evaluated\u0026mdash;partial least squares (PLS) regression, random forest (RF) regression, backpropagation neural network (BPNN), and convolutional neural network (CNN)\u0026mdash;the CNN algorithm demonstrated superior performance in reducing estimation errors for Robinia pseudoacacia aboveground biomass.(3) The mean aboveground biomass (AGB) density of Robinia pseudoacacia in the Caijiachuan plantation watershed was 61.49 Mg\u0026middot;ha⁻\u0026sup1;. Approximately 87% of the total biomass occurred within the 1,050\u0026ndash;1,200 m elevation range. Slope gradient analysis revealed maximum AGB accumulation on moderately steep and steep slopes, whereas minimum values were observed on gentle slopes. The aspect-dependent distribution revealed peak biomass on semii-shaded northeast-facing slopes, whereas the lowest biomass levels occurred on north-facing shaded slopes.\u003c/p\u003e\u003cp\u003eThese findings provide an effective method for assessing the aboveground biomass of Robinia pseudoacacia forests in the region, as well as valuable data for evaluating the carbon sequestration capacity of soil and water conservation forests.\u003c/p\u003e","manuscriptTitle":"Integration of UAV-LiDAR and Sentinel-2A Data for Modeling Robinia pseudoacacia Aboveground Biomass via Artificial Neural Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 23:16:50","doi":"10.21203/rs.3.rs-7432796/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f82179e-f637-4130-b72a-3e87b61c1b67","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-11T12:53:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 23:16:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7432796","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7432796","identity":"rs-7432796","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}