Modeling and Spatialization of Biomass and Carbon Stock Using Lidar Metrics in Tropical Dry Forest, Brazil: Preliminary Results

Modeling and Spatialization of Biomass and Carbon Stock Using Lidar Metrics in Tropical Dry Forest, Brazil: Preliminary Results | 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 Modeling and Spatialization of Biomass and Carbon Stock Using Lidar Metrics in Tropical Dry Forest, Brazil: Preliminary Results Robson Borges de Lima, Cinthia Pereira de Oliveira, Rinaldo Luiz Caraciolo Ferreira, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-55277/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 Background : In recent years, with the growing environmental concern regarding climate change, there has been a search for efficient alternatives in indirect methods for studies on the quantification of biomass and forest carbon stock. In this article, we seek to obtain pioneering and preliminary results of estimates of biomass and carbon using data from conventional forest inventory and LiDAR technology in a dry tropical forest in Brazil. We used data from conventional forest inventory in two areas together with data from the LiDAR overflight, generating local biomass estimates from a developed local equation and the carbon levels obtained from local species. With data from LiDAR technology, we extracted the metrics from the point cloud and were used as an independent variable. For the construction of the biomass and carbon allometric models per hectare, we approach three types of models for data analysis: Multiple linear regression with Principal Components - PCA, Conventional multiple linear regression and Multiple linear regression with Stepwise, the generated equations were analyzed by comparisons of statistical criteria (R²aj and RMSE). After selecting the best equation, we generate the carbon estimates by area by assessing the plot level. Results : The best fit TAGB and TAGC model was the multiple linear regression with Stepwise, concluding, then, that LiDAR data can be used to estimate biomass and total carbon in dry tropical forest, proven by an adjustment considered in the models employed, with a significant correlation between the LiDAR metrics. Conclusions : Our preliminary results provide important information about the spatial distribution of TAGB and TAGC in the study area, which can be used to manage the reserve for optimal carbon sequestration. Other Economics Caatinga domain Forest management Allometry Statistical models Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Full Text 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-55277","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":1320671,"identity":"9c7a0061-6532-491e-80cc-e5e27d7104ba","order_by":0,"name":"Robson Borges de 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area by the nearest weather station. Source: Agritempo adjusted. Source: Agritempo, 2018 (adjusted).","description":"","filename":"Figure3.JPG","url":"https://assets-eu.researchsquare.com/files/rs-55277/v1/Figure3.JPG"},{"id":1882166,"identity":"a0ab4892-f0aa-49a1-9b30-75687ea986f0","added_by":"auto","created_at":"2020-08-11 17:37:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":76381,"visible":true,"origin":"","legend":"Flowchart of the methodology adopted and the resulting products.","description":"","filename":"Figure4.JPG","url":"https://assets-eu.researchsquare.com/files/rs-55277/v1/Figure4.JPG"},{"id":1882167,"identity":"82362826-7480-467f-85b6-01c336b79885","added_by":"auto","created_at":"2020-08-11 17:37:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50608,"visible":true,"origin":"","legend":"Predictions of the best allometric equations for the Correntão area (a) and Transposição (b) using the LiDAR metrics selected step by step in relation to the observed values. 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In this article, we seek to obtain pioneering and preliminary results of estimates of biomass and carbon using data from conventional forest inventory and LiDAR technology in a dry tropical forest in Brazil. We used data from conventional forest inventory in two areas together with data from the LiDAR overflight, generating local biomass estimates from a developed local equation and the carbon levels obtained from local species. With data from LiDAR technology, we extracted the metrics from the point cloud and were used as an independent variable. For the construction of the biomass and carbon allometric models per hectare, we approach three types of models for data analysis: Multiple linear regression with Principal Components - PCA, Conventional multiple linear regression and Multiple linear regression with Stepwise, the generated equations were analyzed by comparisons of statistical criteria (R²aj and RMSE). After selecting the best equation, we generate the carbon estimates by area by assessing the plot level.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The best fit TAGB and TAGC model was the multiple linear regression with Stepwise, concluding, then, that LiDAR data can be used to estimate biomass and total carbon in dry tropical forest, proven by an adjustment considered in the models employed, with a significant correlation between the LiDAR metrics.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Our preliminary results provide important information about the spatial distribution of TAGB and TAGC in the study area, which can be used to manage the reserve for optimal carbon sequestration.\u003c/p\u003e","manuscriptTitle":"Modeling and Spatialization of Biomass and Carbon Stock Using Lidar Metrics in Tropical Dry Forest, Brazil: Preliminary Results","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-08-11 17:37:58","doi":"10.21203/rs.3.rs-55277/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":"e903a8e3-86f4-4981-b3a9-b21dbb4c961c","owner":[],"postedDate":"August 11th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":273604,"name":"Other Economics"}],"tags":[],"updatedAt":"2020-08-17T20:15:53+00:00","versionOfRecord":[],"versionCreatedAt":"2020-08-11 17:37:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-55277","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-55277","identity":"rs-55277","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}