Benchmarking Deep Learning Architectures for Forest Monitoring and Management: A Systematic Review

Benchmarking Deep Learning Architectures for Forest Monitoring and Management: A Systematic Review | 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 Systematic Review Benchmarking Deep Learning Architectures for Forest Monitoring and Management: A Systematic Review Gabriel Osei Forkuo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9088244/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 Environmental engineering relies on the precise, large-scale design and monitoring of ecosystems to ensure the mutual benefit of humans and nature. However, traditional forest assessment methods are constrained by limited spatial and temporal resolution, impeding dynamic habitat reconstruction and ecosystem rehabilitation. This paper presents a systematic review of 186 peer-reviewed articles (2011–2026) to evaluate how Deep Learning (DL) and Computer Vision (CV) are transitioning from observational tools to actionable ecotechnologies for forest restoration. By automating the extraction of multi-modal structural and spectral data, advanced architectures—such as 3D Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs)—are directly empowering evidence-based ecological engineering tasks, including climate-resilient carbon accounting, the tracking of biodiversity shifts during habitat recovery, and early-stage disease mitigation. Quantitative meta-analysis reveals that ViT-based models achieve a pooled species-classification accuracy of 96.3% (95% CI: 95.0–97.5%), offering an absolute improvement of 4.9% over standard CNNs (91.4%). Despite these algorithmic advances, the review identifies three critical barriers to operational deployment in restoration ecology: (1) the absence of standardized benchmarking protocols (73% of studies), (2) a "transferability paradox" causing 23–45% performance degradation when models are applied across diverse ecological biomes, and (3) a profound lack of model interpretability. To bridge the gap between computational research and field-based ecosystem restoration, this study provides a novel computational complexity-performance trade-off analysis and a practitioner’s decision framework. These tools offer a roadmap to overcome edge-deployment limitations, enabling engineers and ecologists to implement robust, real-time AI solutions for the sustainable rehabilitation and management of global forest ecosystems. ecotechnology ecosystem rehabilitation explainable artificial intelligence (XAI) habitat reconstruction multi-sensor data fusion precision conservation Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialsEnvSci.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-9088244","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":605281697,"identity":"9c96028e-2513-43c0-80f4-145c499c2a94","order_by":0,"name":"Gabriel Osei Forkuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYFACHgaGBAYLBvYGBmYgz4YBSLERo0WCgecAWEsaUAszEVoYEFoOAzEBLfLuZ499eFAB1MLe+9iYp+J84nZ2/mOPeRjuyeHSYngmL3lGwhmgFp7jxsk8Z24n7mxmZjfmYSg2xqmlIceYIbFNgsFeIo35MG/b7cQNh5nZJGcwJCQ24NLS/wao5R/QFoiWc4S1yEuAbGmAaEnmbTsA1iLxAY8WA4l3yQwJxyR4eHiOMRvOOZNsDNRibvDBIAGnX+T7cw8z/qixkeNhb2OWeFNhJ7vh/MFnDxIqEnCGmMEBCM2DLo5LA9AWXC4eBaNgFIyCUQAHADWxS35CD5XtAAAAAElFTkSuQmCC","orcid":"","institution":"Transilvania University of Brasov","correspondingAuthor":true,"prefix":"","firstName":"Gabriel","middleName":"Osei","lastName":"Forkuo","suffix":""}],"badges":[],"createdAt":"2026-03-10 23:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9088244/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9088244/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104782813,"identity":"a1369a0a-0a44-47bf-81c4-fce57ffb4276","added_by":"auto","created_at":"2026-03-17 07:57:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1909263,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptMainEnvSci.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9088244/v1_covered_97ca657f-6a13-4813-9a14-45687e27e2b6.pdf"},{"id":104691183,"identity":"911a04f1-499e-42d6-b0d2-36eff050d162","added_by":"auto","created_at":"2026-03-16 06:22:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1196125,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsEnvSci.docx","url":"https://assets-eu.researchsquare.com/files/rs-9088244/v1/e5e4b659a412ed65726e05e6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Benchmarking Deep Learning Architectures for Forest Monitoring and Management: A Systematic Review","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"ecotechnology, ecosystem rehabilitation, explainable artificial intelligence (XAI), habitat reconstruction, multi-sensor data fusion, precision conservation","lastPublishedDoi":"10.21203/rs.3.rs-9088244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9088244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnvironmental engineering relies on the precise, large-scale design and monitoring of ecosystems to ensure the mutual benefit of humans and nature. However, traditional forest assessment methods are constrained by limited spatial and temporal resolution, impeding dynamic habitat reconstruction and ecosystem rehabilitation. This paper presents a systematic review of 186 peer-reviewed articles (2011\u0026ndash;2026) to evaluate how Deep Learning (DL) and Computer Vision (CV) are transitioning from observational tools to actionable ecotechnologies for forest restoration. By automating the extraction of multi-modal structural and spectral data, advanced architectures\u0026mdash;such as 3D Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs)\u0026mdash;are directly empowering evidence-based ecological engineering tasks, including climate-resilient carbon accounting, the tracking of biodiversity shifts during habitat recovery, and early-stage disease mitigation. 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