Applications of artificial intelligence and lidar in forest inventories: a systematic literature review

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dc.creatorWellignton Galvão Rodrigues
dc.creatorGabriel da Silva Vieira
dc.creatorChristian Dias Cabacinha
dc.creatorRenato Freitas Bulcão-Neto
dc.creatorFabrizzio Alphonsus Alves de Melo Nunes Soares
dc.date.accessioned2025-07-22T14:23:00Z
dc.date.accessioned2025-09-08T23:40:27Z
dc.date.available2025-07-22T14:23:00Z
dc.date.issued2024
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.identifier.doihttps://doi.org/10.1016/j.compeleceng.2024.109793
dc.identifier.issn1879-0755
dc.identifier.urihttps://hdl.handle.net/1843/83726
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofComputers and Electrical Engineering
dc.rightsAcesso Restrito
dc.subjectInteligência artificial
dc.subjectLevantamentos florestais
dc.subject.otherInteligência artificial
dc.subject.otherLevantamentos florestais
dc.titleApplications of artificial intelligence and lidar in forest inventories: a systematic literature review
dc.typeArtigo de periódico
local.citation.epage17
local.citation.spage1
local.citation.volume120
local.description.resumoForest inventory is a crucial tool for managing forest resources by providing quantitative and qualitative information about a particular region, much of which is collected manually in the field. Using devices such as Light Detection and Ranging (LiDAR) assists in collecting and analyzing various parameters of forest inventory. Adopting artificial intelligence (AI) techniques has sparked interest among forestry engineers seeking to work with forest LiDAR data. In this context, this study presents a Systematic Literature Review (SLR) to identify, evaluate, and interpret the results of primary studies related to the intersection between AI and Forestry Engineering. The automated search strategy retrieved 218 studies, of which 46 were selected after applying inclusion and exclusion criteria and quality assessment. After analyzing and synthesizing the data, the results showed that deep learning is becoming an increasing trend in recent research and that the direct estimation of tree diameter from aerial scans, although critical, has been minimally explored, highlighting an open field for future research.
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://dl.acm.org/doi/10.1016/j.compeleceng.2024.109793

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