Advancing Forest Degradation and Regeneration Assessment Through Light Detection and Ranging and Hyperspectral Imaging Integration

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorNational Institute for Space Research—INPE-
Autor(es): dc.contributorSCN 211-
Autor(es): dc.contributorFederal University of Maranhão-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversity of Exeter-
Autor(es): dc.creatorAlmeida, Catherine Torres de-
Autor(es): dc.creatorGalvão, Lênio Soares-
Autor(es): dc.creatorOmetto, Jean Pierre H. B.-
Autor(es): dc.creatorJacon, Aline Daniele-
Autor(es): dc.creatorPereira, Francisca Rocha de Souza-
Autor(es): dc.creatorSato, Luciane Yumie-
Autor(es): dc.creatorSilva-Junior, Celso Henrique Leite-
Autor(es): dc.creatorBrancalion, Pedro H. S.-
Autor(es): dc.creatorAragão, Luiz Eduardo Oliveira e Cruz de-
Data de aceite: dc.date.accessioned2025-08-21T19:40:32Z-
Data de disponibilização: dc.date.available2025-08-21T19:40:32Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/rs16213935-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/303604-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/303604-
Descrição: dc.descriptionIntegrating Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) enhances the assessment of tropical forest degradation and regeneration, which is crucial for conservation and climate mitigation strategies. This study optimized procedures using combined airborne LiDAR, HSI data, and machine learning algorithms across 12 sites in the Brazilian Amazon, covering various environmental and anthropogenic conditions. Four forest classes (undisturbed, degraded, and two stages of second-growth) were identified using Landsat time series (1984–2017) and auxiliary data. Metrics from 600 samples were analyzed with three classifiers: Random Forest, Stochastic Gradient Boosting, and Support Vector Machine. The combination of LiDAR and HSI data improved classification accuracy by up to 12% compared with single data sources. The most decisive metrics were LiDAR-based upper canopy cover and HSI-based absorption bands in the near-infrared and shortwave infrared. LiDAR produced significantly fewer errors for discriminating second-growth from old-growth forests, while HSI had better performance to discriminate degraded from undisturbed forests. HSI-only models performed similarly to LiDAR-only models (mean F1 of about 75% for both data sources). The results highlight the potential of integrating LiDAR and HSI data to improve our understanding of forest dynamics in the context of nature-based solutions to mitigate climate change impacts.-
Descrição: dc.descriptionCathie Marsh Centre for Census and Survey Research, University of Manchester-
Descrição: dc.descriptionFaculty of Science and Engineering, University of Manchester-
Descrição: dc.descriptionAlliance Manchester Business School, University of Manchester-
Descrição: dc.descriptionCentre for Epidemiology Versus Arthritis, University of Manchester-
Descrição: dc.descriptionCentre for Paediatrics and Child Health, University of Manchester-
Descrição: dc.descriptionDepartment of Child Health, University of Manchester-
Descrição: dc.descriptionUniversity of Manchester-
Descrição: dc.descriptionFundo Amazônia-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFaculty of Agricultural Sciences of Vale do Ribeira São Paulo State University—UNESP, SP-
Descrição: dc.descriptionNational Institute for Space Research—INPE, Caixa Postal 515, SP-
Descrição: dc.descriptionInstituto de Pesquisa Ambiental da Amazônia (IPAM) SCN 211, Bloco B, Sala 201, GO-
Descrição: dc.descriptionGraduate Program in Biodiversity Conservation Federal University of Maranhão, MA-
Descrição: dc.descriptionDepartment of Forest Sciences “Luiz de Queiroz” College of Agriculture University of São Paulo, SP-
Descrição: dc.descriptionCollege of Life and Environmental Sciences University of Exeter-
Descrição: dc.descriptionFaculty of Agricultural Sciences of Vale do Ribeira São Paulo State University—UNESP, SP-
Descrição: dc.descriptionFundo Amazônia: 14209291-
Descrição: dc.descriptionCNPq: 305054/2016-3-
Descrição: dc.descriptionCNPq: 307792/2021-8-
Descrição: dc.descriptionCNPq: 314416/2020-0-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectairborne laser scanning (ALS)-
Palavras-chave: dc.subjectforest disturbance-
Palavras-chave: dc.subjectforest recovery-
Palavras-chave: dc.subjecthyperspectral remote sensing-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectmultisensor analysis-
Palavras-chave: dc.subjectsuccessional stages-
Título: dc.titleAdvancing Forest Degradation and Regeneration Assessment Through Light Detection and Ranging and Hyperspectral Imaging Integration-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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