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 | Acesso ao texto completo restrito à biblioteca da Embrapa Acre. Para informações adicionais entre em contato com cpafac.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Acre. |
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Data corrente: |
30/08/2021 |
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Data da última atualização: |
19/11/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
OLIVEIRA, M. V. N. d'; FIGUEIREDO, E. O.; ALMEIDA, D. R. A. de; OLIVEIRA, L. C. de; SILVA, C. A.; NELSON, B. W.; CUNHA, R. M. da; PAPA, D. de A.; STARK, S. C.; VALBUENA, R. |
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Afiliação: |
MARCUS VINICIO NEVES D OLIVEIRA, CPAF-AC; EVANDRO ORFANO FIGUEIREDO, CPAF-AC; DANILO ROBERTI ALVES DE ALMEIDA, "Luiz de Queiroz" College of Agriculture, University of São Paulo (USP/ESALQ); LUIS CLAUDIO DE OLIVEIRA, CPAF-AC; CARLOS ALBERTO SILVA, University of Florida, Gainesville, USA; BRUCE WALKER NELSON, National Institute for Amazon Research (INPA); RENATO MESQUITA DA CUNHA, Instituto de Meio ambiente do Acre; DANIEL DE ALMEIDA PAPA, CPAF-AC; SCOTT C. STARK, Michigan State University, East Lansing, MI, USA; RUBEN VALBUENA, Bangor University, Bangor, UK. |
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Título: |
Impacts of selective logging on Amazon forest canopy structure and biomass with a LiDAR and photogrammetric survey sequence. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Forest Ecology and Management, v. 500, 119648, Nov. 2021. |
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ISSN: |
0378-1127 |
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DOI: |
https://doi.org/10.1016/j.foreco.2021.119648 |
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Idioma: |
Inglês |
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Conteúdo: |
Sustainable forest management relies on good knowledge of forest structure obtained from ground surveys combined with remote sensing. Capable of detecting both the forest floor and canopy elements, airborne LiDAR can estimate forest structure parameters with accuracy and precision, but is still difficult to acquire due to the lake of service provider in remote regions of developing countries. Alternatively if ground surface elevations are known (e.g., from LiDAR), they can be tied to a canopy surface model derived from stereo photogrammetry using RGB images from unmanned aerial vehicles (UAV). Here we assessed whether such photogrammetric canopy measurements offer aboveground biomass (AGB) and disturbance impact estimates from logging that are comparable to LiDAR, and whether the use of both in sequence can provide an efficient post-harvest monitoring system. Specifically, through a combination of forest inventory ground plots, airborne LiDAR data, and a UAV-RGB camera system we (i) automatically located and measured canopy disturbance caused by logging, (ii) compared AGB models produced by LiDAR alone and the combination of LiDAR (for terrain elevation model) and RGB-photogrammetry (for forest surface model), and (iii) estimated the AGB stock loss from logging. The study was carried out in the Antimary State forest located in the southwestern Brazilian Amazon. Our results demonstrate that the use of RGB-photogrammetry in regions where the terrain elevation has already been estimated can be an effective way to rapidly identify selective logging and to accurately monitor its impact. MenosSustainable forest management relies on good knowledge of forest structure obtained from ground surveys combined with remote sensing. Capable of detecting both the forest floor and canopy elements, airborne LiDAR can estimate forest structure parameters with accuracy and precision, but is still difficult to acquire due to the lake of service provider in remote regions of developing countries. Alternatively if ground surface elevations are known (e.g., from LiDAR), they can be tied to a canopy surface model derived from stereo photogrammetry using RGB images from unmanned aerial vehicles (UAV). Here we assessed whether such photogrammetric canopy measurements offer aboveground biomass (AGB) and disturbance impact estimates from logging that are comparable to LiDAR, and whether the use of both in sequence can provide an efficient post-harvest monitoring system. Specifically, through a combination of forest inventory ground plots, airborne LiDAR data, and a UAV-RGB camera system we (i) automatically located and measured canopy disturbance caused by logging, (ii) compared AGB models produced by LiDAR alone and the combination of LiDAR (for terrain elevation model) and RGB-photogrammetry (for forest surface model), and (iii) estimated the AGB stock loss from logging. The study was carried out in the Antimary State forest located in the southwestern Brazilian Amazon. Our results demonstrate that the use of RGB-photogrammetry in regions where the terrain elevation has already been ... Mostrar Tudo |
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Palavras-Chave: |
Acre; Aeronave remotamente pilotada; Amazonia Occidental; Amazônia Ocidental; Biomasa aérea; Cubierta forestal; Dossel; Drone; Explotación forestal; FLoresta Estadual do Antimary (AC); RGB images; Sena Madureira (AC); Silvicultura sustentable; Vehículos aéreos no tripulados; Western Amazon. |
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Thesagro: |
Aerofotogrametria; Biomassa; Exploração Florestal; Extração da Madeira; Impacto Ambiental. |
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Thesaurus NAL: |
Aboveground biomass; Environmental impact; Forest canopy; Lidar; Logging; Photogrammetry; Sustainable forestry; Unmanned aerial vehicles. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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Marc: |
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001 2133927
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008 2021 bl uuuu u00u1 u #d
022 $a0378-1127
024 7 $ahttps://doi.org/10.1016/j.foreco.2021.119648$2DOI
100 1 $aOLIVEIRA, M. V. N. d'
245 $aImpacts of selective logging on Amazon forest canopy structure and biomass with a LiDAR and photogrammetric survey sequence.$h[electronic resource]
260 $c2021
520 $aSustainable forest management relies on good knowledge of forest structure obtained from ground surveys combined with remote sensing. Capable of detecting both the forest floor and canopy elements, airborne LiDAR can estimate forest structure parameters with accuracy and precision, but is still difficult to acquire due to the lake of service provider in remote regions of developing countries. Alternatively if ground surface elevations are known (e.g., from LiDAR), they can be tied to a canopy surface model derived from stereo photogrammetry using RGB images from unmanned aerial vehicles (UAV). Here we assessed whether such photogrammetric canopy measurements offer aboveground biomass (AGB) and disturbance impact estimates from logging that are comparable to LiDAR, and whether the use of both in sequence can provide an efficient post-harvest monitoring system. Specifically, through a combination of forest inventory ground plots, airborne LiDAR data, and a UAV-RGB camera system we (i) automatically located and measured canopy disturbance caused by logging, (ii) compared AGB models produced by LiDAR alone and the combination of LiDAR (for terrain elevation model) and RGB-photogrammetry (for forest surface model), and (iii) estimated the AGB stock loss from logging. The study was carried out in the Antimary State forest located in the southwestern Brazilian Amazon. Our results demonstrate that the use of RGB-photogrammetry in regions where the terrain elevation has already been estimated can be an effective way to rapidly identify selective logging and to accurately monitor its impact.
650 $aAboveground biomass
650 $aEnvironmental impact
650 $aForest canopy
650 $aLidar
650 $aLogging
650 $aPhotogrammetry
650 $aSustainable forestry
650 $aUnmanned aerial vehicles
650 $aAerofotogrametria
650 $aBiomassa
650 $aExploração Florestal
650 $aExtração da Madeira
650 $aImpacto Ambiental
653 $aAcre
653 $aAeronave remotamente pilotada
653 $aAmazonia Occidental
653 $aAmazônia Ocidental
653 $aBiomasa aérea
653 $aCubierta forestal
653 $aDossel
653 $aDrone
653 $aExplotación forestal
653 $aFLoresta Estadual do Antimary (AC)
653 $aRGB images
653 $aSena Madureira (AC)
653 $aSilvicultura sustentable
653 $aVehículos aéreos no tripulados
653 $aWestern Amazon
700 1 $aFIGUEIREDO, E. O.
700 1 $aALMEIDA, D. R. A. de
700 1 $aOLIVEIRA, L. C. de
700 1 $aSILVA, C. A.
700 1 $aNELSON, B. W.
700 1 $aCUNHA, R. M. da
700 1 $aPAPA, D. de A.
700 1 $aSTARK, S. C.
700 1 $aVALBUENA, R.
773 $tForest Ecology and Management$gv. 500, 119648, Nov. 2021.
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