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4. | | JONS, V. L.; NELSON, B.; MCMULLEN, M. Downy mildew (Sclerophthora macrospora) of wheat, barley, and oats in north Dakota. Plant Disease, St. Paul, v. 70, n. 9, p. 892, 1986. Biblioteca(s): Embrapa Trigo. |
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5. | | MENDES, N. B.; NELSON, B. W.; MILLER, R. P. Levantamento das espécies arbóreas da região de Manaus - Amazonas. Revista do Instituto Florestal, São Paulo, v. 4, pt. 1, p. 332-338, mar. 1992. Edição dos Anais do Congresso Florestal de Essências Nativas, 2., 1992, São Paulo. Edição especial. Biblioteca(s): Embrapa Florestas. |
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8. | | OLIVEIRA, A. A. de; FERREIRA, L. V.; LLERAS PEREZ, E.; NELSON, B. W.; ALMEIDA, S. S. A flora fanerogâmica amazônica: composição, diversidade, endemismo e conservação. In: ARAÚJO, E. de L.; MOURA, A. do N.; SAMPAIO, E. V. de S. B.; GESTINARI, L. M. de S.; CARNEIRO, J. de M. T. (Ed.). Biodiversidade, conservação e uso sustentável da flora do Brasil. Recife: Sociedade Botânica do Brasil: Universidade Federal Rural de Pernambuco, 2002. p. 15-18. Biblioteca(s): Embrapa Amazônia Ocidental. |
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9. | | SALATI, E.; RIBEIRO, M. de N. G.; ABSY, M. L.; NELSON, B. W. Clima da Amazonia: presente, passado e futuro. In: VAL, A.L.; FIGLIUOLO, R.; FELDBERG, E., ed. Bases cientificas para estrategias de preservacao e desenvolvimento da Amazonia: fatos e perspectivas. Manaus: INPA, 1991. v.1, p.21-34. Biblioteca(s): Embrapa Rondônia. |
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10. | | MORGAN, E. B.; NELSON, B. D.; BROWN, T. F.; McCORMICK, M. E.; SAXTON, A. Comparison of alfalfa, ryegrass, and triticale haylages and corn and sorghum silages with lactating Holstein cows, 1987. Annual Progress Report. Louisiana State University, Southeast Research Station, p. 95-106, 1987. Biblioteca(s): Embrapa Trigo. |
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12. | | EICHHORN JUNIOR, M. M.; OLIVER, W. M.; HALLMARK, W. B.; YOUNG, W. A.; DAVIS, A. V.; NELSON, B. D. Grazer: a new bermudagrass for pastures and moderate hay production. Louisiana Agriculture, v. 29, n. 1, p. 5-7, 1985. Biblioteca(s): Embrapa Caprinos e Ovinos. |
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13. | | NELSON, B. W.; MESQUITA, R.; PEREIRA, J. L. G.; SOUZA, S. G. A. de; BATISTA, G. T.; COUTO, L. B. Allometric regressions for improved estimate of secondary forest biomass in the central Amazon. Forest Ecology and Management, v. 117, n. 1, p. 149-167, 1999. Biblioteca(s): Embrapa Amazônia Ocidental. |
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17. | | NELSON, B.; TAVARES, J.; WU, J.; VALERIANO, D.; LOPES, A.; MAROSTICA, S.; MARTINS, G.; PROHASKA, N.; ALBERT, L.; ARAUJO, A. de; MANZI, A.; SALESKA, S.; HUETE, A. Seasonality of Central Amazon Forest Leaf Flush Using Tower-Mounted RGB Camera. In: AGU FALL MEETING, 2014, San Francisco. [Proceedings]. [San Francisco]: AGU, 2014. Biblioteca(s): Embrapa Amazônia Oriental. |
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18. | | 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. Impacts of selective logging on Amazon forest canopy structure and biomass with a LiDAR and photogrammetric survey sequence. Forest Ecology and Management, v. 500, 119648, Nov. 2021. Biblioteca(s): Embrapa Acre. |
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19. | | KOMIYA, S.; ARAUJO, A. C. de; LAVRIC, J. V.; NELSON, B.; SÖRGEL, M.; WEBER, B.; BOTIA, S.; GOMES-ALVES, E.; WALTER, D.; SÁ, M. de O.; WOLFF, S.; PINHO, D. M.; KONDO, F.; TRUMBORE, S. Seasonal and interannual variations of carbon fluxes at the Amazon Tall Tower Observatory site in 2014-2019. In: EGU GENERAL ASSEMBLY, 2022, Viena. Programme. [S.l.]: EGU, 2022. Biblioteca(s): Embrapa Amazônia Oriental. |
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20. | | WU, J.; KOBAYASHI, H.; STARK, S. C.; MENG, R.; GUAN, K.; TRAN, N. N.; GAO, S.; YANG, W.; RESTREPO-COUPE, N.; MIURA, T.; OLIVEIRA JUNIOR, R. C. de; ROGERS, A.; DYE, D. G.; NELSON, B. W.; SERBIN, S. P.; HUETE, A. R.; SALESKA, S. R. Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest. New Phytologist, v. 217, n. 4, p. 1507-1520, Mar. 2018. Biblioteca(s): Embrapa Amazônia Oriental. |
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Registro Completo
Biblioteca(s): |
Embrapa Acre. |
Data corrente: |
30/08/2021 |
Data da última atualização: |
19/11/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
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. |
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. |
Título: |
Impacts of selective logging on Amazon forest canopy structure and biomass with a LiDAR and photogrammetric survey sequence. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Forest Ecology and Management, v. 500, 119648, Nov. 2021. |
ISSN: |
0378-1127 |
DOI: |
https://doi.org/10.1016/j.foreco.2021.119648 |
Idioma: |
Inglês |
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 |
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. |
Thesagro: |
Aerofotogrametria; Biomassa; Exploração Florestal; Extração da Madeira; Impacto Ambiental. |
Thesaurus NAL: |
Aboveground biomass; Environmental impact; Forest canopy; Lidar; Logging; Photogrammetry; Sustainable forestry; Unmanned aerial vehicles. |
Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
Marc: |
LEADER 03372naa a2200589 a 4500 001 2133927 005 2021-11-19 008 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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