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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: |
12/12/2012 |
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Data da última atualização: |
06/07/2021 |
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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'.; REUTEBUCH, S. E.; MCGAUGHEY, R. J.; ANDERSEN, H. |
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Afiliação: |
MARCUS VINICIO NEVES D OLIVEIRA, CPAF-AC; STEPHEN, E. REUTEBUCH, USDA Forest Service; ROBERT J. MCGAUGHEY, USDA Forest Service; HANS-ERICK ANDERSEN, USDA Forest Service. |
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Título: |
Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Remote Sensing of Environment, v. 124, p. 479-491, Sept. 2012. |
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ISSN: |
0034-4257 |
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DOI: |
10.1016/j.rse.2012.05.014 |
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Idioma: |
Inglês |
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Conteúdo: |
The objectives of this study were to estimate above ground forest biomass and identify areas disturbed by sel ective logging in a 1000 ha Brazilian tropical forest in the Antimary State Forest (FEA) using airborne lidardata. The study area consisted of three management units, two of which were unlogged, while the third unit was selectively logged at a low intensity (approximately 10-15 m3 ha-1 or 5-8% of total volume). A systematic random sample of fifty 0.25-ha ground plots were measured and used to construct lidar-based regression models for above ground biomass (AGB). A lidar model-assisted approach was used to estimate AGB for the logged and unlogged units (using both synthetic and model-assisted estimators). Two lidar explanatory variables, computed at a spatial resolution of 50 m×50 m, were used in these predictions: 1) the first quartile height of all above ground returns (P25); and, 2) variance of the height above ground of all returns (VAR). The model-assisted AGB estimator (total 231,589 Mg±5,477 SE; mean 231.6 Mg ha-1±5.5 SE; ±2.4%) was more precise than plot-only simple random sample estimator (total 230,872 Mg±10,477 SE; mean 230.9 Mg ha-1±10.5 SE; ±4.5%). The total and mean AGB estimates obtained using the synthetic estimator (total 231,694 Mg; mean 231.7 Mg ha-1) were nearly equal those obtained using the modelassisted estimator. In a second component of the analysis lidar metrics were also computed at 1 m×1 m resolution to identify areas impacted by logging activities within the selectively harvested management unit. A high-resolution canopy relative density model (RDM) was used in GIS to identify and delineate roads, skidtrails, landings and harvested tree gaps. The area impacted by selective logging determined from the RDM was 58.4 ha or 15.4% of the total management unit. Using these two spatial resolutions of lidar analyse sit was possible to identify differences in AGB in selectively logged areas that had relatively high levels of residual overstory canopy cover. The mean AGB obtained from the synthetic estimator was significantly lower in impacted areas than in undisturbed areas of the selectively logged management unit (p=0.01). MenosThe objectives of this study were to estimate above ground forest biomass and identify areas disturbed by sel ective logging in a 1000 ha Brazilian tropical forest in the Antimary State Forest (FEA) using airborne lidardata. The study area consisted of three management units, two of which were unlogged, while the third unit was selectively logged at a low intensity (approximately 10-15 m3 ha-1 or 5-8% of total volume). A systematic random sample of fifty 0.25-ha ground plots were measured and used to construct lidar-based regression models for above ground biomass (AGB). A lidar model-assisted approach was used to estimate AGB for the logged and unlogged units (using both synthetic and model-assisted estimators). Two lidar explanatory variables, computed at a spatial resolution of 50 m×50 m, were used in these predictions: 1) the first quartile height of all above ground returns (P25); and, 2) variance of the height above ground of all returns (VAR). The model-assisted AGB estimator (total 231,589 Mg±5,477 SE; mean 231.6 Mg ha-1±5.5 SE; ±2.4%) was more precise than plot-only simple random sample estimator (total 230,872 Mg±10,477 SE; mean 230.9 Mg ha-1±10.5 SE; ±4.5%). The total and mean AGB estimates obtained using the synthetic estimator (total 231,694 Mg; mean 231.7 Mg ha-1) were nearly equal those obtained using the modelassisted estimator. In a second component of the analysis lidar metrics were also computed at 1 m×1 m resolution to identify areas impacted by logging a... Mostrar Tudo |
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Palavras-Chave: |
Acre; Aeronave remotamente pilotada; Amazonia Occidental; Amazônia Ocidental; Análisis estadístico; Biomasa aérea; Drone; Explotación forestal; Floresta Estadual do Antimary (AC); Geoténica; Manejo de baixo impacto; Manejo florestal; Modelo de regressão; Teledetección; Vehículos aéreos no tripulados; Western Amazon. |
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Thesagro: |
Análise estatística; Biomassa; Essência florestal; Extração da madeira; Floresta tropical; Método estatístico; Raio laser; Sensoriamento remoto. |
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Thesaurus NAL: |
Aboveground biomass; Lásers; Lidar; Logging; Remote sensing; Statistical analysis; Tropical forests; Tropical wood; Unmanned aerial vehicles. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 03964naa a2200577 a 4500
001 1942345
005 2021-07-06
008 2012 bl uuuu u00u1 u #d
022 $a0034-4257
024 7 $a10.1016/j.rse.2012.05.014$2DOI
100 1 $aOLIVEIRA, M. V. N. d'.
245 $aEstimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon.$h[electronic resource]
260 $c2012
520 $aThe objectives of this study were to estimate above ground forest biomass and identify areas disturbed by sel ective logging in a 1000 ha Brazilian tropical forest in the Antimary State Forest (FEA) using airborne lidardata. The study area consisted of three management units, two of which were unlogged, while the third unit was selectively logged at a low intensity (approximately 10-15 m3 ha-1 or 5-8% of total volume). A systematic random sample of fifty 0.25-ha ground plots were measured and used to construct lidar-based regression models for above ground biomass (AGB). A lidar model-assisted approach was used to estimate AGB for the logged and unlogged units (using both synthetic and model-assisted estimators). Two lidar explanatory variables, computed at a spatial resolution of 50 m×50 m, were used in these predictions: 1) the first quartile height of all above ground returns (P25); and, 2) variance of the height above ground of all returns (VAR). The model-assisted AGB estimator (total 231,589 Mg±5,477 SE; mean 231.6 Mg ha-1±5.5 SE; ±2.4%) was more precise than plot-only simple random sample estimator (total 230,872 Mg±10,477 SE; mean 230.9 Mg ha-1±10.5 SE; ±4.5%). The total and mean AGB estimates obtained using the synthetic estimator (total 231,694 Mg; mean 231.7 Mg ha-1) were nearly equal those obtained using the modelassisted estimator. In a second component of the analysis lidar metrics were also computed at 1 m×1 m resolution to identify areas impacted by logging activities within the selectively harvested management unit. A high-resolution canopy relative density model (RDM) was used in GIS to identify and delineate roads, skidtrails, landings and harvested tree gaps. The area impacted by selective logging determined from the RDM was 58.4 ha or 15.4% of the total management unit. Using these two spatial resolutions of lidar analyse sit was possible to identify differences in AGB in selectively logged areas that had relatively high levels of residual overstory canopy cover. The mean AGB obtained from the synthetic estimator was significantly lower in impacted areas than in undisturbed areas of the selectively logged management unit (p=0.01).
650 $aAboveground biomass
650 $aLásers
650 $aLidar
650 $aLogging
650 $aRemote sensing
650 $aStatistical analysis
650 $aTropical forests
650 $aTropical wood
650 $aUnmanned aerial vehicles
650 $aAnálise estatística
650 $aBiomassa
650 $aEssência florestal
650 $aExtração da madeira
650 $aFloresta tropical
650 $aMétodo estatístico
650 $aRaio laser
650 $aSensoriamento remoto
653 $aAcre
653 $aAeronave remotamente pilotada
653 $aAmazonia Occidental
653 $aAmazônia Ocidental
653 $aAnálisis estadístico
653 $aBiomasa aérea
653 $aDrone
653 $aExplotación forestal
653 $aFloresta Estadual do Antimary (AC)
653 $aGeoténica
653 $aManejo de baixo impacto
653 $aManejo florestal
653 $aModelo de regressão
653 $aTeledetección
653 $aVehículos aéreos no tripulados
653 $aWestern Amazon
700 1 $aREUTEBUCH, S. E.
700 1 $aMCGAUGHEY, R. J.
700 1 $aANDERSEN, H.
773 $tRemote Sensing of Environment$gv. 124, p. 479-491, Sept. 2012.
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