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Registro Completo |
Biblioteca(s): |
Embrapa Amapá. |
Data corrente: |
07/11/2023 |
Data da última atualização: |
07/11/2023 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
DINIZ, M. F. B. G.; SOUSA, W. B. B. de; YAMADA, P. de O. F.; TAVARES-DIAS, M.; YAMADA, F. H. |
Afiliação: |
MARIA FERNANDA BARROS GOUVEIA DINIZ, UNIVERSIDADE REGIONAL DO CARIRI; WALLAS BENEVIDES BARBOSA DE SOUSA, UNIVERSIDADE REGIONAL DO CARIRI; PRISCILLA DE OLIVEIRA FADEL YAMADA, UNIVERSIDADE FEDERAL DO AMAPÁ; MARCOS TAVARES DIAS, CPAF-AP; FÁBIO HIDEKI YAMADA, UNIVERSIDADE REGIONAL DO CARIRI. |
Título: |
Nova ocorrência de Constrictoanchoratus lemmyi (Monogenea) parasitando Hoplias malabaricus (Characiformes) provenientes de dois açudes do sul do Ceará. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
In: ENCONTRO BRASILEIRO DE PATOLOGISTAS DE ORGANISMOS AQUÁTICOS, 17., 2023, Belo Horizonte. Anais. [São Paulo]: Abrapoa, 2023. |
Idioma: |
Português |
Notas: |
Enbrapoa. |
Conteúdo: |
O objetivo deste estudo foi registrar a ocorrência de Constrictoanchoratus lemmyi parasitando as brânquias de H . malabaricus de dois açudes do estado do Ceará |
Palavras-Chave: |
Ectoparasitos. |
Thesagro: |
Parasito; Peixe; Traíra. |
Thesaurus Nal: |
Monogenea. |
Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1157958/1/NOVA-OCORRENCIA-DE-CONSTRICTOANCHORATUS.pdf
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Marc: |
LEADER 00962nam a2200229 a 4500 001 2157958 005 2023-11-07 008 2023 bl uuuu u00u1 u #d 100 1 $aDINIZ, M. F. B. G. 245 $aNova ocorrência de Constrictoanchoratus lemmyi (Monogenea) parasitando Hoplias malabaricus (Characiformes) provenientes de dois açudes do sul do Ceará.$h[electronic resource] 260 $aIn: ENCONTRO BRASILEIRO DE PATOLOGISTAS DE ORGANISMOS AQUÁTICOS, 17., 2023, Belo Horizonte. Anais. [São Paulo]: Abrapoa$c2023 500 $aEnbrapoa. 520 $aO objetivo deste estudo foi registrar a ocorrência de Constrictoanchoratus lemmyi parasitando as brânquias de H . malabaricus de dois açudes do estado do Ceará 650 $aMonogenea 650 $aParasito 650 $aPeixe 650 $aTraíra 653 $aEctoparasitos 700 1 $aSOUSA, W. B. B. de 700 1 $aYAMADA, P. de O. F. 700 1 $aTAVARES-DIAS, M. 700 1 $aYAMADA, F. H.
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Embrapa Amapá (CPAF-AP) |
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Registro Completo
Biblioteca(s): |
Embrapa Acre. |
Data corrente: |
12/12/2012 |
Data da última atualização: |
06/07/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
OLIVEIRA, M. V. N. d'.; REUTEBUCH, S. E.; MCGAUGHEY, R. J.; ANDERSEN, H. |
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. |
Título: |
Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon. |
Ano de publicação: |
2012 |
Fonte/Imprenta: |
Remote Sensing of Environment, v. 124, p. 479-491, Sept. 2012. |
ISSN: |
0034-4257 |
DOI: |
10.1016/j.rse.2012.05.014 |
Idioma: |
Inglês |
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 |
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. |
Thesagro: |
Análise estatística; Biomassa; Essência florestal; Extração da madeira; Floresta tropical; Método estatístico; Raio laser; Sensoriamento remoto. |
Thesaurus NAL: |
Aboveground biomass; Lásers; Lidar; Logging; Remote sensing; Statistical analysis; Tropical forests; Tropical wood; Unmanned aerial vehicles. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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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