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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
15/12/2022 |
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Data da última atualização: |
14/02/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LIMA, G. S. A. de; FERREIRA, M. E.; MADARI, B. E.; CARVALHO, M. T. de M. |
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Afiliação: |
GABRIELLA SANTOS ARRUDA DE LIMA, UNIVERSIDADE FEDERAL DE GOIÁS; MANUEL EDUARDO FERREIRA, UNIVERSIDADE FEDERAL DE GOIÁS; BEATA EMOKE MADARI, CNPAF; MARCIA THAIS DE MELO CARVALHO, CNPAF. |
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Título: |
Carbon estimation in an integrated crop-livestock system with imaging sensors aboard unmanned aerial platforms. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Remote Sensing Applications: Society and Environment, v. 28, 100867, Nov. 2022. |
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ISSN: |
2352-9385 |
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DOI: |
https://doi.org/10.1016/j.rsase.2022.100867 |
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Idioma: |
Inglês |
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Conteúdo: |
Biomass and carbon estimates in integrated crop-livestock (iLP) systems remain scarce. Recently, sensors aboard an unmanned aerial vehicle (UAV) have been used for such estimation and can represent an efficient and low-cost method for acquiring accurate remote-sensing data. This study aimed to estimate the relationship between vegetation indices (VIs) and carbon stock in an upland rice field intercropped with Brachiaria, an exotic grass species in the savanna ecosystem of Brazil (Cerrado biome), using multispectral aerial imaging with a high cartographic scale. The research was conducted in an experimental iLP system located at the Brazilian Agricultural Research Corporation (Embrapa Arroz e Feijão), Goiás State. During the data collection stage, flights were conducted with a fixed-wing UAV (eBee Plus RTK) on the same day or close to collecting biomass samples in the field. In the processing step, a correlation was performed between the normalized difference vegetation index (NDVI), simple ratio (SR), soil-adjusted vegetation index (SAVI), and modified photochemical reflectance index (MPRI) VIs and the biomass measured in the field. The results indicated that the vegetation index (VI) that used the near-infrared (NIR) band showed a stronger correlation with biomass than the VI that used only the visible band information. All VIs were significantly correlated with the estimated field biomass (P < 0.05) using Pearson's correlation. The regression models were able to predict the biomass and carbon stock in Phase 1 (3.93 Mg ha-1, 1.66 Mg ha-1, R2 = 0.748) and Phase 2 (7.53 Mg ha-1, 3.19 Mg ha-1, R2 = 0.816). From the best model selected for each phase, maps with the spatial and temporal distribution of the biomass of the iLP system were plotted, demonstrating one of the advantages of employing remote sensing techniques based on drones and multispectral imaging sensors. MenosBiomass and carbon estimates in integrated crop-livestock (iLP) systems remain scarce. Recently, sensors aboard an unmanned aerial vehicle (UAV) have been used for such estimation and can represent an efficient and low-cost method for acquiring accurate remote-sensing data. This study aimed to estimate the relationship between vegetation indices (VIs) and carbon stock in an upland rice field intercropped with Brachiaria, an exotic grass species in the savanna ecosystem of Brazil (Cerrado biome), using multispectral aerial imaging with a high cartographic scale. The research was conducted in an experimental iLP system located at the Brazilian Agricultural Research Corporation (Embrapa Arroz e Feijão), Goiás State. During the data collection stage, flights were conducted with a fixed-wing UAV (eBee Plus RTK) on the same day or close to collecting biomass samples in the field. In the processing step, a correlation was performed between the normalized difference vegetation index (NDVI), simple ratio (SR), soil-adjusted vegetation index (SAVI), and modified photochemical reflectance index (MPRI) VIs and the biomass measured in the field. The results indicated that the vegetation index (VI) that used the near-infrared (NIR) band showed a stronger correlation with biomass than the VI that used only the visible band information. All VIs were significantly correlated with the estimated field biomass (P < 0.05) using Pearson's correlation. The regression models were able to predict th... Mostrar Tudo |
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Palavras-Chave: |
Drones. |
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Thesagro: |
Biomassa; Carbono. |
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Thesaurus Nal: |
Biomass; Unmanned aerial vehicles; Vegetation index. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02703naa a2200253 a 4500
001 2149727
005 2023-02-14
008 2022 bl uuuu u00u1 u #d
022 $a2352-9385
024 7 $ahttps://doi.org/10.1016/j.rsase.2022.100867$2DOI
100 1 $aLIMA, G. S. A. de
245 $aCarbon estimation in an integrated crop-livestock system with imaging sensors aboard unmanned aerial platforms.$h[electronic resource]
260 $c2022
520 $aBiomass and carbon estimates in integrated crop-livestock (iLP) systems remain scarce. Recently, sensors aboard an unmanned aerial vehicle (UAV) have been used for such estimation and can represent an efficient and low-cost method for acquiring accurate remote-sensing data. This study aimed to estimate the relationship between vegetation indices (VIs) and carbon stock in an upland rice field intercropped with Brachiaria, an exotic grass species in the savanna ecosystem of Brazil (Cerrado biome), using multispectral aerial imaging with a high cartographic scale. The research was conducted in an experimental iLP system located at the Brazilian Agricultural Research Corporation (Embrapa Arroz e Feijão), Goiás State. During the data collection stage, flights were conducted with a fixed-wing UAV (eBee Plus RTK) on the same day or close to collecting biomass samples in the field. In the processing step, a correlation was performed between the normalized difference vegetation index (NDVI), simple ratio (SR), soil-adjusted vegetation index (SAVI), and modified photochemical reflectance index (MPRI) VIs and the biomass measured in the field. The results indicated that the vegetation index (VI) that used the near-infrared (NIR) band showed a stronger correlation with biomass than the VI that used only the visible band information. All VIs were significantly correlated with the estimated field biomass (P < 0.05) using Pearson's correlation. The regression models were able to predict the biomass and carbon stock in Phase 1 (3.93 Mg ha-1, 1.66 Mg ha-1, R2 = 0.748) and Phase 2 (7.53 Mg ha-1, 3.19 Mg ha-1, R2 = 0.816). From the best model selected for each phase, maps with the spatial and temporal distribution of the biomass of the iLP system were plotted, demonstrating one of the advantages of employing remote sensing techniques based on drones and multispectral imaging sensors.
650 $aBiomass
650 $aUnmanned aerial vehicles
650 $aVegetation index
650 $aBiomassa
650 $aCarbono
653 $aDrones
700 1 $aFERREIRA, M. E.
700 1 $aMADARI, B. E.
700 1 $aCARVALHO, M. T. de M.
773 $tRemote Sensing Applications: Society and Environment$gv. 28, 100867, Nov. 2022.
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