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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente; Embrapa Recursos Genéticos e Biotecnologia. |
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Data corrente: |
08/01/2024 |
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Data da última atualização: |
08/01/2024 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
ALBUQUERQUE, R. W.; VIEIRA, D. L. M.; VICENTE, L. E.; ARAUJO, L. S. de; FERREIRA, M. E.; GROHMANN, C. H. |
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Afiliação: |
RAFAEL WALTER ALBUQUERQUE, UNIVERSIDADE DE SÃO PAULO; DANIEL LUIS MASCIA VIEIRA, Cenargen; LUIZ EDUARDO VICENTE, CNPMA; LUCIANA SPINELLI DE ARAUJO, CNPMA; MANUEL EDUARDO FERREIRA, UNIVERSIDADE FEDERAL DE GOIÁS; CARLOS HENRIQUE GROHMANN, UNIVERSIDADE DE SÃO PAULO. |
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Título: |
Comparing forest restoration canopy cover measurements using RGB and multispectral sensors onboard drones. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. Ref. 155275. |
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Páginas: |
p. 53-56. |
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Idioma: |
Português |
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Conteúdo: |
Abstract: Remotely Piloted Aircrafts (RPA) coupled with Red-Green-Blue (RGB) sensors have a high potential to monitor Forest Restoration (FR), but multispectral sensors onboard RPA are more expensive and still demand more studies when applied to FR monitoring. This work aims to compare an RGB and a multispectral sensor capacity to measure the canopy cover of a FR project. Four canopy cover methods were evaluated using: the point cloud data generated by the RGB sensor; a vegetation index for RGB sensors; the Normalized Difference Vegetation Index (NDVI); and the Near Infra-Red band (Nir) only. The point cloud data method was the most accurate and the only one that presented all accuracies greater than 0.9. However, the multispectral sensor presented more potential for scientific research because it seems to be capable of detecting different photosynthetic activities on the trees and, consequently, different responses to FR treatments, which should be confirmed by future studies. |
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Palavras-Chave: |
Forest Restoration Monitoring; Infra-Red; Red-Green-Blue; Remotely Piloted Aircrafts; Unmanned Aerial Vehicle. |
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Thesagro: |
Reflorestamento. |
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Thesaurus Nal: |
Environmental monitoring; Forest restoration; Remote sensing. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1160538/1/Vicente-Comparing-forest-2023.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1160750/1/ACFrOgBLExK7popwKZHKzuUTNqkbX8kMp1HovKaaEzTXSixyscvPk2XHYX-dveSCNBWu4LRu7-BrCbyoV2pFqOZ4xc-rdoiIpBJbMXbpb4NcY3QOmBAlCpVTW2-oP16P4ltXdOxtl-iDtEdjXOtZ.pdf
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Marc: |
LEADER 02021nam a2200289 a 4500
001 2160538
005 2024-01-08
008 2023 bl uuuu u00u1 u #d
100 1 $aALBUQUERQUE, R. W.
245 $aComparing forest restoration canopy cover measurements using RGB and multispectral sensors onboard drones.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. Ref. 155275.$c2023
300 $ap. 53-56.
520 $aAbstract: Remotely Piloted Aircrafts (RPA) coupled with Red-Green-Blue (RGB) sensors have a high potential to monitor Forest Restoration (FR), but multispectral sensors onboard RPA are more expensive and still demand more studies when applied to FR monitoring. This work aims to compare an RGB and a multispectral sensor capacity to measure the canopy cover of a FR project. Four canopy cover methods were evaluated using: the point cloud data generated by the RGB sensor; a vegetation index for RGB sensors; the Normalized Difference Vegetation Index (NDVI); and the Near Infra-Red band (Nir) only. The point cloud data method was the most accurate and the only one that presented all accuracies greater than 0.9. However, the multispectral sensor presented more potential for scientific research because it seems to be capable of detecting different photosynthetic activities on the trees and, consequently, different responses to FR treatments, which should be confirmed by future studies.
650 $aEnvironmental monitoring
650 $aForest restoration
650 $aRemote sensing
650 $aReflorestamento
653 $aForest Restoration Monitoring
653 $aInfra-Red
653 $aRed-Green-Blue
653 $aRemotely Piloted Aircrafts
653 $aUnmanned Aerial Vehicle
700 1 $aVIEIRA, D. L. M.
700 1 $aVICENTE, L. E.
700 1 $aARAUJO, L. S. de
700 1 $aFERREIRA, M. E.
700 1 $aGROHMANN, C. H.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
