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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
25/10/2022 |
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Data da última atualização: |
25/10/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
BRICEÑO CASTILLO, G. V.; FREITAS, L. J. M. de; CORDEIRO, V. A.; ORELLANA, J. B. P.; REATEGUI-BETANCOURT, J. L.; NAGY, L.; MATRICARDI, E. A. T. |
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Afiliação: |
GUIDO VICENTE BRICEÑO CASTILLO, UNIVERSIDADE DE BRASÍLIA; LUCAS JOSE MAZZEI DE FREITAS, CPATU; VICTOR ALMEIDA CORDEIRO, UNIVERSIDADE DE BRASÍLIA; JORGE BRENO PALHETA ORELLANA, UNIVERSIDADE DE BRASÍLIA; JORGE LUIS REATEGUI-BETANCOURT, UNIVERSIDADE DE BRASÍLIA; LASZLO NAGY, UNIVERSIDADE ESTADUAL DE CAMPINAS; ERALDO APARECIDO TRONDOLI MATRICARDI, UNIVERSIDADE DE BRASÍLIA. |
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Título: |
Assessment of selective logging impacts using UAV, Landsat, and Sentinel data in the Brazilian Amazon rainforest. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Journal of Applied Remote Sensing, v. 16, n. 1, 014526, Mar. 2022. |
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DOI: |
https://doi.org/10.1117/1.JRS.16.014526 |
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Idioma: |
Inglês |
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Conteúdo: |
Several studies have assessed forest disturbance in tropical forests using Landsat imagery. However, the spatial resolution (30 m) of Landsat images has often been considered too coarse to accurately detect the extent and impacts of selective logging. The Sentinel-2 satellite launched in 2015 has been providing images at spatial resolutions of 10 to 20 m and those images have shown an improved potential for detecting forest disturbances in tropical regions. We compared Landsat-8 and Sentinel-2 imagery for detecting selective logging in a rain forest site in the Brazilian Amazon. The aerosol-free modified soil adjusted vegetation index (MSAVI_af) was retrieved from the satellite images acquired in August 2020 immediately following logging. A robust reference dataset of very-high-resolution imagery (0.5 m) acquired using a complementary metal oxide semiconductor sensor (visible bands) onboard of an unmanned aerial vehicle was used to image the area of interest and a map derived from it was used to assess the classification accuracies made using satellite-derived data. The overall accuracy of the classified Sentinel-2 and Landsat-8 images varied between 54% and 83%, depending on the applied classification parameters for distinguishing undisturbed from disturbed forest canopy. Images acquired using the UAV allowed us to detect subtle impacts of canopy openings by selective logging activities. Images acquired using the UAV allowed the detection of small canopy openings, but not Sentinel-2 or Landsat-8. Sentinel-2 provided more details of canopy disturbances than Landsat image. Our classification approach is fully implementable on the Google Earth Engine platform and is a promising technique to monitor selective logging impacts in tropical forests. MenosSeveral studies have assessed forest disturbance in tropical forests using Landsat imagery. However, the spatial resolution (30 m) of Landsat images has often been considered too coarse to accurately detect the extent and impacts of selective logging. The Sentinel-2 satellite launched in 2015 has been providing images at spatial resolutions of 10 to 20 m and those images have shown an improved potential for detecting forest disturbances in tropical regions. We compared Landsat-8 and Sentinel-2 imagery for detecting selective logging in a rain forest site in the Brazilian Amazon. The aerosol-free modified soil adjusted vegetation index (MSAVI_af) was retrieved from the satellite images acquired in August 2020 immediately following logging. A robust reference dataset of very-high-resolution imagery (0.5 m) acquired using a complementary metal oxide semiconductor sensor (visible bands) onboard of an unmanned aerial vehicle was used to image the area of interest and a map derived from it was used to assess the classification accuracies made using satellite-derived data. The overall accuracy of the classified Sentinel-2 and Landsat-8 images varied between 54% and 83%, depending on the applied classification parameters for distinguishing undisturbed from disturbed forest canopy. Images acquired using the UAV allowed us to detect subtle impacts of canopy openings by selective logging activities. Images acquired using the UAV allowed the detection of small canopy openings, but not S... Mostrar Tudo |
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Palavras-Chave: |
Drone; Imagem de satélite; Veículo aéreo não tripulado. |
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Thesagro: |
Degradação Ambiental; Floresta Tropical; Impacto Ambiental. |
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Thesaurus NAL: |
Unmanned aerial vehicles. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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Marc: |
LEADER 02732naa a2200289 a 4500
001 2147739
005 2022-10-25
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1117/1.JRS.16.014526$2DOI
100 1 $aBRICEÑO CASTILLO, G. V.
245 $aAssessment of selective logging impacts using UAV, Landsat, and Sentinel data in the Brazilian Amazon rainforest.$h[electronic resource]
260 $c2022
520 $aSeveral studies have assessed forest disturbance in tropical forests using Landsat imagery. However, the spatial resolution (30 m) of Landsat images has often been considered too coarse to accurately detect the extent and impacts of selective logging. The Sentinel-2 satellite launched in 2015 has been providing images at spatial resolutions of 10 to 20 m and those images have shown an improved potential for detecting forest disturbances in tropical regions. We compared Landsat-8 and Sentinel-2 imagery for detecting selective logging in a rain forest site in the Brazilian Amazon. The aerosol-free modified soil adjusted vegetation index (MSAVI_af) was retrieved from the satellite images acquired in August 2020 immediately following logging. A robust reference dataset of very-high-resolution imagery (0.5 m) acquired using a complementary metal oxide semiconductor sensor (visible bands) onboard of an unmanned aerial vehicle was used to image the area of interest and a map derived from it was used to assess the classification accuracies made using satellite-derived data. The overall accuracy of the classified Sentinel-2 and Landsat-8 images varied between 54% and 83%, depending on the applied classification parameters for distinguishing undisturbed from disturbed forest canopy. Images acquired using the UAV allowed us to detect subtle impacts of canopy openings by selective logging activities. Images acquired using the UAV allowed the detection of small canopy openings, but not Sentinel-2 or Landsat-8. Sentinel-2 provided more details of canopy disturbances than Landsat image. Our classification approach is fully implementable on the Google Earth Engine platform and is a promising technique to monitor selective logging impacts in tropical forests.
650 $aUnmanned aerial vehicles
650 $aDegradação Ambiental
650 $aFloresta Tropical
650 $aImpacto Ambiental
653 $aDrone
653 $aImagem de satélite
653 $aVeículo aéreo não tripulado
700 1 $aFREITAS, L. J. M. de
700 1 $aCORDEIRO, V. A.
700 1 $aORELLANA, J. B. P.
700 1 $aREATEGUI-BETANCOURT, J. L.
700 1 $aNAGY, L.
700 1 $aMATRICARDI, E. A. T.
773 $tJournal of Applied Remote Sensing$gv. 16, n. 1, 014526, Mar. 2022.
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