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| Registros recuperados : 122 | |
| 9. |  | BULLER, L. S.; BERGIER, I.; RECH, R. Greenhouse gas emissions mitigation in more sustainable agroecosystems in Cerrado. In: INTERNATIONAL SYMPOSIUM ON GREENHOUSE GASES IN AGRICULTURE, 2., 2016, Campo Grande, MS. Proceedings... Brasília, DF: Embrapa, 2016. p. 245-248. (Embrapa Gado de Corte. Documentos, 216). Coordenador Roberto Giolo de Almeida. II SIGEE. Disponível em: . Acesso em: 30 nov. 2016.| Biblioteca(s): Embrapa Pantanal. |
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| Registros recuperados : 122 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pantanal. Para informações adicionais entre em contato com cpap.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Pantanal. |
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Data corrente: |
17/04/2023 |
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Data da última atualização: |
27/10/2023 |
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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: |
LOUZADA, R. O.; REIS, L. K.; DINIZ, J. M. F. de S.; ROQUE, F. de O.; GAMA, F. F.; BERGIER, I. |
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Afiliação: |
RÔMULLO OLIVEIRA LOUZADA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; LETÍCIA K. REIS, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; JULIANA MARIA FERREIRA DE SOUZA DINIZ, INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS; FÁBIO DE OLIVEIRA ROQUE, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; FÁBIO F. GAMA, INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS; IVAN BERGIER TAVARES DE LIMA, CPAP. |
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Título: |
Combining optical and microwave remote sensing for assessing gullies in human-disturbed vegetated landscapes. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Catena, v. 228, 107127, July 2023. |
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ISSN: |
1872-6887 |
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DOI: |
https://doi.org/10.1016/j.catena.2023.107127 |
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Idioma: |
Inglês |
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Conteúdo: |
The accurate assessment of the gully is key to stopping soil loss, especially in agricultural landscapes. This study aims to combine freely distributed remote sensing data for the evaluation of gullies located in a tropical watershed with a history of cattle production. Eighty-four vectorized gullies were defined in the Pirizal River basin, part of the highly eroded Upper Taquari (Brazil). We examined 56 variables from Sentinel-1/2 and ALOS-PALSAR-1 datasets, including SAR products and optical products like textures, water, vegetation, and terrain indices. Following a correlation analysis, 19 variables were selected for mapping in a Random Forest classifier by considering samples of active (soil) and stabilized (vegetation) pixels. The method reached an overall accuracy of 89%, in which soil abundance was responsible for 44% of the overall importance in the classification. Optical indices and texture products outperformed SAR products, whose importance represented only 14%. In the studied river basin, about 63% of the gullies were found stabilized, 30% in the process of stabilization, and only 7% active. The method proved effective, low cost, and promptly replicable to general river basins with gullies, mainly those in the tropics where vegetation has a significant role in soil loss control. |
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Palavras-Chave: |
Classificação de floresta aleatória; Desmistura espectral; Detecção de ravinas; Gully detection; Random Forest classification; SAR; Spectral unmixing. |
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Thesagro: |
Sensoriamento Remoto; Uso da Terra. |
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Thesaurus NAL: |
Land use; Landscapes; Remote sensing. |
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Categoria do assunto: |
--
X Pesquisa, Tecnologia e Engenharia |
|
Marc: |
LEADER 02359naa a2200349 a 4500
001 2157557
005 2023-10-27
008 2023 bl uuuu u00u1 u #d
022 $a1872-6887
024 7 $ahttps://doi.org/10.1016/j.catena.2023.107127$2DOI
100 1 $aLOUZADA, R. O.
245 $aCombining optical and microwave remote sensing for assessing gullies in human-disturbed vegetated landscapes.$h[electronic resource]
260 $c2023
520 $aThe accurate assessment of the gully is key to stopping soil loss, especially in agricultural landscapes. This study aims to combine freely distributed remote sensing data for the evaluation of gullies located in a tropical watershed with a history of cattle production. Eighty-four vectorized gullies were defined in the Pirizal River basin, part of the highly eroded Upper Taquari (Brazil). We examined 56 variables from Sentinel-1/2 and ALOS-PALSAR-1 datasets, including SAR products and optical products like textures, water, vegetation, and terrain indices. Following a correlation analysis, 19 variables were selected for mapping in a Random Forest classifier by considering samples of active (soil) and stabilized (vegetation) pixels. The method reached an overall accuracy of 89%, in which soil abundance was responsible for 44% of the overall importance in the classification. Optical indices and texture products outperformed SAR products, whose importance represented only 14%. In the studied river basin, about 63% of the gullies were found stabilized, 30% in the process of stabilization, and only 7% active. The method proved effective, low cost, and promptly replicable to general river basins with gullies, mainly those in the tropics where vegetation has a significant role in soil loss control.
650 $aLand use
650 $aLandscapes
650 $aRemote sensing
650 $aSensoriamento Remoto
650 $aUso da Terra
653 $aClassificação de floresta aleatória
653 $aDesmistura espectral
653 $aDetecção de ravinas
653 $aGully detection
653 $aRandom Forest classification
653 $aSAR
653 $aSpectral unmixing
700 1 $aREIS, L. K.
700 1 $aDINIZ, J. M. F. de S.
700 1 $aROQUE, F. de O.
700 1 $aGAMA, F. F.
700 1 $aBERGIER, I.
773 $tCatena$gv. 228, 107127, July 2023.
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Embrapa Pantanal (CPAP) |
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