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Registro Completo |
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
17/10/2022 |
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Data da última atualização: |
31/10/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
KIYOHARA, B. H.; SANO, E. E. |
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Afiliação: |
BÁRBARA HASS KIYOHARA; EDSON EYJI SANO, CPAC. |
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Título: |
Mapping Secondary Vegetation of a Region of Deforestation Hotspot in the Brazilian Amazon: Performance Analysis of C- and L-Band SAR Data Acquired in the Rainy Season. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Forests, v. 13, n. 1457, 2022. |
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Páginas: |
19 p. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: The re-suppression of secondary vegetation (SV) in the Brazilian Amazon for agriculture or land speculation occurs mostly in the rainy season. The use of optical images to monitor such re-suppression during the rainy season is limited because of the persistent cloud cover. This study aimed to evaluate the potential of C- and L-band SAR data acquired in the rainy season to dis- criminate SV in an area of new hotspot of deforestation in the municipality of Colniza, northwest- ern of Mato Grosso State, Brazil. This is the first time that the potential of dual-frequency SAR data was analyzed to discriminate SV, with an emphasis on data acquired during the rainy season. The L-band ALOS/PALSAR-2 and the C-band Sentinel-1 data acquired in March 2018 were processed to obtain backscattering coefficients and nine textural attributes were derived from the gray level co-occurrence matrix method (GLCM). Then, we classified the images based on the non-parametric Random Forest (RF) and Support Vector Machine (SVM) algorithms. The use of SAR textural attributes improved the discrimination capability of different LULC classes found in the study area. The results showed the best performance of ALOS/PALSAR-2 data classified by the RF algo- rithm to discriminate the following representative land use and land cover classes of the study area: primary forest, secondary forest, shrubby pasture, clean pasture, and bare soil, with an over- all accuracy and Kappa coefficient of 84% and 0.78, respectively. The RF outperformed the SVM classifier to discriminate these five LULC classes in 14% of overall accuracy for both ALOS-2 and Sentinel-1 data sets. This study also showed that the textural attributes derived from the GLCM method are highly sensitive to the moving window size to be applied to the GLCM method. The results of this study can assist the future development of an operation system based on du- al-frequency SAR data to monitor re-suppression of SV in the Brazilian Amazon or in other tropical rainforests. MenosAbstract: The re-suppression of secondary vegetation (SV) in the Brazilian Amazon for agriculture or land speculation occurs mostly in the rainy season. The use of optical images to monitor such re-suppression during the rainy season is limited because of the persistent cloud cover. This study aimed to evaluate the potential of C- and L-band SAR data acquired in the rainy season to dis- criminate SV in an area of new hotspot of deforestation in the municipality of Colniza, northwest- ern of Mato Grosso State, Brazil. This is the first time that the potential of dual-frequency SAR data was analyzed to discriminate SV, with an emphasis on data acquired during the rainy season. The L-band ALOS/PALSAR-2 and the C-band Sentinel-1 data acquired in March 2018 were processed to obtain backscattering coefficients and nine textural attributes were derived from the gray level co-occurrence matrix method (GLCM). Then, we classified the images based on the non-parametric Random Forest (RF) and Support Vector Machine (SVM) algorithms. The use of SAR textural attributes improved the discrimination capability of different LULC classes found in the study area. The results showed the best performance of ALOS/PALSAR-2 data classified by the RF algo- rithm to discriminate the following representative land use and land cover classes of the study area: primary forest, secondary forest, shrubby pasture, clean pasture, and bare soil, with an over- all accuracy and Kappa coefficient of 84% and 0.78,... Mostrar Tudo |
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Palavras-Chave: |
ALOS-2; Sentinel-1. |
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Thesagro: |
Chuva; Vegetação Secundária. |
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Thesaurus Nal: |
Support vector machines. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1147359/1/Sano-Mapping-secondary-vegetation.pdf
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Marc: |
LEADER 02679naa a2200205 a 4500
001 2147359
005 2022-10-31
008 2022 bl uuuu u00u1 u #d
100 1 $aKIYOHARA, B. H.
245 $aMapping Secondary Vegetation of a Region of Deforestation Hotspot in the Brazilian Amazon$bPerformance Analysis of C- and L-Band SAR Data Acquired in the Rainy Season.$h[electronic resource]
260 $c2022
300 $a19 p.
520 $aAbstract: The re-suppression of secondary vegetation (SV) in the Brazilian Amazon for agriculture or land speculation occurs mostly in the rainy season. The use of optical images to monitor such re-suppression during the rainy season is limited because of the persistent cloud cover. This study aimed to evaluate the potential of C- and L-band SAR data acquired in the rainy season to dis- criminate SV in an area of new hotspot of deforestation in the municipality of Colniza, northwest- ern of Mato Grosso State, Brazil. This is the first time that the potential of dual-frequency SAR data was analyzed to discriminate SV, with an emphasis on data acquired during the rainy season. The L-band ALOS/PALSAR-2 and the C-band Sentinel-1 data acquired in March 2018 were processed to obtain backscattering coefficients and nine textural attributes were derived from the gray level co-occurrence matrix method (GLCM). Then, we classified the images based on the non-parametric Random Forest (RF) and Support Vector Machine (SVM) algorithms. The use of SAR textural attributes improved the discrimination capability of different LULC classes found in the study area. The results showed the best performance of ALOS/PALSAR-2 data classified by the RF algo- rithm to discriminate the following representative land use and land cover classes of the study area: primary forest, secondary forest, shrubby pasture, clean pasture, and bare soil, with an over- all accuracy and Kappa coefficient of 84% and 0.78, respectively. The RF outperformed the SVM classifier to discriminate these five LULC classes in 14% of overall accuracy for both ALOS-2 and Sentinel-1 data sets. This study also showed that the textural attributes derived from the GLCM method are highly sensitive to the moving window size to be applied to the GLCM method. The results of this study can assist the future development of an operation system based on du- al-frequency SAR data to monitor re-suppression of SV in the Brazilian Amazon or in other tropical rainforests.
650 $aSupport vector machines
650 $aChuva
650 $aVegetação Secundária
653 $aALOS-2
653 $aSentinel-1
700 1 $aSANO, E. E.
773 $tForests$gv. 13, n. 1457, 2022.
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Registro original: |
Embrapa Cerrados (CPAC) |
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Registro Completo
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Biblioteca(s): |
Embrapa Acre. |
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Data corrente: |
10/11/2020 |
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Data da última atualização: |
04/04/2023 |
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Tipo da produção científica: |
Boletim de Pesquisa e Desenvolvimento |
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Autoria: |
SILVA, L. M. da; COSTA, R. de K. do N.; ALMEIDA, A. S. de; OLIVEIRA, C. H. A. de; ARAÚJO, E. A. de; TAVARES, O. C. H.; PEREIRA, M. G.; SANTOS, O. A. Q. dos. |
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Afiliação: |
LUCIELIO MANOEL DA SILVA, CPAF-AC; Rita de Kássia do Nascimento Costa; Alderlândia Silva de Almeida; Charles Henderson Alves de Oliveira, Instituto de Mudanças Climáticas e Regulação dos Serviços Ambientais; Edson Alves de Araújo, Universidade Federal do Acre (Ufac); Orlando Carlos Huertas Tavares, Pós-Doutorado no Programa de Pós-Graduação em Agronomia – Ciência do Solo, Seropédica, RJ; Marcos Gervasio Pereira, Universidade Federal Rural do Rio de Janeiro (UFRRJ); Otavio Augusto Queiroz dos Santos, Universidade Federal Rural do Rio de Janeiro (UFRRJ). |
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Título: |
Avaliação de métodos de determinação da densidade de solo em amostras com diferentes atividades da fração argila coletadas no município de Rio Branco, estado do Acre. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Rio Branco, AC: Embrapa Acre, 2020. |
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Páginas: |
21 p. |
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Série: |
(Embrapa Acre. Boletim de pesquisa e desenvolvimento, 62). |
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ISSN: |
0101-5516 |
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Idioma: |
Português |
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Notas: |
Selo ODS 15. |
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Conteúdo: |
O objetivo deste trabalho foi avaliar a determinação da densidade do solo por meio dos métodos do anel volumétrico e do torrão parafinado em solos com argila de atividade alta e argila de atividade baixa. Esta publicação está de acordo com o Objetivo de Desenvolvimento Sustentável 15 (Vida Terrestre). Os Objetivos de Desenvolvimento Sustentável (ODS) são uma coleção de 17 metas globais estabelecidas pela Assembleia Geral das Nações Unidas e que tem o apoio da Embrapa para que sejam atingidas. |
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Palavras-Chave: |
Acre; Amazonia Occidental; Amazônia Ocidental; Análisis del suelo; Densidad del suelo; Densidade do solo; Fìsica del suelo; Método do anel volumétrico; Método do torrão parafinado; Minerales arcillosos; Paraffin clod method; Selo ODS 15; Volumetric ring method; Western Amazon. |
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Thesagro: |
Análise do Solo; Argila; Física do Solo. |
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Thesaurus NAL: |
Clay minerals; Soil analysis; Soil density; Soil physics. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/217628/1/27065.pdf
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Marc: |
LEADER 02047nam a2200493 a 4500
001 2126449
005 2023-04-04
008 2020 bl uuuu u0uu1 u #d
022 $a0101-5516
100 1 $aSILVA, L. M. da
245 $aAvaliação de métodos de determinação da densidade de solo em amostras com diferentes atividades da fração argila coletadas no município de Rio Branco, estado do Acre.$h[electronic resource]
260 $aRio Branco, AC: Embrapa Acre$c2020
300 $a21 p.
490 $a(Embrapa Acre. Boletim de pesquisa e desenvolvimento, 62).
500 $aSelo ODS 15.
520 $aO objetivo deste trabalho foi avaliar a determinação da densidade do solo por meio dos métodos do anel volumétrico e do torrão parafinado em solos com argila de atividade alta e argila de atividade baixa. Esta publicação está de acordo com o Objetivo de Desenvolvimento Sustentável 15 (Vida Terrestre). Os Objetivos de Desenvolvimento Sustentável (ODS) são uma coleção de 17 metas globais estabelecidas pela Assembleia Geral das Nações Unidas e que tem o apoio da Embrapa para que sejam atingidas.
650 $aClay minerals
650 $aSoil analysis
650 $aSoil density
650 $aSoil physics
650 $aAnálise do Solo
650 $aArgila
650 $aFísica do Solo
653 $aAcre
653 $aAmazonia Occidental
653 $aAmazônia Ocidental
653 $aAnálisis del suelo
653 $aDensidad del suelo
653 $aDensidade do solo
653 $aFìsica del suelo
653 $aMétodo do anel volumétrico
653 $aMétodo do torrão parafinado
653 $aMinerales arcillosos
653 $aParaffin clod method
653 $aSelo ODS 15
653 $aVolumetric ring method
653 $aWestern Amazon
700 1 $aCOSTA, R. de K. do N.
700 1 $aALMEIDA, A. S. de
700 1 $aOLIVEIRA, C. H. A. de
700 1 $aARAÚJO, E. A. de
700 1 $aTAVARES, O. C. H.
700 1 $aPEREIRA, M. G.
700 1 $aSANTOS, O. A. Q. dos
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Embrapa Acre (CPAF-AC) |
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