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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Cerrados; Embrapa Meio Ambiente. |
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Data corrente: |
01/06/2022 |
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Data da última atualização: |
25/08/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
PARREIRAS, T. C.; BOLFE, E. L.; SANO, E. E.; VICTORIA, D. de C.; SANCHES, I. D.; VICENTE, L. E. |
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Afiliação: |
T. C. PARREIRAS, IG/UNICAMP; EDSON LUIS BOLFE, CNPTIA, IG/UNICAMP; EDSON EYJI SANO, CPAC; DANIEL DE CASTRO VICTORIA, CNPTIA; I. D. SANCHES, INPE; LUIZ EDUARDO VICENTE, CNPMA. |
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Título: |
Exploring the Harmonized Landsat Sentinel (HLS) datacube to map an agricultural landscape in the Brazilian savanna. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 43, B3, p. 967-973, 2022. |
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DOI: |
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-967-2022 |
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Idioma: |
Inglês |
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Notas: |
Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France. Na publicação: E. S. Sano. |
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Conteúdo: |
ABSTRACT: Brazil has established itself as one of the world leaders in food production. Different types of remote sensing mapping techniques have been undertaken to support rural planning in the country. However, due to the complex dynamics of Brazilian agriculture, especially in the Cerrado biome (tropical savanna), there is a need for more feasible crop discrimination and monitoring initiatives, which require a consistent time series of remote sensing data at medium meter and potentially up to 3 day Landsat 8 and Sentinel-2 satellite time series, minimizing the cloud cover limitations for rainfed agricultural monitoring. This paper aims to explore the potential of the Harmonized Landsat 8 Sentinel-2 (HLS) data cube to map agricultural landscapes in the Brazilian Cerrado. The HLS multispectral bands from 27 scenes with less than 10% cloud cover, from October 2020 to September 2021, encompassing one entire crop growing season, were processed by the Random Forest algorithm to produce a map with four land use/cover classes (annual crops, sugarcane, renovated sugarcane fields, cultivated pastures, and native Cerrado). We performed accuracy assessment through 10-fold cross-validation and confusion matrix analyses. The results showed a high level of overall accuracy and Kappa coefficient, both with 99%, as well as high user's and producer's accuracies of at least 99%. The HLS dataset has been continuously improved, showing very promising results for rainfed agricultural mapping and monitoring. MenosABSTRACT: Brazil has established itself as one of the world leaders in food production. Different types of remote sensing mapping techniques have been undertaken to support rural planning in the country. However, due to the complex dynamics of Brazilian agriculture, especially in the Cerrado biome (tropical savanna), there is a need for more feasible crop discrimination and monitoring initiatives, which require a consistent time series of remote sensing data at medium meter and potentially up to 3 day Landsat 8 and Sentinel-2 satellite time series, minimizing the cloud cover limitations for rainfed agricultural monitoring. This paper aims to explore the potential of the Harmonized Landsat 8 Sentinel-2 (HLS) data cube to map agricultural landscapes in the Brazilian Cerrado. The HLS multispectral bands from 27 scenes with less than 10% cloud cover, from October 2020 to September 2021, encompassing one entire crop growing season, were processed by the Random Forest algorithm to produce a map with four land use/cover classes (annual crops, sugarcane, renovated sugarcane fields, cultivated pastures, and native Cerrado). We performed accuracy assessment through 10-fold cross-validation and confusion matrix analyses. The results showed a high level of overall accuracy and Kappa coefficient, both with 99%, as well as high user's and producer's accuracies of at least 99%. The HLS dataset has been continuously improved, showing very promising results for rainfed agricultural mapping a... Mostrar Tudo |
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Palavras-Chave: |
Agricultura brasileira; Bioma Cerrado; Cerrado Biome; Harmonized Landsat Sentinel; Random Forest. |
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Thesagro: |
Agricultura; Sensoriamento Remoto. |
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Thesaurus Nal: |
Agriculture; Classification; Land classification; Remote sensing. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1143597/1/AP-Exploring-harmonized-Landsat-2022.pdf
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Marc: |
LEADER 02738naa a2200337 a 4500
001 2143597
005 2022-08-25
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.5194/isprs-archives-XLIII-B3-2022-967-2022$2DOI
100 1 $aPARREIRAS, T. C.
245 $aExploring the Harmonized Landsat Sentinel (HLS) datacube to map an agricultural landscape in the Brazilian savanna.$h[electronic resource]
260 $c2022
500 $aEdition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France. Na publicação: E. S. Sano.
520 $aABSTRACT: Brazil has established itself as one of the world leaders in food production. Different types of remote sensing mapping techniques have been undertaken to support rural planning in the country. However, due to the complex dynamics of Brazilian agriculture, especially in the Cerrado biome (tropical savanna), there is a need for more feasible crop discrimination and monitoring initiatives, which require a consistent time series of remote sensing data at medium meter and potentially up to 3 day Landsat 8 and Sentinel-2 satellite time series, minimizing the cloud cover limitations for rainfed agricultural monitoring. This paper aims to explore the potential of the Harmonized Landsat 8 Sentinel-2 (HLS) data cube to map agricultural landscapes in the Brazilian Cerrado. The HLS multispectral bands from 27 scenes with less than 10% cloud cover, from October 2020 to September 2021, encompassing one entire crop growing season, were processed by the Random Forest algorithm to produce a map with four land use/cover classes (annual crops, sugarcane, renovated sugarcane fields, cultivated pastures, and native Cerrado). We performed accuracy assessment through 10-fold cross-validation and confusion matrix analyses. The results showed a high level of overall accuracy and Kappa coefficient, both with 99%, as well as high user's and producer's accuracies of at least 99%. The HLS dataset has been continuously improved, showing very promising results for rainfed agricultural mapping and monitoring.
650 $aAgriculture
650 $aClassification
650 $aLand classification
650 $aRemote sensing
650 $aAgricultura
650 $aSensoriamento Remoto
653 $aAgricultura brasileira
653 $aBioma Cerrado
653 $aCerrado Biome
653 $aHarmonized Landsat Sentinel
653 $aRandom Forest
700 1 $aBOLFE, E. L.
700 1 $aSANO, E. E.
700 1 $aVICTORIA, D. de C.
700 1 $aSANCHES, I. D.
700 1 $aVICENTE, L. E.
773 $tThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences$gv. 43, B3, p. 967-973, 2022.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
18/07/2023 |
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Data da última atualização: |
15/04/2024 |
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Tipo da produção científica: |
Documentos |
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Autoria: |
SANTOS, H. G. dos; JACOMINE, P. K. T.; ANJOS, L. H. C. dos; OLIVEIRA, V. A. de; LUMBRERAS, J. F.; COELHO, M. R.; ALMEIDA, J. A. de; ARAUJO FILHO, J. C. de; LIMA, H. N.; MARQUES, F. A. |
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Afiliação: |
HUMBERTO GONCALVES DOS SANTOS, CNPS; PAULO KLINGER TITO JACOMINE, UNIVERSIDADE FEDERAL RURAL DE PERNAMBUCO; LÚCIA HELENA CUNHA DOS ANJOS, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; VIRLEI ÁLVARO DE OLIVEIRA, IBGE; JOSE FRANCISCO LUMBRERAS, CNPS; MAURICIO RIZZATO COELHO, CNPS; JAIME ANTONIO DE ALMEIDA, UNIVERSIDADE DO ESTADO DE SANTA CATARINA; JOSE COELHO DE ARAUJO FILHO, CNPS; HEDINALDO NARCISO LIMA, UNIVERSIDADE FEDERAL DO AMAZONAS; FLAVIO ADRIANO MARQUES, CNPS. |
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Título: |
Proposta de atualização da 5ª edição do Sistema Brasileiro de Classificação de Solos: ano 2023. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Rio de Janeiro: Embrapa Solos, 2023. |
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Páginas: |
143 p. |
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Série: |
(Embrapa Solos. Documentos, 238). |
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ISSN: |
1517-2627 |
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Idioma: |
Português |
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Conteúdo: |
O objetivo do presente trabalho é divulgar as propostas de mudanças no SiBCS, as quais já foram discutidas no âmbito do CE. A partir desta publicação, tais propostas estão prontamente disponíveis para testes e validação pelos usuários, objetivando sua avaliação crítica, que será considerada na próxima edição do SiBCS, prevista para ser publicada no segundo semestre de 2024. |
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Thesagro: |
Classificação do Solo. |
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Thesaurus NAL: |
Soil classification. |
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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/doc/1154993/1/CNPS-DOC-238-2023.pdf
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Marc: |
LEADER 01237nam a2200277 a 4500
001 2154993
005 2024-04-15
008 2023 bl uuuu 00u1 u #d
022 $a1517-2627
100 1 $aSANTOS, H. G. dos
245 $aProposta de atualização da 5ª edição do Sistema Brasileiro de Classificação de Solos$bano 2023.$h[electronic resource]
260 $aRio de Janeiro: Embrapa Solos$c2023
300 $a143 p.
490 $a(Embrapa Solos. Documentos, 238).
520 $aO objetivo do presente trabalho é divulgar as propostas de mudanças no SiBCS, as quais já foram discutidas no âmbito do CE. A partir desta publicação, tais propostas estão prontamente disponíveis para testes e validação pelos usuários, objetivando sua avaliação crítica, que será considerada na próxima edição do SiBCS, prevista para ser publicada no segundo semestre de 2024.
650 $aSoil classification
650 $aClassificação do Solo
700 1 $aJACOMINE, P. K. T.
700 1 $aANJOS, L. H. C. dos
700 1 $aOLIVEIRA, V. A. de
700 1 $aLUMBRERAS, J. F.
700 1 $aCOELHO, M. R.
700 1 $aALMEIDA, J. A. de
700 1 $aARAUJO FILHO, J. C. de
700 1 $aLIMA, H. N.
700 1 $aMARQUES, F. A.
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Registro original: |
Embrapa Solos (CNPS) |
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