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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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Esconder MarcMostrar Marc Completo |
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca |
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Tipo/Formato |
Classificação |
Cutter |
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URL |
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| Registros recuperados : 284 | |
| 162. |  | GOMES, P. B.; BOLFE, E. L.; ARAUJO, L. S. de; VICTORIA, D. de C.; GARRASTAZU, M. C. Classificação de formações vegetais do Pantanal por meio da análise orientada a objeto em imagens de satélite de alta resolução espacial. In: SIMPÓSIO DE GEOTECNOLOGIAS NO PANTANAL, 5., 2014, Campo Grande, MS. Anais... São José dos Campos: INPE, 2014. p. 314-324. 1 CD-ROM.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Florestas. |
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| 163. | | GOMES, P. B.; BOLFE, E. L.; ARAUJO, L. S. de; VICTORIA, D. de C.; GARRASTAZU, M. C. Classificação de formações vegetais do Pantanal por meio da análise orientada a objeto em imagens de satélite de alta resolução espacial. In: SIMPÓSIO DE GEOTECNOLOGIAS NO PANTANAL, 5., 2014, Campo Grande, MS. Anais... São José dos Campos: INPE, 2014. p. 314-324. 1 CD-ROM.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Territorial. |
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| 164. | | BOLFE, E. L.; BARBEDO, J. G. A.; MASSRUHÁ, S. M. F. S.; SOUZA, K. X. S. de; ASSAD, E. D. Challenges, trends and opportunities in digital agriculture in Brazil. In: MASSRUHÁ, S. M. F. S.; LEITE, M. A. de A.; OLIVEIRA, S. R. de M.; MEIRA, C. A. A.; LUCHIARI JUNIOR, A.; BOLFE, E. L. (ed.). Digital agriculture: research, development and innovation in production chains. Brasília, DF: Embrapa, 2023. cap. 16, p. 281-299.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 165. | | SANO, E. E.; BETTIOL, G. M.; MARTINS, E. de S.; VASCONCELOS, V.; BOLFE, E. L.; VICTORIA, D. de C. Características gerais da paisagem do Cerrado. In: BOLFE, E. L.; SANO, E. E.; CAMPOS, S. K. (Ed.). Dinâmica agrícola no cerrado: análises e projeções. Brasília, DF: Embrapa, 2020. v. 1, cap. 1, p. 21-37. p. 21-37| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Cerrados. |
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| 166. | | GOMES, J. B. V.; BOLFE, E. L.; CURI, N.; FONTES, H. R.; BARRETO, A. C.; VIANA, R. D. Variabilidade espacial de atributos de solos em unidades de manejo em área piloto de produção integrada de coco. Revista Brasileira de Ciência do Solo, Campinas, v. 32, n. 6, 2008.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: Internacional - A |
| Biblioteca(s): Embrapa Tabuleiros Costeiros. |
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| 167. | | FENG, Y.; LU, D.; CHEN, Q.; KELLER, M.; MORAN, E.; SANTOS, M. N. dos S.; BOLFE, E. L.; BATISTELLA, M. Examining effective use of data source and modeling algorithms for improving biomass estimation in a moist tropical forest of the brazilian Amazon. International Journal of Digital Earth, London, 2017.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Unidades Centrais. |
|  |
| 168. | | PARREIRAS, T. C.; BOLFE, E. L.; SANO, E. E.; VICTORIA, D. de C.; SANCHES, I. D.; VICENTE, L. E. Exploring the Harmonized Landsat Sentinel (HLS) datacube to map an agricultural landscape in the Brazilian savanna. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 43, B3, p. 967-973, 2022. Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France. Na publicação: E. S. Sano.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 2 |
| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Cerrados; Embrapa Meio Ambiente. |
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| 169. | | ALCÂNTARA, G. C. DE; SILVA, G. B. S. da; DRUCKER, D. P.; VICTORIA, D. de C.; BOLFE, E. L. Geoinfo e MaToPiBa: um Webgis para a disseminação de informações Geoespaciais. In: SIMPÓSIO INTERNACIONAL DE ÁGUAS, SOLOS E GEOTECNOLOGIAS, 1., 2015, Uberaba. Anais... Uberaba: UFMT, 2015. 9 p.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Territorial. |
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| 170. | | BOLFE, E. L.; ANDRADE, R. G.; VICENTE, L. E.; BATISTELLA, M.; GREGO, C. R.; VICTORIA, D. de C. Geospatial monitoring for integrated crop-livestock-forestry systems. In: BUNGENSTAB, D. J.; ALMEIDA, R. G. de (Ed.). Integrated crop-livestock-forestry systems: a brazilian experience for sustainable farming. Brasília, DF: Embrapa, 2014. p. 205-212.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Territorial. |
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| 173. | | NOOJIPADY, P.; MORTON, D. C.; MACEDO, M. N.; VICTORIA, D. de C.; HUANG, C.; GIBBS, H. K.; BOLFE, E. L. Forest carbon emissions from cropland expansion in the Brazilian Cerrado biome. Environmental Research Letters, v. 12, n. 2, p. 1-11, 2017.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 174. | | PENHA, A. R.; BOLFE, E. L.; PEREIRA, P. R. M.; PARREIRAS, T. C.; VICTORIA, D. de C. Imagens Sentinel - 2A para mapeamento de uso e cobertura da terra em áreas de expansão agrícola no Cerrado. In: CONGRESSO INTERINSTITUCIONAL DE INICIAÇÃO CIENTÍFICA, 16., 2022, Campinas. Anais... Campinas: Instituto Agronômico, 2022. p. 1-2. Evento online. CIIC 2022. Nº 22601. Esta pesquisa é financiada pela Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), processo n. 2019/26222-6.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 175. | | BOLFE, E. L.; BARBEDO, J. G. A.; MASSRUHÁ, S. M. F. S.; SOUZA, K. X. S. de; ASSAD, E. D. Desafios, tendências e oportunidades em agricultura digital no Brasil. In: MASSRUHÁ, S. M. F. S.; LEITE, M. A. de A.; OLIVEIRA, S. R. de M.; MEIRA, C. A. A.; LUCHIARI JUNIOR, A.; BOLFE, E. L. (Ed.). Agricultura digital: pesquisa, desenvolvimento e inovação nas cadeias produtivas. Brasília, DF: Embrapa, 2020. cap. 16, p. 380-406.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 284 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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