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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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Circulação/Nível: |
B - 2 |
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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 |
|
Notas: |
Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France. Na publicação: E. S. Sano. |
|
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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Embrapa Agricultura Digital (CNPTIA) |
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