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Registro Completo |
Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
20/06/2017 |
Data da última atualização: |
10/11/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
BELLÓN, B.; BEGUÉ, A.; LO SEEN, D.; ALMEIDA, C. A. de; SIMÕES, M. |
Afiliação: |
BEATRIZ BELLÓN, Cirad, UMR TETIS; AGNÈS BEGUÉ, Cirad, UMR TETIS; DANNY LO SEEN, Cirad, UMR TETIS; CLAUDIO APARECIDO DE ALMEIDA, INPE; MARGARETH GONCALVES SIMOES, CNPS. |
Título: |
A remote sensing approach for regional-scale mapping of agricultural land-use systems based on NDVI time series. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
Remote Sensing, v. 9, n. 6, 600, Jun. 2017. |
DOI: |
https://doi.org/10.3390/rs9060600 |
Idioma: |
Inglês |
Conteúdo: |
In response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of two main steps. First, to obtain relatively homogeneous land units in terms of phenological patterns, a principal component analysis (PCA) is applied to an annual MODIS NDVI time series, and an automatic segmentation is performed on the resulting high-order principal component images. Second, the resulting land units are classified into the crop agriculture domain or the livestock domain based on their land-cover characteristics. The crop agriculture domain land units are further classified into different cropping systems based on the correspondence of their NDVI temporal profiles with the phenological patterns associated with the cropping systems of the study area. A map of the main ALUS of the Brazilian state of Tocantins was produced for the 2013-2014 growing season with the new approach, and a significant coherence was observed between the spatial distribution of the cropping systems in the final ALUS map and in a reference map extracted from the official agricultural statistics of the Brazilian Institute of Geography and Statistics (IBGE). This study shows the potential of remote sensing techniques to provide valuable baseline spatial information for supporting agricultural monitoring and for large-scale land-use systems analysis. MenosIn response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of two main steps. First, to obtain relatively homogeneous land units in terms of phenological patterns, a principal component analysis (PCA) is applied to an annual MODIS NDVI time series, and an automatic segmentation is performed on the resulting high-order principal component images. Second, the resulting land units are classified into the crop agriculture domain or the livestock domain based on their land-cover characteristics. The crop agriculture domain land units are further classified into different cropping systems based on the correspondence of their NDVI temporal profiles with the phenological patterns associated with the cropping systems of the study area. A map of the main ALUS of the Brazilian state of Tocantins was produced for the 2013-2014 growing season with the new approach, and a significant coherence was observed between the spatial distribution of the cropping systems in the final ALUS map and in a reference map extracted from the official agricultural statistics of the Brazilian Institute of Geography and Statistics (IBGE). This study shows the potential of remote sensing techniques to provide valuable baseline spatial information for supporting a... Mostrar Tudo |
Palavras-Chave: |
Estratificação; GEOBIA; MODIS; PCA. |
Thesagro: |
Sistema de Cultivo. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/160871/1/2017-011.pdf
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Marc: |
LEADER 02273naa a2200241 a 4500 001 2071127 005 2021-11-10 008 2017 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.3390/rs9060600$2DOI 100 1 $aBELLÓN, B. 245 $aA remote sensing approach for regional-scale mapping of agricultural land-use systems based on NDVI time series.$h[electronic resource] 260 $c2017 520 $aIn response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of two main steps. First, to obtain relatively homogeneous land units in terms of phenological patterns, a principal component analysis (PCA) is applied to an annual MODIS NDVI time series, and an automatic segmentation is performed on the resulting high-order principal component images. Second, the resulting land units are classified into the crop agriculture domain or the livestock domain based on their land-cover characteristics. The crop agriculture domain land units are further classified into different cropping systems based on the correspondence of their NDVI temporal profiles with the phenological patterns associated with the cropping systems of the study area. A map of the main ALUS of the Brazilian state of Tocantins was produced for the 2013-2014 growing season with the new approach, and a significant coherence was observed between the spatial distribution of the cropping systems in the final ALUS map and in a reference map extracted from the official agricultural statistics of the Brazilian Institute of Geography and Statistics (IBGE). This study shows the potential of remote sensing techniques to provide valuable baseline spatial information for supporting agricultural monitoring and for large-scale land-use systems analysis. 650 $aSistema de Cultivo 653 $aEstratificação 653 $aGEOBIA 653 $aMODIS 653 $aPCA 700 1 $aBEGUÉ, A. 700 1 $aLO SEEN, D. 700 1 $aALMEIDA, C. A. de 700 1 $aSIMÕES, M. 773 $tRemote Sensing$gv. 9, n. 6, 600, Jun. 2017.
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Registro original: |
Embrapa Solos (CNPS) |
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Registros recuperados : 8 | |
2. | | BELLÓN, B.; BÉGUÉ, A.; LO SEEN, D.; LEBOURGEOIS, V.; EVANGELISTA, B. A.; SIMÕES, M.; FERRAZ, R. P. D. Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach. International Journal of Applied Earth Observation and Geoinformation, V. 68, p. 127-138, Jun. 2018.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Pesca e Aquicultura; Embrapa Solos. |
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3. | | BÉGUÉ, A.; ARVOR, D.; BELLON, B.; BETBEDER, J.; ABELLEYRA, D. de; FERRAZ, R. P. D.; LEBOURGEOIS, V.; LELONG, C.; SIMÕES, M.; VERÓN, S. R. Remote sensing and cropping practices: a review. Remote Sensing, v. 10, n. 1, Jan. 2018.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Solos. |
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4. | | SIMÕES, M. G.; FERRAZ, R. P. D.; BÉGUÉ, A.; BELLÓN, B.; FREITAS, P. L.; MACHADO, P. L. O. A.; NEVES, M. L.; SKORUPA, L. Satellite based multi-scale methods to support governance of Brazil's low-carbon agriculture (ABC Plan). In: GEOBIA, 6., 2016, Enschede. Solutions & synergies: conference proceedings. Enschede: University of Twente, 2016.Tipo: Artigo em Anais de Congresso |
Biblioteca(s): Embrapa Meio Ambiente. |
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5. | | SIMÕES, M. G.; FERRAZ, R. P. D.; BÉGUÉ, A.; BELLÓN, B.; FREITAS, P. L.; MACHADO, P. L. O. A.; NEVES, M. L.; SKORUPA, L. Satellite based multi-scale methods to support governance of Brazil's low-carbon agriculture (ABC Plan). In: GEOBIA, 6., 2016, Enschede. Solutions & synergies: conference proceedings. Enschede: University of Twente, 2016.Tipo: Artigo em Anais de Congresso |
Biblioteca(s): Embrapa Solos. |
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6. | | SIMÕES, M.; FERRAZ, R. P. D.; FREITAS, P. L.; SKORUPAE, L.; MANZATTO, C.; PEREIRA, S.; EVANGELISTA, B.; XAUD, H.; XAUD, M.; MACHADO, P. L. O. A.; BÉGUÉ, A.; BELLÓN, B.; BARON, C.; LO SEEN, D.; COSTA, G. Methodologies and technological innovation for satellite monitoring of low carbon agriculture in support to Brazil's ABC Plan - GeoABC Project. Rio de Janeiro: Embrapa Solos, 2016. 1 folder.Tipo: Folder/Folheto/Cartilha |
Biblioteca(s): Embrapa Pesca e Aquicultura; Embrapa Solos. |
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7. | | WALDNER, F.; SCHUCKNECHT, A.; LESIV, M.; GALLEGO, J.; SEE, L.; PÉREZ-HOYOS, A.; D'ANDRIMONT, R.; DE MAET, T.; LASO BAYAS, J. C.; FRITZ, S.; LEO, O.; KERDILES, H.; DÍEZ, M.; VAN TRICHT, K.; GILLIAMS, S.; SHELESTOV, A.; LAVRENIUK, M.; SIMÕES, M.; FERRAZ, R. P. D.; BELLÓN, B.; BÉGUÉ, A.; HAZEU, G.; STONACEK, V.; KOLOMAZNIK, J.; MISUREC, J.; VERÓN, S. R.; ABELLEYRA, D. de; PLOTNIKOV, D.; MINGYONG, L.; SINGHA, M.; PATIL, P.; ZHANG, M.; DEFOURNY, P. Conflation of expert and crowd reference data to validate global binary thematic maps. Remote Sensing of Environment, v. 221, p. 235-246, Feb. 2019.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Solos. |
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8. | | JOLIVOT, A.; LEBOURGEOIS, V.; LEROUX, L.; AMELINE, M.; ANDRIAMANGA, V.; BELLÓN, B.; CASTETS, M.; CRESPIN-BOUCAUD, A.; DEFOURNY, P.; DIAZ, S.; DIEYE, M.; DUPUY, S.; FERRAZ, R. P. D.; GAETANO, R.; GELY, M.; JAHEL, C.; KABORE, B.; LELONG, C.; LE MAIRE, G.; LO SEEN, D.; MUTHONI, M.; NDAO, B.; NEWBY, T.; SANTOS, C. L. M. de O.; RASOAMALALA, E.; SIMÕES, M.; THIAW, I.; TIMMERMANS, A.; TRAN, A.; BÉGUÉ, A. Harmonized in situ datasets for agricultural land use mapping and monitoring in tropical countries. Earth System Science Data, v. 13, n. 2, p. 5951-5967, 2021.Biblioteca(s): Embrapa Solos. |
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Registros recuperados : 8 | |
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Nenhum registro encontrado para a expressão de busca informada. |
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