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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão; Embrapa Solos. |
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Data corrente: |
04/04/2022 |
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Data da última atualização: |
05/04/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
KUCHLER, P. C.; SIMÕES, M.; FERRAZ, R. P. D.; ARVOR, D.; MACHADO, P. L. O. de A.; ROSA, M.; GAETANO, R.; BÉGUÉ, A. |
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Afiliação: |
PATRICK CALVANO KUCHLER, UERJ; MARGARETH GONCALVES SIMOES, CNPS; RODRIGO PECANHA DEMONTE FERRAZ, CNPS; DAMIEN ARVOR, UNIVERSITÉ RENNES; PEDRO LUIZ OLIVEIRA DE A MACHADO, CNPAF; MARCOS ROSA, UNIVERSIDADE ESTADUAL DE FEIRA DE SANTANA; RAFFAELE GAETANO, CIRAD; AGNÈS BÉGUÉ, CIRAD. |
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Título: |
Monitoring complex integrated crop-livestock systems at regional scale in Brazil: a big earth observation data approach. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Remote Sensing, v. 14, n. 7, 1648, 2022. |
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DOI: |
https://doi.org/10.3390/rs14071648 |
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Idioma: |
Inglês |
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Conteúdo: |
Due to different combinations of agriculture, livestock and forestry managed by rotation, succession and intercropping practices, integrated agriculture production systems such as integrated crop-livestock systems (iCL) constitute a very complex target and a challenge for automatic mapping of cropping practices based on remote sensing data. The overall objective of this study was to develop a classification strategy for the annual mapping of integrated Crop-Livestock systems (iCL) at a regional scale. This strategy was designed and tested in the six agro-climatic regions of Mato Grosso, the largest Brazilian soybean producer state, using MODIS satellite time-series images acquired between 2012 and 2019, ground data with heterogeneous distribution in space and time and a Random Forest classifier. The results showed that: 1. the use of unbalanced training samples with a class composition close to the real one was the right classifier training strategy; 2. the use of a single training database (pooling samples from different years and regions) to classify each region and year individually proved to be robust enough to provide similar classification accuracies in comparison to those based on the use of a database acquired for each region and for each year. The final hierarchical classification overall accuracy was 0.89 for Level 1, the cropping pattern level (single and double crops DC); 0.84 for Level 2, the DC category level (integrated system iCL soy-pasture/brachiaria, soy-cotton and soy-cereal); 0.77 for Level 3, the iCL level (iCL1 soy-pasture and iCL2 soy-pasture mixed with corn). The F-scores for DC, iCL and iCL1 cropping systems presented high accuracy (0.89, 0.85 and 0.84), while iCL2 was more difficult to classify (0.63). This approach will next be applied across the entire Brazilian soybean corridor, leading to an operational tool for monitoring the adoption of sustainable intensification practices recognized by Brazil's Agriculture Low Carbon Plan (ABC PLAN). MenosDue to different combinations of agriculture, livestock and forestry managed by rotation, succession and intercropping practices, integrated agriculture production systems such as integrated crop-livestock systems (iCL) constitute a very complex target and a challenge for automatic mapping of cropping practices based on remote sensing data. The overall objective of this study was to develop a classification strategy for the annual mapping of integrated Crop-Livestock systems (iCL) at a regional scale. This strategy was designed and tested in the six agro-climatic regions of Mato Grosso, the largest Brazilian soybean producer state, using MODIS satellite time-series images acquired between 2012 and 2019, ground data with heterogeneous distribution in space and time and a Random Forest classifier. The results showed that: 1. the use of unbalanced training samples with a class composition close to the real one was the right classifier training strategy; 2. the use of a single training database (pooling samples from different years and regions) to classify each region and year individually proved to be robust enough to provide similar classification accuracies in comparison to those based on the use of a database acquired for each region and for each year. The final hierarchical classification overall accuracy was 0.89 for Level 1, the cropping pattern level (single and double crops DC); 0.84 for Level 2, the DC category level (integrated system iCL soy-pasture/brachiaria, soy-c... Mostrar Tudo |
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Palavras-Chave: |
Big data; Hierarchical classification; Machine learning; MODIS; Samples balancing; Satellite image time series; Training sample designs. |
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Thesagro: |
Agricultura Sustentável. |
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Thesaurus Nal: |
Cropping systems; Double cropping; Sustainable agriculture. |
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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/1141783/1/Monitoring-complex-integrated-crop-livestock-systems-at-regional-scale-in-Brazil-2022.pdf
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Marc: |
LEADER 03062naa a2200349 a 4500
001 2141783
005 2022-04-05
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/rs14071648$2DOI
100 1 $aKUCHLER, P. C.
245 $aMonitoring complex integrated crop-livestock systems at regional scale in Brazil$ba big earth observation data approach.$h[electronic resource]
260 $c2022
520 $aDue to different combinations of agriculture, livestock and forestry managed by rotation, succession and intercropping practices, integrated agriculture production systems such as integrated crop-livestock systems (iCL) constitute a very complex target and a challenge for automatic mapping of cropping practices based on remote sensing data. The overall objective of this study was to develop a classification strategy for the annual mapping of integrated Crop-Livestock systems (iCL) at a regional scale. This strategy was designed and tested in the six agro-climatic regions of Mato Grosso, the largest Brazilian soybean producer state, using MODIS satellite time-series images acquired between 2012 and 2019, ground data with heterogeneous distribution in space and time and a Random Forest classifier. The results showed that: 1. the use of unbalanced training samples with a class composition close to the real one was the right classifier training strategy; 2. the use of a single training database (pooling samples from different years and regions) to classify each region and year individually proved to be robust enough to provide similar classification accuracies in comparison to those based on the use of a database acquired for each region and for each year. The final hierarchical classification overall accuracy was 0.89 for Level 1, the cropping pattern level (single and double crops DC); 0.84 for Level 2, the DC category level (integrated system iCL soy-pasture/brachiaria, soy-cotton and soy-cereal); 0.77 for Level 3, the iCL level (iCL1 soy-pasture and iCL2 soy-pasture mixed with corn). The F-scores for DC, iCL and iCL1 cropping systems presented high accuracy (0.89, 0.85 and 0.84), while iCL2 was more difficult to classify (0.63). This approach will next be applied across the entire Brazilian soybean corridor, leading to an operational tool for monitoring the adoption of sustainable intensification practices recognized by Brazil's Agriculture Low Carbon Plan (ABC PLAN).
650 $aCropping systems
650 $aDouble cropping
650 $aSustainable agriculture
650 $aAgricultura Sustentável
653 $aBig data
653 $aHierarchical classification
653 $aMachine learning
653 $aMODIS
653 $aSamples balancing
653 $aSatellite image time series
653 $aTraining sample designs
700 1 $aSIMÕES, M.
700 1 $aFERRAZ, R. P. D.
700 1 $aARVOR, D.
700 1 $aMACHADO, P. L. O. de A.
700 1 $aROSA, M.
700 1 $aGAETANO, R.
700 1 $aBÉGUÉ, A.
773 $tRemote Sensing$gv. 14, n. 7, 1648, 2022.
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Embrapa Solos (CNPS) |
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