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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
24/08/2022 |
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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: |
TORO, A. P. S. G. D.; WERNER, J. P. S.; REIS, A. A. dos; ESQUERDO, J. C. D. M.; ANTUNES, J. F. G.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; FIGUEIREDO, G. K. D. A. |
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Afiliação: |
FEAGRI/UNICAMP; FEAGRI/UNICAMP; UNICAMP; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA, FEAGRI/UNICAMP; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; UNICAMP; UNICAMP; FEAGRI/UNICAMP. |
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Título: |
Evaluation of early season mapping of integrated crop livestock systems using Sentinel-2 data. |
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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. 1335-1340, 2022. |
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DOI: |
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1335-2022 |
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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. |
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Conteúdo: |
ABSTRACT. Various approaches were developed considering the need to increase agricultural productivity in cultivated areas without more deforestation, such as the Integrated Crop livestock systems (ICLS). The ICLS could be composed of annual crops followed by pastureland with the presence of cattle. Due to the high temporal dynamic of rotation between crops over the season, monitoring these areas is a big challenge. Also, agricultural organizations worldwide highlight the need for early-season maps for this kind of work. In this context, this study evaluated the potential of open data (Sentinel-2) data to map ICLS areas. The performance of two classifiers was evaluated: one of Machine Learning (random forest) and the other of Deep Learning (LSTM). Three different time windows of data were tested (Entire season, 180 days, and 120 days). Using the RF classifier, it was possible to achieve satisfactory results (Overall accuracy higher than 80%) for the early season (180 days). However, further studies are needed to explain better the lower(when compared to Random Forest) accuracy achieved by LSTM net (0.79 % for 180 days) and compare the results achieved here with results for a study area with different rates of cloud cover. |
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Palavras-Chave: |
Agricultura regenerativa; Aprendizado profundo; Crop identification; Floresta aleatória; Identificação de culturas; LSTM; Random forest; Regenerative agriculture. |
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Thesagro: |
Sensoriamento Remoto. |
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Thesaurus Nal: |
Remote sensing. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1145714/1/AP-Evalution-early-season-2022.pdf
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Marc: |
LEADER 02542naa a2200361 a 4500
001 2145714
005 2022-08-25
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1335-2022$2DOI
100 1 $aTORO, A. P. S. G. D.
245 $aEvaluation of early season mapping of integrated crop livestock systems using Sentinel-2 data.$h[electronic resource]
260 $c2022
500 $aEdition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France.
520 $aABSTRACT. Various approaches were developed considering the need to increase agricultural productivity in cultivated areas without more deforestation, such as the Integrated Crop livestock systems (ICLS). The ICLS could be composed of annual crops followed by pastureland with the presence of cattle. Due to the high temporal dynamic of rotation between crops over the season, monitoring these areas is a big challenge. Also, agricultural organizations worldwide highlight the need for early-season maps for this kind of work. In this context, this study evaluated the potential of open data (Sentinel-2) data to map ICLS areas. The performance of two classifiers was evaluated: one of Machine Learning (random forest) and the other of Deep Learning (LSTM). Three different time windows of data were tested (Entire season, 180 days, and 120 days). Using the RF classifier, it was possible to achieve satisfactory results (Overall accuracy higher than 80%) for the early season (180 days). However, further studies are needed to explain better the lower(when compared to Random Forest) accuracy achieved by LSTM net (0.79 % for 180 days) and compare the results achieved here with results for a study area with different rates of cloud cover.
650 $aRemote sensing
650 $aSensoriamento Remoto
653 $aAgricultura regenerativa
653 $aAprendizado profundo
653 $aCrop identification
653 $aFloresta aleatória
653 $aIdentificação de culturas
653 $aLSTM
653 $aRandom forest
653 $aRegenerative agriculture
700 1 $aWERNER, J. P. S.
700 1 $aREIS, A. A. dos
700 1 $aESQUERDO, J. C. D. M.
700 1 $aANTUNES, J. F. G.
700 1 $aCOUTINHO, A. C.
700 1 $aLAMPARELLI, R. A. C.
700 1 $aMAGALHÃES, P. S. G.
700 1 $aFIGUEIREDO, G. K. D. A.
773 $tThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences$gv. 43, B3, p. 1335-1340, 2022.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca |
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Tipo/Formato |
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| Registros recuperados : 31 | |
| 1. |  | FERREIRA, M. D.; MAGALHÃES, P. S. G. Colheita. In: FERREIRA, M. D. (Ed.). Colheita e beneficiamento de frutas e hortaliças. São Carlos, SP: Embrapa Instrumentação Agropecuária, 2008. p. 15-22.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Instrumentação. |
|   |
| 11. | | DIAS, H. B.; CUADRA, S. V.; BOOTE, K. J.; LAMPARELLI, R. A. C.; FIGUEIREDO, G. K. D. A.; SUYKER, A. E.; MAGALHÃES, P. S. G.; HOOGENBOOM, G. Coupling the CSM-CROPGRO-Soybean crop model with the ECOSMOS Ecosystem Model: an evaluation with data from an AmeriFlux site. Agricultural and Forest Meteorology, v. 342, 109697, 2023.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agricultura Digital. |
|  |
| 12. | | GREGO, C. R.; ARAUJO, L. S. de; VICENTE, L. E.; NOGUEIRA, S. F.; MAGALHÃES, P. S. G.; VICENTE, A. K.; BRANCALIÃO, S. R.; VICTORIA, D. de C.; BOLFE, E. L. Agricultura de precisão em cana-de-açúcar. In: BERNARDI, A. C. de C.; NAIME, J. de M.; RESENDE, A. V. de; BASSOI, L. H.; INAMASU, R. Y. (Ed.). Agricultura de precisão: resultados de um novo olhar. Brasília, DF: Embrapa, 2014. p. 442-457.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Territorial. |
|  |
| 13. | | REIS, A. A. dos; WERNER, J. P. S.; SILVA, B. C. da; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; FIGUEIREDO, G. K. D. A.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G. Can canopy height of mixed pastures in integrated crop-livestock systems be estimated using planetscope imagery? In: WORLD CONGRESS ON INTEGRATED CROP-LIVESTOCK-FORESTRY SYSTEMS, 2., 2021. Proceedings reference. Brasília, DF: Embrapa, 2021. p. 658-663. WCCLF 2021. Evento online.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 14. | | ALMEIDA, H. S. L.; REIS, A. A. dos; WERNER, J. P. S.; ANTUNES, J. F. G.; ZHONG, L.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G. Deep neural networks for mapping integrated crop-livestock systems using PlanetScope time series. IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2021, Brussels. Proceedings [...]. [S. l.]: IEEE, 2021. p. 4224-4227. IGARSS 2021. Paper WE2.MM-8.3.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 15. | | CARVALHO, J. R. P. de; VIEIRA, S. R.; DECHEN, S. C. F.; MARIA, I. C. de; MARINHO, P. R. R.; POTT, C. A.; DUFRANC, G.; MAGALHÃES, P. S. G. Spatial variability of soil water movement properties on a medium texture soil in São Paulo - Brazil. In: EUROPEAN CONFERENCE OF THE EUROPEAN FEDERATION FOR INFORMATION TECHNOLOGY IN AGRICULTURE FOOD AND THE ENVIRONMENT, 3., 2001, Montpellier. Book of abstracts. Montpellier: Agro Montpellier: Enita de Bourdeaux, 2001. p. 14. EFITA 2001.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 16. | | PEREIRA, F. R. da S.; REIS, A. A. dos; FREITAS, R. G.; OLIVEIRA, S. R. de M.; AMARAL, L. R. do; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; LAMPARELLI, R. A. C.; MORO, E.; MAGALHÃES, P. S. G. Imputation of missing parts in UAV orthomosaics using PlanetScope and Sentinel-2 data: a case study in a grass-dominated área. International Journal of Geo-Information, v. 12, n. 2, 41, Feb. 2023.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 17. | | TORO, A. P. S. G. D.; WERNER, J. P. S.; REIS, A. A. dos; ESQUERDO, J. C. D. M.; ANTUNES, J. F. G.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; FIGUEIREDO, G. K. D. A. Evaluation of early season mapping of integrated crop livestock systems using Sentinel-2 data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 43, B3, p. 1335-1340, 2022. Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 2 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 18. | | BUENO, I. T.; ANTUNES, J. F. G.; TORO, A. P. S. G. D.; WERNER, J. P. S.; COUTINHO, A. C.; FIGUEIREDO, G. K. D. A.; LAMPARELLI, R. A. C.; ESQUERDO, J. C. D. M.; MAGALHÃES, P. S. G. Land use/land cover classification in a heterogeneous agricultural landscape using PlanetScope data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. XLVIII-M-1-2023, p. 49-55, 2023. Edition of proceedings of the 39th International Symposium on Remote Sensing of Environment (ISRSE-39) "From Human Needs to SDGs", 2023, Antalya, Türkiye.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 4 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 19. | | BUENO, I. T.; ANTUNES, J. F. G.; TORO, A. P. S. G. D.; WERNER, J. P. S.; LAMPARELLI, R. A. C.; COUTINHO, A. C.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; MAGALHÃES, P. S. G. Land use/land cover classification and scale effect analysis for a multi-temporal superpixel-based segmentation using PlanetScope data. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. p. 304-307. Editores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 20. | | BUENO, I. T.; ANTUNES, J. F. G.; REIS, A. A. dos; WERNER, J. P. S.; TORO, A. P.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; LAMPARELLI, R. A. C.; COUTINHO, A. C.; MAGALHÃES, P. S. G. Mapping integrated crop-livestock systems in Brazil with planetscope time series and deep learning. Remote Sensing of Environment, v. 299, 113886, Dec. 2023.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 31 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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