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68. | | MERCANTE, E.; ANTUNES, J. F. G.; ROCHA, J. V. da; LAMPARELLI, R. A. C. Simulação do traçado de um gasoduto utilizando análise de múltiplos critérios. In: CONGRESSO BRASILEIRO DE ENGENHARIA AGRÍCOLA, 33., 2004, São Pedro, SP. A inserção da engenharia agrícola em projetos nacionais: caderno de resumos. Campinas: Unicamp, Feagri: Embrapa Informática Agropecuária, 2004. p. 145. CONBEA 2004. Biblioteca(s): Embrapa Agricultura Digital. |
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Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
21/09/2020 |
Data da última atualização: |
14/12/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
B - 2 |
Autoria: |
REIS, A. A. dos; SILVA, B. C.; WERNER, J. P. S.; SILVA, Y. F.; ROCHA, J. V.; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C; MAGALHÃES, P. S. G. |
Afiliação: |
Feagri, Nipe/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; Nipe/Unicamp; Nipe/Unicamp. |
Título: |
Exploring the potential of high-resolution PlanetScope imagery for pasture biomass estimation in an integrated crop-livestock system. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42-3, W12, p. 419-424, 2020. |
DOI: |
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-419-2020 |
Idioma: |
Inglês |
Notas: |
Publicado também em: IEEE LATIN AMERICAN GRSS; ISPRS REMOTE SENSING CONFERENCE, Santiago, 2020. Proceedings... [Piscataway]: IEEE, 2020. p. 675-680. LAGIRS 2020. |
Conteúdo: |
ABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring. MenosABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promisi... Mostrar Tudo |
Palavras-Chave: |
Aprendizado de máquina; Dove satellites; Floresta aleatória; Índice de vegetação; Integração lavoura-pecuária; Integrated crop-livestock system; Machine Learning; Nano-Satellites; Pastureland; Random Forest; Vegetation Indices. |
Thesagro: |
Biomassa; Pastagem. |
Thesaurus NAL: |
Biomass; Pasture management; Vegetation index. |
Categoria do assunto: |
-- |
Marc: |
LEADER 03286naa a2200457 a 4500 001 2125045 005 2021-12-14 008 2020 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-419-2020$2DOI 100 1 $aREIS, A. A. dos 245 $aExploring the potential of high-resolution PlanetScope imagery for pasture biomass estimation in an integrated crop-livestock system.$h[electronic resource] 260 $c2020 500 $aPublicado também em: IEEE LATIN AMERICAN GRSS; ISPRS REMOTE SENSING CONFERENCE, Santiago, 2020. Proceedings... [Piscataway]: IEEE, 2020. p. 675-680. LAGIRS 2020. 520 $aABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring. 650 $aBiomass 650 $aPasture management 650 $aVegetation index 650 $aBiomassa 650 $aPastagem 653 $aAprendizado de máquina 653 $aDove satellites 653 $aFloresta aleatória 653 $aÍndice de vegetação 653 $aIntegração lavoura-pecuária 653 $aIntegrated crop-livestock system 653 $aMachine Learning 653 $aNano-Satellites 653 $aPastureland 653 $aRandom Forest 653 $aVegetation Indices 700 1 $aSILVA, B. C. 700 1 $aWERNER, J. P. S. 700 1 $aSILVA, Y. F. 700 1 $aROCHA, J. V. 700 1 $aFIGUEIREDO, G. K. D. A. 700 1 $aANTUNES, J. F. G. 700 1 $aESQUERDO, J. C. D. M. 700 1 $aCOUTINHO, A. C. 700 1 $aLAMPARELLI, R. A. C 700 1 $aMAGALHÃES, P. S. G. 773 $tThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences$gv. 42-3, W12, p. 419-424, 2020.
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