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Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
18/09/2020 |
Data da última atualização: |
14/12/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
REIS, A. A. dos; WERNER, J. P. S.; SILVA, B. C.; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; ROCHA, J. V.; MAGALHÃES, P. S. G. |
Afiliação: |
ALINY A. DOS REIS, Nipe, Feagri/Unicamp; JOÃO P. S. WERNER, Feagri/Unicamp; BRUNA C. SILVA, Feagri/Unicamp; GLEYCE K. D. A. FIGUEIREDO, Feagri/Unicamp; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; RUBENS A. C. LAMPARELLI, Nipe/Unicamp; JANSLE V. ROCHA, Feagri/Unicamp; PAULO S. G. MAGALHÃES, Nipe/Unicamp. |
Título: |
Monitoring pasture aboveground biomass and canopy height in an integrated crop-livestock system using textural information from PlanetScope imagery. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Remote Sensing, v. 12, n. 16, p. 1-21, Aug. 2020. |
DOI: |
10.3390/rs12162534 |
Idioma: |
Inglês |
Notas: |
Article number: 2534. |
Conteúdo: |
Abstract: Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions oered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures. |
Palavras-Chave: |
Extreme gradient boosting; Floresta aleatória; Integrated systems; Mixed pastures; Pastagem tropical; Pasto; Random forest; Texture measures. |
Thesagro: |
Biomassa; Pastagem Mista; Sensoriamento Remoto. |
Thesaurus Nal: |
Aboveground biomass; Biomass; Pastures; Remote sensing; Tropical pastures. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/216114/1/AP-Monitoring-pasture-aboveground-2020.pdf
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Marc: |
LEADER 02709naa a2200445 a 4500 001 2125026 005 2021-12-14 008 2020 bl uuuu u00u1 u #d 024 7 $a10.3390/rs12162534$2DOI 100 1 $aREIS, A. A. dos 245 $aMonitoring pasture aboveground biomass and canopy height in an integrated crop-livestock system using textural information from PlanetScope imagery.$h[electronic resource] 260 $c2020 500 $aArticle number: 2534. 520 $aAbstract: Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions oered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures. 650 $aAboveground biomass 650 $aBiomass 650 $aPastures 650 $aRemote sensing 650 $aTropical pastures 650 $aBiomassa 650 $aPastagem Mista 650 $aSensoriamento Remoto 653 $aExtreme gradient boosting 653 $aFloresta aleatória 653 $aIntegrated systems 653 $aMixed pastures 653 $aPastagem tropical 653 $aPasto 653 $aRandom forest 653 $aTexture measures 700 1 $aWERNER, J. P. S. 700 1 $aSILVA, B. C. 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 $aROCHA, J. V. 700 1 $aMAGALHÃES, P. S. G. 773 $tRemote Sensing$gv. 12, n. 16, p. 1-21, Aug. 2020.
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Embrapa Agricultura Digital (CNPTIA) |
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1. | | BRUNETTO, G.; CIOTTA, M. N.; STE, P. B.; COMIN, J. J.; NAVA, G.; SALUME, J. A.; LOURENZI, C. R.; LOOS, A.; ROZANE, D. E.; TIECHER, T.; GATIBONI, L. C.; PASA, M. da S.; STEFANELLO, L.; PAULA, B. V. de. Respostas de pereiras à adubação nitrogenada, fosfatada e potássica In: CIOTTA, M.N.; BRUNETTO, G.; COMIN, J.; PASA, M.S.; SETE, P.B (orgs.). Workshop sobre frutificação e adubação de pereiras, 1.; Florianópolis, SC: Epagri, 2017. 120p. (Documentos 276) p. 69-86Tipo: Capítulo em Livro Técnico-Científico |
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