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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
18/09/2020 |
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Data da última atualização: |
14/12/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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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. |
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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. |
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Título: |
Monitoring pasture aboveground biomass and canopy height in an integrated crop-livestock system using textural information from PlanetScope imagery. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing, v. 12, n. 16, p. 1-21, Aug. 2020. |
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DOI: |
10.3390/rs12162534 |
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Idioma: |
Inglês |
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Notas: |
Article number: 2534. |
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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. |
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Palavras-Chave: |
Extreme gradient boosting; Floresta aleatória; Integrated systems; Mixed pastures; Pastagem tropical; Pasto; Random forest; Texture measures. |
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Thesagro: |
Biomassa; Pastagem Mista; Sensoriamento Remoto. |
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Thesaurus Nal: |
Aboveground biomass; Biomass; Pastures; Remote sensing; Tropical pastures. |
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Categoria do assunto: |
-- |
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URL: |
https://www.alice.cnptia.embrapa.br/alice/bitstream/doc/1125026/1/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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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Registros recuperados : 8 | |
| 1. |  | SANTOS, L. M.; FERRAZ, G. A. e S.; CARVALHO, M. A. de F.; CAMPOS, A. A. V.; MENICUCCI NETO, P.; XAVIER, L. A. G.; MATTIA, A.; BECCIOLINI, V.; ROSSI, G. A spatial analysis of coffee plant temperature and its relationship with water potential and stomatal conductance using a thermal camera embedded in a remotely piloted aircraft. Agronomy, v. 14, n. 10, 2024.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Café. |
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| 2. | | SANTOS, L. M. D.; FERRAZ, G. A. E. S.; XAVIER, L. A. G.; MENICUCCI NETTO, P.; VILELA, M. S.; CARVALHO, M. A. de F. Imagem termográfica obtida por aeronave remotamente pilotada para análise de cafeeiros submetidos a diferentes tratamentos. In: CONGRESSO BRASILEIRO DE ENGENHARIA AGRÍCOLA, 50., 2021, On-line. Anais... Campinas: SBEA, 2021. CONBEA 2021. Congresso On-line. 4 p.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Café. |
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| 3. | | SANTOS, L. M. dos; FERRAZ, G. A. E. S.; MARIN, D. B.; CARVALHO, M. A. de F.; GUIMARÃES, R. J.; ALECRIM, A. de O. NDVI aplicado em imagens multiespectrais de cafeeiros sadios e cafeeiros infestados com bicho-mineiro. In: CONGRESSO BRASILEIRO DE ENGENHARIA AGRÍCOLA, 49., 2020, On-line. Anais... Campinas: SBEA, 2020. CONBEA 2020. Congresso On-line. 4 p.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Café. |
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| 4. | | FERRAZ, P. F. P.; YANAGI JUNIOR, T.; LIMA, R. R. de; FERRAZ, G. A. e S.; XIN, H. Performance of chicks subjected to thermal challenge. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 52, n. 2, p. 113-120, fev. 2017. Título em português: Desempenho de pintinhos submetidos a estresse térmico.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 6. | | BENTO, N. L.; FERRAZ, G. A. e S.; OLIVEIRA, M. de L.; CAMPOS, A. A. V.; CARVALHO, M. A. de F.; CASTANHEIRA, D. T.; SOUZA, A. C. de; PIRES, T. de P.; ROSSI, G.; BECCIOLINI, V. Remotely piloted aircraft system and machine learning for detection of coffeeplants subjected to foliar application of chitosan. European Journal of Remote Sensing, v. 58, n. 1, 2025. 11 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Café. |
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| 7. | | SANTOS, L. M. dos; FERRAZ, G. A. e S.; CARVALHO, M. A. de F.; TEODORO, S. A.; CAMPOS, A. A. V.; MENICUCCI NETO, P. Use of RPA images in the mapping of the chlorophyll index of coffee plants. Sustainability , v. 14, n. 20, 13118, 2022. 16 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Café. |
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| 8. | | SANTOS, L. M. dos; FERRAZ, G. A. e S.; MARIN, D. B.; CARVALHO, M. A. de F.; DIAS, J. E. L.; ALECRIM, A. de O.; SILVA, M. de L. O. e. Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner. AgriEngineering, v. 4, n. 1, p. 311-319, Mar. 2022.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 2 |
| Biblioteca(s): Embrapa Café. |
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| Registros recuperados : 8 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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