Prediction of aboveground biomass and dry-matter content in brachiaria pastures by combining meteorological data and satellite imagery.
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1133603 https://doi.org/10.1111/gfs.12517 |
Resumo: | Aboveground biomass (AGB) data are important for profitable and sustainable pasture management. In this study, we hypothesized that vegetation indexes (VIs) obtained through analysis of moderate spatial resolution satellite data (Landsat-8 and Sentinel-2) and meteorological data can accurately predict the AGB of Brachiaria (syn. Urochloa) pastures in Brazil. We used AGB field data obtained from pastures between 2015 and 2019 in four distinct regions of Brazil to evaluate (i) the relationship between three different VIs?normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2) and optimized soil adjusted vegetation index (OSAVI)?and meteorological data with pasture aboveground fresh biomass (AFB), aboveground dry biomass (ADB) and dry-matter content (DMC); and (ii) the performance of simple linear regression (SLR), multiple linear regression (MLR) and random forest (RF) algorithms for the prediction of pasture AGB based on VIs obtained through satellite imagery combined with meteorological data. The results highlight a strong correlation (r) between VIs and AGB, particularly NDVI (r = 0.52 to 0.84). The MLR and RF algorithms demonstrated high potential to predict AFB (R2 = 0.76 to 0.85) and DMC (R2 = 0.78 to 0.85). We conclude that both MLR and RF algorithms improved the biomass prediction accuracy using satellite imagery combined with meteorological data to determine AFB and DMC, and can be used for Brachiaria (syn. Urochloa) AGB prediction. Additional research on tropical grasses is needed to evaluate different VIs to improve the accuracy of ADB prediction, thereby supporting pasture management in Brazil. |
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Prediction of aboveground biomass and dry-matter content in brachiaria pastures by combining meteorological data and satellite imagery.Pastagem tropicalÍndice de vegetaçãoBiomassaSensoriamento RemotoSatélitePastagemBiomassRemote sensingTropical grasslandsVegetation indexAboveground biomass (AGB) data are important for profitable and sustainable pasture management. In this study, we hypothesized that vegetation indexes (VIs) obtained through analysis of moderate spatial resolution satellite data (Landsat-8 and Sentinel-2) and meteorological data can accurately predict the AGB of Brachiaria (syn. Urochloa) pastures in Brazil. We used AGB field data obtained from pastures between 2015 and 2019 in four distinct regions of Brazil to evaluate (i) the relationship between three different VIs?normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2) and optimized soil adjusted vegetation index (OSAVI)?and meteorological data with pasture aboveground fresh biomass (AFB), aboveground dry biomass (ADB) and dry-matter content (DMC); and (ii) the performance of simple linear regression (SLR), multiple linear regression (MLR) and random forest (RF) algorithms for the prediction of pasture AGB based on VIs obtained through satellite imagery combined with meteorological data. The results highlight a strong correlation (r) between VIs and AGB, particularly NDVI (r = 0.52 to 0.84). The MLR and RF algorithms demonstrated high potential to predict AFB (R2 = 0.76 to 0.85) and DMC (R2 = 0.78 to 0.85). We conclude that both MLR and RF algorithms improved the biomass prediction accuracy using satellite imagery combined with meteorological data to determine AFB and DMC, and can be used for Brachiaria (syn. Urochloa) AGB prediction. Additional research on tropical grasses is needed to evaluate different VIs to improve the accuracy of ADB prediction, thereby supporting pasture management in Brazil.IGOR L. BRETAS, Universidade Federal de ViçosaDOMINGOS S. M. VALENTE, Universidade Federal de ViçosaFABYANO F. SILVA, Universidade Federal de ViçosaMARIO L. CHIZZOTTI, Universidade Federal de ViçosaMÁRIO F. PAULINO, Universidade Federal de ViçosaANDRÉ P. D’ÁUREA, PremixDOMINGOS SAVIO CAMPOS PACIULLO, CNPGLBRUNO CARNEIRO E PEDREIRA, CPAMTFERNANDA H. M. CHIZZOTTI, Universidade Federal de Viçosa.BRETAS, I. L.VALENTE, D. S. M.SILVA, F. F.CHIZZOTTI, M. L.PAULINO, M. F.D’ÁUREA, A. P.PACIULLO, D. S. C.PEDREIRA, B. C. eCHIZZOTTI, F. H. M.2021-09-24T12:00:47Z2021-09-24T12:00:47Z2021-08-162021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleGrass and Forage Science, v. 76, p. 340-362, 2021.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1133603https://doi.org/10.1111/gfs.12517enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2021-09-24T12:00:57Zoai:www.alice.cnptia.embrapa.br:doc/1133603Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542021-09-24T12:00:57falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542021-09-24T12:00:57Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Prediction of aboveground biomass and dry-matter content in brachiaria pastures by combining meteorological data and satellite imagery. |
title |
Prediction of aboveground biomass and dry-matter content in brachiaria pastures by combining meteorological data and satellite imagery. |
spellingShingle |
Prediction of aboveground biomass and dry-matter content in brachiaria pastures by combining meteorological data and satellite imagery. BRETAS, I. L. Pastagem tropical Índice de vegetação Biomassa Sensoriamento Remoto Satélite Pastagem Biomass Remote sensing Tropical grasslands Vegetation index |
title_short |
Prediction of aboveground biomass and dry-matter content in brachiaria pastures by combining meteorological data and satellite imagery. |
title_full |
Prediction of aboveground biomass and dry-matter content in brachiaria pastures by combining meteorological data and satellite imagery. |
title_fullStr |
Prediction of aboveground biomass and dry-matter content in brachiaria pastures by combining meteorological data and satellite imagery. |
title_full_unstemmed |
Prediction of aboveground biomass and dry-matter content in brachiaria pastures by combining meteorological data and satellite imagery. |
title_sort |
Prediction of aboveground biomass and dry-matter content in brachiaria pastures by combining meteorological data and satellite imagery. |
author |
BRETAS, I. L. |
author_facet |
BRETAS, I. L. VALENTE, D. S. M. SILVA, F. F. CHIZZOTTI, M. L. PAULINO, M. F. D’ÁUREA, A. P. PACIULLO, D. S. C. PEDREIRA, B. C. e CHIZZOTTI, F. H. M. |
author_role |
author |
author2 |
VALENTE, D. S. M. SILVA, F. F. CHIZZOTTI, M. L. PAULINO, M. F. D’ÁUREA, A. P. PACIULLO, D. S. C. PEDREIRA, B. C. e CHIZZOTTI, F. H. M. |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
IGOR L. BRETAS, Universidade Federal de Viçosa DOMINGOS S. M. VALENTE, Universidade Federal de Viçosa FABYANO F. SILVA, Universidade Federal de Viçosa MARIO L. CHIZZOTTI, Universidade Federal de Viçosa MÁRIO F. PAULINO, Universidade Federal de Viçosa ANDRÉ P. D’ÁUREA, Premix DOMINGOS SAVIO CAMPOS PACIULLO, CNPGL BRUNO CARNEIRO E PEDREIRA, CPAMT FERNANDA H. M. CHIZZOTTI, Universidade Federal de Viçosa. |
dc.contributor.author.fl_str_mv |
BRETAS, I. L. VALENTE, D. S. M. SILVA, F. F. CHIZZOTTI, M. L. PAULINO, M. F. D’ÁUREA, A. P. PACIULLO, D. S. C. PEDREIRA, B. C. e CHIZZOTTI, F. H. M. |
dc.subject.por.fl_str_mv |
Pastagem tropical Índice de vegetação Biomassa Sensoriamento Remoto Satélite Pastagem Biomass Remote sensing Tropical grasslands Vegetation index |
topic |
Pastagem tropical Índice de vegetação Biomassa Sensoriamento Remoto Satélite Pastagem Biomass Remote sensing Tropical grasslands Vegetation index |
description |
Aboveground biomass (AGB) data are important for profitable and sustainable pasture management. In this study, we hypothesized that vegetation indexes (VIs) obtained through analysis of moderate spatial resolution satellite data (Landsat-8 and Sentinel-2) and meteorological data can accurately predict the AGB of Brachiaria (syn. Urochloa) pastures in Brazil. We used AGB field data obtained from pastures between 2015 and 2019 in four distinct regions of Brazil to evaluate (i) the relationship between three different VIs?normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2) and optimized soil adjusted vegetation index (OSAVI)?and meteorological data with pasture aboveground fresh biomass (AFB), aboveground dry biomass (ADB) and dry-matter content (DMC); and (ii) the performance of simple linear regression (SLR), multiple linear regression (MLR) and random forest (RF) algorithms for the prediction of pasture AGB based on VIs obtained through satellite imagery combined with meteorological data. The results highlight a strong correlation (r) between VIs and AGB, particularly NDVI (r = 0.52 to 0.84). The MLR and RF algorithms demonstrated high potential to predict AFB (R2 = 0.76 to 0.85) and DMC (R2 = 0.78 to 0.85). We conclude that both MLR and RF algorithms improved the biomass prediction accuracy using satellite imagery combined with meteorological data to determine AFB and DMC, and can be used for Brachiaria (syn. Urochloa) AGB prediction. Additional research on tropical grasses is needed to evaluate different VIs to improve the accuracy of ADB prediction, thereby supporting pasture management in Brazil. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09-24T12:00:47Z 2021-09-24T12:00:47Z 2021-08-16 2021 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Grass and Forage Science, v. 76, p. 340-362, 2021. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1133603 https://doi.org/10.1111/gfs.12517 |
identifier_str_mv |
Grass and Forage Science, v. 76, p. 340-362, 2021. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1133603 https://doi.org/10.1111/gfs.12517 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503509842853888 |