Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1125026
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dc.contributor.authorREIS, A. A. dos
dc.contributor.authorWERNER, J. P. S.
dc.contributor.authorSILVA, B. C.
dc.contributor.authorFIGUEIREDO, G. K. D. A.
dc.contributor.authorANTUNES, J. F. G.
dc.contributor.authorESQUERDO, J. C. D. M.
dc.contributor.authorCOUTINHO, A. C.
dc.contributor.authorLAMPARELLI, R. A. C.
dc.contributor.authorROCHA, J. V.
dc.contributor.authorMAGALHÃES, P. S. G.
dc.date.accessioned2020-09-19T04:39:18Z-
dc.date.available2020-09-19T04:39:18Z-
dc.date.created2020-09-18
dc.date.issued2020
dc.identifier.citationRemote Sensing, v. 12, n. 16, p. 1-21, Aug. 2020.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1125026-
dc.descriptionAbstract: 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.
dc.language.isoeng
dc.rightsopenAccesseng
dc.subjectPasto
dc.subjectPastagem tropical
dc.subjectFloresta aleatória
dc.subjectRandom forest
dc.subjectMixed pastures
dc.subjectIntegrated systems
dc.subjectTexture measures
dc.subjectExtreme gradient boosting
dc.titleMonitoring pasture aboveground biomass and canopy height in an integrated crop-livestock system using textural information from PlanetScope imagery.
dc.typeArtigo de periódico
dc.subject.thesagroBiomassa
dc.subject.thesagroPastagem Mista
dc.subject.thesagroSensoriamento Remoto
dc.subject.nalthesaurusPastures
dc.subject.nalthesaurusTropical pastures
dc.subject.nalthesaurusBiomass
dc.subject.nalthesaurusAboveground biomass
dc.subject.nalthesaurusRemote sensing
dc.description.notesArticle number: 2534.
riaa.ainfo.id1125026
riaa.ainfo.lastupdate2020-09-18
dc.identifier.doi10.3390/rs12162534
dc.contributor.institutionALINY 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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