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dc.contributor.authorREIS, A. A. dos
dc.contributor.authorSILVA, B. C.
dc.contributor.authorWERNER, J. P. S.
dc.contributor.authorSILVA, Y. F.
dc.contributor.authorROCHA, J. V.
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.authorMAGALHÃES, P. S. G.
dc.date.accessioned2025-07-09T10:49:35Z-
dc.date.available2025-07-09T10:49:35Z-
dc.date.created2020-09-21
dc.date.issued2020
dc.identifier.citationThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42-3, W12, p. 419-424, 2020.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1125045-
dc.descriptionABSTRACT: 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.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectIntegração lavoura-pecuária
dc.subjectAprendizado de máquina
dc.subjectFloresta aleatória
dc.subjectÍndice de vegetação
dc.subjectIntegrated crop-livestock system
dc.subjectMachine Learning
dc.subjectPastureland
dc.subjectVegetation Indices
dc.subjectDove satellites
dc.subjectNano-Satellites
dc.subjectRandom Forest
dc.titleExploring the potential of high-resolution PlanetScope imagery for pasture biomass estimation in an integrated crop-livestock system.
dc.typeArtigo de periódico
dc.subject.thesagroBiomassa
dc.subject.thesagroPastagem
dc.subject.nalthesaurusVegetation index
dc.subject.nalthesaurusBiomass
dc.subject.nalthesaurusPasture management
dc.description.notesPublicado também em: IEEE LATIN AMERICAN GRSS; ISPRS REMOTE SENSING CONFERENCE, Santiago, 2020. Proceedings... [Piscataway]: IEEE, 2020. p. 675-680. LAGIRS 2020.
riaa.ainfo.id1125045
riaa.ainfo.lastupdate2025-07-08
dc.identifier.doihttps://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-419-2020
dc.contributor.institutionFeagri, 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.
Aparece nas coleções:Artigo em anais de congresso (CNPTIA)

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