Please use this identifier to cite or link to this item: https://repositorio.inpa.gov.br/handle/1/38633
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dc.contributor.authorCruz, Luiz Eduardo de Oliveira e-
dc.contributor.authorGraça, Paulo Maurício Lima de Alencastro-
dc.contributor.authorSilva, Camila Valéria de Jesus-
dc.contributor.authorDutra, Andeise Cerqueira-
dc.contributor.authorDalagnol, Ricardo-
dc.contributor.authorLopes, Aline Pontes-
dc.date.accessioned2022-05-26T20:14:31Z-
dc.date.available2022-05-26T20:14:31Z-
dc.date.issued2022-
dc.identifier.urihttps://repositorio.inpa.gov.br/handle/1/38633-
dc.description.abstractFire is a major forest degradation component in the Amazon forests. Therefore, it is important to improve our understanding of how the post-fire canopy structure changes cascade through the spectral signals registered by medium-resolution satellite sensors over time. We contrasted accumulated yearly temporal changes in forest aboveground biomass (AGB), measured in permanent plots, and in traditional spectral indices derived from Landsat-8 images. We tested if the spectral indices can improve Random Forest (RF) models of post-fire AGB losses based on pre-fire AGB, proxied by AGB data from immediately after a fire. The delta normalized burned ratio, non-photosynthetic vegetation, and green vegetation (ΔNBR, ΔNPV, and ΔGV, respectively), relative to pre-fire data, were good proxies of canopy damage through tree mortality, even though small and medium trees were the most affected tree size. Among all tested predictors, pre-fire AGB had the highest RF model importance to predicting AGB within one year after fire. However, spectral indices significantly improved AGB loss estimates by 24% and model accuracy by 16% within two years after a fire, with ΔGV as the most important predictor, followed by ΔNBR and ΔNPV. Up to two years after a fire, this study indicates the potential of structural and spectral-based spatial data for integrating complex post-fire ecological processes and improving carbon emission estimates by forest fires in the Amazon. © 2022 by the authors.pt_BR
dc.language.isoenpt_BR
dc.relation.ispartofVolume 14, Edição 7, Número 1545pt_BR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Brazil*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/br/*
dc.subjectBiomasspt_BR
dc.subjectChange detectionpt_BR
dc.titleQuantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Datapt_BR
dc.typeArtigopt_BR
dc.identifier.doi10.3390/rs14071545-
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