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Campo DC | Valor | Idioma |
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dc.contributor.author | Cruz, Luiz Eduardo de Oliveira e | - |
dc.contributor.author | Graça, Paulo Maurício Lima de Alencastro | - |
dc.contributor.author | Silva, Camila Valéria de Jesus | - |
dc.contributor.author | Dutra, Andeise Cerqueira | - |
dc.contributor.author | Dalagnol, Ricardo | - |
dc.contributor.author | Lopes, Aline Pontes | - |
dc.date.accessioned | 2022-05-26T20:14:31Z | - |
dc.date.available | 2022-05-26T20:14:31Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | https://repositorio.inpa.gov.br/handle/1/38633 | - |
dc.description.abstract | Fire 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.iso | en | pt_BR |
dc.relation.ispartof | Volume 14, Edição 7, Número 1545 | pt_BR |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Brazil | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/br/ | * |
dc.subject | Biomass | pt_BR |
dc.subject | Change detection | pt_BR |
dc.title | Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data | pt_BR |
dc.type | Artigo | pt_BR |
dc.identifier.doi | 10.3390/rs14071545 | - |
Aparece nas coleções: | Artigos |
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