@Article{PontesLopesSiDuSiGrAr:2022:QuPoCh,
               author = "Pontes Lopes, Aline and Silva, Ricardo Dalagnol da and Dutra, 
                         Andeise Cerqueira and Silva, Camila Val{\'e}ria de Jesus and 
                         Gra{\c{c}}a, Paulo Maur{\'{\i}}cio Lima de Alencastro and 
                         Arag{\~a}o, Luiz Eduardo de Oliveira e Cruz de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Lancaster University} and 
                         {Instituto Nacional de Pesquisas da Amaz{\^o}nia (INPA)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Quantifying Post-Fire Changes in the Aboveground Biomass of an 
                         Amazonian Forest Based on Field and Remote Sensing Data",
              journal = "Remote Sensing",
                 year = "2022",
               volume = "14",
               number = "7",
                pages = "e1545",
                month = "Apr.",
             keywords = "Biomass, Change detection, Degradation, Forest fire, Google Earth 
                         Engine, Landsat-8.",
             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.",
                  doi = "10.3390/rs14071545",
                  url = "http://dx.doi.org/10.3390/rs14071545",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-14-01545.pdf",
        urlaccessdate = "02 maio 2024"
}