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@Article{MartinsSantGalvMaga:2016:SeALIm,
               author = "Martins, Flora da Silva Ramos Vieira and Santos, Jo{\~a}o Roberto 
                         dos and Galv{\~a}o, L{\^e}nio Soares and Magalh{\~a}es, Xaud. 
                         Haron Abrahim",
          affiliation = "Funda{\c{c}}{\~a}o de Ci{\^e}ncia, Aplica{\c{c}}{\~o}es e 
                         Tecnologia Espaciais (FUNCATE) and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Empresa Brasileira de Pesquisa 
                         Agropecu{\'a}ria - Roraima (Embrapa)}",
                title = "Sensitivity of ALOS/PALSAR imagery to forest degradation by fire 
                         in northern Amazon",
              journal = "International Journal of Applied Earth Observation and 
                         Geoinformation",
                 year = "2016",
               volume = "49",
                pages = "163--174",
                month = "July",
             keywords = "Amazon, Aboveground biomass, Forest fire, L-band, ALOS/PALSAR, 
                         Polarimetric response.",
             abstract = "We evaluated the sensitivity of the full polarimetric Phased Array 
                         type L-band Synthetic Aperture Radar (PALSAR), onboard the 
                         Advanced Land Observing Satellite (ALOS), to forest degradation 
                         caused by fires in northern Amazon, Brazil. We searched for 
                         changes in PALSAR signal and tri-dimensional polarimetric 
                         responses for different classes of fire disturbance defined by 
                         fire frequency and severity. Since the above-ground biomass (AGB) 
                         is affected by fire, multiple regression models to estimate AGB 
                         were obtained for the whole set of coherent and incoherent 
                         attributes (general model) and for each set separately (specific 
                         models). The results showed that the polarimetric L-band PALSAR 
                         attributes were sensitive to variations in canopy structure and 
                         AGB caused by forest fire. However, except for the unburned and 
                         thrice burned classes, no single PALSAR attribute was able to 
                         discriminate between the intermediate classes of forest 
                         degradation by fire. Both the coherent and incoherent polarimetric 
                         attributes were important to explain AGB variations in tropical 
                         forests affected by fire. The HV backscattering coefficient, 
                         anisotropy, double-bounce component, orientation angle, volume 
                         index and HH-VV phase difference were PALSAR attributes selected 
                         from multiple regression analysis to estimate AGB. The general 
                         regression model, combining phase and power radar metrics, 
                         presented better results than specific models using coherent or 
                         incoherent attributes. The polarimetric responses indicated the 
                         dominance of VV-oriented backscattering in primary forest and 
                         lightly burned forests. The HH-oriented backscattering 
                         predominated in heavily and frequently burned forests. The results 
                         suggested a greater contribution of horizontally arranged 
                         constituents such as fallen trunks or branches in areas severely 
                         affected by fire. (C) 2016 Elsevier B.V. All rights reserved.",
                  doi = "10.1016/j.jag.2016.02.009",
                  url = "http://dx.doi.org/10.1016/j.jag.2016.02.009",
                 issn = "0303-2434",
             language = "en",
           targetfile = "martins_sensitivity.pdf",
        urlaccessdate = "26 nov. 2020"
}


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