@Article{ShimabukuroDuArDuCaPeCa:2020:MaBuAr,
               author = "Shimabukuro, Yosio Edemir and Dutra, Andeise Cerqueira and Arai, 
                         Eg{\'{\i}}dio and Duarte, Valdete and Cassol, Henrique Luis 
                         Godinho and Pereira, Gabriel and Cardozo, Francielle da Silva",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Universidade Federal de S{\~a}o Jo{\~a}o del-Rei 
                         (UFSJ)} and {Universidade Federal de S{\~a}o Jo{\~a}o del-Rei 
                         (UFSJ)}",
                title = "Mapping burned areas of mato grosso state brazilian amazon using 
                         multisensor datasets",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "22",
                pages = "1--23",
                month = "Nov.",
             keywords = "burned areas detection, shade fraction image, linear spectral 
                         mixing model, VIIRS, PROBA-V, Landsat-8 OLI.",
             abstract = "Quantifying forest fires remain a challenging task for the 
                         implementation of public policies aimed to mitigate climate 
                         change. In this paper, we propose a new method to provide an 
                         annual burned area map of Mato Grosso State located in the 
                         Brazilian Amazon region, taking advantage of the high spatial and 
                         temporal resolution sensors. The method consists of generating the 
                         vegetation, soil, and shade fraction images by applying the Linear 
                         Spectral Mixing Model (LSMM) to the Landsat-8 OLI (Operational 
                         Land Imager), PROBA-V (Project for On-Board AutonomyVegetation), 
                         and Suomi NPP-VIIRS (National Polar-Orbiting Partnership-Visible 
                         Infrared Imaging Radiometer Suite) datasets. The shade fraction 
                         images highlight the burned areas, in which values are represented 
                         by low reflectance of ground targets, and the mapping was 
                         performed using an unsupervised classifier. Burned areas were 
                         evaluated in terms of land use and land cover classes over the 
                         Amazon, Cerrado and Pantanal biomes in the Mato Grosso State. Our 
                         results showed that most of the burned areas occurred in 
                         non-forested areas (66.57%) and old deforestation (21.54%). 
                         However, burned areas over forestlands (11.03%), causing forest 
                         degradation, reached more than double compared with burned areas 
                         identified in consolidated croplands (5.32%). The results obtained 
                         were validated using the Sentinel-2 data and compared with active 
                         fire data and existing global burned areas products, such as the 
                         MODIS (Moderate Resolution Imaging Spectroradiometer product) 
                         MCD64A1 and MCD45A1, and Fire CCI (ESA Climate Change Initiative) 
                         products. Although there is a good visual agreement among the 
                         analyzed products, the areas estimated were quite different. Our 
                         results presented correlation of 51% with Sentinel-2 and agreement 
                         of r2 = 0.31, r2 = 0.29, and r2 = 0.43 with MCD64A1, MCD45A1, and 
                         Fire CCI products, respectively. However, considering the active 
                         fire data, it was achieved the better performance between active 
                         fire presence and burn mapping (92%). The proposed method provided 
                         a general perspective about the patterns of fire in various biomes 
                         of Mato Grosso State, Brazil, that are important for the 
                         environmental studies, specially related to fire severity, 
                         regeneration, and greenhouse gas emissions.",
                  doi = "10.3390/rs12223827",
                  url = "http://dx.doi.org/10.3390/rs12223827",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-12-03827-v2.pdf",
        urlaccessdate = "28 abr. 2024"
}