author = "Santos, Filippe and Rodrigues, Julia and Libonati, Renata and 
                         Peres, Leonardo and Pereira, Allan and Setzer, Alberto Waingort",
          affiliation = "{Universidade Federal do Rio de Janeiro (UFRJ)} and {Universidade 
                         Federal do Rio de Janeiro (UFRJ)} and {Universidade Federal do Rio 
                         de Janeiro (UFRJ)} and {Universidade Federal do Rio de Janeiro 
                         (UFRJ)} and {Instituto Federal de Ci{\^e}ncia e Tecnologia do Sul 
                         de Minas Gerais} and {Instituto Nacional de Pesquisas Espaciais 
                title = "Burned area mapping in Brazil using NPP-VIIRS imagery and One 
                         Class Support Vector Machine",
                 year = "2019",
         organization = "EGU General Assembly",
             keywords = "burned area, VIIRS, SVM, Cerrado.",
             abstract = "Remote sensing observations has improved the understanding of 
                         spatial and temporal fire patterns in Brazil in the last decades 
                         based on quantitative metrics such as severity, location, 
                         extension and duration. Nevertheless, large discrepancies and 
                         uncertainties persist in the currently burned area (BA) products 
                         in determining BA extension, location, and occurrence time. 
                         Visible Infrared Imaging Radiometer Suite (VIIRS) sensor was 
                         launched in 2011 to upgrade and to maintain the Earth long-term 
                         monitoring initiated by Advanced Very High Resolution Radiometer 
                         (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) 
                         sensors, but to our knowledge, none BA product has been developed 
                         using VIIRS data imagery. Accordingly, we present a BA mapping 
                         algorithm based on VIIRS imagery which includes two-steps. 
                         Firstly, monthly composites of (V, W) burned index are computed 
                         using spectral information of near infrared (NIR) and middle 
                         infrared (MIR) channels. Secondly, multispectral samples extracted 
                         by VIIRS active fires are used for training a One-Class Support 
                         Vector Machine (OC-SVM) classification that uses cumulative 
                         distribution functions criteria. The active fire data were 
                         screened to prevent extraction of unrepresentative BA samples and 
                         combined with burn index (V, W) monthly composites to produce BA 
                         scars. The procedure was applied over Brazilian savanna for 2015, 
                         a biome that has been increasingly affected by deforestation due 
                         to cropland and pasture expansion, consequently rising and 
                         changing the natural fire regime in region. Then, the developed 
                         algorithm was validated by reference scars obtained from Landsat 
                         imagery and compared with other BA product (e.g., MCD64A1). 
                         Results show that VIIRS BA product based on OC-SVM are able to map 
                         smaller areas more accurately than other products, including 
                         burned areas without active fires, due OC-SVM classification 
                         characterizes BA through active fire samples, thus eliminating a 
                         potential source of omission error.",
  conference-location = "Vienna, Austria",
      conference-year = "07-12 apr.",
             language = "en",
           targetfile = "EGU2019-17840-2.pdf",
        urlaccessdate = "15 abr. 2021"