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@Article{SchroederOlGiQuLoMo:2016:AcFiDe,
               author = "Schroeder, Wilfrid and Oliva, Patricia and Giglio, Louis and 
                         Quayle, Brad and Lorenz, Eckehard and Morelli, Fabiano",
          affiliation = "{University of Maryland} and {University of Maryland} and 
                         {University of Maryland} and {USDA Forest Service Remote Sensing 
                         Applications Cente} and {German Aerospace Center} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Active fire detection using Landsat-8/OLI data",
              journal = "Remote Sensing of Environment",
                 year = "2016",
               volume = "185",
                pages = "210--220",
                month = "Nov.",
             abstract = "The gradual increase in Landsat-class data availability creates 
                         new opportunities for fire science and management applications 
                         that require higher-fidelity information about biomass burning, 
                         improving upon existing coarser spatial resolution (\≥ 1 
                         km) satellite active fire data sets. Targeting those enhanced 
                         capabilities we describe an active fire detection algorithm for 
                         use with Landsat-8 Operational Land Imager (OLI) daytime and 
                         nighttime data. The approach builds on the fire-sensitive 
                         short-wave infrared channel 7 complemented by visible and 
                         near-infrared channel 16 data (daytime only), while also expanding 
                         on the use of multi-temporal analysis to improve pixel 
                         classification results. Despite frequent saturation of OLI's 
                         fire-affected pixels, which includes radiometric artifacts 
                         resulting from folding of digital numbers, our initial assessment 
                         based on visual image analysis indicated high algorithm fidelity 
                         across a wide range of biomass burning scenarios, gas flares and 
                         active volcanoes. Additional field data verification confirmed the 
                         sensor's and algorithm's ability to resolve fires of significantly 
                         small areas compared to current operational satellite fire 
                         products. Commission errors were greatly reduced with the addition 
                         of multi-temporal analysis tests applied to co-located pixels, 
                         averaging less than 0.2% globally. Because of its overall quality, 
                         Landsat-8/OLI active fire data could become part of a network of 
                         emerging earth observation systems providing enhanced spatial and 
                         temporal coverage of biomass burning at global scales.",
                  doi = "10.1016/j.rse.2015.08.032",
                  url = "http://dx.doi.org/10.1016/j.rse.2015.08.032",
                 issn = "0034-4257",
             language = "en",
           targetfile = "schroeder_active.pdf",
        urlaccessdate = "25 nov. 2020"
}


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