@Article{FerreiraVegZhaCarMac:2020:GlFiSe,
               author = "Ferreira, Leonardo N. and Vega-Oliveros, Didier A. and Zhao, Liang 
                         and Cardoso, Manoel Ferreira and Macau, Elbert Einstein Nehrer",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Indiana 
                         University} and {Universidade de S{\~a}o Paulo (USP)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Global fire season severity analysis and forecasting",
              journal = "Computers and Geosciences",
                 year = "2020",
               volume = "134",
                pages = "UNSP 104339",
                month = "Jan.",
             keywords = "Global fire activity, Wildfire, Fire season length, Fire severity, 
                         Climate change, Time series prediction.",
             abstract = "Fire activity has a huge impact on human lives. Different models 
                         have been proposed to predict fire activity, which can be 
                         classified into global and regional ones. Global fire models focus 
                         on longer timescale simulations and can be very complex. Regional 
                         fire models concentrate on seasonal forecasting but usually 
                         require inputs that are not available in many places. Motivated by 
                         the possibility of having a simple, fast, and general model, we 
                         propose a seasonal fire prediction methodology based on time 
                         series forecasting methods. It consists of dividing the studied 
                         area into grid cells and extracting time series of fire counts to 
                         fit the forecasting models. We apply these models to estimate the 
                         fire season severity (FSS) from each cell, here defined as the sum 
                         of the fire counts detected in a season. Experimental results 
                         using a global fire detection data set show that the proposed 
                         approach can predict FSS with a relatively low error in many 
                         regions. The proposed approach is reasonably fast and can be 
                         applied on a global scale.",
                  doi = "10.1016/j.cageo.2019.104339",
                  url = "http://dx.doi.org/10.1016/j.cageo.2019.104339",
                 issn = "0098-3004",
             language = "en",
           targetfile = "ferreira_global.pdf",
        urlaccessdate = "28 abr. 2024"
}