@InProceedings{FletcherMuFrLiShAr:2013:ReSPMo,
               author = "Fletcher, Imogen Nancy and Murray-Totarolo, Guillermo and 
                         Friedlingstein, Pierre and Lima, Andr{\'e} and Shimabukuro, Yosio 
                         Edemir and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
          affiliation = "{} and {} and {} and {} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Fire emissions in tropical forests: refining the SPITFIRE model 
                         using remote sensing data",
            booktitle = "Anais...",
                 year = "2013",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "7337--7344",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 16. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Tropical fires are the most poorly represented fire type in 
                         Dynamic Global Vegetation Models (DGVMs), due to an incomplete 
                         understanding of the factors driving them. As the time period 
                         increases for which remote sensing fire data is available, it 
                         becomes possible to assess long-term trends and distinguish 
                         between natural interannual variability and the effects of changes 
                         in anthropogenic drivers of fire. The SPITFIRE model captures the 
                         broad features of global fire regimes, but includes several 
                         processes that rely heavily on the accuracy of the input data, 
                         products of earlier calculations, and prescribed parameters. In 
                         this paper, we develop two alternative approaches for calculating 
                         fire danger and burnt areas, whose substitution into SPITFIRE 
                         would increase computational efficiency and reduce the required 
                         number of weakly constrained input variables and datasets. We 
                         first model fire danger as a function of water stress and fuel 
                         availability proxies. Second, we test a new burnt area model using 
                         a Pareto distribution, which relies on fire counts, thus 
                         eliminating the need for rate of fire spread information. 
                         Parameters for the fire danger model have yet to be estimated, but 
                         the structure is plausible. The burnt area model performs well for 
                         Amazonia for a range of grid resolutions and parameter estimates; 
                         coarse resolutions produce the most accurate results. More data is 
                         required to calibrate the equations across the tropics. These 
                         changes could potentially improve predictions of which areas are 
                         at risk of burning, but not the extent of damage to standing 
                         biomass: this will require further model improvements.",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "13-18 abr. 2013",
                 isbn = "{978-85-17-00066-9 (Internet)} and {978-85-17-00065-2 (DVD)}",
                label = "851",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW34M/3E7GGCS",
                  url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7GGCS",
           targetfile = "p0851.pdf",
                 type = "Mudan{\c{c}}a de Uso e Cobertura da Terra",
        urlaccessdate = "29 jun. 2024"
}