@InProceedings{NogueiraMartSetzMore:2019:CoLaCo,
               author = "Nogueira, Joana and Martins, Guilherme and Setzer, Alberto 
                         Waingort and Morelli, Fabiano",
          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)}",
                title = "A comparison of land cover maps to define vegetation classes of 
                         fire risk in Brazil",
            booktitle = "Anais...",
                 year = "2019",
                pages = "789",
         organization = "International Wildland Fire Ecology and Fire Management Congress, 
                         7.",
             keywords = "Fire modelling, fire prone ecosystems, land use, Mapbiomas, 
                         IGBP.",
             abstract = "Natural fires are essential in the structure and functioning of 
                         many ecosystems in the world. Some vegetation types are more 
                         vulnerable to fire, e.g.tropical forests, whereas others are fire 
                         dependent, like savannas. However, the constant and uncontrolled 
                         use of fire as an agricultural tool, particularly in developing 
                         regions, has contributed to accelerate land cover (LC) changes and 
                         to disrupt spatial distribution patterns of the original 
                         vegetation even in fire-prone ecosystems. Satellite-derived LC 
                         global products have been developed to quantify frequencies, 
                         processes and drivers of annual LC changes, where accurate 
                         characterization and mapping of LC is key to define the most fire 
                         affected vegetation types. In this context, the aim of this study 
                         was to evaluate LC maps to define the fire risk (FR) vegetation 
                         classes used in the INPE \́s FR model. We compared the 
                         global ESA CCI Land cover derived from the Meris-300m sensor and 
                         the NASA/MCD12Q1-IGBP from Modis-500m data products with the 
                         regional LC Mapbiomas derived from 30-m Landsat images, used here 
                         as a reference for the Brazilian territory. All maps were 
                         evaluated for the year 2012 at 1 km spatial resolution and 
                         reclassified in the seven LC classes used in the FR model: 
                         1-Grasslands, 2-Croplands and Cropland/Natural vegetation mosaic, 
                         3-Open Shrublands/ Savannas, 4-Closed Shrublands/Woody Savannas, 
                         5-Evergreen Needleleaf Forests, 6-Deciduous Needleleaf/Mixed 
                         Forests, and 7-Evergreen Broadleaf Forests/Permanent wetlands. All 
                         LC datasets showed >40% of coincident pixels to 7. The main 
                         differences were observed in the fire-prone ecosystems comparing 
                         global products and Mapbiomas, with ~34% of reduction to 3 and 4 
                         and an increase of ~14% in 1, showing that global products tend to 
                         classify typical Brazilian savannas as grasslands. Our results 
                         demonstrate the importance of a reliable regional LC map to 
                         improve the quality of spatial vegetation distribution to estimate 
                         fire risk. An accurate fire risk LC type characterization can 
                         support decision strategies in fire management and fire modelling. 
                         From these results, Mapbiomas and its yearly updates were adopted 
                         as the vegetation map input for INPE \́s FR, replacing 
                         MCD12Q1-IGBP.",
  conference-location = "Bras{\'{\i}}lia, DF",
      conference-year = "2019",
                  doi = "10.37002/biodiversidadebrasileira.v9i1.1174",
                  url = "http://dx.doi.org/10.37002/biodiversidadebrasileira.v9i1.1174",
             language = "en",
           targetfile = "1174-Texto do Artigo-6020-1-10-20191030.pdf",
        urlaccessdate = "05 maio 2024"
}