@Article{AlvesGoÁvGaMaNaCo:2024:NeClVe,
               author = "Alves, Laurizio Emanuel Ribeiro and Gon{\c{c}}alves, 
                         Lu{\'{\i}}s Gustavo Gon{\c{c}}alves de and {\'A}vila, 
                         {\'A}lvaro Vasconcelos Ara{\'u}jo de and Galetti, Giovana 
                         Deponte and Maske, Bianca Buss and Nascimento, Giuliano Carlos do 
                         and Correia Filho, Washington Luiz Feliz",
          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)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Centro de Monitoramento de Alerta e Alarme da Defesa 
                         Civil (CEMADEC)} and {Universidade Federal do Rio Grande (FURG)}",
                title = "A New Climatology of Vegetation and Land Cover Information for 
                         South America",
              journal = "Sustainability",
                 year = "2024",
               volume = "16",
               number = "7",
                pages = "e2606",
                month = "Apr.",
             keywords = "land parameters, land input, climatology.",
             abstract = "Accurate information on vegetation and land cover is crucial for 
                         numerical forecasting models in South America. This data aids in 
                         generating more realistic forecasts, serving as a tool for 
                         decision-making to reduce environmental impacts. Regular updates 
                         are necessary to ensure the data remains representative of local 
                         conditions. In this study, we assessed the suitability of 
                         'Catchment Land Surface Models-Fortuna 2.5' (CLSM), Noah, and 
                         Weather Research and Forecasting (WRF) for the region. The 
                         evaluation revealed significant changes in the distribution of 
                         land cover classes. Consequently, it is crucial to adjust this 
                         parameter during model initialization. The new land cover 
                         classifications demonstrated an overall accuracy greater than 80%, 
                         providing an improved alternative. Concerning vegetation 
                         information, outdated climatic series for Leaf Area Index (LAI) 
                         and Greenness Vegetation Fraction (GVF) were observed, with 
                         notable differences between series, especially for LAI. While some 
                         land covers exhibited good performance for GVF, the Forest class 
                         showed limitations. In conclusion, updating this information in 
                         models across South America is essential to minimize errors and 
                         enhance forecast accuracy.",
                  doi = "10.3390/su16072606",
                  url = "http://dx.doi.org/10.3390/su16072606",
                 issn = "2071-1050",
             language = "en",
           targetfile = "sustainability-16-02606-v2.pdf",
        urlaccessdate = "04 maio 2024"
}