@Article{SimõesPCMSASFC:2020:LaUsCo,
               author = "Sim{\~o}es, Rolf Ezequiel de Oliveira and Picoli, Michelle 
                         Cristina Ara{\'u}jo and C{\^a}mara, Gilberto and Maciel, Adeline 
                         Marinho and Santos, Lorena Alves dos and Andrade Neto, Pedro 
                         Ribeiro de and Sanchez Ipia, Alber Hamersson and Ferreira, Karine 
                         Reis and Carvalho, Alexandre",
          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 {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto de 
                         Pesquisas Econ{\^o}micas Aplicadas (IPEA)}",
                title = "Land use and cover maps for Mato Grosso State in Brazil from 2001 
                         to 2017",
              journal = "Scientific Data",
                 year = "2020",
               volume = "7",
               number = "1",
                pages = "e34",
                month = "Dec.",
             abstract = "This paper presents a dataset of yearly land use and land cover 
                         classification maps for Mato Grosso State, Brazil, from 2001 to 
                         2017. Mato Grosso is one of the worlds fast moving agricultural 
                         frontiers. To ensure multi-year compatibility, the work uses MODIS 
                         sensor analysis-ready products and an innovative method that 
                         applies machine learning techniques to classify satellite image 
                         time series. The maps provide information about crop and pasture 
                         expansion over natural vegetation, as well as spatially explicit 
                         estimates of increases in agricultural productivity and trade-offs 
                         between crop and pasture expansion. Therefore, the dataset 
                         provides new and relevant information to understand the impact of 
                         environmental policies on the expansion of tropical agriculture in 
                         Brazil. Using such results, researchers can make informed 
                         assessments of the interplay between production and protection 
                         within Amazon, Cerrado, and Pantanal biomes.",
                  doi = "10.1038/s41597-020-0371-4",
                  url = "http://dx.doi.org/10.1038/s41597-020-0371-4",
                 issn = "2052-4436",
             language = "en",
           targetfile = "simoes_land.pdf",
        urlaccessdate = "28 abr. 2024"
}