author = "Picoli, Michelle Cristina Ara{\'u}jo and Camara, Gilberto and 
                         Sanches, Ieda Del'Arco and Sim{\~o}es, Rolf Ezequiel de Oliveira 
                         and Carvalho, Alexandre and Maciel, Adeline Marinho and Coutinho, 
                         Alexandre and Esquerdo, Julio and Antunes, Jo{\~a}o and Begotti, 
                         Rodrigo Anzolin and Arvor, Damien and Almeida, Cl{\'a}udio 
                         Aparecido de",
          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 de Pesquisa Economica Aplicada 
                         (IPEA)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Empresa Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and 
                         {Empresa Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and 
                         {Empresa Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Universite 
                         de Rennes} and {Instituto Nacional de Pesquisas Espaciais 
                title = "Big earth observation time series analysis for monitoring 
                         Brazilian agriculture",
              journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
                 year = "2018",
               volume = "145",
               number = "B",
                pages = "328--339",
                month = "Nov.",
             keywords = "Big earth observation data, Land use science, Satellite image time 
                         series, Crop expansion, Brazilian Amazonia biome, Brazilian 
                         Cerrado biome, Tropical deforestation.",
             abstract = "This paper presents innovative methods for using satellite image 
                         time series to produce land use and land cover classification over 
                         large areas in Brazil from 2001 to 2016. We used Moderate 
                         Resolution Imaging Spectroradiometer (MODIS) time series data to 
                         classify natural and human-transformed land areas in the state of 
                         Mato Grosso, Brazil's agricultural frontier. Our hypothesis is 
                         that building high-dimensional spaces using all values of the time 
                         series, coupled with advanced statistical learning methods, is a 
                         robust and efficient approach for land cover classification of 
                         large data sets. We used the full depth of satellite image time 
                         series to create large dimensional spaces for statistical 
                         classification. The data consist of MODIS MOD13Q1 time series with 
                         23 samples per year per pixel, and 4 bands (Normalized Difference 
                         Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), 
                         near-infrared (nir) and mid-infrared (mir)). By taking a series of 
                         labelled time series, we fed a 92 dimensional attribute space into 
                         a support vector machine model. Using a 5-fold cross validation, 
                         we obtained an overall accuracy of 94% for discriminating among 
                         nine land cover classes: forest, cerrado, pasture, soybean fallow, 
                         fallow-cotton, soybean-cotton, soybean-corn, soybean-millet, and 
                         soybean-sunflower. Producer and user accuracies for all classes 
                         were close to or better than 90%. The results highlight important 
                         trends in agricultural intensification in Mato Grosso. Double crop 
                         systems are now the most common production system in the state, 
                         sparing land from agricultural production. Pasture expansion and 
                         intensification has been less studied than crop expansion, 
                         although it has a stronger impact on deforestation and greenhouse 
                         gas (GHG) emissions. Our results point to a significant increase 
                         in the stocking rate in Mato Grosso and to the possible 
                         abandonment of pasture areas opened in the state's frontier. The 
                         detailed land cover maps contribute to an assessment of the 
                         interplay between production and protection in the Brazilian 
                         Amazon and Cerrado biomes.",
                  doi = "10.1016/j.isprsjprs.2018.08.007",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2018.08.007",
                 issn = "0924-2716",
             language = "en",
           targetfile = "picoli_big.pdf",
        urlaccessdate = "15 jan. 2021"