@InProceedings{MüllerRufGriSiqHos:2015:MiDeLa,
               author = "M{\"u}ller, Hannes and Rufin, Philippe and Griffiths, Patrick and 
                         Siqueira, Auberto Jos{\'e} Barros and Hostert, Patrick",
                title = "Mining dense Landsat time series for separating cropland and 
                         pasture in a heterogeneous Brazilian savanna landscape",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1113--1120",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Better remote sensing based information on the global distribution 
                         of croplands and pastures is urgently needed. Without reliable 
                         cropland-pasture separation it will be impossible to retrieve 
                         high-quality information on agricultural expansion or land use 
                         intensification, and on related ecosystem service provision. In 
                         this context, the savanna biome is critically important but 
                         information on land use and land cover (LULC) is notoriously 
                         inaccurate in these areas. This is due to pronounced 
                         spatial-temporal dynamics of agricultural land use and spectral 
                         similarities between cropland, pasture, and natural savanna 
                         vegetation. In this study, we investigated the potential to 
                         reliably separate cropland, pasture, natural savanna vegetation, 
                         and other relevant land cover classes employing Landsat-derived 
                         spectral-temporal variability metrics for a savanna landscape in 
                         the Brazilian Cerrado. Spectral-temporal variability metrics were 
                         derived from 344 Landsat images across four footprints between 
                         2009 and 2012. Our results showed a reliable separation between 
                         cropland, pasture, and natural savanna vegetation achieving an 
                         overall accuracy of 93%. There is great potential for expanding 
                         our approach towards large parts of the Cerrado biome and to other 
                         savanna systems which still suffer from inaccurate LULC 
                         information.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "208",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM47PA",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM47PA",
           targetfile = "p0208.pdf",
                 type = "An{\'a}lise de s{\'e}ries de tempo de imagens de sat{\'e}lite",
        urlaccessdate = "11 maio 2024"
}