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@InProceedings{PavanelliNeveCampKort:2015:ReSeIm,
               author = "Pavanelli, Jo{\~a}o Arthur Pompeu and Neves, Bruna Virginia and 
                         Camphora, Vanessa Priscila and Korting, Thales Sehn",
          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 = "Remote sensing image processing to identify spatial units of human 
                         occupation along Trans-Amazonian Highway (BR-230), Brazil",
            booktitle = "Proceedings...",
                 year = "2015",
         organization = "Joint Urban Remote Sensing Event, (JURSE).",
             abstract = "To investigate the urban phenomenon in the Amazon is necessary to 
                         observe the cities and communities. Identifying these population 
                         nuclei can provide information about where the population is 
                         concentrated and how it relates to the space and environment, 
                         therefore, how Amazonian urban is structured. This study 
                         identified spatial units of human occupation along the 
                         Trans-Amazonian Highway (BR-230) by applying remote sensing image 
                         processing techniques. The study site is located in Par{\'a} 
                         state, Brazil, in the municipalities of Altamira, Brasil Novo, 
                         Medicil{\^a}ndia and Uruar{\'a}, inside a 15 km buffered from 
                         the Highway. Four Landsat-5 Thematic Mapper orthorrectfied scenes 
                         from 2011 were processed using software SPRING. The processing 
                         steps consisted in mosaicking the scenes, the application of 
                         dilation filter, segmentation and maximum likelihood 
                         classification. The validation was based on manual classification 
                         of middle resolution RapidEye images (5 metres) and ancillary data 
                         from Brazilian Institute of Geography and Statistics (IBGE). 
                         Twenty three spatial units of human occupation were mapped and the 
                         validation showed a Kappa coefficient of 0.6785. The application 
                         of dilation filter during the processing was able to identify 
                         spatial units of human occupation in the study site, although some 
                         misclassified pixels occurred mainly in small patches.",
  conference-location = "Lausanne, Switzerland",
           targetfile = "pavanelli_remote.pdf",
        urlaccessdate = "04 dez. 2020"
}


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