@InProceedings{LuzOlRoGaFrJrFr:2015:ClHíIm,
               author = "Luz, Na{\'{\i}}ssa Batista da and Oliveira, Yeda Maria Malheiros 
                         de and Rosot, Maria Augusta Doetzer and Garrastazu, Marilice 
                         Cordeiro and Franciscon, Luziane and Jr. , Humberto Navarro de 
                         Mesquita and Freitas, Joberto Veloso de",
                title = "Classifica{\c{c}}{\~a}o h{\'{\i}}brida de imagens Landsat-8 e 
                         RapidEye para o mapeamento do uso e cobertura da terra nas 
                         Unidades Amostrais de Paisagem do Invent{\'a}rio Florestal 
                         Nacional do Brasil.",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "7222--7230",
         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 = "In response to the growing demand for reliable information on 
                         forest and tree resources as well as for land use/land cover 
                         (LULC) maps at larger scales, the Brazilian National Forest 
                         Inventory (NFI-BR) is now being conducted. Besides the traditional 
                         approaches related to forest assessment, the NFI-BR includes a 
                         geospatial component to provide such information at landscape 
                         scale. Using a sampling grid of 20 km × 20 km, field registry 
                         sample units were established, and 100 km2 landscape sample units 
                         (LSU) were located on a 40 km × 40 km grid. LULC maps are being 
                         prepared for each LSU using RapidEye and Landsat-8 imagery. 
                         Different remote sensing techniques are being tested to 
                         characterize LULC in order to identify patterns in different 
                         themes using spatial analysis, such as forest fragmentation, state 
                         of conservation, production and forest health. The mapping 
                         approach uses a hybrid approach, here understood as the 
                         combination of automatic unsupervised pixel-by-pixel 
                         classification and object based image classification. Attributes 
                         from image objects such as spectral characteristics, texture, and 
                         context are also involved in process tree classification, as well 
                         as ancillary data such as roads, water bodies and digital terrain 
                         models. LULC maps are the basis for analyzing landscape-scale 
                         forest fragmentation analysis as well as for evaluating compliance 
                         of permanent preservation areas under recently approved 
                         environmental legislation.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "1606",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4JQJ",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4JQJ",
           targetfile = "p1606.pdf",
                 type = "Floresta e vegeta{\c{c}}{\~a}o",
        urlaccessdate = "01 maio 2024"
}