@InProceedings{AmaralShimAher:1997:PABrAm,
               author = "Amaral, Silvana and Shimabukuro, Yosio Edemir and Ahern, Frank J",
                title = "Multitemporal Radarsat fine mode images to identify different 
                         forest types at Tapajos National Forest - PA, Brazilian Amazon",
                 year = "1997",
         organization = "Simp{\'o}sio Latino-Americano de Percepcion Remota, 8.",
             keywords = "VEGETACAO, Floresta Nacional de Tapaj{\'o}s (PA), florestas, 
                         Radarsat, imagens de radar, mapeador tem{\'a}tico (Landsat), 
                         m{\'a}xima verossimilhanca, analise multitemporal.",
             abstract = "This work describes the use of multitemporal Radarsat images to 
                         identify different forest types at Tapajos National Forest, Para, 
                         Brazilian Amazon. Color composition and principal components 
                         derived from Radarsat fine mode images acquired in dry and wet 
                         season were analyzed to evaluate the discrimination of forest 
                         classes based on the phytoecological map. Landsat-TM image was 
                         used as ancillary data.Image segmentation and classification 
                         proceddure were applied to Radarsat data. Radar images from dry 
                         season showed better results for discrimination of forest types 
                         and land use than images from wet season. Radarsat principal 
                         component images generated the best color composition when 
                         combined with Landsat-TM principal components to discriminate both 
                         forest types and deforestation. The segmentation procedure did not 
                         present good result for individual Radarsat fine mode images. 
                         Radarsat image classification, using Maximum Likelihood - ICM 
                         algorithm, showed a potential to discriminate different forest 
                         types, but it still requires some visual interpretation or 
                         geographical manipulation in order to extract homogeneous forest 
                         classes. The improvement of the information extraction from 
                         Radarsat images will provide an useful tool for forest managemeent 
                         and monitoring.",
  conference-location = "Merida, VE",
      conference-year = "2-7 nov. 1997",
           copyholder = "SID/SCD",
                label = "8409",
             language = "en",
         organisation = "SELPER",
           targetfile = "INPE 7089.pdf",
        urlaccessdate = "11 maio 2024"
}