@InProceedings{BarbosaNovoCost:2002:MePr,
               author = "Barbosa, Cl{\'a}udio Clemente Faria and Novo, Evlyn M{\'a}rcia 
                         le{\~a}o de Moraes and Costa, Maycira Pereira de Freitas",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Remote sensing for sampling station selection in the study of 
                         water circulation from river system to Amazon floodplain lakes: a 
                         methological proposal",
            booktitle = "Posters",
                 year = "2002",
         organization = "International LBA Scientific Conference, 2.",
             keywords = "water circulation, Amazon.",
             abstract = "Although remote sensing is a suitable tool for monitoring vast 
                         remote areas such as the Amazon floodplain, the accurate 
                         extraction of information must rely on ground validation sampling, 
                         through burdensome and expensive field campaigns. This paper 
                         proposes a methodology for planning and optimizing the acquisition 
                         of water quality parameters during field campaigns aiming the 
                         study of water circulation between Amazon River and Amazon 
                         floodplains lakes and wetlands. The objective of the approach is 
                         to settle an optimized geographic position data set spatially 
                         representative of water quality parameters revealing water 
                         circulation patterns. The first step in the study was to build a 
                         georeferenced image database consisting of seven dates of 
                         Landsat-TM/ETM+ images selected according to Amazon River water 
                         level. Each image date was then submitted to the following 
                         processing: 1)atmospheric correction 2)region growing 
                         segmentation, 3)unsupervised segmented-based classification. Each 
                         resulting class for each date was then characterized by the 
                         statistical attributes estimated from bands TM1, TM2 and TM3 of 
                         Landsat Thematic Mapper, which are the bands sensitive to water 
                         optical properties. Changes in the spatial dynamic of each class 
                         from images acquired at different water level were then mapped and 
                         the number of sampling stations and the geographic position of 
                         each station were defined analyzing the results of the previous 
                         step.",
  conference-location = "Manaus",
      conference-year = "July 7-10, 2002.",
                label = "10034",
             language = "en",
           targetfile = "painel_LBA_Manaus.pdf",
        urlaccessdate = "05 jun. 2024"
}