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@InProceedings{BarbosaNFCCLASA:2015:BaDaBi,
               author = "Barbosa, Claudio Clemente Faria and Novo, Evlyn M{\'a}rcia 
                         Le{\~a}o de Moraes and Ferreira, Renato Martins Passos and 
                         Carvalho, Lino Augusto Sander de and Cairo, Carolline Tressmann 
                         and Lopes, Fernando Bezerra and Ara{\'u}jo, Carlos Alberto 
                         Sampaio and Stech, Jos{\'e} Luiz and Alc{\^a}ntara, Enner 
                         Herenio",
          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)} and {} and {} and {} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Base de dados bio-{\'o}pticos como suporte a estudos de ambientes 
                         aqu{\'a}ticos por sensoriamento remoto",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4337--4344",
         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 = "The Amazonian floodplain has more than 8,000 lakes larger than one 
                         hectare of which less than 1% has been adequately studied. Given 
                         that the Brazilian electric power matrix is predominantly 
                         hydroelectric, the area flooded by the 150 largest reservoirs is 
                         nearly 45000 km2. The anthropogenic impacts in the Amazonian 
                         floodplain lakes as well as the net carbon budget in hydroelectric 
                         reservoirs are not well known and needs to be determined and 
                         monitored. Due to these aquatic system dimensions, its monitoring 
                         is only feasible by means of remote sensing. This work presents 
                         ongoing efforts and preliminary results for building a dataset 
                         which represents the first and the most comprehensive bio-optical 
                         information available on Brazilian inland waters to support the 
                         development of remote sensing algorithms for monitoring of aquatic 
                         systems. From 2012 to 2014 thirteen field campaigns were carried 
                         out in five hydroelectric reservoirs representing different 
                         Brazilian biomes, in a irrigation and domestic water supplier 
                         reservoir located in the semi-arid region and in Amazonian 
                         floodplain lakes. On average, 25 stations were sampled at each 
                         site and the following profiles were acquired: absorption, 
                         attenuation, scattering and backscattering coefficients, 
                         radiances/irradiances spectra above and in-water. Water samples 
                         were collected concurrently with profiles for determining 
                         concentrations of optically active constituents, CDOM absorption 
                         and specific coefficients of algal and no algal particulate. These 
                         data are being used to support the development of graduate student 
                         in order to foster the scientific development in the remote 
                         sensing of inland waters.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "850",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4CLS",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM4CLS",
           targetfile = "p0850.pdf",
                 type = "{\'A}reas {\'u}midas",
        urlaccessdate = "01 dez. 2020"
}


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