@InProceedings{ArellanoTansBalzDore:2017:HyReSe,
               author = "Arellano, Paul and Tansey, Kevin and Balzter, Heiko and Doreen, 
                         Boyd",
                title = "Hyperspectral remote sensing to detect petroleum pollution in the 
                         Amazon forest",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "2138--2145",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The global demand for fossil energy is triggering oil exploration 
                         and production projects in remote areas of the world. During the 
                         last few decades hydrocarbon production has caused pollution in 
                         the Amazon forest inflicting considerable environmental impact. 
                         Until now it is not clear how hydrocarbon pollution affects the 
                         health of the tropical forest flora. During a field campaign in 
                         polluted and pristine forest, more than 1100 leaf samples were 
                         collected and analysed for biophysical and biochemical parameters. 
                         The results revealed that tropical forests exposed to hydrocarbon 
                         pollution show reduced levels of chlorophyll content, higher 
                         levels of foliar water content and leaf structural changes. In 
                         order to map this impact over wider geographical areas, vegetation 
                         indices were applied to hyperspectral Hyperion satellite imagery. 
                         Three vegetation indices (SR, NDVI and NDVI705) were found to be 
                         the most appropriate indices to detect the effects of petroleum 
                         pollution in the Amazon forest.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59384",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLQ3C",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLQ3C",
           targetfile = "59384.pdf",
                 type = "Sensoriamento remoto hiperespectral",
        urlaccessdate = "27 abr. 2024"
}