author = "Cho, Hyun Jung and Ogashawara, Igor and Mishra, Deepak and White, 
                         Joseph and Kamerosky, Andrew and Morris, Lori and Clarke, 
                         Christopher and Simpson, Ali and Banisakher, Deya",
          affiliation = "{Bethune-Cookman University} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {University of Georgia} and {Bethune-Cookman 
                         University} and {Bethune-Cookman University} and {St. Johns River 
                         Water Management District} and {Bethune-Cookman University} and 
                         {Bethune-Cookman University} and {Bethune-Cookman University}",
                title = "Evaluating Hyperspectral Imager for the Coastal Ocean (HICO) data 
                         for seagrass mapping in Indian River Lagoon, FL",
              journal = "Giscience \& Remote Sensing",
                 year = "2014",
               volume = "51",
               number = "2",
                pages = "120--138",
             keywords = "coastal lagoon, image analysis, image classification, macroalga, 
                         mapping, remote sensing, seagrass, Florida [United States], Indian 
                         River [Florida], United States, algae.",
             abstract = "Differentiation between benthic habitats, particularly seagrass 
                         and macroalgae, using satellite data is complicated because of 
                         water column effects plus the presence of chlorophyll-a in both 
                         seagrass and algae that result in similar spectral patterns. 
                         Hyperspectral imager for the coastal ocean data over the Indian 
                         River Lagoon, Florida, USA, was used to develop two benthic 
                         classification models, SlopeRED and SlopeNIR. Their performance 
                         was compared with iterative self-organizing data analysis 
                         technique and spectral angle mapping classification methods. The 
                         slope models provided greater overall accuracies (63-64%) and were 
                         able to distinguish between seagrass and macroalgae substrates 
                         more accurately compared to the results obtained using the other 
                         classifications methods.",
                  doi = "10.1080/15481603.2014.895577",
                  url = "http://dx.doi.org/10.1080/15481603.2014.895577",
                 issn = "1548-1603",
                label = "scopus 2014-05 ChoOMWKMCSB:2014:EvHyIm",
             language = "en",
        urlaccessdate = "26 nov. 2020"