@InProceedings{NagelTerrOlivArag:2017:EsCaAr,
               author = "Nagel, Gustavo Willy and Terra, Fabr{\'{\i}}cio Silva and 
                         Oliveira, Jade Silva de and Aragona, M{\'a}rcio Pagano",
                title = "Compara{\c{c}}{\~a}o entre classifica{\c{c}}{\~o}es de imagem 
                         RapidEye para o c{\'a}lculo CN de bacia hidrogr{\'a}fica urbana: 
                         estudo de caso do Arroio Pepino (Pelotas/RS)",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "3822--3829",
         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 runoff curve-number (CN) is an empirical parameter used for 
                         predicting direct runoff from rainfall excess, and it depends on 
                         land use and cover changes. High spatial resolution images have 
                         been important to identify these changes. This research aimed to 
                         compare effects of different land use and cover maps produced from 
                         K-means, MaxVer, and SAM classifications of high spatial 
                         resolution orbital image on calculation of CN value in the urban 
                         watershed of Arroio Pepino (Pelotas/RS). Our hypothesis was that 
                         different classification algorithms have produced divergent maps 
                         that in turn have affect the CN value of an urban watershed. A 
                         RapidEye image was classified in order to map the surface, and the 
                         following 10 classes were identified: water, asphalt, dirt road, 
                         vegetation (three types), roofs (three types), and building shade. 
                         The CN value of each class was obtained by comparing to 
                         corresponding tabulated values, and the total CN value was 
                         calculated taking into account the proportional area of each 
                         class. The MaxVer was the best-performed classifier (global 
                         accuracy: 64.89 % and kappa index: 0.59). The three CN values 
                         based on the distinct maps had different intensities where values 
                         calculated from K-means (CNtotal: 88.91 %) and SAM (CNtotal: 88.88 
                         %) classifications were similar to each other and different of the 
                         value from MaxVer (90.71 %). Differences on proportions of land 
                         use and cover classes obtained from different classifiers affect 
                         the CN value of this urban watershed where its quality is highly 
                         dependent on accuracy of the classified image.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "60066",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLTMD",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLTMD",
           targetfile = "60066.pdf",
                 type = "Hidrologia",
        urlaccessdate = "27 abr. 2024"
}