@Article{RiedelGomFerSamStu:2010:IdLaSc,
               author = "Riedel, Paulina Setti and Gomes, Alessandra Rodrigues and 
                         Ferreira, Mateus Vidotti and Sampaio Lopes, Eymar Silva and 
                         Sturaro, Jose Ricardo",
          affiliation = "Sao Paulo State Univ UNESP, Dept Appl Geol, BR-13506900 Bela Vista 
                         Rio Claro, SP Brazil and Sao Paulo State Univ UNESP, Postgrad 
                         Program Geosci \& Environm Studies, BR-13506900 Bela Vista Rio 
                         Claro, SP Brazil and Sao Paulo State Univ UNESP, Postgrad Program 
                         Geosci \& Environm Studies, BR-13506900 Bela Vista Rio Claro, SP 
                         Brazil and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         Sao Paulo State Univ UNESP, Dept Appl Geol, BR-13506900 Bela Vista 
                         Rio Claro, SP Brazil",
                title = "Identification of Landslide Scars in the Region of the Serra do 
                         Mar, Sao Paulo State, Brazil, Using Digital Image Processing and 
                         Spatial Analysis Tools",
              journal = "GIScience and Remote Sensing",
                 year = "2010",
               volume = "47",
               number = "4",
                pages = "498--513",
                month = "Oct.-Dec.",
             keywords = "digital image, error analysis, IKONOS, image analysis, image 
                         processing, landslide, spatial analysis, Brazil, Serra do Mar.",
             abstract = "The objective of the present study, developed in a mountainous 
                         region in Brazil where many landslides occur, is to present a 
                         method for detecting landslide scars that couples image processing 
                         techniques with spatial analysis tools. An IKONOS image was 
                         initially segmented, and then classified through a Batthacharrya 
                         classifier, with an acceptance limit of 99%, resulting in 216 
                         polygons identified with a spectral response similar to landslide 
                         scars. After making use of some spatial analysis tools that took 
                         into account a susceptibility map, a map of local drainage 
                         channels and highways, and the maximum expected size of scars in 
                         the study area, some features misinterpreted as scars were 
                         excluded. The 43 resulting features were then compared with 
                         visually interpreted landslide scars and field observations. The 
                         proposed method can be reproduced and enhanced by adding filtering 
                         criteria and was able to find new scars on the image, with a final 
                         error rate of 2.3%.",
                  doi = "10.2747/1548-1603.47.4.498",
                  url = "http://dx.doi.org/10.2747/1548-1603.47.4.498",
                 issn = "1548-1603",
             language = "en",
        urlaccessdate = "20 maio 2024"
}