@InProceedings{EeckhautKerlPoesHerv:2012:IdVeLa,
               author = "Eeckhaut, Miet Van Den and Kerle, Norman and Poesen, Jean and 
                         Herv{\'a}s, Javier",
                title = "Identification of vegetated landslides using only a LiDAR-based 
                         Terrain Model and derivatives in an object-oriented environment",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Feitosa, Raul Queiroz and Costa, Gilson Alexandre Ostwald Pedro da 
                         and Almeida, Cl{\'a}udia Maria de and Fonseca, Leila Maria Garcia 
                         and Kux, Hermann Johann Heinrich",
                pages = "211--216",
         organization = "International Conference on Geographic Object-Based Image 
                         Analysis, 4. (GEOBIA).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Landslide identification, Dense vegetation, Conceptualisation, 
                         LiDAR, Segmentation, Classification, Geomorphometry, Belgium, 
                         Support vector machines.",
             abstract = "Light Detection and Ranging (LiDAR) and its derivative products 
                         have become a powerful tool in landslide research, particularly 
                         for landslide identification and landslide inventory mapping. In 
                         contrast to the many studies that use expert-based analysis of 
                         LiDAR derivatives to identify landslides only few studies, all 
                         pixel-based, have attempted to develop computer-aided methods for 
                         extracting landslides from LiDAR. It has not been tested whether 
                         object-oriented analysis (OOA) could be an alternative. Therefore, 
                         this study investigates the application of OOA using 2 m 
                         resolution slope gradient, roughness, curvature, and openness maps 
                         calculated from single pulse LiDAR data, without the support of 
                         any spectral information. More specifically, the focus is on the 
                         possible use of these derivatives for segmentation and 
                         classification of slow-moving landslides in densely vegetated 
                         areas, where spectral data do not facilitate accurate landslide 
                         identification. A semi-quantitative method based on support vector 
                         machines (SVM) was developed for a test area in the Flemish 
                         Ardennes (Belgium). The procedure was then applied without further 
                         modification to a validation area in the same region. The results 
                         show that OOA using LiDAR derivatives allows recognition and 
                         characterization of profound morphologic properties of deep-seated 
                         landslides on soil-covered hillslopes such as those in the Flemish 
                         Ardennes, because approximately 70% of the landslides of an 
                         expert-based inventory were also included in the object-oriented 
                         inventory. For mountain areas with bedrock, on the other hand, it 
                         is expected more difficult to create a transferable model.",
  conference-location = "Rio de Janeiro",
      conference-year = "May 7-9, 2012",
                 isbn = "978-85-17-00059-1",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP8W/3BTBB4P",
                  url = "http://urlib.net/ibi/8JMKD3MGP8W/3BTBB4P",
           targetfile = "061.pdf",
                 type = "LiDAR and SAR Applications",
        urlaccessdate = "20 maio 2024"
}