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Landslide identification is a fundamental step enabling the assessment of landslide susceptibility and determining the associated risks. Landslide identification by conventional methods is often time-consuming, therefore alternative techniques, including automatic approaches based on remote sensing data, have captured the interest among researchers in recent decades. By providing a highly detailed digital elevation model (DEM), airborne laser scanning (LiDAR) allows effective landslide identification, especially in forested areas. In the present study, object-based image analysis (OBIA) was applied to landslide detection by utilizing LiDAR-derived data. In contrast to previous investigations, our analysis was performed on forested and agricultural areas, where cultivation pressure has degraded specific landslide geomorphology. A diverse variety of aspects that influence OBIA accuracy in landslide detection have been considered: DEM resolution, segmentation scale, and feature selection. Finally, using DEM delivered layers and OBIA, landslide was identified with an overall accuracy (OA) of 85% and a kappa index (KIA) equal to 0.60, which illustrates the effectiveness of the proposed approach. In the end, a field investigation was performed in order to evaluate the results achieved by applying an automatic OBIA approach. The advantages and challenges of automatic approaches for landslide identification for various land use were also discussed. Final remarks underline that effective landslide detection in forested areas could be achieved while this is still challenging in agricultural areas.

Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features