In the framework of the doctoral thesis, methodology for landslide mapping using LiDAR (Light Detection and Ranging) technology was established, encompassing inventory mapping and derivation of prognostic susceptibility maps. Airborne laser scanning (ALS) allows generation of bare-earth digital terrain model (DTM) optimal for identification and mapping of small and shallow landslides under dense vegetation. Acquisition of the LiDAR data for the Podsljeme area (180 km2) in the City of Zagreb was performed in December 2013, following the extreme precipitation period in winter 2012/2013 that caused multiple occurrences of regional landslides (MORLE). The first result of the doctoral research is high-resolution DTM optimal for mapping of targeted landslides, generated from a point cloud based on suitable interpolation method and spatial resolution (0,3x0,3 m and 1x1 m). A series of topographic derivative datasets were generated from the LiDAR DTM for landslide mapping that was performed for the pilot area of 21 km2, which represents 12 % of the hilly Podsljeme area in the Zagreb City. Landslide inventory maps for the pilot area were prepared using two different methods: visual identification of landslides on LiDAR DTM derivatives and automated mapping based on morphometric characteristics of landslide areas compared to non-landslide areas. The second phase of research involved visual interpretation of three LiDAR DTM derivative maps (hillshade, slope and contours) with a spatial resolution of 0,3 x 0,3 m which resulted with inventory map containing 702 landslide polygons in the pilot area (21 km2). Seventy-five percent of the landslides in inventory have an area between 150 and 2000 m2 while the area of the smallest identified landslide is 43 m2. Around 10 % of visually identified landslides were additionally checked and evaluated in the field, while the whole inventory map for the pilot area was compared with conventionally prepared historical landslide inventory maps. A comparison of landslide inventories and the frequency–area distribution of visually interpreted landslides proves that LiDAR DTM enables preparation of detailed and complete landslide inventory maps (after MORLE) in the Podsljeme area. In the third phase of research, the quantitative geomorphological analysis on visually interpreted landslides was performed to define the topographic signature of landslide morphology using statistical parameters. In soil-covered areas, such as the Podsljeme area, landslides are generally characterized by a different morphometric values, i.e., higher surface roughness compared to the surrounding landslide-free areas. Based on defined morphological parameters which indicate hummocky landslide morphology in the test area (10.5 km2), the pixel-based model for landslide mapping was developed. Verification showed that landslides and unstable slopes can be predicted with an accuracy of 70 % and it can be concluded that developed model allows recognition and characterization of morphologic properties of forested small and shallow landslides on soil-covered hillslopes. In the last phase of the study, visually and automatically prepared landslide inventories were used to perform the landslide susceptibility maps. Landslide causal factors analysis and susceptibility assessment was done using bivariate statistical method and comparison of three landslide susceptibility maps were performed due to the area under the receiver operating characteristic (ROC) curves. The final landslide susceptibility map, using automatically prepared landslide inventory as input, resulted with high prediction rate (AUC=83 %), due to validation with visually interpreted landslides. It can be concluded that the model developed for automated landslide mapping is a useful tool for fast and cost-efficient production of reliable landslide susceptibility maps. Therefore, the methodology established for landslide mapping using LiDAR technology has practical implication in landslide risk reduction.