Landslides in the Podsljeme area of the City of Zagreb cause significant economic losses, which have been increasing over the last several decades due to the urbanisation of hilly areas and the influence of climate changes. An airborne LiDAR digital terrain model (DTM) with a spatial resolution of 30 × 30 cm was used to prepare a landslide inventory map of the pilot area (21 km²) with more than 700 identified landslides. The area of the smallest identified landslide is 43 m², while 90% of the landslides are between 100 and 2,000 m². The frequency–size distribution of all mapped landslides in the pilot area shows a very high level of landslide inventory completeness. Therefore, it is concluded that the LiDAR-based terrain model is a valuable tool for the preparation of detailed landslide inventories in heavily vegetated regions such as the hilly area of Medvednica Mt.

Soil erosion processes are a type of geological hazard. They cause soil loss and sediment production, landscape dissection, and economic damage, which can, in the long term, result in land abandonment. Thus, identification of soil erosion processes is necessary for sustainable land management in an area. This study presents the potential of visual interpretation of high resolution LiDAR (light detection and ranging) imagery for direct and unambiguous identification and mapping of soil erosion processes, which was tested in the study area of the Vinodol Valley (64.57 km²), in Croatia. Eight LiDAR images were derived from the 1 m airborne LiDAR DTM (Digital Terrain Model) and were used to identify and map gully erosion, sheet erosion, and the combined effect of rill and sheet erosion, with the ultimate purpose to create a historical erosion inventory. The two-step procedure in a visual interpretation of LiDAR imagery was performed: preliminary and detailed. In the preliminary step, possibilities and limitations for unambiguous identification of the soil erosion processes were determined for representative portions of the study area, and the exclusive criteria for the accurate and precise manual delineation of different types of erosion phenomena were established. In the detailed step, the findings from the preliminary step were used to map the soil erosion phenomena in the entire studied area. Results determined the highest potential for direct identification and mapping of the gully erosion phenomena. A total of 236 gullies were identified and precisely delineated, although most of them were previously unknown, due to the lack of previous investigations on soil erosion processes in the study area. On the other hand, the used method was proven to be inapplicable for direct identification and accurate mapping of the sheet erosion. Sheet erosion, however, could have been indirectly identified on certain LiDAR imagery, based on recognition of colluvial deposits accumulated at the foot of the eroded slopes. Furthermore, the findings of this study present which of the used LiDAR imagery, and what features of the imagery used, are most effective for identification and mapping of different types of erosion processes.

The objective of the study is to show that landslide conditioning factors derived from different source data give significantly different relative influences on the weight factors derived with statistical models for landslide susceptibility modelling and risk analysis. The analysis of the input data for large-scale landslide hazard assessment was performed on a study area (20.2 km²) in Hrvatsko Zagorje (Croatia, Europe), an area highly susceptible to sliding with limited geoinformation data, including landslide data. The main advantage of remote sensing technique (i.e., LiDAR, Light Detection and Ranging) data and orthophoto images is that they enable 3D surface models with high precision and spatial resolution that can be used for deriving all input data needed for landslide hazard assessment. The visual interpretation of LiDAR DTM (Digital Terrain Model) morphometric derivatives resulted in a detailed and complete landslide inventory map, which consists of 912 identified and mapped landslides, ranging in size from 3.3 to 13,779 m². This inventory was used for quantitative analysis of 16 input data layers from 11 different sources to analyse landslide presence in factor classes and thus comparing landslide conditioning factors from available small-scale data with high-resolution LiDAR data and orthophoto images, pointing out the negative influence of small-scale source data. Therefore, it can be concluded that small-scale landslide factor maps derived from publicly available sources should not be used for large-scale analyses because they will result in incorrect assumptions about conditioning factors compared with LiDAR DTM derivative factor maps. Furthermore, high-resolution LiDAR DTM and orthophoto images are optimal input data because they enable derivation of the most commonly used landslide conditioning factors for susceptibility modelling and detailed datasets about elements at risk (i.e., buildings and traffic infrastructure data layers).

The study presents a landslide susceptibility assessment on a large scale in the City of Zagreb (Croatia). The susceptibility analysis was performed using the Weight of Evidence model in the pilot area (21 km²) and applying the obtained weight values for each class of conditioning factors in the study area (130 km²). The input data were LiDAR-based landslide inventory and six conditioning factors derived from 5 m LiDAR DTM, 5 m SfM DEM, and geological and land-use maps. The validation of the susceptibility assessment for the study area was evaluated with a ROC curve, which showed a high prediction rate (AUC = 84.4%), similar to the prediction rate for the pilot area (AUC = 86.9%). Based on the results, it can be concluded that the proposed method for large-scale landslide susceptibility assessment, where susceptibility conditions are defined in smaller pilot areas, can be applied to larger research areas with similar geomorphological and geological conditions.