In this paper, for the first time, a regional-scale 1:100,000 landslide-susceptibility map (LSM) is presented for Sisak-Moslavina County in Croatia. The spatial relationship between landslide occurrence and landslide predictive factors (engineering geological units, relief, roughness, and distance to streams) is assessed using the integration of a statistically based frequency ratio (FR) into the analytical hierarchy process (AHP). Due to the lack of landslide inventory for the county, LiDAR-based inventories are completed for an area of 132 km². From 1238 landslides, 549 are chosen to calculate the LSM and 689 for its verification. Additionally, landslides digitized from available geological maps and reported via the web portal “Report a landslide” are used for verification. The county is classified into four susceptibility classes, covering 36% with very-high and high and 64% with moderate and low susceptibility zones. The presented approach, using limited LiDAR data and the extrapolation of the correlation results to the entire county, is encouraging for primary regional-level studies, justifying the cost-benefit ratio. Still, the positioning of LiDAR polygons prerequires a basic statistical analysis of predictive factors.

The Gajevo landslide in the Kravarsko area (Vukomeričke Gorice hilly area, northern Croatia) was chosen for investigation due to the existing landslide risk for the households at the landslide crown. Available data are limited, but a new landslide map and cross-section was developed within the presented research, mostly based on detailed light detection and ranging (LiDAR) data and field mapping. By comparing available orthophotos of the landslide, resident testimonies, precipitation data, and media releases, it was concluded that the landslide was activated in February 2014. The landslide was primarily triggered by increased precipitation (its measured variations could be in direct connection with ongoing global climate changes), but natural terrain features and anthropogenic factors also affected slope stability. New findings have led to the conclusion that the existing landslide area is large and complex and the crown and head scarp area should be stabilized by urgent remediation measures.

This study aims to improve the accuracy of small-scale landslide susceptibility maps (LSMs) by comparing two critical terrain factors—slope and relief energy. Slope is commonly used in LSMs, but its values are significantly sensitive to the spatial resolution of digital elevation models (DEMs). Although some studies have also addressed the effect of DEM resolution on relief parameters, direct comparisons between slope and relief energy remain limited. This research examines how these factors perform at different DEM resolutions and compare them to identify the most effective predictor for small-scale LSMs. Using the frequency ratio method, two LSM scenarios were evaluated: one using slope alongside geological units, and another using relief energy instead of slope, with various neighborhood distances. The study was conducted over a 29,785 km² area in the Pannonian part of Croatia. The findings indicate that relief energy is more stable across different DEM resolutions and enhances the accuracy of LSMs, particularly in large and geologically diverse regions. These results suggest that relief energy may serve as a more reliable factor for small-scale LSMs, offering practical implications for improving landslide risk prediction and land management strategies.

Climate change is anticipated to have predominantly negative impacts on human habitats in the coming decades, leading to significant environmental transformations, including an increase in landslide frequency. This paper introduces a novel approach to evaluating the susceptibility of geological units (GUs) to landslides, based on an analysis of the energy relief index. The method is applied to Pannonian Croatia, specifically the North Croatian Basin and the Hrvatsko Zagorje Basin, where landslides are the predominant mass movement process. Previous attempts to create landslide susceptibility maps for Croatia did not adequately analyze landslide inventories. Our approach utilizes three key datasets: landslide inventory, geological maps, and relief energy, to clearly identify which GUs are most frequently in an unstable equilibrium state. The findings highlight that the youngest isolated Miocene unit (M₇) has the highest landslide susceptibility. This method shows significant promise by enabling precise identification of landslide-prone GUs and offering the potential for integration into the production of small-scale landslide susceptibility maps, particularly for determining weighting factors for GUs within specific study areas. However, it is crucial to ensure that the landslide inventory accurately represents the entire landslide population in the study area.

The Gajevo landslide is located in a hilly area of northern Croatia, where numerous landslides endanger and damage houses, roads, water systems, and power lines. Nevertheless, available landslide data are relatively scarce. Therefore, the Gajevo landslide location was chosen for detailed research and the development of a typical landslide model for this area. During initial research, the geographical and geological settings were reviewed and historical orthophotos were analysed. Due to the complexity and vulnerability of the area, the location required detailed investigations and the integration of multi-level data: remote (based on high-resolution LiDAR data) and field landslide mapping were performed and a map of the landslide area was developed. Precipitation data were reviewed, while shallow boreholes with material sampling and geophysical measurements provided information on material characteristics and 3D (depth) insight. As a result, knowledge was gained about material resistivity and composition along with the depth of sliding surfaces, and an engineering geological map of the Gajevo landslide area with the landslide and directly endangered areas marked was developed to be used by the local community in landslide risk assessment. As it is reasonable to expect that an extreme rainfall event will occur in combination with snowmelt in the coming years, resulting in the reactivation of Gajevo landslide, further research and continuous landslide monitoring are recommended.