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.

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.

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.

This dataset provides a comprehensive inventory of landslides in Pannonian Croatia, serving as an essential resource for assessing landslide susceptibility, hazard, and risk. Given the lack of a national-level landslide inventory in Croatia, this dataset represents an initial effort to systematically collect and digitize landslide data. Landslide point data were extracted from archived geological maps created at various scales and from publicly reported landslide locations submitted through the online portal “Report a Landslide”, managed by the Croatian Geological Survey. The data are compiled in ESRI shapefile format using ArcMap 10.2.1 software and standardized in the World Geodetic System 1984 (WGS84) coordinate system.