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 analyses of input data impact on the quality of the landslide susceptibility large-scale maps. For comparison, two input data sets were used to produce two landslide susceptibility maps. The first input data set included free-available, small-scale data with low spatial accuracy, while the second set included high-resolution remote sensing data. The same nine types of landslide causal factors were derived and used for susceptibility analyses. Furthermore, LiDAR-based landslide inventory and bivariate statistical method, i.e. Information Value method, were used for susceptibility modelling. The resulting landslide susceptibility maps were compared with ROC curves. Success and prediction rates showed that the landslide susceptibility model based on causal factors derived from high-resolution remote sensing data is approximately 10% more accurate than the model based on causal factors derived from small-scale input data. Furthermore, based on the conducted research, it can be concluded that susceptibility modelling based on small-scale data and LiDAR-based inventories enables reliable landslide susceptibility assessments at the regional level.

Quantitative characterisation of the geometrical properties of discontinuities in fractured rock masses is fundamental for understanding their mechanical behaviour, structural characterisation, and for performing reliable rockfall susceptibility assessments. Discontinuity abundance parameters, such as intensity and density, play a key role in rock mass classification and hazard analysis. Yet, accurately estimating them remains challenging due to limited accessibility, scale effects, and censoring bias in conventional field surveys.Recent advances in remote sensing techniques, particularly UAV-based digital photogrammetry, enable the acquisition of high-resolution three-dimensional point clouds and ortho-view images, commonly referred to as Digital Outcrop Models (DOMs). These datasets significantly improve access to steep or unstable rock faces and enable detailed, reproducible discontinuity mapping. However, standardised, open-source tools for the quantitative analysis of discontinuity abundance from 2D ortho-view images remain limited.Here, we present a new toolbox within the open-source MATLAB application QDC-2D (Quantitative Discontinuity Characterization, 2D) (Loiotine et al., 2021), focused on the calculation and spatial visualization of discontinuity abundance parameters. The toolbox computes commonly used linear (P10) and areal (P20, P21) discontinuity intensity and density metrics using two approaches. It uses well-established Mauldon estimators (Mauldon et al., 2001) and introduces a circular scan window approach that improves fracture intensity and density estimation through direct calculation of discontinuity trace segment lengths and number within the circular scan window. The toolbox further allows user-defined regions of interest (ROI) and cluster-based abundance calculation to capture spatial variability in discontinuity density and intensity. This approach enables the detection of high-fracturing zones with high certainty.The toolbox's capabilities have been thoroughly tested and validated using multiple synthetic discontinuity datasets, demonstrating robust, reliable performance. This extension toolbox for QDC-2D provides a reproducible, accessible framework for quantitative discontinuity analysis, thereby supporting improved structural characterisation of fractured rock masses. References:Mauldon, M., Dunne, W. M., & Rohrbaugh, M. B., Jr. (2001). Circular scanlines and circular windows: New tools for characterizing the geometry of fracture traces. Journal of Structural Geology, 23(2–3), 247–258.Loiotine, L., Wolff, C., Wyser, E., Andriani, G. F., Derron, M.-H., Jaboyedoff, M., & Parise, M. (2021). QDC-2D: A Semi-Automatic Tool for 2D Analysis of Discontinuities for Rock Mass Characterization. Remote Sensing, 13(24), 5086. https://doi.org/10.3390/rs13245086