sustainability Article Regional-Scale Landslide Susceptibility Mapping Using Limited LiDAR-Based Landslide Inventories for Sisak-Moslavina County, Croatia Iris Bostjanˇ ci´ c *, Marina Filipovi´ c, Vlatko Gulam and Davor Pollak   Citation: Bostjanˇ ci´ c, I.; Filipovi´ c, M.; Gulam, V.; Pollak, D. Regional-Scale Landslide Susceptibility Mapping Using Limited LiDAR-Based Landslide Inventories for Sisak-Moslavina County, Croatia. Sustainability 2021, 13, 4543. https://doi.org/10.3390/su13084543 Academic Editor: Zoran Naki´ c Received: 30 March 2021 Accepted: 18 April 2021 Published: 19 April 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Department of Hydrogeology and Engineering Geology, Croatian Geological Survey, Sachsova 2, 10000 Zagreb, Croatia; marina.filipovic@hgi-cgs.hr (M.F.); vlatko.gulam@hgi-cgs.hr (V.G.); davor.pollak@hgi-cgs.hr (D.P.) * Correspondence: iris.bostjancic@hgi-cgs.hr Abstract: 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 km 2 . 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. Keywords: landslide susceptibility; regional-scale; LiDAR; frequency ratio; AHP; Croatia 1. Introduction Over the last few decades, we have witnessed frequent occurrences of natural hazards, among which landslides are no exception. This trend is expected to continue in the future, according to [1], due to increased urbanization, continued deforestation, and increased regional precipitation caused by changing climate patterns. Keeping in mind that climate changes undoubtedly affect the stability of natural and engineered slopes [2,3], it is nec- essary to emphasize the need for future landslide risk reduction. Since landslides have a great impact on humans, their property, and the environment [4], knowledge improve- ment and raising awareness among the wider public should be highlighted. Considering landslides, one way that helps land-use planners and policy-makers to achieve sustainable land management is terrain zonation according to slope stability through susceptibility, hazard, and risk assessment [5,6]. Landslide susceptibility maps (LSM) show the spatial probability of landslide oc- currence [7,8]. In areas where the possibility of their occurrence exists, the production of LSMs is an important step to define the geoenvironmental characteristics of the area in the aspect of spatial planning. The zonation of terrain according to the degree of landslide susceptibility is the foundation of rational land-use management, with an emphasis on safe and planned construction. The basic concept of landslide-susceptibility assessment includes the spatial distri- bution of prevalent geoenvironmental factors to determine areas prone to landsliding without temporal implications [9]. LSM divides the area of interest into zones of the same Sustainability 2021, 13, 4543. https://doi.org/10.3390/su13084543 https://www.mdpi.com/journal/sustainability