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Research Article Open access CC BY 4.0

Landslide Risk Assessment of Settlements Using GIS and Remote Sensing in Kashar, Albania

Enkela BEGU, Kosovrasti Albana, Huta Aida

Journal of Geography, Environment and Earth Science International · pp. 191–202 · Published 22 Jul 2025

10.9734/jgeesi/2025/v29i7926

Abstract

Landslides are complex situations that derive from a variety of interconnected processes, which include both causative and triggering factors. In residential areas, this phenomenon causes significant loss of life and infrastructural damage. Therefore, it is crucial for local and national decision makers to have up-to-date, suitable, and detailed information based on a complete landslide vulnerability assessment of the target area. In this context, the use of Geographic Information Systems (GIS) and Remote Sensing (RS) plays a key role in identifying and analyzing infrastructure at risk from landslides. Both these technologies offer the opportunity to integrate data on various contributing factors, such as geology, terrain slope, hydrography, and land use, to create maps that indicate the level of spatially distributed areas at risk and develop prevention strategies. This study focuses on identifying spatial distribution patterns of landslides occurring in the Kashar administrative unit, Tirana, Albania. This is done using the Multicriteria Decision Making (MCDA) technique in ArcGIS Desktop 10.8/ArcGIS Pro 3.2 by analyzing four main contributing factors: land cover, slope, distance from water flows, and soil texture. Their relation to the Kashar unit’s settlements has been examined, and hot spots of high risk of landslides have been identified. The study successfully identifies areas within the Kashar administrative unit that are most at risk of landslides. The results of this study can be highly valuable for urban planners and decision-makers. By identifying spatial patterns and hotspots of landslide risk in the Kashar administrative unit, authorities can prioritize areas for intervention, infrastructure reinforcement, or land-use restrictions. The integration of GIS and Remote Sensing provides a scientific basis for informed urban development, disaster preparedness, and zoning policies. These insights allow for targeted investment in resilient infrastructure and guide sustainable planning efforts to minimize future loss of life and property.

landslide GIS remote sensing MCDA satellite images socioeconomic

Cited by 4

Machine Learning-Based Landslide Risk Prediction in Tirana, Albania

Suela Vasil, E. Koçi, Flavio Corradini · Conference of the Open Innovations Association · 2026

Application Research on Geological Hazard Risk Zonation Evaluation Based on GIS Information Fusion:A Case Study of Bama County

Qiongzhen Tang, Shenye Liu, Ji-Jun Zeng · Journal of Safety Science and Engineering · 2025

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