Resulting Landslide Susceptibility Maps Research Articles

GIS Based Spatial Analysis of Landslide Hazard in Tehsil Balakot, KP, Pakistan

The Himalayan region is highly susceptible to landslides due to intense rainfall and seismic activity, posing significant risks to slope stability. Due to landslides, Tehsil Balakot valley has suffered substantial damage to infrastructure, roads, and the tourism sector. This research aims to develop a landslide susceptibility map and identify hazard-prone areas to support sustainable development and mitigate risks in Balakot valley. The study employs a weighted overlay analysis using primary and secondary data raster layers, including slope, aspect, precipitation, and seismic raster maps. The slope and aspect maps are derived from a 30-meter ASTER digital elevation model, while precipitation maps are generated using Inverse Distance Weighted (IDW) interpolating data from the Pakistan Meteorological Department. Seismic data is acquired from the Geological Survey of Pakistan (GSP). The resulting landslide susceptibility map classifies the region into five hazard classes: very high, high, medium, low, and very low. The landslide susceptibility maps provide valuable insights for identifying areas at varying levels of landslide risk.

Leveraging artificial neural networks for robust landslide susceptibility mapping: A geospatial modeling approach in the ecologically sensitive Nilgiri District, Tamil Nadu

Landslides pose a significant threat to the lives and livelihoods of marginalised communities residing in rural areas and the delicate ecological balance of the environment. Implementing advanced technologies is crucial for improving hazard risk assessment and enhancing preparedness measures in regions characterised by diverse topography and complex geological formations. Geospatial applications and modelling techniques have emerged as indispensable in mitigating landslide risks, particularly in environmentally sensitive areas. This study presents a comprehensive approach to landslide susceptibility mapping in the Nilgiri District of Tamil Nadu, India, leveraging the power of Artificial Neural Networks (ANNs) and integrating multi-dimensional geospatial datasets. Integrating ANN-based modelling and geospatial techniques offers significant advantages in terms of statistical robustness, reproducibility, and the ability to analyse the complex interplay of factors influencing landslide hazards quantitatively. The methodology involves rigorous pre-processing and integrating spatial data, including landslide event occurrences as the dependent variable and ten independent parameters influencing landslide susceptibility. These parameters encompass elevation, slope aspect, slope degree, distance to roads, land use patterns, geomorphology, lithology, drainage density, lineament density, and rainfall distribution. Feature extraction and selection techniques are employed to effectively model the complex interactions between these factors and landslide occurrences. This process identifies the most relevant variables influencing landslide susceptibility, enhancing the model's predictive capabilities. The state-of-the-art ANNs are trained using historical landslide occurrence data and the selected influencing factors, enabling the development of a robust and accurate landslide susceptibility model. The performance of the developed model is rigorously evaluated using a comprehensive suite of metrics, including accuracy, precision, and the Area under the Receiver Operating Characteristic (ROC) curve. Preliminary results indicate that the ANN-based landslide susceptibility model outperforms traditional zonation methods, demonstrating higher accuracy and reliability in predicting landslide-prone areas. The resulting Landslide Susceptibility Map (LSM) categorises the study area into five distinct hazard zones, ranging from very high (664.1 ​km2), high (598.9 ​km2), moderate (639.7 ​km2), low (478.9 ​km2) and to very low (170.9 ​km2). Notably, the eastern and central regions of the district emerge as particularly vulnerable to landslide occurrences. The study's findings have far-reaching implications for disaster risk reduction efforts, land-use planning, and sustainable development strategies in the ecologically sensitive Nilgiri District and beyond.

Geology and Landslide Susceptibility Using GIS at Kampung Belahat, Jeli, Kelantan

The geological study of Kampung Belahat in Jeli, Kelantan, Malaysia, which is located close to the Thai border in northeastern Peninsular Malaysia, is the main subject of this research. The Jeli district, which spans an area of roughly 2,552 square kilometers, has three different geomorphological types: low, hills, and mountainous. Kampung Belahat is surrounded by a mix of flat, hilly, and mountainous terrain due to the area’s height range of 80m to 900m. The goal of the study is to update Kampung Belahat’s geological map, with a particular emphasis on geomorphology, lithology, and structural geology and to determine the landslide susceptibility of study area. Comprehensive geological data was gathered by laboratory work, sampling, and traversing during the geological mapping process. The area’s lithology consists of high-grade metamorphic rock types and intrusive igneous rock, with granite and gneiss being the main rock types. The granite rock shows signs of metamorphic processes, including 33% biotite, 46% quartz, 11% alkali feldspar, and 10% plagioclase. Gneiss rock, on the other hand, has flattened minerals as a result of metamorphism; it contains 13% alkali feldspar, 35% quartz, and 52% biotite. Granite was the primary geological structure observed, combined with visible quartz veins and joint and fault structures. The study took into account five factors: aspect, drainage density, lithology, slope, and land use. The Weightage Overlay Method (WOM) was used to process the data. The study area is categorized into three risk levels on the resulting landslide susceptibility map: low (5%), moderate (30%), and high (65%). This knowledge is essential for putting safety precautions in place, reducing potential harm during future development in the area, and reducing the likelihood of landslides.

Spatial Prediction of Landslide Susceptibility Using Logistic Regression (LR), Functional Trees (FTs), and Random Subspace Functional Trees (RSFTs) for Pengyang County, China

Landslides pose significant and serious geological threat disasters worldwide, threatening human lives and property; China is particularly susceptible to these disasters. This paper focuses on Pengyang County, which is situated in the Ningxia Hui Autonomous Region of China, an area prone to landslides. This study investigated the application of machine learning techniques for analyzing landslide susceptibility. To construct and validate the model, we initially compiled a landslide inventory comprising 972 historical landslides and an equivalent number of non-landslide sites (Data sourced from the Pengyang County Department of Natural Resources). To ensure an impartial evaluation, both the landslide and non-landslide datasets were randomly divided into two sets using a 70/30 ratio. Next, we extracted 15 landslide conditioning factors, including the slope angle, elevation, profile curvature, plan curvature, slope aspect, TWI (topographic wetness index), TPI (topographic position index), distance to roads and rivers, NDVI (normalized difference vegetation index), rainfall, land use, lithology, SPI (stream power index), and STI (sediment transport index), from the spatial database. Subsequently, a correlation analysis between the conditioning factors and landslide occurrences was conducted using the certainty factor (CF) method. Three landslide models were established by employing logistic regression (LR), functional trees (FTs), and random subspace functional trees (RSFTs) algorithms. The landslide susceptibility map was categorized into five levels: very low, low, medium, high, and very high susceptibility. Finally, the predictive capability of the three algorithms was assessed using the area under the receiver operating characteristic curve (AUC). The better the prediction, the higher the AUC value. The results indicate that all three models are predictive and practical, with only minor discrepancies in accuracy. The integrated model (RSFT) displayed the highest predictive performance, achieving an AUC value of 0.844 for the training dataset and 0.837 for the validation dataset. This was followed by the LR model (0.811 for the training dataset and 0.814 for the validation dataset) and the FT model (0.776 for the training dataset and 0.760 for the validation dataset). The proposed methods and resulting landslide susceptibility map can assist researchers and local authorities in making informed decisions for future geohazard prevention and mitigation. Furthermore, they will prove valuable and be useful for other regions with similar geological characteristics features.

Insights into large landslide mechanisms in tectonically active Agadir, Morocco: The significance of lithological, geomorphological, and soil characteristics

Landslide susceptibility assessment is crucial for land use planning, infrastructure development, and hazard mitigation, particularly in tectonically active regions where lithology and soil characteristics significantly influence slope stability. This study aims to evaluate landslide susceptibility in the Agadir Ida-Ou-Tanane province, a tectonically active area, using a hierarchical multicriteria analysis approach. Eight factors influencing landslide occurrence were considered: lithology, slope, LULC, distance to main faults, hypsometry, drainage density, road density, and aspect. Pairwise comparison matrices and expert opinion were employed to determine the relative importance of these factors. The consistency ratio (CR) was calculated to ensure the reliability of the judgments. The resulting landslide susceptibility map reveals a spatial distribution ranging from very low to very high susceptibility zones. Lithology, mainly clay, and limestone formations, emerged as the most influential factor due to the active tectonic context of the study area. The model was validated using observed landslide locations and the Area Under the Curve (AUC) method, yielding a reasonable validation accuracy. The findings indicate that integrating these factors, with a particular emphasis on lithology and soil characteristics, effectively identifies areas prone to landslides, making the resulting map a valuable tool for land use planning and risk management in the Agadir Ida-Ou-Tanane region. This study contributes to understanding landslide susceptibility in tectonically active areas and provides a practical foundation for future research and decision-making. Regularly monitoring and updating the landslide susceptibility map is necessary to ensure its continued relevance and utility for stakeholders.

An integrated approach based landslide susceptibility mapping: case of Muzaffarabad region, Pakistan

Landslides result in the devastation of property and loss of lives. This study assesses landslide susceptibility by employing geographic information systems (GIS) and machine learning techniques, that is, support vector machine (SVM) and artificial neural network (ANN), with the integration of advanced optimization techniques, that is, particle swarm optimization (PSO). The landslide-inducing factors considered in this study include fault density, lithology, road density, slope, elevation, flow direction, aspect, earthquake intensity, curvature, Normalized Difference Water Index (NDWI), waterways density, rainfall, and Normalized Difference Vegetation Index (NDVI). The resulting landslide susceptibility maps (LSMs) showed that the areas falling under the high and very high susceptibility class have higher rainfall levels, weak lithology, high NDWI, and flow direction. The accuracy assessment of the techniques showed that ANN with an Area Under the Curve (AUC) of 0.81 performed better than SVM with an AUC of 0.78 without the optimization. Similarly, the performance of ANN was also better than SVM using PSO. During the integrated modeling, the AUC of PSO-ANN was 0.87, whereas the AUC of PSO–SVM was 0.84. The accuracy assessment of the produced LSMs also showed a similar trend in terms of accuracy percentage as that of the models.

Utilization of frequency ratio method for the production of landslide susceptibility maps: Karaburun Peninsula case, Turkey.

Geographical information systems (GIS) facilitate both current landslide mapping processes and the prediction of potential landslides that may be experienced in the future. Within the scope of the study, landslide susceptibility maps were created to reduce the damage of possible landslides in the Karaburun Peninsula of İzmir province. A landslide inventory map was produced from related databases in the first place, followed by the creation of parameter maps (elevation, aspect, slope, curvature, land use, vegetation cover, lithology, distance to roads, distance to rivers, and distance to fault lines). The frequency ratio (FR) method was utilized for producing the landslide susceptibility maps on a 5-level risk scale ranging from very low to very high-risk categories. Receiver operating characteristic (ROC) analysis was performed for accuracy testing. The resulting landslide susceptibility map revealed that 3% and 46% of the study area had high- and medium-risk categories, and the low landslide risk areas comprised 47% of the region. These results provide important inputs to guide sustainable strategic and physical planning processes in the region, which has been declared a special protection area and is a popular destination for tourism activities and energy facilities.

Landslide Susceptibility Mapping using Frequency Ratio, a case study of Vythiriblock in Wayanad, the northern part of Kerala, India

Landslides are common and disastrous events in India's Western Ghats during the rainy season and various factors influence the occurrence of landslides. The present research aims to establish landslide susceptibility zones to control landslide hazards and risk effectively. Landslides occurred in the entire area of the Wayanad district. The study was carried out in vythiri taluk to do detailed surveys using remote sensing and GIS, GPS and field investigation. A total of 162 landslide locations are used to develop a landslide susceptibility map of this area. Landslide susceptibility map of this area was done by using the frequency ratio (FR) model. The landslide susceptibility map is based on factors that affect directly or indirectly land subsidence process. There are twelve landslide influencing factors taken for this study: Slope, Aspect, Land use/land cover, Rainfall, Vegetation, Soil, Distance to lineament, Distance to the road, Elevation, Curvature, Geomorphology and Lithology. The resulting landslide susceptibility map shows that 3.6 % of the region is in a low susceptible zone, 40.5 % is in a moderate susceptible zone, 49 % is in a high susceptible zone and 6.9 % is in a very high susceptible zone to landslides. According to this model, the area under the curve (AUC) for the success rate is 75.3 %. This study would support the relevant authorities in identifying potential risks in particular parts which could be used for landslide mitigation activities as construction development in the region increases.

An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping

In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor.

Landslide susceptibility mapping with r.landslide: A free open-source GIS-integrated tool based on Artificial Neural Networks

This study presents r.landslide, a free and open source add-on to the open source Geographic Information System (GIS) GRASS software for landslide susceptibility mapping. The tool was written in Python language and works on the top of an Artificial Neural Network (ANN) fed with environmental parameters and landslide databases. In order to illustrate the application and effectiveness of the developed tool, a case study is presented for the municipality of Porto Alegre, Brazil. The resulting landslide susceptibility maps are compared with the map published by the Brazilian Geological Survey (CPRM) and a direct comparison using unseen (new) landslide records indicate that the r.landslide can identify and pinpoint susceptible areas with better accuracy. The module can be used by natural disaster management bodies and land use planning organs as a support tool for the elaboration of landslide susceptibility maps in an agile and efficient manner.

Landslide susceptibility evaluating using artificial intelligence method in the Youfang district (China)

This study assesses the landslide susceptibility of the Youfang area, China. For this purpose, four advanced artificial intelligence models, namely, Naive Bayes (NB), multilayer perceptron (MLP), kernel logistic regression (KLR), and J48-bagging methods, were applied and compared. The relationship between landslides happening and landslide conditioning factors which include: slope, aspect, altitude, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), landuse, lithology, distance to faults, distance to roads, distance to rivers, and rainfall were analyzed by the frequency ratios method. These results indicated that MLP model exhibits the most stable and reasonable result, and the resultant landslide susceptibility maps are a useful tool for local government managers and policy planners for this study area and other areas.

A Hybrid Spatial Multi-Criteria Evaluation Method for Mapping Landslide Susceptible Areas in Kullu Valley, Himalayas

In this paper we report our results from analysing a hybrid spatial multi-criteria evaluation (SMCE) method for generating landslide susceptibility mapping (LSM). This study is the first of its kind in the Kullu valley, Himalayas. We used eight related geospatial conditioning factors from three main groups: geological, morphological and topographical factors. Our landslide inventory dataset has a total of 149 GPS points of landslide locations, collected based on a field survey in July 2018. The relationships between landslide locations and conditioning factors were determined using the GIS-based statistical methods of frequency ratio (FR), multi-criteria decision-making (MCDM) and the integration method of hybrid SMCE. We compared the performance of applied methods by dividing the inventory into testing (70%) and validation (30%) datasets. The area under the curve (AUC) was used to validate the results. The integration method of hybrid SMCE gave the highest accuracy rate (0.910) compared to the other two methods, with 0.797 and 0.907 accuracy rates for the analytical hierarchy process (AHP) and FR, respectively. The applied methodologies are easily transferable to other areas, and the resulting landslide susceptibility maps (LSMs) can be useful for risk mitigation and development planning purposes in the Kullu valley, Himalayas.

Identification of landslide susceptible settlements using geographical information system of Yelwandi river basin, Maharashtra (India)

Landslides are one of the critical geological processes, which cause enormous damage to civil engineering structures and also lead to loss of life. The present study is an attempt toward the development of a landslide methodology by using geographical information system (GIS) and remote sensing techniques for landslide susceptibility mapping. This involves the generation of thematic data layers and their spatial analysis within the Yelwandi river basin in Maharashtra, India. A weight rating system for the factors was developed for spatial data analysis in a GIS. The resulting landslide susceptibility map divides the area into different zones of four relative susceptibility classes: high, moderate, low and very low. The susceptibility map was correlated by field observations. The 67 settlements present in Yelwandi river basin area were classified according to severity of landslide. It also gives guidelines for feature activities of planning.

Robustness evaluation of fuzzy expert system and extreme learning machine for geographic information system-based landslide susceptibility zonation: A case study from Indian Himalaya

In the last few decades, with the development of computers and geographic information system (GIS), a wide range of landslide susceptibility zonation (LSZ) techniques were orchestrated by various researchers around the globe. Among them, the artificial intelligence (AI) have been distinctly regarded as the most effective and suitable approach to part with GIS for LSZ. Though, suitability of AI for LSZ is well addressed in the landslide literature, noises of processing data, choice of causative factors and landslide density of study area are the number of hindrances that cause quandary over preference of ideal AI technique among many. The current study intends to analyse and compare the predictive performance of two entirely different AI techniques, fuzzy expert system (FES), a bivariate statistical technique, and extreme learning machine (ELM), a multivariate statistical technique for GIS based LSZ. The Mussoorie Township, a famous tourist destination in the Indian State of Uttarakhand was taken as the study area. Thematic layers of relevant causative factors and landslide inventory were prepared for the study area through field survey, remote sensing, and GIS. The resultant landslide susceptibility maps (LSM) of the study area, LSM-I of FES and LSM-II of ELM were critically evaluated and compared with the aid of landslide inventory of the study area.

A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping

In recent years, ensemble learning methods have become popular in landslide susceptibility mapping (LSM) with varying degrees of success. Within classifier ensemble concept, decision tree based ensemble learners such as random forest (RF) (i.e. decision forest) and rotation forest (RotFor) have gained a great interest due to their robustness against conventional statistical methods. This study proposes canonical correlation forest (CCF), a new member of ensemble learning family, in the prediction of landslide susceptibility for Yenice district of Karabuk in Turkey. To test the robustness and suitability of the CCF method, its prediction performance was compared to two well-known machine learning ensemble algorithms, RF and RotFor, and a commonly used statistical method, the logistic regression (LR). Furthermore, the effects of variations in ratio of training/testing datasets were assessed on the performances of RF, CCF, RotFor and LR models using the root-mean square error (RMSE). The quality of resulting landslide susceptibility maps was evaluated using overall accuracy (OA), Kappa coefficient (KC), success rate curves and receiver operating characteristic (ROC) curves. Wilcoxon’s signed rank test was also applied to measure the statistical differences of the accuracies of susceptibility maps. The estimated area under curve (AUC) values for RF, CCF, RotFor and LR models were 0.982, 0.970, 0.966 and 0.826, respectively. It was clear that ensemble learning algorithms outperformed the LR method. The results also showed that selection of sampling ratio had significant effect on model performance of RF, CCF, RotFor and LR models, and the lowest RMSE values were estimated with the use of 70:30 ratio for training and test datasets.

Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along Upper Bhote Koshi, Nepal

The 2015 Mw7.8 Gorkha earthquake triggered thousands of landslides of various types scattered over a large area. In the current study, we utilized pre- and post-earthquake high-resolution satellite imagery to compile two landslide inventories before and after earthquake and prepared three landslide susceptibility maps within 404 km(2) area using frequency ratio (FR) model. From the study, we could map about 519 landslides including 178 pre-earthquake slides and 341 coseismic slides were identified. This study investigated the relationship between landslide occurrence and landslide causative factors, i.e., slope, aspect, altitude, plan curvature, lithology, land use, distance from streams, distance from road, distance from faults, and peak ground acceleration. The analysis showed that the majority of landslides both pre-earthquake and coseismic occurred at slope >30 degrees, preferably in S, SE, and SW directions and within altitude ranging from 1000 to 1500 m and 1500 to 3500 m. Scatter plots between number of landslides per km(-2) (LN) and percentage of landslide area (LA) and causative factors indicate that slope is the most influencing factor followed by lithology and PGA for the landslide formation. Higher landslide susceptibility before earthquake is observed along the road and rivers, whereas landslides after earthquake are triggered at steeper slopes and at higher altitudes. Combined susceptibility map indicates the effect of topography, geology, and land cover in the triggering of landslides in the entire basin. The resultant landslide susceptibility maps are verified through AUC showing success rates of 78, 81, and 77%, respectively. These susceptibility maps are helpful for engineers and planners for future development work in the landslide prone area.

Landslide susceptibility mapping of Blue Nile and Tekeze River Basins using oblique rainfall-aspect rasters

This research was carried out to prepare the regional level landslide susceptibility maps by incorporating the oblique rainfall raster in the upper Blue Nile and Tekeze River basins. The oblique rainfall is the amount that actually falls on sloping surfaces, and varies considerably with slope inclination and aspect with respect to the prevailing trend of the wind direction. The monthly averaged precipitation data for the Kermit (July–September) and the Belg (March–April) rainfall seasons for the study area were acquired for the period of 1950 to 2000, and utilized to compute the oblique rainfall vectors at 40°, 45°, 50°, 55°, and 60° angles (representing “wind-driven” rainfall vectors). The weighted overlay index method using ArcGIS software was applied for this regional landslide susceptibility mapping (scales >1:100,000) by incorporating vertical rainfall intensity maps and aspect separately and as a combination (rainfall raster coupled with the slope aspect raster). The resulting landslide susceptibility maps were compared which reveals that the results obtained from using integrated rainfall/aspect raster’s (combined) were found to be more reasonable towards computing high to very high hazards than using aspect and rainfall rasters as separate layers. The susceptibility maps were validated with landslide inventory maps as well as documented rockslides, scattered throughout the study area. This reconnaissance level study could serve as guide maps in identifying those areas where more detailed landslide hazard mapping might, or should be, undertaken in the future for detailed investigations.

Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping

This study compares the predictive performance of GIS-based landslide susceptibility mapping (LSM) using four different kernel functions in support vector machines (SVMs). Nine possible causal criteria were considered based on earlier similar studies for an area in the eastern part of the Khuzestan province of southern Iran. Different models and the resulting landslide susceptibility maps were created using information on known landslide events from a landslide inventory dataset. The models were trained using landslide inventory dataset. A two-step accuracy assessment was implemented to validate the results and to compare the capability of each function. The radial basis function was identified as the most efficient kernel function for LSM with the resulting landslide susceptibility map showing the highest predictive accuracy, followed by the polynomial kernel function. According to the obtained results, it concluded that using SVMs can generally be considered to be an effective method for LSM while it demands careful consideration of kernel function. The results of the present research will also assist other researchers to select the best SVM kernel function to use for LSM.

POTENTIAL OF SENTINEL-1A FOR NATION-WIDE ROUTINE UPDATES OF ACTIVE LANDSLIDE MAPS

Slope deformation is one of the typical geohazards that causes an extensive economic damage in mountainous regions. As such, they are usually intensively monitored by means of modern expertise commonly by national geological or emergency services. Resulting landslide susceptibility maps, or landslide inventories, offer an overview of areas affected by previously activated landslides as well as slopes known to be unstable currently. Current slope instabilities easily transform into a landslide after various triggering factors, such as an intensive rainfall or a melting snow cover. In these inventories, the majority of the existing landslide-affected slopes are marked as either stable or active, after a continuous investigative work of the experts in geology. In this paper we demonstrate the applicability of Sentinel-1A satellite SAR interferometry (InSAR) to assist by identifying slope movement activity and use the information to update national landslide inventories. This can be done reliably in cases of semi-arid regions or low vegetated slopes. We perform several analyses based on multitemporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides.

POTENTIAL OF SENTINEL-1A FOR NATION-WIDE ROUTINE UPDATES OF ACTIVE LANDSLIDE MAPS

Abstract. Slope deformation is one of the typical geohazards that causes an extensive economic damage in mountainous regions. As such, they are usually intensively monitored by means of modern expertise commonly by national geological or emergency services. Resulting landslide susceptibility maps, or landslide inventories, offer an overview of areas affected by previously activated landslides as well as slopes known to be unstable currently. Current slope instabilities easily transform into a landslide after various triggering factors, such as an intensive rainfall or a melting snow cover. In these inventories, the majority of the existing landslide-affected slopes are marked as either stable or active, after a continuous investigative work of the experts in geology. In this paper we demonstrate the applicability of Sentinel-1A satellite SAR interferometry (InSAR) to assist by identifying slope movement activity and use the information to update national landslide inventories. This can be done reliably in cases of semi-arid regions or low vegetated slopes. We perform several analyses based on multitemporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides.