The accuracy paradox: comparing high-accuracy LiDAR and topographic DEMs for landslide susceptibility assessment in the Slanské vrchy Mountains, Slovakia

The recent census in India revealed that India is now housing 17% of the world’s population, and India is on the way to become the most populated country. Landslides are an increasing concern in India due to the rapid population expansion in hilly and mountainous terrain. Landslides affect vast areas within India, in particular in the Himalayan chain in the North and Eastern part of the country and the Western Ghats in the Southwest. The Geological Survey of India (GSI) has been designated as the nodal agency for landslides by the Indian government, and they are responsible for landslide inventory, susceptibility and hazard assessment. Until recently their landslide susceptibility assessment was based on a heuristic approach using fixed weights or ranking of geofactors, based on guidelines of the Bureau of Indian Standards (BIS). However, this method is disputed as it doesn’t provide accurate results. This paper gives an overview of recent research on how the existing methods for landslide inventory, susceptibility and hazard assessment in India could be improved, and how these could be used in (semi)quantitative risk assessment. Due to the unavailability of airphotos in large parts of India, satellite remote sensing data has become the standard data input for landslide inventory mapping. The National Remote Sensing Center (NRSC) has developed an approach using semi-automatic image analysis algorithms that combine spectral, shape, texture, morphometric and contextual information derived from high resolution satellite data and DTMs for the preparation of new as well as historical landslide inventories. Also the use of existing information in the form of maintenance records, and other information to generate event-based landslide inventories is presented. Event-based landslide inventories are used to estimate the relation between temporal probability, landslide density and landslide size distribution. Landslide susceptibility methods can be subdivided in heuristic, statistical and deterministic methods. Examples are given on the use of these methods for different scales of analysis. For medium scales a method is presented to analyze the spatial association between landslides and causal factors, including those related to structural geology, to select the most appropriate spatial factors for different landslide types, and combine them using the multivariate methods. For transportation corridors a method is presented for quantitative hazard and risk assessment based on a landslide database. Deterministic methods using several dynamic slope-hydrology and slope stability models have been applied to evaluate the relation between land use changes and slope stability in a steep watershed. The paper ends with an overview how the susceptibility maps can be combined with the landslide databases to convert them into hazard maps which are subsequently used in (semi) quantitative risk assessment at different scales of analysis, and how the results can be used in risk reduction planning.

As a critical predisaster warning tool, landslide susceptibility assessment is crucial in disaster prevention and mitigation efforts. However, earlier methods for assessing landslide susceptibility have often ignored the impact of similarities in geographical attributes, restricting their feasibility in regions with diverse characteristics. The geographical-optimal-similarity (GOS) model effectively captures similarity relations within geospatial data and can isolate region-specific landslide features, thus overcoming this challenge. Consequently, a landslide susceptibility assessment method was developed by integrating the information value (IV) model with the GOS model. Huangshan City in Anhui Province, China, was selected as the study region. This research used 11 remote sensing feature factors and 657 historical landslide points, combined with the IV model, to construct a dataset for landslide prediction and susceptibility assessment using the GOS model. The findings indicate that, compared to conventional methods such as random forest, logistic regression, and radial basis function classifier, the GOS model enhances the area under the curve (AUC) value by 2.81% to 8.92%, reaching 0.846. This demonstrates superior performance and confirms the effectiveness and accuracy of the method in landslide susceptibility assessment. Furthermore, compared to the basic-configuration-similarity (BCS) model, the GOS model increases the AUC value by 9.64%, achieving 0.846. This approach substantially diminishes the effects of historical data accuracy, revealing upgraded applicability in landslide susceptibility evaluations. Landslides in Huangshan City are primarily influenced by rainfall and vegetation cover. High-susceptibility zones are predominantly located in areas with high precipitation and low vegetation cover. In contrast, low-susceptible and non-susceptible zones are primarily found in flat areas with high vegetation cover and farther from fault lines. The majority of the study region lies within landslide-prone zones, with non-susceptible areas comprising only 12.43% of the total area. Historical landslides are largely concentrated in moderate- to high-susceptibility zones, accounting for 92.24% of all landslide occurrences. Landslide density increases with the susceptibility level, with a density of 0.15 landslides per square kilometre in high-susceptibility zones. This study brings forward a reliable strategy for establishing the spatial relationship between geographical attribute similarity and landslide susceptibility, bolstering the method’s adaptability across various regions.

Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions

Large areas in southern Kyrgyzstan are subjected to high and ongoing landslide activity; however, an objective and systematic assessment of landslide susceptibility at a regional level has not yet been conducted. In this paper, we investigate the contribution that remote sensing can provide to facilitate a quantitative landslide hazard assessment at a regional scale under the condition of data scarcity. We performed a landslide susceptibility and hazard assessment based on a multi-temporal landslide inventory that was derived from a 30-year time series of satellite remote sensing data using an automated identification approach. To evaluate the effect of the resulting inventory on the landslide susceptibility assessment, we calculated an alternative susceptibility model using a historical inventory that was derived by an expert through combining visual interpretation of remote sensing data with already existing knowledge on landslide activity in this region. For both susceptibility models, the same predisposing factors were used: geology, stream power index, absolute height, aspect and slope. A comparison of the two models revealed that using the multi-temporal landslide inventory covering the 30-year period results in model coefficients and susceptibility values that more strongly reflect the properties of the most recent landslide activity. Overall, both susceptibility maps present the highest susceptibility values for similar regions and are characterized by acceptable to high predictive performances. We conclude that the results of the automated landslide detection provide a suitable landslide inventory for a reliable large-area landslide susceptibility assessment. We also used the temporal information of the automatically detected multi-temporal landslide inventory to assess the temporal component of landslide hazard in the form of exceedance probability. The results show the great potential of satellite remote sensing for deriving detailed and systematic spatio-temporal information on landslide occurrences, which can significantly improve landslide susceptibility and hazard assessment at a regional scale, particularly in data-scarce regions such as Kyrgyzstan.

Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides.

Nepal is facing the threat of landslides each year causing huge loss of lives and properties. Landslide prediction and susceptibility assessments help in identifying the potential zones of landslide occurrences and provide opportunities to treat them prior to their occurrence. Among different methods of landslide susceptibility mapping, the InfoVal method is one of the simple and useful methods. In this study, this method is used to study the landslide susceptibility in the Marin Khola Watershed within the Siwaliks of central Nepal as this area comprises of the weak geological formations that contribute to high potentialities of landslides, yet there are no studies for predicting landslides. A total of 217 landslides were taken for the study and they were divided into two groups: working landslides and validating landslides. 75% of these total landslides were selected as working landslides and the remaining 25% were selected for validating landslides. Spatial relationships of the landslide distribution with different causative factors including topographic factors, hydrologic factors, geological factors and landuse factors were employed and analyzed. The results depict that very high, high, moderate, low and very low susceptibility classes cover 1.15%, 49.93%, 30.17%, 11.48%, and 11.28% area, respectively. The Middle Siwaliks are most susceptible to landslides compared to the Upper Siwaliks and Lower Siwaliks. The accuracy values are found to be affected by the difference in the landslide characteristics and types occurring in the study area. The model accuracy remains at 66% and predictive accuracy at 75%.

The aim of this study was to identify some new factors that may impact the occurrence and distribution of landslides based on light detection and ranging digital elevation model (LiDAR DEM), and to examine whether these factors can apply to distinguish between landslide and non-landslide pixels. Twenty-one landslide influential factors were identified. Thereinto, there were ten novel factors, namely the texture factors of slope and surface roughness, including the contrast (Con), correlation (Cor), angular second moment, entropy, and homogeneity (Hom) textures. Qualitative and quantitative analysis and feature selection method were applied to examine the application of these factors. The analysis results indicate that these factors have certain abilities to distinguish between landslide and non-landslide objects. And the selected optimal factors combination that derived from feature selection method was DEM, slope, Hom_d, Con_s, Cor_s, Hom_s, Con_r, Cor_r, and Hom_r (_d, _s, and _r represent DEM, slope, and surface roughness textures, respectively). In conclusion, the identified landslide influential factors can provide effective information for landslide identification. And the new texture factors of slope and surface roughness could act as important measurements that can improve the precision of landslide inventory mapping, susceptibility mapping, and risk assessment.

Landslides pose a significant threat to the safety of reservoirs, particularly those situated in canyon terrains. This study aims to enhance the safety and security of reservoir areas by proposing an integrated method for the automatic identification and assessment of landslides. By combining the SBAS-InSAR technique with spatial clustering analysis, we successfully delineated landslide areas and developed a new landslide susceptibility assessment model. This model operates independently of historical landslide inventory data. Based on the delineated landslide areas, we enhanced the information value model using the inverse tangent function, which was then integrated with Random Forest and Extreme Gradient Boosting methods for landslide susceptibility assessment. The identified landslides were validated through field tests, demonstrating a high degree of consistency with actual conditions. The results indicated that, in canyon-type reservoirs, aspect was a critical factor influencing landslide occurrence, with susceptibility being greater near water bodies. In model comparisons, the RF-NIV model outperformed, providing a more realistic representation of landslide susceptibility distribution. These findings offer valuable insights for landslide safety management in canyon-type reservoirs, such as those in Hekou Village and Baihetan.

Landslide susceptibility assessment is a major research topic in geo-disaster management. In recent days, various landslide susceptibility and landslide hazard assessment methodologies have been introduced with diverse thoughts of assessment and validation method. Fundamentally, in landslide susceptibility zonation mapping, the susceptibility predictions are generally made in terms of likelihoods and probabilities. An overview of landslide susceptibility zoning practices in the last few years reveals that susceptibility maps have been prepared to have different accuracies and reliabilities. To address this issue, the work in this paper focuses on extreme event-based landslide susceptibility zonation mapping and its evaluation. An ideal terrain of northern Shikoku, Japan, was selected in this study for modeling and event-based landslide susceptibility mapping. Both bivariate and multivariate approaches were considered for the zonation mapping. Two event-based landslide databases were used for the susceptibility analysis, while a relatively new third event landslide database was used in validation. Different event-based susceptibility zonation maps were merged and rectified to prepare a final susceptibility zonation map, which was found to have an accuracy of more than 77 %. The multivariate approach was ascertained to yield a better prediction rate. From this study, it is understood that rectification of susceptibility zonation map is appropriate and reliable when multiple event-based landslide database is available for the same area. The analytical results lead to a significant understanding of improvement in bivariate and multivariate approaches as well as the success rate and prediction rate of the susceptibility maps.

China-Pakistan Economic Corridor (CPEC) is a framework of regional connectivity, which will not only benefit China and Pakistan but will have positive impact on Iran, Afghanistan, India, Central Asian Republic, and the region. The surrounding area in CPEC is prone to frequent disruption by geological hazards mainly landslides in northern Pakistan. Comprehensive landslide inventory and susceptibility assessment are rarely available to utilize for landslide mitigation strategies. This study aims to utilize the high-resolution satellite images to develop a comprehensive landslide inventory and subsequently develop landslide susceptibility maps using multiple techniques. The very high-resolution (VHR) satellite images are utilized to develop a landslide inventory using the visual image classification techniques, historic records and field observations. A total of 1632 landslides are mapped in the area. Four statistical models i.e., frequency ratio, artificial neural network, weights of evidence and logistic regression were used for landslide susceptibility modeling by comparing the landslide inventory with the topographic parameters, geological features, drainage and road network. The developed landslides susceptibility maps were verified using the area under curve (AUC) method. The prediction power of the model was assessed by the prediction rate curve. The success rate curves show 93%, 92.8%, 92.7% and 87.4% accuracy of susceptibility maps for frequency ratio, artificial neural network, weights of evidence and logistic regression, respectively. The developed landslide inventory and susceptibility maps can be used for land use planning and landslide mitigation strategies.

BackgroundThousands of landslides were triggered by the Hokkaido Eastern Iburi earthquake on 6 September 2018 in Iburi regions of Hokkaido, Northern Japan. Most of the landslides (5627 points) occurred intensively between the epicenter and the station that recorded the highest peak ground acceleration. Hundreds of aftershocks followed the major shocks. Moreover, in Iburi region, there is a high possibility of earthquakes occurring in the future. Effective prediction and susceptibility assessment methods are required for sustainable management and disaster mitigation in the study area. The aim of this study is to evaluate the performance of an autoencoder framework based on deep neural network for prediction and susceptibility assessment of regional landslides triggered by earthquakes.ResultsBy applying 12 sampling sizes and 12 landslide-influencing factors, 12 landslide susceptibility maps were produced using an autoencoder framework. The results of the model were evaluated using qualitative and quantitative assessment methods. The ratios of the sampling sizes on the non-landslide points randomly generated from the combination zone including plain and mountain (PM) and a mountainous only zone (M) affected different prediction abilities of the model’s performance.ConclusionsThe 12 susceptibility maps, including the landslide susceptibility index, indicated the various spatial distributions of the landslide susceptibility values in both PM and the M. The highly accurate models explicitly distinguished the potential areas of landslide from stable areas without expanding the spatial extent of the potential landslide areas. The autoencoder is proved to be an effective and efficient method for extracting spatial patterns through unsupervised learning for the prediction and susceptibility assessment of landslide areas.

The constant threat from landslides in the northeastern part of Istria, Croatia, calls for the need to apply accurate and reliable methods in landslide hazard assessment in order to prevent landslide damage and to set an early warning system if necessary. Furthermore, landslide susceptibility and hazard assessment enable optimal area management and regional urban planning. The study area is in the northeastern and central part of the Istrian Peninsula, well known as an area of frequent, small and shallow slope instability phenomena. Landslide susceptibility assessment in the area around the city of Buzet was performed using a deterministic landslide susceptibility model in the LS-RAPID software. LS-RAPID was developed to analyze stability at one single location, but the performed analysis has shown that LS-RAPID can be used as a powerful tool in landslide susceptibility and hazard assessment on regional scale. The objective of this paper is to establish the influence of the runout potential on the enlargement of the landslide-susceptible zones, due to expansion of the failure area around the initial failure zone. Performed analysis of rainfall return periods shows the frequency of landslide occurrence and provides the possible correlation with the time component of landslide hazard in the area.

Generally, pixels are the basic unit for assessment of landslide susceptibility. However, even if the results facilitate the comparison, a pixel-based analysis does not clearly illustrate the distribution relationships. To eliminate this deficiency, the concept of the Landslide Response Unit (LRU) is proposed in this study, for which adjacent pixels that have similar properties are combined as a basic unit for susceptibility assessment. The Subao River basin, seriously impacted by the Wenchuan Earthquake, was selected as the study area, and three factors including slope gradient, slope aspect, and slope shape, which have a significant impact on landslides, were chosen to divide the basin into 25,984 LRUs. Then topographic, geologic, and distance factors were applied for the landslide susceptibility evaluation. The logistic regression method was used to establish the susceptibility assessing model by analyzing 2,000 susceptible LRUs and 2,000 un-susceptible LRUs. The model accuracy was defined in terms of the ROC curve value and the κ value, 0.531 and 0.84, respectively. The susceptibility of landslides was divided into low, moderate, high, and very high in Subao River basin, and 73% of historical landslides and all four new landslides are in the highly susceptible zone and very highly susceptible zones. Finally, the LRUs with houses, farmlands, and roads prone to sliding and burial hazard were assessed separately. On the basis of considering the potential movement directions of the LRUs, the result found that 1,001 and 835 LRUs probably would be destroyed by slope sliding and landslide burial, respectively.

Landslide susceptibility assessment is a critical step in preventing and mitigating landslide disaster risks, providing a basis for avoiding potential landslide hazards. Although machine learning has been widely adopted in this field, existing studies often rely on single models without rigorous comparative validation and frequently overlook the spatiotemporal dynamics of exposed elements at risk, limiting the accuracy and practical applicability of risk assessments. Addressing these gaps, this study employs the Qinba Mountain Area, a region prone to landslides, as a case study to integrate multi‐model susceptibility mapping with dynamic exposure analysis. Four machine learning methods, Random Forest (RF), Multivariate Adaptive Regression Splines (MARS), Generalized Additive Model (GAM), and Support Vector Machine (SVM), are used to construct landslide susceptibility models for the region, using 18 conditioning factors. The results indicate that the RF model achieved the highest predictive accuracy (AUC = 0.815), outperforming MARS (0.765), GAM (0.763), and SVM (0.760). The susceptibility map derived from the optimal RF model reveals that high‐risk zones exhibit a clustered distribution in the mountainous terrains of Nanyang, Hanzhong, and Ankang. Furthermore, by integrating time‐series GDP and population grid data (2000–2010), this study uncovers a significant expansion of exposure in high‐susceptibility zones due to urbanization. These findings demonstrate the necessity of coupling susceptibility modeling with dynamic exposure analysis, providing a scientific basis for spatial planning, early warning systems, and sustainable urban development in complex mountainous regions.

Landslide susceptibility assessment is crucial for preventing landslide risks. However, existing methods only consider local environmental features related to landslides, neglecting remote yet interconnected geographical features, leading to unreliable landslide susceptibility maps. This study fully considers the complex terrain and landform features of mountainous areas where landslides occur. From the perspectives of mapping units and susceptibility assessment models, it introduces geographical environmental correlations to achieve a comprehensive association between landslides and affected environments, thereby improving the accuracy of landslide susceptibility assessments. At the same time, since the world's first scientific satellite dedicated to serving the United Nations 2030 Agenda for Sustainable Development, the Sustainable Development Goals Scientific Satellite 1 (SDGSAT-1), was launched in 2021, its potential in monitoring and assessing landslide disasters remains to be developed. Therefore, this study innovatively applies SDGSAT-1 data in the field of landslide research and conducts landslide susceptibility assessment in Jiulong County, Ganzi, based on the optimal scale slope units and Graph Neural Networks (GNN).We propose the following method: First, establish appropriately sized slope units using R.Slopeunits to simulate complex mountainous terrain. Second, extract various landslide influencing factors using SDGSAT-1 satellite imagery data. Then, select the most representative graph nodes by constraining environmental similarity and influencing factor feature similarity, constructing a graph structure. Finally, perform landslide susceptibility assessment in the study area using the GraphSage model, which includes environmental information aggregation.This study's distinctive feature lies in fully considering the complex terrain and landform characteristics of mountainous areas where landslides occur. From the perspectives of mapping units and evaluation models, it introduces geographical environmental correlations to achieve a comprehensive association between landslides and affected environments. Furthermore, to validate the effectiveness of the proposed method, we selected raster units and the classic Artificial Neural Network (ANN) model as control experiments. Simultaneously, we conducted comparative experiments using Landsat and SDGSAT-1 satellite imagery, analyzing differences from two aspects: landslide influencing factors and landslide susceptibility evaluation results.The results indicate that: (1) Compared to the commonly used Landsat series satellite data in previous studies, SDGSAT-1 satellite imagery offers higher spatial resolution, capturing more spectral information with richer hue and detail. Additionally, it can generate more angles of landslide influencing factors compared to Landsat satellite data. (2) Employing global heterogeneity evaluation metrics allows for reasonable determination of slope unit scales, thereby maximizing internal consistency and external heterogeneity control within slope units. (3) By utilizing the Graph Neural Network (GNN) model that incorporates environmental information aggregation for landslide susceptibility assessment in the study area, it can, to some extent, overcome spatial limitations and integrate complex mountainous environmental information, facilitating the induction of reliable landslide characteristics.