Landslide Inventory Map Research Articles

Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan

Karakoram Highway (KKH) is frequently disrupted by geological hazards mainly landslides which pose a serious threat to its normal operation. Using documented inventory, optical imagery interpretation, and frequency-area statistics, the features of slope failure, the spatial distribution, and their link to numerous contributing factors have all been effectively explored along the KKH. An updated inventory for the area was recreated using the interferometric synthetic aperture radar (InSAR) persistent scatterer (PS) technology to further investigate millimetre-accurate measurements of slope deformation (Vslope). Utilizing the PS approach, Sentinel-1 data from Jan 2018 to Jan 2022 were processed by which we obtained a deformation rate (VSlope) that varies between 0 and 364 mm/year. A total number of 234 landslides were cited from the literature and classified while 29 new potential landslides were detected and several pre-existing landslides were redefined by the InSAR approach, which was incorporated to generate an updated landslide susceptibility model with 86.6% of prediction precision in the area under curve method. As previous studies done by applying the InSAR technique incorporated a short span temporally and they missed some highly deforming zones like Budalas and Khanabad landslides, contain mean velocities > 50 mm/yr, which we studied individually in this work. In this study, a comprehensive application of the InSAR technique to assessing its performance in detecting and analysing landslides has been applied. The deformation velocity (Vslope) model shows high displacement in some regions, which needed to be further investigated by geoscientists, and the updated developed landslide inventory and susceptibility map can be used for land use planning and landslide mitigation strategies.

Investigating the Feasibility of Using Satellite Rainfall for the Integrated Prediction of Flood and Landslide Hazards over Shaanxi Province in Northwest China

Rainfall-triggered flood and landslide hazards pose significant threats to human lives and infrastructure worldwide. This study aims to evaluate the applicability of three satellite rainfall data sets—namely, CMORPH, GPM, and TRMM—for the prediction of flood and landslide hazards using a coupled hydrological-slope stability model. The spatial distribution of annual rainfall from the three satellite data sets was similar to that of gauge rainfall, with an increasing trend from the north to the south of Shaanxi Province. The average annual rainfall of CMORPH was the lowest, while that of TRMM was the highest. The modeled discharges forcing by satellite rainfall generally matched the observed discharges at four hydrological stations for the period 2010–2012, with average correlation coefficients of 0.51, 0.61, and 0.57 for the CMORPH, GPM, and TRMM rainfall, respectively. The exceedance probabilities of modeled discharges for the three satellite rainfall data sets were close to those of the observations, particularly when the discharges were low. Moreover, the landslide prediction results demonstrated that the three satellite rainfall data sets could simulate the spatial distribution of landslide events well; these simulations were consistent with the information in the landslide inventory map. Furthermore, when compared to the classical Intensity-Duration (ID) rainfall threshold method, the physically based slope stability model presented higher global accuracy under all three satellite rainfall data sets. The global accuracy of GPM rainfall was the highest among the three data sets (0.973 for GPM vs. 0.951 for CMORPH and 0.965 for TRMM), indicating that GPM rainfall provides the highest quality compared to CMORPH and TRMM rainfall. These findings provide a crucial basis for the application of satellite rainfall data in the context of flood and landslide prediction.

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

Predicting landslides is becoming a crucial global challenge for sustainable development in mountainous areas. This research compares the landslide susceptibility maps (LSMs) prepared from five GIS-based data-driven bivariate statistical models, namely, (a) Frequency Ratio (FR), (b) Index of Entropy (IOE), (c) Statistical Index (SI), (d) Modified Information Value Model (MIV) and (e) Evidential Belief Function (EBF). These five models were tested in the high landslides-prone humid sub-tropical type Upper Tista basin of the Darjeeling-Sikkim Himalaya by integrating the GIS and remote sensing. The landslide inventory map consisting of 477 landslide locations was prepared, and about 70% of all landslide data was utilized for training the model, and 30% was used to validate it after training. A total of fourteen landslide triggering parameters (elevation, slope, aspect, curvature, roughness, stream power index, TWI, distance to stream, distance to road, NDVI, LULC, rainfall, modified fournier index, and lithology) were taken into consideration for preparing the LSMs. The multicollinearity statistics revealed no collinearity problem among the fourteen causative factors used in this study. Based on the FR, MIV, IOE, SI, and EBF approaches, 12.00%, 21.46%, 28.53%, 31.42%, and 14.17% areas, respectively, identified in the high and very high landslide-prone zones. The research also revealed that the IOE model has the highest training accuracy of 95.80%, followed by SI (92.60%), MIV (92.20%), FR (91.50%), and EBF (89.90%) models. Consistent with the actual distribution of landslides, the very high, high, and medium hazardous zones stretch along the Tista River and major roads. The suggested landslide susceptibility models have enough accuracy for usage in landslide mitigation and long-term land use planning in the study area. Decision-makers and local planners may utilise the study's findings. The techniques for determining landslide susceptibility can also be employed in other Himalayan regions to manage and evaluate landslide hazards.

Boosting Ensemble Learning Technique for Landslide Activity Classification using Vegetation Anomalies Indicators

Having detailed information and inventories about landslides is important for studying landslides. These inventories have been created for various purposes. However, detecting and mapping landslides in highly dense vegetated areas and evaluating their activity state is a major challenge due to various factors, such as the dense tree canopy, undulating terrain, and fast-growing vegetation. Therefore, this paper presents a new technique for categorising landslide activity using vegetation anomalies indicators (VAIs) extracted from high-resolution remote sensing data. The data were utilised to support manual landslide inventory and VAI production. The landslide inventory map was divided randomly into two groups of datasets, one for training (70%) and the other for validation (30%). The classification process used a boosting ensemble learning approach, specifically Decision Tree (DT) and Stochastic Gradient Boosting (SGB), with seven primary VAIs as inputs. The study compared the classification models’ performance against various parameters, including spatial resolution and landslide depth. To evaluate the accuracy of the classification methods, metrics such as overall accuracy, kappa, producer’s accuracy, and user’s accuracy were measured from the validation dataset. The results demonstrated that both methods performed best under high spatial resolution. Among the two approaches, DT performed better, with an overall accuracy value of 89.6% for deep-seated translational, 64.0% for shallow translational, 67.0% for deep-seated rotational, and 80.0% for shallow rotational. This reliable accuracy that has been attained in landslide activity classification from VAI allows (i) to map and classify the landslide activity in the forested area, (ii) characterise the different types of vegetation characteristics to specific landslide activity, and (iii) permits for the continuous landslide activity monitoring in the area where monitoring activity is not practically feasible to be conducted.

Rapid Mapping of Slow-Moving Landslides Using an Automated SAR Processing Platform (HyP3) and Stacking-InSAR Method

The increasing number of landslide hazards worldwide has placed greater demands on the production and updating of landslide inventory maps. As an important data source for landslide detection, interferometric synthetic aperture radar (InSAR) data processing is time-consuming and also requires specialized knowledge, which severely hinders its widespread application. At present, a new cloud-based online platform, i.e., Alaska Satellite Facility’s Hybrid Pluggable Processing Pipeline (ASF HyP3) was developed for massive SAR data processing. In this study, combining the HyP3 online platform and Stacking-InSAR method, we constructed a new easy-to-use processing chain for rapidly identifying slow-moving landslides over large areas. With this processing chain, a total of 923 interferometric pairs covering an area of over 1800 km2 were processed within a few hours (about 4 to 5 h). A total of 81 slow-moving landslides were immediately detected and mapped using Stacking-InSAR method, of which 65 landslides were confirmed by previous studies and 16 landslides were newly detected. Results show that the new processing chain can greatly improve the efficiency of wide-area landslide mapping and is expected to serve as an effective tool for rapid updating the existing landslide inventories and contribute to the prevention and management of geological hazards.

Persistent Scatterer Interferometry (PSI) Technique for the Identification and Monitoring of Critical Landslide Areas in a Regional and Mountainous Road Network

A reliable road network is a vital local asset, connecting communities and unlocking economic growth. Every year landslides cause serious damage and, in some cases, the full disruption of many road networks, which can last from a few days to even months. The identification and monitoring of landslides with conventional methods on an extended and complex road network can be a rather difficult process, as it requires a significant amount of time and resources. The road network of the Chania regional unit on the island of Crete in Greece is a typical example, as it connects, over long distances, many remote mountainous villages with other local communities, as well as with the main urban centers, which are mainly located across the shore. Persistent scatterer interferometry (PSI) is a remote-sensing technique that can provide a reliable and cost-effective solution, as it can be used to identify and monitor slow-moving and ongoing landslides over large and complex areas such as those of the mountainous road networks. This study applied PSI in the Chania regional unit, using the novel parallelized PSI (P-PSI) processing chain, developed by the Operational Unit Center for Earth Observation Research and Satellite Remote Sensing BEYOND of the Institute of Astronomy and Astrophysics, Space Applications and Remote Sensing of the National Observatory of Athens (BEYOND) for the rapid identification of the areas, most critical to landslide in a local road network. The application of P-PSI speeded up the total required processing time by a factor of five and led to the rapid identification and monitoring of 235 new slow-moving landslides. The identified landslides were correlated with a pre-existing landslide inventory and open access visual data to create a complete landslide inventory and a relative landslide inventory map, thus offering a valuable tool to local stakeholders.

Geomorphological landslide inventory map of the Daunia Apennines, southern Italy

Abstract. Detailed and accurate geomorphological historical landslide inventory maps are an invaluable source of information for many research topics and applications. Their systematic preparation worldwide has been advised by many researchers as it may foster our knowledge on landslides, their spatial and temporal distribution, their potential interaction with the built environment, their contribution to landscape dynamics, and their response to climate change in the past. Due to the extreme variability of the morphological and radiometric elements that can reveal historical landslides, geomorphological historical landslide inventory maps are produced by expert interpretation, which makes it a time-consuming and expensive process, which often discourages wide-area mapping activities. In this paper we present a new geomorphological historical landslide inventory map for a 1460 km2 area in the Daunia Apennines, the north-western sector of the Apulia (Puglia) region, in southern Italy. The inventory contains 17 437 landslides classified according to relative age, type of movement, and estimated depth. Landslides were mapped according to rigorous and reproducible criteria applied by two teams of expert photo interpreters to two sets of stereoscopic aerial photographs taken in 1954/55 and 2003. The dataset consists of a digital archive publicly available at https://doi.org/10.1594/PANGAEA.942427 (Cardinali et al., 2022).

GIS-Based Landslide Susceptibility Modeling: A Comparison between Best-First Decision Tree and Its Two Ensembles (BagBFT and RFBFT)

This study aimed to explore and compare the application of current state-of-the-art machine learning techniques, including bagging (Bag) and rotation forest (RF), to assess landslide susceptibility with the base classifier best-first decision tree (BFT). The proposed two novel ensemble frameworks, BagBFT and RFBFT, and the base model BFT, were used to model landslide susceptibility in Zhashui County (China), which suffers from landslides. Firstly, we identified 169 landslides through field surveys and image interpretation. Then, a landslide inventory map was built. These 169 historical landslides were randomly classified into two groups: 70% for training data and 30% for validation data. Then, 15 landslide conditioning factors were considered for mapping landslide susceptibility. The three ensemble outputs were estimated with a receiver operating characteristic (ROC) curve and statistical tests, as well as a new approach, the improved frequency ratio accuracy. The areas under the ROC curve (AUCs) for the training data (success rate) of the three algorithms were 0.722 for BFT, 0.869 for BagBFT, and 0.895 for RFBFT. The AUCs for the validating groups (prediction rates) were 0.718, 0.834, and 0.872, respectively. The frequency ratio accuracy of the three models was 0.76163 for the BFT model, 0.92220 for the BagBFT model, and 0.92224 for the RFBFT model. Both BagBFT and RFBFT ensembles can improve the accuracy of the BFT base model, and RFBFT was relatively better. Therefore, the RFBFT model is the most effective approach for the accurate modeling of landslide susceptibility mapping (LSM). All three models can improve the identification of landslide-prone areas, enhance risk management ability, and afford more detailed information for land-use planning and policy setting.

Landslide Hazard Assessment Method Considering the Deformation Factor: A Case Study of Zhouqu, Gansu Province, Northwest China

Landslides are geological disasters that can cause great damage to natural and social environments. Landslide hazard assessments are crucial for disaster prevention and mitigation. Conventional regional landslide hazard assessment results are static and do not take into account the dynamic changes in landslides; thus, areas with landslides that have been treated and stabilized are often still identified as high-risk areas. Therefore, a new hazard assessment method is proposed in this paper that combines the deformation rate results obtained by interferometric synthetic aperture radar (InSAR) with the results of conventional hazard assessments to obtain the hazard assessment level while considering the deformation factor of the study area, with Zhouqu, Gansu Province, selected as the case study. First, to obtain the latest landslide inventory map of Zhouqu, the hazard assessment results of the study area were obtained based on a neural network and statistical analysis, and an innovative combination of the deformation rate results of the steepest slope direction from the ascending and descending data were obtained by InSAR technology. Finally, the hazard assessment level considering the deformation factor of Zhouqu was obtained. The method proposed in this paper allows for a near-term hazard assessment of the study area, which in turn enables dynamic regional landslide hazard assessments and improves the efficiency of authorities when conducting high-risk-area identification and management.

Landslide susceptibility mapping in the Loess Plateau of northwest China using three data-driven techniques-a case study from middle Yellow River catchment

Accurate landslide susceptibility maps are relevant for stakeholders to take effective measures and perform land use planning. The present research aims at using three data-driven approaches to generate landslide susceptibility map for the middle Yellow River catchment (northwest China) and comparing their performances, including the weighted information value (WIV), where the IV model was integrated with analytic hierarchy process (AHP), the support vector machine (SVM) and the random forest (RF) models. A landslide inventory map including 684 historical landslides was generated first by visual interpretation of remote sensing images combined with a field survey. A total of 14 thematic layers were applied to serve as the landslides influencing factors. The Pearson correlation coefficient analyzed the correlation among these factors, and the C5.0 decision tree algorithm determined the factor importance. The results demonstrated the correlation between every two factors were all less than 0.5. Three factors (including distance to road, distance to river, and slope) were the most important contributions to the landslide occurrences in the region, whereas five factors (including NDWI, plan curvature, profile curvature, surface roughness, and aspect) had minor importance. All the models predict that most of the historical landslides are identified in moderate and high susceptibility areas. For the prediction percentage of landslides in high susceptibility areas, both SVM and RF models exceed 70%. The RF model represented the best performance, with high susceptibility zones accounting for 21.9% and landslide numbers accounting for 90.5%. The comparison among the receiver operating characteristic curves indicated that the accuracy was higher in the RF model than in the other two models: the area under the curve (AUC) for the RF was 0.904, whereas that for the WIV and the SVM were 0.845 and 0.847, respectively. Hence, the RF was proven suitable for assessing the landslide susceptibility in the region. Current results can provide valuable references for future studies and landslide risk mitigation strategies.

Stacking ensemble of machine learning methods for landslide susceptibility mapping in Zhangjiajie City, Hunan Province, China

The current study aims to apply and compare the performance of six machine learning algorithms, including three basic classifiers: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGB), as well as their hybrid classifiers, using the logistic regression (LR) method (RF + LR, GBDT + LR, and XGB + LR), to map the landslide susceptibility of Zhangjiajie City, Hunan Province, China. First, a landslide inventory map was created with 206 historical landslide points and 412 non-landslide points, which was randomly divided into two datasets for model training (80%) and model testing (20%). Second, a landslide factor database was initially established by selecting 15 landslide conditioning factors from the topography, hydrology, climate, geology, and artificial activities. Thereafter, the multicollinearity test and information gain ratio (IGR) technique were applied to rank the importance of the factors. Subsequently, we used a series of metrics (e.g., accuracy, precision, recall, f-measure, area under the ROC (receiver operating characteristic) curve (AUC), kappa index, mean absolute error (MAE), and root mean square error (RMSE)) to evaluate the accuracy and performance of the six models. Based on the AUC values derived from the models, the GBDT + LR model with the highest AUC value (0.8168) was identified as the most efficient model for mapping landslide susceptibility, followed by the XGB + LR, XGB, RF + LR, GBDT, and RF models, which achieved AUC values of 0.8124, 0.8118, 0.8060, 0.7927, and 0.7883, respectively. The results from this study suggest that the stacking ensemble machine learning method is promising for use in landslide susceptibility mapping in the Zhangjiajie area and is capable of targeting the areas prone to landslides.

Application and validation of AHP and FR methods for landslide susceptibility mapping around choke mountain, northwestern ethiopia

The present research was conducted around Choke Mountain, East Gojjam, Northwestern Ethiopia. This study aimed to apply and validate analytical hierarchy process (AHP) and frequency ratio (FR) Methods and produce landslide susceptibility maps of the area. At first, a landslide inventory map was produced with information on past and active landslide locations. To produce a susceptibility map, nine influencing factors including lithology, slope, elevation, distance to drainage, distance to road, profile curvature, plane curvature, NDVI, and aspect were determined and integrated into the ArcGIS platform. All these influencing factors were statistically analyzed to determine their relationship to past landslides. The landslide susceptibility maps generated in the GIS environment by the AHP and FR methods were classified into five susceptibility classes: very low, low, moderate, high, and very high. Accordingly, the AHP method showed that very high susceptibility zones and high susceptibility zones cover 9.8% and 19.3% of the area, respectively. Whereas, moderate, low, and very low susceptibility zones cover 26.1%, 28.0%, and 16.8% of the area, respectively. Similarly, the FR method showed that very high susceptibility zones and high susceptibility zones cover 12.2% and 22.5% of the area, respectively. Whereas, moderate, low, and very low susceptibility zones cover 27.6%, 24.2%, and 13.4% of the area, respectively. The final results were compared and validated using the area under the curve (AUC) method. According to the AUC graph, the AHP and FR methods had success rates of 85.8 and 82.0%, respectively, and prediction rates of 88.2 and 84.8%. According to the AUC validation results, the map obtained from the AHP method is more accurate than that obtained from the FR method. Thus, the validation of the landslide susceptibility map demonstrated that the methodologies used resulted in acceptable results. This type of map can be useful for damage and risk prevention management and land use planning.

Evaluation of Different Landslide Susceptibility Analysis Methods: A Case Study of Bagmati Rural Municipality

This study is focused on the evaluation of the different statistical methods of landslide susceptibility analysis in the Himalaya. For the evaluation, Bagmati Rural Municipality is selected as a typical study site. Various statistical methods are used to determine the relationship between thirteen landslide causative factors (viz., slope, aspect, profile curvature, tangential curvature, Normalized Difference Vegetation Index, Normalized Difference Water Index, elevation, distance from fault, distance from river network, distance from road network, geology, land use pattern and soil type) and landslide occurrence. Furthermore, a landslide inventory map of 154 landslides was prepared and 70% landslides were randomly selected for generating a model and the remaining 30% were used for validation proposes. These factor maps and the inventory are analyzed in a geographic information system. The evaluation between the three statistical bivariate methods was performed using the success rate and prediction rate. The study revealed that all susceptibility analysis techniques performed well, with the Certainty Factor Method having the best success and prediction rates for the study area, at 90.53% and 92.07%, respectively.

Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote Sensing

Landslide inventory mapping (LIM) is a key prerequisite for landslide susceptibility evaluation and disaster mitigation. It aims to record the location, size, and extent of landslides in each map scale. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), have been increasingly applied to landslide detection using remote sensing images in recent decades. However, their limitations have impeded their wide application. Furthermore, despite the widespread use of deep learning algorithms in remote sensing, for LIM, deep learning algorithms are limited to less unbalanced landslide samples. To this end, in this study, full convolution networks with focus loss (FCN-FL) were adopted to map historical landslides in regions with imbalanced samples using an improved symmetrically connected full convolution network and focus loss function to increase the feature level and reduce the contribution of the background loss value. In addition, K-fold cross-validation training models (FCN-FLK) were used to improve data utilization and model robustness. Results showed that the recall rate, F1-score, and mIoU of the model were improved by 0.08, 0.09, and 0.15, respectively, compared to FCN. It also demonstrated advantages over U-Net and SegNet. The results prove that the method proposed in this study can solve the problem of imbalanced sample in landslide inventory mapping. This research provides a reference for addressing imbalanced samples in the deep learning of LIM.

GIS and geomorphological mapping applied to landslide inventory and susceptibility mapping in the El Estado River basin, Pico de Ori-zaba, Mexico

With the purpose of strengthening the knowledge and prevention of landslide disasters, this work develops a methodology that integrates geomorphological mapping with the elaboration of landslide susceptibility maps using geographic information systems (GIS) and the multiple logistic regression method (MLR). In Mexico, some isolated works have been carried out with GIS to evaluate slope stability. However, to date, no practical and standardized method has been developed to integrate geomorphological maps with landslide inventories using GIS. This paper shows the analysis carried out to develop a multitemporal landslide inventory together with the morphometric analysis and mapping technique for the El Estado River basin where, selected as the study area, is located on the southwestern slope of the Citlaltepetl or Pico de Orizaba volcano. The geological and geomorphological factors in combination with the high seasonal precipitation, the high degree of weathering and the steep slopes predispose its surfaces to landslides. To assess landslide susceptibility, a landslide inventory map was prepared using aerial photographs, followed by geomorphometric mapping (altimetry, slopes and geomorphology) and field work. With this information, landslide susceptibility was modeled using multiple logistic regression (MLR) within a GIS platform and the landslide susceptibility map was obtained.

Landslides and predisposing factors of the Southern Apennines, Italy

ABSTRACT The Fiumarella di Corleto drainage basin is an emblematic sector of the southern Apennine thrust belt in the Basilicata region (Southern Italy), which is strongly affected by landslides. Landslides both affect the urban area and the facilities related to hydrocarbon exploitation, such as the pipelines and oil wells connected with the oil centre located just outside the eastern border of the study area. Based on a detailed field survey, supported by stereoscopic analysis, a landslide inventory map has been realized, which also reports the associated processes and landforms. The relationships between the different types of landslides and the various predisposing factors like outcropping lithologies, slopes steepness, slope exposition and land use have been also investigated. The acquired data relate to a geologically complex area of the southern Apennines subject to recent uplift and represent a fundamental contribution useful for the correct management of the territory.

Integration of Sentinel-1A, ALOS-2 and GF-1 Datasets for Identifying Landslides in the Three Parallel Rivers Region, China

In the process of using InSAR technology to identify active landslides, phenomena such as steep terrain, dense vegetation, and complex clouds may lead to the missed identification of some landslides. In this paper, an active landslide identification method combining InSAR technology and optical satellite remote sensing technology is proposed, and the method is successfully applied to the Three Parallel Rivers Region (TPRR) in the northwest of Yunnan Province, China. The results show that there are 442 landslides identified in the TPRR, and the fault zone is one of the important factors affecting the distribution of landslides in this region. In addition, 70% of the active landslides are distributed within 1 km on both sides of the fault zone. The larger the scale of the landslide, the closer the relationship between landslides and the fault zone. In this identification method, the overall landslide identification rate based on InSAR technology is 51.36%. The combination of Sentinel-1 and ALOS-2 data is beneficial to improve the active landslide identification rate of InSAR. In the northern region with large undulating terrain, shadows and overlaps occur easily. The southern area with gentle terrain is prone to the phenomenon where the scale of landslides is too small. Both phenomena are not conducive to the application of InSAR. Thus, in the central region, with moderate terrain and slope, the identification rate of active landslides based on InSAR is highest. The active landslide identification method proposed in this paper, which combines InSAR and optical satellite remote sensing technology, can integrate the respective advantages of the two technical methods, complement each other’s limitations and deficiencies, reduce the missed identification of landslides, and improve the accuracy of active landslide inventory maps.

Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India

Landslides, a natural hazard, can endanger human lives and gravely affect the environment. A landslide susceptibility map is required for managing, planning, and mitigating landslides to reduce damage. Various approaches are used to map landslide susceptibility, with varying degrees of efficacy depending on the methodology utilized in the research. An analytical hierarchy process (AHP), a fuzzy-AHP, and an artificial neural network (ANN) are utilized in the current study to construct maps of landslide susceptibility for a part of Darjeeling and Kurseong in West Bengal, India. On a landslide inventory map, 114 landslide sites were randomly split into training and testing with a 70:30 ratio. Slope, aspect, profile curvature, drainage density, lineament density, geomorphology, soil texture, land use and land cover, lithology, and rainfall were used as model inputs. The area under the curve (AUC) was used to examine the models. When tested for validation, the ANN prediction model performed best, with an AUC of 88.1%. AUC values for fuzzy-AHP and AHP are 86.1% and 85.4%, respectively. According to the statistics, the northeast and eastern portions of the study area are the most vulnerable. This map might help development in the area by preventing human and economic losses.

Spatio-temporal landslide inventory and susceptibility assessment using Sentinel-2 in the Himalayan mountainous region of Pakistan.

The 2005 Kashmir earthquake has triggered widespread landslides in the Himalayan mountains in northern Pakistan and surrounding areas, some of which are active and are still posing a significant risk. Landslides triggered by the 2005 Kashmir earthquake are extensively studied; nevertheless, spatio-temporal landslide susceptibility assessment is lacking. This can be partially attributed to the limited availability of high temporal resolution remote sensing data. We present a semi-automated technique to use the Sentinel-2 MSI data for co-seismic landslide detection, landslide activities monitoring, spatio-temporal change detection, and spatio-temporal susceptibility mapping. Time series Sentinel-2 MSI images for the period of 2016-2021 and ALOS PALSAR DEM are used for semi-automated landslide inventory map development and temporal change analysis. Spectral information combined with topographical, contextual, textural, and morphological characteristics of the landslide in Sentinel-2 images is applied for landslide detection. Subsequently, spatio-temporal landslide susceptibility maps are developed utilizing the weight of evidence statistical modeling with seven causative factors, i.e., elevation, slope, geology, aspect, distance to fault, distance to roads, and distance to streams. The results reveal that landslide occurrence increased from 2016 to 2021 and that the coverage of areas of relatively high susceptibility has increased in the study area.

The large deep-seated landslide induced by the March 12th, 2012 rainfall event in the city of Azazga, Northern Algeria: deformation characteristics and failure mechanisms

A rainfall-induced deep large landslide occurred on March 12, 2012, in the city of Azazga (Northern Algeria), causing heavy damages to residential buildings and public infrastructures. In this work, we investigate the inventory mapping, the deformation characteristics, the controlling factors, and the failure mechanisms of this landslide based on the field investigation, aerial photographs and satellite images interpretation, deep boreholes equipped with piezometers and inclinometers, as well as subsurface geophysical imaging by electric resistivity tomography (ERT). The obtained landslide inventory map shows that the landslide of March 2012 affected an area of 0.40 km2. This landslide is considered as a partial reactivation of a large pre-existing one (0.606 km2), which represents 6.65% of the total urban area. Moreover, the analyses identified two types of causative factors: (1) the triggering factor related to the high intensity and antecedent rainfall as well as human activity through slope excavations and embankments; and (2) the susceptibility factors related to the lithological nature and the internal structure of the flysch deposits, their weak mechanical resistance characteristics, the presence of shallow aquifers, and basal undercutting erosion of the Iazoughen and Aboud river torrents. The inclinometer measurements and ERT imaging reveal a complex, deep-seated and rapidly moving landslide whose failure surfaces are located at a depth of 11–29 m with an average velocity of 1–29 cm year−1. The entire slip surface is located along the geotechnical interface between the flysch bedrock and the overlying scree. This comprehensive study provides useful information on rainfall-induced landslides and may constitute guidance for landslide hazard mitigation and prevention.