Landslide Inventory Research Articles

Landslide Susceptibility Mapping and Driving Mechanisms in a Vulnerable Region Based on Multiple Machine Learning Models

Landslides can cause severe damage to both the environment and society, and many statistical, index-based, and inventory-based methods have been developed to assess landslide susceptibility; however, it is still challenging to choose the most effective method and properly identify major driving factors for specific regions. Here, we applied four machine learning algorithms, adaptive boosting (AdaBoost), gradient-boosting decision tree (GBDT), multilayer perceptron (MLP), and random forest (RF), to predict the landslide susceptibility at 30 m spatial scale based on thirteen landslide conditioning factors (LCFs) in a landslide-vulnerable region. Based on inventory landslide points, the classification results were evaluated, and indicated that the performance of the RF (F1-score: 0.85, AUC: 0.92), AdaBoost (F1-score: 0.83, AUC: 0.91), and GBDT (F1-score: 0.83, AUC: 0.88) methods were significantly better than the MLP (F1-score: 0.76, AUC: 0.79) method. The results further indicated that the areas with high and very high landslide risk (susceptibility greater than 0.5) accounted for about 40% of the study region. All four models matched well and predicted similar spatial distribution patterns in landslide susceptibility, with the very high risk areas mostly distributed in the western and southeastern regions. Daoshi, Qingliangfeng, Jinnan, and Linglong towns have the highest landslide risk, with mean susceptibility levels greater than 0.5. The leading contributing factors to landslide susceptibility were slightly different for the four models; however, population density, distance to road, and relief amplitude were generally among the top leading factors for most towns. Our study provided significant information on the highly landslide-prone areas and the major contributing factors for decision-makers and policy planners, and suggested that different areas should take unique precautions to mitigate or avoid severe damage from landslide events.

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.

Freely accessible inventory and spatial distribution of large-scale landslides in Xianyang City, Shaanxi Province, China

In this study, we used high-resolution optical satellite images on the Google Earth platform to map large-scale landslides in Xianyang City, Shaanxi Province, China. After mapping, a comprehensive and detailed large-scale landslide inventory that contains 2924 large-scale landslides was obtained. We analyzed the spatial distribution of landslides with seven influencing factors, including elevation, slope angle, aspect, curvature, lithology, distance to a river, and distance to the fault. Landslide Number, Landslide Area, Landslide Number Density (LND), and Landslide Area Percentage (LAP) were selected as indexes for the spatial distribution analysis. The results show that the number and area of landslides in the elevation range of 1000–1200 ​m is the highest. The highest number of landslides was observed in the slope angle of 25°–30°. North-facing slopes are prone to sliding. The area and number of landslides are the largest when the slope curvature ranges from −1.28 to 0. The LND and LAP reach their maxima when the slope curvature is less than −2.56. Areas covered by the Tertiary stratum with weakened fine-grained sandstone and siltstone show the highest LND and LAP values. Regarding distance to a river, the LAP peaks in the range of 300–600 ​m, whereas the LND peaks in an area larger than 2100 ​m. The values of LND and LNP rise as the distance from the faults increases, except for the locations 30 ​km away from active faults. This phenomenon is because active faults in this area pass through the plain areas, while landslides mostly occur in mountainous areas. The cataloging of landslide development in Xianyang City provides a significant scientific foundation for future research on landslides. In addition, the spatial distribution results are useful for landslide hazard prevention decisions and provide valuable references in this area.

Exploring the uncertainty of landslide susceptibility assessment caused by the number of non–landslides

Identifying the uncertainty caused by the number of non-landslides is necessary to obtain a precise landslide susceptibility map. Hence, the objective of this study is to investigate the evolution of landslide susceptibility under different numbers of non-landslides, and thus to answer the question of how many non-landslides are required for susceptibility modeling. For this purpose, Heilongjiang province was selected as the study area to build the landslide inventory, and the number of non-landslides at different ratios to landslides was set. Later, 12 conditioning factors were constructed and applied to the susceptibility model. Finally, the susceptibility uncertainty was ascertained via fluctuations in sample prediction, variations in classifier performance, and changes in the susceptibility map. Results show that the fluctuation in landslide prediction grows, while that for non-landslides decreases, as the number of non-landslides increases. Quantitatively, the sensitivity is 0.93 at 0.5/1, followed by 0.90 at 1/1, 0.87 at 2/1, 0.72 at 5/1, and 0.61 at 10/1; the specificity is 0.86 at 0.5/1, followed by 0.90 at 1/1, 0.92 at 2/1, 0.97 at 5/1, and 0.99 at 10/1; while the accuracy is 0.91 at 0.5/1, followed by 0.90 at 1/1, 0.89 at 2/1, 0.84 at 5/1, and 0.96 at 10/1, in the training phase. Also, the predictive ability of samples shows similar trends, in the test phase. Besides, the area with high susceptibility gradually shrinks with increasing non-landslides, and the R-value reaches the top, 2.92, at the equilibrium state. This study provides an in-depth understanding to emphasize the balanced value for non-landslides when applying the idea of presence-absence.

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.

How Spatial Resolution of Remote Sensing Image Affects Earthquake Triggered Landslide Detection: An Example from 2022 Luding Earthquake, Sichuan, China

The magnitude 6.8 Luding earthquake that occurred on 5 September 2022, triggered multiple large-scale landslides and caused a heavy loss of life and property. The investigation of earthquake-triggered landslides (ETLs) facilitates earthquake disaster assessments, rescue, reconstruction, and other post-disaster recovery efforts. Therefore, it is important to obtain landslide inventories in a timely manner. At present, landslide detection is mainly conducted manually, which is time-consuming and laborious, while a machine-assisted approach helps improve the efficiency and accuracy of landslide detection. This study uses a fully convolutional neural network algorithm with the Adam optimizer to automatically interpret the aerial and satellite data of landslides. However, due to the different resolutions of the remote sensing images, the detected landslides vary in boundary and quantity. In this study, we conducted an assessment in the study area of Wandong village in the earthquake-affected area of Luding. UAV images, GF-6 satellite images, and Landsat 8 satellite images, with a resolution of 0.2 m, 2 m, and 15 m, respectively, were selected to detect ETLs. Then, the accuracy of the results was compared and verified with visual detection results and field survey data. The study indicates that as the resolution decreases, the accuracy of landslide detection also decreases. The overall landslide area detection rate of UAV imagery can reach 82.17%, while that of GF-6 and Landsat 8 imagery is only 52.26% and 48.71%. The landslide quantity detection rate of UAV imagery can reach 99.07%, while that of GF-6 and Landsat 8 images is only 48.71% and 61.05%. In addition, for each landslide detected, little difference is found in large-scale landslides, and it becomes more difficult to correctly detect small-scale landslides as the resolution decreases. For example, landslides under 100 m2 could not be detected from a Landsat 8 satellite image.

Mapping landslides through a temporal lens: an insight toward multi-temporal landslide mapping using the u-net deep learning model

ABSTRACT Repeated temporal mapping of landslides is essential for investigating changes in landslide movements, legacy effects of the landslide triggering events, and susceptibility changes in the area. However, in order to perform such investigations, multi-temporal (MT) inventories of landslides are required. The traditional approach of visual interpretation from cloud-free optical remote sensing imageries is time consuming and expensive. Recent endeavors exploring Convolutional Neural Networks and deep learning models have made rapid and accurate mapping of landslides feasible but have not been applied for multi-temporal landslide mapping in the Himalayas, yet. Earlier models used a standard supervised learning approach, with a small landslide inventory over a limited area used for training, which is then utilized to predict landslides in nearby areas. We propose a new strategy, using geographically separate training samples to design a standard approach which can be utilized to create multi-temporal landslide inventories. RapidEye images of 5-meters spatial resolution are used to generate MT landslide inventories in the study area of Rasuwa district, Nepal. We test the effectiveness of the model by training with only 55 landslides and predicting for a different area. Then, using the weights attained from this first training phase, we use transfer learning to map landslides over a time period between 2013 and 2019 in the Rasuwa district attaining an average F1-score of 0.69 for the study area. We also perform a spatial comparison between the manual (observed) and predicted inventories to evaluate the differences between landslide densities and overall landslide statistics of landslide area distribution. The benefit of a transfer learning-based model training is that it circumvents the need for generating annual inventories for training a deep learning. A single event-based inventory is enough to generate landslide inventories over a number of years, at least until landslide conditioning factors do not change significantly. This application can enable automated workflows to generate MT landslide inventories of particular areas as the basis for landslide evolution and movement change analysis.

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.

Assessing global parameters of slope stability model using Earth data observations for forecasting rainfall – induced shallow landslides

Landslides are one of the most widespread natural hazards on earth and pose an intricate challenge in many mountainous terrains, especially in the Himalayas. Rainfall-induced shallow landslides are ubiquitous on steep terrains of the Himalayas and are accountable for substantial damage to properties and loss of human lives and livestock. Climate change has led to an unprecedented increase in frequency and intensity of cloudbursts, and a greater number of landslides are triggered by rainfall, along the hillslopes. In the present study, Transient Rainfall Infiltration and Grid-Based Slope Stability Model (TRIGRS), a dynamic physically based slope stability model with optimised hydro-geomechanical parameters is used for grid-wise simulation of stability of the terrain in the central Himalayas. The present study utilised Earth observation datasets and a comprehensive spatio-temporal landslide inventory to optimise hydro-geomechanical parameters of the slope stability model. The calibrated model with optimised hydro-geomechanical parameters is validated at 9 locations, where rainfall induced landslides have been triggered. The optimised model has better predictive capabilities and could accurately forecast 67% of the landslide events in the study area in comparison with the non-optimised model. Based on the present study, the new calibrated model with optimised hydro-geomechanical parameters is more sensitive and reliable in computing Factor of Safety (FS) and Pore Water Pressure (PWP) during and after the precipitation events and can help policy makers and local administration to propose appropriate mitigation strategies for vulnerable terrains.

An identification method of potential landslide zones using InSAR data and landslide susceptibility

Landslides are destructive to property, infrastructure and people in potential landslide zones. Identifying potential landslides is an important step in landslide preparedness and will help develop sustainable landslide risk management. Interferometric synthetic aperture radar (InSAR) and landslide susceptibility assessment (LSA) have poor reliability in individually identifying potential landslide zones. This study proposes a threshold model for identifying potential landslide zones that fuses the InSAR two-dimensional (vertical direction and east-west direction) deformation rates and LSA results. The deformation rate threshold for this threshold model is |DU| or |DE|>10 mm/year (DU and DE are the vertical and the east-west deformation rates, respectively), with threshold levels of LSA set to high and very high susceptibility. The criterion of potential landslide zones is ((|DU| or |DE|>10 mm/year) ∩ (high or very high susceptibility of LSA)), and points with similar deformation and susceptibility are clustered by the K-means algorithm, and the potential landslide zones are obtained by elimination, smooth and speckle removal operations. The results showed that the InSAR two-dimensional deformation rates DU and DE were −32.71 − 12.72 mm/year and −14.88 − 24.81 mm/year, respectively, during 2015–2020 in Lanzhou city. The LSA showed that very low, low, medium, high, and very high susceptibility accounted for 55.36%, 10.54%, 21.37%, 9.63%, 3.1% of the total area, respectively. Using the proposed threshold model, 117 potential landslide zones were identified in Lanzhou. The overlap rate between potential landslide zones and the landslide inventory was 40.17%, indicating that about 40% of the potential landslide zones overlapped with the landslide inventory and that about 60% were new potential landslide zones in Lanzhou. The feasibility of the threshold model in identifying potential landslides was confirmed by field research and time-series InSAR analysis on typical areas (L1, L2, L3, and L4), which had large deformation variables and landslide features. The proposed method can quickly determine the spatial location of potential landslides, providing targeting data for landslide field investigations, technical support for rapid early landslide identification, and data support for landslide management and prevention in Lanzhou.

Combining Statistical, Displacement and Damage Analyses to Study Slow-Moving Landslides Interacting with Roads: Two Case Studies in Southern Italy

Slow-moving landslides are widespread natural hazards that can affect social and economic activities, causing damage to structures and infrastructures. This paper aims at proposing a procedure to analyze road damage induced by slow-moving landslides based on the joint use of landslide susceptibility maps, a road-damage database developed using Google Street View images and ground-displacement measurements derived from the interferometric processing of satellite SAR images. The procedure is applied to the municipalities of Vaglio Basilicata and Trivigno in the Basilicata region (southern Italy) following a matrix-based approach. First, a susceptibility analysis is carried out at the municipal scale, using data from landslide inventories and thematic information available over the entire municipalities. Then, the susceptibility index, the class of movement and the level of damage are calculated for the territorial units corresponding to the road corridors under investigation. Finally, the road networks are divided into stretches, each one characterized by a specific level of risk (or attention required) following the aggregation of the information provided by the performed analyses. The results highlight the importance of integrating all of these different approaches and data for obtaining quantitative information on the spatial and temporal behavior of slow-moving landslides affecting road networks.

A Scenario-Based Case Study: Using AI to Analyze Casualties from Landslides in Chittagong Metropolitan Area, Bangladesh

Understanding the complex dynamics of landslides is crucial for disaster planners to make timely and effective decisions that save lives and reduce the economic impact on society. Using the landslide inventory of the Chittagong Metropolitan Area (CMA), we have created a new artificial intelligence (AI)-based insight system for the town planners and senior disaster recovery strategists of Chittagong, Bangladesh. Our system generates dynamic AI-based insights for a range of complex scenarios created from 7 different landslide feature attributes. The users of our system can select a particular kind of scenario out of the exhaustive list of 1.054 × 1041 possible scenario sets, and our AI-based system will immediately predict how many casualties are likely to occur based on the selected kind of scenario. Moreover, an AI-based system shows how landslide attributes (e.g., rainfall, area of mass, elevation, etc.) correlate with landslide casualty by drawing detailed trend lines by performing both linear and logistic regressions. According to the literature and the best of our knowledge, our CMA scenario-based AI insight system is the first of its kind, providing the most comprehensive understanding of landslide scenarios and associated deaths and damages in the CMA. The system was deployed on a wide range of platforms including Android, iOS, and Windows systems so that it could be easily adapted for strategic disaster planners. The deployed solutions were handed down to 12 landslide strategists and disaster planners for evaluations, whereby 91.67% of users found the solution easy to use, effective, and self-explanatory while using it via mobile.

Data-driven landslide forecasting: Methods, data completeness, and real-time warning

Various data-driven methods, including empirical, statistical, and machine learning methods, have been developed to promptly forecast rain-induced landslides. Their abilities differ considerably in spatio-temporal landslide prediction and in handling datasets of varying qualities. A challenging issue that significantly hinders the applications of data-driven methods is the data incompleteness in most landslide inventories, particularly the lack of accurate landslide time that is a vital link between each landslide and its triggering rainstorm. This study systematically compares the performances of three categories of data-driven methods for landslide prediction and proposes a novel machine learning model featured by probabilistic landslide modelling for spatio-temporal landslide prediction. The integrated machine learning model can be developed on a realistic landslide database, regardless of whether the landslide timing information is known or not. It not only promptly predicts the spatio-temporal evolution of landslides during a rainstorm but also reliably characterises the factual landslide risk, which provides a powerful real-time decision-making tool for landslide early warning and risk management. The model is validated against the landslide incidents in Hong Kong in the past 35 years both spatially and temporally, and outperforms other data-driven models in both prediction ability and accuracy.

An Ensemble Approach of Feature Selection and Machine Learning Models for Regional Landslide Susceptibility Mapping in the Arid Mountainous Terrain of Southern Peru

This study evaluates the utility of the ensemble framework of feature selection and machine learning (ML) models for regional landslide susceptibility mapping (LSM) in the arid climatic condition of southern Peru. A historical landslide inventory and 24 different landslide influencing factors (LIFs) were prepared using remotely sensed and auxiliary datasets. The LIFs were evaluated using multi-collinearity statistics and their relative importance was measured to select the most discriminative LIFs using the ensemble feature selection method, which was developed using Chi-square, gain ratio, and relief-F methods. We evaluated the performance of ten different ML algorithms (linear discriminant analysis, mixture discriminant analysis, bagged cart, boosted logistic regression, k-nearest neighbors, artificial neural network, support vector machine, random forest, rotation forest, and C5.0) using different accuracy statistics (sensitivity, specificity, area under curve (AUC), and overall accuracy (OA)). We used suitable combinations of individual ML models to develop different ensemble ML models and evaluated their performance in LSM. We assessed the impact of LIFs on ML performance. Among all individual ML models, the k-nearest neighbors (sensitivity = 0.72, specificity = 0.82, AUC = 0.86, OA = 78%) and artificial neural network (sensitivity = 0.71, specificity = 0.85, AUC = 0.87, OA = 79%) algorithms showed the best performance using the top five LIFs, while random forest, rotation forest, and C5.0 (sensitivity = 0.76–0.81, specificity = 0.87, AUC = 0.90–0.93, OA = 82–84%) outperformed other models when developed using all twenty-four LIFs. Among ensemble models, the ensemble of k-nearest neighbors and rotation forest, k-nearest neighbors and artificial neural network, and artificial neural network and rotation forest outperformed other models (sensitivity = 0.72–0.73, specificity = 0.83–0.84, AUC = 0.86, OA = 79%) using the top five LIFs. The landslide susceptibility maps derived using these models indicate that ~2–3% and ~10–12% of the total study area fall within the “very high” and “high” susceptibility. The obtained susceptibility maps can be efficiently used to prioritize landslide mitigation activities.

SBAS-InSAR based validated landslide susceptibility mapping along the Karakoram Highway: a case study of Gilgit-Baltistan, Pakistan

Geological settings of the Karakoram Highway (KKH) increase the risk of natural disasters, threatening its regular operations. Predicting landslides along the KKH is challenging due to limitations in techniques, a challenging environment, and data availability issues. This study uses machine learning (ML) models and a landslide inventory to evaluate the relationship between landslide events and their causative factors. For this, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Artificial Neural Network (ANN), Naive Bayes (NB), and K Nearest Neighbor (KNN) models were used. A total of 303 landslide points were used to create an inventory, with 70% for training and 30% for testing. Susceptibility mapping used Fourteen landslide causative factors. The area under the curve (AUC) of a receiver operating characteristic (ROC) is employed to compare the accuracy of the models. The deformation of generated models in susceptible regions was evaluated using SBAS-InSAR (Small-Baseline subset-Interferometric Synthetic Aperture Radar) technique. The sensitive regions of the models showed elevated line-of-sight (LOS) deformation velocity. The XGBoost technique produces a superior Landslide Susceptibility map (LSM) for the region with the integration of SBAS-InSAR findings. This improved LSM offers predictive modeling for disaster mitigation and gives a theoretical direction for the regular management of KKH.

Spatial Pattern and Intensity Mapping of Coseismic Landslides Triggered by the 2022 Luding Earthquake in China

On 5 September 2022, an Mw 6.6 earthquake occurred in Luding County in China, resulting in extensive surface rupture and casualties. Sufficient study on distribution characteristics and susceptibility regionalization of the earthquake-induced disasters (especially coseismic landslides) in the region has great significance to mitigation of seismic hazards. In this study, a complete coseismic landslide inventory, including 6233 landslides with 32.4 km2 in area, was present through multi-temporal satellite images. We explored the distribution and controlling conditions of coseismic landslides induced by the 2022 Luding event from the perspective of epicentral distance. According to the maximum value of landslide area density, the geographical location with the strongest coseismic landslide activity intensity under the influence of seismic energy, the macro-epicenter, was determined, and we found a remarkable relationship with the landslide distribution and macro-epicentral distance, that is, both the landslide area and number density associatively decreased with the increase in macro-epicentral distance. Then, a fast and effective method for coseismic landslide intensity zoning based on the obvious attenuation relationship was proposed, which could provide theoretical reference for susceptibility mapping of coseismic landslides induced by earthquakes in mountainous areas. Additionally, to quantitatively assess the impact of topographic, seismogenic and lithological factors on the spatial pattern of coseismic landslides, the relationships between the occurrences of coseismic landslides and influencing factors, i.e., elevation, slope angle, local relief, aspect, distance to fault and lithology, were examined. This study provides a fresh perspective on intensity zoning of coseismic landslides and has important guiding significance for post-earthquake reconstruction and land use in the disaster area.

Landslide susceptibility mapping using maximum entropy (MaxEnt) and geographically weighted logistic regression (GWLR) models in the Río Aguas catchment (Almería, SE Spain)

A landslide susceptibility analysis has been made in the Río Aguas catchment (Almeria, Southeast Spain), using two statistical models, Maximum Entropy (MaxEnt) and Geographically Weighted Logistic Regression (GWLR). For this purpose, a previous landslide inventory has been used and re-elaborated, reaching a total incidence of 2.58% of the whole area. Different types of movements have been distinguished, being rock falls, slides and complex movements the predominant. From the inventory, the centroid of the rupture zone has been extracted to represent the landslides introduced in the models. A previous factor analysis has been made, using 12 predictors related to morphometry, hydrography, geology and land cover, with 5 m grid spacing, allowing the selection of factors to be used in the analysis and discarding those showing correlation between them. Then, MaxEnt and GWLR models are applied using different distributions of training and testing samples from the landslide inventory. For the validation, the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) has been used but additionally, the degree of fit (DF) has allowed to validate the rupture zones themselves, not only the centroids. Results show an excellent prediction with both metrics in all the methods and samples, but the better results are obtained in the GWLR method for AUC and in the MaxEnt for the degree of fit. Therefore, a consensus model of both methods has been obtained, that improves even more the results reaching an AUC value of 0.99 and a degree of fit of 90%.

Automatic detection and update of landslide inventory before and after impoundments at the Lianghekou reservoir using Sentinel-1 InSAR

There is an urgent demand for continuous detection and monitoring of active slopes in wide reservoir areas, as reservoir impoundments may activate unstable slopes. Although satellite time-series InSAR has been widely used in mapping active landslides, the tedious artificial interpretation of InSAR results limits the efficiency and reliability of landslides detection. We propose a set of procedures for deformation monitoring and continuous automatic landslide identification in wide reservoir areas. The local time window estimation can extract the nonlinear deformation in the time series InSAR signal, thus enhancing the deformation field. Spatial adaptive clustering enables the effective extraction of unstable slopes. The abnormal deformation trends of landslides can be updated through the continuous identification of new results and comparison with historical results. The procedure is used to continuously detect unstable slopes in the Lianghekou reservoir area before and after the impoundment. Combining the ascending and descending SAR data of Sentinel-1, 109 historically active landslides were found and 18 new landslides were activated within one year after the first water storage. The distribution and types of unstable slopes were analyzed, followed by two case studies. The first case shows the different effects of the two-stage water storage on slope deformations, indicating rising water levels' critical role in landslide activation. The second case shows the consolidation settlement of the embankment dam and the influence of water storage on slope deformation monitoring. The results demonstrate the effectiveness of the InSAR automatic landslide identification and this study provides a technical reference for similar reservoir area.

Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog

Rainfall-triggered landslides can result in devastating loss of life and property damage and are a growing concern from a local to global scale. NASA’s global landslide catalog (GLC) compiles a record of rainfall-triggered landslide events from media reports, academic articles, and existing databases at global scale. The database consists of all types of mass movement events that are triggered by rainfall and represents a minimum number of events occurring between 2007 and 2018. The GLC collection is no longer being compiled, and the dataset will not be updated past 2018. The research presented here evaluates global patterns in landslide reporting from events in the GLC. The evaluation includes an analysis of the spatial and temporal distribution of global landslide events and associated casualties and comparisons with other landslide inventories. This database has been used to estimate landslide hotspots, evaluate geographic patterns in landslides, and train and validate landslide models from local to global scales. The most notable landslide hotspots are in the Pacific Northwest of North America, High Mountain Asia, and the Philippines. Additionally, the relationship between country GDP and income status with landslide occurrence was determined to have a positive correlation between economic status and landslide reporting. The GLC also indicates a reporting bias towards English-speaking countries. The general goal of this research is to assess the decade of global landslide reports from the GLC and show how this database can be used for rainfall-triggered landslide research.

Integrated Approach for Landslide Risk Assessment Using Geoinformation Tools and Field Data in Hindukush Mountain Ranges, Northern Pakistan

Landslides are one of the most recurring and damaging natural hazards worldwide, with rising impacts on communities, infrastructure, and the environment. Landslide hazard, vulnerability, and risk assessments are critical for landslide mitigation, land use and developmental planning. They are, however, often lacking in complex and data-poor regions. This study proposes an integrated approach to evaluate landslide hazard, vulnerability, and risk using a range of freely available geospatial data and semi-quantitative techniques for one of the most landslide-prone areas in the Hindukush mountain ranges of northern Pakistan. Very high-resolution satellite images and their spectral characteristics are utilized to develop a comprehensive landslide inventory and predisposing factors using bi-variate models to develop a landslide susceptibility map. This is subsequently integrated with landslide-triggering factors to derive a Landslide Hazard Index map. A geospatial database of the element-at-risk data is developed from the acquired remote sensing data and extensive field surveys. It contains the building’s footprints, accompanied by typological data, road network, population, and land cover. Subsequently, it is analyzed using a spatial multi-criteria evaluation technique for vulnerability assessment and further applied in a semi-quantitative technique for risk assessment in relative risk classes. The landslide risk assessment map is classified into five classes, i.e., very low (18%), low (39.4%), moderate (26.3%), high (13.3%), and very high (3%). The developed landslide risk index map shall assist in highlighting the landslide risk hotspots and their subsequent mitigation and risk reduction.