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In the present paper, an active landslide located to the southeast of the urban area of Alcoy (Alicante, Spain) is analysed. This landslide affects the N-340 road, as well as an area in which some industrial buildings dated from the fifteenth century and catalogued as Places of Cultural Interest are placed. This region is characterised by the existence of a steep relief composed of materials from the Miocene to the present, on which many gravitational movements are developed. The displacements have been analysed using high-resolution spotlight mode SAR images captured by the Spanish SAR satellite PAZ (which operates at X-band), which were processed by advanced differential SAR Interferometry (A-DInSAR). The behaviour of the instability has been determined using InSAR-derived time series displacement maps. Additionally, the study has been completed with a detailed geomorphological mapping at 1:2000 scale and an analysis of structural damage carried out in situ. The landslide was modelled in 3D, from a deterministic and probabilistic point of view, confirming the global instability of the area and the relationship of the landslide with rainfall and the construction of a new road. The integration of all this information obtained and its subsequent analysis has made possible to precisely define the extension of the instability, its kinematics, and to identify the main triggering factors. This information will be crucial for the proper management of this landslide by the local authorities. [ABSTRACT FROM AUTHOR]

Landslides which occur at unexpected locations and times, and which have unrecognizable risk areas, often cause mass casualties. Despite there being few preceding landslides in the same season, the catastrophic Qijiaba landslide occurred in the new government-selected settlement of Wufu village, Sichuan Province, China, on September 5, 2021, killing three people and destroying 13 houses. In this paper, the landslide features and causes are discussed based on field investigations, remote sensing interpretations, and laboratory-based soil experiments. The results showed that it was a typical soil landslide within a geomorphic hollow of the slope with a volume of 20,230 m3, recurrence interval of at least 69 years, and runout distance of 137.5 m. This type of landslide is difficult to predict. The soil strength in the sliding surface decreased suddenly as the water content increased, and the soil's rich clay mineral and high clay particle contents determined its high mobility (Heim's ratio of 0.44). The occurrence time of the Qijiaba landslide lagged 1.3 days behind the peak rainfall, and the 2-day cumulative rainfall was a crucial factor in the landslide occurrence. Finally, the large water confluence area behind the main scarp was determined to be 3.9 times the area of the landslide itself, thereby providing abundant water conditions to induce failure. The factors underlying the casualties included inappropriate site selection, insufficient awareness of potential landslides, and failure to evacuate in time. The results of this study provide a basis for future prevention, mitigation, and early warning of similar landslides. [ABSTRACT FROM AUTHOR]

A landslide is the movement of rock, debris or earth down along the slope under the gravity. It may cause to loss of people's life and their private and public properties. Landslide is a common hazard in steep slope areas, especially during the rainy season. A study of landslide helps urban planners, engineers and local communities to reduce losses caused by existing and future landslides by means of prevention, mitigation and avoidance. Therefore, the prime aim of this paper is to produce acceptable landslide hazard map for Chenab valley, Jammu and Kashmir. Semiautomatic extraction of the landslide is a suitable method that has been used in this paper to extract the location and extent of the landslides. IRS LISS-IV and CartoDEM have been used for object-based image analysis to extract and prepare a landslide inventory map. About 84 landslide potential sites have been identified by the semiautomatic extraction approach. Landslide numerical risk factor model is derived by using thirteen thematic layers with landslide inventory to prepare the landslide hazard map. The result showed that 21% area of the Chenab valley is falling under the very high hazard zone category of the landslide. The final result of the investigation will definitely be useful in the decision-making procedure at the time of emergency and will be used to prepare a preparedness plan for high-risk areas of Chenab valley. The ROC curve method is used for accuracy assessment that signifies the acceptable result for landslide susceptibility zonation of Chenab valley with 0.956 AUC value. [ABSTRACT FROM AUTHOR]

Rapid detection of landslides using remote sensing images plays a key role in hazard assessment and mitigation. Many deep convolutional neural network-based models have been proposed for this purpose; however, for small-scale landslide detection, excessive convolution and pooling process may cause potential texture information loss, which can lead to misclassification of landslide target. In this paper, we present a novel UNet model for the automatic detection of landslides, wherein the reversed image pyramid features (RIPFs) are adapted to mitigate the information loss caused by a succession of convolution and pooling. The proposed RIPF-Unet model is trained and validated using the open-source landslides dataset of the Bijie area, Guizhou Province, China, wherein the precision of the proposed model is observed to increase by 3.5% and 4.0%, compared to the conventional UNet and UNet + + model, respectively. The proposed RIPF-Unet model is further applied to the case of the Longtoushan region after the 2014 Ms.6.5 Ludian earthquake. Results show that the proposed model achieves a 96.63% accuracy for detecting landslides using remote sensing images. And the RIPF-Unet model is also advanced in its compact parameter size; notably, it is 31% lighter compared to the UNet + + model. [ABSTRACT FROM AUTHOR]

Landslide is one of the most morpho-dynamic patterns of slopes in the coastal region of Syria. The assessment of landslide susceptibility is a complex procedure because of limited data and lack of related literature in Syria. Therefore, the current paper explored the landslide susceptibility in Al-Fawar basin, based on remote sensing data in the GIS environment. Inventorization of the current landslide was carried out as points utilizing satellite images, fieldwork, and photographs. Thirteen dominant factors of landslide including slope angle, slope aspect, curvature, plan curvature, profile curvature, elevation, proximity to faults, proximity to streams, proximity to roads, NDVI, rainfall, lithology, and TWI were utilized for landslide susceptibility mapping. In this study, two bivariate statistical models, i.e., frequency ratio (FR) and statistical index (SI), were used for spatial calibration between the resulting inventory map and the geofactors. The quality of models performance in mapping the landslide susceptibility was assessed utilizing receiver operating characteristic (ROC) curves. The prediction accuracy curves showed that the landslide susceptibility map using FR model has the greatest prediction accuracy (AUC = 0.824) compared with SI model that showed 0.801 of AUC. Identically, the success rate curve graph showed that the AUC for FR and SI models was 0.799 and 0.778, respectively. Although the susceptibility assessment utilizing the FR model produced higher accurate output, the validation results indicate that the two models used in this study can be encouraging approaches for the assessment of landslide susceptibility hazard in the Syrian coastal basin, thus fruitfully creating spatial mitigation and conservation measures. [ABSTRACT FROM AUTHOR]

When an active landslide is first identified in an artificial reservoir, a comprehensive study has to be quickly conducted to analyse the possible hazard that it may represent to such a critical infrastructure. This paper presents the case of the El Arrecife Landslide, located in a slope of the Rules Reservoir (Southern Spain), as an example of geological and motion data integration for elaborating a preliminary hazard assessment. For this purpose, a field survey was carried out to define the kinematics of the landslide: translational in favour of a specific foliation set, and rotational at the foot of the landslide. A possible failure surface has been proposed, as well as an estimation of the volume of the landslide: 14.7 million m3. At the same time, remote sensing and geophysical techniques were applied to obtain historical displacement rates. A mean subsidence rate of the landslide around 2 cm/year was obtained by means of synthetic aperture radar interferometry (InSAR) and ground-penetrating radar (GPR) data, during the last 5 and 22 years, respectively. The structure-from-motion (SfM) technique provided a rate up to 26 cm/year during the last 14 years of a slag heap located within the foot of the landslide, due to compaction of the anthropical deposits. All of this collected information will be valuable to optimise the planning of future monitoring surveys (i.e. differential global positioning systems, inclinometers, ground drilling, and InSAR) that should be applied in order to prevent further damage on the reservoir and related infrastructures. [ABSTRACT FROM AUTHOR]

The recent assessment of the Mitchell Creek Landslide (MCL) in northern British Columbia is a good case history of engineering geomorphological analysis of a large landslide. It was completed using historic aerial photographs, with approximately 20-year time intervals dating back to the mid-twentieth century and field investigations completed between 2008 and 2014. The large bedrock slide initiated between 1956 and 1972 and continues to experience ongoing annual movements. Significant glacial downwasting and retreat has been observed in the photographic record, and it is hypothesized that alpine glaciation has contributed to development of the MCL. This paper documents four aspects of the engineering geomorphological assessment completed at the MCL: (i) topographic evolution, (ii) slope morphology, (iii) deformation features, and (iv) displacement behavior. Four distinct geomorphic zones have been defined at the MCL based on these analyses, controlled by different failure mechanisms. The extents of these zones have changed little over the documented history of the landslide, and rates of movement estimated from aerial photography have been consistent over the last 60 years. Retreat of the Mitchell Valley Glacier appears to have played an important role in landslide initiation, as the ice mass receded the kinematic freedom of the slope increased. This study of the initiation and development of the MCL demonstrates the capabilities of a multi-faceted approach to engineering geomorphology. The combination of historical aerial photographs with digital photogrammetric modeling and point cloud analysis techniques, and geomorphological mapping, allows for development of a robust understanding of landslide behavior. [ABSTRACT FROM AUTHOR]

Multi-platform remote sensing using space-, airborne and ground-based sensors has become essential tools for landslide assessment and disaster-risk prevention. Over the last 30 years, the multiplicity of Earth Observation satellites mission ensures uninterrupted optical and radar imagery archives. With the popularization of Unmanned Aerial Vehicles, free optical and radar imagery with high revisiting time, ground and aerial possibilities to perform high-resolution 3D point clouds and derived digital elevation models, it can make it difficult to choose the appropriate method for risk assessment. The aim of this paper is to review the mainstream remote-sensing methods commonly employed for landslide assessment, as well as processing. The purpose is to understand how remote-sensing techniques can be useful for landslide hazard detection and monitoring taking into consideration several constraints such as field location or costs of surveys. First we focus on the suitability of terrestrial, aerial and spaceborne systems that have been widely used for landslide assessment to underline their benefits and drawbacks for data acquisition, processing and interpretation. Several examples of application are presented such as Interferometry Synthetic Aperture Radar (InSAR), lasergrammetry, Terrestrial Optical Photogrammetry. Some of these techniques are unsuitable for slow moving landslides, others limited to large areas and others to local investigations. It can be complicated to select the most appropriate system. Today, the key for understanding landslides is the complementarity of methods and the automation of the data processing. All the mentioned approaches can be coupled (from field monitoring to satellite images analysis) to improve risk management, and the real challenge is to improve automatic solution for landslide recognition and monitoring for the implementation of near real-time emergency systems. [ABSTRACT FROM AUTHOR]

This paper presents rainfall-induced landslide thresholds and predicts landslide hazard in Kuala Lumpur metropolitan city and surrounding areas. Landslide events from 2000 to 2012 were collected. The long and short antecedent rainfall days were prepared for landslide and non-landslide days simultaneously. First, threshold analysis was conducted by using data obtained from rainfall stations located in highly urbanized areas of Kuala Lumpur metropolis. Six rainfall gauges were selected, and the study area was divided into six zones according to rainfall gauges: Taman Desa Station (TD-KL), Genting Klang (GK-KL), LDG Edinburgh Station (LDGE-KL), SG Raya Hulu Langat Station (SRHL-Slg), Puchong Drop Station (PD-Slg), and Bukit Antarabangsa (BTA-Slg). After the threshold analysis was conducted for different periods (10, 15, and 30 days) in each station, reliability index test was conducted to optimize the best threshold that limits the predicted events along the study period for each region. Second, the threshold analysis results were used as input in the Poisson probability model to estimate landslide temporal probability ( P). Third, the spatial probability ( P) analysis was prepared by using the evidential belief function multiplied by the P to obtain the hazard maps for 1-, 3-, and 5-year scenarios. Finally, a validation process was conducted to test the prediction performance of the resultant hazard map for a 1- and 2-year prediction by using the landslide inventory of 2012 to early 2014, which was not included in the modeling of the hazard map. Results showed a valid correlation between the high and moderate hazardous areas for the six zones. The predicted hazard maps indicated a quantitative assessment of the prone areas and proved to be a valid disaster management tool. The produced hazard maps may play a vital role as input component in risk analysis. [ABSTRACT FROM AUTHOR]

Landslides are one of the most destructive phenomena in nature and damage both property and lives every year. In this paper, a logistic regression model with datasets developed via a geographic information system and remotely sensed data was used to create a landslide spatial susceptibility map for the Three Gorges Project reservoir region on the Yangtze River in Zigui County. The five causative factors used in the logistic regression model were evaluated in different ways: topographic slope and topographic aspect were derived from a topographical map at 1:50,000 scale; bed rock-slope relationship and lithology were obtained from a geological map at 1:50,000 scale; and fractional vegetation cover (FVC), which represents the reduced frequency of landslides due to the vegetation canopy and ground cover and is also one of the most difficult parameters to estimate over broad geographic areas, was generated using a back propagation neural network (BPNN) method based on CBERS (China-Brazil Earth Resources Satellite) data, the results of which were compared with values measured in the field. The obtained Pearson correlation coefficient ( r) was 0.899. Then, the FVC factor and the other four factors were used as the input to a logistic regression model. By integrating the five factor maps in the geographical information system (GIS) via pixel-based computing, the landslide spatial susceptibility map was obtained. The study area was reclassified into four categories of landslide susceptibility: severe, moderate, low, and very low. Approximately 15.0 % of the study area was identified as severe susceptibility, and very low, low, and moderate susceptibility zones covered 21.8, 41.7, and 21.5 % of the area, respectively. These results have an accuracy of 78.90 %. Thus, by using a logistic regression model in a GIS environment, a spatial susceptibility map of landslides can be obtained, and the regions in Zigui County that are susceptible to landslides and need immediate protective and mitigation measures can be identified. [ABSTRACT FROM AUTHOR]

The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance. [ABSTRACT FROM AUTHOR]

An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. CHAID as a multivariate method has an automatic classification capacity to analyze large numbers of landslide conditioning factors. Moreover, it results two or more nodes for each independent variable, where every node contains numbers of presence or absence of landslides (dependent variable). Other DT methods such as Quick, Unbiased, Efficient Statistic Tree (QUEST) and Classification and Regression Trees (CRT) are not able to produce multi branches based tree. Thus, the main objective of this paper is to use CHAID method to perform the best classification fit for each conditioning factors, then, combined it with logistic regression (LR) to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. In the first step, a landslide inventory map with 296 landslide locations were extracted from various sources over the Pohang-Kyeong Joo catchment (South Korea). Then, the inventory was randomly split into two datasets, 70 % was used for training the models, and the remaining 30 % was used for validation purpose. Thirteen landslide conditioning factors were used for the susceptibility modeling. Then, CHAID was applied and revealed that some conditioning factors such as altitude, soil drain, soil texture and TWI, as terminal nodes and reflected the best classification fit. Then, a proposed ensemble technique was applied and the interpretations of the coefficients showed that the relationship between the decision tree branch nodes distance from drain, soil drain, and TWI, respectively, leads to better consequences assessment of landslides in the current study area. The validation results showed that both success and prediction rates, 75 and 79 %, respectively. This study proved the efficiency and reliability of ensemble DT and LR model in landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of two bivariate statistical approaches, frequency ratio (FR) and weights-of-evidence (WoE) for landslide susceptibility mapping (LSM) at south-western part of Saudi Arabia, Jizan region. Landslide locations were identified and mapped from interpretation of high resolution satellite images (GeoEye 0.5 m and QuikeBird 0.6m), topographic maps (scale of 1:10,000), historical records, and extensive field surveys. A total of 106 landslide locations were mapped using Arc-GIS software and divided into two groups, 75% and 25% of landslide locations were used for training and validation of models, respectively. Eleven landslide conditioning factors such as elevation, slope, curvature, aspect, lithology, topographic wetness index (TWI), normalized difference vegetation index (NDVI), proximity to lineament, roads and rivers were considered in this evaluation. The effects of these factors on landslide occurrence were assessed using aforementioned bivariate statistical approaches. For validation, the models results were compared with landslide locations which were not used during the models building. Subsequently, the receiver operating characteristic (ROC) curves were established and area under the curves (AUC) was calculated for the landslide susceptibility maps using the success (training data) and prediction (validation data) rates. The results showed that the area under the curve for success rates are 0.861 (86.1%) and 0.839 (83.9%) and for prediction rates are 0.796 (79.6%) and 0.791 (79.1%), respectively for frequency ratio and weight-of-evidence models. The resulting landslide susceptibility maps showed five classes of susceptibility such as very high, high, moderate, low, and very low. The percentage of existing training and validating landslides data in high and very high zones of the susceptibility maps were calculated to be 90.02% and 76.03% for frequency ratio model and 88.33% and 79.3% for weight-of-evidence model, respectively. The results revealed that the frequency ratio and weights-of-evidence models produced reasonable accuracy. The resultant maps would be useful and can also help planners for general choosing favorable locations for development schemes, such as infrastructural, buildings, road constructions, and environmental protection. [ABSTRACT FROM AUTHOR]

Landslides represent a major type of natural hazards worldwide. For development of risk mitigation capabilities, an effective system for monitoring dynamic process of slope failure, capable of gathering spatially distributed information before, during and after a landslide occurrence at real-time manner is essential. A spatial sensor network (SSN), which integrates the real-time communication infrastructure and observations from in situ sensors and remote sensing platforms, offers an efficient and effective approach for such purpose. In this paper, a SSN-based landslide monitoring system was designed and evaluated through a model test study conducted at Tongji University, China. This system, MUNOLD ( MUlti-Sensor Network for Observing Landslide Disaster), has been designed as a comprehensive monitoring framework, including sensor observations, multi-channel wireless communication, remote data storage, visualization, data processing and data analysis. In this model test study, initial experimentation demonstrated the capabilities of the MUNOLD system for collecting real-time information about the dynamic process and propagation of slope failure. Innovatively, generated from the high-speed stereo images, the sequential surface deformation vector field can be created and may exhibit the dynamic process during the extremely critical and short period of the slope failure. After this model test study, the MUNOLD system is going to be further improved and extended in a landslide prone region in Sichuan Province, China. [ABSTRACT FROM AUTHOR]

Mountain areas in the southern western corner of the Kingdom of Saudi Arabia (KSA) frequently suffer from various types of landsides induced by rainstorms and anthropogenic activities. To mitigate these problems, landslide susceptibility mapping and classification is important to develop quick and safe mitigation or remediation measures and to help the decision making in future planning by identifying the most vulnerable areas. This paper summarizes the findings of a landslide susceptibility analysis in the Ar-Rayth area, Jizan, KSA, using an analytical hierarchy process (AHP), frequency ratio (FR), and logistic regression (LR) models with the aid of GIS tools and remote sensing data. The landslide inventory map was prepared according to historical data, interpretation of high-resolution satellite images (Geo-Eye 0.5 m resolution and QuickBird 0.6 m resolution), topographic maps (1:10,000), and verified by field investigations. An inventory map with 253 landslides locations was extracted from many sources. The landslide inventory was randomly divided into two datasets: 75 % for training the models and 25 % for validation. An enhanced thematic mapper plus 15 m satellite image acquired in 2006 was used for determining the Normalized Difference Vegetation Index. A contour map (1:10,000 with a 10 m contour interval) was converted to digital elevation model 10 m resolution and subsequently converted to slope, slope aspect, curvature, elevation, and drainage networks. Geological data obtained with the help of satellite images were used to prepare lithological units and lineaments. Roads were digitized from the high-resolution image and a rainfall map was prepared from the rainfall gauges distributed throughout the study area. All these data were used to construct a spatial database using GIS and image processing. Three landslide susceptibility maps were constructed; one on the basis of a heuristic method using an AHP, the other two on the basis of landslide inventories and causative factors, using FR and LR. Furthermore, each causative factor's weight was determined according to these three models and landslide susceptibility indices were calculated. For verification, receiver operating characteristic curves were calculated and the areas under the curve (AUC) for success rate are 0.755, 0.828, and 0.749 and for prediction rate are 0.708, 0.781, and 0.727 for the AHP, FR, and LR, respectively. In addition, the percentage of existing training landslides data in high and very high zones of the susceptibility maps is of 73.2, 85.31, and 88.59 % and for the validation landslides data is 73.2, 85.31, and 88.59 % for the AHP, FR, and LR, respectively. The findings indicated that the values of AUC are above 0.7 for both success and prediction rate which indicate these three models are suitable for susceptibility analysis. Also, percent of training and validation landslides data of high and very high zones indicate that LR model is more accurate followed by FR model and AHP model. [ABSTRACT FROM AUTHOR]

Landslide is one of the most widely distributed mass movements in mountainous areas. With its wide spreading, abrupt, and seasonal characteristics, landslide always causes huge risks towards transportation, human settlements, industrial and mining plants, water resources facilities, and hydropower stations. Abe Barek landslide, which happened in the morning of May 2, 2014, in Ago District, Badakhshan Province, Afghanistan, buried 86 houses and took the lives of almost 2700 people. Many factors triggered the occurrence of this disaster. Firstly, the landslide-impacted area has a complex geologic structure that bears concentrated faults with mountain slopes covered by thick loess. Secondly, at the time of landslide, a continuous rainfall had deepened the level of moisture in the loess layer, which made the loess mass heavier and changed the soil body's mechanical properties. Thirdly, a similar landslide once happened on the same slope, which destroyed the land cover and transformed the topology of the slope. In addition, farming and irrigating activities may have also affected the stability of loess mass in this area. Upon an initial examination of landslide distribution in Badakhshan Province by using high-resolution remote sensing images from Google Earth, a total number of 609 landslide sites were identified in this area, and a landslide susceptibility assessment was completed by utilizing weight-of-evidence method. Several suggestions on landslide risk reduction in this remote mountainous area are proposed at the end of this paper. [ABSTRACT FROM AUTHOR]

Mountain areas in the southern western corner of the Kingdom of Saudi Arabia frequently suffer from various types of landslides due to rain storms and anthropogenic activities. To resolve the problem related to landslides, landslide susceptibility map is important as a quick and safe mitigation measure and to help making strategic planning by identifying the most vulnerable areas. This paper summarizes findings of landslide susceptibility analysis at Al-Hasher area, Jizan, KSA, using two statistical models: frequency ratio and index of entropy models with the aid of GIS tools and remote sensing data. The landslide locations (inventory map) were identified in the study area using historical records, interpretation of high-resolution satellite images that include Geo-Eye in 2.5 m and Quickbird in 0.6m resolution, topographic maps of 1:10,000 scale, and multiple field investigations. A total of 207 landslides (80% out of 257 detected landslides) were randomly selected for model training, and the remaining 50 landslides (19%) were used for the model validation. Ten landslide conditioning factors including slope angle, slope-aspect, altitude, curvature, lithology, distance to lineaments, normalized difference vegetation index (NDVI), distance to roads, precipitation, and distance to streams, were extracted from spatial database. Using these conditioning factors and landslide locations, landslide susceptibility and weights of each factor were analyzed by using frequency ratio and index of entropy models. Our findings showed that the existing landslides of high and very high susceptibility classes cover nearly 80.4% and 79.1% of the susceptibility maps produced by frequency ratio and index of entropy models respectively. For verification, receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) were calculated for success and prediction rates. For success rate the results revealed that for the index of entropy model (AUC = 77.9%) is slightly lower than frequency ratio model (AUC = 78.8%). For the prediction rate, it was found that the index of entropy model (AUC = 74.9%) is slightly lower than the frequency ratio model (AUC = 76.7%). The landslide susceptibility maps produced from this study could help decision makers, planners, engineers, and urban areas developers to make suitable decisions. [ABSTRACT FROM AUTHOR]

The Longchi area with the city of Dujiangyan, in the Sichuan province of China, is composed of Permian stone and diorites and Triassic sandstones and mudstones intercalated with slates. An abundance of loose co-seismic materials were present on the slopes after the May 12, 2008 Wenchuan earthquake, which in later years served as source material for rainfall-induced debris flows or shallow landslides. A total of 48 debris flows, all triggered by heavy rainfall on 13th August 2010, are described in this paper. Field investigation, supported by remote sensing image interpretation, was conducted to interpret the co-seismic landslides in the debris flow gullies. Specific characteristics of the study area such as slope, aspect, elevation, channel gradient, lithology, and gully density were selected for the evaluation of debris flow susceptibility. A score was given to all the debris flow gullies based on the probability of debris flow occurrence for the selected factors. In order to get the contribution of the different factors, principal component analyses were applied. A comprehensive score was obtained for the 48 debris flow gullies which enabled us to make a susceptibility map for debris flows with three classes. Twenty-two gullies have a high susceptibility, twenty gullies show a moderate susceptibility and six gullies have a low susceptibility for debris flows. [ABSTRACT FROM AUTHOR]

The combination between two remote surveying methods is presented herein as a pioneering approach for landslide airborne monitoring. The survey of an active landslide by UAV-based RGB photogrammetry and infrared thermography, sided by the knowledge of the field condition, allowed increasing the scientific experience on the remote sensing of slope instability phenomena by analyzing multiple aspects related to the evolution of key slope features. In this research, the state and distribution of activity of a landslide was monitored by matching data arising from three-dimensional models of the slope, built by exploiting the aerial RGB photogrammetric technology, and thermal outcomes, resulting from the airborne application of infrared thermography principles. In this frame, thermal anomalies detected during different monitoring campaigns allowed recognizing peculiar features along the unstable slope that could be related to specific kinematic elements involved in the landslide activity. Forming cracks, developing scarps, wet terrain portions, and loose material are some of the elements that could be located by integrating thermal outcomes with Digital Surface Models of the slope. Thanks to the different thermal behavior of such elements, strengthened herein by a novel approach of thermal data processing (i.e. the study of thermal slope profiles), the lateral and retrogressive evolution of the studied movement was first hypothesized and then verified in field. Achieved results show that the location of thermal anomalies well corresponds to field structures, which sometimes are hardly detectable by in situ or RGB surveys, thus suggesting the high potential of the methodological approach developed for this study. The scientific validity of presented data gains relevance thanks to the positive field validation. This paves the way to further studies aimed at implementing the infrared aerial survey of landslides, which surely could bring benefits to practical applications in terms of survey speed and spatial coverage, especially in areas characterized by bad field logistics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

It is widely recognized that post-seismic mass movements amplify and decay. Previous studies have found that most post-seismic mass movement is concentrated in the first few years following an earthquake. A major debris flow occurred in Wenchuan County in 2019, 11 years after the 2008 Wenchuan earthquake, showing that there might be a different temporal evolution pattern. In Wenchuan Country, the area struck by the 2019 disaster was investigated to explore whether a pattern existed. Remote sensing and field surveys investigate the initiation processes, sediment supply, and triggering rainfall. The result shows that most of the active landslides occurred in high-elevation areas where vegetation cover was lacking, and nearly half of them were reactivated landslides. The debris flows were mainly initiated by run-off erosion of debris in steep channels, and more than half of the sediment supply was from deposition along the channels in some catchments. The spatial and temporal evolution of debris flows was analyzed by combining our investigation results with historical events. More debris flows occurred in the northern part of the study area, where the relative annual rainfall and coseismic landslide density were low. The average values of debris-active catchments' size and relief are increasing, while the average values of their coseismic landslide density and the annual rainfall received were decreasing from 2008 to 2019. Larger catchments receiving less annual rainfall tend to have a more prolonged and enhanced mass movement. [ABSTRACT FROM AUTHOR]

With the increasing of extreme rainfall frequency, landslides accompanied by mudslides often lead to serious casualties and property damage. On 3rd July 2021, a debris flow occurred in Izu Mountain area, Shizuoka County, Japan, and then resulted in 26 deaths and 131 houses destroyed, where houses were mainly built along the banks of the creek. In order to analyse the landslide state and distribution, a two-dimensional debris flow dynamic model (Massflow) was selected to simulate the process of the landslide-generated debris flow. When the model results are considered together with remote sensing images, the volume distribution of the unstable landslide is also able to be determined. The results show that (1) the affected areas are mainly concentrated at the outfall of the gully and on both sides of the streets. (2) The pore pressure ratio is an important factor affecting the damage range of this debris flow. (3) The increase of the pore pressure ratio in the landslide make the movement distance of debris flow increase significantly. [ABSTRACT FROM AUTHOR]

The main objective of the study was to determine the landslide hazard in the drainage basin of the Niraj River taking into account as far as possible the dichotomous relationship between space and time. A broad inventory of the Romanian approaches concerned with natural hazard prediction reveals a clear preference for spatial analysis, while the temporal scale was almost totally neglected. To fill this gap, the proposed methodological approach combines GIS techniques for quantitative analysis with statistical analysis and detailed observation in the field, both directly and indirectly through remote sensing. Niraj River drainage basin is extended over an area of 658 km in the east-central part of Transylvania Depression, one of Romania's major geographic units. Due to the substrate composition, consisting essentially of marls, clays and sands, this area is often affected by rotational and shallow landslides causing important material damages. The first step of this approach was to develop a spatial model for determining the susceptibility to landslide occurrence related to several causative factors: lithology, geomorphology, structural, hydroclimatic, seismic, land use and anthropogenic factors. In order to develop the temporal side of the prediction, two subsidiary objectives were assumed. The first one, to obtain a more detailed database which contains also the temporal moments of the landslides activation or reactivation. Secondly, to realise a statistical analysis of cumulative rainfall, over a period of 90 days prior to the time of each sliding event, which allows us to identify the correlation between cumulative rainfall which had triggered recorded landslides (beginning with the year 2005) and the landslide occurrence moments. On this basis, the main results obtained were: (1) the identification the recurring interval of each value of precipitation amount; (2) the assessment of the temporal probability of landslide occurrence, accepting the assumption that the precipitation amount which led to landslide activation in the past will have the same effects in the future; and (3) the development of four scenarios of landslide occurrence, starting from the events of May 2005, April 2006, July 2010 and February 2013, which were used to determine the annual probability of landslide occurrence under similar precipitation conditions. Finally, by identifying the correlation curves between the probability of landslides and time, taking into account the susceptibility classes, additional scenarios were determined for representative years in the future (2021, 2050, 2071 and 2100). The probability values were used to create correlation curves for each susceptibility class, the corresponding mathematical expressions facilitating the computation of annual probability. The results from the study area highlight the fact that there is an acceleration trend of landslide processes in the high susceptibility area. [ABSTRACT FROM AUTHOR]

Remote sensing was used to visualize the West region with the purpose of investigating recent natural hazards observed in this area. Various approaches used based on multi-spectral information such as PCA, band compositions, NDVI, and texture analysis are used to investigate landslide and new faults in the region of West Cameroon. The combination of remote sensing techniques with fieldwork and photogrammetry has been probed again and helped to detect three (03) new faults and to characterise the recently assumed landslide as a rock fall characterised by an accumulation of fresh rock deposits downwards, from the edge to the valley. In this study, we extracted in this region all linear and curvilinear edges resulted from geomorphological features, geological structures, rock composition, vegetal tonal contrasts, soil moisture, and human activities. These features are terraces, linear valleys and cliffs, faults, joints, and other line weaknesses, tracks, buildings, roads and rivers. They are overlain on rivers, highways and digital elevation model maps for preliminary validation. Then, a post-processing fieldwork has been conducted to avoid any confusion with faults. The digital elevation model and the derived slope maps enabled fracturing to be discerned. Existing known faults have also been identified in this study. [ABSTRACT FROM AUTHOR]

Considering the ever-increasing landslide incidences in Indian Himalayas, a methodology has been presented to assess the risk to buildings constructed in the landslide-prone areas. Since landslide is a dynamic phenomenon, an inter-disciplinary approach is required for the assessment of elements at risk (buildings in this case). Therefore, a novel remote sensing and GIS-based semi-quantitative technique has been developed by integrating the concepts of landslide susceptibility zonation (LSZ), physical vulnerability (PV) and the proximity (Prox) of buildings from the influence zone (i.e. LSZ and drainage channels). In order to understand the acceptability of risk, the landslide risk (LR) has been categorized into three risk classes as class I (low risk), class II (moderate risk) and class III (high risk). This study aims to develop a systematic and easy to adopt methodology for hilly terrains of India in a scenario of historical data scarcity and also in line with the codal provisions of the country as well as the geographical conditions. The developed methodology is implemented in a test site of Gopeshwar Township, Chamoli District Headquarter, Uttarakhand State of India, covering an area of 8.39 km2 situated in the upper Alaknanda valley. This study will be useful in increasing the safety aspects of the infrastructures and lives and also for strategic governance of developmental activities in the times ahead, especially in developing countries. [ABSTRACT FROM AUTHOR]

Possibilities of using radar polarimetry methods for identifying landslide zones are analyzed. The transformation of the dominant mechanism of signal scattering by the reflecting surface was used as a key feature of landslide zones. The polarimetric data from the PALSAR-2 radar of the ALOS-2 satellite are processed using the Freeman–Durden and Cloude–Pottier decompositions at four test sites selected in the region of the landslide caused by the collapse of the bank of the Bureya River. It is found that the results of decompositions are consistent with each other; however, in some areas there are significant differences due to the specific features of the basic model assumptions. It is shown that, before the descent of the landslide masses, three main mechanisms of radar signal scattering existed in the analyzed region: single surface, volumetric, and double scattering. After the collapse, this area was dominated by single scattering characteristic of the reflective surface with large-scale irregularities free of vegetation, due to which the landslide descent zone can be confidently recognized. The significant potential of using radar polarimetry for remote diagnostics of the consequences of landslide phenomena has been demonstrated. [ABSTRACT FROM AUTHOR]

Majority of the Iraqi Kurdistan Region is a mountainous area with relief differences that attain up to 1000 m, and locally more. The presence of dense net of rivers, streams and large valleys with diverse lithologies have initiated hundreds of mass movements of different types and sizes, active and dormant. In the current study, we have selected different landslides from different parts of the Iraqi Kurdistan Region. In all selected cases, the landslides have shifted the facing rivers, streams or valleys. The slid masses are of hard and thick carbonates and/or claystones; however, clastic rocks are encountered too. At each studied landslide, we have presented the satellite image, detailed description of the geology, size of the involved area, cause of the slide, marking the original trace of the shifted river, stream or valley; if still exists. We have performed field work for some of them and presented more detailed data and field photos. Moreover, we have applied Landslide Possibility Index (LPI) at each case to indicate the possibility of re-activation or otherwise. [ABSTRACT FROM AUTHOR]

On September 28, 2016, the torrential rainfall carried by Typhoon Megi induced a catastrophic mudstone landslide in the Yanchao District of Kaohsiung City, Taiwan. The total volume of the sliding mass was approximately 8700 m3, which blocked an industrial road and displaced a duplex house in the downslope, leading to three deaths. Unprecedentedly, the 2016 Yanchao landslide was large and quickly raised public awareness regarding the possibility of large landslides that could occur in the nearby mudstone areas. Based on field surveys, topographic and geological maps, unmanned aerial vehicle (UAV) photography, and remote-sensing imagery, the failure mechanism of the 2016 Yanchao landslide is described in this study. The results revealed that steep anti-dip layered sandstones embedded with the mudstones play a critical role in large, rainfall-induced landslides. In the case of Yanchao landslide, the bedding joint between two formations was associated with the runoff infiltrating the low permeability mudstone. The kinematic process of the landslide consisted of two phases: a circular failure in the lower formation (alternation of mudstone and sandstone) and the subsequent regressive failure in upper formation (thick sandstone interbedded with mudstone). This study discloses the role of steep anti-dip layered sandstone influencing mudstone slope failure behaviors and presents a preliminary guide for assessing large, rainfall-induced landslides in mudstones. The results could also provide useful information for calibrating the numerical modeling of large landslides that may occur in the mudstone area in the future. [ABSTRACT FROM AUTHOR]

During the extreme precipitation event of 15th–17th June 2013 in Garhwal Himalaya, glacial lake outburst flooding accompanied by numerous landslides and flash flood events caused widespread death and destruction. Many hill slopes and steep river banks had developed fractures and fissures indicative of landslides and one such scarp was observed at Kunjethi (Kalimath) village on satellite images during routine analysis. Rainfall threshold analysis for years 2012, 2013, 2014 and 2015 has confirmed that it was the main triggering factor which happened due to extreme precipitation in June 2013 leading to scarp development and initiation of landslide. In order to characterize the landslide and get subsurface information, two highly cost-effective and fast non-invasive geophysical techniques, electrical resistivity tomography (ERT) and ground penetrating radar (GPR), were used. Four ERT profiles and one GPR radargram profile were used to determine the approximate depth to slip surface, which is inferred around 15–19 m. Integration of satellite remote sensing, geophysical studies and field observations have been used to demarcate the maximum possible slide zone. This study reiterates that earth observation tools in integration with faster, non-invasive and cost-effective geophysical techniques can establish the slip surface, which is an essential information required for landslide hazard mitigation measures. [ABSTRACT FROM AUTHOR]

Jiuzhaigou National Park, located in northwest plateau of Sichuan Province, is a UNESCO World Heritage Site, and one of the most popular scenic areas in China. On August 8, 2017, a Mw 6.5 earthquake occurred 5 km to the west of a major scenic area, causing 25 deaths and injuring 525, and the Park was seriously affected. The objective of this study was to explore the controls of seismogenic fault and topographic factors on the spatial patterns of these landslides. Immediately after the main shock, field survey, remote-sensing investigations, and statistical and spatial analysis were undertaken. At least 2212 earthquake-triggered landslides were identified, covering a total area of 11.8 km2. These were mainly shallow landslides and rock falls. Results demonstrated that landslides exhibited a close spatial correlation with seismogenic faults. More than 85% of the landslides occurred at 2200 to 3700 m elevations. The largest quantity of landslides was recorded in places with local topographic reliefs ranging from 200 to 500 m. Slopes in the range of ~20°-50° are the most susceptible to failure. Landslides occurred mostly on slopes facing east-northeast (ENE), east (E), east-southeast (ESE), and southeast (SE), which were nearly vertical to the orientation of the seismogenic fault slip. The back-slope direction and thin ridge amplification effects were documented. These results provide insights on the control of the spatial pattern of earthquake-triggered landslides modified by the synergetic effect of seismogenic faults and topography. [ABSTRACT FROM AUTHOR]

Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems. [ABSTRACT FROM AUTHOR]

A new approach combining the certainty factor (CF) and analytic hierarchy process (AHP) methods was proposed to assess landslide susceptibility in the Ziyang district, which is situated in the Qin-Ba Mountain region, China. Landslide inventory data were collected based on field investigations and remote sensing interpretations. A total of 791 landslides were identified. A total of 633 landslides were randomly selected from this data set as the training set, and the remaining landslides were used for validation as the test set. Nine factors, including the slope angle, slope aspect, slope curvature, lithology, distance to faults, distance to streams, precipitation, road network intensity degree and land use were chosen as the landslide causal factors for further susceptibility assessment. The weight of each factor and its subclass were calculated by AHP and CF methods. Landslide susceptibility was compared between the bivariate statistical method and the proposed CF-AHP method. The results indicate that the distance to streams, distance to faults and lithology are the most dominant causal factors associated with landslides. The susceptibility zonation was categorized into five classes of landslide susceptibility, i.e., very high, high, moderate, low and very low level. Lastly, the relative operating characteristics (ROC) curve was used to validate the accuracy of the new approach, and the result showed a satisfactory prediction rate of 78.3%, compared to 69.2% obtained with the landslide susceptibility index method. The results indicate that the CF-AHP combined method is more appropriate for assessing the landslide susceptibility in this area. [ABSTRACT FROM AUTHOR]

Landslide risk assessment for large areas at a country level requires a different approach and data than what is standard practice at large scales. The main goal of this research was to design a methodology for a nationwide landslide risk assessment for Georgia taking into account the limitations in data availability and detail, which do not allow the use of physically based models or statistical methods. Given these limitations, we decided to generate a qualitative landslide risk index using spatial multicriteria evaluation (SMCE). An attempt was made to compile a national landslide inventory, using old and partly destroyed archives from the Soviet period, combined with information from annual field surveys. A web-based interface for the reporting of landslide events was developed to improve the updating of the inventory in future. Relevant factor maps were prepared for the entire country, partly based on remote sensing data. As the available landslide inventory was not sufficient to use statistical methods, the factor maps were weighted using the expert-based SMCE method, and the resulting susceptibility map was validated using the available landslide inventory. The inventory was also used to make an estimation of the spatial probability of landslide occurrence within the various susceptibility classes. The resulting map was used in combination with element-at-risk maps to calculate exposure maps and to make a tentative assessment of the expected landslide losses in a 50-year time period . [ABSTRACT FROM AUTHOR]

Garhwal Himalaya in northern India has emerged as one of the most prominent hot spots of landslide occurrences in the Himalaya mainly due to geological causes related to mountain building processes, steep topography and frequent occurrences of extreme precipitation events. As this region has many pilgrimage and tourist centres, it is visited by hundreds of thousands of people every year, and in the recent past, there has been rapid development to provide adequate roads and building infrastructure. Additionally, attempts are also made to harness hydropower by constructing tunnels, dams and reservoirs and thus altering vulnerable slopes at many places. As a result, the overall risk due to landslide hazards has increased many folds and, therefore, an attempt was made to assess landslide susceptibility using 'Weights of Evidence (WofE)', a well-known bivariate statistical modelling technique implemented in a much improved way using remote sensing and Geographic Information System. This methodology has dual advantage as it demonstrates how to derive critical parameters related to geology, geomorphology, slope, land use and most importantly temporal landslide distribution in one of the data scarce region of the world. Secondly, it allows to experiment with various combination of parameters to assess their cumulative effect on landslides. In total, 15 parameters related to geology, geomorphology, terrain, hydrology and anthropogenic factors and 2 different landslide inventories (prior to 2007 and 2008-2011) were prepared from high-resolution Indian remote sensing satellite data (Cartosat-1 and Resourcesat-1) and were validated by field investigation. Several combinations of parameters were carried out using WofE modelling, and finally using best combination of eight parameters, 76.5 % of overall landslides were predicted in 24 % of the total area susceptible to landslide occurrences. The study has highlighted that using such methodology landslide susceptibility assessment can be carried out in vast stretches of Himalaya in short time in order to assess the impact of development as well as climate change/variability. The resultant map can play a critical role in selecting areas for remedial measures for slope stabilisation as well planning for future development of the region. [ABSTRACT FROM AUTHOR]

Even small number of built-up areas experienced landslide problems; however, the loss could rise when the built-up areas develop in and on the unstable slopes. Landslide hazard information serves one of the many components in an integrated development planning study. A study on spatial vulnerability assessment is carried out for the part of Nilgiris district in the Western Ghats of India with the help of remote sensing and geographical information system to make use of landslide planning scenario. A spatial database is created for past landslide histories in the study area. The critical facilities, transportation and lifeline utilities, which include tourist spots, banks, ATMs, educational institutions, hospitals, bus stops, railway stations, that are potential threat under the very high to severe landslide hazard areas were identified. These are the elements at risk during landslides. From the study, it has been found that the eastern part of the study area is more prone to landslides. The past landslide inventories very clearly reveal that most of the landslides are triggered by the intense rainfall, human and cultural activities. Out of 36 utilities in the study area, about 65 % critical facilities and 36 % of transportation and lifeline utilities are vulnerable to severe landslide threat. The output of the present study can be used as first-hand information for the decision makers and planners and also helpful for the further developmental activities in the landslide-prone areas of the Nilgiris district of India. [ABSTRACT FROM AUTHOR]

Chelungpu fault reactivation produced the 1999 Chi-Chi earthquake in Central Taiwan in 1999, triggering many landslides over a broad area. The ground damage caused by the Chi-Chi earthquake still influences successive rainfall-triggered landslides. The landslide distribution triggered by heavy rainfall during typhoon Mindulle in 2004 has a high correlation to the landslide sites triggered by the previous Chi-Chi earthquake. This research calculated and ordered landslide data for both the area and distance from the Chelungpu fault. The rank correlation coefficient for measuring two covarying variables is employed to quantify the degree of correlation between the landslide distribution and the Chelungpu fault. A high rank correlation coefficient value demonstrates a highly correlated relationship between the subsequent landslides in 2004 and previous Chelungpu fault activity in 1999. [ABSTRACT FROM AUTHOR]

The Al-Hada highway that descends towards the west of the city of At-Taif is a major connecting highway in the western part of the Kingdom of Saudi Arabia. It is one of the series of descending roads connecting the holy city of Makkah and the city of Jeddah with the city of At-Taif and the cities farther south along the escarpment. The length of the Al-Hada highways is about 22 km. The Al-Hada highway has been historically exposed to landslides and other geohazards since the day it was opened to public some 60 years ago. The road has been reconstructed and many slope instabilities have been remediated and the road has been expanded to two lanes in each direction. Heavy rainfalls occurred on the 14th of April 2012 in the province of At-Taif, causing huge debris flows in two places along the Al-Hada highway. As a result of that, these debris flows closed all four lanes of the highway for 2 weeks in order to remove the debris. The current research deals with mapping of all debris flows along Al-Hada highway and determining their volumes and their impact on the road. Finally, suitable solutions have been suggested to address these critical sites to minimize and/or avoid the debris flow hazards in the future. [ABSTRACT FROM AUTHOR]