PFR model and GiT for landslide susceptibility mapping: a case study from Central Alborz, Iran

In northern parts of Iran such as the Alborz Mountain belt, frequent landslides occur due to a combination of climate and geologic conditions with high tectonic activities. This results in millions of dollars of financial damages annually excluding casualties and unrecoverable resources. This paper evaluates the landslide susceptible areas in Central Alborz using the probabilistic frequency ratio (PFR) model and Geo-information Technology (GiT). The landslide location map in this study has been generated based on image elements interpreted from IRS satellite data and field observations. The display, manipulation and analysis have been carried out to evaluate layers such as geology, geomorphology, soil, slope, aspect, land use, distance from faults, lineaments, roads and drainages. The validation group of actual landslides and relative operation curve method has been used to increase the accuracy of the final landslide susceptibility map. The area under the curve evaluates how well the method predicts landslides. The results showed a satisfactory agreement of 91% between prepared susceptibility map and existing data on landslide locations.     

长江三峡工程库首区滑坡与线性构造的关系

三峡工程库首区分布着许多大型滑坡 ,在空间上具有丛集性规律。为探明滑坡空间上丛集性与线性构造关系 ,并查明线性构造的发育特征 ,采用了SPOT卫星像片线性构造解译及线性构造密度统计和失真指数分析方法。结果显示 ,滑坡密集区与线性构造的高密度区有较好的吻合关系     

The slope aspect: A predisposing factor for landsliding?

The influence of slope aspect on the distribution of landslides was studied in the Milia and Roglio basins in Tuscany, Italy. For each basin, the new Tuscany region landslide inventory that was initiated in 2010 was used. The landslides were split into separate datasets based on their prevailing movement typology. To assess the results that were obtained from the different slope aspect values, maps of the lithology, slope angle, distances to streams, and distances to tectonic lineaments were included in the bivariate statistical analysis as comparison terms. For each basin, all of the geo-environmental factor maps were compared with the different landslide typologies with GIS software. Pearson's Chi² (χ²) coefficient was used to test the degree of spatial association between each predictor variable and landslide type. In addition, Cramer's V test was used to quantify the strength of the degree of association. Next, a conditional analysis was applied to all of the possible combinations that occurred between the slope aspect and other landslide-predisposing factors. Overall, the slope aspect significantly affected the distribution of superficial landslide types, but apparently not that of other landslide types.     

An InSAR and depth-integrated coupled model for potential landslide hazard assessment

Assessing the hazard of potential landslides is crucial for developing mitigation strategies for landslide disasters. However, accurate assessment of landslide hazard is limited by the lack of landslide inventory maps and difficulty in determining landslide run-out distance. To address these issues, this study developed a novel method combining the InSAR technique with a depth-integrated model. Within this new framework, potential landslides are identified through InSAR and their potential impact areas are subsequently estimated using the depth-integrated model. To evaluate its capability, the proposed method was applied to a landslide event that occurred on November 3, 2018 in Baige village, Tibet, China. The simulated results show that the area with a probability of more than 50% to be affected by landslides matched the real trimlines of the landslide and that the accuracy of the proposed method reached 85.65%. Furthermore, the main deposit characteristics, such as the location of maximum deposit thickness and the main deposit area, could be captured by the proposed method. Potential landslides in the Baige region were also identified and evaluated. The results indicate that in the event of landslides, the collapsed mass has a high probability to block the Jinsha River. It is therefore necessary to implement field monitoring and prepare hazard mitigation strategies in advance. This study provides new insights for regional-scale landslide hazard management and further contributes to the implementation of landslide risk assessment and reduction activities.

     

西藏昌都白格滑坡斜坡地质结构特征及成因机制

在西藏昌都市江达县波罗乡发生的两次高位大型滑坡,形成堰塞体阻断金沙江,其溃决洪水对下游造成了巨大损失.本文基于野外地质调查与工程验证、遥感影像、倾斜摄影测量、岩体微观特征,结合区域地质资料进行分析研究.结果表明:(1)白格滑坡发育于金沙江构造混杂岩带,坡体属于河谷型构造破碎松散体;(2)坡体物质主要由弱变形构造透镜体岩块和强变形错动带(糜棱岩带、碎裂岩带、断层泥)组成,镜下岩石结构破坏严重,岩石强度显著降低;(3)断层破碎带控制滑坡体两侧及后缘边界,为滑坡提供了侧向及后缘的切割面;(4)不连续错动带为白格滑坡的滑动层,在重力卸荷作用下发生贯通,导致坡体发生多期次崩滑;(5)综合坡体失稳启动分析,白格滑坡为"推移式+牵引式"混合型滑坡;(6)白格滑坡是在内动力和外动力耦合作用相互交替下促进形成,加之金沙江对坡脚掏蚀,松散体在重力卸荷作用下剪切破坏致使滑坡发生.白格滑坡事件可为研究金沙江构造混杂岩带中大型滑坡形成机制提供依据,同时也为该区域防灾减灾研究提供理论指导.      

Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography

Large deep-seated landslides can be reactivated during intense events, and they can evolve into destructive failures. They are generally difficult to recognize in the field, especially when they develop in densely forested areas. A detailed and constantly updated inventory map of such phenomena, and the recognition of their topographic signatures is absolutely a key tool for landslide risk mitigation.The aim of this work is to test in forested areas, the performance of the new automatic and objective methodology developed by Tarolli et al. (2012) for geomorphic features extraction (landslide crowns) from high resolution topography (LiDAR derived Digital Terrain Models – DTMs). The methodology is based on the detection of landslides through the use of thresholds obtained by the statistical analysis of variability of landform curvature. The study was conducted in a high-risk area located in the central-south Taiwan, where an accurate field survey on landsliding processes and a high-quality set of airborne laser scanner elevation data are available. The area has been chosen because some of the deep-seated landslides are located near human infrastructures and their reactivation is highly dangerous. Thanks to LiDAR’s capability to detect the bare ground elevation data in forested areas, it was possible to recognize in detail landslide features also in remote regions difficult to access. The results, if compared with the previous work of Tarolli et al. (2012), mainly focused on shallow landslides, and in a not forested area, indicate that for deep-seated landslides, where the crowns are more evident, and they are present at large scale, the tested methodology performs better (higher quality index). The method can be used to interactively assist the interpreter/user on the task of deep-seated landslide hazard mapping, and risk assessment planning of such regions.     

Tectonic and lithologic control over landslide activity within the Larji–Kullu Tectonic Window in the Higher Himalayas of India

The purpose of this study is to analyze and characterize recent landslide events in the Larji–Kullu Tectonic Window (LKTW), and to establish a relationship between the tectonic and lithologic characters of the terrain and the landslides activity. Using multispectral satellite image analysis with selected field investigation, a landslide occurrence database has been generated for the period between 1984 and 2015. To decipher the accelerated occurrences of landslides in the region, an integrated study is undertaken in the Kullu (also known as Kulu) valley of Beas River basin within the LKTW complex, to analyze the litho-structural and terrain slope interactions using morpho-tectonic parameters such as Topographic/Bedding Plane Interaction Angle (TOBIA) index, terrain surface roughness index and lithological competency analysis. A prominent clustering of landslides is observed in the north of Sainj River, contained within the tectonic window. Major sites of landslides are found to be located in the intensely fractured Manikaran Quartzite occurring within the core of the LKTW. The landslides are mostly associated with southern and southwestern-facing slopes and activations are pronounced in the ‘Orthoclinal’ slope class with gradient of 37°–48°. Thematic maps, e.g., geological, structural, geomorphological, slope and slope-aspect maps are generated and considered together to understand the morpho-tectonic scenario of the tectonic window. Observations from the above-stated thematic maps along with the occurrences of moderate magnitude earthquake epicenters helped to infer neotectonic movements along the Sainj River fault. Tectonic upliftment of the northern bank of the Sainj River along with increased precipitation through decades has resulted in recurrent landslides within the LKTW.     

An open dataset for landslides triggered by the 2016 Mw 7.8 Kaikōura earthquake,New Zealand

On November 14, 2016, the northeastern South Island of New Zealand was hit by the magnitude Mw 7.8 Kaikōura earthquake, which is characterized by the most complex rupturing mechanism ever recorded. The widespread landslides triggered by the earthquake make this event a great case study to revisit our current knowledge of earthquake-triggered landslides in terms of factors controlling the spatial distribution of landslides and the rapid assessment of geographic areas affected by widespread landsliding. Although the spatial and size distributions of landslides have already been investigated in the literature, a polygon-based co-seismic landslide inventory with landslide size information is still not available as of June 2021. To address this issue and leverage this large landslide event, we mapped 14,233 landslides over a total area of approximately 14,000 km². We also identified 101 landslide dams and shared them all via an open-access repository. We examined the spatial distribution of co-seismic landslides in relation to lithologic units and seismic and morphometric characteristics. We analyzed the size statistics of these landslides in a comparative manner, by using the five largest co-seismic landslide inventories ever mapped (i.e., Chi-Chi, Denali, Wenchuan, Haiti, and Gorkha). We compared our inventory with respect to these five ones to answer the question of whether the landslides triggered by the 2016 Kaikōura earthquake are less numerous and/or share size characteristics similar to those of other strong co-seismic landslide events. Our findings show that the spatial distribution of the Kaikōura landslide event is not significantly different from those belonging to other extreme landslide events, but the average landslide size generated by the Kaikōura earthquake is relatively larger compared to some other large earthquakes (i.e., Wenchuan and Gorkha).

     

Landslide-hazard mapping through multi-technique activity assessment: an example from the Betic Cordillera (southern Spain)

Landslide hazard in a region limited to data from a regional scale about triggering factors is assessed via cross tabulation between determining factors and landslides with recent activity. Firstly, landslide susceptibility was evaluated and validated through a bivariate statistical method between the previously identified stability conditioning factors and the mapped landslides. In this way, the most susceptible areas for assessing landslide hazards were selected. The main problem to solve in this type of research is the landslide activity. For this purpose, several techniques were applied: news reports, differential interferometric synthetic aperture radar, digital photogrammetry, light detection and ranging, photointerpretation, and dendrochronology. Both the strong and weak points of these techniques are also mentioned. The landslide return period was computed via the association between landslide activity and triggering factors, in this case annual rainfall. Finally, landslide hazard was mapped solely based on landslides with recent activity and their computed return period. The relationship between landslide occurrence and triggering factors shows that, according to both the considered assumptions and the observations made, deep-seated landslides are triggered or reactivated together with superficial landslides once every 18 years, while superficial landslides as flows or falls occur once every 5 years. The results show that there is generally a low landslide hazard in the study zone, especially when compared to landslide susceptibility. This means that landslides are mainly dormant from a natural evolution point of view, but could be reactivated as a result of geomorphological, climate, or human changes. In any case, the landslide hazard is successfully assessed, with a prediction of a 6% annual probability of a high hazard in 5% of the area, intersecting with the main infrastructures of the region; thus, control strategies are justified in order to avoid damage in extraordinary rainfall periods.     

Post-failure evolution analysis of an irrigation-induced loess landslide using multiple remote sensing approaches integrated with time-lapse ERT imaging: lessons from Heifangtai,China

The Heifangtai terrace, in Northwest China, is a typical area where loess landslides have been induced by agricultural irrigation, and many of the landslides are prone to reactivation. However, the spatiotemporal evolution and hydrological-triggering mechanisms of loess landslide reactivation are not well understood. In this research, multiple remote sensing (SBAS-InSAR, TLS, and optical remote sensing), integrated with time-lapse ERT (tl-ERT) imaging, was used to monitor the post-failure evolution of the Luojiapo landslide in Heifangtai during the period of May 2015 to Nov. 2020. Pronounced temporal and spatial differences in the deformation and hydrological evolution of landslides after sliding were observed. The largest displacement rates occurred in the landslide source area, and the lateral extension of the landslide source area caused by spatial differences in reactivation is an important feature of landslide evolution. In the landslide area, the groundwater table (GWT) decreased at first ascribed to the spring hole caused by the exposure of the GWT after sliding and then increased due to the subsequent continuous irrigation, and the lag time of the GWT response to irrigation decreased significantly. Spatial differences in GWT evolution are one of the main causes of spatial differences in landslide reactivation, and reactivation was more likely to occur where the GWT fluctuated at a high level. The GWT also fell with local reactivation. Our findings highlight the potential for obtaining internal and external spatiotemporal information of loess landslide evolution using multiple remote sensing integrated with tl-ERT. Our results also help to understand the reactivation process of irrigated loess landslides and provide a reference for the monitoring and early warning of such landslides.

     

Probabilistic landslide susceptibility mapping in the Lai Chau province of Vietnam: focus on the relationship between tectonic fractures and landslides

This study evaluates the susceptibility of landslides in the Lai Chau province of Vietnam using Geographic Information System (GIS) and remote sensing data to focus on the relationship between tectonic fractures and landslides. Landslide locations were identified from aerial photographs and field surveys. Topographic, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS data and image-processing techniques. A scheme of the tectonic fracturing of crust in the Lai Chau region was established. Lai Chau was identified as a region with many crustal fractures, where the grade of tectonic fracture is closely related to landslide occurrence. The influencing factors of landslide occurrence were: distance from a tectonic fracture, slope, aspect, curvature, soil, and vegetative land cover. Landslide prone areas were analyzed and mapped using the landslide occurrence factors employing the probability–frequency ratio model. The results of the analysis were verified using landslide location data and showed 83.47% prediction accuracy. That emphasized a strong relationship between the susceptibility map and the existing landslide location data. The results of this study can form a basis stable development and land use planning for the region.     

Landslide susceptibility analysis using probabilistic likelihood ratio model—a geospatial-based study

The crucial and difficult task in landslide susceptibility analysis is estimating the probability of occurrence of future landslides in a study area under a specific set of geomorphic and topographic conditions. This task is addressed with a data-driven probabilistic model using likelihood ratio or frequency ratio and is applied to assess the occurrence of landslides in the Tevankarai Ar sub-watershed, Kodaikkanal, South India. The landslides in the study area are triggered by heavy rainfall. Landslide-related factors—relief, slope, aspect, plan curvature, profile curvature, land use, soil, and topographic wetness index proximity to roads and proximity to lineaments—are considered for the study. A geospatial database of the related landslide factors is constructed using Arcmap in GIS environment. Landslide inventory of the area is produced by detailed field investigation and analysis of the topographical maps. The results are validated using temporal data of known landslide locations. The area under the curve shows that the accuracy of the model is 85.83%. In the reclassified final landslide susceptibility map, 14.48% of the area is critical in nature, falling under the very high hazard zone, and 67.86% of the total validation dataset landslides fall in this zone. This landslide susceptibility map is a vital tool for town planning, land use, and land cover planning and to reduce risks caused by landslides.     

Geophysical surveys to study a landslide body (north-eastern Sicily)

Almost the entire Italian territory is prone to hydrogeological risk mostly due to landslides and flooding. The high frequency of extreme weather events in areas prone to geological–hydraulic hazards contributes significantly to increasing the risk for cities and its infrastructure, but above all for the people living there. Therefore, it is vital to research into rapid monitoring techniques to be applied following a disaster such as a landslide, so that important background information, useful for planning interventions aimed at hazard mitigation, can be obtained. This work seeks to identify the depth of the sliding surface of a landslide affecting the municipal area of Tripi (village in the Messina province, Sicily), through two geophysical field surveys (MASW and HVSR) undertaken in the same area at an interval of about 5 years. The MASW surveys have enabled to reconstruct the distribution of shear wave velocity with depth. The HVSR surveys have provided information on resonant frequency and directional effects. The data integration obtained by the two methods allowed to reconstruct impedance contrast sections indicating the depth of the sliding surface of the landslide.

     

MARLI: a mobile application for regional landslide inventories in Ecuador

The regions of Central and South America most susceptible to the occurrence of landslides will become even more vulnerable in the context of climate change. The Josefina disaster, in 1993, demonstrated both the vulnerability of local infrastructures and communities in the Paute River basin (Ecuador). Since this natural phenomena, several landslide inventories and susceptibility studies were developed, revealing the vulnerability of the Paute River basin to unstable terrain and the need for further studies throughout the basin. Despite this, no studies have been done since then to update the information generated. This paper describes a Mobile Application for Regional Landslide Inventories (MARLI), a simple but efficient open-access platform to report landslide events using the Open Data Kit system. Its design makes reporting fast, simple and cost-effective with an added benefit, and a specialized knowledge is not required for its use. MARLI was tested for the collection of landslides in Cuenca (Ecuador). From the data taken in the field, it was possible to analyze the performance and suitability of collected data and compare the results with regional inventories in the same area. Additionally, these results can be used for the elaboration and update of large-scale inventories or the training of automatic identification systems of landslides and later evaluation of their precision in a small-medium scale. Likewise, this product constitutes a fundamental input for the formulation of mitigation strategies, to formulate the appropriate response and in time, also the elaboration of reconstruction plans before the increase in the occurrence of such phenomena.

     

Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS

The purpose of this study is to assess the susceptibility of landslides in parts of Western Ghats, Kerala, India, using a geographical information system (GIS). Landslide inventory of the area was made by detailed field surveys and the analysis of the topographical maps. The landslide triggering factors are considered to be slope angle, slope aspect, slope curvature, slope length, distance from drainage, distance from lineaments, lithology, land use and geomorphology. ArcGIS version 8.3 was used to manipulate and analyse all the collected data. Probabilistic-likelihood ratio was used to create a landslide susceptibility map for the study area. The result was validated using the Area under Curve (AUC) method and temporal data of landslide occurrences. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations. As the result, the success rate of the model was (84.46%) and the prediction rate of the model was (82.38%) shows high prediction accuracy. In the reclassified final landslide susceptibility zone map, 5.68% of the total area is classified as critical in nature. The landslide susceptibility map thus produced can be used to reduce hazards associated with landslides and to land cover planning.     

Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya

This study considers landslide susceptibility mapping by means of frequency ratio and artificial neural network approaches using geographic information system (GIS) techniques as a basic analysis tool. The selected study area was that of the Panchthar district, Nepal. GIS was used for the management and manipulation of spatial data. Landslide locations were identified from field survey and aerial photographic interpretation was used for location of lineaments. Ten factors in total are related to the occurrence of landslides. Based on the same set of factors, landslide susceptibility maps were produced from frequency ratio and neural network models, and were then compared and evaluated. The weights of each factor were determined using the back-propagation training method. Landslide susceptibility maps were produced from frequency ratio and neural network models, and they were then compared by means of their checking. The landslide location data were used for checking the results with the landslide susceptibility maps. The accuracy of the landslide susceptibility maps produced by the frequency ratio and neural networks is 82.21 and 78.25%, respectively.     

再论“滑坡群”——以三峡库区为例

在前人有关地质灾害的研究中,滑坡地质灾害以群体式出现的专业术语有"滑坡群"和"滑坡带"两种,但对其内涵的认识存在模糊性和局限性,缺乏层次性,多数情况下其只是对多个滑坡聚集的简称,没有体现出对地质灾害研究的指导意义。本文修正了"滑坡群"的概念,丰富了"滑坡群"的内涵,将"滑坡群"与相同的局部构造、活动构造相联系,使"滑坡群"内部的个体滑坡、相同新构造活动区域"滑坡群"之间的对比成为可能,实现了基础地质研究与灾害地质研究的有机结合。在此指导下提出了"滑坡群"评价方法,包括局部构造样式以及形成机理、局部构造与地质灾害空间演化规律、"滑坡群"内部个体滑坡对比,并建立了"滑坡群"时空演化模式,为预测滑坡破坏过程提供依据。     

A new global landslide dam database (RAGLAD) and analysis utilizing auxiliary global fluvial datasets

To address the current data and understanding knowledge gap in landslide dam inventories related to geomorphological parameters, a new global-scale landslide dam dataset named River Augmented Global Landslide Dams (RAGLAD) was created. RAGLAD is a collection of landslide dam records from multiple data sources published in various languages and many of these records we have been able to precisely geolocate. In total, 779 landslide dam records were compiled from 34 countries/regions. The spatial distribution, time trend, triggers, and geomorphological characteristic of the landslides and catchments where landslide dams formed are summarized. The relationships between geomorphological characteristics for landslides that form river dams are discussed and compared with those of landslides more generally. Additionally, a potential threshold for landslide dam formation is proposed, based on the relationship of landslide volume to river width. Our findings from our analysis of the value of the use of additional fluvial datasets to augment the database parameters indicate that they can be applied as a reliable supplemental data source, when the landslide dam records were accurately and precisely geolocated, although location precision in smaller river catchment areas can result in some uncertainty at this scale. This newly collected and supplemented dataset will allow the analysis and development of new relationships between landslides located near rivers and their actual propensity to block those particular rivers based on their geomorphology.

     

Rainfall thresholds for prediction of shallow landslides around Chamoli-Joshimath region,Garhwal Himalayas,India

Majority of landslides in the Indian sub-continent are triggered by rainfall. Several attempts in the global scenario have been made to establish rainfall thresholds in terms of intensity-duration and antecedent rainfall models on global, regional and local scales for the occurrence of landslides. However, in the context of the Indian Himalayas, the rainfall thresholds for landslide occurrences are not yet understood fully. Neither on regional scale nor on local scale, establishing such rainfall thresholds for landslide occurrences in Indian Himalayas has yet been attempted. This paper presents an attempt towards deriving local rainfall thresholds for landslides based on daily rainfall data in and around Chamoli-Joshimath region of the Garhwal Himalayas, India. Around 128 landslides taken place in last 4 years from 2009 to 2012 have been studied to derive rainfall thresholds. Out of 128 landslides, however, rainfall events pertaining to 81 landslides were analysed to yield an empirical intensity–duration threshold for landslide occurrences. The rainfall threshold relationship fitted to the lower boundary of the landslide triggering rainfall events is I?=?1.82 D ^?0.23 (I?=?rainfall intensity in millimeters per hour and D?=?duration in hours). It is revealed that for rainfall events of shorter duration (≤24 h) with a rainfall intensity of 0.87 mm/h, the risk of landslide occurrence in this part of the terrain is expected to be high. Also, the role of antecedent rainfall in causing landslides was analysed by considering daily rainfall at failure and different period cumulative rainfall prior to failure considering all 128 landslides. It is observed that a minimum 10-day antecedent rainfall of 55 mm and a 20-day antecedent rainfall of 185 mm are required for the initiation of landslides in this area. These rainfall thresholds presented in this paper may be improved with the hourly rainfall data vis-à-vis landslide occurrences and also data of later years. However, these thresholds may be used in landslide warning systems for this particular region of the Garhwal Himalayas to guide the traffic and provide safety to the tourists travelling along this pilgrim route during monsoon seasons.     

Methodological Validation for Automated Lineament Extraction by LINE Method in PCI Geomatica and MATLAB based Hough Transformation

Spatial distribution and pattern recognition of geologic lineaments are very effective in hazard risk evaluation. To make the process automated, various algorithms with various data sources were applied. This paper aims to make a review on such algorithms as well as data sources used. Comparative analysis of the LINE algorithm in PCI Geomatica and Hough Transformation in MATLAB has been conducted to explore the procedural accuracy with reference to existing landslides in the Darjeeling Himalayan region. Another comparison was made based on data sources like Landsat-8 OLI and digital elevation model, with reference to the same. As lineament density affects the landslide occurrences predominantly, the output lineament density by these two methods as well as with ground reflection and ground elevation data, were correlated with that of the landslide inventory map. A relational illustration was also done in between the lithological thrust direction and the direction of the individual lineament outputs. Hence, this study provides a decision on the use of a reliable method and data source for the automated lineament extraction which can be considered as generating an output with higher accuracy and, hence, safer for using it in the structural planning and execution of projects on various natural hazard studies.