Multi-temporal Landslide Inventory Research Articles

Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam.

Recurrent landslide events triggered by typhoons and tropical storms over Vietnam pose a longstanding threat to the nation's population and infrastructure. Changes in hydroclimatic conditions, especially the growing intensity and frequency of storms, have elevated landslide susceptibility in many parts of the country. This research examines the spatio-temporal variations in landslide susceptibility across central Vietnam over several years, using multi-temporal landslide inventories from Typhoon Ketsana (2009), Tropical Storm Podul (2013), and Typhoon Molave (2020). Additionally, the research explores the impact of individual landslide causative factors on the probabilistic occurrences of landslides. The post-event landslide susceptibility models of these three climate extreme events were developed using nine causative factors and a Random Forest machine learning algorithm. The results indicate a notable areal expansion of high to very high landslide susceptibility in the northern and eastern regions and a moderate reduction in the central and southern areas during the post-Molave period compared to the post-Ketsana period. These changes may be early indicators of increasing landslide susceptibility in response to changing hydro-climatic conditions. The research found that annual average rainfall and topographic elevation are the two most important variables influencing landslide prediction, showing a nonlinear relationship with landslide probability. The landslide susceptibility models achieved high Area Under the Receiver Operating Characteristic Curve (AUC) (>95%), accuracy (>89%), and sensitivity (>90%) scores, signifying the robustness of the models. Additionally, the uncertainty of the models was quantified and spatially mapped. This multi-temporal analysis of landslide susceptibility is crucial for understanding the regional susceptibility trends and identifying areas with increasing, decreasing, and consistently high susceptibility to landslides. These insights are invaluable for prioritizing mitigation and risk reduction strategies in landslide-prone regions and guiding appropriate land use planning.

Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya

This paper presents a remote sensing-based method to efficiently generate multi-temporal landslide inventories and identify recurrent and persistent landslides. We used free data from Landsat, nighttime lights, digital elevation models, and a convolutional neural network model to develop the first multi-decadal inventory of landslides across the Himalaya, spanning from 1992 to 2021. The model successfully delineated >265,000 landslides, accurately identifying 83 % of manually mapped landslide areas and 94 % of reported landslide events in the region. Surprisingly, only 14 % of landslide areas each year were first occurrences, 55–83 % of landslide areas were persistent and 3–24 % had reactivated. On average, a landslide-affected pixel persisted for 4.7 years before recovery, a duration shorter than findings from small-scale studies following a major earthquake event. Among the recovered areas, 50 % of them experienced recurrent landslides after an average of five years. In fact, 22 % of landslide areas in the Himalaya experienced at least three episodes of landslides within 30 years. Disparities in landslide persistence across the Himalaya were pronounced, with an average recovery time of 6 years for Western India and Nepal, compared to 3 years for Bhutan and Eastern India. Slope and elevation emerged as significant controls of persistent and recurrent landslides. Road construction, afforestation policies, and seismic and monsoon activities were related to changes in landslide patterns in the Himalaya.

Multitemporal landslide inventory and susceptibility map for the Arun River Basin, Nepal

AbstractThe transboundary Arun River Basin (ARB) spreads across Nepal and Tibet. Nearly 95% of the basin lies in Tibet through which the Pumqu River flows, forming the Arun River once it enters Nepal. The ARB has five large hydropower projects undergoing construction or planned for the future. Rainfall and earthquake‐induced landslides, landslide‐dammed lakes and landslide‐induced glacial lake outburst floods pose major risks to smooth operation of these projects. To safeguard upcoming hydropower projects, areas susceptible to landslides in the ARB must be identified. We used high‐resolution satellite imagery and open‐source tools to generate a multitemporal landslide inventory for the basin. The rigorously quality‐controlled inventory represents a yearly record of landslides from 2011 to 2020. A data‐driven approach was used to map areas susceptible to landslides within the ARB. The multitemporal landslide inventory combined with other readily available Earth observation‐based variables was used to create a landslide susceptibility map. The susceptibility analysis provides a valuable initial estimate of where landslides are likely to initiate. These landslide products could form the basis of more comprehensive local studies to inform hydropower project development.

Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be

The geoscientific community primarily focuses on predicting where landslides are likely to occur through data-driven susceptibility models. Recently, few researchers have turned to statistical estimation of landslide planimetric area within a given terrain unit and exploration of the spatiotemporal distribution of landslide occurrence. However, these data-driven approaches cannot fulfill the commonly accepted definition of landslide hazard and cannot predict the location, time/frequency, and magnitude of landslide occurrence simultaneously. This study proposes a unified and data-driven framework for landslide hazard spatiotemporal modeling, enabling dynamic and probabilistic estimation of landslide occurrence within a given slope unit at a specific time period and for a specific landslide magnitude. This framework not only involves static and dynamic factors in modelling, but also considers spatial and temporal interactions to explore the spatiotemporal variation effects of landslide hazard. We test this framework on the main island of Taiwan with a multi-temporal landslide inventory from 2004 to 2018. Specifically, this framework assumes that the occurrence and size of landslides spatiotemporally follows a binomial and a Log-Gaussian distribution, respectively, and then uses generalized additive models to achieve the estimation of landslide hazard probability. Finally, the performance is validated by a spatiotemporal leave-one-out cross-validation scheme. We believe that this framework will lay the foundation for the community to estimate landslide hazard in a unified and probabilistic data-driven prototype. We envision it could lead to studies of dynamic hazard responses to climate change.

CO-SEISMIC LANDSLIDE BASED VALIDATION OF SUSCEPTIBILITY MAPPING AFTER KAHRAMANMARAS EARTHQUAKES (FEB 6, 2023) IN AMANOS MOUNTAINS

Abstract. The quality of landslide susceptibility maps is often assessed using a part of learning data that represents geographical and land use characteristics over a quasi-fixed time. However, when validated with multi-temporal landslide inventories, more realistic insights on the susceptibility maps can be obtained. In addition, extreme events may trigger landslides in regions which are not considered as landslide-prone. The February 6, 2023, Kahramanmaras Earthquakes (Mw 7.7 and Mw 7.6), also known as the disaster of the century, triggered numerous landslides. Amanos Mountains located in southern Türkiye were also within the earthquake-affected area and had a very small amount of inventory recorded in official databases. The aim of this study was to evaluate the performance of the random forest method for producing landslide susceptibility maps. The official inventory of General Directorate of Mineral Research and Exploration (MTA) was used for map production. The resulting susceptibility map was assessed using the co-seismic landslide inventory produced in the study. The model’s performance evaluated using a part of the learning data yielded high accuracy expressed with area under receiver operating characteristics curve (AUC), precision, and recall values and F1 score using (AUC = 97%, recall = 97%, precision = 96%, F1 = 98%). However, multi-temporal evaluation with co-seismic landslides showed that 80% of the landslide pixels with moderate, high, and very high susceptibility levels could be predicted with the model. The results suggest that special attention should be given to features underrepresented in the inventory, such as low altitudes and types of lithology.

An updating of landslide susceptibility prediction from the perspective of space and time

Due to the similarity of conditioning factors, the aggregation feature of landslides and the multi-temporal landslide inventory, the spatial and temporal effects of landslides need to be considered in landslide susceptibility prediction (LSP). The ignorance of this issue will result in some biases and time-invariance in landslide susceptibility. Hence, a novel framework has been proposed to update landslide susceptibility by simultaneously considering the spatial and temporal effects of landslides at the regional scale. In this framework, the landslide inventory of Chongyi County has been divided into pre- and fresh-landslide inventories. According to the LSP results predicted by the support vector machine (SVM) model using the slope unit-based conditioning factors and pre-landslide inventory, a normalized spatial distance index (NSDI) is calculated to quantitatively represent the spatial correlation between landslides and surrounding slope units to develop the SVM-NSDI model. Furthermore, the SVM-Updating model, which incorporates the LSP results of the SVM-NSDI model and fresh-landslide inventory, could be developed to update the LSP results. Subsequently, the confusion matrix, the area under the receiver operating characteristic curve (AUC) and frequency ratio (FR) accuracy are used to evaluate the prediction performance of the above LSP models. The F1-score values of the SVM, SVM-NSDI and SVM-Updating models are 0.776, 0.816 and 0.831, respectively. The AUC values are 0.869, 0.903 and 0.914 and the FR accuracies are 0.795, 0.853 and 0.873. It can be concluded that landslide susceptibility is a time-variant variable, which can be updated by considering the spatial correlation between landslides and surrounding slope units as well as the temporal effects of multi-temporal landslide inventory. This study provides a new framework to update landslide susceptibility over time and also provides more accurate LSP results for decision-makers.

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

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

Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data

Mapping of landslides over space has seen an increasing attention and good results in the last decade. While current methods are chiefly applied to generate event-inventories, whereas multi-temporal (MT) inventories are rare, even using manual landslide mapping. Here, we present an innovative deep learning strategy which employs transfer learning that allows for the Attention Deep Supervision Multi-Scale U-Net model to be adapted for landslide detection tasks in new areas. The method also provides the flexibility of re-training a pretrained model to detect both rainfall- and earthquake-triggered landslides on new target areas. For the mapping, we used archived Planet Lab remote sensing images spanning a period between 2009 till 2021 with spatial resolution of 3–5 m to systematically generate MT landslide inventories. When we examined all cases, our approach provided an average F1 score of 0.8 indicating that we successfully identified the spatiotemporal occurrences of landslides. To examine the size distribution of mapped landslides we compared the frequency-area distributions of predicted co-seismic landslides with manually mapped products from the literature. Results showed a good match between calculated power-law exponents where the difference ranges between 0.04 and 0.21. Overall, this study showed that the proposed algorithm could be applied to large areas to generate polygon-based MT landslide inventories.

Event-based landslide susceptibility models in Shihmen watershed, Taiwan: accounting for the characteristics of rainfall events.

When performing a landslide susceptibility analysis, a model is usually established on the basis of a multi-temporal or event-triggered landslide inventory. Because multi-temporal landslide inventories for most areas are rarely available, an event-triggered landslide inventory is often used, but the result depends on the selection of single event. In order to establish a landslide susceptibility model with a good prediction performance, the present study tried to find out how to select a single event-triggered landslide inventory, and investigated the effect of various combinations of event inventories. We selected Shihmen reservoir watershed as the research area, conducted a logistic regression analysis to build 23 event-based landslide susceptibility models and one multi-year landslide susceptibility model, and estimated the performance of these models. In addition, this study further assessed the influence of event characteristics on the model prediction performance, used the above results to merge two different events, and then established models based on these combinations. The results indicated that when establishing an event-based landslide susceptibility model, selecting events with suitable rainfall return periods and landslide density can yield robust models with relatively high predictive ability. Furthermore, the combination of two events which negatively correlate with each other in rainfall spatial distributions can enhance a model's predictive ability and modeling efficiency.

Landslide susceptibility evolution in an area affected by the 2015 Nepal earthquakes, derived from multitemporal landslide inventories

The Mw 7.8 and Mw 7.3 Nepal earthquakes in 2015 triggered >47,000 landslides and significantly enhanced landslide activity in the affected region in the following years, likely due to a drop in rock strength of the slopes there. This work analyzed the evolution of landslide susceptibility of an area close to the Zhangmu border crossing on the Araniko Highway in Nepal, which was seriously affected by the mainshock, aftershock, and monsoon rainfalls. Using eight landslide inventories we constructed to register landslides triggered by monsoon rainfalls before 2015, the mainshock, the aftershock, and the monsoon rainfalls in 2015–2019, eight susceptibility assessment models were built using the logistic regression method considering 11 control factors of landslides. The changes in the high-susceptibility zones before, during, and after the earthquakes were examined. The backward prediction abilities of the models were checked by taking landsliding and nonlandsliding data in the following years. The results show that the best susceptibility mapping results are obtained based on landslides triggered by the mainshock and the aftershock, which both have successful prediction rates >87%. The high-susceptibility regions have moved from the slope surfaces to channels since 2015, suggesting that transportation of residual debris induced by the strong ground motion resulted from erosion related to the strong post-seismic rainfall and the rivers. A series of attempts using susceptibility models to backward predict landslides in the following years demonstrate that the combination of the susceptibility results of pre-seismic and mainshock-triggered landslides, which have a higher prediction accuracy, has prediction rates >70% until 2018. This study not only will further the understanding of post-seismic landsliding effects, but also will aid in efforts toward future seismic landslide mitigation and post-seismic reconstruction.

Topography and geology effects on travel distances of natural terrain landslides: Evidence from a large multi-temporal landslide inventory in Hong Kong

Landslides pose threats to the safety and property of people living in mountainous areas like Hong Kong. Dependence of landslide travel distance on initial conditions and travel routes has broad implications for assessing the landslide risks. In this study, the controlling topographic and geologic factors for travel distances of open hillslope landslides and channelized debris flows are examined based on the Enhanced Natural Terrain Landslide Inventory (ENTLI) in Hong Kong with 11,622 records during the period of 1984–2013. The mean values of the horizontal travel distances of open hillslope landslides and channelized debris flows are 24.1 and 86.3 m, respectively. Correlations combining horizontal travel distances, volumes and fall heights of open hillslope landslides have been derived. The horizontal travel distances (L) and fall heights (H) are closely related for both types of landslides. The use of the L/H ratio alone as a representation of natural terrain landslide mobility is not suggested. Because of the entrainment of loose materials, the travel distances of channelized debris flows are enlarged significantly on colluvium lands (with mean value of 142.6 m), compared with those on weathered mantles (with mean value of 74.2 m).

New Insight into Post-seismic Landslide Evolution Processes in the Tropics

Earthquakes do not only trigger landslides in co-seismic phases but also elevate post-seismic landslide susceptibility either by causing a strength reduction in hillslope materials or by producing co-seismic landslide deposits, which are prone to further remobilization under the external forces generated by subsequent rainfall events. However, we still have limited observations regarding the post-seismic landslide processes. And, the examined cases are rarely representative of tropical conditions where the precipitation regime is strong and persistent. Therefore, in this study, we introduce three new sets of multi-temporal landslide inventories associated with subsets of the areas affected by 1) 2016 Reuleuet (Indonesia, Mw = 6.5), 2) 2018 Porgera (Papua New Guinea, Mw = 7.5) and 3) 2012 Sulawesi (Indonesia, Mw = 6.3), 2017 Kasiguncu (Indonesia, Mw = 6.6) and 2018 Palu (Indonesia, Mw = 7.5) earthquakes. Overall, our findings show that the landslide susceptibility level associated with the occurrences of new landslides return to pre-seismic conditions in less than a year in the study areas under consideration. We stress that these observations might not be representative of the entire area affected by these earthquakes but the areal boundaries of our study areas.

A multi-temporal landslide inventory and hazard zonation using relative effect method along the Mughal road Shopian, India

Landslide inventory and thematic data are of utmost importance in the domain of landslide hazard mapping. The union territory of Jammu and Kashmir, India surrounded by the Himalayan and the Pir-Panjal mountain range is prone to landslides and has already caused havoc at many places. The present study aims to provide the landslide inventory of the Mughal Road, Shopian, which lies in the Pir Panjal range of Kashmir valley. Multidate satellite data of the years 2008 to 2020 are utilized to create an inventory of landslides in this area.The use of high-resolution satellite imagery made it possible to delineate the shallow as well as the deep landslides along the roadside where they occur frequently. To understand the landslide causes, a statistical technique, relative effect method has been implemented in this study. This method helped in mapping the hazard zone areas. The relative effect of each causative factor on landslides is determined by calculating the ratio of coverage and slide which were analyzed in GIS environment. The resulting landslide hazard zone map has been classified as very low, low, moderate, high and very high zones. Out of the total area, 12.62% is critical to landslides, 21.45% is highly prone and 24.84% is moderately prone while 21.94% is low and 19.13% is very low prone to landslides. The outcome of this susceptibility modeling will be beneficial for handling and monitoring the forthcoming landslides as well as the fortification of the general public and environmental hazards of the study area. It will also help the planners in the development around the study area.

Multitemporal Landslide Inventory and Activity Analysis by Means of Aerial Photogrammetry and LiDAR Techniques in an Area of Southern Spain

This paper deals with the use of aerial photogrammetry and LiDAR techniques to analyze landslide activity over a long time span—just over 32 years. The data correspond to several aerial surveys (1984, 1996, 2001, 2005, 2009, 2010, 2011, 2013 and 2016) covering an area of about 50 km2 along highway A-44, near Jaén (Southern Spain). An ad hoc combined photogrammetric and LiDAR aerial survey of 2010 was established as the reference flight. This flight was processed by means of direct orientation methods and iterative adjustments between both data sets. Meanwhile, historical flights available in public geographical data servers were oriented by transferring ground control points from the reference flight. Then, digital surface models (DSMs) and orthophotographs were generated, as well as the corresponding differential models (DoDs), which, after the application of filters and taking into account the estimated uncertainty of ± 1 m, allowed us to identify true changes on the ground surface. This analysis, complemented by photointerpretation, led us to obtain a landslide multitemporal inventory in the study area that was analyzed in order to characterize the landslide type, morphology and activity. Three basic typologies were identified: rock falls–collapses, slides and flows. These types present different morphometric properties (area, perimeter and height interval) and are associated with different conditions (height, slope, orientation and lithology). Moreover, a set of monitoring areas, common for the different flights, was also used to analyze the activity throughout the study period. Thus, some more active periods were identified (2009–2010, 2010–2011, 2011–2013 and 1996–2001) among other less active ones (1984–1996, 2001–2005, 2005–2009 and 2013–2016), which are related to rainy events and dry years, respectively.

Rapidly Evolving Controls of Landslides After a Strong Earthquake and Implications for Hazard Assessments

AbstractStrong earthquakes, especially on mountain slopes, can generate large amounts of unconsolidated deposits, prone to remobilization by aftershocks and rainstorms. Assessing the hazard they pose and what drives their movement in the years following the mainshock has not yet been attempted, primarily because multitemporal landslide inventories are lacking. By exploiting a multitemporal inventory (2005–2018) covering the epicentral region of the 2008 Wenchuan Earthquake and a set of conditioning factors (seismic, topographic, and hydrological), we perform statistical tests to understand the temporal evolution of these factors affecting debris remobilizations. Our analyses, supported by a random‐forest susceptibility assessment model, reveal a prediction capability of seismic‐related variables declining with time, as opposed to hydro‐topographic parameters gaining importance and becoming predominant within a decade. These results may have important implications on the way conventional susceptibility/hazard assessment models should be employed in areas where coseismic landslides are the main sediment production mechanism on slopes.

Activity maps of multi-source mudslides from the Daunia Apennines (Apulia, southern Italy)

Multi-source mudslides are common in the Daunia Apennines, i.e., the Apulian sector of the Southern Apennines of Italy, and play an important role in the historical evolution of the landscape, with significant impact on the social and economic activities of the area. They also represent the most relevant geological hazard along the front of the Apennine Chain and expose to a considerable risk the local population and infrastructures. The outer sector of the Daunia Apennines is part of the foreland thrust belt system, made up of fissured fine-grained materials, belonging to the Daunia tectonic unit formations. Concerning landslide typology, the most frequent slope movements are represented by flows and composite mudslides, the latter typically starting as slumps evolving from flows. This paper deals with the production of landslide activity maps of three case studies of composite mudslides from the front of the Daunia Apennines. The maps were produced integrating traditional interpretation of multi-year aerial photograph coverage and field surveys with the available digital elevation models to analyze surface morphology. Each landslide activity map corresponds to multi-temporal landslide inventories compiled for the last 15–20 years. This paper outlined that in the last 20 years the front of the Daunia Apennines underwent a significant reactivation of slope failure phenomena after years of quiescence, or of limited activity. The three case studies clearly confirm that: (i) the active portions of the mudslides are located inside or near preexisting slope failures, and (ii) the spatial distribution of instability phenomena is strictly dependent upon the presence of older, larger and dormant, phenomena. Landslide activity maps are an important tool for the evaluation of landslide susceptibility and hazard in the study area, and for quantitative geomorphological analyses it aimed at understanding the long-term geomorphological evolution of this portion of the Southern Apennines. Moreover, we argue that high-quality multi-temporal inventories could have positive effects on all derivative products and analyses, including erosion studies and landscape modeling, susceptibility and hazard assessments, and risk evaluations as well.

Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan

Extreme rainfall has caused severe road damage and landslide disasters in mountainous areas. Rainfall forecasting derived from remote sensing data has been widely adopted for disaster prevention and early warning as a trend in recent years. By integrating high-resolution radar rain data, for example, the QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system provides a great opportunity to establish the extreme climate-based landslide susceptibility model, which would be helpful in the prevention of hillslope disasters under climate change. QPESUMS was adopted to obtain spatio-temporal rainfall patterns, and further, multi-temporal landslide inventories (2003–2018) would integrate with other explanatory factors and therefore, we can establish the logistic regression method for prediction of landslide susceptibility sites in the Laonong River watershed, which was devastated by Typhoon Morakot in 2009. Simulations of landslide susceptibility under the critical rainfall (300, 600, and 900 mm) were designed to verify the model’s sensitivity. Due to the orographic effect, rainfall was concentrated at the low mountainous and middle elevation areas in the southern Laonong River watershed. Landslide change analysis indicates that the landslide ratio increased from 1.5% to 7.0% after Typhoon Morakot in 2009. Subsequently, the landslide ratio fluctuated between 3.5% and 4.5% after 2012, which indicates that the recovery of landslide areas is still in progress. The validation results showed that the calibrated model of 2005 is preferred in the general period, with an accuracy of 78%. For extreme rainfall typhoons, the calibrated model of 2009 would perform better (72%). This study presented that the integration of multi-temporal landslide inventories in a logistic regression model is capable of predicting rainfall-triggered landslide risk under climate change.

The future of landslides’ past—a framework for assessing consecutive landsliding systems

Landslides often happen where they have already occurred in the past. The potential of landslides to reduce or enhance conditions for further landsliding has long been recognized and has often been reported, but the mechanisms and spatial and temporal scales of these processes have previously received little specific attention. Despite a preponderance of qualitative and anecdotal evidence, analysis has been limited. As a result, there is little consensus on the meaning of terms such as landslide repetition, recurrence, and reactivation. This source of confusion is evident when such terms are also used to describe systems where landsliding is prevalent but unrelated to landslide history. Recent findings, partly based on a rare multi-temporal landslide inventory for an area in Italy, show that the impacts of earlier landslides affect a substantial fraction of landslides, that landslides following earlier landslides differ from those that do not, and that accounting for the effect of previous landslides can improve susceptibility assessments. These findings await confirmation in other landslide-prone landscapes but show that consecutive landsliding deserves more attention, which requires consistent terminology. No such terminology is presently available, and we therefore propose it in this manuscript. We use the term “uncorrelated landsliding” to describe situations where landslides are common, but where a correlation with environmental variables such as terrain steepness is not implied. We propose “correlated landsliding” to describe situations where landslides are common and correlations with environmental variables exist, and “path-dependent landsliding” to describe situations where causal relations exist between consecutive landslides, for instance, when landslides occur at the scarp of previous landslides. These are situations where past landslides impact future landslides. Within the path-dependent category, we distinguish three subcategories based on the spatial distance between earlier and later landslides: “reactivation” or “continuation” if essentially the same material recommences or continues to slide, “local activation” if an earlier slide causes changes in a local hillslope that cause a later slide, and “remote activation” if an earlier slide causes changes elsewhere in the landscape that cause a later landslide. We use this proposed set of terms to outline some prominent knowledge gaps and potential research questions.

Comparing characteristics of rainfall- and earthquake-triggered landslides in the Upper Minjiang catchment, China

The Upper Minjiang catchment is located at the eastern margin of the Tibetan Plateau and is frequently affected by landslides. The two main triggering factors in the region are rainfall and high magnitude seismic shocks, such as the 2008 Wenchuan earthquake. In this paper, we present a comparison study of pre-conditioning factors that drive the spatial occurrence of landslides in this high elevation region. We used a multi-temporal landslide inventory that differentiates between rainfall and earthquake triggers based on the analyses of satellite imagery, aerial photos, and field investigations. Our investigation revealed that the strongest influences on landslides are lithology and topographic factors. The difference between these two triggering factors can also be observed with respect to the relative slope positions: rainfall-triggered landslides are more frequently found at lower slopes, while seismic-induced landslides are more evenly distributed on very steep slope sections. In particular, the steep and deeply incised river terraces in this region are prone to landslides due to their rapid formation. This study presents the first analysis that differentiates two main triggering factors in order to understand the influence of rainfall and earthquakes on landslide occurrences in this region. Our results can also aid in the sustainable management of performance-based slope engineering and landslide risk reduction by reporting the differences regarding the spatial distribution of rainfall- and seismic-triggered slope failures. Consequently, these results can be used by local and regional decision-makers for risk management and spatial planning.

Analysing post-earthquake landslide susceptibility using multi-temporal landslide inventories — a case study in Miansi Town of China

The earthquake that occurred on May 12, 2008, in Wenchuan County aroused a great deal of research on co-seismic landslide susceptibility assessment, but there is still a lack of an evaluation method that considers the activity state of the landslide itself. Therefore, this paper establishes a new susceptibility evaluation model that superimposes the active landslide state based on previous susceptibility evaluation models. Based on a multi-phase landslide database, the probabilistic approach was used to evaluate landslide susceptibility in the Miansi town over many years. We chose the elevation, slope, aspect, and distance from the channel as trigger factors and then used the probability comprehensive discrimination method to calculate the probability of landslide occurrence. Then, the susceptibility results of each period were calculated by superposition with the activity rate. The results show that between 2008 and 2014, the proportion of areas with low landslide susceptibility in the study area was the largest, and the proportion of areas with the highest susceptibility was minimal. The landslide area with highest susceptibility gradually decreased from 2014 to 2017. However, in 2017, 15.06% of the area was still with high susceptibility, and relevant disaster prevention and reduction measures should be taken in these areas. The larger area under the receiver operating characteristic curve (AUC) indicates that the results of the landslide susceptibility assessment in this study are more objective and reliable than those of previous models. The difference in the AUC values over many years shows that the accuracy of the evaluation results of this model is not constant, and a greater number of landslides or higher landslide activity corresponds to a higher accuracy of the evaluation results.