Landslide Investigation Research Articles

Unmanned Aerial Vehicles (UAVs) in Landslide Investigation and Monitoring: A Review

Over the past decade, Unmanned Aerial Vehicles (UAVs) have emerged as essential tools for landslide studies, particularly in on-site investigations. This paper reviews UAV applications in landslide studies, with a focus on static geological characteristics, monitoring temporal and spatial dynamics, and responses post-events. We discuss the functions and limitations of various types of UAVs and sensors (RGB cameras, multi-spectral cameras, thermal IR cameras, SAR, LiDAR), outlining their roles and data processing methods in landslide applications. This review focuses on the UAVs’ roles in landslide geology surveys, emphasizing landslide mapping, modeling and characterization. For change monitoring, it provides an overview of the temporal and spatial evolution through UAV-based monitoring, shedding light on dynamic landslide processes. Moreover, this paper underscores UAVs’ crucial role in emergent response scenarios, detailing strategies and automated detection using machine learning algorithms. The discussion on challenges and opportunities highlights the need for ongoing UAV technology advancements, addressing regulatory hurdles, hover time limitations, 3D reconstruction accuracy and potential integration with technologies like UAV swarms.

Investigation of offshore landslides impact on bucket foundations using a coupled SPH–FEM method

BackgroundAs the exploitation of marine resources intensifies, the impact of submarine landslides on underwater structures has become a significant issue. Existing research primarily focuses on the impact on pipelines, often neglecting the actual deformation and mechanical response of underwater structures under impact loads in numerical simulations, thus complicating the evaluation of the reliability of these engineering structures in extreme conditions. Moreover, the dynamic response of bucket foundations, a common form of underwater base, under the effect of submarine landslide impacts remains unclear.MethodsTo address this knowledge gap, we have developed a fluid-structure coupling system that employs the coupled Smoothed Particle Hydrodynamics (SPH)-Finite Element Method (FEM) to investigate a single impact process and analyze the displacement response of bucket foundations within a water-offshore landslide-bucket foundationsubgrade context. The accuracy of this developed method has been systematically verified through comparisons with previous experimental and numerical results.ResultsDuring a submarine landslide impact event, the impact force demonstrates a distinct decrease followed by stabilization, and the displacement response of the bucket foundation exhibits a rebound effect after reaching its maximum value. Furthermore, we conducted an extensive analysis of different impact angles for underwater data centers equipped with multi-bucket foundations. Our study revealed that group-bucket foundations experience a combined translation-turnover failure when subjected to submarine landslide impacts, and the most unfavorable scenario for such impact is identified. The research introduces a novel numerical simulation approach for investigating the impact of submarine landslides on complex underwater structures.

Quantifying impacts of precipitation scenarios projected under climate change on annual probability of rainfall-induced landslides at a specific slope

Because of climate change, the intensity, duration, and frequency of future extreme rainfalls are projected to increase in many regions worldwide according to results from global climate models (GCMs). As rainfall is a major triggering factor for landslides, the frequency and risks of landslides are expected to increase. Consequently, it is crucial to quantify the impacts of climate change on landslide risks at a specific site. This study develops a statistical downscaling method based on the generalized extreme value (GEV) distribution and a probabilistic method for assessing the annual probability of rainfall-induced landslides at a specific site under projected precipitation scenarios from GCMs. The GEV-based statistical downscaling method bridges the gap between site-specific extreme rainfall (e.g., annual maximum 1-day rainfall) projection for a future scenario and GCM outputs with large spatial and temporal scales. A physics-based slope model is also used to properly depict slope failure mechanisms. The results indicate that the projected increase in extreme rainfall due to climate change leads to increases in annual frequency of heavy rainstorms and the annual slope failure probability, which are primarily controlled by the increase in the standard deviation, instead of the mean, of future extreme rainfalls.

Landslide mapping using geospatial techniques: A case study of the Bokoya Massif (Central Rif, Morocco)

The terrestrial zone of Al-Hoceima National Park is faced with a multitude of natural and man-made hazards that threaten the stability of the soil and the ecological balance: earthquakes, coastal erosion, floods, fires and landslides. The creation of landslide susceptibility maps is a crucial step in making informed decisions for preventing and mitigating landslide risks. This work focuses on the analysis of landslide potential in the Bokoya massif, located in the central Moroccan Rif, using the Analytical Hierarchical Process (AHP). Our aim is to develop a landslide probability map, which will be an important tool for land-use planning and risk management, helping to identify priority areas for further research.

Reconstruction of Natural Slope Stability by Limit Equilibrium Methods and Finite Element Methods

Slope stability analysis is particularly important for natural slopes that are relatively undesigned to be technically safe. Natural slopes are prone to collapse depending on the condition of the slope material and rainfall. A comprehensive stability analysis using numerical methods to calculate the factor of safety and probability of collapse can be used as a reference to assess the safety of a slope. This study discusses boundary equilibrium and finite element in the analysis of slope safety factor and landslide probability by reconstructing the slope with landslide history. The reconstruction of the slope is based on the actual slope condition with saturated material due to rain that has a safety factor of 0.5 using mathematical methods which are then analyzed numerically in this study. The results of the factor of safety on the saturated condition slope have a value that is not much different from the actual condition with a 100% probability of landslide. The results of low shear strength values in each slice of the limit equilibrium analysis for saturated conditions also indicate that the slope is in an unsafe condition which is supported by the development of shear and tensile strains in the finite element method analysis in the slope slide plane area which causes the stress distribution in the actual landslide area to be unstable.

A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

The accurate prediction of landslide susceptibility shortly after a violent earthquake is quite vital to the emergency rescue in the 72-h “golden window”. However, the limited quantity of interpreted landslides shortly after a massive earthquake makes landslide susceptibility prediction become a challenge. To address this gap, this work suggests an integrated method of Crossing Graph attention network and xgBoost (CGBoost). This method contains three branches, which extract the interrelations among pixels within a slope unit, the interrelations among various slope units, and the relevance between influencing factors and landslide probability, respectively, and obtain rich and discriminative features by an adaptive fusion mechanism. Thus, the difficulty of susceptibility modeling under a small number of coseismic landslides can be reduced. As a basic module of CGBoost, the proposed Crossing graph attention network (Crossgat) could characterize the spatial heterogeneity within and among slope units to reduce the false alarm in the susceptibility results. Moreover, the rainfall dynamic factors are utilized as prediction indices to improve the susceptibility performance, and the prediction index set is established by terrain, geology, human activity, environment, meteorology, and earthquake factors. CGBoost is applied to predict landslide susceptibility in the Gorkha meizoseismal area. 3.43% of coseismic landslides are randomly selected, of which 70% are used for training, and the others for testing. In the testing set, the values of Overall Accuracy, Precision, Recall, F1-score, and Kappa coefficient of CGBoost attain 0.9800, 0.9577, 0.9999, 0.9784, and 0.9598, respectively. Validated by all the coseismic landslides, CGBoost outperforms the current major landslide susceptibility assessment methods. The suggested CGBoost can be also applied to landslide susceptibility prediction in new earthquakes in the future.

Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy

Shallow landslides represent potentially damaging processes in mountain areas worldwide. These geomorphic processes are usually caused by an interplay of predisposing, preparatory, and triggering environmental factors. At regional scales, data-driven methods have been used to model shallow landslides by addressing the spatial and temporal components separately. So far, few studies have explored the integration of space and time for landslide prediction. This research leverages generalized additive mixed models to develop an integrated approach to model shallow landslides in space and time. We built upon data on precipitation-induced landslide records from 2000 to 2020 in South Tyrol, Italy (7400 km2). The slope unit-based model predicts landslide occurrence as a function of static and dynamic factors while seasonal effects are incorporated. The model also accounts for spatial and temporal biases inherent in the underlying landslide data. We validated the resulting predictions through a suite of cross-validation techniques, obtaining consistent performance scores above 0.85. The analyses revealed that the best-performing model combines static ground conditions and two precipitation time windows: a short-term cumulative precipitation of 2 days before the landslide event and a medium-term cumulative precipitation of 14 days. We demonstrated the model's predictive capabilities by predicting the dynamic landslide probabilities over historical data associated with a heavy precipitation event on August 4th and August 5th, 2016, and hypothetical non-spatially explicit precipitation (what-if) scenarios. The novel approach shows the potential to integrate static and dynamic landslide factors for large areas, accounting for the underlying data structure and data limitations.

Assessment of Soil Erosion and its Correlation with Landslide Incidents using Geospatial Techniques

The present attempt is made to assess soil erosion and its relationship to landslide incidents in the Panchganga river basin using geospatial techniques. The RUSLE model is used for assessing soil erosion and the FR model is employed for correlation analysis. The static and dynamic components of RUSLE are obtained using multi-source information and an integrated analysis is conducted in the ArcGIS software environment. Landslides are detected using remote sensing datasets and the change detection approach. According to the results of the soil erosion model, 81 % of the study area has a very low risk of erosion while about 21 % has a high and very high risk. The average rate of soil loss in the basin is 83 t/ha/yr. In the correlation analysis, the ratio represents the region where landslides took place to a specific area of an erosion-prone zone. As per the findings, the correlation between soil erosion and landslide incidences indicates that areas with high and very high erosion zones have a higher probability of landslides. The ROC is used to assess the correlation between the soil erosion model and landslide occurrence. According to the assessment results, the AUC indicates an 0.89 correlation between the resulting soil erosion model and landslides.

Investigation of Landslide by Comprehensive Geophysical Exploration in Northeast Chongqing: Taking Xiawafang Landslide as Example

Aiming at the characteristics of geological structure of Xiawafang landslide in Wuxi County, northeast Chongqing, high density resistivity method and seismic image method are used to comprehensively analyze the differences between conductivity and impedance of rock stratum in this area. At the same time, the geological survey results and drilling data are taken as the basis. With the fusion of geology-geophysics analysis and interpretation, this paper shows the characteristics of geological structure and spatial distribution in the exploration area, and landslides can be analyzed according to their mode of deformation and destruction, which provides a basis of comprehensive geology-geophysics interpretation for the treatment of geological landslide.

The Geomorphic Effectiveness of Landslides

AbstractLandslides are widely recognized as key components of landscape evolution in areas of steep topography. Here, we present a new framework for examining landslide inventories in the context of the volume‐based impact that different landslide sizes have on shaping the landscape, that is, their geomorphic effectiveness (GE). Focusing on an actively retreating coastal cliff in the Eastern Mediterranean and utilizing a LiDAR‐derived inventory of over 1,100 cliff landslides that occurred between 2014 and 2019, we show that segments of the cliff are characterized by two principal types of GE distributions: (a) A “humped” GE distribution where the accumulated erosion volume of the largest and rarest collapses in the inventory is similar or lower than that of more frequent, mid‐range collapses and (b) Nearly monotonically increasing GE distribution where the cumulative volume of larger collapses consistently surpasses that of smaller magnitude collapses. Regardless of the GE distribution type, we found that the cumulative geomorphic impact of the small and most probable collapses was negligible. Extending this new GE framework to 9 other previously published landslide inventories (coastal and mountainous), we demonstrate that precipitation and seepage‐induced landslide inventories are commonly characterized by monotonic‐type GE distributions, dominated by large landslides (>10−1 of the volume of the largest landslide), and that hump‐shaped GE distributions, dominated by more frequent mid‐size landslides, commonly occur under “dry” triggers (e.g., earthquakes). We propose that the humped GE distribution could reflect the lack of deep mechanical weakening, which exerts a higher probability of the largest landslides in the inventories triggered by “wet” factor.

Investigation and Slope Improvement of Landslides on Bodor River Slopes

The Bodor River is an important source of rice irrigation because most of the population works in the agricultural sector. The slopes of the Bodor River have experienced landslides, causing a major negative impact on the community. Many studies state that soil type, consistency, and friction angle influence slope failure. However, the relationship between soil characteristics and landslides on slopes, especially on the Bodor River, has not been studied. This research aims to identify soil characteristics and their influence on landslides on the slopes of the Bodor River, along with recommendations for improvement. The soil was taken after a landslide occurred at a depth of 80 cm for a sieve gradation test, Atterberg limit test, direct shear test, and slope stability analysis using the Cullman method. The research results show that the SW-SM soil type on the slopes of the Bodor River is highly vulnerable to landslides. A steep slope of 50° and a low soil friction angle between 1.38 and 14.11 have less than one safety factor. Changes in soil conditions, such as increased water content, contribute to a higher risk of landslides. Therefore, strengthening the slope is necessary, with the recommendation to increase the slope to 34.5° so that the slope safety factor increases by 73%. The results of this research provide an overview of the relationship between geotechnical soil parameters that influence slope failure in river areas and recommendations for slope improvement to prevent future collapse.

Estimating the period of probable landslide event using advanced D-InSAR technique for time-series deformation study of Kotrupi region

Himalayan region is prone to landslides and every year many landslides occur. Some of these events are reported while, many of these landslides are not reported due to their remote location of occurrence, or nature of impact, and its date of occurrence is also unknown. This study tries to find a method to estimate the probable dates of landslide occurrences using time-series deformation analysis applying advanced DInSAR approach by adopting a short temporal baseline graph. We used 118 Sentinel 1-A SLC images to identify the nature, pattern, and trends of landslide deformation. Based on the time series cumulative deformation values, we found a ‘V’-shaped kink that matches with the landslide events. Such kinks were observed around 12 June 2015, 25 May 2016, 05 August 2016, 23 June 2017, 15 September 2019, 27 May 2018, 08 June 2018, and 15 July 2020. The majority of these kinks are visible during monsoon season which is expected. High-resolution Planet satellite data around these time ranges were used to verify the existence of new landslides. Moreover, the performance is evaluated using AUC-ROC, precision, recall; and F-Score. This approach has good prediction capabilities with the precision of 87%, AUC-ROC of 77% and F-score of 74%.

Integrating data-driven and physically based landslide susceptibility methods using matrix models to predict reservoir landslides

One of the most hazardous types of landslide disasters is reservoir landslides. Spatially predicting the probability of these events when the hydroelectric power station is not impounded can provide a foundation for their prevention and management. A reservoir landslide prediction study was conducted using the reservoir of the Baihetan Hydropower Station in Southwest China as the research area. The study applied both the widely used data-driven landslide susceptibility assessment method and the physics-based landslide stability evaluation approach. Additionally, a matrix prediction model based on a combination of the aforementioned techniques was suggested for reservoir landslide prediction. The applicability of the three approaches was assessed based on the findings of a field investigation of reservoir landslides during the initial impoundment period. The results demonstrated that, in the absence of historical reservoir landslide data, the prediction success rate of the susceptibility mapping method was incredibly poor, at only 30.19%. While the stability prediction approach had an 88.68% success rate, its frequency ratio was only 5.22. The approach significantly overpredicted reservoir landslides, according to a study of the prediction results for regions. On the other hand, the frequency ratio of the matrix prediction model, which combines the two approaches, was 2.24 times that of the stability prediction approach. Thus, the matrix prediction model was found to be more reliable than other methods and capable of predicting the vast majority of reservoir landslides with fewer extremely high probability reservoir landslide areas. To further increase the accuracy of reservoir landslide prediction, it is recommended to use the matrix prediction model.

Climate change-derived environmental and physical factors influencing the socioeconomic development in the Himalayan region

Spatio-temporal fluctuation of climatic variables with the terrain characteristics and their inter-relationship is a priority for predicting flash-flood-induced landslide hazards over the fragile Himalayas. The present study addressed this anxiety by assimilating satellite data products and auxiliary datasets in the Bhagirathi River basin of the Indian Himalayas. Snow Covered Area (SCA) is a critical indicator of the ecosystem that influenced the flash flood along different terrain features such as Altitude, Hill-Gradient, and Aspect. GIS-based multi-criteria decision analysis (GIS-MCDA) technique is used to analyze the possible landslide zones and flood extent along the river basin, and MODIS Terra (MOD10A2) data products derived annual SCA is 4278 km2 for the year 2021, the analysis of geospatial maps at 25° intervals of Altitude, hill-gradient, and Aspect. The SCA distribution reveals that apart from the Altitude, the Aspect of the hill gradient significantly impacts snow accumulation. Hill-Gradient, ranging from 13.06 to 19.52, occupies 24.7% of the total area, and 45.3 to 51.83 are found without snow. The highest variation of SCA is along the Western direction (9.19%), followed by North-East (8.79%), while the least (3.78%) variance is in the Southwest direction. Additionally, it was found that many bridges, roads, and other properties are under threat in this study area, even with a moderate flash flood. Findings from this study provide the spatiotemporal status of SCA in various geological stress conditions during the last decades and probable landslide zones. This will be a preliminary pathway to policymakers in rehabilitation and early evacuation of human lives due to flash flood occurrence.

Advances on the investigation of landslides by space-borne synthetic aperture radar interferometry

ABSTRACT Landslides are destructive geohazards to people and infrastructure, resulting in hundreds of deaths and billions of dollars of damage every year. Therefore, mapping the rate of deformation of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated risks. In this paper, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59,339 “Earth observation for seismic hazard assessment and landslide early warning system” are reported. The primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks, detect potential landslides in wide regions, and demonstrate EO-based landslide early warning system over selected landslides. This work only focuses on the landslide hazard content of the project, and thus, in order to achieve these objectives, the following tasks were developed up to now: a) a procedure for phase unwrapping errors and tropospheric delay correction; b) an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements; c) the application of polarimetric SAR interferometry (PolInSAR) to increase the number and quality of monitoring points in landslide-prone areas; d) the semiautomatic mapping and preliminary classification of active displacement areas on wide regions; e) the modeling and identification of landslides in order to identify triggering factors or predict future displacements; and f) the application of an InSAR-based landslide early warning system on a selected site. The achieved results, which mainly focus on specific sensitive regions, provide essential assets for planning present and future scientific activities devoted to identifying, mapping, characterizing, monitoring and predicting landslides, as well as for the implementation of early warning systems.

Regional-scale InSAR investigation and landslide early warning thresholds in Umbria, Italy

In this work, we use conventional two-pass differential satellite interferometry for the investigation of active landslides at regional scale in the Umbria Region of Italy. First, we use InSAR for the detection of active slope movements during the study period (October 2019 to January 2021). Then, we characterize the displacement rates and their variation through time for selected active slopes, by analysing 6-days interferograms. The evolutionary trends of the phenomena are compared to the pertaining climate and soil parameters that are used by the regional landslide early warning system (LEWS) to predict the possibility of landslide occurrence over the territory. We aim to explore the potential of differential interferometry to detect and monitor active landslides and, through that, to validate a geographical LEWS:Through the analysis of stacked interferograms, we identified 256 InSAR deformation signals (IDS) corresponding to active slope movements. Their distribution is compared to the geological map and the landslide inventory, illustrating how active landslides are favoured by weak lithologies and pre-existing slope instability. Also, the relative orientation of the satellite's line of sight with respect to the slope influences the results indicating a bias in the completeness of remotely sensed data. However, in the context of land management and civil protection, the results constitute a valuable information dataset for plans managed by local authorities.The analysis, at the interferogram scale (6-days), of 13 clearly recognizable IDSs illustrates the dynamics of the slopes through the variation of the displacement rates. Due to decorrelation, only about half of the interferograms can be used for this purpose, and interpretation is required. The comparison to the alert thresholds of the Umbria region shows the remarkable relationship between InSAR-derived landslide activity and the parameters used for landslide prediction (i.e., 48 h rainfall and soil moisture conditions). The maximum 48 h cumulative rainfall and the soil saturation index prove effective to predict landslide reactivations and accelerations. We provide uncertainty measures of the landslide prediction through binary classification metrics. Such results are only preliminary, given the limited number of landslides and timespan of our dataset. However, it represents one of the few attempts to validate the forecast of a regional LEWS quantitatively.

Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA

Abstract. Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

BUILDING A REAL-TIME INFORMATION AND FLASH FLOODS, LANDSLIDES EARLY WARNING SYSTEM IN MOUNTAINOUS AND MIDLAND AREAS OF VIET NAM

Flash flood is a natural disaster occurring shortly on small scale albeit with great devastation. In fact, the current flash flood events, landslide investigations and surveys from research agencies and local authorities play an important role in creating diverse data sources for researching and exploring the signs as well as modeling adjustment for detecting the risks. This article presents the results of making a Web portal used for the reception and integration of early warnings from citizens, local authorities and governing bodies against flash floods and landslides. This system collects real time rainfall data from the CDH of Meteorology and Hydrology Administration, integrates the numerical weather prediction WRF (the Weather Research and Forescast) model with 6 h, 24 h, 48 h forecast lead time and SEAFFGS product results. Also, it would elaborate on the system providing tools for aiding, analysing, producing and disseminating warning bulletins against flash floods and landslides for mountainous and midland areas, sending warning message about high risk flash flood and landslide with the total observed rainfall or rainfall forecast over the threshold via email or Zalo automatically. This information which can be accessed everywhere via the internet, will effectively support the forecasters in mornitoring and publishing the flash flood and landslide warning bulletins; help the authorities and citizens become aware of natural disasters early and mitigate damage caused by flash floods and landslides.

Internal structure of an active landslide based on ERT and DP data: New insights from the Hofermühle landslide observatory in Lower Austria

Slopes with clay-rich, deeply weathered soils, such as those present in the Flysch Zone of Lower Austria, are prone to landslide processes. Even though generally being of low magnitude and velocity, they can cause substantial economic losses and threaten settlements as well as infrastructure. Previous studies have demonstrated the importance of sub-surface information to further increase our understanding on landslide processes and triggers in different areas as well as to support any decision makers to determine any appropriate landslide mitigation measures. The combination of direct and indirect methods thereby has proven to be an adequate methodology, yet case studies are still needed to develop a universal strategy for the investigation of clay-rich landslides. In this study, we applied Electrical Resistivity Tomography (ERT) and Dynamic Probing Medium (DPM) to further investigate the complex Hofermühle landslide in the Flysch Zone of Lower Austria in order to improve our knowledge on sub-surface conditions. The focus is on the initially activated main landslide area, which has already formed earth-flow-like processes in the past. The methodology facilitated a representative characterization of the sub-surface. We were able to approximate the geometry and depth of slip surfaces, as well as the transition to bedrock, allowing us to delimit those parts of the slope which are likely to fail and, respectively be re-activated in the future. The results show that a range of thicknesses can be found in the investigated part of the Hofermühle landslide. In particular, near the surface, which has been altered by creep displacement and also by anthropogenic alteration, the resistivity is more variable compared to deeper, presumably less disturbed, layers. Furthermore, it is assumed that clay particles are transported with the surface runoff and deposited in the surroundings of the drainage channels, resulting in lower resistivity values and higher saturation at this locations. The location of the slip surface is interpreted to be on the surface of the bedrock, where there is a highly disturbed, clay-rich mass with no interbedded layers. Layers with lower resistivity directly above the bedrock indicate weathered material and remolding of materials during creep.

Impact of Vegetation Differences on Shallow Landslides: A Case Study in Aso, Japan

Climate change has increased the frequency and scale of heavy rainfall, increasing the risk of shallow landslides due to heavy rainfall. In recent years, ecosystem-based disaster risk reduction (Eco-DRR) has attracted attention as one way to reduce disaster risks. Vegetation is known to increase soil strength through its root system and reduce the risk of shallow landslides. To reduce the risk of shallow landslides using vegetation, it is necessary to quantitatively evaluate the effects that vegetation has on shallow landslides. In this study, we constructed a generalized linear model (GLM) and random forest (RF) model to quantitatively evaluate the impact of differences in the vegetation, such as grasslands and forests, on the occurrence of shallow landslides using statistical methods. The model that resulted in the lowest AIC in the GLM included elevation, slope angle, slope aspect, undulation, TWI, geology, and vegetation as primary factors, and the hourly rainfall as a trigger factor. The slope angle, undulation, and hourly rainfall were selected as significant explanatory variables that contribute positively to shallow landslides. On the other hand, elevation and TWI were selected as significant explanatory variables that contribute negatively to shallow landslides. Significant differences were observed among multiple categories of vegetation. The probability of shallow landslide in secondary grasslands was approximately three times that of coniferous and broadleaf forests, and approximately nine times that of broadleaf secondary forests. The landslide probability of shrubs was approximately four times that of coniferous and broadleaf forests, and approximately ten times that of broadleaf secondary forests. The results of constructing the RF model showed that the importance was highest for the hourly rainfall, followed by geology, then elevation. AUC values for the GLM and RF model were 0.91 and 0.95, respectively, indicating that highly accurate models were constructed. We quantitatively showed the impact of differences in vegetation on shallow landslides. The knowledge obtained in this study will be essential for considering appropriate vegetation management to reduce the risk of future shallow landslides.