Slope Units Research Articles

Quantitatively detecting ground surface changes of slope failure caused by heavy rain using ALOS-2/PALSAR-2 data: a case study in Japan

Many SAR images have been utilized for geologic disasters investigations with the continuous launch of new Synthetic Aperture Radar (SAR) satellites such as ALOS-2/PALSAR-2. However, to proactively respond to transient slope failures caused by heavy rainfall, rapid extraction of areas of surface change accompanying slope failures is required. This study proposes two methods for quantitatively extracting slope failure areas using L-band SAR observations with slope units (SUs) as the evaluation units. The first method is based on the threshold method, which automates the selection of thresholds for various disaster-affected conditions, such as land use and topography. The second method is a machine-learning-based density ratio estimation method, which uses multi-temporal periodic observation data and pre- and post-disaster data to detect outliers through feature selection optimization. In the observation direction with the shortest satellite observation period, the F1 score (The F1 score is the harmonic mean of the precision and recall) of the threshold method for accuracy evaluation is 61.91%, and the F1 score of the density ratio method is 65.87%. Both methods can reduce the problem of low extraction accuracy caused by the effect of speckle noise. When slope failure occurs, both methods can extract the area of surface change within hours of a disaster. The method proposed in this study displays good applicability in supporting emergency rescue and the prevention of secondary disasters.

Unveiling spatial pattern in slope units-based landslides using a data-driven GAMM and InSAR techniques

ABSTRACT Spatial slope deformation pattern is crucial for landslide early warning and management. Quantitative modelling of the effects of multiple driving factors on the landslide spatial pattern has not yet been fully explored. To address this, this study utilizes data-driven models and Interferometric Synthetic Aperture Radar (InSAR) techniques to unveil the spatial pattern of landslides based on SUs (slope units). Specifically, we propose a methodology that establishes a correlation between InSAR deformation and topographic/meteorological factors by constructing a Generalized Additive Mixed Model (GAMM). Using this method, we analysed ALOS-2 ascending SAR images from April 2020 to September 2021 and successfully obtained cumulative minimum deformation during the period. Through the GAMM, we linked the observed cumulative minimum deformation in each SU to various factors, including topographic and meteorological factors. In terms of accuracy, there is an excellent agreement between the observed and fitted deformation, with a Pearson Correlation Coefficient (PCC) of 0.83. In terms of interpretability, our method can well explain the contribution of each factor to landslide deformation. We conclude that using the data-driven GAMM and InSAR techniques can effectively reveal the spatial pattern of landslides based on SUs. Furthermore, based on the method proposed in this study, it is possible to predict landslide deformation in the future.

An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland

AbstractAt the time of its development, GeoSure was created using expert knowledge based on a thorough understanding of the engineering geology of the rocks and soils of Great Britain. The ability to use a data-driven methodology to develop a national-scale landslide susceptibility was not possible due to the relatively small size of the landslide inventory at the time. In the intervening 20 years, the National Landslide Database has grown from around 6000 points to over 18,000 records today and continues to be added to. With the availability of this additional inventory, new data-driven solutions could be utilised. Here, we tested a Bernoulli likelihood model to estimate the probability of debris flow occurrence and a log-Gaussian Cox process model to estimate the rate of debris flow occurrence per slope unit. Scotland was selected as the test site for a preliminary experiment, which could potentially be extended to the whole British landscape in the future. Inference techniques for both of these models are applied within a Bayesian framework. The Bayesian framework can work with the two models as additive structures, which allows for the incorporation of spatial and covariate information in a flexible way. The framework also provides uncertainty estimates with model outcomes. We also explored consideration on how to communicate uncertainty estimates together with model predictions in a way that would ensure an integrated framework for master planners to use with ease, even if administrators do not have a specific statistical background. Interestingly, the spatial predictive patterns obtained do not stray away from those of the previous GeoSure methodology, but rigorous numerical modelling now offers objectivity and a much richer predictive description.

Incorporating Effects of Slope Units and Sliding Areas into Seismically Induced Landslide Risk Modeling in Tectonically Active Mountainous Areas

Traditional Newmark models estimate earthquake-induced landslide hazards by calculating permanent displacements exceeding the critical acceleration, which is determined from static factors of safety and hillslope geometries. However, these studies typically predict the potential landslide mass only for the source area, rather than the entire landslide zone, which includes both the source and sliding/depositional areas. In this study, we present a modified Newmark Runout model that incorporates sliding and depositional areas to improve the estimation of landslide chain risks. This model defines the landslide runout as the direction from the source area to the nearest river channel within the same slope unit, simulating natural landslide behavior under gravitational effects, which enables the prediction of the entire landslide zone. We applied the model to a subset of the Minjiang Catchment affected by the 1933 MW 7.3 Diexi Earthquake in China to assess long-term landslide chain risks. The results indicate that the predicted total landslide zone closely matches that of the Xinmo Landslide that occurred on 24 June 2017, despite some uncertainties in the sliding direction caused by the old landslide along the sliding path. Distance-weighted kernel density analysis was used to reduce the prediction uncertainties. The hazard levels of the buildings and roads were determined by the distance to the nearest entire landslide zone, thereby assessing the landslide risk. The landslide dam risks were estimated using the kernel density module for channels blocked by the predicted landslides, modeling intersections of the total landslide zone and the channels. High-risk landslide dam zones spatially correspond to the locations of the knickpoints primarily induced by landslide dams, validating the model’s accuracy. These analyses demonstrate the effectiveness of the presented model for Newmark-based landslide risk estimations, with implications for geohazard chain risk assessments, risk mitigation, and land use planning and management.

Analyzing spatial non-stationarity effects of driving factors on landslides: a multiscale geographically weighted regression approach based on slope units

Analyzing spatial non-stationarity effects of driving factors on landslides: a multiscale geographically weighted regression approach based on slope units

At the Junction Between Deep Learning and Statistics of Extremes: Formalizing the Landslide Hazard Definition

AbstractThe most adopted definition of landslide hazard combines spatial information about landslide location (susceptibility), threat (intensity), and frequency (return period). Only the first two elements are usually considered and estimated when working over vast areas. Even then, separate models constitute the standard, with frequency being rarely investigated. Frequency and intensity are dependent on each other because larger events occur less frequently and vice versa. However, due to the lack of multi‐temporal inventories and joint statistical models, modeling such properties via a unified hazard model has always been challenging and has yet to be attempted. Here, we develop a unified model to estimate landslide hazard at the slope unit level to address such gaps. We employed deep learning, combined with extreme‐value theory to analyze an inventory of 30 years of observed rainfall‐triggered landslides in Nepal and assess landslide hazard for multiple return periods. We also use our model to further explore landslide hazard for the same return periods under different climate change scenarios up to the end of the century. Our model performs excellently (with an accuracy of 0.78 and an area under the curve of 0.86) and can be used to model landslide hazard in a unified manner. Geomorphologically, we find that under climate change scenarios (SSP245 and SSP585), landslide hazard is likely to increase up to two times on average in the lower Himalayan regions (Siwalik and lower Himalayas; ≈110–3,500 m) while remaining the same in the middle Himalayan region (≈3,500–5,000 m) whilst decreasing slightly in the upper Himalayan region (≳5,000 m) areas.

An ensemble neural network approach for space–time landslide predictive modelling

There is an urgent need for accurate and effective Landslide Early Warning Systems (LEWS). Most LEWS are currently based on temporally-aggregated measures of rainfall derived from either in-situ measurements or satellite-based rainfall estimates. Relying on a summary metric of precipitation may not capture the complexity of the rainfall signal and its dynamics in space and time in triggering landslides. Here, we present a proof-of-concept for constructing a LEWS based on an integrated spatio-temporal modelling framework. Our proposed methodology builds upon a recent approach that uses a daily rainfall time series instead of the traditional scalar aggregation. Specifically, we partition the study area into slope units and use a Gated Recurrent Unit (GRU) to process satellite-derived rainfall time series and combine the output features with a second neural network (NN) tasked with capturing the effect of terrain characteristics. To assess if our approach enhances accuracy, we applied it in Vietnam and benchmarked it against a modelling counterpart where we replaced the rainfall time series with the corresponding scalar representative of the cumulated precipitation. The corresponding duration was set at 14 days as it proved to produce the best performance. Our results show that our protocol leads to better performance in hindcasting landslides when making use of the rainfall as a continuous signal over time. While not tested here, our approach can be extended to rainfall obtained from weather forecasts, potentially leading to actual landslide forecasts.

Integrated risk assessment of landslide in karst terrains: Advancing landslides management in Beiliu City, China

Beiliu City, Guangxi Zhuang Autonomous Region, China, like other karst landscape areas, is also suffering from the threat of landslides. This research applied the circular variance method to delineate 31,465 slope units and then combined it with a geographic information system and a well-established risk assessment system to synthesize three perspectives-susceptibility, hazard, and vulnerability to realize the risk assessment of karst landslides in Beiliu City. Using perennial field surveys, aerial images-derived landslide inventories, slope unit investigations, and a literature review combined with a Fully Convolutional Neural Networks approach, we present a culminating in the derivation of landslide susceptibility assessment outcomes within the study area. Because Beiliu City is located in a karst area, this research employs the quantification of soil erosion intensity as a metric to assess landslide hazard. In this study, based on a comprehensive analysis of the basic characteristics of landslides and their associated disaster-bearing bodies, a robust landslide vulnerability assessment index system was constructed for risk assessment of karst landslides in Beiliu City. The landslide risk assessment model in karst area is constructed by combining the landslide susceptibility and the vulnerability in the danger range. Detailed field surveys verified the scientific validity and accuracy of the assessment results. This research extends beyond merely furnishing scientific data support for local governmental efforts in disaster prevention and mitigation. It impressively enhances the methodological framework for conducting landslide risk assessments within karst landscapes, thereby offering a robust foundation for both immediate and strategic responses to the inherent geological vulnerabilities of these areas.

Object-based image analysis for extracting regional slope units and its application

Object-based image analysis for extracting regional slope units and its application

Integrated Risk Assessment of Mountainous Long-Distance Oil and Gas Pipelines Based on Multisource Spatial Data.

Pipeline risk assessment is crucial for pipeline safety management and operation. The aim of this study is to develop a comprehensive assessment model that accurately evaluates pipeline risks and ensures the safe and reliable operation of the pipeline system. The model is based on multisource spatial data and is primarily applicable to long-distance oil and gas pipelines that traverse complex geological conditions in mountainous areas. The research is conducted using the example of the Jinliwen natural gas pipeline in Zhejiang Province, China. By analyzing the geological data of the study area and the potential risks that the pipeline may encounter, a comprehensive risk assessment indicator system for the pipeline was developed using slope units to divide pipeline sections. The pipeline risk levels are classified using the K-means clustering-entropy weighted-random forest algorithm. The model is evaluated using accuracy (Acc), precision (Pre), recall (R), F1-score, and the ROC curve. The results show that the model has an accuracy of 0.917, a precision of 0.92, a recall of 0.916, an F1-score of 0.914, and an AUC (Area Under Curve) of 0.93, indicating its strong predictive capability. The risk assessment results demonstrate a strong consistency when compared with actual incident events. This indicates that the constructed model effectively reflects the influencing factors of pipeline risk, providing a basis for pipeline risk assessment and disaster prevention and mitigation efforts in similar regions.

Modeling landslide susceptibility using alternating decision tree and support vector

Globally, but especially in the Chinese Loess Plateau, landslides are considered to be one of the most severe and significant geological hazards. The purpose of this study is to design two ensemble machine learning methods, which are denoted as ADTree-Dagging and SVM-Dagging, for modeling landslide susceptibility in Lanzhou City (China). For this aim, the slope units extracted by the curvature watersheds method are used to construct landslide susceptibility modeling, and ten landslide conditioning factors are included in the landslide susceptibility evaluation (altitude, slope angle, slope aspect, cutting depth, surface roughness, relief amplitude, gully density, rainfall, distance to roads, and lithology). The conditioning factors selection and spatial correlation analysis were implemented by using the correlation attribute evaluation method and the frequency ratio model. The comprehensive performance of the models was tested using the receiver operating characteristic (ROC), area under the ROC curve (AUC), the root mean square error (RMSE), and several other performance metrics. For the training dataset, the results show that the SVM-Dagging model acquire the largest AUC value (0.953), lowest RMSE (0.3125), highest positive predictive value (96.0%), highest negative predictive value (91.2%), highest sensitivity (91.6%), highest specificity (95.8%), highest accuracy (93.6%), and highest Kappa (0.873). Similar results are observed in the validation dataset. Results demonstrated that the Dagging technique has improved significantly the prediction ability of SVM and ADTree models. The Dagging method can combine different models by leveraging the strengths of each model to create methods with higher flexibility than traditional machine learning methods. Therefore, in this study, the proposed new models can be applied for land-use planning and management of landslide susceptibility in the study area and in other areas containing similar geological conditions.

Landslide Susceptibility Assessment Based on Slope Unit and Information Value Method in Changbai Mountain District

Landslide Susceptibility Assessment Based on Slope Unit and Information Value Method in Changbai Mountain District

A landslide susceptibility assessment method considering the similarity of geographic environments based on graph neural network

Landslide susceptibility assessment (LSA) is vital for landslide mitigation and management. Existing LSA methods only consider local environmental characteristics associated with landslides, while neglecting the characteristics of remote yet interconnected geographic environments. In addition, the datasets for landslide samples do not consider the equilibrium among various geographic environmental characteristics. Ultimately, the landslide susceptibility mapping (LSM) generated is unreliable. We propose an LSA method based on the similarity of geographic environments and the equilibrium of landslide samples. We selected the Bailong River Basin (BRB) as the study area. Our proposed LSA method has four steps: (1) We constructed slope units using the curvature watershed method (CWM) based on the digital elevation model (DEM) to generate geographic nodes and selected 12 landslide influencing factors (LIFs) through multicollinearity analysis to extract geographic node features. (2) We constructed an association graph of geographic environment similarity constraints based on geographic node features using the cosine similarity method to correlate landslide and geographical environments. (3) We constructed sample datasets for an equal number of landslide and non-landslides based on various geographic environments. (4) We constructed a graph neural network (GNN) model based on the association graph to predict the susceptibility of unknown geographic nodes, obtaining a reliable LSM. Compared with random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) models, our proposed model framework outperforms in the LSA. The area under the receiver operating characteristic curve (AUC) values improved by 6.2%, 5.7%, and 1.8%, respectively. Specifically, our model can predict landslides across space in areas with few samples. Our proposed model offers an effective approach and essential technical support for landslide disaster investigation and prevention.

To explore the optimal solution of different mapping units and classifiers and their application in the susceptibility evaluation of slope geological disasters

Slope geological disaster is a type of natural disaster that is prevalent in the plateau mountainous area. Its occurrence frequently brings substantial harm to the local human population and living environment. The aim of this research is to investigate the suitability of various mapping units for different classifiers in the process of creating susceptibility zoning maps for slope geological disasters and to evaluate the effectiveness of each model. Concurrently, the susceptibility map produced by the high-accuracy model will assist relevant authorities in conducting disaster management tasks and also serve as a reference for modeling the susceptibility assessment of environmental geological disasters in other mountainous regions. Fuyuan County of China was considered as the research area, based on two different mapping units and four classifiers. Nine factors leading to disasters, such as elevation and topographic relief, were chosen as the evaluation indexes. With the help of the ArcGIS platform, the zoning map of geo-logical disaster susceptibility is drawn. Ultimately, the accuracy of the evaluation results was verified by the receiver operating characteristic curve and the confusion matrix. The findings indicate that all approaches are capable of producing susceptibility maps for geological disasters. However, the outcomes from the four classifiers that utilize slope units are more precise and logical than those that employ grid units. Lithology and the water system emerge as the most significant factors causing disasters in the study area, while the influence of fault zones is found to be minimal. The integration of the slope unit with the random forest classifier achieves the highest accuracy in predictions, maximizing the capabilities of both, and presents a promising application in the susceptibility mapping of slope geological disasters.

Geological Hazard Susceptibility Analysis and Developmental Characteristics Based on Slope Unit, Using the Xinxian County, Henan Province as an Example.

Geological hazards in Xinxian County, Xinyang City, Henan Province, are characterized by their small scale, wide distribution, and significant influence from regional tectonics. This study focuses on collapses and landslide hazards within the area, selecting twelve evaluation factors: aspect, slope shape, normalized difference vegetation index (NDVI), topographic relief, distance from geological structure, slope, distance from roads, land use cover type, area of land change (2012-2022), average annual rainfall (2012-2022), and river network density. Utilizing data from historical disaster sites across the region, the information quantity method and hierarchical analysis method are employed to ascertain the information quantity and weight of each factor. Subsequently, a random forest model is applied to perform susceptibility zoning of geological hazards in Xinxian County and to examine the characteristics of these geological disasters. The results show that in the study area, the primary factors influencing the development of geohazards are the distance from roads, rock groups, and distance from geological structure areas. A comparison of the susceptibility results obtained through two methods, the analytic hierarchy process information quantity method and the random forests model, reveals that the former exhibits a higher accuracy. This model categorizes the geohazard susceptibility in the study area into four levels: low, medium, high, and very high. Notably, the areas of very high and high susceptibility together cover 559.17 km2, constituting 35.99% of the study area's total area, and encompass 57 disaster sites, which represent 72.15% of all disaster sites. Geological hazards in Xinxian County frequently manifest on steep canyon inclines, along the curved and concave banks of mountain rivers, within watershed regions, on gully inclines, atop steep cliffs, and on artificially created slopes, among other sites. Areas with very high and high vulnerability to these hazards are mainly concentrated near the county's geological formations. The gneiss formations are widely exposed in Xinxian County, and the gneisses' strength is significantly changed under weathering, which makes the properties of the different degrees of weathering of the rock and soil bodies play a decisive role in the stability of the slopes. This paper provides a basis for evaluating and preventing geologic hazards in the Dabie mountainous area of the South Henan Province, and the spatial planning of the national territory.

Uncertainties of landslide susceptibility prediction: influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction (LSP). To illustrate various study area scales, Ganzhou City in China, its eastern region (Ganzhou East), and Ruijin County in Ganzhou East were chosen. Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m, as well as slope units that were extracted by multi-scale segmentation method. The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs. Then, landslide susceptibility maps (LSMs) of Ganzhou City, Ganzhou East and Ruijin County are produced using a support vector machine (SVM) and random forest (RF), respectively. The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City, along with the LSMs of Ruijin County from Ganzhou East. Additionally, LSMs of Ruijin at various mapping unit scales are generated in accordance. Accuracy and landslide susceptibility indexes (LSIs) distribution are used to express LSP uncertainties. The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City, Ganzhou East to Ruijin County, whereas those under slope units are less affected by study area scales. Of course, attentions should also be paid to the broader representativeness of large study areas. The LSP accuracy of slope units increases by about 6%–10% compared with those under grid units with 30 m and 60 m resolution in the same study area's scale. The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large. The importance of environmental factors varies greatly with the 60 m grid unit, but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.Graphic abstract

Constructing Rainfall Threshold for Debris Flows of a Defined Hazardous Magnitude

Debris flow can cause damage only when its discharge exceeds the drainage capacity of the prevention engineering. At present, most rainfall thresholds for debris flows mainly focus on the initiation of debris flow and do not adequately consider the magnitude and drainage measures of debris flows. These thresholds are likely to initiate numerous warnings that may not be related to hazardous processes. This study proposes a method for calculating the rainfall threshold that is related to a defined level of debris flow magnitude, over which certain damage may be caused. This method is constructed by using the transient rainfall infiltration analysis slope stability model (TRIGRS) and the fluid dynamics process simulation model (MassFlow). We first use the TRIGRS model to analyze slope stability in the study area and obtain the distribution of unstable slopes under different rainfall conditions. Afterward, the MassFlow model is employed to simulate the movement process of unstable slope units and to predict the depositional processes at the mouth of the catchment. Lastly a rainfall threshold is constructed by statistically analyzing the rainfall conditions that cause debris flows flushing out of the given drainage ditch. This method is useful to predict debris flow events of a hazardous magnitude, especially for areas with limited historical observational data.

Space-time modeling of landslide size by combining static, dynamic, and unobserved spatiotemporal factors

Landslide spatial prediction using data-driven models has predominantly concentrated on predicting where landslides may occur. Nevertheless, few researchers have turned to jointly modeling how large and when landslides will be for a given terrain unit. This study proposes a data-driven model capable of estimating how large landslides may be, for the entire Taiwan main island in a fourteen-year time window. To address this task, we implement a space–time generalized additive model to fit the complex relationships between environmental factors and landslide size. In addition to incorporating static and dynamic covariates into the modeling process, the model takes into account spatial and temporal interactions to elucidate the spatiotemporal variations affecting landslide size. To test the effectiveness of the model, we employ a comprehensive set of cross-validation (CV) procedures, includes a randomized 10fold-CV, a spatially constrained CV, a temporal leave-one-year-out CV, and a spatio-temporal CV. The experimental results demonstrate that the space–time model delivers acceptable and interpretable prediction outcomes, demonstrating the ability to predict landslide area for a given slope unit within a specified time period. We believe that the space–time landslide modeling will lay the foundation for landslide community to analyze landslide characteristic within a dynamic context. Furthermore, given its inherent spatio-temporal nature, we anticipate that this approach may pave the way for simulation studies exploring diverse climate scenarios.

SPATIALLY AWARE LANDSLIDE SUSCEPTIBILITY PREDICTION USING A GEOGRAPHICAL RANDOM FOREST APPROACH

Abstract. Landslide susceptibility prediction practices have been increasingly reliant on non-geographically-oriented (i.e., aspatial) machine learning algorithms. While these approaches have exhibited increasing success, they have often faced criticism for their limited consideration of spatial autocorrelations and local variations across geographical space, thereby neglecting the concept of spatial non-stationarity. To fulfill the research gap, this work applies a geographical random forest (GRF) approach, contrasting it with the conventional random forest (RF) algorithm. To this end, the study area, encompassing the Lake Sapanca Basin and its surroundings, was subdivided into 4,452 slope-based mapping units. The effectiveness of both predictive models was then measured by using overall accuracy (OA) and area under the curve (AUC). The results revealed that the GRF (OA = 80.82% and AUC = 85.22%) outperformed the RF algorithm (OA = 75.34% and AUC = 82.50%) by approximately 5% in OA, and demonstrated a 3% improvement in AUC score. The Wilcoxon signed-rank test confirmed significant differences (95% level) between the predictions of both models. The slope parameter emerged as the globally most influential factor, but local interpretations disclosed notable variations in the importance of causative factors contingent upon location. For instance, the curvature parameter was the most important geospatial covariate in around one-third (34.23%) of the slope units, mostly concentrated in the northernmost zones of the study area. On the other hand, elevation was the most important factor for 14.67% of the slope units primarily located in the southern region.

Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling

Abstract. Slope units are terrain partitions bounded by drainage and divide lines. In landslide modeling, including susceptibility modeling and event-specific modeling of landslide occurrence, slope units provide several advantages over gridded units, such as better capturing terrain geometry, improved incorporation of geospatial landslide-occurrence data in different formats (e.g., point and polygon), and better accommodating the varying data accuracy and precision in landslide inventories. However, the use of slope units in regional (> 100 km2) landslide studies remains limited due, in part, to the large computational costs and/or poor reproducibility with current delineation methods. We introduce a computationally efficient algorithm for the parameter-free delineation of slope units that leverages tools from within TauDEM and GRASS, using an R interface. The algorithm uses geomorphic laws to define the appropriate scaling of the slope units representative of hillslope processes, avoiding the often ambiguous determination of slope unit size. We then demonstrate how slope units enable more robust regional-scale landslide susceptibility and event-specific landslide occurrence maps.