Landslide Susceptibility Evaluation Research Articles

An Investigation into the Susceptibility to Landslides Using Integrated Learning and Bayesian Optimization: A Case Study of Xichang City

In the middle southern section of the Freshwater River–Small River Fault system, Xichang City, Daliang Prefecture, Sichuan Province, is situated in the junction between the Anning River Fault and the Zemu River Fault. There has been a risk of increased activity in the fault zone in recent years, and landslide susceptibility evaluation for the area can effectively reduce the risk of disaster occurrence. Using integrated learning and Bayesian hyperparameter optimization, 265 landslides in Xichang City were used as samples in this study. Thirteen influencing factors were chosen to assess landslide susceptibility, and the BO-XGBoost, BO-LightGBM, and BO-RF models were evaluated using precision, recall, F1, accuracy, and AUC curves. The findings indicated that after removing the terrain relief evaluation factor, the four most significant factors associated with landslide susceptibility were NDVI, distance from faults, slope, and distance from rivers. The study demonstrates that the AUC value of the BO-XGBoost model in the study area is 0.8677, demonstrating a better generalization ability and higher prediction accuracy than the BO-LightGBM and BO-RF models. After Bayesian optimization of hyperparameters, the model offers a significant improvement in prediction accuracy.

Evaluating landslide susceptibility: the impact of resolution and hybrid integration approaches

The present study investigates the effectiveness of various landslide susceptibility machine learning (ML) models at multiple spatial resolutions. Using various conditioning factors, including topography, hydrology, and human influences, the study analyzed the predictive power of single, integrated, and comparative ML models. Alternating Decision Tree (ADT), Forest by Penalizing Attributes (FPA), and Random Forest (RAF), and integrated approaches such as Rotation Forest (RF) and Random Subspace (RS) that were based on ADT and FPA were utilized for the study. All models were trained and validated using a ten-fold cross-validation technique and evaluated by various statistical metrics, across resolutions from 12.5 m to 200 m. Shenmu City in China was chosen as an ideal test site to evaluate the developed methodology. The study reveals that finer spatial resolutions significantly enhance the accuracy of landslide predictions and that integrated models have superior performance over single models. The Frequency Ratio method identified elevation, slope, and hydrological factors as the key predictors concerning landslide occurrences. According to the results the RS-ADT model at a 12.5 m resolution achieved the highest evaluation accuracy (ROC = 0.907). The research highlights the possibility of combining multiple modeling techniques and high-resolution data to improve the predictive accuracy of landslide susceptibility models.

Identification of Potential Landslides in the Gaizi Valley Section of the Karakorum Highway Coupled with TS-InSAR and Landslide Susceptibility Analysis

Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional soil erosion, and landslide disasters occur frequently along this section, which severely affects the smooth flow of traffic through the China-Pakistan Economic Corridor (CPEC). In this study, 118 views of Sentinel-1 ascending- and descending-orbit data of this highway section are collected, and two time-series interferometric synthetic aperture radar (TS-InSAR) methods, distributed scatter InSAR (DS-InSAR) and small baseline subset InSAR (SBAS-InSAR), are used to jointly determine the surface deformation in this section and identify unstable slopes from 2021 to 2023. Combining these data with data on sites of historical landslide hazards in this section from 1970 to 2020, we constructed 13 disaster-inducing factors affecting the occurrence of landslides as evaluation indices of susceptibility, carried out an evaluation of regional landslide susceptibility, and identified high-susceptibility unstable slopes (i.e., potential landslides). The results show that DS-InSAR and SBAS-InSAR have good agreement in terms of deformation distribution and deformation magnitude and that compared with single-orbit data, double-track SAR data can better identify unstable slopes in steep mountainous areas, providing a spatial advantage. The landslide susceptibility results show that the area under the curve (AUC) value of the artificial neural network (ANN) model (0.987) is larger than that of the logistic regression (LR) model (0.883) and that the ANN model has a higher classification accuracy than the LR model. A total of 116 unstable slopes were identified in the study, 14 of which were determined to be potential landslides after the landslide susceptibility results were combined with optical images and field surveys. These 14 potential landslides were mapped in detail, and the effects of regional natural disturbances (e.g., snowmelt) and anthropogenic disturbances (e.g., mining projects) on the identification of potential landslides using only SAR data were assessed. The results of this research can be directly applied to landslide hazard mitigation and prevention in the Gaizi Valley section of the Karakorum Highway. In addition, our proposed method can also be used to map potential landslides in other areas with the same complex topography and harsh environment.

Landslide Susceptibility Assessment Based on Multisource Remote Sensing Considering Inventory Quality and Modeling

The completeness of landslide inventories and the selection of evaluation models significantly impact the accuracy of landslide susceptibility assessments. Conventional field geological survey methods and single remote-sensing technology struggle to reliably identify landslides under complex environmental conditions. Moreover, prevalent landslide susceptibility evaluation models are often plagued by issues such as subjectivity and overfitting. Therefore, we investigated the uncertainty in susceptibility modeling from the aspects of landslide inventory quality and model selection. The study focused on Luquan County in Yunnan Province, China. Leveraging multisource remote-sensing technologies, particularly emphasizing optical remote sensing and InSAR time-series deformation detection, the existing historical landslide inventory was refined and updated. This updated inventory was subsequently used to serve as samples. Nine evaluation indicators, encompassing factors such as distance to faults and tributaries, lithology, distance to roads, elevation, slope, terrain undulation, distance to the main streams, and average annual precipitation, were selected on the basis of the collation and organization of regional geological data. The information value and two coupled machine-learning models were formulated to evaluate landslide susceptibility. The evaluation results indicate that the two coupled models are more appropriate for susceptibility modeling than the single information value (IV) model, with the random forest model optimized by genetic algorithm in Group I2 exhibiting higher predictive accuracy (AUC = 0.796). Furthermore, comparative evaluation results reveal that, under equivalent model conditions, the incorporation of a remote-sensing landslide inventory significantly enhances the accuracy of landslide susceptibility assessment results. This study not only investigates the impact of landslide inventories and models on susceptibility outcomes but also validates the feasibility and scientific validity of employing multisource remote-sensing technologies in landslide susceptibility assessment.

Landslide susceptibility prediction based on landform predisposing indexes − An example from the Beiluo River Basin

The Beiluo River basin, which flows through the central part of the Loess Plateau, has experienced intense soil erosion and significant geomorphic change, which has provided favorable conditions for the occurrence of a large number of landslides. Landform indexes, which can express geomorphologic development state and internal rules, can transfer the development process information of surface morphology into the evaluation of landslide susceptibility, and help get more accurate landslide susceptibility prediction results. Taking the Beiluo River Basin as an example, a landslide susceptibility prediction model based on landform index is proposed by comparing the importance of landform index. In order to improve the accuracy of LSP, 10 kinds of general predictors indexes, 5 kinds of landform predisposing indexes and 1821 landslide points were compiled, and the geographic information system of Beiluo River Basin was constructed. Through the correlation test and CF model, the environmental indexes were evaluated to obtain the sensitive index results, and the combination of different environmental predictors indexes were classified according to the sensitive index results. Based on the combined classification results, the Max Entropy (MaxEnt) model was used to evaluate the Landslide susceptibility prediction (LSP), while the calculated results were evaluated and compared using the receiver operating characteristic (ROC) curve and landslide density. The results show that the vertical erosion factor, elevation, rainfall, horizontal erosion factor, slope angle and NDVI play a key role in controlling the spatial distribution of landslides in the study area. At the same time, the accuracy of landslide susceptibility is compared by AUC value. According to the calculation results, the Group5 (AUC = 0.803) with reasonable terrain index performs better in the training and test stages, and the relative accuracy is improved by 6.22 % compared with the non-introduction of terrain index and the omission rate difference is the best (omission rate difference = 0.0005), indicating that the introduction of landform index can effectively improve the landslide susceptibility prediction. The distribution of different sensitive areas was observed. The high sensitive areas and very high sensitive areas are mainly distributed in the southern Luochuan loess tableland and the northern Wuqi loess hilly area. The research results provide a scientific basis for landslide susceptibility prediction with rational introduction of landform indexes and regional infrastructure construction.

Co-seismic landslides susceptibility evaluation of Bayesian random forest considering InSAR deformation: a case study of the Luding Ms6.8 earthquake

Strong earthquakes can frequently trigger a large number of co-seismic landslides. Obtaining an accurate susceptibility map of co-seismic landslides is crucial for post-disaster rescue and reconstruction efforts. In this study, the pre-seismic average annual surface deformation rate of the study area was obtained using Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology. Furthermore, the landslide hazards prior to the earthquake in multiple locations within the study area were analyzed. Subsequently, the deformation data was combined with 11 evaluation factors, including the distance to the fault and the Peak Ground Acceleration (PGA), to model the susceptibility of landslides. We constructed four models to evaluate the susceptibility of landslides in the study area: the Bayesian optimization random forest (BO-RF) model, the random forest model (RF), the logistic regression model (LR), and the support vector machine model (SVM). The BO-RF model outperformed the other models, achieving an AUC value of 0.984, an accuracy of 0.952, a precision of 0.953, a recall of 0.952, and an F1 score of 0.953. Furthermore, incorporating pre-seismic deformation features in the evaluation of co-seismic landslide susceptibility can effectively improve the reliability of model predictions, as compared to the evaluation model results without incorporating deformation factors. The obtained research results provide valuable data support for the rescue and management of disaster-stricken areas.

Deterministic approach to assess landslide susceptibility and landslide activity in the Central-Western Region of Slovakia

Quantitative assessments in landslide susceptibility evaluation are typically dominated by statistical analyses. However, this paper employs a deterministic approach to construct such maps, specifically focusing on the stability assessment of shallow landslides with slip surfaces generally not exceeding 5 m in depth at a regional scale. The study area chosen for this research is the landslide-prone region between Hlohovec and Sereď towns in the central-western part of Slovakia. Our focus is on the activation of shallow landslides, representing the youngest, third generation of landslide evolution in the area. For the stability assessment, three scenarios were considered using deterministic analysis: a scenario involving dry slopes, a scenario under partially saturated conditions, and a scenario of fully saturated slopes. This approach enables the assessment of stability conditions based on qualitative parameters derived from both field and laboratory research. The results from the deterministic analysis aid in defining different stages of activity within the landslide body. These Landslide susceptibility maps were verified directly in the field, and most landslides reactivated from 2011 to the present fell into the class with the safety factor below 1.0. In comparison with the landslide hazard map created by using bivariate statistical analysis, it was observed that 89.97% of the area is in a very high level of landslide hazard, given a safety factor below 1.0. This highlights the effectiveness of the deterministic approach in capturing the vulnerability of the region to shallow landslides.

Evaluation of Landslide Susceptibility in Tekes County, Yili Prefecture Based on the Information Quantity Method

Evaluation of Landslide Susceptibility in Tekes County, Yili Prefecture Based on the Information Quantity Method

Seismic landslide susceptibility evaluation model based on historical data and its application to areas with similar environmental settings

Seismic landslide susceptibility evaluation models are usually built on the basis of historical sample data; however, the evaluation results are often unsatisfactory when the environmental settings differ between the historical sample data region and application region. Therefore, similarity between the environmental settings is important for the application of such models. In this paper, a seismic landslide susceptibility evaluation model was first built using data from the 2008 Ms 8.0 Wenchuan earthquake-induced landslide, and the model was then used to evaluate the 2022 Ms 6.8 Luding earthquake area. In addition, the grade of susceptibility is typically represented by the landslide density, which is insufficient for capturing the details of landslides, such as their sizes, frequencies, and spatial distribution patterns. The authors therefore use a large and concentrated landslide as the susceptibility grade for the Luding earthquake area. The test results demonstrate that these two areas have similar background environments. The area under the curve (AUC) value of the receiver operating characteristics (ROC) curve of the evaluation accuracy for the model applied to the Luding earthquake area is 0.889, which indicates relatively high accuracy. Besides, the results also demonstrate that the evaluations are consistent with the disaster situation of the Moxi Platform, Wandong Village, as well as the Dagangshan Hydropower Station area. Therefore, it is reliable to apply the susceptibility evaluation model based on the Wenchuan earthquake data to the Luding earthquake area. These results show that better evaluations can be obtained based on environmental similarity tests between the areas used for historical data modeling and areas to which the models are applied.

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.

GIS-based statistical analysis for landslide susceptibility evaluation and zonation mapping: A case from Blue Nile Gorge, Gohatsion-Dejen road corridor, Central Ethiopia

Globally, landslides in mountainous regions are considered to be major geo-hazards which are frequent, intensive and devastating that result in extensive destruction to people's lives, land, property and other infrastructure. To mitigate landslide-associated risks, identification, evaluation, and delineation of susceptible areas is essential for proper strategic planning and management. The present study was conducted in Blue Nile Gorge in the highlands of Central Ethiopia, about 185 km from Addis-Ababa, along the Gohatsion-Dejen road corridor. Landslides are frequently reported in the study area, particularly during the rainy season. The objective of the present study was to evaluate landslide susceptibility and to prepare a landslide susceptibility zoning (LSZ) map. For this purpose a statistical information value (IV) model was used that took into account various factors including slope material, elevation, slope, aspect, land-use and land-cover, proximity to the road, and proximity to streams. Each of these factors was classified based on different conditions like landslide concentration, topography, geology, and land-use and land-cover types. The landslide inventory mapping was conducted through field observations and Google Earth image interpretation. Later, the influence of each causative factor on past landslides was used to calculate their respective IVs. The statistical IVs were further used to evaluate the landslide susceptibility. The LSZ map produced from this study shows that 22.3 % of the study area falls into a very high susceptibility zone, 23.1 % into a high, 18.8 % in moderate, 23.1 % into a low, and the remaining 12.8 % into a low susceptibility zone. The LSZ map was validated using the Receiver Operating Characteristics Curve (ROC), landslide density index (LDI), and past landslide data. All validation methods showed acceptable results. The prepared LSZ map can be used for safe land-use planning of the area.

Optimized landslide susceptibility prediction based on SBAS-InSAR: case study of the Jiuzhaigou Ms7.0 earthquake

Earthquake-induced landslides can cause severe surface damage and casualties, posing a serious threat to the overall ecological environment and social stability. Traditional landslide susceptibility prediction (LSP) techniques often suffer from low effectiveness and precision, necessitating the exploration of remote sensing technology. However, this research in this area is limited, and the development of high-performance prediction models remains a pressing scientific issue. This study focuses on the Ms7.0 earthquake in Jiuzhaigou on 8 August 2017. To investigate the optimal integration of remote sensing technology with traditional LSP techniques, the study applies collaborative factor analysis and contingency matrix methods to create four new coupling models (SVM-I, SVM-II, RF-I, RF-II), followed by a comprehensive performance evaluation of these models. The results indicate that the integration of SAR-derived surface deformation data significantly enhances the accuracy of Landslide Susceptibility Mapping (LSM). Comparing the model performance with the receiver operating characteristic curve and landslide density, the reliability and prediction performance of the RF-I model are outstanding, reflecting that the improved method based on the InSAR collaborative machine learning model with shape variables along the slope direction can optimize the accuracy of the LSM, and has better performance and robustness in earthquake landslide susceptibility evaluation.

Evaluating landslide susceptibility and landscape changes due to road expansion using optimized machine learning

Evaluating landslide susceptibility and landscape changes due to road expansion using optimized machine learning

Evaluation of Landslide Susceptibility of Mangshan Mountain in Zhengzhou Based on GWO-1D CNN Model

Evaluation of Landslide Susceptibility of Mangshan Mountain in Zhengzhou Based on GWO-1D CNN Model

An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest

This study proposed an interpretable model that combines Random Forest (RF), Optuna hyperparameter optimization, and SHapley Additive exPlanations (SHAP) to achieve optimal landslide susceptibility evaluation and provide explanations in the northwest region of Yunnan Province in China. First, an inventory of 4447 landslides and 23 related factors was considered for the landslide susceptibility assessment. Subsequently, a hyperparameter-optimized RF model was developed using the Optuna framework and the training dataset to generate landslide susceptibility maps. The performance of the models were evaluated using accuracy (ACC), precision (PPV), recall (TPR), F1-score (F1), and the Area Under the Curve (AUC) based on the Receiver Operating Characteristic. Furthermore, the interpretability of the model was enhanced through the implementation of SHAP. The proposed model demonstrated outstanding performance on the test set, achieving an ACC of 0.7792, PPV of 0.7448, TPR of 0.8769, F1 of 0.8055, and an AUC of 0.8387. The interpretability analysis revealed that elevation, population density, distance from roads, and normalized difference vegetation index were the primary factors influencing landslide occurrences in the study area. This study provides a comprehensive framework for evaluating landslide susceptibility in specific regions and offers invaluable insights for the prevention and management of landslide disasters.

Interpretable Landslide Susceptibility Evaluation Based on Model Optimization

Machine learning (ML) is increasingly utilized in Landslide Susceptibility Mapping (LSM), though challenges remain in interpreting the predictions of ML models. To reveal the response relationship between landslide susceptibility and evaluation factors, an interpretability model was constructed to analyze how the results of the ML model are realized. This study focuses on Zhenba County in Shaanxi Province, China, employing both Random Forest (RF) and Support Vector Machine (SVM) to develop LSM models optimized through Random Search (RS). To enhance interpretability, the study incorporates techniques such as Partial Dependence Plot (PDP), Local Interpretable Model-Agnostic Explanations (LIMEs), and Shapley Additive Explanations (SHAP). The RS-optimized RF model demonstrated superior performance, achieving an Area Under the Curve (AUC) of 0.965. The interpretability model identified the NDVI and distance from road as important factors influencing landslides occurrence. NDVI plays a positive role in the occurrence of landslides in this region, and the landslide-prone areas are within 500 m from the road. These analyses indicate the importance of improved hyperparameter selection in enhancing model accuracy and performance. The interpretability model provides valuable insights into LSM, facilitating a deeper understanding of landslide formation mechanisms and guiding the formulation of effective prevention and control strategies.

New method for landslide susceptibility evaluation in alpine valley regions that considers the suitability of InSAR monitoring and introduces deformation rate grading

ABSTRACT Incorporating Interferometric Synthetic Aperture Radar (InSAR) data sources into the landslide susceptibility evaluation process has yielded favorable outcomes in some studies. However, there are fewer analyses of the applicability of InSAR data monitoring, and a framework of accurate and effective grading criteria needs to be developed. To overcome these limitations, this study presents a novel and precise approach for landslide susceptibility assessment in alpine valley regions. This method incorporates the suitability of InSAR monitoring and introduces a grading system based on deformation rates, enhancing accuracy and efficiency. Taking the Dongchuan district, the most typical high mountain valley area in southwest China, as the research object, the SAR is quantitatively simulated and analyzed in this study using the improved R-index shadow layover method. Then, the optimal Synthetic Aperture Radar (SAR) monitoring scheme is derived, calculate deformation rates using LiCSBA (small baseline subset with the automated sentinel-1 InSAR processor) technology, and accurately establish the extent of landslide hazards (including potential landslides) by incorporating high-resolution images. The criteria for grading susceptibility within the landslide hazard range are based on deformation rates. Evaluation factors for corresponding grid cells are obtained. The best evaluation factors are selected using covariance diagnosis and gray correlation analysis. The landslide susceptibility model is developed utilizing the Particle Swarm Optimization-Back Propagation (PSO-BP) algorithm. It includes evaluation techniques for regions without deformation rates. The study findings demonstrate that: (i) Analyzing SAR suitability in alpine and canyon areas is crucial. Complementary monitoring with SAR lift tracks may only sometimes resolve geometric distortion issues in all these regions. (ii) The InSAR deformation rate can be an essential evaluation factor for landslide susceptibility evaluation. (iii) The proposed method effectively addresses low coherence challenges in certain alpine valley regions, where grading based on deformation rate is complex. The landslide susceptibility evaluation model is validated using performance evaluation indexes (Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-Square (R2)), confirming its reliability and effectiveness. (iv) The proposed method improves the grading accuracy by 37.84 ~ 60.91%. Overall, our proposed new landslide susceptibility method brings a new way of evaluating landslide susceptibility in alpine valley regions.

Integrating Remote Sensing, GIS and Machine Learning Approaches in Evaluation of Landslide Susceptibility in Mountainous Region of Nghe An Province, Vietnam

This study applied remote sensing methods combining GIS and machine learning (ML) in landslide assessment and zonation for the western mountainous area of Nghe An province, Vietnam. Factors affecting landslide susceptibility are analyzed and included in the assessment model including terrain elevation, slope, aspect, flow accumulation, geomorphology, profile curvature, Topographic Position Index (TPI), fault density, road density, rainfall and land use. A field survey was conducted on July, 2023 to collect the ground truth data of landslide areas in Nghe An and used as input for the training and validating process of landslide model with ratios of 70 and 30 percentage. The landslide estimation algorithms which derived from the machine learning approach including Support Vector Machine, Random Forest, and Logistic Regression have been investigated with 11 input layers and field survey training data. The results indicated that among the causative parameters of landslides in the study area, the most important factor was the Standardized Precipitation Index, derived from the rainfall data. Additionally, traffic, terrain slope, and elevation were also significant factors. In terms of the landslide estimation algorithms, the Random Forest model exhibited the highest accuracy for mapping landslide susceptibility in the western mountainous region of Nghe An province, with a correlation coefficient (R2) of 0.97. The research findings demonstrate the effectiveness of integrating remote sensing, GIS, and ML techniques for landslide research in mountainous areas of Vietnam. This approach provides valuable insights on landslide susceptibility, and a better understanding of landslide dynamics in the study area.

Comparing shallow landslide susceptibility maps in Northeastern Türkiye (Beşikdüzü, Trabzon): a multivariate statistical, machine learning, and physical data-based analysis

This study endeavors to assess and compare the efficacy of various modeling approaches, including statistical, machine learning, and physical-based models, in the creation of shallow landslide susceptibility maps within the Besikduzu district of Trabzon province, situated in the Black Sea Region of Türkiye. The landslide inventory data, spanning from 2000 to 2018, was acquired through meticulous field surveys and analysis of Google Earth satellite imagery. Key topographic and geologic input parameters, such as slope, aspect, topographic wetness index, stream power index, plan and profile curvature, and geologic units, were extracted from a high-resolution 10 m spatial DEM (Digital Elevation Model) and a 1:25,000 scaled digital geology map, respectively. Additionally, soil unit weight and shear strength parameters, critical for the physical-based model, were determined through field samples. To evaluate landslide susceptibility, logistic regression, random forest, and Shalstab were employed as the chosen methods. The accuracy of susceptibility maps generated by each method was assessed using the area under the curve method, yielding impressive values of 0.99 for the random forest model, 0.97 for the logistic regression model, and 0.93 for the Shalstab model. These results underscore the robust performance of all three methods, suggesting their applicability for generating shallow landslide susceptibility maps not only in the Black Sea Region but also in analogous areas with similar geological characteristics.

Detailed Landslide Traces Database of Hancheng County, China, Based on High-Resolution Satellite Images Available on the Google Earth Platform

Hancheng is located in the eastern part of China’s Shaanxi Province, near the west bank of the Yellow River. It is located at the junction of the active geological structure area. The rock layer is relatively fragmented, and landslide disasters are frequent. The occurrence of landslide disasters often causes a large number of casualties along with economic losses in the local area, seriously restricting local economic development. Although risk assessment and deformation mechanism analysis for single landslides have been performed for landslide disasters in the Hancheng area, this area lacks a landslide traces database. A complete landslide database comprises the basic data required for the study of landslide disasters and is an important requirement for subsequent landslide-related research. Therefore, this study used multi-temporal high-resolution optical images and human-computer interaction visual interpretation methods of the Google Earth platform to construct a landslide traces database in Hancheng County. The results showed that at least 6785 landslides had occurred in the study area. The total area of the landslides was about 95.38 km2, accounting for 5.88% of the study area. The average landslide area was 1406.04 m2, the largest landslide area was 377,841 m2, and the smallest landslide area was 202.96 m2. The results of this study provides an important basis for understanding the spatial distribution of landslides in Hancheng County, the evaluation of landslide susceptibility, and local disaster prevention and mitigation work.