Landslide Inventory Map Research Articles

The influence of land use and land cover change on landslide susceptibility in the Lower Mekong River Basin

The Lower Mekong River Basin in Southeast Asia experiences frequent rainfall-triggered landslides especially during the monsoon season. In this study, the influence of land use and land cover change and other causative factors on landslide susceptibility is evaluated in the Lower Mekong Basin. Frequency ratio analysis is performed to quantify the relationship between LULC change and susceptibility. Detailed landslide inventory maps are used for analysis with yearly LULC maps. The LULC change is used as a contributing variable in a logistic regression-based susceptibility model with other variables including distance to roads, slope, aspect, forest loss, and soil properties. The Receiver Operating Characteristic (ROC) curve and Area Under the Curve are estimated for the model trained by each landslide inventory. The models show good performance, with AUC values ranging from 0.697 to 0.958 and an average AUC equal to 0.820. Both the Frequency Ratio analysis and the Logistic Regression models indicate LULC change from agricultural land to forest has a positive correlation with landslide occurrence. The most significant factors in the models are found to be distance to roads, slope, and aspect. A better understanding of the effects of LULC on landslide susceptibility can be useful for local land and disaster management and for the implementation of LULC as a factor in future susceptibility models. Using datasets that are unique to the Lower Mekong region, this study provides additional insights into the relationship between causative factors and landslide activity to better inform regional and global landslide susceptibility modeling.

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

The main purpose of this research is to apply the logistic regression (LR) model, the support vector machine (SVM) model based on radial basis function, the random forest (RF) model, and the coupled model of the whale optimization algorithm (WOA) and genetic algorithm (GA) with RF, to make landslide susceptibility mapping for the Ankang City of Shaanxi Province, China. To this end, a landslide inventory map consisting of 4278 identified landslides is randomly divided into training and test landslides in a ratio of 7 : 3. The 15 landslide influencing factors are selected as follows: slope aspect, slope degree, elevation, terrain curvature, plane curvature, profile curvature, surface roughness, distance to faults, distance to roads, landform, lithology, distance to rivers, rainfall, stream power index (SPI), and normalized difference vegetation index (NDVI), and the potential multicollinearity problem among these factors is detected by Pearson correlation coefficient (PCC), variance inflation factor (VIF), and tolerance (TOL). We evaluate the performance of the model separately by statistical training and test dataset metrics, including sensitivity, specificity, accuracy, kappa, mean absolute error (MSE), root mean square error (RMSE), and area under the receiver operating characteristic curve. The training success rates of LR, SVM, RF, WOA-RF, and GA-RF models are 0.7546, 0.8317, 0.8561, 0.8804, and 0.8957; the testing success rates are 0.7551, 0.8375, 0.8395, 0.8348, and 0.85007. The results show that the GA significantly improves the predictive power of the RF model. This study provides a scientific reference for disaster prevention and control in this area and its surrounding areas.

Landslide susceptibility assessment in the Rangun Khola watershed of far western Nepal

Considering the serious threat of landslides to life, property, and the environment, this study aimed at exploring past landslides (2005-2020) to evaluate landslide susceptibility. The study is carried out in the Rangun Khola watershed, in western Nepal covering an area of 488 km2. The landslide inventory map was prepared, recognizing 494 landslides, among them 70% were used for susceptibility mapping, and the rest 30% for validation purposes. The size of the landslide was found in the range of 103.53 m2 to 149120.1 m2, with an average of 4677.35 m2. Frequency ratio (FR) and logistic regression (LR) models were implemented for landslide susceptibility assessment based on the various intrinsic factors. The validity of the models was assessed by using receiver operating characteristic (ROC) curves. The success rate was 87.6% for the LR model with a prediction rate of 87.3% indicating a good degree of fit. Similarly, with a success rate of 76.4% and a prediction rate of 75.1%, the result obtained from the FR model was a fairly good performance. Thus, both exhibited reasonably good accuracy in predicting the susceptibility of the landslide and are considered to be in land management and hazard mitigation, and policy formulating purposes.

A simple method for landslide risk assessment in the Rivière Aux Vases basin, Quebec, Canada

This research offers a simple approach for landslide risk assessment on a basin-scale in the Quebec Province of Canada, where landslides are common. Landslide inventory and distribution mapping are developed for the Aux Vases Watershed, the most landslide-affected area in the Saguenay region, for a detailed landslide hazard and risk assessment process. The study uses publicly available data from Google Earth images, published literature, Government data sources for topographic maps and landuse information, as well as the 2016 Census Profile of the Saguenay region of Quebec. ArcGIS software is used for spatial data processing and analyzing datasets to develop maps of landslide susceptibility, vulnerability, and lack of coping capacity, and a landslide risk map of the Aux Vases Watershed was developed. Landslide risk assessment is critical for minimizing the loss of life and property from potential landslides in the study area and empowers communities with useful information so they can make informed decisions to mitigate the risk and build coping capacities. In addition, risk reduction policies and strategies, public safety, and future development planning initiatives will benefit from this research.

The Use of High-Resolution Remote Sensing Data in Preparation of Input Data for Large-Scale Landslide Hazard Assessments

The objective of the study is to show that landslide conditioning factors derived from different source data give significantly different relative influences on the weight factors derived with statistical models for landslide susceptibility modelling and risk analysis. The analysis of the input data for large-scale landslide hazard assessment was performed on a study area (20.2 km2) in Hrvatsko Zagorje (Croatia, Europe), an area highly susceptible to sliding with limited geoinformation data, including landslide data. The main advantage of remote sensing technique (i.e., LiDAR, Light Detection and Ranging) data and orthophoto images is that they enable 3D surface models with high precision and spatial resolution that can be used for deriving all input data needed for landslide hazard assessment. The visual interpretation of LiDAR DTM (Digital Terrain Model) morphometric derivatives resulted in a detailed and complete landslide inventory map, which consists of 912 identified and mapped landslides, ranging in size from 3.3 to 13,779 m2. This inventory was used for quantitative analysis of 16 input data layers from 11 different sources to analyse landslide presence in factor classes and thus comparing landslide conditioning factors from available small-scale data with high-resolution LiDAR data and orthophoto images, pointing out the negative influence of small-scale source data. Therefore, it can be concluded that small-scale landslide factor maps derived from publicly available sources should not be used for large-scale analyses because they will result in incorrect assumptions about conditioning factors compared with LiDAR DTM derivative factor maps. Furthermore, high-resolution LiDAR DTM and orthophoto images are optimal input data because they enable derivation of the most commonly used landslide conditioning factors for susceptibility modelling and detailed datasets about elements at risk (i.e., buildings and traffic infrastructure data layers).

Time-series metrics applied to land use and land cover mapping with focus on landslide detection

Landslides are a recurring phenomenon in Brazil and have caused many socioeconomic losses and casualties. To monitor them, land use and land cover (LULC) and landslide inventory maps are essential to identifying high susceptibility areas. In this sense, the main aim of this study is to produce LULC classification focused on landslide detection via semi-automatic methods, using data mining techniques with remote sensing time-series imagery. For that, different indices, such as the normalized difference vegetation index, the normalized difference built-up index (NDBI), and the soil adjusted vegetation index were extracted from Sentinel-2 imagery. Basic, polar, and fractal metrics were extracted from the time series. From the Shuttle Radar Topography Mission digital elevation model, six geomorphometric features were extracted. Then, classification was performed with random forest with four different approaches: mono-temporal, bi-temporal, metrical, and all. In every approach, the NDBI index or metric derived from it presented the highest importance, and the slope was ranked among the six first predictors. The all approach showed the highest overall accuracy (OA) (88.96%), followed by metrical (87.90%), bi-temporal (82.59%), and mono-temporal (74.95%). Briefly, the metrical approach presented the most beneficial result, presenting high OA and low levels of commission and omission errors.

Utilization of frequency ratio method for the production of landslide susceptibility maps: Karaburun Peninsula case, Turkey.

Geographical information systems (GIS) facilitate both current landslide mapping processes and the prediction of potential landslides that may be experienced in the future. Within the scope of the study, landslide susceptibility maps were created to reduce the damage of possible landslides in the Karaburun Peninsula of İzmir province. A landslide inventory map was produced from related databases in the first place, followed by the creation of parameter maps (elevation, aspect, slope, curvature, land use, vegetation cover, lithology, distance to roads, distance to rivers, and distance to fault lines). The frequency ratio (FR) method was utilized for producing the landslide susceptibility maps on a 5-level risk scale ranging from very low to very high-risk categories. Receiver operating characteristic (ROC) analysis was performed for accuracy testing. The resulting landslide susceptibility map revealed that 3% and 46% of the study area had high- and medium-risk categories, and the low landslide risk areas comprised 47% of the region. These results provide important inputs to guide sustainable strategic and physical planning processes in the region, which has been declared a special protection area and is a popular destination for tourism activities and energy facilities.

A national landslide inventory for Denmark

Abstract. Landslides are a frequent natural hazard occurring globally in regions with steep topography. Additionally, landslides play an important role in landscape evolution by transporting sediment downslope. Landslide inventory mapping is a common technique to assess the spatial distribution and extent of landslides in an area of interest. High-resolution digital elevation models (DEMs) have proven to be useful databases to map landslides in large areas across different land covers and topography. So far, Denmark had no national landslide inventory. Here, we create the first comprehensive national landslide inventory for Denmark derived from a 40 cm resolution DEM from 2015 supported by several 12.5 cm resolution orthophotos. The landslide inventory is created based on a manual expert-based mapping approach, and we implemented a quality control mechanism to assess the completeness of the inventory. Overall, we mapped 3202 landslide polygons in Denmark with a level of completeness of 87 %. The complete landslide inventory is freely available for download at https://doi.org/10.6084/m9.figshare.16965439.v2 (Svennevig and Luetzenburg, 2021) or as a web map (https://data.geus.dk/landskred/, last access: 6 June 2022) for further investigations.

The Role of Citrus Groves in Rainfall-Triggered Landslide Hazards in Uwajima, Japan

Landslides often cause deaths and severe economic losses. In general, forests play an important role in reducing landslide probability because of the stabilizing effect of the tree roots. Although fruit groves consist of trees, which are similar to forests, practical land management, such as the frequent trampling of fields by laborers and compression of the terrain, may cause such land to become prone to landslides compared with forests. Fruit groves are widely distributed in hilly regions, but few studies have examined their role in landslide initiation. This study aims at filling this gap evaluating the predisposing and triggering conditions for rainfall-triggering landslides in part of Uwajima City, Japan. A large number of landslides occurred due to a heavy rainfall event in July 2018, where citrus groves occupied about 50% of the study area. In this study, we combined geodata with a regression model to assess the landslide hazard of fruit groves in hilly regions. We developed maps for five conditioning factors: slope gradient, slope aspect, normalized difference vegetation index (NDVI), land use, and geology. Based on these five maps and a landslide inventory map, we found that the landslide area density in citrus groves was larger than in forests for the categories of slope gradient, slope aspect, NDVI, and geology. Ten logistic regression models along with different rainfall indices (i.e., 1-h, 3-h, 12-h, 24-h maximum rainfall and total rainfall) and different land use (forests or citrus groves) in addition to the other four conditioning factors were produced. The result revealed that “citrus grove” was a significant factor with a positive coefficient for all models, whereas “forest” was a negative coefficient. These results suggest that citrus groves have a higher probability of landslide initiation than forests in this study area. Similar studies targeting different sites with various types of fruit groves and several rainfall events are crucial to generalize the analysis of landslide hazard in fruit groves.

Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia

Landslides are a natural hazard that can endanger human life and cause severe environmental damage. A landslide susceptibility map is essential for planning, managing, and preventing landslides occurrences to minimize losses. A variety of techniques are employed to map landslide susceptibility; however, their capability differs depending on the studies. The aim of the research is to produce a landslide susceptibility map for the Langat River Basin in Selangor, Malaysia, using an Artificial Neural Network (ANN). A landslide inventory map contained a total of 140 landslide locations which were randomly separated into training and testing with ratio 70:30. Nine landslide conditioning factors were selected as model input, including: elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), distance to road, distance to river, lithology, and rainfall. The area under the curve (AUC) and several statistical measures of analyses (sensitivity, specificity, accuracy, positive predictive value, and negative predictive value) were used to validate the landslide predictive model. The ANN predictive model was considered and achieved very good results on validation assessment, with an AUC value of 0.940 for both training and testing datasets. This study found rainfall to be the most crucial factor affecting landslide occurrence in the Langat River Basin, with a 0.248 weight index, followed by distance to road (0.200) and elevation (0.136). The results showed that the most susceptible area is located in the north-east of the Langat River Basin. This map might be useful for development planning and management to prevent landslide occurrences in Langat River Basin.

LANDSLIDE EVOLUTION PATTERN REVEALED BY MULTI-TEMPORAL DSMS OBTAINED FROM HISTORICAL AERIAL IMAGES

Abstract. Landslides are a widespread natural hazard that cause damages to people and to the built up environment. Accurate knowledge of landslide distribution is crucial to develop planning strategies, prevention and resilient communities worldwide. One of the most diffuse way of reporting landslides distribution in a territory is by preparing landslide inventory maps. Such a task is mostly accomplished by expert photo-interpretation of historical aerial photographs, which are an invaluable source of information because they portray the landscape when the anthropic pressure was lower than the present day, providing an observation of the landscape closer to the natural conditions. Despite such a common use of aerial photographs, they are poorly exploited to obtain quantitative measures to support landslide mapping activities. In this paper we present a comparison of two photogrammetric approaches to measure elevation changes in a 50-years period for an area densely affected by landslides in Southern Italy. The obtained results allowed to revisit the original expert mapping proving that such a method is a useful tool to support geomorphological mapping and to improve the overall accuracy of landslide inventories.

Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan

The notion of this research is based on the two devastating earthquake events that happened on October 8, 2005, and September 24, 2019, in the regions of Azad Kashmir and Muzaffarabad. This study aims to (i) identification of the susceptible zones where landslides can occur in the future; (ii) preparation of landslide inventory maps using vector data, satellite imagery, Shuttle Radar Topographic Mission (STRM) and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) DEM; (iii) implementation of Analytical Hierarchy Process (AHP) model using weighted overlay analysis (WOA). For this purpose, key factors such as land use, faults, slope, contours, soil, and seismology maps are used to develop a landslide hazard zonation map. The output landslide susceptibility map has four susceptibility levels such as low, medium, high, and very high vulnerable zones. The results indicated that a highly susceptible landslide zone is found in the northwestern part of Muzaffarabad, which is a metropolitan region. Moreover, there are 127 active landslides are identified and collectively about 9% of the study area is very highly susceptible to future landslides. Furthermore, research findings are helpful in tactful thinking for future infrastructure development, and ecological protection in high-susceptible landslide regions in Muzaffarabad. It also helps the Government to make strategies based on any specific zones on a priority basis to reduce the casualties and destruction in future landslide events.

LANDSLIDE INVENTORY MAPPING FROM LANDSAT-8 NDVI TIME SERIES USING ADAPTIVE LANDSLIDE INTERVAL DETECTION

Abstract. A landslide inventory map is one of the essential sources of geospatial information for land resource management. This study proposes an interval-based landslide detection strategy using satellite images' time series vegetation index. Landslide trends to be abruptly changed with the landscape can be clearly detected using the time series vegetation index. The proposed adaptive landslide interval detection (LID) method is a two stage algorithm. The first stage extracts local extremes and divides the time series to obtain discriminative intervals. Then, a predefined threshold is applied to determine whether a landslide occurs in each interval. The experiment compares the proposed scheme and the traditional time series forest (TSF) algorithm on a pre-labeled dataset. In the comparison of the results obtained from TSF and LID, the validation results show that the proposed LID has better discriminative ability in real scenarios, and the landslide detection rate for large areas reaches 85%. But the TSF can only provide good results on well-defined and unmixing datasets. In addition, LID does not require a large number of training datasets and can be applied to irregular time series with various lengths. In summary, this study demonstrated that the LID global pattern concerned splitting strategy is more effective than TSF random interval segmentation.

GIS-based assessment of landslide susceptibility and inventory mapping using different bivariate models

The frequency ratio (FR) and Shannon entropy (SE) two bivariate models are used to produce LSM for CHA, Bangladesh, and a comparison is established between the two models through validation. A landslide inventory map of CHA with 730 landslides was randomly dispersed into the testing dataset, with 70% (511 landslides) used for modeling and 30% (219 landslides) used for validation processes. The LSM shows that very high LS, high LS, moderate LS, low LS, and very low LS regions. The validity of the two methods was determined using the receiver operating characteristic curve and the area under the curve. The AUC for the success rate curve findings is 0.8437 for FR and 0.7962 for SE. Furthermore, the prediction rate curve accuracy for FR was 0.8364, whereas it was 0.8324 for SE. In calculating the LSM in the study region, the FR model is the more accurate of the two models.

Application of Certainty Factor and Bayesian statistics models for evaluation of landslides and environmental factors at Bao Thang district and Lao Cai city, Lao Cai province

The study area is heavily affected by landslides with increasing frequency and intensity, causing serious damages and affecting the sustainable socio-economic development of the region. The use of mathematical methods in landslide research is increasingly interested due to the quantitative nature of parameters and calculation results. This study aims to apply the Certainty Factor (CF) and Bayesian statistics models for geological hazard evaluation. Landslide distribution is identified from remote sensing images and field surveys. Landslide inventory maps (428 landslides) were compiled by reference to historical reports, Google Earth, and field mapping. All landslides were randomly separated into two data sets: 70% were used to establish the models (training data sets) and the rest for validation (validation data sets). Fifteen environmental factors from geology, topography and hydrological information of the studied area were extracted from the spatial database. Results show that the group of factors of slope angle, Terrain Ruggedness Index, fault/lineament density, stratigraphy, geoengineering characteristics, weathering types, and maximum daily rainfall play the most important role in the formation of landslides in the study area. Validation from Certainty Factor (CF) and Bayesian statistics models show 87% and 92% prediction accuracy between hazard maps and existing landslide locations. These models show reasonably accurate landslide predictions in the study area and can be served as the basis of landslide risk-management studies in the future.

Application of novel deep boosting framework-based earthquake induced landslide hazards prediction approach in Sikkim Himalaya

A major earthquake (6.9 Moment magnitude) occurred in the Sikkim and Darjeeling areas of the Indian Himalaya as well as in the adjacent Nepal on 18th September 2011, triggering a large number of landslides. A total of 188 landslide locations were extracted in order to create the landslide inventory map (LIM). The earthquake-induced landslide susceptibility maps (LSMs) were created using an Artificial Neural Network (ANN) model and three novel deep learning approaches (DLAs), namely Deep Boosting (DB), Deep Learning Neural Network (DLNN), and Deep Learning Tree (DLT), as well as training points and 22 conditioning factors. The earthquake-induced LSMs validated using several statistical indices and the results showed optimal accuracy for all models, where DB yielding the highest prediction rate curve (PRC) of 98.5%. This is followed by DLT (97%), DLNN (96%), and ANN (91%). The results demonstrate maximum efficacy of the proposed earthquake-induced LSM.

Evaluation of Different Landslide Susceptibility Models for a Local Scale in the Chitral District, Northern Pakistan.

This work evaluates the performance of three machine learning (ML) techniques, namely logistic regression (LGR), linear regression (LR), and support vector machines (SVM), and two multi-criteria decision-making (MCDM) techniques, namely analytical hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), for mapping landslide susceptibility in the Chitral district, northern Pakistan. Moreover, we create landslide inventory maps from LANDSAT-8 satellite images through the change vector analysis (CVA) change detection method. The change detection yields more than 500 landslide spots. After some manual post-processing correction, the landslide inventory spots are randomly split into two sets with a 70/30 ratio for training and validating the performance of the ML techniques. Sixteen topographical, hydrological, and geological landslide-related factors of the study area are prepared as GIS layers. They are used to produce landslide susceptibility maps (LSMs) with weighted overlay techniques using different weights of landslide-related factors. The accuracy assessment shows that the ML techniques outperform the MCDM methods, while SVM yields the highest accuracy of 88% for the resulting LSM.

Landslide Susceptibility Mapping Using Machine Learning Algorithm Validated by Persistent Scatterer In-SAR Technique.

Landslides are the most catastrophic geological hazard in hilly areas. The present work intends to identify landslide susceptibility along Karakorum Highway (KKH) in Northern Pakistan, using landslide susceptibility mapping (LSM). To compare and predict the connection between causative factors and landslides, the random forest (RF), extreme gradient boosting (XGBoost), k nearest neighbor (KNN) and naive Bayes (NB) models were used in this research. Interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technology was used to explore the displacement movement of retrieved models. Initially, 332 landslide areas alongside the Karakorum Highway were found to generate the landslide inventory map using various data. The landslides were categorized into two sections for validation and training, of 30% and 70%. For susceptibility mapping, thirteen landslide-condition factors were created. The area under curve (AUC) of the receiver operating characteristic (ROC) curve technique was utilized for accuracy comparison, yielding 83.08, 82.15, 80.31, and 72.92% accuracy for RF, XGBoost, KNN, and NB, respectively. The PS-InSAR technique demonstrated a high deformation velocity along the line of sight (LOS) in model-sensitive areas. The PS-InSAR technique was used to evaluate the slope deformation velocity, which can be used to improve the LSM for the research region. The RF technique yielded superior findings, integrating with the PS-InSAR outcomes to provide the region with a new landslide susceptibility map. The enhanced model will help mitigate landslide catastrophes, and the outcomes may help ensure the roadway’s safe functioning in the study region.

Landslide Displacement Prediction via Attentive Graph Neural Network

Landslides are among the most common geological hazards that result in considerable human and economic losses globally. Researchers have put great efforts into addressing the landslide prediction problem for decades. Previous methods either focus on analyzing the landslide inventory maps obtained from aerial photography and satellite images or propose machine learning models—trained on historical land deformation data—to predict future displacement and sedimentation. However, existing approaches generally fail to capture complex spatial deformations and their inter-dependencies in different areas. This work presents a novel landslide prediction model based on graph neural networks, which utilizes graph convolutions to aggregate spatial correlations among different monitored locations. Besides, we introduce a novel locally historical transformer network to capture dynamic spatio-temporal relations and predict the surface deformation. We conduct extensive experiments on real-world data and demonstrate that our model significantly outperforms state-of-the-art approaches in terms of prediction accuracy and model interpretations.

Landslide Susceptibility Mapping Using Novel Hybrid Model Based on Different Mapping Units

Landslide is a most widespread geohazards around the world. Reasonable landslide susceptibility mapping can aid decision-makers in landslide prevention. Therefore, drawing regional landslide susceptibility map is of great significance to landslide prevention. This research mainly aims to explore a new method and carry out the landslide susceptibility mapping based on terrain mapping unit (TMU) and grid cells mapping unit (GCMU) in Wuqi County, Yan'an City, China. Firstly, the landslide inventory map was prepared based on 717 landslides that were extracted. Secondly, the index of entropy model (IOE) was applied to quantify landslide predisposing factors based on TMU and GCMU, respectively. Finally, the systematically developed forest of multiple trees (SysFor) was integrated with IOE to construct a novel hybrid model (IOE-SysFor) and the landslide susceptibility maps based on TMU and GCMU were obtained. Statistical indices and receiver operating characteristic curve (ROC) were applied to evaluate the results and compare the approaches. From the results, it indicated that the accuracy of landslide susceptibility maps generated by the IOE-SysFor based on TMU and GCMU was satisfactory, furthermore, the performance of IOE-SysFor model running on the TMU was better than GCMU. Therefore, the TMU was considered as a more suitable landslide mapping unit for similar regions and the approach developed by this study can provide a reference for future researches.