Landslide Mapping Research Articles

Geohazard Mapping and Mitigations of Landslides Along the Road Corridor Gasera–Indeto, Oromia Regional State, Southeast Ethiopia

Geohazard Mapping and Mitigations of Landslides Along the Road Corridor Gasera–Indeto, Oromia Regional State, Southeast Ethiopia

Automating global landslide detection with heterogeneous ensemble deep-learning classification

With changing climatic conditions, we are already seeing an increase in extreme weather events and their secondary consequences, including landslides. Landslides threaten infrastructure, including roads, railways, buildings, and human life. Hazard-based spatial planning and early warning systems are cost-effective strategies to reduce the risk to society from landslides. However, these both rely on data from previous landslide events, which is often scarce. Many deep learning (DL) models have recently been applied for landslide mapping using medium-to high-resolution satellite images as input. However, they often suffer from sensitivity problems, overfitting, and low mapping accuracy. This study addresses some of these limitations by using a diverse global landslide dataset, using different segmentation models, such as Unet, Linknet, PSP-Net, PAN, and DeepLab and based on their performances, building an ensemble model. The ensemble model achieved the highest F1-score (0.69) when combining both Sentinel-1 and Sentinel-2 bands, with the highest average improvement of 6.87 % when the ensemble size was 20. On the other hand, Sentinel-2 bands performed well, with an F1 score of 0.61 only when the ensemble size is 20 with an improvement of 14.59 %. This result shows considerable potential in building a robust and reliable monitoring system to minimise landslide hazards by building the ensemble globally trained system based on changes in vegetation index dNDVI only.

Mapping Landslide Risk in the United States and Puerto Rico

A new method provides highly accurate continental-scale landslide susceptibility maps that are being used in the aftermath of Hurricane Helene.

Mapping landslide susceptibility with the consideration of spatial heterogeneity and factor optimization

Mapping landslide susceptibility with the consideration of spatial heterogeneity and factor optimization

CATALOG OF LANDSLIDES OF THE CITY OF ZENICE WITH SPECIFIC CAUSES

As part of its activities, the Federal Institute of Geology carries out mapping of active landslides in the territory of the Federation of Bosnia and Herzegovina, and my task is to map instability in the area of the City of Zenica. A very large number of landslides were registered during the catastrophic rainfall that occurred in 2014. Following these phenomena and certain records, detailed mapping of unstable slopes and records of unstable surfaces was started. The cadastre is defined in such a way that it displays all input data with location polygons in GIS. Not in the field, quite recognizable causes were observed, which are always a combination of bad geological and hydrogeological conditions with a significant anthropogenic factor. The slope of the terrain is also very unfavourable, and gravity also played a significant role in the instability of these slopes. Almost all registered landslides have unregulated water (surface, underground, waste). The frequent occurrence of earthquakes on this section also significantly threatened the stability of slopes and buildings, and on the spot, the consequences of cracking and shearing of buildings were evident as a result of the earthquake. A special review will be given to the activation of mudflows during extensive rainfall in this area, which is not rare. A significant share in the formation of mudflows belongs to heavy precipitation, which over time formed the so-called "watersheds" where water was transported from higher to lower parts of the slope. However, during the climate changes, there was a significant increase in the amount and frequency of precipitation, which carried the surface cover and rock blocks that were moved down the steep slopes. Road approaches to the villages, as well as a significant number of buildings, made artificial dams and cut cuts that were a natural way of draining water. So you have a significant number of buildings built exactly on these "watersheds" that represent the dam. In the newly created situation, by changing the natural paths of movement of surface and underground water, it would be necessary to regulate the water below and around the road strip, as well as the drainage of water around buildings. Only after the water has been drained, certain geotechnical procedures can be carried out to stabilize the slope and to permanently rehabilitate it, so that the inhabitants of these villages do not constantly fear during rainfall.

Unsupervised Change Detection Methods Applied to Landslide Mapping: Case Study in São Sebastião, Brazil

ABSTRACTLandslides represent a growing global geological hazard, further intensified by climate‐induced changes. Remote sensing data, through its capacity for repetitive collection and change detection techniques, that compare and quantify the spatio‐temporal alterations over time, plays a critical role in landslide detection. Considering the February 2023 São Sebastião event and Sentinel‐2 imagery, we assessed diverse unsupervised change detection techniques, encompassing both traditional and recent machine learning‐based approaches. Notably, the Floating References (FR) and Homogeneous Blocks Single‐class Classification (HBSC) methods outperform classic approaches and deliver the most accurate results with F1‐Score and kappa coefficient exceeding 0.96 and 0.92, respectively. These outcomes demonstrate the efficacy of machine learning in automating landslide delineation and underscore the necessity of meticulous data and parameter selection in achieving high‐accuracy automatic landslide mapping. Lastly, this study fills a significant gap in the existing literature by evaluating unsupervised change detection methods for landslide mapping within the Brazilian context.

Application of Remote Sensing and GIS in Landslide Mapping and Management

Application of Remote Sensing and GIS in Landslide Mapping and Management

Landslides: A Review from the Southern Western Ghats of India

ABSTRACT Landslides are the most unpredictable catastrophic events in mountainous and hilly regions. South Western Ghats one among of the regions in India that have experienced recurring landslides due to increase in pre-and post-monsoon rains. Landslides often cause damage to buildings, roads, natural vegetation, even result in the loss of human lives in South Western Ghats with indirect impact on the socioeconomic status of the country. It is crucial to depict the current trend technologies and their performance in landslide studies, offering valuable insights into mitigation strategies. This article highlights on prevalent techniques for mapping landslide zones, along with major conditional factors and assess the performance of these techniques in landslide susceptibility mapping of the South Western Ghats. Landslide studies of the South Western Ghats reveal 20% of the research work with the Landsat 8 OLI for mapping landslides, 13.3% of the studies using the higher spatial resolutions 2.5m and 10m of Cartosat DEM, the rest of the studies were based on the spatial resolution of 12.5m and 30m from ALOS PALSAR, ASTER and SRTM DEM. About 26% landslide studies have employed quantitative approaches primarily the frequency ratio of bivariate. Subsequently machine learning techniques such as random forest and support vector machine have gained prominence, accounting for 33% landslide studies. The literature depicts precipitation as a significant triggering factor and slope as the major conditional factor in the occurrence of landslides within the South Western Ghats. Prioritized densely populated areas in landslide studies have increased attention to smaller geographical areas and forest regions in the South Western Ghats to protect both inhabitants and ecosystems.

Multi-hazard susceptibility mapping of landslides and earthquakes in Bhagirathi Valley region of Uttarakhand Himalaya, India

ABSTRACT This study addresses impact of increasing frequency of natural hazards, particularly in mountainous regions like the Himalayas. The research develops a comprehensive multi-hazard susceptibility mapping methodology. . The integrated methodology involving majorly machine learning, remote sensing and GIS tools is applied to generate the multi-hazard susceptibility map of the test site. This integrated approach offers a nuanced understanding of hazard interactions, providing a robust framework for disaster risk reduction, government and non-government agencies in planning future developmental projects and infrastructures in the study area. The study’s findings show that 11.30% of the area is labeled as highly susceptible to multi-hazards.

ML-CASCADE: A machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data

ML-CASCADE: A machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data

GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)

Abstract Landslides and slope processes constitute one of the most frequent natural hazards in valleys near major rivers and mountainous regions. The surface layer, characterized by its relatively loose composition, is prone to sliding due to a combination of distinct natural and human-related factors. Specific sections along the right bank of the Danube River in Smederevo city exhibit significant susceptibility to landslide activation, often leading to substantial material losses and posing a risk to the local population. The initial step in the provided research involves analyzing existing literature and mapping landslides within the study area. The initial analysis covers both natural conditions and anthropogenic activities. The second step includes establishing a geospatial database in the Geographic Information System and generating eight thematic maps. In the third step, different weight coefficients were assigned to the criteria, which facilitated the creation of the Landslide Susceptibility Index using the Best–Worst Method. Subsequently, in the fourth step, a composite map illustrating landslide susceptibility was produced. According to this research, about 4% of the territory of Smederevo, or 19.3 km2, is highly or very highly susceptible to landslides. These localities are located on the right bank of the Danube River and around the Ralja River. Receiver operating characteristic-area under the curve value indicates very high predictive power (approximately 1), thus suggesting the reliability of the used methodology. This visualization of areas highly prone to such occurrences empowers policymakers to implement more effective environmental protection measures and institute sustainable management practices for agricultural parcels in this region. Also, the provided research represents the inaugural integration of advanced remote sensing techniques and interdisciplinary investigations, offering deeper insights into landslide activity in the study area and yielding more comprehensive results.

Landslide susceptibility zonation mapping using geospatial technologies and multi criteria evaluation techniques in the upper Didessa sub-basin, Southwest Ethiopia

ABSTRACT Landslides have a profound impact on landscape geology, resulting in extensive devastation and loss of human lives. Mapping landslide susceptibility is crucial for effective land use planning in mountainous country like Ethiopia. This study was conducted in the upper Didessa sub-basin, southwestern parts of Ethiopia using Geographic Information System (GIS) and multi criteria evaluation (MCE) technique. This study employed a blend of primary data, encompassing field surveys and interviews with experts, as well as secondary data derived from diverse source, such as remote sensing data, digital soil maps, and geological maps. A total of eleven critical factors were employed to assess the triggers of landslides. These factors include slope, aspect, drainage density, topographic wetness index (TWI), stream power index (SPI), topographic ruggedness index (TRI), hypsometric integral, lithology, land use land cover (LULC), soil texture, and distance from roads. The analytical hierarchy process (AHP) method was used to determine the significance of each indicator through pairwise comparison matrix. The study area was categorized into different zones based on the susceptibility to landslides, namely very high, high, moderate, low, and very low. Results revealed that cultivated land had the highest likelihood of experiencing landslides, with a total of nine incidents out of 25, followed by built-up areas with seven landslides. Conversely, dense forests, sparse forests, and grazing land experienced a lower likelihood of landslides. Out of the 11 factors contributing to landslides, 24% of the surveyed region was deemed to have a moderate susceptibility, with 12% and 6% falling into the categories of high and very high susceptibility to landslides, respectively. The findings of this research provide important information for policymakers to develop efficient measures for preventing and reducing the risks of landslides.

Landslide Recognition Based on Machine Learning Considering Terrain Feature Fusion

Landslides are one of the major disasters that exist worldwide, posing a serious threat to human life and property safety. Rapid and accurate detection and mapping of landslides are crucial for risk assessment and humanitarian assistance in affected areas. To achieve this goal, this study proposes a landslide recognition method based on machine learning (ML) and terrain feature fusion. Taking the Dawan River Basin in Detuo Township and Tianwan Yi Ethnic Township as the research area, firstly, landslide-related data were compiled, including a landslide inventory based on field surveys, satellite images, historical data, high-resolution remote sensing images, and terrain data. Then, different training datasets for landslide recognition are constructed, including full feature datasets that fusion terrain features and remote sensing features and datasets that only contain remote sensing features. At the same time, different ratios of landslide to non-landslide (or positive/negative, P/N) samples are set in the training data. Subsequently, five ML algorithms, including Extreme Gradient Boost (XGBoost), Adaptive Boost (AdaBoost), Light Gradient Boost (LightGBM), Random Forest (RF), and Convolutional Neural Network (CNN), were used to train each training dataset, and landslide recognition was performed on the validation area. Finally, accuracy (A), precision (P), recall (R), F1 score (F1), and intersection over union (IOU) were selected to evaluate the landslide recognition ability of different models. The research results indicate that selecting ML models suitable for the study area and the ratio of the P/N samples can improve the A, R, F1, and IOU of landslide identification results, resulting in more accurate and reasonable landslide identification results; Fusion terrain features can make the model recognize landslides more comprehensively and align better with the actual conditions. The best-performing model in the study is LightGBM. When the input data includes all features and the P/N sample ratio is optimal, the A, P, R, F1, and IOU of landslide recognition results for this model are 97.47%, 85.40%, 76.95%, 80.95%, and 71.28%, respectively. Compared to the landslide recognition results using only remote sensing features, this model shows improvements of 4.51%, 35.66%, 5.41%, 22.27%, and 29.16% in A, P, R, F1, and IOU, respectively. This study serves as a valuable reference for the precise and comprehensive identification of landslide areas.

Landslide susceptibility mapping using modified frequancy ratio method in Correb area, South Wollo, North-Western Ethiopia

Correb area in Mekdela Woreda, South-Wollo Zone, North-Western Ethiopia is one of the most landslide affected region. The primary aim of this study was to assess the landslide susceptibility of the area and create a landslide susceptibility map. A bivariate statistical frequency ratio model was used to accomplish this goal. Using fieldwork and Google Earth picture interpretation, a map of landslides was made. A total of 524 landslides were identified and then classified in to training landslide (75%) for model development and validation landslide (25%) for model validation. Eight landslide causative factors slope, aspect, elevation, distance to drainage, distance to spring, slope material, distance to lineament, and vegetation cover were integrated with training landslides in order to determine frequency ratio value(weight) for each class of landslide causative factors. Furthermore, each causative factor’s prediction rate or weight was established. Relative frequency values were allocated to the appropriate factor classes and prediction rate values were allocated to the appropriate causative factors and summed using a raster calculator algorithm in order to produce the landslide susceptibility map. The final landslide susceptibility map revealed that 27.54% (31.52 km2) of the study area falls under very low susceptible, 30.35% (34.74 km2) low susceptible, 21.36% (24.44 km2) moderate susceptible, 14.74% (16.88 km2) high susceptible, and the rest 6.01% (6.88 km2) as very high susceptible. This shows that more than 20% of the area has probability and risk of landslide while about 80% has relatively low probability and risk of this natural hazard. The performance of modified frequency ratio model for landslide susceptibility mapping was validated using simply overlay, relative landslide density index, and receiver operating characteristics curve. Both training and validation landslides were rare in the very low and low susceptibility classes and strongly concentrated in the high and very high susceptibility classes, according to validation using simple overlay and the relative landslide density index. The implemented model performed exceptionally well, as seen by the receiver operating characteristics curve, which showed 82.6% success rate and an 83.1% prediction rate. In general, the model produced reasonable accuracy. The resultant map would be useful for general land use planning, site selection, and landslide prevention and mitigation programs.

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.

Evaluating Coseismic Landslide Susceptibility Following the 2022 Luding Earthquake: A Comparative Analysis of Six Displacement Regression Models Integrating Epicentral and Seismogenic Fault Distances within the Permanent-Displacement Framework

Coseismic landslides have the potential to cause catastrophic disasters. Thus, it is of crucial importance to conduct a comprehensive regional assessment of susceptibility to coseismic landslides. This study rigorously interprets 13,759 coseismic landslides triggered by the 2022 Luding earthquake within the seismic zone. Employing the Newmark method, we systematically assess the susceptibility to coseismic landslides through the application of six distinct displacement regression models. The efficacy of these models is validated against the actual landslide inventory using the area under the receiver operating characteristic (ROC) curve. A hazard map of coseismic landslides is generated based on the displacement regression model with the highest degree of fit. The results show that Moxi Town, Detuo Town, the flanks of the Daduhe River, Wandonghe River, Hailuogou River, and Yanzigou River are high-susceptibility areas for coseismic landslides. This study explores factors influencing model fit, revealing that the inclusion of the epicentral distance and the distance to the seismogenic fault in displacement prediction enhances model performance. Nevertheless, in close proximity to fault zones, the distance to the seismogenic fault exerts a more significant influence on the spatial distribution density of coseismic landslides compared to the epicentral distance. Conversely, in regions situated further from fault zones, the epicentral distance has a greater impact on the spatial distribution density of coseismic landslides compared to the distance to the seismogenic fault. These findings contribute to a nuanced understanding of coseismic landslide susceptibility and offer valuable insights for future Newmark method-based coseismic landslide displacement calculations.

A Novel Attention-Based Generalized Efficient Layer Aggregation Network for Landslide Detection from Satellite Data in the Higher Himalayas, Nepal

Landslide event detection poses a significant challenge in the remote sensing community, especially with the advancements in computer vision technology. As computational capabilities continue to grow, the traditional manual and partially automated methods of landslide recognition from remote sensing data are transitioning towards automatic approaches using deep learning algorithms. Moreover, attention models, encouraged by the human visual system, have emerged as crucial modules in diverse applications including natural hazard assessment. Therefore, we suggest a novel and intelligent generalized efficient layer aggregation network (GELAN) based on two prevalent attention modules, efficient channel attention (ECA) and convolutional block attention module (CBAM), to enrich landslide detection techniques from satellite images. CBAM and ECA are separately integrated into GELAN at different locations. The experiments are conducted using satellite images of the Nepal Himalayan region. Standard metrics such as precision, recall, F-score, and mAP (mean average precision) are considered for quantitative evaluation. GELANc+CBAM (F-score = 81.5%) demonstrates the best performance. This study underscores the suitability of the proposed approach in up-to-date inventory creation and accurate landslide mapping for disaster recovery and response efforts. Moreover, it contributes to developing early prediction models for landslide hazards.

CResU-Net: a method for landslide mapping using deep learning

Landslides, which can occur due to earthquakes and heavy rainfall, pose significant challenges across large areas. To effectively manage these disasters, it is crucial to have fast and reliable automatic detection methods for mapping landslides. In recent years, deep learning methods, particularly convolutional neural and fully convolutional networks, have been successfully applied to various fields, including landslide detection, with remarkable accuracy and high reliability. However, most of these models achieved high detection performance based on high-resolution satellite images. In this research, we introduce a modified Residual U-Net combined with the Convolutional Block Attention Module, a deep learning method, for automatic landslide mapping. The proposed method is trained and assessed using freely available data sets acquired from Sentinel-2 sensors, digital elevation models, and slope data from ALOS PALSAR with a spatial resolution of 10 m. Compared to the original ResU-Net model, the proposed architecture achieved higher accuracy, with the F1-score improving by 9.1% for the landslide class. Additionally, it offers a lower computational cost, with 1.38 giga multiply-accumulate operations per second (GMACS) needed to execute the model compared to 2.68 GMACS in the original model. The source code is available at https://github.com/manhhv87/LandSlideMapping.git.

Evaluation of Various Deep Learning Algorithms for Landslide and Sinkhole Detection from UAV Imagery in a Semi-arid Environment

AbstractSinkholes and landslides occur due to soil collapse in different slope types, often triggered by heavy rainfall, presenting challenges in the semi-arid Golestan province, Iran. This study primarily focuses on the detection of these phenomena. Recent advancements in unmanned aerial vehicle (UAV) image acquisition and the incorporation of deep learning (DL) algorithms have enabled the creation of semi-automated methods for highly detailed soil landform detection across large areas. In this study, we explored the efficacy of six state-of-the-art deep learning segmentation algorithms—DeepLab-v3+, Link-Net, MA-Net, PSP-Net, ResU-Net, and SQ-Net—applied to UAV-derived datasets for mapping landslides and sinkholes. Our most promising outcomes demonstrated the successful mapping of landslides with an F1-Score of 0.95% and sinkholes with an F1-Score of 89% in a challenging environment. ResUNet exhibited an outstanding Precision of 0.97 and Recall of 0.92, culminating in the highest F1-Score of 0.95, indicating the best landslide detection model. MA-Net and SQ-Net resulted in the highest F1-Score for sinkhole detection. Our study underscores the significant impact of DL segmentation algorithm selection on the accuracy of landslide and sinkhole detection tasks. By leveraging DL segmentation algorithms, the accuracy of both landslide and sinkhole detection tasks can be significantly improved, promoting better hazard management and enhancing the safety of the affected areas.

High-resolution landslide mapping and susceptibility assessment: Landslide temporal variations and vegetation recovery

In mountainous terrains, the frequent landslides and their associated impacts on human lives and the economy is increasing globally. Development of landslide inventory and afterward landslide susceptibility mapping are the main prerequisites for implementing landslide mitigation measures and protection in mountainous regions. The 2005 Kashmir earthquake induced different small and large landslides and some were active for the long term. So far many studies have used medium and high-resolution data to develop landslide inventory. This study aims to develop a 1st detailed, comprehensive and accurate landslide inventory using a very high-resolution image using a semi-automatic technique. The precise landslide inventory is employed to develop an accurate and comprehensive landslide susceptibility map considering the landslide inventory data using a logistic regression training model. Furthermore, the landslide’s temporal recovery from the earthquake and its reactivation due to rainfall in spare vegetation areas have been evaluated. Fine-resolution satellite images of Worldview-2 are applied to develop a detailed landslide inventory using a Support Vector Machine (SVM) classifier. A total of 63,630 landslides were identified using a semi-automatic technique within a study area of 265 km2. From regression modeling, the results show that geology, topography, and road networks have a significant impact on the spatial distribution of landslides. Model performance was evaluated based on the testing data, the model gives an AUC of 0.93 and the kappa value of 0.9353. The spatiotemporal NDVI has been assessed to identify the landslide recovery and its reactivation due to extreme rainfall. The results show that 72.1 % of the landslides occurred in Muzaffarabad formation in the study area. The developed landslide susceptibility map can be further used for land-use planning and implementing mitigation measures for the safety of roads and other infrastructure in the area.