Landslide Mapping Research Articles

Application of InSAR satellite method for mapping of active landslides in Bulgaria – opportunities and perspectives

Abstract. Landslides are geological phenomena that are spread on Bulgarian territory mainly along the northern Black Sea coast and on the right banks of the Danube in the western part of the country. Mitigation of the negative effects of these destructive geological phenomena is the compilation of inventory maps of their distribution and registers with the main characteristics of the individual landslides. Conventional methods for making such maps are time-consuming and resource-intensive. Modern satellite, air and ground-based remote sensing technologies facilitate the production of landslide maps, reducing the time and resources required to compile and systematically update them. In this paper, we demonstrate the applicability of Differential Sentinel-1A satellite SAR interferometry (DInSAR) to assess the movement activity and use the information for further updating the national landslide inventories in Bulgaria. We perform several analyses based on multi-temporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides. The use of new opportunities for free access to satellite images, which can be applied in conjunction with other methods, greatly facilitates the processes of inventory, mapping and study of landslides.

Distribution of landslides in the area of the town of Polski Trambesh, Northern Bulgaria

The article presents the results of the landslide mapping in the area of the town of Polski Trambesh, central Northern Bulgaria. A total of 37 new landslides have been described, which have not been included in the landslide register so far. Landslides are classified by area, type and activity. Their location is mapped in a GIS environment.

Deep learning-based landslide susceptibility mapping

Landslides are considered as one of the most devastating natural hazards in Iran, causing extensive damage and loss of life. Landslide susceptibility maps for landslide prone areas can be used to plan for and mitigate the consequences of catastrophic landsliding events. Here, we developed a deep convolutional neural network (CNN–DNN) for mapping landslide susceptibility, and evaluated it on the Isfahan province, Iran, which has not previously been assessed on such a scale. The proposed model was trained and validated using training (80%) and testing (20%) datasets, each containing relevant data on historical landslides, field records and remote sensing images, and a range of geomorphological, geological, environmental and human activity factors as covariates. The CNN–DNN model prediction accuracy was tested using a wide range of statistics from the confusion matrix and error indices from the receiver operating characteristic (ROC) curve. The CNN–DNN model was evaluated comprehensively by comparing it to several state-of-the-art benchmark machine learning techniques including the support vector machine (SVM), logistic regression (LR), Gaussian naïve Bayes (GNB), multilayer perceptron (MLP), Bernoulli Naïve Bayes (BNB) and decision tree (DT) classifiers. The CNN–DNN model for landslide susceptibility mapping was found to predict more accurately than the benchmark algorithms, with an AUC = 90.9%, IRs = 84.8%, MSE = 0.17, RMSE = 0.40, and MAPE = 0.42. The map provided by the CNN–DNN clearly revealed a high-susceptibility area in the west and southwest, related to the main Zagros trend in the province. These findings can be of great utility for landslide risk management and land use planning in the Isfahan province.

The Relevance of Geotechnical-Unit Characterization for Landslide-Susceptibility Mapping with SHALSTAB

Given the increasing occurrence of landslides worldwide, the improvement of predictive models for landslide mapping is needed. Despite the influence of geotechnical parameters on SHALSTAB model outputs, there is a lack of research on models’ performance when considering different variables. In particular, the role of geotechnical units (i.e., areas with common soil and lithology) is understudied. Indeed, the original SHALSTAB model considers that the whole basin has homogeneous soil. This can lead to the under-or-overestimation of landslide hazards. Therefore, in this study, we aimed to investigate the advantages of incorporating geotechnical units as a variable in contrast to the original model. By using locally sampled geotechnical data, 13 slope-instability scenarios were simulated for the Jaguar creek basin, Brazil. This allowed us to verify the sensitivity of the model to different input variables and assumptions. To evaluate the model performance, we used the Success Index, Error Index, ROC curve, and a new performance index: the Detective Performance Index of Unstable Areas. The best model performance was obtained in the scenario with discretized geotechnical units’ values and the largest sample size. Results indicate the importance of properly characterizing the geotechnical units when using SHALSTAB. Hence, future applications should consider this to improve models’ predictivity.

Landslide Mapping And Characterization For Agriculturally Intensive Mountainous Region Of Cameron Highlands, Malaysia

Relatively cool tropical climate with high annual rainfall experienced in mountainous tropical region creates favorable agricultural areas where intensive agricultural practices often correlate with increasing landslide events. While it has long been associated with landslides, agricultural practice in such areas especially in Cameron Highlands, Malaysia continues to flourish with minimal regard to environmental sustainability and safety. Therefore, this study aims to confirm the relationship between human activities and landslide occurrences and identify human practices that directly or indirectly cause landslides. Aerial photograph was used to identify landslides and anthropogenic features while other remotely sensed data including Interferometric Synthetic Aperture Radar (IFSAR) were used to establish the geomorphology of study area. A total of 207 landslides were identified in a 25 km2 area from remote sensing study and field mapping. They were superimposed over several potential contributing factors including geology, slope gradient and human activities. This study found that landslides are more likely to occur in areas greater than 15° of slope angle. The main factor, however, is the human factor where 164 landslides (79.22% of total identified landslides) were located in agricultural lands. Poorly planned and engineered agricultural farms were identified as the main causal factors for landslide occurrences at the study area, either directly or indirectly. These issues were observed through remote sensing study using aerial photograph which were then verified through site observations. New district development plan called for a stricter regulations in new tourism and agricultural developments. Improvements should also be implemented on existing agricultural activities where most of these landslides were located at this area. Therefore, in addition to controlling new developments, improving of existing agricultural practices in Cameron Highlands shall be a major focus in ensuring slope safety and their sustainability.

Exploring event landslide mapping using Sentinel-1 SAR backscatter products

Landslides triggered by meteorological phenomena occur worldwide and cause extensive and severe damages to properties, and life loss. Detailed maps of event landslides can sensibly shorten emergency response time, possibly resulting in reduced death tolls. In most cases, however, optical post-event images are not always available right after the event, due to dense cloud cover. Since Synthetic Aperture Radar (SAR) sensors overcome the limitation of cloud cover, in this work we explore the use of C-band Sentinel-1 SAR amplitude images to map event landslides. A team of four expert photo-interpreters first defined interpretation criteria of SAR amplitude post-event images of the backscatter coefficient (β₀) and of the derived images of change. The same team mapped two large event landslides occurred in Villa Santa Lucia (Chile) and Tonzang (Myanmar). Maps were prepared on a total of 72 images for the Chile test case and 54 for the Myanmar test case. Images included VV (vertical transmit, vertical receive) and VH (vertical transmit, horizontal receive) polarisation, ascending and descending orbits, multilook processing, adaptive and moving window filters, post-event images and images of change. In the first case, interpreters were asked to map the event landslide on an optical post-event image before mapping on SAR images, whereas in Myanmar it was done in the end. Results were quantitatively compared to the maps prepared on post-event optical images, assumed as benchmark. Results revealed a good agreement between the SAR-derived maps and the benchmark. Locally, errors can be due to geometrical distortions, and speckling-like effects. Also polarisation plays an important role, as opposed to filtering. Despite the preliminary nature of this study, it proved that SAR amplitude derived products are suitable to prepare accurate maps of large event landslides, and that they should be further tested to prepare event inventories.

GIS-based Landslide Susceptibility Zoning Using Multi-Criteria Decision-making Method: A Case Study in Binalood Mountains, Iran

INTRODUCTION: Landslides are one of the recurrent natural problems that are widespread throughout the world, especially in mountainous areas, and cause a significant injury to and loss of human life and damage to properties and infrastructures. This study aimed to assess landslide susceptibility using the analytic hierarchy process (AHP) in Binalood Mountains, Razavi Khorasan Province, Iran. METHODS: Since the Binalood Mountains range has a high potential for landslides occurrence, the present study went through to map landslide susceptibility. To accomplish this, the AHP method was used, and then, receiver operating characteristic/area under the curves (AUCs) were prepared to evaluate the performance of the susceptibility map. Multiple data, such as lithology, distance to faults, land use, distance to roads, altitude, slope, aspect, stream power index, topographic wetness index, rainfall, distance from rivers, slope length index, and topographic location index, were considered for delineating the landslide susceptibility maps. These thematic layers were assigned suitable weights on the Saaty's scale according to their relative importance in landslide occurrence in the study area. The assigned weights of the thematic layers and their features were subsequently normalized using the AHP technique. Finally, all thematic layers were integrated by a weighted linear combination method in a geographic information system tool to generate landslide susceptibility maps. FINDINGS: The landslide susceptibility maps are split into five classes, namely very low, low, moderate, high, and very high. The results showed that the geological factor was the most important factor affecting the occurrence of landslides in the study area. Generally, 47.8% of the total area was considered high and very high-risk areas. The prediction accuracy of this map showed the values of AUC equal to 81.7% that showed the AHP model had very good accuracy. CONCLUSION: Overall, AHP is acceptable for landslide susceptibility mapping in the study area. A landslide susceptibility map is a useful tool to help with land management in landslide-prone areas. The results revealed that the predicted susceptibility levels were found to be in good agreement with the past landslide occurrences. Possibly, this map can be used by the concerned authorities in disaster management planning to prepare rescue routes, service centers, and shelters

Application of the group method of data handling (GMDH) approach for landslide susceptibility zonation using readily available spatial covariates

Landslide susceptibility (LS) mapping is an essential tool for landslide risk assessment. This study aimed to provide a new approach with better performance for landslide mapping and adopting readily available variables. In addition, it investigates the capability of a state-of-the-art model developed using the group method of data handling (GMDH) to spatially model LS. Furthermore, hybridized models of GMDH were developed using different metaheuristic algorithms. The study area was the Bonghwa region of South Korea, for which an accurate landslide inventory dataset is available. We considered a total of 13 spatial covariates (altitude, slope, aspect, topographic wetness index, valley depth, plan curvature, profile curvature, distance from fault, distance from river, distance from road, land use, density of forest, and lithology were chosen as independent variables). Two benchmark models—random forest and boosted regression trees—were used to compare their results with the standalone GMDH and hybridized models. We compared model accuracy using the two most robust evaluation metrics, root mean square error (RMSE) and area under the receiver operating characteristic curve (AUROC). The validation results showed that hybridized models outperformed the standalone GMDH model. Moreover, the hybridized GMDH-PSO (AUC = 0.83, RMSE = 0.108), GMDH-IWO (AUC = 0.81, RMSE = 0.111), GMDH-BBO (AUC = 0.8; RMSE = 0.12), and GMDH-ICA (AUC = 0.8; RMSE = 0.117) had a better predictive performance than both RF and BRT. Therefore, the proposed approach could successfully produce landslide susceptibility maps using relatively few readily available variables and can be repeated in data-scarce regions.

Landslide mapping and analysis along the China-Pakistan Karakoram Highway based on SBAS-InSAR detection in 2017

Landslide mapping and analysis along the China-Pakistan Karakoram Highway based on SBAS-InSAR detection in 2017

Development and distribution of ancient landslides in the northwest corner of the Tibetan Plateau

The periphery of the Tibetan Plateau is a landslide-prone area, yet there is a lack of satisfactory results on the development and distribution of landslides due to low human activity and inconvenient transportation within this area. In this study, taking 38–40°N and 73–76°E as the study region, a detailed distribution map of ancient landslides, including 5378 landslides with a total area of 2705.02 km2, is established with the help of Google Earth Engine platform using manual visual interpretation. The numbers and areas of landslides, classified in terms of elevation, slope angle, slope aspect, fault distance, and water system distance, are analyzed. The development of landslides is closely correlated with topographical and geological factors. After comprehensive analysis, we conclude that both the number and total area of landslides in the elevation range of 4–5 km are the greatest; more landslides happen in the slope angle range of 40°–50°; the total area of landslides in the slope angle range of 50°–60° is the largest; both the number and total area of landslides with slope aspects of N, NE, and NW are the greatest; the ancient landslides in this region were weakly impacted by tectonic movement and seismic activity, and more likely caused by reasons like climate change; and the occurrence of ancient landslides in this region is insignificantly correlated with water systems.

Deep Multiple Instance Learning for Landslide Mapping

In this letter, a novel neural network (CDMI-Net) that combines change detection and multiple instance learning (MIL) is proposed for landslide mapping. After obtaining a score map of landslides provided by the network, the final binary map is generated by fast postprocessing. The benefits of the proposed method are threefold. First, using the MIL framework, the network is trained only by the scene-level samples and it reduces the need for pixel-level samples. Second, a change-detection network architecture using a two-stream U-Net with shared weights is designed to learn the deep features of the landslide from the two-period aerial images, reducing the false-positive results. Third, integrating a gated attention-based pooling layer and a fast level-set evolution algorithm can finally produce the pixel-level results. Experimental results show that the proposed CDMI-Net achieves comparable and even better performance on the testing image pairs than all other methods and has great potential for the landslide mapping application.

Application of Machine Learning Algorithm in China National Geohazard Susceptibility Assessment

Landslide susceptibility is an important activity in landslide hazard assessment. With the advancement of artificial intelligence, the machine learning algorithm is applied in the landslide susceptibility assessment, has recently gained immense attention due to the advantages of obtaining the insights of landslide events and conditioning parameters based on data mining, which is important when tackling the challenge of mapping landslide prone areas in regional scale due to the complex nonlinear correlations among landslides and parameters and uncertainties associated during parameters reclassification. Therefore, the machine learning algorithm has become a standard approach for modeling landslide susceptibility over large regions. In this study, the random forest method is applied to produce the China national landslide susceptibility mapping based on the national landslide database containing more than 300 thousand landslide events. Thirty different categories of conditioning parameters related to the development, triggering, and potentially vulnerable elements of the landslide were collected using a scale of approximately 1:1,000,000. Through the data mining process, lithology, faults, topography, soil erosion, precipitation, and human activities were found to be the top six important contribution factors to landslide susceptibility. The mapping results show the areas of four degrees of susceptibility from high to low are 101, 191, 337, and 331 thousand square kilometers, respectively. The receiver operating curve (ROC) and area under curve (AUC) value was calculated to 0.81, indicating that results are well satisfying and could guide landslide mitigation on the national level.

Landslide Hazard Zonation along the MH SH-73 at Kelghar Ghat, Satara, Maharashtra

Road transportation is the most common victim of landslide in the world. The present study investigates the landslide hazard zonation along the MH SH-73 at Kelghar ghat between Medha and Mahabaleshwar hill station of Maharashtra. Remote Sensing and GIS were used for the landslide hazard zonation of this section. The ghat section was buffered 100 m on both side to define the extent of study area based on the field investigation. The study incorporated predefined important landslide causative factors, viz. slope, rainfall, relief, lithology, soil depth, soil erosion, soil texture, land use / land cover, drainage distance, drainage density, lineament distance, lineament density, aspect, temperature, landslide inventory and in this approach fifteen thematic layers were prepared in GIS platform. The weight and score were assigned to each thematic layer based on heuristic approach on their relative importance in causing landslide. Multi-criteria model in ArcGIS 10.5 software were used for the mapping landslide hazard zones and it were classified into six zones: very high (1.3 %), high (1.7 %), moderate (3.4 %), low (7.6 %) very low (8.4 %) and no risk zone (77.7 %). The final result of this research can help for proper mitigation and adaptation measures for engineers, planners and administrators for this ghat section. Keywords: GIS, Landslide Hazard Zonation, Remote Sensing, Susceptibility, Sahyadri, Kelghar.

Beyond 2D landslide inventories and their rollover: synoptic 3D inventories and volume from repeat lidar data

Abstract. Efficient and robust landslide mapping and volume estimation is essential to rapidly infer landslide spatial distribution, to quantify the role of triggering events on landscape changes, and to assess direct and secondary landslide-related geomorphic hazards. Many efforts have been made to develop landslide mapping methods, based on 2D satellite or aerial images, and to constrain the empirical volume–area (V–A) relationship which, in turn, would allow for the provision of indirect estimates of landslide volume. Despite these efforts, major issues remain, including the uncertainty in the V–A scaling, landslide amalgamation and the underdetection of landslides. To address these issues, we propose a new semiautomatic 3D point cloud differencing method to detect geomorphic changes, filter out false landslide detections due to lidar elevation errors, obtain robust landslide inventories with an uncertainty metric, and directly measure the volume and geometric properties of landslides. This method is based on the multiscale model-to-model cloud comparison (M3C2) algorithm and was applied to a multitemporal airborne lidar dataset of the Kaikōura region, New Zealand, following the Mw 7.8 earthquake of 14 November 2016. In a 5 km2 area, the 3D point cloud differencing method detects 1118 potential sources. Manual labeling of 739 potential sources shows the prevalence of false detections in forest-free areas (24.4 %), due to spatially correlated elevation errors, and in forested areas (80 %), related to ground classification errors in the pre-earthquake (pre-EQ) dataset. Combining the distance to the closest deposit and signal-to-noise ratio metrics, the filtering step of our workflow reduces the prevalence of false source detections to below 1 % in terms of total area and volume of the labeled inventory. The final predicted inventory contains 433 landslide sources and 399 deposits with a lower limit of detection size of 20 m2 and a total volume of 724 297 ± 141 087 m3 for sources and 954 029 ± 159 188 m3 for deposits. Geometric properties of the 3D source inventory, including the V–A relationship, are consistent with previous results, except for the lack of the classically observed rollover of the distribution of source area. A manually mapped 2D inventory from aerial image comparison has a better lower limit of detection (6 m2) but only identifies 258 landslide scars, exhibits a rollover in the distribution of source area of around 20 m2, and underestimates the total area and volume of 3D-detected sources by 72 % and 58 %, respectively. Detection and delimitation errors in the 2D inventory occur in areas with limited texture change (bare-rock surfaces, forests) and at the transition between sources and deposits that the 3D method accurately captures. Large rotational/translational landslides and retrogressive scars can be detected using the 3D method irrespective of area's vegetation cover, but they are missed in the 2D inventory owing to the dominant vertical topographic change. The 3D inventory misses shallow (< 0.4 m depth) landslides detected using the 2D method, corresponding to 10 % of the total area and 2 % of the total volume of the 3D inventory. Our data show a systematic size-dependent underdetection in the 2D inventory below 200 m2 that may explain all or part of the rollover observed in the 2D landslide source area distribution. While the 3D segmentation of complex clustered landslide sources remains challenging, we demonstrate that 3D point cloud differencing offers a greater detection sensitivity to small changes than a classical difference of digital elevation models (DEMs). Our results underline the vast potential of 3D-derived inventories to exhaustively and objectively quantify the impact of extreme events on topographic change in regions prone to landsliding, to detect a variety of hillslope mass movements that cannot be captured by 2D landslide mapping, and to explore the scaling properties of landslides in new ways.

Detection and Mapping of Active Landslides before Impoundment in the Baihetan Reservoir Area (China) Based on the Time-Series InSAR Method

Many potential landslides occured in the Baihetan reservoir area before impoundment. After impoundment, these landslides may still slide, affecting the safe operation of the reservoir area (e.g., causing barrier lakes and floods). Identifying the locations of landslides and their distribution pattern has attracted attention in China and globally. In addition, due to the rolling terrain of the reservoir area, synthetic aperture radar (SAR) imaging will affect the interactive synthetic aperture radar (InSAR) deformation results. Only by obtaining effective deformation information can active landslides be accurately identified. Therefore, the banks of the Hulukou Xiangbiling section of the Baihetan reservoir area before impoundment in the Jinsha River Basin were studied in this paper. Using terrain data and the satellite parameters from Sentinel-1A ascending and descending orbits and ALOS PALSAR ascending orbit, the line-of-sight visibility was quantitatively analyzed, and an analysis method was proposed. Based on the SAR data visibility analysis, the small baseline subset (SBAS) technique was used to process the SAR data to acquire effective deformation. InSAR deformation data was combined with Google Earth imagery to identify 25 active landslides. After field verification, 21 active landslides (14 new) were determined. Most of the active landslides are controlled by faults, and the strata of the other landslides are relatively weak. This InSAR analysis method based on SAR data visibility can provide a reference for identifying and analyzing active landslides in other complicated terrain.

A Comparison of an Adaptive Neuro-Fuzzy and Frequency Ratio Model to Landslide-Susceptibility Mapping along Forest Road Networks

In this research, we used the integration of frequency ratio and adaptive neuro-fuzzy modeling (ANFIS) to predict landslide susceptibility along forest road networks in the Hyrcanian Forest, northern Iran. We began our study by first mapping landslide locations during an extensive field survey. In addition, we then selected landslide-conditioning factors, such as slope, aspect, altitude, rainfall, geology, soil, road age, and slip position from the available Geographic Information System (GIS) data. Following this, we developed Adaptive Neuro-Fuzzy Inference System (ANFIS) models with two different membership functions (MFs) in order to generate landslide susceptibility maps. We applied a frequency ratio model to the landslide susceptibility mapping and compared the results with the probabilistic ANFIS model. Finally, we calculated map accuracy by evaluating receiver-operating characteristics (ROC). The validation results yielded 70.7% accuracy using the triangular MF model, 67.8% accuracy using the Gaussian MF model, and 68.8% accuracy using the frequency ratio model. Our results indicated that the ANFIS is an effective tool for regional landslide susceptibility assessment, and the maps produced in the study area can be used for natural hazard management in the landslide-prone area of the Hyrcanian region.

Matrix SegNet: A Practical Deep Learning Framework for Landslide Mapping from Images of Different Areas with Different Spatial Resolutions

Practical landslide inventory maps covering large-scale areas are essential in emergency response and geohazard analysis. Recently proposed techniques in landslide detection generally focused on landslides in pure vegetation backgrounds and image radiometric correction. There are still challenges in regard to robust methods that automatically detect landslides from images with multiple platforms and without radiometric correction. It is a significant issue in practical application. In order to detect landslides from images over different large-scale areas with different spatial resolutions, this paper proposes a two-branch Matrix SegNet to semantically segment input images by change detection. The Matrix SegNet learns landslide features in multiple scales and aspect ratios. The pre- and post- event images are captured directly from Google Earth, without radiometric correction. To evaluate the proposed framework, we conducted landslide detection in four study areas with two different spatial resolutions. Moreover, two other widely used frameworks: U-Net and SegNet, were adapted to detect landslides via the same data by change detection. The experiments show that our model improves the performance largely in terms of recall, precision, F1-score, and IOU. It is a good starting point to develop a practical, deep learning landslide detection framework for large scale application, using images from different areas, with different spatial resolutions.

Rapid Terrain Assessment for Earthquake-Triggered Landslide Susceptibility With High-Resolution DEM and Critical Acceleration

Earthquake ground motion often triggers landslides in mountainous areas. A simple, robust method to quickly evaluate the terrain’s susceptibility of specific locations to earthquake-triggered landslides is important for planning field reconnaissance and rescues after earthquakes. Different approaches have been used to estimate coseismic landslide susceptibility using Newmark’s sliding block model. This model requires an estimate of the landslide depth or thickness, which is a difficult parameter to estimate. We illustrate the use of Newmark sliding block’s critical acceleration for a glaciated valley affected by the 2015 Gorkha earthquake in Nepal. The landslide data came from comparing high-resolution pre- and post-earthquake digital elevation models (DEMs) derived from Spot 6/7 images. The areas where changes were detected provided an inventory of all the landslides triggered by the earthquake. The landslide susceptibility was modeled in a GIS environment using as inputs the pre-earthquake terrain and slope angles, the peak ground acceleration from the 2015 Gorkha earthquake, and a geological map. We exploit the depth information for the landslides (obtained by DEM difference) to apply the critical acceleration model. The spatial distribution of the predicted earthquake-triggered landslides matched the actual landslides when the assumed landslide thickness in the model is close to the median value of the actual landslide thickness (2.6 m in this case). The landslide predictions generated a map of landslide locations close to those observed and demonstrated the applicability of critical acceleration for rapidly creating a map of earthquake-triggered landslides.

Evaluation of Machine Learning Algorithms for Object-Based Mapping of Landslide Zones Using UAV Data

Landslides are a critical geological phenomenon with devastating and catastrophic consequences. With the recent advancements in the geoinformation domain, landslide documentation and inventorization can be achieved with automated workflows using aerial platforms such as unmanned aerial vehicles (UAVs). As a result, ultra-high-resolution datasets are available for analysis at low operational costs. In this study, different segmentation and classification approaches were utilized for object-based landslide mapping. An integrated object-based image analysis (OBIA) workflow is presented incorporating orthophotomosaics and digital surface models (DSMs) with expert-based and machine learning (ML) algorithms. For segmentation, trial and error tests and the Estimation of Scale Parameter 2 (ESP 2) tool were implemented for the evaluation of different scale parameters. For classification, machine learning algorithms (K- Nearest Neighbor, Decision Tree, and Random Forest) were assessed with the inclusion of spectral, spatial, and contextual characteristics. For the ML classification of landslide zones, 60% of the reference segments have been used for training and 40% for validation of the models. The quality metrics of Precision, Recall, and F1 were implemented to evaluate the models’ performance under the different segmentation configurations. Results highlight higher performances for landslide mapping when DSM information was integrated. Hence, the configuration of spectral and DSM layers with the RF classifier resulted in the highest classification agreement with an F1 value of 0.85.

A comparative geological, tectonic and geomorphological assessment of the Charlotte, Regent and Madina landslides, Western area, Sierra Leone

This work focused on three landslide events that have attracted significant public concern due to the associated calamities they recorded in 1945, 2017 and 2019, i.e. the Charlotte, Regent and Madina landslides, respectively. Their geology, tectonics (structural discontinuities) and geomorphology, i.e. their GTG characteristics were studied to establish links between them and the landslide events.Field surveys were conducted, particularly on the Charlotte landslide, where the identification of geological structures was impeded to an extent by its obliteration by vegetation and sediment accumulations on relatively planar sections of the landslide area. Remote sensing and GIS techniques (earth imagery and drone images) enhanced the mapping and determination of landslides’ geometric and geomorphic parameters. Laboratory analyses of rock and soil samples provided the landslides’ petrological characterisation and were used to determine the particle-size distribution in the slide-prone soil.The study indicated a change in the gabbroic rock composition, variable geomorphological characteristics, and nature/pattern and density of the discontinuities. These factors, to a large extent, determined the nature and magnitude of the rainfall-triggered landslides. Charlotte lithology slightly differed from the other two landslides and recorded higher Silica (Si) and Aluminum (Al) and lower iron (Fe) from X-Ray Fluorescence (XRF) than rocks of Regent and Madina landslides. The study also revealed only a tenuous correlation between rock composition and weathering depth. The slope angles at the landslides’ prominent scarps (depletion zone) are steep (> 45 degrees) with altitudes of approximately 270 m, 200 m and 470 m above sea level for Charlotte, Regent and Madina, respectively. Unlike the Charlotte landslide, both Regent and Madina landslides are active, but geometrically, their area, length and run-out distances have relatively high variance with a coefficient of variance equals to 1. Information derived from this work can help understand the spatial variation in landslide characteristics and develop a susceptibility map.