Landslide Inventory Research Articles

The landslide traces inventory in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau: a case study of Jianzha County, China

The upper reaches of the Yellow River in China, influenced by erosion of the Yellow River and tectonic activities, are prone to landslides. Therefore, it is necessary to investigate the existing landslide traces. Based on visual interpretation on high-resolution satellite images and terrain data, supplemented and validated by existing landslide records, this paper prepared the most complete and detailed landslide traces inventory in Jianzha County, Huangnan Tibetan Autonomous Prefecture, Qinghai Province, to date. The results indicate that within the study area of 1714 km2, there are at least 713 landslide traces, ranging in scale from 3,556 m2 to 11.13 km2, with a total area of 134.46 km2. The total landslide area excluding the overlap area is 126.30 km2. The overall landslide point density and area density in the study area are 0.42 km-2 and 7.37% respectively. The maximum point density and maximum area density of landslide traces in the area are as high as 5.69 km-2 and 98.0% respectively. The landslides are primarily distributed in the relatively low-elevation northeastern part of Jianzha County, characterized mainly by large-scale loess landslides, with 14 landslides exceeding 1×106 m2. This inventory not only supplements the landslide trace data in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau, but also provides an important basis for subsequent landslide risk zoning, response to climate change, and landscape evolution. Additionally, it holds significant reference value for compiling landslide inventories in similar geological environments.

The unsuPervised shAllow laNdslide rapiD mApping: PANDA method applied to severe rainfalls in northeastern appenine (Italy)

Shallow landslides, frequently triggered by extreme events such as heavy rainfall, snowmelt, or earthquakes, affect vast areas with remarkable density. In the immediate aftermath of such events, it becomes crucial to rapidly assess landslides distribution and pinpoint the most severely affected areas to prioritize damage assessments and guide field survey operations effectively. Once the emergency phase subsides, the attention can shift to enhancing the accuracy of landslide inventory. In this work, we introduce the two-phase methodology “PANDA”, the unsuPervised shAllow laNdslide rapiD mApping, for the low-cost mapping of the potential landslides, firstly in the emergency phase and then, with an improved version, in the post-emergency one. This approach utilizes variations in NDVI derived from Sentinel-2 satellite imagery and geomorphological filters. We applied PANDA to rainfall events in the northeastern Apennine range, Italy, occurred in May 2023, causing dramatic social and economic consequences for this mountain territory. Within just five days of obtaining Sentinel-2 post-event imagery, we produced a reliable, ready-to-use map covering a vast area (∼4000 km2). The map tested during emergency field mapping shows positive feedback. In the post-emergency phase, accuracy was enhanced using completely cloud-free imagery, a filter to identify false positives associated with land use changes, a higher resolution digital terrain model (DTM), and an iterative approach to optimize NDVI and slope thresholds. Potential landslide density related with rainfall, indicating that the most severely affected region attained a density of approximately 50 landslides/km2. Validation against an independent manual inventory based on high-resolution imagery demonstrated encouraging accuracy results from both inventories, with a noticeable increase in the F1 score for the post-emergency version.

A framework for automated landslide dating utilizing SAR-Derived Parameters Time-Series, An Enhanced Transformer Model, and Dynamic Thresholding

Determining the timing of landslide occurrence is crucial for establishing an accurate, comprehensive and systematic landslide inventory while assessing the potential for reducing landslide risk. Unfortunately, many existing landslide inventories lack temporal information such as the precise time of landslide events. Optical and Synthetic Aperture Radar (SAR) sensors are the most commonly used remote sensing technologies for landslide detection. Unlike optical sensors, SAR sensors are not affected by cloudy conditions and provide valuable imagery regardless of sunlight availability. Therefore, SAR-derived parameters, i.e., SAR amplitude, interferometric coherence, and polarimetric features (alpha and entropy), offer a higher temporal resolution for detecting landslide occurrence times compared to optical data. Despite the advantages, there is currently no universally accepted automatic method for determining the time of landslide events using SAR data. This is due to the lack of anomaly labels and the high time-series volatility in detecting landslide occurrence times. Despite advances in deep-learning methods for anomaly detection in time-series, only a few of them can address these challenges in our case. In this paper, we propose an unsupervised multivariate transformed-based deep-learning model to automatically and efficiently estimate landslide occurrence times using multivariate SAR-derived parameters time-series analysis. The designed gated relative position can increase robustness and temporal context information, by learning global temporal trends in the time-series. Subsequently, the time-series of the anomaly score derived from the proposed Transformer model is analyzed using an adaptive thresholding strategy to dynamically and automatically mark anomalies related to the landslide occurrence. Our research focuses on collapsed landslides characterized by dramatic changes in ground surface topography, with a particular attention for the need of a prior knowledge about landslide boundaries. We assess the performance of the proposed methodology for several collapsed landslides including the July 21, 2020 Shaziba and 23 July, 2019 Shuicheng landslides in China, March 19, 2019 Takht landslide in Iran, June 15, 2018 Jalgyz-Jangak and May 25, 2018 Kugart landslides in Kyrgyzstan, July 7, 2018 Hitardalur landslide in Iceland, and January 25, 2019 Brumadinho landslide in Brazil. In comparison to commonly used neural networks like the LSTM algorithm, our proposed framework leads to a more accurate estimate for the time of landslide failure using time-series of SAR-derived parameters. Furthermore, our results suggest the great potential of SAR data to narrow the time period detected from optical data when used in conjunction with them.

Assessing landslide susceptibility in Lake Abya catchment, Rift Valley, Ethiopia: A GIS-based frequency ratio analysis

Ethiopia's varied landscape, significant rainfall, and diverse geological characteristics pose risks of landslides. The specific research area spans 40 km2 within the Lake Abaya catchment area in the Rift Valley of Ethiopia. This investigation aimed to map landslide susceptibility using remote sensing information, GIS technology, and frequency ratio analysis. It evaluated multiple factors influencing landslide susceptibility. The process involved meticulous mapping of thematic layers, utilizing GIS techniques and diverse data sources, including primary data, satellite imagery, and secondary sources. A combination of Google Earth image analysis and field surveys was used to map landslide susceptibility in inaccessible areas. It was determined that 138 landslide sites existed. Of these, 30% (41 points) were assigned to the test of the model and another 30% to the training of the model, for a total of 97 points. The landslide susceptibility was classified into five categories based on frequency ratio analysis of the landslide susceptibility index (LSI): very low, low, moderate, high, and very high. The northeastern sector of the study area demonstrated a comparatively diminished susceptibility to landslides, ranging from low to moderate, whereas the central and southern regions showcased markedly elevated vulnerability. An evaluation of the model's accuracy using the area under the curve (AUC) method based on test inventory landslide data produced encouraging results: 84.8% accuracy on the success rate curve and 78.8% accuracy on the prediction rate curve. Based on the frequency ratio model, a susceptibility map is derived to represent susceptibility levels accurately.

Landslide susceptibility mapping of the main boundary thrust region in Thungsingdanda-Bandipur section of Nawalparasi and Palpa Districts, Gandaki and Lumbini Provinces, Nepal

This research assesses the results of a landslide susceptibility analysis employing frequency ratios (FR). It was conducted in collaboration between CNES/Airbus and Maxar Technologies using Google Earth. Landslides in the Thungsingdanda-Bandipur area were identified through imagery with a 50 cm spatial resolution. A comprehensive dataset for training and testing was established based on the landslide inventory. Nine causative variables: slope, aspect, relief, distance from the stream, distance from the road, curvature, distance from the thrust, geology, and land use were used. FR ratings were assigned to these causative variables based on training events. The resultant landslide susceptibility map was generated by integrating causative variables with their respective FR ratings. The validation rate, determined using the ROC-AUC curve, was found to be 82.2 percent. Notably, distance from the thrust (MBT), land use, and distance from the road emerged as the more influential factors among the nine causative variables in landslide occurrences.

National-scale assessment of railways exposure to rapid flow-like landslides

In landslide-prone regions, railway networks are vulnerable to rapid flow-like landslides, which can cause extensive damage, even at significant distances from the landslide's point of origin. The prioritization of protective measures on a national scale depends on the accurate assessment of each railway segment's exposure to landslides.This study introduces a methodology for evaluating the exposure of transportation infrastructure, with a specific focus on flow-like landslides. The application of this methodology is demonstrated on Italy's national railway network.The methodology involves a multi-phase approach, including a training phase using DEM data and landslide inventories, a modeling phase for landslide source areas and runout, and a final phase for infrastructure exposure assessment. Key outputs of this approach include the creation of a landslide susceptibility map and, critically, the generation of an exposure map that quantifies the risk to transportation infrastructure posed by flow-like landslides.Validation using an independent landslide dataset demonstrated satisfactory results, especially in regions with area under the ROC curve values ranging from 0.7 to 0.95. The resulting exposure map shows that approximately 13.4% (2376 km) of the entire national railway network is exposed to a medium-high level of risk from flow-like landslides.This methodology highlights its effectiveness in nationwide exposure analysis of transportation infrastructure, providing valuable insights for risk mitigation and resource allocation, while relying solely on existing landslide inventories and digital elevation models.

Inventory of Landslides in the Northern Half of the Taihang Mountain Range, China

The Taihang Mountains are a critical mountain range and geographical boundary in eastern China. Landslide disasters are particularly common in this region and usually cause serious casualties and property damage. However, previous landslide inventories in the region are limited and lack comprehensive landslide cataloguing. To address this gap, the northern half of the Taihang Mountain Range was selected for this study. A landslide database for the area was constructed using multi-temporal high-resolution optical imagery from the Google Earth and human–computer interactive visual interpretation technology. The results indicate that at least 8349 landslides have occurred in the Taihang Mountain Range, with a total landslide area of about 151.61 km2. The size of the landslides varies, averaging about 18,159.23 m2, with the largest landslide covering 2.83 km2 and the smallest landslide only 5.95 m2. The significance of this study lies in its ability to enhance our understanding of the distribution of landslides in the northern half of the Taihang Mountains. Furthermore, it offers valuable data references and supports for landslide assessment, early warning systems, disaster management, and ecological protection efforts.

Feature adaptation for landslide susceptibility assessment in “no sample” areas

Given the time-consuming nature of compiling landslide inventories, it is increasingly important to develop transferable landslide susceptibility models that can be applied to regions without existing data. In this study, we propose a feature-based domain adaptation method to improve the transferability of landslide susceptibility models, especially in “no sample” areas. Two typical landslide-prone areas in Fujian province, southeastern China, were chosen as research cases to test the practicality of the transfer effect. Five conventional machine learning algorithms (Support vector machines (SVM), Random Forest (RF), Logistic Regression (LOG), K-nearest neighbor (KNN), and Decision tree (C4.5)) are used to model landslide susceptibility in sampled areas (source domain), and a feature transfer-based landslide susceptibility evaluation model is constructed under coupled feature transfer methods to evaluate the susceptibility of landslide in un-sampled areas (target domain). The results showed that feature transfer can effectively improve the transferability of different machine learning models for cross-regional prediction (The indicators have improved overall by 8.49%), with SVM (increased by 13.68%) and LOG (increased by 10.19%) models showing the most significant improvements. The feature-based domain adaptive method can alleviate the burden of collecting and labeling new data, and effectively improve the assessment performance of machine learning-based landslide susceptibility models in un-sampled areas. This is a new solution for landslide susceptibility assessment in completely “no sample” areas.

Environmental effects following a seismic sequence: the 2019 Cotabato—Davao del Sur (Philippines) earthquakes

In the period of October–December 2019, the Cotabato–Davao del Sur region (Philippines) was hit by a seismic sequence comprising four earthquakes with magnitude MW > 6.0 (EQ1-4; max magnitude MW 6.8). The earthquakes triggered widespread environmental effects, including landslides and liquefaction features. We documented such effects by means of field surveys, which we supplemented with landslide mapping from satellite images. Field surveys allowed us to gather information on 43 points after EQ1, 202 points after EQs2–3 and 87 points after EQ4. Additionally, we built a multi-temporal inventory of landslides from remote sensing, comprising 190 slope movements triggered by EQ1, 4737 after EQs2–3, and 5666 at the end of the sequence. We assigned an intensity value to each environmental effect using the environmental seismic intensity (ESI-07) scale. Our preferred estimates of ESI-07 epicentral intensity are VIII for the first earthquake and IX at the end of the sequence, which is in broad agreement with other events of similar magnitude globally. This study, which is the first case of the application of the ESI-07 scale to a seismic sequence in the Philippines, shows that repeated documentation of environmental damage and the evaluation of the progression through time may be useful for providing input data for derivative products, such as susceptibility assessment, evaluation of residual risk or investigation of the role played by ground shaking and by other mechanisms able to trigger environmental effects.

Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya

This paper presents a remote sensing-based method to efficiently generate multi-temporal landslide inventories and identify recurrent and persistent landslides. We used free data from Landsat, nighttime lights, digital elevation models, and a convolutional neural network model to develop the first multi-decadal inventory of landslides across the Himalaya, spanning from 1992 to 2021. The model successfully delineated >265,000 landslides, accurately identifying 83 % of manually mapped landslide areas and 94 % of reported landslide events in the region. Surprisingly, only 14 % of landslide areas each year were first occurrences, 55–83 % of landslide areas were persistent and 3–24 % had reactivated. On average, a landslide-affected pixel persisted for 4.7 years before recovery, a duration shorter than findings from small-scale studies following a major earthquake event. Among the recovered areas, 50 % of them experienced recurrent landslides after an average of five years. In fact, 22 % of landslide areas in the Himalaya experienced at least three episodes of landslides within 30 years. Disparities in landslide persistence across the Himalaya were pronounced, with an average recovery time of 6 years for Western India and Nepal, compared to 3 years for Bhutan and Eastern India. Slope and elevation emerged as significant controls of persistent and recurrent landslides. Road construction, afforestation policies, and seismic and monsoon activities were related to changes in landslide patterns in the Himalaya.

The Yinshan Mountains Record over 10,000 Landslides

China boasts a vast expanse of mountainous terrain, characterized by intricate geological conditions and structural features, resulting in frequent geological disasters. Among these, landslides, as prototypical geological hazards, pose significant threats to both lives and property. Consequently, conducting a comprehensive landslide inventory in mountainous regions is imperative for current research. This study concentrates on the Yinshan Mountains, an ancient fault-block mountain range spanning east–west in the central Inner Mongolia Autonomous Region, extending from Langshan Mountains in the west to Damaqun Mountains in the east, with the narrow sense Xiao–Yin Mountains District in between. Employing multi-temporal high-resolution remote sensing images from Google Earth, this study conducted visual interpretation, identifying 10,968 landslides in the Yinshan area, encompassing a total area of 308.94 km2. The largest landslide occupies 2.95 km2, while the smallest covers 84.47 m2. Specifically, the Langshan area comprises 331 landslides with a total area of 11.96 km2, the narrow sense Xiao–Yin Mountains include 3393 landslides covering 64.13 km2, and the Manhan Mountains, Damaqun Mountains, and adjacent areas account for 7244 landslides over a total area of 232.85 km2. This research not only contributes to global landslide cataloging initiatives but also serves as a robust foundation for future geohazard prevention and management efforts.

Multitemporal landslide inventory and susceptibility map for the Arun River Basin, Nepal

AbstractThe transboundary Arun River Basin (ARB) spreads across Nepal and Tibet. Nearly 95% of the basin lies in Tibet through which the Pumqu River flows, forming the Arun River once it enters Nepal. The ARB has five large hydropower projects undergoing construction or planned for the future. Rainfall and earthquake‐induced landslides, landslide‐dammed lakes and landslide‐induced glacial lake outburst floods pose major risks to smooth operation of these projects. To safeguard upcoming hydropower projects, areas susceptible to landslides in the ARB must be identified. We used high‐resolution satellite imagery and open‐source tools to generate a multitemporal landslide inventory for the basin. The rigorously quality‐controlled inventory represents a yearly record of landslides from 2011 to 2020. A data‐driven approach was used to map areas susceptible to landslides within the ARB. The multitemporal landslide inventory combined with other readily available Earth observation‐based variables was used to create a landslide susceptibility map. The susceptibility analysis provides a valuable initial estimate of where landslides are likely to initiate. These landslide products could form the basis of more comprehensive local studies to inform hydropower project development.

Landslide Susceptibility and Risk Mapping in the Tectonic Ensemble Comprising of Eastern Himalayan Zone, Northeast India and Bhutan using Logistic Regression and Random Forest Techniques

Abstract The mountainous terrain of the East-Northeast tectonic ensemble covers an approximate area of 3,11,420 km2 comprising the Eastern Himalayan zone, Northeast India and Bhutan is prone to mass movements. The increasing trend of landslide occurrences in the last few decades sets loud alarm bells for mapping the landslide hotspot zones in terms of Landslide Susceptibility and Risk. Initially, a landslide inventory of around 9751 landslides has been prepared, of which 6826 (70%) has been randomly picked as training dataset and the remaining 2925 (30%) as the testing dataset. Thereafter, Random Forest (RF) and binary Logistic Regression (LR) based Landslide Susceptibility Zonation (LSZ) have been prepared through twelve predictor layers viz. Slope Angle, Slope Aspect, Slope Curvature, Distance to Drainage, Distance to Lineament, Landform, Surface Geology, Distance to Road, Normalized Difference Vegetation Index (NDVI), Landuse/ Landcover (LULC), Rainfall and Epicentre Proximity. Both the Landslide Susceptibility Index (LSI) maps are divided into five classes, viz. ‘low’, ‘moderate’, ‘high’, ‘very high’ and ’severe’, which are then validated statistically by drawing a comparison with the 30% testing chronological landslide inventory database. The statistical index based accuracy assessment in terms of Area Under the Curve (AUC) exhibits an LR model AUC of 0.776 and RF model AUC of 0.820. However, it has been observed that the RF model strongly correlates with the testing inventory dataset exhibiting that around 46% of the landslide-prone terrain is classified in the ‘high to severe’ zones with 41% inventory landslides occurring in these zones. Integrating the Random Forest (RF) based LSI thematic layer with the socio-economic vulnerability layers like the Number of Households and Population Density have demarcated around 20.78% of the region under ‘very high to severe’ socio-economic risk as convoluted by landslide susceptibility in the terrain. The present findings are expected to be useful in urban development and town planning with appropriate slope management and land-use planning.

Seismic Landslide Susceptibility Assessment Using Newmark Displacement Based on a Dual-Channel Convolutional Neural Network

Landslide susceptibility assessment (LSA) is an essential tool for landslide hazard warning. The selection of earthquake-related factors is pivotal for seismic LSA. In this study, Newmark displacement (Dn) is employed as the earthquake-related factor, providing a detailed representation of seismic characteristics. On the algorithmic side, a dual-channel convolutional neural network (CNN) model is built, and the last classification layer is replaced with two machine learning (ML) models to facilitate the extraction of deeper features related to landslide development. This research focuses on Beichuan County in Sichuan Province, China. Fifteen landslide predisposing factors, including hydrological, geomorphic, geological, vegetation cover, anthropogenic, and earthquake-related features, were extensively collected. The results demonstrate some specific issues. Dn outperforms conventional earthquake-related factors such as peak ground acceleration (PGA) and Arias intensity (Ia) in capturing seismic influence on landslide development. Under the same conditions, the OA improved by 5.55% and AUC improved by 0.055 compared to the PGA; the OA improved by 3.2% and AUC improved by 0.0327 compared to the Ia. The improved CNN outperforms ML models. Under the same conditions, the OA improved by 4.69% and AUC improved by 0.0467 compared to RF; the OA improved by 4.47% and AUC improved by 0.0447 compared to SVM. Additionally, historical landslides validate the reasonableness of the landslide susceptibility maps. The proposed method exhibits a high rate of overlap with the historical landslide inventory. The proportion of historical landslides in the very high and high susceptibility zones exceeds 87%. The method not only enhances accuracy but also produces a more fine-grained susceptibility map, providing a reliable basis for early warning of seismic landslides.

Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi

Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi

Landslide hazard assessment and mapping at national scale for Malawi

Malawi, a landlocked country characterized by its mountainous terrain crisscrossed by the Great Rift Valley and Lake Malawi, is facing an increasing threat of landslides primarily triggered by heavy rainfall from tropical cyclones and depressions. While there are general landslide susceptibility maps available on a global and continental scale, Malawi lacks its own specific landslide hazard maps that take into account regional nuances, different types of landslides, and their triggering factors. However, factoring in these parameters is essential for accurately quantifying hazards. This contribution aims to fill this gap by proposing hazard maps that consider both spatial and temporal probabilities of landslide events. The methodology employed here is based on quantifying the probability of failure at both spatial and temporal levels, following the guidelines set forth by the Joint Technical Committee – 1 for slopes and landslides (JTC-1). To enhance the accuracy of the landslide inventory, a combination of literature review, visual remote sensing, and field surveys was used. This comprehensive data collection approach included information on the types of landslides, their activity levels, and the periods during which they are most likely to be triggered. Subsequently, susceptibility analyses were conducted for various types of landslides using a data-driven approach. Temporal analyses were carried out, taking into consideration two key factors: (i) the recurrence time of different phenomena, such as debris-flows, debris-slides, and slides from 1946 to 2019, and (ii) the rainfall patterns induced by various tropical meteorological events, as defined by the World Meteorological Organization and Meteo-France. the computation of exceedance probabilities based on the Poisson distribution, predicting the likelihood of landslide reactivation for six different return periods, ranging from 1 to 100 years, following various typical meteorological events. Subsequently, susceptibility analyses were conducted for various types of landslides using a data-driven approach. Temporal analyses were carried out, taking into consideration two key factors: (i) the recurrence time of different phenomena, such as debris-flows, debris-slides, and slides from 1946 to 2019, and (ii) the rainfall patterns induced by various tropical meteorological events, as defined by the World Meteorological Organization and Meteo-France. the computation of exceedance probabilities based on the Poisson distribution, predicting the likelihood of landslide reactivation for six different return periods, ranging from 1 to 100 years, following various typical meteorological events. Ultimately, this methodology facilitates the development of various spatio-temporal landslide risk scenarios on a national scale.

Long‐Term Landslide Evolution and Restoration After the Wenchuan Earthquake Revealed by Time‐Series Remote Sensing Images

AbstractA powerful earthquake, particularly in mountainous regions, has the ability to trigger an immense number of landslides, resulting in severe damages. However, effectively assessing their recoveries in the years following the mainshocks has not yet been attempted due to the lack of continuously updated landslide inventories. Therefore, we proposed a novel detection framework utilizing time‐series Landsat images from 2000 to 2021, which enabled us to achieve a long‐term landslide inventory in the seismic zone for the first time. The response patterns of landslide development under different conditions were explored in detail. The restoration models were quantitatively established, revealing that the recoveries of post‐earthquake landslide activity and vital environmental indicators require a range from ∼5.29 to ∼25.00 years. This pioneering research is highly beneficial in comprehending the impact of earthquake‐induced hazards on short‐term land surface damage and long‐term ecological equilibrium.

Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping

Mapping shallow landslide inventories represents the initial stage in conducting susceptibility, vulnerability, and risk analyses. The identification of areas with a history of landslides, both past and current events, enables the detection of spatio-temporal patterns and facilitates the evaluation of morphological and geological factors. Shallow landslides are frequent in Brazil, especially along the Brazilian south and southeastern coast. Despite their high frequency, the recognition and mapping of these landslides are inadequately developed due to the absence of established mapping guidelines. This resulted of limited investments by Brazilian authorities in initiatives aimed at preventing and mitigating mass movements. Thus, the aim of this study is to establish criteria for the recognition and mapping of shallow landslides in Brazil and to evaluate their applicability in susceptibility analysis. The study area is located in Itaóca and Apiaímunicipalities, São Paulo state, southeastern Brazil. Criteria for shallow landslide recognition were created based on the absence of vegetation, shape and size, drainage network distance, slope position, planar rupture surface, and altimetric variation. An extensive landslide interpretation was carried out using a set of free access Google Earth Pro images. Susceptibility maps were developed through a bivariate statistical approach, specifically the Informative Value method. The outcomes demonstrated that incorporating the deposition area in susceptibility studies for shallow landslides led to modifications in the final mapping, a slight reduction in validation rates, and, notably, an impact on the influence of the thematic variable curvature. Therefore, it is clear that the criteria used for landslide inventory maps have a significant effect on the accuracy of susceptibility maps and should be a critical consideration in studies related to this subject.

Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China)

On August 8, 2017, a magnitude 7.0 earthquake struck Jiuzhaigou County in Sichuan Province, triggering numerous coseismic landslides. The prompt identification of these landslides is imperative for emergency rescue efforts and post-earthquake hazard assessments. Optical satellite and unmanned aerial vehicle (UAV) images are often obstructed by cloud cover and fog following earthquakes. In contrast, polarimetric synthetic aperture radar (PolSAR), unaffected by adverse weather conditions, emerges as an indispensable tool. However, the utilization of spaceborne single-temporal SAR for mapping the inventory of coseismic landslides is infrequent and encounters constraints due to several limitations. In this study, we analyzed the amplitude feature and polarimetric decomposition of multiple ground categories in a full PolSAR image, and proposed an automated method to accurately identify coseismic landslides using a single-temporal full PolSAR image. The coseismic landslide inventory following the Jiuzhaigou Earthquake was mapped and validated using high-resolution UAV images. Detailed analysis was conducted to identify error sources leading to omissions and false positives. Additionally, we evaluated various machine learning models to compare their performance with our proposed method. Finally, we conducted a comprehensive discussion on the strengths and weaknesses of different data types (PolSAR, optical satellite, and UAV) for coseismic landslide identification. Our results indicate that the proposed PolSAR-based method achieves high accuracy in coseismic landslide inventory mapping, offering an effective solution for timely post-earthquake emergency responses in complex environments and all weather conditions in the future.

Determination of alternative forest road routes using produced landslide susceptibility maps: A case study of Tonya (Trabzon), Türkiye

Firstly, Landslide Susceptibility Maps of the study area were produced using Frequency Ratio and Modified Information Value models. Nine factors were defined and the Landslide Inventory Map was used to produce these maps. In the Landslide Susceptibility Maps obtained from the Frequency Ratio and Modified Information Value models, the total percentages of high and very high-risk areas were calculated as 10% and 15%, respectively. To determine the accuracy of the produced Landslide Susceptibility Maps, the success and the prediction rates were calculated using the receiver operating curve. The success rates of the Frequency Ratio and Modified Information Value models were 82.1% and 83.4%, respectively, and the prediction rates were 79.7% and 80.9%. In the second part of the study, the risk situations of 125 km of forest roads were examined on the map obtained by combining the Landslide Susceptibility Maps. As a result of these investigations, it was found that 4.28% (5.4 km) of the forest roads are in very high areas and 4.27% (5.3 km) in areas with high landslide risk areas. In the last part of the study, as an alternative to forest roads with high and very high landslide risk, 9 new forest road routes with a total length of 5.77 km were produced by performing costpath analysis in with geographic information systems.