Search Results (174)

This study applies the Weighted Overlay Analysis (WOA) method integrated with GIS to assess landslide susceptibility along Al Figrah Road in Al-Madinah Al-Munawarah, western Saudi Arabia. Seven key conditioning factors, elevation, slope, aspect, drainage density, lithology, soil type, and precipitation were integrated using high-resolution remote sensing data and expert-assigned weights. The output susceptibility map categorized the region into three zones: low (93.5 million m²), moderate (271.2 million m²), and high risk (33.1 million m²). Approximately 29% of the road corridor lies within the low-risk zone, 48% in the moderate zone, and 23% in the high-risk zone. Ten critical sites with potential landslide activity were detected along the road, correlating well with the high-risk zones on the map. Structural weaknesses in the area, such as faults, joints, foliation planes, and shear zones in both igneous and metamorphic rock units, were key contributors to slope instability. The findings offer practical guidance for infrastructure planning and geohazard mitigation in arid, mountainous environments and demonstrate the applicability of WOA in data-scarce regions. Full article

This research paper presents an assessment of geohazards on Lefkas Island, Greece, using Geographic Information System (GIS) technology to map risk and enhance public awareness through an interactive web application. Natural hazards such as landslides, floods, wildfires, and desertification threaten both the safety of residents and the island’s tourism-dependent economy, particularly due to its seismic activity and Mediterranean climate. By combining the Sendai Framework for Disaster Risk Reduction with GIS capabilities, we created detailed hazard maps that visually represent areas of susceptibility and provide critical insights for local authorities and the public. The web application developed serves as a user-friendly platform for disseminating hazard information and educational resources, thus promoting community preparedness and resilience. The findings highlight the necessity for proactive land management strategies and community engagement in disaster risk reduction efforts. This study underscores GIS’s pivotal role in fostering informed decision making and enhancing the safety of Lefkas Island’s inhabitants and visitors in the face of environmental challenges. Full article

Active continental margins, generally characterized by narrow shelves incised by canyons, are pervasively shaped by submarine landslides that can occur near coastal areas. In this context, creating landslide susceptibility maps is the first step in landslide geohazard assessment. This paper focuses on shallow-water submarine landslides along the Capo d’Orlando continental margin and presents a related susceptibility map using the Weight of Evidence method. This method quantifies the strength of the association between a landslide inventory and predisposing factors. A geomorphological analysis of the continental shelf and upper slope yielded a landslide inventory of 450 initiation points, which were combined with five specifically selected preconditioning factors. The results revealed that the most favourable conditions for shallow-water landslides include slopes between 5° and 15°, proximity to faults (<1 km), proximity to river mouths (<2 km), the presence of consolidated lithologies and sandy terraces, and slopes facing NE and E. The landslide susceptibility map indicates that susceptible areas are in canyon heads and flanks, as well as in undisturbed slope portions near canyon heads where retrogressive landslides are likely. The model results are robust (AUC = 0.88), demonstrating that this method can be effectively applied in areas with limited geological data for preliminary susceptibility assessments. Full article

Data bias in geohazard artificial intelligence (AI) systems, particularly class distribution imbalances, critically undermines the reliability of landslide detection models. While active learning (AL) offers promise for mitigating annotation costs and addressing data biases, the interplay between landslide class proportions and AL efficiency remains poorly quantified; additionally, self-optimizing mechanisms to adaptively manage class imbalances are underexplored. This study bridges these gaps by rigorously evaluating how landslide-to-non-landslide ratios (1:1, 1:12, and 1:30) influence the effectiveness of a widely used AL strategy—margin sampling. Leveraging open-source landslide inventories, we benchmark margin sampling against random sampling using the area under the receiver operating characteristic curve (AUROC) and partial AUROC while analyzing spatial detection accuracy through classification maps. The results reveal that margin sampling significantly outperforms random sampling under severe class imbalances (1:30), achieving 12–18% higher AUROC scores and reducing false negatives in critical landslide zones. In balanced scenarios (1:1), both strategies yield comparable numerical metrics; however, margin sampling produces spatially coherent detections with fewer fragmented errors. These findings indicate that regardless of the landslide proportion, AL enhances the generalizability of landslide detection models in terms of predictive accuracy and spatial consistency. This work also provides actionable guidelines for deploying adaptive AI systems in data-scarce, imbalance-prone environments. Full article

Urban expansion in coastal areas involves infrastructure development, industrial growth, and mining activities. These coastal environments face various environmental and geological hazards that require geo-engineers to devise solutions. An integrated geophysical approach aims to address such complex challenges as sea level rise, sea water intrusion, shoreline erosion, landslides and previous anthropogenic activity in coastal settings. In this study, the proposed methodology involves the systematic application of geophysical methods (FDEM, 3D GPR, 3D ERT, seismic), starting with a broad-scale survey and then proceeding to a localized exploration, in order to identify lithostratigraphy, bedrock depth, sea water intrusion and detect anthropogenic buried features. The critical aspect is to leverage the unique strengths and limitations of each method within the coastal environment, so as to derive valuable insights for survey design (extension and orientation of measurements) and data interpretation. The coastal zone of Throrikos valley, Attica, Greece, serves as the test site of our geophysical investigation methodology. The planning of the geophysical survey included three phases: The application of frequency-domain electromagnetic (FDEM) and 3D ground penetrating radar (GPR) methods followed by a 3D electrical resistivity tomography (ERT) survey and finally, using the seismic refraction tomography (SRT) and multichannel analysis of surface waves (MASW). The FDEM method confirmed the geomorphological study findings by revealing the paleo-coastline, superficial layers of coarse material deposits and sea water preferential flow due to the presence of anthropogenic buried features. Subsequently, the 3D GPR survey was able to offer greater detail in detecting the remains of an old marble pier inland and top layer relief of coarse material deposits. The 3D ERT measurements, deployed in a U-shaped grid, successfully identified the anthropogenic feature, mapped sea water intrusion, and revealed possible impermeable formation connected to the bedrock. ERT results cannot clearly discriminate between limestone or deposits, as sea water intrusion lowers resistivity values in both formations. Finally, SRT, in combination with MASW, clearly resolves this dilemma identifying the lithostratigraphy and bedrock top relief. The findings provide critical input for engineering decisions related to foundation planning, construction feasibility, and preservation of coastal infrastructure. The methodology supports risk-informed design and sustainable development in areas with both natural and cultural heritage sensitivity. The applied approach aims to provide a complete information package to the modern engineer when faced with specific challenges in coastal settings. Full article

Understanding the relationship between geological and geomorphological processes is essential for reconstructing landscape evolution. This study examines how geology and geomorphology shape landscape development in central Brazil, focusing on the Natividade Group area. Sentinel-2 and SRTM data were integrated with geospatial analyses to produce two key maps: (i) a pedo-geomorphological map, classifying landforms and soil–landscape relationships, and (ii) a predictive geological–geomorphological map, based on a machine learning-based prediction of geomorphic units, which employed a Random Forest classifier trained with 15 environmental predictors from remote sensing datasets. The predictive model classified the landscape into six classes, revealing the ongoing interactions between geology, geomorphology, and surface processes. The pedo-geomorphological map identified nine pedoforms, grouped into three slope classes, each reflecting distinct lithology–relief–soil relationships. Resistant lithologies, such as quartzite-rich metasedimentary rocks, are associated with shallow, poorly developed soils, particularly in the Natividade Group. In contrast, phyllite, schist, and Paleoproterozoic basement rocks from the Almas and Aurumina Terranes support deeper, more weathered soils. These findings highlight soil formation as a critical indicator of landscape evolution in tropical climates. Although the model captured geological and geomorphological patterns, its moderate accuracy suggests that incorporating geophysical data could enhance the results. The landscape bears the imprint of several tectonic events, including the Rhyacian amalgamation (~2.2 Ga), Statherian taphrogenesis (~1.6 Ga), Neoproterozoic orogeny (~600 Ma), and the development of the Sanfranciscana Basin (~100 Ma). The results confirm that the interplay between geology and geomorphology significantly influences landscape evolution, though other factors, such as climate and vegetation, also play crucial roles in landscape development. Overall, the integration of remote sensing, geospatial analysis, and machine learning offers a robust framework for interpreting landscape evolution. These insights are valuable for applications in land-use planning, environmental management, and geohazard assessment in geologically complex regions. Full article

Slope-related geological disasters occur frequently in various countries, posing significant threats to surrounding infrastructure, ecosystems, and human lives and property. Traditional manual monitoring methods for slope hazards are inefficient and have limited coverage. To enhance the monitoring and analysis of geological hazards, a study was conducted on the legacy slopes of an abandoned quarry in Jinan, Shandong Province, China. High-resolution images of the slopes were captured using unmanned aerial vehicle (UAV) phase tilt photogrammetry, and three-dimensional models were subsequently constructed. Software tools, including LiDAR360 5.2 and ArcMap 10.8, were employed to extract slope geological information, identify disaster-prone areas, and conduct stability analyses. The Analytic Hierarchy Process (AHP) was employed to further evaluate the stability of hazardous slopes. The results reveal the presence of two geohazard-prone areas in the study area. Geological analysis shows that both areas exhibit instability, with a high susceptibility to small-scale rockfalls and landslides. The integration of UAV remote sensing technology with AHP represents a novel approach, and the combination of multiple analytical methods enhances the accuracy of slope stability assessments. Full article

The 2012 Emilia earthquake highlighted the vulnerability of fortified architecture. Based on the observed seismic behaviors, this research proposes a GIS geodatabase, designed with a proactive approach, for the prediction and prevention—at a territorial scale—of the most frequent damage mechanisms of the investigated typology. The designed geo-database allows for the identification of possible correlations between constructive features and the occurrence of damage, through statistical and geo-referenced analysis. Moreover, the designed geodatabase, by enabling the comparison of the damage level data with the seismic action of the site, through INGV (National Institute of Geophysics and Volcanology) shakemaps, allowed the definition of experimental fragility curves, for three of the most common damage mechanisms. By applying these functions to castles in the province of Parma, it was possible to define future seismic risk scenarios for the mechanisms considered, thanks to the use of the seismic hazard map. Therefore, the described methodology could be functional to identify the most urgent and high-priority interventions in order to optimize the management of economic resources. The final aim is to promote the application of the concept of minimum intervention, and more in general to preserve the architectural heritage, avoiding emergency interventions and aiming instead to apply planned conservation strategies. Full article

Aba Tibetan and Qiang Autonomous Prefecture (Aba Prefecture), located in Southwest China, has complex geological conditions and frequent seismic activity, facing an increasing landslide risk that threatens the safety of local communities. This study aims to improve the regional geohazard database by identifying slow-moving landslides in the area. We combined Stacking Interferometric Synthetic Aperture Radar (Stacking-InSAR) technology for deformation detection, optical satellite imagery for landslide boundary mapping, and field investigations for validation. A total of 474 slow-moving landslides were identified, covering an area of 149.84 km², with landslides predominantly concentrated in the river valleys of the southern and southeastern regions. The distribution of these landslides is strongly influenced by bedrock lithology, fault distribution, topographic features, proximity to rivers, and folds. Additionally, 236 previously unknown landslides were detected and incorporated into the local geohazard database. This study provides important scientific support for landslide risk management, infrastructure planning, and mitigation strategies in Aba Prefecture, offering valuable insights for disaster response and prevention efforts. Full article

Landslides are one of the most severe geohazards globally, causing extreme financial and social losses. While InSAR time-series analyses provide valuable insights into landslide detection, mapping, and monitoring, AI is also implemented in a variety of geohazards, including landslides. In the present study, a machine learning (ML) landslide susceptibility map is proposed that integrates the geotectonic units of Greece and incorporates various sources of landslide data. Satellite data from Persistent Scatterer Interferometry analysis, validated by geotechnical experts, resulted in an extremely large dataset of more than 3000 landslides in an area of interest, including the most landslide-prone area in Greece. The gradient-boosted decision tree was employed in the landslide susceptibility mapping. The model was trained on three geotectonic units and five prefectures of Western Greece and performed well in predicting landslide events. Finally, a SHAP (SHapley Additive exPlanations) analysis verified that precipitation and geology, which are the main landslide-triggering and preparatory factors, respectively, in Greece, positively affected landslide characterization. The innovation of the proposed research lies in the uniqueness of this newly created dataset, comprising a remarkably large number of landslide and non-landslide locations in Western Greece. By adopting a strict machine learning methodology, the spatial autocorrelation effect, which is overlooked in similar studies, was reduced. Also, leveraging the unique features of the geological formations, the model was trained to incorporate differences in the landslide susceptibility of formations located in different geotectonic units with variant geotechnical characteristics. The proposed approach facilitates the generalization of the model and sets a strong base for the creation of a national-scale landslide susceptibility mapping and forecasting system. Full article

China’s first L-band fully polarimetric Synthetic Aperture Radar (SAR) constellation, LuTan-1 (LT-1), was designed for terrain mapping and geohazard monitoring. This study evaluates LT-1’s capability in identifying landslides in the southern hilly regions of China, focusing on Longshan County, Hunan Province. Using both ascending and descending orbit data from LT-1, we conducted landslide identification experiments. First, deformation was obtained using Differential Interferometric SAR (D-InSAR) technology, and the deformation rates were derived through the Stacking technique. A landslide identification method that integrates C-index, slope, and ascending/descending orbit deformation information was then applied. The identified landslides were validated against existing geohazard points and medium-to-high-risk slope and gully unit data. The experimental results indicate that LT-1-ascending orbit data identified 88 landslide areas, with 39.8% corresponding to geohazard points and 65.9% within known slope units. Descending orbit data identified 90 landslide areas, with 37.8% matching geohazard points and 61.1% within known slope units. The identification results demonstrated good consistency with existing data. Comparative analysis with Sentinel-1 data revealed that LT-1’s combined ascending and descending orbit data outperformed Sentinel-1’s single ascending orbit data. LT-1’s L-band characteristics, comprehensive ascending and descending orbit coverage, and high-precision deformation detection make it highly promising for landslide identification in the southern hilly regions. This study underscores LT-1’s robust technical support for early landslide identification, highlighting its potential to enhance geohazard monitoring and mitigate risks in challenging terrains. Full article

Land subsidence, a slow-onset geohazard, poses a severe threat to cities worldwide. However, the lack of quantification in terms of intensity, probability, and hazard zoning complicates the assessment and understanding of the land subsidence risk. In this study, we employ a weighted Bayesian model to explicitly present the spatial distribution of land subsidence probability and map hazard zoning in Shanghai. Two scenarios based on distinct aquifers are analyzed. Our findings reveal the following: (1) The cumulative land subsidence probability density functions in Shanghai follow a skewed distribution, primarily ranging between 0 and 50 mm, with a peak probability at 25 mm for the period 2017–2021. The proportions of cumulative subsidence above 100 mm and between 50 and 100 mm are significantly lower for 2017–2021 compared to those for 2012–2016, indicating a continuous slowdown in land subsidence in Shanghai. (2) Using the cumulative subsidence from 2017–2021 as a measure of posterior probability, the probability distribution of land subsidence under the first scenario ranges from 0.02 to 0.97. The very high probability areas are mainly located in the eastern peripheral regions of Shanghai and the peripheral areas of Chongming District. Under the second scenario, the probability ranges from 0.04 to 0.98, with high probability areas concentrated in the eastern coastal area of Pudong District and regions with intensive construction activity. (3) The Fit statistics for Scenario I and Scenario II are 67% and 70%, respectively, indicating a better fit for Scenario II. (4) High-, medium-, low-, and very low-hazard zones in Shanghai account for 14.2%, 48.7%, 23.6%, and 13.5% of the city, respectively. This work develops a method based on the weighted Bayesian model for assessing and zoning land subsidence hazards, providing a basis for land subsidence risk assessment in Shanghai. Full article

Floods are considered to be among the most dangerous and destructive geohazards, leading to human victims and severe economic outcomes. Yearly, many regions around the world suffer from devasting floods. The estimation of flood aftermaths is one of the high priorities for the global community. One such flood took place in northern Libya in September 2023. The presented study is aimed at evaluating the flood aftermath for Derna city, Libya, using high resolution GEOEYE-1 and Sentinel-2 satellite imagery in Google Earth Engine environment. The primary task is obtaining and analyzing data that provide high accuracy and detail for the study region. The main objective of study is to explore the capabilities of different algorithms and remote sensing datasets for quantitative change estimation after the flood. Different supervised classification methods were examined, including random forest, support vector machine, naïve-Bayes, and classification and regression tree (CART). The various sets of hyperparameters for classification were considered. The high-resolution GEOEYE-1 images were used for precise change detection using image differencing (pixel-to-pixel comparison and geographic object-based image analysis (GEOBIA) for extracting building), whereas Sentinel-2 data were employed for the classification and further change detection by classified images. Object based image analysis (OBIA) was also performed for the extraction of building footprints using very high resolution GEOEYE images for the quantification of buildings that collapsed due to the flood. The first stage of the study was the development of a workflow for data analysis. This workflow includes three parallel processes of data analysis. High-resolution GEOEYE-1 images of Derna city were investigated for change detection algorithms. In addition, different indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed NDVI (TNDVI), and normalized difference moisture index (NDMI)) were calculated to facilitate the recognition of damaged regions. In the final stage, the analysis results were fused to obtain the damage estimation for the studied region. As the main output, the area changes for the primary classes and the maps that portray these changes were obtained. The recommendations for data usage and further processing in Google Earth Engine were developed. Full article

Studying natural hazards in the context of human-induced landscape transformation is complex, especially in regions with limited information. The narratives of the elderly can play a role in filling these knowledge gaps at the multi-decadal timescale. Here, we build upon a citizen-based elderly approach to understanding natural hazard patterns and landscape transformation in a tropical mountainous environment, the Kigezi Highlands (SW Uganda). We engaged 98 elderly citizens (>70 years old) living in eight small watersheds with different characteristics. Through interviews and focus group discussions, we reconstructed historical timelines and used participatory mapping to facilitate the interview process. We cross-checked the information of the elderly citizens with historical aerial photographs, archives, and field visits. Our results show that major land use/cover changes are associated with a high population increase over the last 80 years. We also evidence an increase in reported natural hazard events such as landslides and flash floods from the 1940s until the 1980s. Then, we notice a stabilization in the number of hazard events per decade, although the two most impacted decades (1980s and 2000s) stand out. Despite this new information, an increase in natural hazard frequency due to land use/cover change cannot yet be quantitatively validated, especially when the probable modulator effect of climate variability is considered. Nevertheless, the increase in the exposure of a vulnerable population to natural hazards is clear, and population growth together with poor landscape management practices are the key culprits that explain this evolution. This study demonstrates the added value of historical narratives in terms of understanding natural hazards in the context of environmental changes. This insight is essential for governments and non-governmental organizations for the development of policies and measures for disaster risk reduction that are grounded in the path dependence of local realities. Full article

Submarine slides, gas hydrates, shallow gas, and volcanoes considered to be potential geohazards have been well delineated using three-dimensional (3D) seismic data and well log data in the Pearl River Mouth Basin, South China Sea. Seismic characteristics, distribution maps, and controlling factors of these geohazards have been well analyzed showing the influence of regional tectonics and sedimentary processes. Recently, shallow gas and gas hydrates are confirmed by drilling expeditions, which are considered important unconventional resources. Moreover, the mapped features of various geohazards show the spatial overlays and that they are connected each other. To delineate well the relationships between gas hydrates, shallow gas, and the potential geological features such as submarine slides, gas chimneys, faults, and volcanoes, the seismic attributes and interpretations are displayed using 3D seismic data to show the interplay between them. Gas hydrates and shallow gas occur and are widely distributed above sills, volcanoes, gas chimneys, and faults within the submarine slides and migrating canyon because large amount of hydrocarbon gas can migrate from the deep layer to the shallow layer along different pathways. This study aims to show the correlation among various geological bodies and their effects on shallow gas and gas hydrate distributions. Full article