Search Results (155)

Landslide susceptibility modelling represents an efficient approach to enhance disaster management and mitigation strategies. The focus of this paper lies in the development of a landslide susceptibility evaluation in northwestern Tunisia using the Analytical Hierarchy Process (AHP) and Artificial Neural Network (ANN) approaches. The used database covers 286 landslides, including ten landslide factor maps: rainfall, slope, aspect, topographic roughness index, lithology, land use and land cover, distance from streams, drainage density, lineament density, and distance from roads. The AHP and ANN approaches were applied to classify the factors by analyzing the correlation relationship between landslide distribution and the significance of associated factors. The Landslide Susceptibility Index result reveals five susceptible zones organized from very low to very high risk, where the zones with the highest risks are associated with the combination of extreme amounts of rainfall and steep slope. The performance of the models was confirmed utilizing the area under the Relative Operating Characteristic (ROC) curves. The computed ROC curve (AUC) values (0.720 for ANN and 0.651 for AHP) convey the advantage of the ANN method compared to the AHP method. The overlay of the landslide inventory data locations of historical landslides and susceptibility maps shows the concordance of the results, which is in favor of the established model reliability. Full article

On 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention. Rapid acquisition of landslide inventories, distribution patterns, and key controlling factors is critical for post-disaster emergency response and reconstruction. Based on high-resolution Planet satellite imagery, landslide areas in Jiangwan Town were automatically extracted using the Normalized Difference Vegetation Index (NDVI) differential method, and a detailed landslide inventory was compiled. Combined with terrain, rainfall, and geological environmental factors, the spatial distribution and causes of landslides were analyzed. Results indicate that the extreme rainfall induced 1426 landslides with a total area of 4.56 km², predominantly small-to-medium scale. Landslides exhibited pronounced clustering and linear distribution along river valleys in a NE–SW orientation. Spatial analysis revealed concentrations on slopes between 200–300 m elevation with gradients of 20–30°. Four machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to assess landslide susceptibility mapping (LSM) accuracy. RF and XGBoost demonstrated superior performance, identifying high-susceptibility zones primarily on valley-side slopes in Jiangwan Town. Shapley Additive Explanations (SHAP) value analysis quantified key drivers, highlighting elevation, rainfall intensity, profile curvature, and topographic wetness index as dominant controlling factors. This study provides an effective methodology and data support for rapid rainfall-induced landslide identification and deep learning-based susceptibility assessment. Full article

Understanding and mapping forest fire vulnerability is essential for informed landscape management and disaster risk reduction, especially in the context of increasing anthropogenic and climatic pressures. This study aims to model and spatially predict forest fire vulnerability across Romania using two machine learning algorithms: MaxEnt and XGBoost. We integrated forest fire occurrence data from 2006 to 2024 with a suite of climatic, topographic, ecological, and anthropogenic predictors at a 250 m spatial resolution. MaxEnt, based on presence-only data, achieved moderate predictive performance (AUC = 0.758), while XGBoost, trained on presence–absence data, delivered higher classification accuracy (AUC = 0.988). Both models revealed that the impact of environmental variables on forest fire occurrence is complex and heterogeneous, with the most influential predictors being the Fire Weather Index, forest fuel type, elevation, and distance to human proximity features. The resulting vulnerability and uncertainty maps revealed hotspots in Sub-Carpathian and lowland regions, especially in Mehedinți, Gorj, Dolj, and Olt counties. These patterns reflect historical fire data and highlight the role of transitional agro-forested landscapes. This study delivers a replicable, data-driven approach to wildfire risk modelling, supporting proactive management and emphasising the importance of integrating vulnerability assessments into planning and climate adaptation strategies. Full article

Landscape visualization plays a crucial role in various scientific and artistic fields, including geography, environmental sciences, and digital arts. Recent advancements in computer graphics have enabled more sophisticated approaches to landscape representation. The integration of artificial intelligence (AI) image generation has further improved accessibility for researchers, allowing efficient creation of landscape visualizations. This study presents a comprehensive workflow for the rapid and cost-effective generation of photorealistic still images. The methodology combines AI applications, computational techniques, and photographic methods to reconstruct the historical landscapes of the Great Hungarian Plain, one of Europe’s most significantly altered regions. The most accurate and visually compelling results are achieved by using historical maps and drone imagery as compositional and stylistic references, alongside a suite of AI tools tailored to specific tasks. These high-quality landscape visualizations offer significant potential for scientific research and public communication, providing both aesthetic and informative value. The article, which primarily presents a methodological description, does not contain numerical results. To test the method, we applied a procedure: we ran the algorithm on a current topographic map of a sample area and compared the resulting image with the view model provided by Google Earth. Full article

Augmented Reality (AR) is an innovative tool in education, enhancing learning experiences across multiple domains. This literature review explores the application of AR in education, with a particular focus on geographical learning. The study begins by tracing the historical development of AR, distinguishing it from Virtual Reality (VR) and highlighting its advantages in an educational context. The integration of AR into learning environments has been shown to improve engagement, comprehension of abstract concepts, and collaboration among students. The use of AR in geographical education through interactive applications, such as GeoAR and AR Sandbox, improves the exploration of spatial relationships, topographic maps, and environmental changes. Studies demonstrate that AR enhances students’ ability to recall information and understand geographical processes more effectively than with traditional methods. Furthermore, AR Sandbox implementations, including Illuminating Clay, SandScape, and AR Sandbox, are analyzed and compared. The paper also discusses future developments in AR for geography education for AR Sandbox, such as the integration of a mobile application for extended learning and improving computing solutions through Raspberry Pi. These advancements aim to make AR systems more accessible and to increase the benefits to both students and professors. Full article

Landslides triggered by extreme rainfall often cause severe casualties and property losses. Therefore, it is essential to accurately assess and predict building vulnerability under landslide scenarios for effective risk mitigation. This study proposed a quantitative framework for vulnerability assessments of structures. It integrated extreme rainfall analysis, landslide kinematic assessment, and the dynamic response of structures. The study area is located in the northern mountainous region of Tianjin, China. It lies within the Yanshan Mountains, serving as a key transportation corridor linking North and Northeast China. The Sentinel-1A satellite imagery consisting of 77 SLC scenes (from October 2014 to November 2023) identified a slow-moving landslide in the region by using the SBAS-InSAR technique. High-resolution topographic data of the slope were first acquired through UAV-based remote sensing. Next, historical rainfall data from 1980 to 2017 were analyzed. The Gumbel distribution was used to determine the return periods of extreme rainfall events. The potential slope failure range and kinematic processes of the landslide were then simulated by using numerical simulations. The dynamic responses of buildings impacted by the landslide were modeled by using ABAQUS. These simulations allowed for the estimation of building vulnerability and the generation of vulnerability maps. Results showed that increased rainfall intensity significantly enlarged the plastic zone within the slope. This raised the likelihood of landslide occurrence and led to more severe building damage. When the rainfall return period increased from 50 to 100 years, the number of damaged buildings rose by about 10%. The vulnerability of individual buildings increased by 10% to 15%. The maximum vulnerability value increased from 0.87 to 1.0. This model offers a valuable addition to current quantitative landslide risk assessment frameworks. It is especially suitable for areas where landslides have not yet occurred. Full article

Historical maps contain valuable topographic information, including altimetry in the form of annotated elevation contours. These contours are essential for understanding past terrain configurations, particularly in areas affected by human activities such as mining or dam construction. To make this data usable in modern GIS applications, the contours must be vectorized—a process that often requires extensive manual work due to noise, inconsistent symbology, and topological disruptions like annotations or sheet boundaries. In this study, we apply a convolutional neural network (U-Net) to improve the automation of this vectorization process. Leveraging a recent benchmark for historical map vectorization, our method demonstrates increased robustness to disruptive factors and reduces the need for manual corrections. Full article

Floods and landslides are two distinct natural phenomena influenced by different conditioning factors, though some environmental triggers may overlap. This study applied eXtreme Gradient Boosting (XGBoost) to develop susceptibility maps for both phenomena, using a unified approach based on the same geospatial predictors. The approach integrated topographical, geological, and remote sensing datasets. Flood event data were collected from institutional sources using multi-source and high-resolution remotely sensed data. The landslide inventory was compiled based on historical records and geomorphological analysis. Key conditioning factors such as elevation, slope, lithology, and land cover were analyzed to identify areas prone to floods and landslides. The methodology was applied to the Basento River basin in Southern Italy, a region frequently impacted by both hazards, to assess its vulnerability and inform risk management strategies. While flood susceptibility is primarily associated with low-lying areas near river networks, landslides are more influenced by steep slopes and geological instability. The XGBoost model achieved a classification accuracy close to 1 for flood-prone areas and 0.92 for landslide-prone areas. Results showed that flood susceptibility was primarily associated with low Elevation and Relative Elevation, and high Drainage Density, whereas landslide susceptibility was more influenced by a broader and balanced set of factors, including Elevation, Drainage Density, Relative Elevation, Distance and Lithology. The resulting susceptibility maps offered critical approaches for land use planning, emergency management, and risk mitigation. Overall, the results demonstrated the effectiveness of XGBoost in multi-hazard assessments, offering a scalable and transferable approach for similar at-risk regions worldwide. Full article

Post-industrial and post-mining areas are an important element of cities historically associated with industrial activity. The transformation of degraded areas is a challenge for spatial policy, which is characterized by a substantial impact on the cultural heritage of mining and industry. The case of Piekary Śląskie shows the consequences of deindustrialization, which leads to the degradation of urban space and requires innovative revitalization strategies considering the principles of sustainable development and the concept of blue–green infrastructure. Archived topographic maps and current interactive maps of the study city were used in a spatial data analysis. The aim was to determine the directions of the spatial development of post-industrial and post-mining areas using the example of a medium-sized city located in the core of the Katowice conurbation, while considering the role of blue–green infrastructure in the revitalization process. Integrating blue–green infrastructure into the city’s planning documents may serve as a model for other urban areas, highlighting the synergy benefits between urban development and environmental protection. Such solutions support the development of a green economy to improve residents’ living conditions and increase the city’s competitiveness in the region. The specific examples of the revitalization of the areas in the Andaluzja and Julian mines and the reclamation of the brickyard in the area of Kozłowa Góra in Piekary Śląskie show that a multifunctional approach to revitalization contributes to the harmonious development of urban spaces. Full article

Understanding glacier and climate variations since pre-Industrial times is crucial for evaluating the present-day glacier response to climate change. Here, we focus on twelve small glaciers (≤2.0 km²) on both the northern and southern slopes of the Greater Caucasus to assess post-Little Ice Age glacier–climate fluctuations in this region. We reconstructed the Little Ice Age glacier extent using a manual detection method based on moraines. More recent glacier fluctuations were reconstructed using historical topographical maps and satellite imagery. Digital elevation models were used to estimate the topographic characteristics of glaciers. We also used the accumulation area ratio (AAR) method and a regional temperature lapse rate to reconstruct glacier snowlines and corresponding temperatures since the 1820s. The results show that all selected glaciers have experienced area loss, terminus retreat, and equilibrium line altitude (ELA) uplift over the last 200 years. The total area of the glaciers has decreased from 19.1 ± 0.9 km² in the 1820s to 9.7 ± 0.2 km² in 2020, representing a −49.2% loss, with an average annual reduction of −0.25%. The most dramatic reduction occurred between the 1960s and 2020, when the glacier area shrank by −35.5% or −0.59% yr⁻¹. The average terminus retreat for all selected glaciers was −1278 m (−6.4 m/yr⁻¹) during the last 200 years, while the average retreat over the past 60 years was −576 m (−9.6 m/yr⁻¹). AAR-based (0.6 ± 0.05) ELA reconstructions from all twelve glaciers suggest that the average ELA in the 1820s was about 180 m lower (3245 ± 50 m a.s.l.) than today (3425 ± 50 m a.s.l.), corresponding to surface air temperatures <1.1 ± 0.3 °C than today (2001–2020). The largest warming occurred between the 1960s and today, when snowlines rose by 105 m and air temperatures increased by <0.6 ± 0.3 °C. This study represents a first attempt at using glacier evidence to estimate climate changes in the Caucasus region since the Little Ice Age, and it can be used as a baseline for future studies. Full article

The integration of AI tools like ChatGPT in education has sparked debates on their benefits and limitations, particularly in subjects requiring region-specific knowledge. This study examines ChatGPT’s ability to generate accurate and contextually rich responses to assignments in Romanian history and geography, focusing on topics with limited digital representation. Using a document-based analysis, this study compared ChatGPT’s responses to local archival sources, monographs, and topographical maps, assessing coverage, accuracy, and local nuances. Findings indicate significant factual inaccuracies, including misidentified Dacian tribes, incorrect historical sources, and geographic errors such as misplaced landmarks, elevation discrepancies, and incorrect infrastructure details. ChatGPT’s reliance on widely digitized sources led to omissions of localized details, highlighting a fundamental limitation when applied to non-digitized historical and geographic topics. These results suggest that while ChatGPT can be a useful supplementary tool, its outputs require careful verification by educators to prevent misinformation. Future research should explore strategies to improve AI-generated educational content, including better integration of regional archives and AI literacy training for students and teachers. The study underscores the need for hybrid AI-human approaches in education, ensuring that AI-generated text complements rather than replaces verified academic sources. Full article

Badland morphologies are prominent examples of linear erosion occurring on clay-rich slopes and are critical hotspots for sediment production. Traditional field-based mapping of these features can be both time-consuming and costly, particularly over larger basins. This research proposes a novel methodology for assessing badland susceptibility through a multi-criteria decision-making framework known as the Analytical Hierarchy Process (AHP). This methodology, developed and tested in the Foglia River basin of the Marche region (Italy), facilitates the identification and mapping of badland areas. More in detail, our study resulted in the creation of a comprehensive badland inventory and susceptibility map for the 102 km² study area, identifying 276 badlands using a combination of satellite imagery, historical orthophotos, existing regional inventories, and field inspections. Key predisposing factors, including geological, land use, topographical, and hydrometric elements, were systematically analyzed using the AHP approach. The research findings indicate that badlands develop in medium to steep slopes oriented towards the southern quadrants and in proximity to watercourses; their formation is predominantly influenced by clayey–sandy lithology. The resulting inventory and susceptibility map serve as relevant tools for monitoring, preventing, and mitigating slope instability risks within the region. Full article

The closure of coal and lignite mines has the potential to result in long-term environmental risks and socio-economic issues. To solve these, this research aims to improve the hazard assessment and risk management of former mining regions in a European project funded by the Research Fund for Coal and Steel. A multidisciplinary approach integrated historic, geological, topographical, environmental, and socio-economic data to create a methodology to support stakeholders at different decision-making levels in risk assessment and possible mitigation. For this purpose, a spatial decision support system was developed using a multi-hazard, multi-risk methodology. The individual hazards (post-mining, natural, and technical) are weighted using expert knowledge, their interaction analyzed, and then combined into a spatial multi-hazard index. Together with the other risk factors of social vulnerability and exposure, a comprehensive spatial risk map can be created automatically for individual regions using open-source components. In addition, GIS and statistical tools enable further analysis and visualization for decision-making by the relevant stakeholders. The methodology was validated through the examination of a first case study conducted in the post-mining region of the southern Ruhr area in Germany. The methodology and tool created significant results in two test scenarios, and will be tested and improved using other European mining sites during the next stages of the project. Full article

Drought represents a recurring natural event that holds notable socio-economic and environmental consequences. This research aims to analyze drought patterns in Greece by employing the standardized precipitation index (SPI) and several vegetation indices within a Geographic Information System (GIS) framework. GIS is a potent tool for integrating geospatial data, encompassing climatic, topographic, and hydrological information, enabling a comprehensive assessment of drought conditions. By examining historical precipitation data, the SPI quantifies the severity and duration of drought relative to long-term average precipitation. In addition, the SPI is calculated from precipitation data from a total of 152 meteorological stations. Subsequently, geostatistical techniques are applied to generate drought maps (SPI 6- and 12-timescale) and to examine the secondary effects of drought on different land uses. Satellite data are utilized to calculate indices. This is completed using satellite data by calculating the corresponding indices such as the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). Drought maps extracted using these methods and based on indicators and remote sensing data are useful tools for policymakers, stakeholders, and water experts. The resulting drought maps, based on the indicators and remote sensing data, serve as valuable tools for policymakers and stakeholders. Full article

Mozambique Island, a UNESCO World Heritage property, faces significant challenges due to climate change and extreme weather events. This study proposes a comprehensive framework for assessing morphological vulnerabilities and enhancing urban resilience in this unique historical urban landscape. The research methodology involves a thorough analysis of historical cartography, urban evolution, topography, and vernacular architecture, combined with recent conservation assessments and case studies from other climate-vulnerable regions. This study reveals the island’s dual urban structure, comprising the Stone and Lime town and the Macuti town, each with distinct morphological characteristics and vulnerabilities. Historical maps and topographical analysis demonstrate how the island’s geography has shaped its urban development, with the Stone and Lime town built on higher ground and the Macuti town situated at or below sea level, increasing its flood risk. The research highlights the importance of integrating traditional knowledge with resilience strategies while respecting the authenticity and integrity of the World Heritage property. Key findings include the need for a GIS-based management tool for continuous conservation assessment, and the crucial role of community engagement in implementing resilience mechanisms. This study contributes to the broader discourse on cultural heritage as a contributor to architectural and urban resilience, offering valuable insights for other World Heritage properties facing similar climate challenges. The proposed framework emphasizes the importance of balancing heritage preservation with adaptive strategies, while enhancing the island’s resilience facing climate-related threats. Full article