Search Results (255)

Knowledge and perceptions are prerequisites for contributing to CC mitigation and adaptation. This paper developed a framework and a tool (scale) to capture farmers’ knowledge and perceptions of all aspects of CC. We involved 15 extremely qualified (those with PhD degrees in agriculture and allied disciplines and experience in scale construction and CC research) experts and 83 highly qualified (a minimum of a PhD degree in agriculture and allied fields was the prerequisite criterion for acting as a judge) judges in the construction of this scale. Further, we adopted factor analysis to draw valid conclusions. We proposed 138 items/statements related to 14 dimensions/issues (General, GHGs, Temperature, Rainfall, Agricultural emissions, shifting cultivation, rice cultivation, Mitigation, C-sequestration, Impact on Agriculture, Livestock, Wind, Natural disaster, Impact, and Adaptation) associated with agriculture and CC scenarios. Finally, 102 items/statements were retained with six indicators/dimensions. The results indicate that the scale explains 83% of variance. The scale is highly consistent (Cronbach alpha = 0.985) and widely applicable to future research and policy decisions. Further, the scale was adopted (with 100 respondents) to assess consistency and validity. Finally, the tool (scale) for assessing farmers’ knowledge and perceptions of CC was prepared for further use and replication. The policy and research system may adopt the framework and scale to assess stakeholders’ inclusive knowledge and perceptions of CC. The findings of this study may be helpful for policymakers, researchers, development workers, and extension functionaries. Full article

Cross-domain building segmentation in high-resolution remote sensing imagery underpins urban change monitoring, disaster assessment, and exposure mapping. However, differences in sensors, regions, and imaging conditions create structural and radiometric domain gaps that degrade model generalization. Most existing methods adopt model-centric domain adaptation with additional networks or losses, complicating training and deployment. We propose a data-centric framework, Hybrid Object-Based Augmentation and Histogram Matching (Hybrid OBA–HM), which improves cross-domain building segmentation without modifying the backbone architecture or using target-domain labels. The proposed framework comprises two stages: (i) object-based augmentation to increase structural diversity and building coverage, and (ii) histogram-based normalization to mitigate radiometric discrepancies across domains. Experiments on OpenEarthMap and cross-city transfer among three KOMPSAT-3A scenes show that Hybrid OBA–HM improves F1-scores from 0.808 to 0.840 and from 0.455 to 0.652, respectively, while maintaining an object-level intersection over union of 0.89 for replaced buildings. Domain-indicator analysis further reveals larger gains under stronger radiometric and geometric mismatches, indicating that the proposed framework strengthens cross-domain generalization and provides practical guidance by relating simple domain diagnostics (e.g., brightness/color and orientation mismatch indicators) to the expected benefits of augmentation and normalization when adapting to new domains. Full article

Floods are among the most destructive natural disasters, posing major challenges to human safety, property, and urban resilience. Effective communication of flood risk is therefore crucial for disaster preparedness and the sustainable management of smart cities. This study explores how interface design elements of flood hazard maps, including interaction modes and legend color schemes, influence users’ risk perception, decision support, and usability. An online questionnaire survey (N = 776) and a controlled 2 × 2 experiment (N = 40) were conducted to assess user comprehension, cognitive load, and behavioral responses when interacting with different visualization formats. Results show that slider-based interaction significantly reduces task completion and map-reading times compared with drop-down menus, enhancing usability and information efficiency. Multicolor legends, although requiring higher cognitive effort, improve users’ risk perception, engagement, and memory of flood-related information. These findings suggest that integrating cognitive principles into interactive design can enhance the effectiveness of digital disaster communication tools. By combining human–computer interaction, visual cognition, and smart governance, this study provides evidence-based design strategies for developing intelligent and user-centered flood hazard mapping systems. The proposed framework contributes to the advancement of smart urban resilience and supports the broader goal of building safer and more sustainable cities. Full article

Landslide disasters are complex spatiotemporal phenomena. Existing deep learning (DL) models for remote sensing (RS) image analysis primarily exploit shallow visual features, inadequately incorporating critical geological, geographical, and environmental knowledge. This limitation impairs detection accuracy and generalization, especially in complex terrains and diverse vegetation conditions. We propose Knowledge Graph Embedding in Swin Feature Pyramid Networks (KGE–SwinFpn), a novel RS landslide segmentation framework that integrates explicit domain knowledge with deep features. First, a comprehensive landslide knowledge graph is constructed, organizing multi-source factors (e.g., lithology, topography, hydrology, rainfall, land cover, etc.) into entities and relations that characterize controlling, inducing, and indicative patterns. A dedicated KGE Block learns embeddings for these entities and discretized factor levels from the landslide knowledge graph, enabling their fusion with multi-scale RS features in SwinFpn. This approach preserves the efficiency of automatic feature learning while embedding prior knowledge guidance, enhancing data–knowledge–model coupling. Experiments demonstrate significant outperformance over classic segmentation networks: on the Yuan-yang dataset, KGE–SwinFpn achieved 96.85% pixel accuracy (PA), 88.46% mean pixel accuracy (MPA), and 82.01% mean intersection over union (MIoU); on the Bijie dataset, it attained 96.28% PA, 90.72% MPA, and 84.47% MIoU. Ablation studies confirm the complementary roles of different knowledge features and the KGE Block’s contribution to robustness in complex terrains. Notably, the KGE Block is architecture-agnostic, suggesting broad applicability for knowledge-guided RS landslide detection and promising enhanced technical support for disaster monitoring and risk assessment. Full article

Timely and accurate urban change detection is vital for sustainable urban development, infrastructure management, and disaster response. Traditional two-dimensional approaches often overlook vertical and structural variations in dense urban areas. This study proposes a three-dimensional (3D) change detection framework that integrates high-resolution optical imagery and Digital Surface Models (DSMs) from two time points to capture both horizontal and vertical transformations. The method is based on a deep learning architecture combining a ResNet34 encoder with a UNet++ decoder, enabling the joint learning of spectral and elevation features. The research was carried out in two stages. First, a binary classification model was trained to detect areas of change and no-change, allowing direct comparison with conventional methods such as Principal Component Analysis (PCA), Change Vector Analysis (CVA) with thresholding, K-Means clustering, and Random Forest classification. In the second stage, a multi-class model was developed to categorize the types of structural changes, including new building construction, complete destruction, building height increase, and height decrease. Experiments conducted on a high-resolution urban dataset demonstrated that the proposed CNN-based framework significantly outperformed traditional methods, achieving an overall accuracy of 96.58%, an F1-score of 96.58%, and a recall of 96.7%. Incorporating DSM data notably improved sensitivity to elevation-related changes. Overall, the ResNet34–UNet++ architecture offers a robust and accurate solution for 3D urban change detection, supporting more effective urban monitoring and planning. Full article

In high-mountain canyon areas, complex geological environments lead to frequent cascading disasters with unclear triggering mechanisms, posing severe threats to human life and property. Existing knowledge graph research in geology predominantly focuses on single-hazard types or general geological entities, lacking structured modeling and specialized datasets for cascading disaster processes, particularly the evolutionary chains in high-mountain canyon settings. To address this gap, this study proposes a method for constructing a knowledge graph tailored to cascading disasters in high-mountain canyon regions. First, a three-layer schema framework—comprising concept, relation, and instance layers—was designed to systematically characterize the knowledge elements and evolutionary relationships of disaster chains. To address the lack of a knowledge dataset for cascade disasters, this paper integrates multi-source heterogeneous data to construct a high-mountain canyon cascading disasters entity–relation dataset (DCER-MC), providing a reliable benchmark for related tasks. Based on this dataset, we implemented the knowledge graph and conducted disaster chain analysis. Experiments and applications demonstrate that the constructed knowledge graph effectively supports structured storage, centralized management, and scenario-based application of regional cascading disaster information. The main contributions of this work are (1) proposing a targeted schema framework for cascading-disaster knowledge graphs; (2) releasing a specialized dataset for cascading disasters in high-mountain canyon regions; and (3) establishing a complete pipeline from data to knowledge to scenario-based services, offering a novel knowledge-driven paradigm for disaster chain risk identification, inference prediction, and emergency decision-making in these areas. Full article

The integration of Neural Radiance Field (NeRF)-based 3D reconstruction with text-guided diffusion models enables flexible editing of real-world scenes. However, for large-scale UAV-captured scenes, existing methods struggle to achieve strong semantic consistency (e.g., in local editing) and suffer from cross-view inconsistency, primarily due to the globally free generative behavior and the lack of scene continuity constraints in diffusion models. To address these issues, we propose the UAVEdit-NeRFDiff framework, which ensures the maintenance of overall symmetry by restricting the editing operations to the target region. First, we leverage both visual priors and semantic masks to achieve semantically consistent editing for key views, and then design Optimal Editing Propagation (OEP) and Progressive Inheritance Propagation (PIP) methods to achieve cross-view geometric consistency propagation for Single-View-Dependent Regions (SVDRs) and Multi-View-Dependent Regions (MVDRs). Finally, experiments on diverse editing tasks demonstrate our method’s superiority in semantic alignment, cross-view consistency, and visual fidelity on UAV scenes, with promising applications in weather and disaster scenario simulations. On the proposed TDB metric, our approach delivers more than 50% improvement over prior methods. To the best of our knowledge, this is the first text–visual bimodal-guided diffusion editing framework for NeRF-reconstructed UAV-captured scenes, offering a practical and effective route for related research. Full article

Internally displaced persons (IDPs) face severe physical, emotional, and social challenges due to conflict, climate change, and other crises. Ethiopia has the highest number of IDPs in Africa, primarily due to ethnic conflicts and climate-related disasters, placing them at a high risk for post-traumatic stress disorder (PTSD) and psychological distress (anxiety, emotional well-being, and depression, referred to as mental health (MH)). This study examines PTSD’s direct predictive role on IDPs’ (MH) in Debre Berhan Town, Ethiopia, the mediating role of psychological capital (PsyCap), and the moderating role of perceived social support (PSS). It also explores the interaction between PSS and PsyCap in the PTSD and MH relationship. A sample of 273 IDPs (129 females, 144 males) was selected using simple random sampling from a total population of 19,349 IDPs. Data were collected using validated instruments, including the PTSD Checklist-Civilian Version (PCL-C), PsyCap, PSS, and the General Health Questionnaire (GHQ). A structural equation modeling (SEM) analysis revealed that PTSD significantly and negatively predicts the MH of IDPs. Additionally, PsyCap positively influences their mental well-being and partially mediates the relationship between PTSD and depressive symptoms. Furthermore, PSS moderates the PTSD and MH relationship, reducing its negative impact. The finding concludes that despite PTSD directly predicting the MH of IDPs, PsyCap helps mitigate these effects. Key components of PsyCap, including hope, resilience, self-efficacy, and optimism, buffer the adverse effects of PTSD on MH. IDPs with stronger psychological resources are less likely to experience psychological distress. PSS further weakens PTSD’s negative impact, as individuals with higher PSS are less likely to suffer from trauma-related distress or depression after displacement. This study highlights the importance of PsyCap in enhancing the mental well-being of IDPs. Future research should expand on these findings and explore the integration of PsyCap-based interventions into IDP mental health programs. Strengthening social support can also provide vital support in helping IDPs cope with trauma and improve their overall psychological health. Full article

Rain-on-snow (ROS) events profoundly influence mixed rain–snow flooding and the water resource cycle. However, current research regarding ROS events remains predominantly reliant on existing datasets, lacking detailed controlled experiments under variable conditions. This study employed control variables and an orthogonal experimental design to conduct laboratory-controlled experiments simulating ROS events with different temperatures, rainfall intensities, and rainfall durations. Observations and analyses were performed on the snowmelt volumes during and after events. The results indicate that ROS events significantly accelerate snowmelt rates and increase total snowmelt volume. Under low-intensity ROS, snowmelt volume exhibits greater sensitivity to temperature changes. A temperature threshold exists between 2 °C and 6 °C; beyond this threshold, the melting rate accelerates and ablation volume increases. Under high-intensity ROS, rainwater becomes the dominant factor driving snowpack ablation. When rainfall intensity exceeds 60 mm·h⁻¹, it triggers a sharp increase in snowmelt volume. Concurrently, following an ROS event, snowpacks subjected to low-intensity rainfall exhibit a stronger rainwater retention capacity, an effect that becomes more pronounced at lower temperatures. Additionally, snowmelt volume increases with prolonged rainfall duration, with the increment in snowmelt volume attributable to extended rainfall time being greater under weaker rainfall intensities. These findings provide a scientific reference for better understanding ROS-related disasters mechanisms. Full article

Floods are among the most frequent and damaging natural hazards in the Mekong River Basin, where the interplay of monsoon-driven climate variability, complex topography, and rapid socio-economic change creates high exposure and vulnerability. This study presents a comprehensive assessment of flood disaster patterns, loss distribution, and regional disparities across five countries in the Lower Mekong Basin—Cambodia, Laos, Myanmar, Thailand, and Vietnam. Using multivariate spatiotemporal analysis based on EM-DAT, MRC, and national government datasets, the study quantifies flood frequency, casualties, and affected population to reveal cross-country differences in disaster impact and timing. Results show that while Vietnam and Thailand experience high flood frequency and storm-induced events, Laos and Cambodia face riverine flooding under constrained economic and infrastructural conditions. The findings highlight a basin-wide increase in flood frequency over recent decades, driven by climate change, land use transitions, and uneven development. The analysis identifies critical gaps in adaptive governance, particularly the need for dynamic policy frameworks that can adjust to spatial disparities in flood typologies (e.g., Vietnam’s storm floods vs. Cambodia’s riverine floods) and improve transboundary coordination of reservoir operations. Despite the region’s extensive reservoir capacity, most infrastructure prioritizes hydropower over flood mitigation. The study evaluates the role of regional cooperation frameworks such as the Lancang–Mekong Cooperation (LMC), demonstrating how strengthened institutional flexibility and knowledge-sharing mechanisms could enhance progress toward Sustainable Development Goals (SDGs) related to water governance (SDG 6), resilient infrastructure (SDG 9), and disaster risk reduction (SDG 11). By constructing the first integrated national-level flood disaster database for the basin and conducting comparative analysis across countries, this research provides empirical evidence to support differentiated yet coordinated flood risk governance strategies at both national and transboundary levels. Full article

Today’s rapidly increasing number and performance of Remotely Piloted Aircraft Systems (RPASs) and sensors allows for an innovative approach in monitoring, mitigating, and responding to natural disasters and risks. At present, there are 100s of different RPAS platforms and smaller and more affordable payload sensors. As natural disasters pose ever increasing risks to society and the environment, it is imperative that these RPASs are utilized effectively. In order to exploit these advances, this study presents the development and validation of a Natural Disaster Information System (NDIS), a geospatial decision-support framework for RPAS-based natural hazard missions. The system integrates a global geohazard database with specifications of geophysical sensors and RPAS platforms to automate mission planning in a generalized form. NDIS v1.0 uses decision tree algorithms to select suitable sensors and platforms based on hazard type, distance to infrastructure, and survey feasibility. NDIS v2.0 introduces a Random Forest method and a Critical Path Method (CPM) to further optimize task sequencing and mission timing. The latest version, NDIS v3.8.3, implements a staggered decision workflow that sequentially maps hazard type and disaster stage to appropriate survey methods, sensor payloads, and compatible RPAS using rule-based and threshold-based filtering. RPAS selection considers payload capacity and range thresholds, adjusted dynamically by proximity, and ranks candidate platforms using hazard- and sensor-specific endurance criteria. The system is implemented using ArcGIS Pro 3.4.0, ArcGIS Experience Builder (2025 cloud release), and Azure Web App Services (Python 3.10 runtime). NDIS supports both batch processing and interactive real-time queries through a web-based user interface. Additional features include a statistical overview dashboard to help users interpret dataset distribution, and a crowdsourced input module that enables community-contributed hazard data via ArcGIS Survey123. NDIS is presented and validated in, for example, applications related to volcanic hazards in Indonesia. These capabilities make NDIS a scalable, adaptable, and operationally meaningful tool for multi-hazard monitoring and remote sensing mission planning. Full article

Persons with disabilities are disproportionately and differentially impacted by climate change, particularly in low-income settings. Our novel study reports findings from a survey of 104 Nigerians with disabilities and focus groups; examines the climate change impacts perceived by persons with disabilities; enumerates the barriers to climate responses they experience; and identifies disability-inclusive key climate action priorities and climate solutions in Abia State, Nigeria. Our findings indicate that the dominant climate impacts perceived by respondents with disabilities were poverty, loss of agricultural productivity and livelihood, and effects on wellbeing. Climate response measures were predominantly inaccessible to participants with disabilities facing structural barriers including stigma and discrimination, a lack of meaningful inclusion in decision-making, and a scarcity of disability-inclusive climate resources. Key climate action priorities identified by respondents included advancing understanding of the disparate impact of climate change on persons with disabilities, promoting inclusive disaster risk reduction, centering and prioritizing disability equity within climate action, and enabling inclusive sustainable livelihoods. Experiential insights at the micro-level from persons with disabilities are vital to formulating climate-related policy and climate decision-making. We recommend innovative cross-cutting policies and interventions to repair structural disability discrimination and promote urgent inclusive climate action that benefits all of society. Full article

Climate change is associated with an increase in the frequency of extreme weather that threatens emotional well-being, with some research pointing to increased vulnerability among older adults. We investigated how age relates to depression and anxiety following adversities due to extreme weather or natural disaster. Socioemotional selectivity theory (SST) posits that older age buffers against emotional distress. The strength and vulnerability integration model (SAVI) posits that this age-related advantage is attenuated during periods of acute stress. Members (n = 9761, M age = 52.22, SD = 16.36 yrs) of a nationally representative, probability-based US internet panel, the Understanding America Study (UAS), reported their experience with extreme weather or natural disaster (e.g., severe storms, tornado, flood), associated adversities (e.g., property loss), and depression and anxiety over the past month. Of the 1075 respondents experiencing extreme weather or natural disaster, 216 reported related adversity. Those experiencing adversity reported more anxiety and depression than those with no events, while extreme weather or disaster alone made no significant difference. Consistent with SST, older age was associated with less depression and anxiety. This age-related benefit was most apparent among those experiencing weather- or disaster-related adversity, even when controlling for socio-demographic correlates. Findings highlight age-related emotional resilience with implications for climate change policy and practice. Full article

Background: Disasters, although predictable, often occur unexpectedly, and efforts must be directed towards reducing their impact. Emergency healthcare workers, key players in disaster response, should maintain a high level of preparedness to act in catastrophic situations. Data on knowledge, attitude, and disaster preparedness among emergency healthcare workers is scarce, particularly for developed countries in Europe. This study aimed to measure the perceived preparedness of various health practitioners in emergency care in Iași county (Romania) and identify factors that influence it. Materials and methods: A self-assessment web-based questionnaire was developed to measure knowledge (K), attitude (A), and preparedness (P). Nonparametric tests compared measurements between demographic groups. Spearman correlation, linear univariate, and multivariate regression models were used to test the effect of perceived knowledge, attitude, and other work-related factors (such as experience, training, and leadership) on disaster preparedness. Results: 211 valid entries were recorded (114 female and 97 male), of which 33.6% were doctors, 25.1% were nurses, and 23.7% were paramedics. There were differences in exposure to training across health professions for disasters and trauma management (p = 0.03 and p = 0.009). The sample’s overall scores for the three primary domains assessed were moderate. Univariate analyses identified a significant effect of knowledge and attitude on preparedness (B = 0.9, 95% CI: 0.79–1.01, p < 0.001, and B = 0.81, 95% CI: 0.66–0.97, p < 0.001, respectively), which was maintained in multivariate regression. Workplace factors (disaster plans and institutional collaboration), along with experience in disaster management and emergency care, were determinants of preparedness, while the effect of training was insignificant. Conclusions: Most healthcare workers displayed moderate preparedness for disasters, while exposure to training and practice was found to be inadequate. Focus should be placed on identifying barriers and enhancing training delivery, strengthening institutional involvement in staff preparedness, and improving inter-professional collaborations. Adequate training methods must be developed and validated in further studies. Full article

The gas desorption behavior of coal is a key basis for guiding gas parameter determination, optimizing gas extraction, and preventing gas-related disasters. Coal in mine working faces typically exhibits a mixed particle size distribution. However, research on the gas desorption behavior of mixed-size coal samples and comparative studies with single-sized samples remains insufficient. This study employed a self-developed experimental system for the multi-field coupled seepage desorption of gas-bearing coal to conduct comparative experiments on gas desorption behavior between single-sized and mixed-size coal samples. Systematic analysis revealed significant differences in their desorption and diffusion patterns: smaller particle sizes and higher proportions of small particles correlate with greater total gas desorption amounts and higher desorption rates. The desorption process exhibits distinct stages: the initial desorption amount is primarily influenced by the particle size, while the later stage is affected by the proportion of coal samples with different particle sizes. The desorption intensity for both single-sized and mixed-size samples decays exponentially over time, with the decay rate weakening as the proportion of small particles decreases. The gas diffusion coefficient decays over time during desorption, eventually approaching zero, and increases as the proportion of small particles rises. Conversely, the gas desorption attenuation coefficient increases with a higher proportion of fine particles. Based on the desorption laws of coal samples with single and mixed particle sizes, this study can be applied to coalbed gas content measurements, emission prediction, and extraction design, thereby providing a theoretical foundation and technical support for coal mine operations. Full article