During the summer of 2022, Chongqing experienced record-breaking stress from extremely high temperatures, persistent decreases in precipitation, and severe wildfires. Remote sensing has been widely applied to monitor natural disasters and vegetation recovery because of its ability to collect continuous spatiotemporal dynamics observations. In this study, the normalised difference vegetation index (NDVI) and normalised burn ratio (NBR) were comprehensively used to assess the effects of high temperatures and wildfires on vegetation. The statistical results indicate that the average values of the NDVI and NBR decreased by 28% and 22%, respectively, between June and September. Moreover, both the NDVI and NBR sharply decreased across temporal and spatial dimensions after wildfires. The NBR outperforms the NDVI in wildfire warnings and burned area extractions. In early-stage recovery, the NDVI and NBR in the burned areas began to gradually recover but had not yet reached normal levels. The NDVI is superior to the NBR in terms of assessing the resistance and resilience of vegetation to extreme climate events. Furthermore, vegetation recovery response mechanisms are correlated with fire intensities, burned areas and vegetation types. Severely burned areas exhibited weaker resistance. Forests demonstrated strong resistance but weaker resilience; grasslands and shrubs, conversely, showed weaker resistance but stronger resilience.
Long-term flood risk assessment is vital for identifying persistent hazard-prone areas and supporting risk-informed spatial planning in rapidly urbanising regions. This study developed an interpretable geospatial framework integrating AHP, LightGBM–SHAP, and reliability-weighted game-theoretic weight fusion for the Beijing–Tianjin–Hebei (BTH) region using annual flood-related dataset for 2001–2025. Negative sample selection was refined using HAND-based low-likelihood screening and buffer-based exclusion around flood-labelled pixels. The LightGBM component showed strong internal predictive performance, with AUC, TSS, F1-score, and Kappa values of 0.92, 0.69, 0.84, and 0.69, respectively. Independent validation of the final flood risk index for 2022–2025 further confirmed its ability to identify flood-prone areas, with annual AUC values ranging from 0.63 to 0.83. High-risk areas were mainly concentrated in the central-eastern and southeastern plains, especially along the Beijing–Langfang–Tianjin–Cangzhou corridor. Flood risk fluctuated markedly and showed a weak increasing trend, with stable and increasing-risk areas accounting for 87.8% and 11.7%. Topographic factors dominated risk patterns, followed by hydrometeorological, urbanisation, and ecological factors. The framework supports long-term flood risk mapping, driver attribution, and spatial prioritisation of risk reduction measures.
Despite frequent catastrophic flooding in Bangladesh, national- vulnerability assessments integrating multi-dimensional indicators remain sparse. This study develops a country-scale flood vulnerability map by integrating ten criteria across physical (e.g. elevation, drainage density), social (land use and land cover (LULC), distance from roads), and resilience dimensions. Parameters were standardized into uniform risk classes, weighted via the analytical hierarchy process (AHP), and aggregated within a geographic information system (GIS) environment using a multi-criteria decision-making (MCDM) weighted overlay framework. Spatial analysis reveals that 10.8% of Bangladesh is classified as having ‘high’ to ‘very high’ vulnerability, with risk hotspots concentrated in the north-eastern (Sylhet region) and south-western riverine corridors, whereas higher-elevation zones show lower vulnerability. Model validation using the normalized difference water index (NDWI) and historical flood data yielded a root mean square error (RMSE) of 0.47. Bridging physical hazard assessment and socio-economic resilience, this study provides a scientific foundation aligned with the sendai framework for disaster risk reduction (SFDRR). The resulting flood vulnerability index (FVI) serves as a critical tool for prioritizing infrastructure investment and flood mitigation across susceptible administrative zones.
Most susceptibility models fail to capture the coupled effects of fault-controlled seismic attenuation and directional wave propagation, limiting their ability to represent spatial heterogeneity. This research proposes a Fault Azimuth Attenuation Coupling Factor (FAACF) to quantitatively characterise the synergistic ‘direction–distance’ effects of seismic dynamics. Fourteen conditioning factors were selected from five categories: topography, seismic activity, geological conditions, vegetation cover, and human activity. Together with the newly proposed FAACF, these yielded a total of 15 factors for landslide susceptibility modelling. The results show that landslides predominantly concentrated in areas characterised by strong positive (>0.17) or strong negative (<−0.16) FAACF values. Incorporating FAACF significantly improves model performance, increasing the AUC to 0.926 for the XGBoost model and to 0.915 for the RF model. SHAP-based interpretability analysis identifies elevation, FAACF, slope, and peak ground acceleration (PGA) as the dominant controlling factors and reveals clear threshold effects. In addition, strong positive interactions are observed between elevation and PGA, as well as between FAACF and elevation, jointly regulating slope instability. This study highlights the critical role of seismic dynamic characteristics in landslide susceptibility modelling and demonstrates that the proposed FAACF provides a transferable reference for assessing earthquake-induced landslide susceptibility in other tectonically active regions.
Rainfall-induced landslide clusters are widespread hazards in the hilly regions of southeastern China. In August 2018, an intense rainfall event triggered numerous landslides in Luhe County, Guangdong Province. Based on a detailed landslide inventory, this study analyses the geometric and kinematic characteristics of landslides and investigates their spatial patterns. Ten environmental factors were integrated using the GeoDetector model to quantify the dominant controls on landslide distribution and their interactions. The results show that the total landslide area is approximately 3 km2, with an estimated total volume of about 0.0063 km3. Most landslides exhibit heights of less than 50 m and horizontal lengths ranging from 0 to 200 m. The average height-length ratio (H/L) is 0.22, indicating relatively high mobility. Elevation and rainfall are identified as the primary controlling factors, with elevation showing the highest explanatory power. Interaction analysis reveals significant enhancement effects, particularly between elevation and rainfall, highlighting the coupled influence of topographic and triggering conditions. Furthermore, the landslides mainly occurred on granite weathering crust hillslopes within elevations of 150–600 m and slopes of 10–30°, reflecting the typical geomorphic and lithological settings of rainfall-induced landslides in southeastern China.
