This study evaluates soil erosion in the Kabul River Basin (KRB), Khyber Pakhtunkhwa (KPK), Pakistan, using the revised universal soil loss equation (RUSLE) integrated with geographic information system (GIS) and remote sensing (RS) techniques. We analyzed two benchmark years, 1994 and 2024, to quantify net multi-decadal changes in soil-loss magnitude, erosion severity, and spatial erosion patterns. Rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practice (P) factors were integrated within a harmonized spatial framework, while Landsat-derived land use and land cover (LULC) maps represented temporal changes in surface conditions. Forest cover increased from 4892.76 to 6751.36 km2, whereas barren land decreased from 18,284.99 to 14,316.69 km2. Mean modelled soil loss declined from 4.70 t ha−1 yr−1 in 1994 to 3.04 t ha−1 yr−1 in 2024, while the maximum localized soil-loss rate decreased from approximately 5003 to 3912 t ha−1 yr−1. Barren land consistently exhibited the highest mean soil loss, decreasing from 14.44 to 11.24 t ha−1 yr−1, whereas forest remained comparatively low at 1.52 and 1.38 t ha−1 yr−1. Spatial soil-loss change ranged from −3184 to +2518 t ha−1 yr−1, with localized increases concentrated mainly in steep and runoff-convergent areas. Overall, the endpoint comparison indicates generally lower erosion conditions in 2024, while persistent mountainous hotspots remain priorities for targeted soil-conservation measures.
This study develops a Monthly Fire Probability Index (MFPI) framework for China using Random Forest and XGBoost, driven by 17 predictors spanning meteorology, vegetation/fuel properties, topography, human activity, and lightning, with a one-month lead to represent antecedent conditions. Predictor–target pairs from 2015 to 2022 were used for model fitting, those from 2023 to 2024 for temporal hold-out selection, and selected models were refitted using all valid 2015–2024 samples; 2025 was retained for independent testing. RF achieved stronger probability and broad-pattern performance in the test period (ROC-AUC 0.858, Brier score 0.143, log loss 0.412, Expected Calibration Error 0.263, and Pearson spatial correlation 0.204) than XGBoost (0.845, 0.174, 0.483, 0.288, and 0.195, respectively). Systematic multi-lag experiments indicate that the 1-month lag represents an appropriate trade-off among predictive accuracy, spatial fidelity, and operational early-warning timeliness. In 2025, the RF predictions adequately capture China's seasonal fire spatial migration patterns, including winter aggregation in South and Southwest China, spring northward expansion to Northeast and North China, summer contraction to northern peripheral margins, and autumn resurgence in Southwest and South China. These findings provide an interpretable framework with national-scale applicability for fire risk assessment, monthly early warning, and adaptive ecosystem management.
Flood mapping requires workflows that are automated and robust under cloud-prone conditions. Recent reference-guided adaptive thresholding studies have improved threshold selection in change-detection-based flood mapping, but they still depend on usable optical reference data and may become constrained when cloud-free observations are unavailable. To address this limitation, this study develops NDFI-SC, a robust flood-mapping workflow implemented on Google Earth Engine that combines normalised difference flood index (NDFI) change detection with a Sentinel-1 Dual-Polarisation Water Index (SDWI)-derived stable reference and consistency-driven threshold calibration. Unlike existing optical-reference-guided approaches, NDFI-SC derives the stable reference directly from Sentinel-1, thereby reducing the dependence of threshold calibration on cloud-free optical observations. Near-date Dynamic World data are incorporated for inundated land-cover reporting. It is evaluated across three complementary scenarios: cross-method comparison in China, applicability under optically constrained conditions in Vietnam, and land-cover inundation reporting in Australia. Results show that NDFI-SC suppresses false alarms, reaches a critical success index (CSI) of 0.809 and a false alarm ratio (FAR) of 0.141 in China, shows good agreement with Copernicus Global Flood Monitoring in Vietnam, and supports near-real-time (NRT) reporting of land-cover types in Australia. These results indicate that NDFI-SC provides an NRT framework for flood delineation and impact assessment.
Assessing regional economic recovery after major earthquakes is critical for disaster risk reduction. This study aims to evaluate the disaster resilience of the Wenchuan earthquake-stricken area and explore its spatially varying influencing factors. Based on night-time light data, we constructed a counterfactual development baseline to quantify four resilience indices: Instantaneous Disturbance (D), Recovery Rate (R), Recovery Time (T), and Cumulative Economic Loss (L). Ordinary Least Squares and Multi-scale Geographically Weighted Regression models were applied to analyse the spatial heterogeneity of the relationships between resilience and its influencing factors. The results indicate that the initial post-disaster recovery trajectories in extremely severely affected areas exhibited distinct spatial deviations. The core influencing factors associated with resilience show a phased pattern: environmental and disaster factors are more closely associated with the initial disturbance and short-term recovery (D, R), whereas socioeconomic factors show stronger associations with the medium- and long-term recovery and cumulative losses (T, L). The relationships between these factors and resilience vary spatially. In conclusion, regional disaster resilience and its associated influencing factors exhibit spatial heterogeneity. Enhancing long-term economic resilience requires differentiated, localised recovery strategies tailored to specific geographical and socioeconomic conditions rather than uniform reconstruction approaches.
Against the backdrop of climate change and rapid urbanisation, frequent extreme rainfall events have exposed urban underground rail transit systems to increasingly severe flood risks. Metro stations exhibit substantial differences in flood susceptibility, vulnerability, and recovery capacity, making it difficult for conventional risk assessment methods to accurately characterise their flood resilience. To address this challenge, this study proposes an integrated framework combining Positive–Unlabelled Learning (PU), the Best–Worst Method (BWM), and Criteria Importance Through Intercriteria Correlation (CRITIC) for assessing the flood resilience of metro stations (PU–BWM–CRITIC). The framework establishes a comprehensive indicator system comprising 23 indicators across three dimensions: flood susceptibility, vulnerability, and recovery capacity. Based on classical resilience-loss theory, an integrated resilience quantification model is developed to comprehensively evaluate resilience characteristics across the pre-disaster, during-disaster, and post-disaster stages. The proposed framework is applied to the Chongqing metro system as a case study. The results reveal pronounced spatial heterogeneity in the flood resilience of metro stations in Chongqing. Stations with low resilience are primarily concentrated in central urban areas and major transfer hubs, whereas highly resilient stations are more frequently distributed in peripheral urban areas. Although stations in central urban areas generally exhibit greater recovery capacity, their higher flood susceptibility and vulnerability contribute to greater potential resilience losses. Comparative experiments demonstrate that the PU–BWM–CRITIC framework maintains relatively stable station rankings and spatial patterns under different weighting schemes and extreme rainfall scenarios, indicating good robustness and adaptability across scenarios. The proposed framework provides a quantitative basis for identifying low-resilience stations, prioritising flood protection improvements, and allocating emergency resources.
