Jingxuan Liu

The 21st‑century Maritime Silk Road initiative highlights the importance of oceans as hubs for resources, ecology, and trade, yet a comprehensive understanding of marine natural disaster risks within this region remains limited. This study focused on 30 countries along the Maritime Silk Road and developed a multi-hazard natural disaster risk assessment framework tailored for large-scale regional evaluation. It goes beyond single-factor or single-disaster assessments to enhance disaster resilience and support effective disaster response strategies. The framework integrates 65 indicators across four dimensions: disaster-causing factors, disaster-conceiving environments, disaster-bearing bodies, and disaster reduction capacities. It employs five single-indicator evaluation models alongside a combination assessment method based on maximum deviations to evaluate national-scale natural disaster risks. Results reveal spatial consistency in risk evaluations and capture the exposure and sensitivity of 30 countries to different hazards. South Asia exhibits higher seismic risks, while Saudi Arabia consistently receives the lowest risk. Tropical countries like Vietnam and the Philippines face significant storm risks. Drought hazard risk is higher in the Middle East and East Africa, while it is lower in Brunei, Indonesia, and Malaysia. Flood risks are notably higher in Bangladesh, while Iran and Tanzania consistently receive lower risk ratings. Overall, South Asia exhibits higher multi-hazard risks, with medium-to-low risks along the Mediterranean and Southeast Asia. These findings provide technical support for disaster risk reduction by identifying high-risk areas, prioritising resource allocation, and strengthening disaster reduction strategies.

Land-use and land-cover changes in the Eastern European Plain have important implications for regional and global ecological environments, food security, and socio-economic development. Here, three 30 m resolution global land cover data products (FROM_GLC, GlobeLand30, and GLC_FCS30) from the Eastern European Plain were analyzed and evaluated for component similarity, type confusion degree, spatial consistency, and accuracy verification. The research found that the three products provided consistent descriptions of land-cover types in the East European Plain. There was a strong correlation in the type area between the different products, with a correlation coefficient >0.85. Medium-to-high-consistency areas represented 92.31% of the total plains area. The low-consistency areas were mainly concentrated on Yuzhny Island, Kola Peninsula, and Pechora River Basin. The comparison revealed high consistency among the three products in identifying forest, cropland, water, and permanent ice/snow types. However, the consistency was poor for shrubs, wetlands, and bare land. Using the GLCVSS_V1 validation dataset, the highest overall accuracy among the assessed land cover data products was observed in the FROM_GLC (73.96%), followed by GlobeLand30 (69.80%) and GLC_FCS30 (67.29%). The FROM_GLC dataset is suitable for studying forests, tundra, water, and providing an overall representation of the region’s land cover. The GLC_FCS30 dataset is more suitable for agricultural research. The differences between products arise from the differences in classification systems, algorithms, and data correction. In the future, it will be necessary to utilize the advantages of different products for data fusion, focusing on areas with high heterogeneity and easily confused types, and improving the reliability of land-cover data products.

Rapid and accurate identification of mangroves using remote sensing images is of great significance for assisting ecological conservation efforts in coastal zones. With the rapid development of artificial intelligence, deep learning methods have been successfully applied to a variety of fields. However, few studies have applied deep learning methods to the automatic detection of mangroves and few scholars have used medium-resolution Landsat images for large-scale mangrove identification. In this study, cloud-free Landsat 8 OLI imagery of the Indus Delta was acquired using the GEE platform, and NDVI and land use data were used to produce integrated labels to reduce the complexity and subjectivity of manually labeled samples. We proposed the use of MSNet, a semantic segmentation model fusing multiple-scale features, for mangrove extraction in the Indus Delta, and compared the performance of the MSNet model with three other semantic segmentation models, FCN-8s, SegNet, and U-Net. The overall performance ranking of the deep learning methods was MSNet > U-Net > SegNet > FCN-8s. The parallel-structured MSNet model was easy to train, had the fewest parameters and the highest validation accuracy, and provided the best results for the extraction of mangrove pixels with weak features. The MSNet model not only maintains the high-resolution features of the image and fully learns the pixels with weak features during the training process but also fuses the multiple-scale underlying features at different scales to enhance the semantic information and improve the accuracy of feature recognition and segmentation localization. Finally, the areas covered by mangroves in the Indus Delta in 2014 and 2022 were extracted using the best-performing MSNet. The statistics show an increase in mangrove-covered areas in the Indus Delta between 2014 and 2022, with a reduction of 44.37 km², an increase of 170.48 km², and a net increase of 126.11 km².