Fang Luo

The coastal zone plays an essential part in maintaining the balance of the ecosystem and promoting the development of human society and economy. It is significant to assess the extent to which the Jiaozhou Bay coastal zone withstands floods and coastal erosion during storms. The coastal exposure index (CEI) of the Jiaozhou Bay in 1984, 2000 and 2019 was obtained by the coastal vulnerability model based on data including coastline, bathymetry and coastal terrain elevation. The spatial distribution and aggregation characteristics of CEI in Jiaozhou Bay were analyzed through spatial autocorrelation analysis. The results show that the north coast of Jiaozhou Bay is highly vulnerable, that is, prone to coastal erosion, while the south and east are less vulnerable, meaning that they can basically withstand natural disasters such as storm surges and floods. The CEI shows significant spatial autocorrelation, with little spatial heterogeneity. The type of coastline, elevation, distance to continental shelf and socio-economic development are the main factors that cause the north–south vulnerability differences in the Jiaozhou Bay coast. The results can identify the districts along the Jiaozhou Bay that are at greater risk of marine disasters, and provide scientific theoretical support for the coastal protection and sustainable development of the Jiaozhou Bay.

Urban wetlands provide cities with unique and valuable ecosystem services but are under great degradation pressure. Correctly identifying urban wetlands from remote sensing images is fundamental for developing appropriate management and protection plans. To overcome the semantic limitations of traditional pixel-level urban wetland classification techniques, we proposed an urban wetland identification framework based on an advanced scene-level classification scheme. First, the Sentinel-2 high-resolution multispectral image of Shenzhen was segmented into 320 m × 320 m square patches to generate sample datasets for classification. Next, twelve typical convolutional neural network (CNN) models were transformed for the comparison experiments. Finally, the model with the best performance was used to classify the wetland scenes in Shenzhen, and pattern and composition analyses were also implemented in the classification results. We found that the DenseNet121 model performed best in classifying urban wetland scenes, with overall accuracy (OA) and kappa values reaching 0.89 and 0.86, respectively. The analysis results revealed that the wetland scene in Shenzhen is generally balanced in the east–west direction. Among the wetland scenes, coastal open waters accounted for a relatively high proportion and showed an obvious southward pattern. The remaining swamp, marsh, tidal flat, and pond areas were scattered, accounting for only 4.64% of the total area of Shenzhen. For scattered and dynamic urban wetlands, we are the first to achieve scene-level classification with satisfactory results, thus providing a clearer and easier-to-understand reference for management and protection, which is of great significance for promoting harmony between humanity and ecosystems in cities.