As one of the most important wetland systems, coastal wetlands play an important role in conserving water, regulating the climate and protecting biodiversity. However, due to large-scale and long-term tidal flat reclamations, the landscape structure and function of the coastal wetlands have been greatly affected. Therefore, it is necessary to understand the spatio-temporal characteristics of the impact of tidal flat reclamation on regional ecology and to quantitatively assess the relationships between them. In this study based on long-term, multiperiod remote sensing data, the main spatio-temporal variation characteristics of stability, and the relationship between stability and tidal flat reclamation were analyzed with regard to the influence scope of tidal flat reclamation. The results showed that a substantial decrease in natural wetlands in 1980, mainly caused by tidal flat reclamation, was discovered in the Jiangsu coastal area, and the influence scope of tidal flat reclamation on regional landscape ecology was roughly 30 km. In the affected area, the overall stability had a tendency to improve, but the stability change characteristics between reclamation area and non-reclamation area varied greatly. Especially in the reclamation area, the stability of construction wetlands and non-wetlands deteriorated. Spatially, the stability outside the reclamation area had the characteristics of first deteriorating and then improving as the distance from the reclamation area increased. Under the influence of tidal flat reclamation, the influence of different use types of TFR on stability was not completely consistent, and the influence of the same uses type of tidal flat reclamation on different landscapes was also different.

Global warming is predicted to lead to a new geographic and spatial distribution of storm-surge events and an increase in their activity intensity. Therefore, it is necessary to detect storm-surge events in order to reveal temporal and spatial variations in their activity intensity. This study attempted to detect storm-surge events from the perspective of detecting outliers. Four common outlier-detection methods, the Pauta criterion (PC), Chauvenet criterion (CC), Pareto distribution (PD) and kurtosis coefficient (KC), were used to detect the storm-surge events from the hourly residual water level data of 14 tide gauges along the coasts of China. This paper evaluates the comprehensive ability of the four methods to detect storm-surge events by combining historical typhoon-storm-surge events and deep-learning target-detection-evaluation indicators. The results indicate that (1) all of the four methods are feasible for detecting storm surge events; (2) the PC has the highest comprehensive detection ability for storm-surge events (F1 = 0.66), making it the most suitable for typhoon-storm-surge detection in coastal areas of China; the CC has the highest detection accuracy for typhoon-storm-surge events (precision = 0.89), although the recall of the CC is the lowest (recall = 0.42), as only severe storm surges were detected. This paper therefore evaluates four storm-surge-detection methods in coastal areas of China and provides a basis for the evaluation of storm-surge-detection methods and detection algorithms.