Predicting the regime shift of coastal wetlands based on the bistability features in the intertidal zone: A case study in the Liaohe estuary

Influenced by human activities and natural interference, the worldwide distribution of coastal wetlands is now undergoing rapid evolution. The prediction on the locations of vegetation conversion is greatly important for the management of these coastal ecosystems in terms of early warning. In this paper, a series of waterlines extracted from multiple satellite images were used to generate a high-precision digital elevation model (DEM) in the intertidal zone of the Liaohe estuary. Based on the characteristics of the alternative stable states in elevation and normalized difference vegetation index (NDVI), the Logistic Regression Model was applied to predict the potential locations of vegetation expansion with geomorphic factors, such as elevation, slope, and annual changing rate of elevation. Before the prediction, the existence of two stable states in the landscape was confirmed in the study area, i.e., low-lying tidal flats and high-lying saltmarshes. When the geomorphic parameters exceeded the thresholds, the stable state transition would occur. By using the Logistic Regression Model, the elevation was the best explainer for determining the vegetation conversion in the single-factor simulation, while the slope was the worst. When multiple factors were integrated in simulations, the prediction with the elevation, slope, and annual elevation change rate was the best, with R2 = 0.739, and the overall accuracy of prediction reached 88.6%. The prediction result indicated that the area of saltmarshes in the Liaohe estuary increased by 16.7 km2 at a rate of 0.8% per year between 2011 and 2015. When considering the popularization in restoration practice, it is necessary to evaluate the reliability and flexibility of the Logistic Regression Model in predicting vegetation conversion in more types of estuarine wetlands.