Spatial Distribution of Wetland Plants of Saltmarsh Wetlands and its Dem Dependence—Taking Yancheng Wetland in China as an Example

Monitoring spatial distribution of wetland plants of a salt marsh and its Digital Elevation Model (DEM) dependence is critical in understanding the present wetland plants variation and predicting future development under the global climate change. The objective of this study was to utilize the GaoFen-2 (GF-2) high-resolution satellite to classify the coastal salt marsh wetland plants in Yancheng, Jiangsu Province, China. The BP method, a machine learning method, was adopted.Additionally, the DEM was retrieved using the 2022 ZY302 high-resolution data, and five different periods of DEM from 2000-2015 were analyzed. The results show that: 1) BP neural network method can be used to classify salt marsh vegetation. The overall accuracy is 97%, and the kappa coefficient is 0.91; 2) Due to the physical effects of wave action, tidal activity, and biological soil fixation on salt marsh vegetation, the elevation of coastal wetlands undergoes continuous temporal variations; 3) The scale of tidal gully increases gradually from north to south. The density of salt marsh tidal gully is much higher than that of bare land tidal gully, and most of them are perpendicular to the river bank; 4) Spartina alterniflora, Suaeda salsa, and Phragmites australis exhibit distinct zonation patterns in their distribution from the sea towards inland regions. In terms of terrain distribution, the lower zone is characterized by tidal flat and bare land, while the middle zone is occupied by Spartina alterniflora and Suaeda salsa. The upper zone is dominated by Phragmites australis. This zonation pattern reflects the adaptability of these species to specific environmental conditions and their ecological niches within the coastal landscape. This distribution pattern is further characterized by a narrow elevation range, suggesting specific environmental constraints on its growth.