Fine-scale (2m) wetland plant communities maps in Honghu Lake, China.

Wetland ecosystems have been subject to prolonged and severe ecological degradation. In recent years, wetland restoration has made significant contributions to addressing this issue. However, wetland plant communities are undergoing drastic changes during the restoration, and different plant communities are experiencing diverse restoration approaches. Existing technological methods struggle to achieve fine-scale monitoring of wetland plant restoration when access to high-cost remote sensing data such as hyperspectral imagery is limited. To address this challenge, this study proposes a fine-scale wetland plant communities mapping method that integrates image resolution enhancement and high-dimensional multi-index feature classification. The study employs a spatiotemporal-spectral fusion model, TemPanSharpening net, to improve the spatial resolution of long-term multispectral image sequences. Subsequently, multiple spectral features are selected and conveyed to a Transformer variant classification model. This approach is applied to map 2m resolution annual dynamics of wetland plant communities including Phragmites australis, Zizania latifolia, and Nelumbo nucifera in the Honghu Lake South, China. Compared to conventional remote sensing-based wetland plant communities classification approaches, this method significantly improves mapping granularity and achieves an overall accuracy of 88.21%. This research overcomes the limitations of fine-scale wetland plant communities mapping under constrained imaging conditions. It provides technical support for accurately monitoring the effectiveness of wetland plant restoration.