Wetland ecosystems have suffered from prolonged and severe global degradation. In recent years, wetland restoration has made significant contributions to addressing this issue. However, restoration interventions have induced complex and dynamic shifts in plant community composition. Conventional remote sensing approaches often fail 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 emergent plant mapping method that integrates spatiotemporal-spectral fusion for resolution enhancement with a transformer-based classifier utilizing high-dimensional features. 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 2 m resolution annual dynamics of emergent plant communities in the Honghu Lake South, China. Compared to conventional approaches, our method significantly enhances mapping granularity with an overall accuracy of 88.21%, and reveals that 9.5% of the carbon storage might be overlooked. This research overcomes the limitations of fine-scale emergent plant monitoring under constrained imaging conditions. It provides technical support for accurately monitoring the effectiveness of wetland plant restoration.
Han et al. (Tue,) studied this question.