Rapid and automated mapping method of Spartina alterniflora combines tidal imagery and phenological characteristics

Spartina alterniflora exhibits vigorous growth and remarkable adaptability, enabling its rapid expansion throughout the intertidal zones of Shandong Province. As an invasive species, it not only disrupts native coastal ecosystems but also incurs significant economic burdens. Although substantial resources have been allocated by local authorities for its control, a comprehensive evaluation of these management efforts remains lacking. In particular, the influence of tidal dynamics on the spatial distribution of S. alterniflora has been largely overlooked, underscoring the need for advanced remote sensing approaches to accurately monitor. In this study, we propose a method to monitor S. alterniflora by combining low-tide imagery with phenological features. By utilizing low-tide images, we effectively overcome the impact of tidal fluctuations on monitoring accuracy. Using the Maximum Spectral Index Synthesis and Otsu algorithms, we achieved efficient, automated classification and change detection of S. alterniflora in Shandong Province, with an overall accuracy of 90.55%. In 2019, the total area of S. alterniflora was 11,386.05 ha, which decreased to 1787.13 ha by 2023. The distribution of S. alterniflora in Shandong followed a trend of initial increase followed by a decrease from 2019 to 2023. By 2023, the eradication rate had reached 91.10%, demonstrating the outstanding success of the province's management efforts. Although the management efforts have been somewhat effective, they have also led to significant carbon storage loss. These results validate the effectiveness of combining low-tide imagery with phenological features, offering a reference for similar studies in other coastal regions. Future S. alterniflora management should incorporate more scientific approaches, including the consideration of carbon emissions, to promote the sustainable development of coastal ecosystems.