Mapping Invasive Spartina alterniflora Using Phenological Information and Red-Edge Bands of Sentinel-2 Time-Series Data

The accurate mapping of Spartina alterniflora (S. alterniflora) invasion is crucial for controlling its spread and reducing severe ecological problems. Satellite images have been extensively employed for S. alterniflora invasion monitoring; however, there are still several issues that need to be addressed. The spectral similarities between S. alterniflora and surrounding ground objects make it challenging for traditional classifiers to achieve satisfactory extraction accuracy. Since the phenological information and red-edge spectral differences have been considered as informative features for identifying S. alterniflora, current studies mainly used them separately as classification features and seldom considered the differences of red-edge information at different phenological periods. Therefore, we proposed a pixel-based phenological and red-edge feature composite method (PpRef-CM) for S. alterniflora extraction considering both phenological information and red-edge bands derived from Sentinel-2 time series based on the existing pixel-based phenological feature composite method (Ppf-CM). The proposed PpRef-CM and machine-learning algorithms were employed for S. alterniflora extraction in two typical mangrove forests along coastal China. Results indicated that red-edge information at different phenological periods is essential for detecting S. alterniflora. S. alterniflora extraction achieved the highest accuracy of 96.57% by using the eXtreme gradient boost algorithm when compared with other machine-learning algorithms. The PpRef-CM gave 2.72% and 2.61% more extraction accuracies of S. alterniflora than the Ppf-CM in two study sites, separately. These findings provide insights for selecting suitable classification features for S. alterniflora extraction studies and serve as an effective control and management of S. alterniflora.