Pixel-based unique phenological feature composite method: tackle the challenges of Spartina alterniflora identification and salt marsh classification in mixed vegetations area

Abstract

Coastal salt marshes are valuable wetland ecosystems, and obtaining accurate information on their spatial and temporal distribution is crucial. However, due to the strong spectral similarity among various types of salt marshes, distinguishing them is difficult, especially in areas with mixed salt marsh growth. In this study, we developed a pixel-based unique phenological feature composite method (PUpf-CM). First, we identified a unique phenological period for each type of salt marsh by analyzing the normalized difference vegetation index time-series of Spartina alterniflora (S. alterniflora) and native salt marshes [Suaeda salsa (S. salsa), Phragmites australis (P. australis), and Imperata cylindrica (I. cylindrica)] in Yancheng National Nature Reserve, Jiangsu Province, China. Then, we generated a composite image for each unique phenological period, maximizing the spectral separability between the corresponding salt marsh and background species. Second, we used the composite images obtained at the unique phenological period of S. alterniflora as input to the random forest classifier, achieving high precision identification of S. alterniflora. Compared with the previously developed pixel-based PUpf-CM, the overall accuracy (OA) of PUpf-CM was 98.26% to 99.42%, and the Kappa was 0.940 to 0.975, indicating an improvement of 0.87% to 4.94% and 0.031 to 0.179, respectively. Finally, we tested the possibility of applying PUpf-CM to salt marsh classification by stacking four composite images obtained at different unique phenological periods. We found that the maximum spectral separability was achieved for the combination of four unique phenological periods, and the OA and Kappa of the PUpf-CM-based salt marsh classification reached 97.54% and 0.969, respectively.