Plant species classification in salt marshes using phenological parameters derived from Sentinel-2 pixel-differential time-series

Abstract

Salt marshes are one of the most productive but vulnerable ecosystems on Earth; and due to the continued intensification of natural and anthropogenic pressures on them, accurate and timely information on the distribution of plant species in salt marshes is needed for effective coastal management. Time-series approaches have been widely applied to classify plant species; however, developing time-series with high spatial and temporal resolution over coastal zones remains challenging due to the influence caused by frequent cloud cover and periodic tidal fluctuations. In this study, aiming at the above challenges, we presented a saltmarsh vegetation classification method using phenological parameters derived from Sentinel-2 pixel-differential time-series (PDTS): first, a PDTS that each pixel has a different distribution of observations was constructed using a time-series cloud mask; second, a tidal filter determined by the threshold and frequency of the modified normalized difference water index (MNDWI) was used to exclude tide-related observations from the PDTS; third, phenological parameters that highlight the differences among salt marsh vegetation were extracted from a two-term Fourier fitting curve as classification features; and finally, the random forest algorithm was used for plant species classification with the assistance of sample data. Six common plant species (Spartina alterniflora, Phragmites australis, Suaeda salsa, Tamarix chinensis, Imperata cylindrica, and Scirpus mariqueter) from three representative coastal sites in China were analyzed. The major results were as follows: (1) The MNDWI demonstrated superior ability in identifying flooding pixels, with an overall accuracy of ~0.91. After tidal filtering, the R2 of the fitting curve for more than 70% of the vegetated salt marsh pixels was improved with an average increase of 0.113. (2) The six plant species exhibit unique phenological characteristics. In particular, P. australis has an advanced green-up season, 19–42 days earlier than that of the other plant species mentioned above, whereas S. alterniflora senesces one to two months later than the native plant species. (3) The average overall accuracy of the plant species classification based on the PDTS was 81.5%. Compared with a single-image-based classification, the PDTS-based classification demonstrated a ~ 5.1% improvement in overall accuracy, which is expected to serve the annual monitoring dynamics of the salt marsh.