Mapping Plant Communities in the Intertidal Zones Using Sentinel-2 and Sentinel-L Data

Abstract

Intertidal habitats are important not only for the ecosystem services they provide but also because they represent a key ecotone between land and ocean. In this paper we explore the synergistic use of time series Sentinel-1 and Sentinel-2 data for mapping intertidal plant communities, using a Random Forest algorithm. We compare the performances of ten models with different input data. Results showed that the use of multi-seasonal Sentinel-2 images could significantly improve the mapping accuracy compared to the use of any single-season image. There was no statistically significant difference in mapping accuracies between the use of multi-seasonal and time series Sentinel-2 images. However, when combining time series Sentinel-1 data, time series Sentinel-2 data and NDVI statistic metrics, the highest mapping accuracy was achieved with an overall mapping accuracy of 77.7% and the Kappa coefficient of 0.75.