Mapping and Monitoring Heterogeneous Plant Communities in Restored and Established Salt Marshes Using UAVs and Machine Learning

Species composition is an important indicator for the condition, functioning, and ecosystem service provision of salt marshes, making the mapping of species composition valuable for their management. Previous studies have demonstrated that the combined use of unoccupied aerial vehicle (UAV)-mounted multispectral cameras and machine learning (ML) can provide effective mapping of vegetation communities in these habitats. However, to date, these studies have predominantly focused on relatively species-poor salt marshes in North America. There has been no published testing of these combined UAV-ML methods in the salt marshes of northwestern Europe, which contain different often more diverse assemblages. Consequently, this study investigated whether applying recent methodological advances can accurately map National Vegetation Classification communities in three locations in the United Kingdom, each comprising two salt marsh sites, one established and one restored. Sites consisted of a mix of established and restored salt marshes of different ages, enabling a complementary assessment of how these methods perform in communities at different stages of development. The applied random forest ML models were found to produce highly accurate maps of salt marsh vegetation communities, with a mean overall accuracy of 94.7%. No relationship was found between the age of restoration sites and the accuracy of the classifications, showing these methods may be applied at a range of stages of community development and offer wider applicability for saltmarsh management and monitoring. The findings of this study demonstrate that advances in the combined use of drones and machine learning provide a readily transferrable method for mapping standardised vegetation communities in both established and restored northwestern European salt marshes and therefore likely other salt marshes globally. Consequently, this study demonstrates that both researchers and practitioners may confidently use these methods to create improved assessments of both marsh condition and function.