Comparing ultra-high spatial resolution remote-sensing methods in mapping peatland vegetation

Questions How to map floristic variation in a patterned fen in an ecologically meaningfully way? Can plant communities be delineated with species data generalized into plant functional types? What are the benefits and drawbacks of the two selected remote-sensing approaches in mapping vegetation patterns, namely: (a) regression models of floristically defined fuzzy plant community clusters and (b) classification of predefined habitat types that combine vegetation and land cover information? Location Treeless 0.4 km(2) mesotrophic string-flark fen in Kaamanen, northern Finland. Methods We delineated plant community clusters with fuzzy c-means clustering based on two different inventories of plant species and functional type distribution. We used multiple optical remote-sensing data sets, digital elevation models and vegetation height models derived from drone, aerial and satellite platforms from ultra-high to very high spatial resolution (0.05-3 m) in an object-based approach. We mapped spatial patterns for fuzzy and crisp plant community clusters using boosted regression trees, and fuzzy and crisp habitat types using supervised random forest classification. Results Clusters delineated with species-specific data or plant functional type data produced comparable results. However, species-specific data for graminoids and mosses improved the accuracy of clustering in the case of flarks and string margins. Mapping accuracy was higher for habitat types (overall accuracy 0.72) than for fuzzy plant community clusters (R-2 values between 0.27 and 0.67). Conclusions For ecologically meaningful mapping of a patterned fen vegetation, plant functional types provide enough information. However, if the aim is to capture floristic variation in vegetation as realistically as possible, species-specific data should be used. Maps of plant community clusters and habitat types complement each other. While fuzzy plant communities appear to be floristically most accurate, crisp habitat types are easiest to interpret and apply to different landscape and biogeochemical cycle analyses and modeling.