A novel UAV-based approach for biomass prediction and grassland structure assessment in coastal meadows
Miguel Villoslada, Thaisa Bergamo, Raymond D. Ward, Christopher Joyce, Kalev Sepp
Abstract
Coastal meadows provide a wide range of ecosystem services worldwide. In order to better target conservation efforts in these ecosystems, it is necessary to develop highly accurate models that account for the spatial nature of ecosystem structure, processes and functions. In this study, above-ground biomass was predicted at very high spatial resolution in nine study sites in Estonia. A combination of UAV-derived datasets were used to produce vegetation indices and micro topographic models. A random forest algorithm was used to generate above-ground biomass maps and assess the contribution of each predictor variable. The model successfully predicted above-ground biomass at very high accuracies. Additionally, grassland structural heterogeneity was assessed using UAV-derived datasets and vegetation indices. The results were subsequently related to management history at each study site, showing that continuous, monospecific grazing management tends to simplify grassland structure, which could in turn reduce the supply of a key regulation and maintenance ecosystem services: nursery and reproduction habitat for waders. These results also indicate that UAV-based surveys can serve as reliable grassland monitoring tools and could aid in the development of site-specific management strategies.