A multi-sensor approach to the study of geomorphic, vegetation and hydrogeologic patterns of Alpine peatlands

Geomorphic and vegetation patterns within peatlands are strictly related, and reflect the interactions among topography, hydrogeology, and climate. Vegetation patterns are closely related to soil moisture, drainage patterns, bulk density and carbon content, and the spatial distribution of different plant species as well as the spatial variability of vegetation density may provide important information on key hydrogeological variables at the peatland scale. Therefore, the accurate mapping of vegetation patterns is a fundamental step to study the spatial distribution of peat properties and hydrogeological variables in the near-surface layer, where the roots of living plants develop, and peat accumulation and degradation processes occur. In this study we present the results obtained on two Alpine peatlands located in the Italian Dolomitic area, using field and UAV-based observations. Concurrent acquisitions of LiDAR, VIS/NIR Hyperspectral and VIS/NIR Multispectral sensors onboard of UAV systems were performed in July 2021 and July 2022. Field observations started in spring 2020 and ended in October 2022, including: water table summer monitoring (levels and temperature), soil sampling and analyses (bulk density, carbon content, peat layer thickness), vegetation sampling (plant associations, above- and below-ground biomass), and organic matter degradation assessment (based on the Tea Bag Index – TBI, Keuskamp et al. 2013). The combined analysis of field and UAV data allowed us to explore the correlation between vegetation, microtopography and hydrogeological patterns across the studied peatlands, determining the plant associations that best adapt to specific hydrogeological conditions (a phenomenon called “zonation”). Our results show that plant distribution, leaf area index and biomass are related to microtopography and water table levels and that they can be successfully mapped and monitored using UAV systems. Moreover, applying the TBI we explored the variability of the organic matter decomposition across the different plant associations as well as with depth (from the soil surface to the saturated zone). Our results show that the decomposition rate decreases with depth at all sites, while the stabilization factor increases, showing a significant correlation with the depth of the water table. Since the microtopography spatial variation is strongly linked to different soil moisture conditions, and therefore to different vegetation associations, we show that such associations can be used to map different hydrogeological conditions. The results of this study will be used to calibrate and validate an eco-hydrological model to forecast the future development of Alpine peatlands in different climate-change scenarios.