Leaf area index and aboveground biomass estimation of an alpine peatland with a UAV multi-sensor approach

Aboveground biomass (AGB) can serve as an indicator when estimating various biogeochemical processes in peatlands, an ecosystem which provides countless ecosystem services and plays a key role in climate regulation. While remote sensing has been extensively employed to assess AGB across vast areas in forested peatlands its application to small and treeless peatlands, which are typical of the alpine regions, has been limited. Due to the characteristics of peatlands, innovative approaches capable of capturing their fine-scale, highly heterogeneous and short-stature vegetation cover are needed. Likewise, other key requirements include an ability to overcome site accessibility barriers, cost-effective acquisition of datasets and minimizing damage of these protected habitats. Hence, the utilization of Unmanned Aerial Vehicles (UAVs) offers a viable means for mapping AGB in alpine peatlands. In this study, the AGB of the Val di Ciampo alpine peatland (Veneto Region, Italy) was estimated by combining datasets derived from in situ vegetation samples as well as UAV-based LiDAR, hyperspectral and RGB sensors. A limited number of vegetation samples were used to reduce the impact of the study on the ecosystem. The results indicate that a linear regression can model the relationship between AGB and Leaf Area Index (LAI) with a significant explanatory ability (R-2 = 0.72; p < 0.001). Several indices derived from digital terrain model (DTM) morphologies, hyperspectral data, and orthophotos were tested using a multiple regression approach to determine their potential to enhance the model's performance. Among these only the Double Difference (DD) index, derived from hyperspectral data, was found to slightly improve the model's explanatory ability (R-2 = 0.76). Overall, the findings of this study suggest that UAV LiDAR data provides the most reliable solution for estimating AGB in alpine peatlands, while the inclusion of hyperspectral data provides only a minor improvement in accuracy.

Automated summary

This study estimates aboveground biomass (AGB) in alpine peatlands in the Veneto Region, Italy, using a combination of in situ vegetation samples and datasets from UAV-based LiDAR, hyperspectral, and RGB sensors. The results indicate that UAV LiDAR data provides the most reliable solution for estimating AGB in alpine peatlands, while the inclusion of hyperspectral data provides only a minor improvement in accuracy.