Integrating UAS-based lidar data in eddy covariance flux footprint modelling

Peatlands, covering 3% of the global land area, store twice the carbon of all the world's forests combined, acting as crucial carbon sinks. However, under varying environmental conditions induced by global warming and land cover changes, they can transition into carbon sources. Monitoring gas exchanges in peatland ecosystems involves employing the eddy covariance method, often interpreted using flux footprint models. This study focuses on the application of the FFP model, specifically addressing the influence of spatially varying roughness parameters.

Utilizing Unoccupied Aerial Vehicle (UAS)-based high-resolution LIDAR data, we incorporated spatially varying roughness values into the FFP model, comparing the results with traditional scalar roughness length values. Our findings reveal that spatially varying roughness introduces spatial heterogeneity, resulting in more irregular and smaller footprints. The inclusion of spatially varying roughness based on the surface reduced the area contribution to fluxes by 40%, emphasizing the significance of accounting for this spatial variability.

Moreover, we investigated the impact of surface and terrain conditions on footprint modeling in a peatland previously subjected to extraction. Our analysis indicates that variations in terrain (both natural and extraction-induced) reduced the footprint contours by 18% compared to the original FFP model footprints. This underscores the importance of considering terrain changes in footprint modeling, especially in peatlands with a history of extraction activities.

In conclusion, this research enhances our understanding of (1) the impacts of spatially varying roughness on modeled footprints and (2) the influences of surface and terrain on footprint size in peatland ecosystems. These insights contribute to improved modeling accuracy and aid in effective carbon management strategies for peatland conservation.