Leveraging synergies between UAV and Landsat 8 sensors to evaluate the impact of pale lichen biomass on land surface temperature in heath tundra ecosystems

• Published in: Science of The Total Environment
• Main Authors: Villoslada, Miguel, Bergamo, Thaísa, Kolari, Tiina, Erlandsson, Rasmus, Korpelainen, Pasi, Räsänen, Aleksi, Tahvanainen, Teemu, Tømmervik, Hans, Virtanen, Tarmo, Winquist, Emelie, Kumpula, Timo
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2025
• Subjects: VDP::Matematikk og naturvitenskap: 400; VDP::Mathematics and natural scienses: 400; Arctic; Norway; albedo; Tundra
• Online Access: https://hdl.handle.net/10037/36636; https://doi.org/10.1016/j.scitotenv.2025.178982

Pale terricolous lichens are a vital component of Arctic ecosystems, significantly contributing to carbon balance, energy regulation, and serving as a primary food source for reindeer. Their characteristically high albedo also impacts land surface temperature (LST) dynamics across various spatial scales. However, remote sensing of lichens is challenging due to their complex spectral signatures and large spatial variations in coverage and biomass even within local landscape scales. This study evaluates the influence of pale lichens on LST at local and landscape scales by integrating RGB, multispectral, and thermal infrared imagery from an Unmanned Aerial Vehicle (UAV) with multi-temporal Landsat 8 thermal data. An Extreme Gradient Boosting algorithm was employed to map pale lichen biomass, areal extent, and the occurrence of major plant functional types in the sub-arctic heath tundra landscape in the Jávrrešduottar and Sieiddečearru areas on the Finland-Norway border. Generalized Additive Models (GAMs) were used to elucidate the factors affecting LST. The UAV model accurately predicted pale lichen biomass (R 2 0.63) and vascular vegetation cover (R 2 0.70). GAMs revealed that pale lichens significantly influence thermal regimes, with increased biomass leading to decreased LST, an effect more pronounced at the landscape scale (deviance explained 47.26 % and 65.8 % for local and landscape models, respectively). Pale lichen biomass was identified as the second most important variable affecting LST at both scales, with elevation being the most important variable. This research demonstrates the capability of UAV-derived models to capture the heterogeneous and fine-scale structure of tundra ecosystems. Furthermore, it underscores the effectiveness of combining high spatial resolution UAV and high temporal resolution satellite platforms. Finally, this study highlights the pivotal role of pale lichens in Arctic thermal dynamics and showcases how advanced remote sensing techniques can be used for ecological monitoring and ...