July 23, 2021
Landscape-Scale Characterization of Arctic Tundra Vegetation Composition, Structure, and Function with a Multi-Sensor Unoccupied Aerial System (UAS)
UAS imagery reveals that, in higher abundance, tall shrubs tend to cool arctic landscapes and reduce species richness
The Arctic is warming faster than anywhere else on Earth, driving important changes in vegetation composition, structure, and function. Traditionally, satellite remote sensing has been used to monitor changes in the Arctic; however, the heterogeneity of tundra landscapes and the coarse resolution of satellite data have left critical gaps in our understanding of Arctic vegetation. Unoccupied Aerial Systems (UASs) can provide the rich, spatially detailed information on vegetation dynamics necessary to improve the remote sensing of tundra vegetation. Using a novel UAS, scientists found that deciduous tall shrubs had strong localized effects on surface energy balance and vegetation composition, where increased tall shrub cover led to a significant reduction in surface temperature and the abundance of other plant species.
Increasing shrub cover and height in the Arctic is a key control on large-scale changes in plant biodiversity, energy balance, and biogeochemical cycling. The resolution of traditional satellite remote sensing is too coarse to capture the fine-scale surface heterogeneity in arctic landscapes, creating significant challenges in understanding the impacts of “shrubification” on tundra ecosystems. To address this challenge, scientists employed a novel multi-sensor UAS to investigate the influence of arctic shrubs on plant community composition and surface energy balance at a very high spatial resolution. The use of this UAS platform allowed for a deeper understanding of fine-scale controls on vegetation structure, composition, and energy cycling. This information will be used to improve scaling efforts that employ other airborne or satellite platforms.
Changes in vegetation composition, structure, and function have strong impacts on terrestrial ecosystems and feedbacks to global climate. In the Arctic, average temperatures are warming twice as fast as the global average, with important implications for tundra vegetation dynamics. Remotely monitoring these changes, however, is challenging given the high spatial heterogeneity of tundra vegetation and surface properties in these ecosystems. To address the challenges of characterizing the fine-scale patterns of arctic vegetation and primary controls on composition, structure, and surface energy cycling, researchers used a novel multi-senor UAS platform to map the spatial patterns of vegetation composition, structure, and function across multiple arctic watersheds. Results show that the fine-scale details provided by UAS platforms can significantly improve understanding of the drivers of arctic vegetation distribution, structure, and thermoregulation. In particular, the researchers found a significant localized ‘cooling’ effect in areas of higher tall shrub abundance that has important implications for surface energy balance. The establishment of tall shrub individuals also reduced the abundance of other vegetation types in arctic plant communities, due to increased competition for light and resources, as well as creating a more closed canopy. Importantly, these fine-scale patterns of tundra vegetation also drive the emergent, landscape-scale cycling of carbon, water, and energy in Arctic ecosystems.
Brookhaven National Laboratory
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
This work, the associated field data, and UAS collection campaigns were supported by the Next-Generation Ecosystem Experiments in the Arctic (NGEE-Arctic) project (DE-SC0012704), which is funded by the Office of Biological and Environmental Research (BER), within the U.S. Department of Energy’s (DOE) Office of Science.
Yang, D., et al. "Landscape-Scale Characterization of Arctic Tundra Vegetation Composition, Structure, and Function with a Multi-Sensor Unoccupied Aerial System." Environmental Research Letters 16 (8), 085005 (2021). https://doi.org/10.1088/1748-9326/ac1291.
Yang, D. et al. "A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra." Remote Sensing 12 (16), 2638 (2020). https://doi.org/10.3390/rs12162638.