Dynamic Vegetation Changes in the Arctic: Circumpolar Trends based on Earth Observation

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Abstract

The Arctic, characterised by severe climatic conditions and sparse vegetation, is experiencing rapid warming, with temperatures increasing up to four times the global rate since 1979. Extensive impacts from these changes have far-reaching consequences for the global climate and energy balance. Observing and quantifying the changes of the Arctic vegetation is therefore not only important to local stake holders, as changes in the Arctic region will affect the rest of the world. Satellite remote sensing is a valuable tool for monitoring Arctic vegetation dynamics, particularly in regions with limited ground observations. To investigate the impact of climate change on Arctic and subarctic vegetation dynamics, a literature review of 154 studies published between 2000 and 2023 was conducted based on the PRISMA guidelines and the Web of Science database. The research objectives, spatial distribution of study areas, methods and the temporal and spatial resolution of utilised satellite data were extracted for further analysis. The results show circumpolar greening trends for all major optical remote sensing satellite products, widespread decline in lichen coverage often accompanied by an increase of shrubs and increasing plant productivity. However, start and end of growing season dynamics are heterogeneous throughout the circumpolar Arctic, while the growing season length generally increased. The disturbance and recovery mechanisms in the tundra region are diverse and no uniform development derived. Although an increase in temperature has been linked to greening, shrub expansion and increased plant productivity, the driving forces behind the diverse and localised ecological changes observed in the Arctic and subarctic regions are numerous and complex. Subsequent studies could benefit from methodological advancements, such as the fusion of satellite data to overcome data gaps particular in the shoulder months of the growing season. Merging optical satellite data to achieve a better temporal and spatial resolution would benefit all examined research objectives. The fusion of SAR and optical data (e.g. by merging the Sentinel-1 and Sentinel-2 data) might be beneficial for research objectives focusing on plant productivity, species composition and disturbance analysis. The application of machine learning methods, and deep learning methods in particular, is expected to increase in the future as more (open-source) models become available, which benefits the analysis of disturbance mechanisms and species composition in the tundra. At the Living Planet Symposium, the central findings of this review will be summarised, providing an overview of the most prevalent circumpolar developments and ecological conditions. Finally, the remaining challenges of satellite remote sensing are identified.

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