Tipping point detection on Greenland with vegetation indices
Photo source: https://www.flickr.com/photos/gridarendal/31941518052/in/photostream/
Vegetation is currently scarce on Greenland, and does not occur on the ice-sheet, as it grows around the edges. However, vegetation plays an important role in the energy budget of a surface (Oehri et al. 2022). Besides, vegetation has the potential to act as an instigator to increase energy budget imbalances because it plays a major role in an important positive feedback loop. Increasing global temperatures are beneficial for plants that are mainly temperature limited in Greenland. When plant density increases, this will have a negative effect on the albedo of the land surface, resulting in less reflection of solar energy and more absorption of it. Any changes in vegetation characteristics will potentially increases the surface temperature and indirectly thus the air temperature, having a positive effect on plant growth.
Monitoring vegetation dynamics with remote sensing is a well-developed field offering a good way to generate indicators at high spatial resolution and with dense time intervals. Remote sensing has been used in earlier studies on tipping point detection but it is currently not yet used on Greenland.
To enhance our understanding of vegetation dynamics and changes, we propose to generate consistent and localized timeseries of important vegetation indices (VI’s) by combining data sources for the global satellite archives (mainly Sentinel 2 and Landsat) and analyze them for any early warning signals (autocorrelation, skewness etc). Then Early warning indicators will be extracted from these timeseries using different regions of interest along the Green Land Ice-sheet. These regions of interest will be based on vegetation zones, topographic and geographic zones, and based on unsupervised clustering of the time series (for example using k-means clustering) , to find out if there are clusters that are showing significantly different trends in terms of temporal behavior.
Oehri, J., Schaepman-Strub, G., Kim, JS. et al. Vegetation type is an important predictor of the arctic summer land surface energy budget. Nat Commun 13, 6379 (2022). https://doi.org/10.1038/s41467-022-34049-3