Mapping and modeling land cover change: impacts on terrestrial ecosystems

Part of my past collaborative research projects involved evaluating the effect of uncertainties in the prescribed global vegetation distribution maps on predictions of carbon, water and energy-related variables – an aspect of model uncertainty that has mostly been ignored in the past. We found that uncertainty due to vegetation distribution can be of the same order of magnitude as inter-model spread due to parametric and structural uncertainty, therefore we suggested that considered efforts need to be made by both the satellite and modelling communities to derive better maps of vegetation distribution in the future.

• Poulter et al. (2014) Plant functional type classification for Earth System Models: results from the European Space Agency’s Land Cover Climate Change Initiative, Geoscientific Model Development, 8, 2315-2328.
• Hartley et al. (2017) Uncertainty in plant functional type distributions and its impact on land surface models. Remote Sensing of Environment Special Issue on Earth Observation of Essential Climate Variables, 203, 71-89.

We also investigated the influence of uncertainties in maps of historical land cover change on carbon, water and energy flux simulations – a key topic given that land cover and land use change is the second biggest source of anthropogenic CO₂ emissions to the atmosphere.

• Li et al. (2016) Major forest changes and land cover transitions based on plant functional types derived from the ESA CCI Land Cover product. International Journal of Applied Earth Observation and Geoinformation, 47, 30-39.
• Li et al. (2018) Gross and net land cover changes based on plant functional types derived from the annual ESA CCI land cover maps. Earth Syst. Sci. Data, 10, 219-234

This is not a research topic that I am currently pursuing, except for exploring the role of fractional cover/land cover mapping uncertainty in dryland regions. However, in future research I plan on returning to this topic — especially to determine how historical land cover change has affected modeled carbon stocks, and how state data assimilation could be used to alleviate this source of modeling uncertainty.

This work was funded by:

• European Space Agency CCI Land Cover Project: an ESA Climate Change Initiative project aiming to provide new land cover maps for earth system modelers based on a state of the science satellite mapping algorithm.