2021 ESA Annual Meeting (August 2 - 6)

Visualizing forest futures: Transdisciplinary approaches to forest management under climate change by integrating immersive virtual reality, modeling, and robust decision analysis

On Demand
Erica AH Smithwick, The Pennsylvania State University;
Background/Question/Methods

Visualizing how forests might change with climate change is essential to enact management and policy changes today, but reliance on model output, graphs, and projections may not be sufficient to fully capture values and tradeoffs that underpin decision making under deep uncertainties. Recent research in cognitive psychology and learning show that immersive virtual reality (iVR) can be used to inform knowledge co-production and transdisciplinary decision-making. Moreover, alternative objectives around forest stewardship and uncertainties in climate futures require approaches to examine a range of future potential outcomes. We use a combination of ecosystem modeling, data-driven iVR, and robust decision-making data assimilation approaches to visualize forest conditions and tradeoffs among competing objectives. The work is conducted in collaboration with the College of Menominee Nation, located in northern Wisconsin, U.S.A.

Results/Conclusions

Using procedural modeling and the Unreal game engine, we visualized a forest plot in Wisconsin, U.S.A. under scenarios of climate change and forest disturbance (windthrow). Users can interact with the scene using point-and-click features and compare outcomes among scenarios. Results show high accuracy of visualizations compared to comparable Forest Inventory and Analysis plots. Robust decision making analysis yields insights into how biodiversity and carbon outcomes vary as a function of future climate pathways and management strategies. We conclude that values-informed mental models that use iVR to elicit and make transparent a fuller range of values around forest futures are likely to be increasingly important when assessing tradeoffs under joint climate and ecological uncertainties.