Walking through the forests of the future: using data-driven virtual reality to visualize forests under climate change

References

• Ahlqvist, O., Khodke, N., and Ramnath, R., 2018. GeoGame analytics – A cyber-enabled petri dish for geographic modeling and simulation. Computers, Environment and Urban Systems, 67, 1–8. doi:10.1016/j.compenvurbsys.2017.08.013
   Web of Science ®Google Scholar
• Alley, R.B., et al., 2003. Abrupt climate change. Science, 299 (5615), 2005–2010. doi:10.1126/science.1081056
   PubMed Web of Science ®Google Scholar
• Bao, G., et al., 2012. Large-scale forest rendering. Real-time, realistic, and progressive. Computers & Graphics, 36 (3), 140–151. doi:10.1016/j.cag.2012.01.005
   Web of Science ®Google Scholar
• Bastin, J.-F., et al., 2019. The global tree restoration potential. Science (New York, N.Y.), 365 (6448), 76–79. doi:10.1126/science.aax0848
   PubMed Web of Science ®Google Scholar
• Bolliger, J., Lischke, H., and Green, D.G., 2005. Simulating the spatial and temporal dynamics of landscapes using generic and complex models. Ecological Complexity, 2 (2), 107–116. doi:10.1016/j.ecocom.2004.11.005
   Web of Science ®Google Scholar
• Bruneton, E. and Neyret, F., 2012. Real-time Realistic Rendering and Lighting of Forests. Computer Graphics Forum, 31 (2pt1), 373–382. doi:10.1111/j.1467-8659.2012.03016.x
   Google Scholar
• Canadell, J.G. and Schulze, E.D., 2014. Global potential of biospheric carbon management for climate mitigation. Nature Communications, 5 (1), 5282. doi:10.1038/ncomms6282
   PubMedGoogle Scholar
• Ch’ng, E., 2009. Experiential archaeology. Is virtual time travel possible? Journal of Cultural Heritage, 10 (4). [Accessed 27 August 2017, pp. 458–470. doi:10.1016/j.culher.2009.02.001
   Web of Science ®Google Scholar
• Chou, C.-Y., et al., 2012. Three-dimensional landscape visualizations of forest restoration scenarios for southern pine beetle-infected forests. Tree and Forestry Science and Biotechnology, 6 (1), 40–50. [Accessed 31 May 2018].
   Google Scholar
• Clayton, S., et al., 2015. Psychological research and global climate change. Nature Climate Change, 5 (7), 640–646. doi:10.1038/nclimate2622
   Web of Science ®Google Scholar
• Decaudin, P. and Neyret, F., 2009. Volumetric Billboards. Computer Graphics Forum, 26 (3), 553.
   Google Scholar
• Dede, C., 2009. Immersive interfaces for engagement and learning. Science, 323 (5910), 66–69. Available from: http://www.jstor.org/stable/20177121 [ Accessed: [25 october 2017].
   PubMed Web of Science ®Google Scholar
• Dockerty, T., et al., 2005. Visualising the potential impacts of climate change on rural landscapes. Computers, Environment and Urban Systems, 29 (3), 297–320. doi:10.1016/j.compenvurbsys.2004.05.004
   Google Scholar
• Esri, 2019. About CityEngine [online]. Available from: https://doc.arcgis.com/en/cityengine/latest/get-started/get-started-about-cityengine.htm [Accessed 4 February 2020].
   Google Scholar
• Fabrika, M., Valent, P., and Scheer, Ľ., 2018. Thinning trainer based on forest-growth model, virtual reality and computer-aided virtual environment. Environmental Modelling and Software, 100, 11–23. doi:10.1016/j.envsoft.2017.11.015
   Web of Science ®Google Scholar
• Favorskaya, M.N. and Jain, L.C., 2017. Handbook on Advances in Remote Sensing and Geographic Information Systems, Paradigms and Applications in Forest Landscape Modeling. Berlin: Springer.
   Google Scholar
• Fiore, S.M., et al., 2009. Virtual experiments and environmental policy. Journal of Environmental Economics and Management, 57 (1), 65–86. doi:10.1016/j.jeem.2008.08.002
   Web of Science ®Google Scholar
• Fisher, P. and Unwin, D., eds.. 2001. Virtual reality in geography. Boca Raton: CRC Press.
   Google Scholar
• Fujisaki, I., et al., 2008. Stand assessment through Lidar-based forest visualization using immersive virtual environment technology. Forest Science, 54 (1), 1–7.
   Web of Science ®Google Scholar
• Griffon, S., et al., 2011. Virtual reality for cultural landscape visualization. Virtual Reality, 154, 279–294. [Accessed 13 January 2017 doi:10.1007/s10055-010-0160-z
   Web of Science ®Google Scholar
• Gumbau, J., et al., 2011. View-dependent pruning for real-time rendering of trees. Computers & Graphics, 35 (2), 364–374. doi:10.1016/j.cag.2010.11.014
   Web of Science ®Google Scholar
• Gustafson, E.J., 2013. When relationships estimated in the past cannot be used to predict the future. Using mechanistic models to predict landscape ecological dynamics in a changing world. Landscape Ecology, 28 (8), 1429–1437. [Accessed 21 July 2018. doi:10.1007/s10980-013-9927-4
   Web of Science ®Google Scholar
• Gustafson, E.J. and Sturtevant, B.R., 2013. Modeling forest mortality caused by drought stress. Implications for climate change. Ecosystems, 16 (1), 60–74. doi:10.1007/s10021-012-9596-1
   Web of Science ®Google Scholar
• Hartmann, T., et al., 2016. The spatial presence experience scale (SPES). Journal of Media Psychology, 28 (1), 1–15. doi:10.1027/1864-1105/a000137
   Web of Science ®Google Scholar
• He, H.S., 2008. Forest landscape models. Definitions, characterization, and classification. Forest Ecology and Management, 254 (3), 484–498. doi:10.1016/j.foreco.2007.08.022
   Web of Science ®Google Scholar
• Heilig, M.L., 1962. Sensorama Patent. United States patent application 3050870. 28 August.
   Google Scholar
• Huang, J., et al., 2018. From archive, to access, to experience – historical documents as a basis for immersive virtual reality. Journal of Map & Geography Libraries, 14 (1), 40–63. Available from: https://www.tandfonline.com/doi/full/10.1080/15420353.2018.1498427.
   Web of Science ®Google Scholar
• Huang, J., et al., 2019a. Visualizing ecological data in virtual reality. In:. 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 23–27 March 2019 Osaka. Piscataway, NJ: IEEE, 1311–1312.
   Google Scholar
• Huang, J., et al., 2019b. Visualizing natural environments from data in virtual reality: combining realism and uncertainty. In:. 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 23–27 March 2019 Osaka. Piscataway, NJ: IEEE, 1485–1488.
   Google Scholar
• Incropera, F.P., 2015. Climate Change: A Wicked Problem:. Complexity and Uncertainty at the Intersection of Science, Economics, Politics. and Human Behavior. New York: Cambridge University Press.
   Google Scholar
• Joos, F., Prentice, I.C., and House, J.I., 2002. Growth enhancement due to global atmospheric change as predicted by terrestrial ecosystem models: consistent with US forest inventory data. Global Change Biology, 8 (4), 299–303. doi:10.1046/j.1354-1013.2002.00505.x
   Web of Science ®Google Scholar
• Kohek, Š., et al., 2019. Interactive synthesis and visualization of self-organizing trees for large-scale forest succession simulation. Multimedia Systems, 25 (3), 213–227. doi:10.1007/s00530-018-0597-6
   Web of Science ®Google Scholar
• Kohek, Š. and Strnad, D., 2018. Interactive large-scale procedural forest construction and visualization based on particle flow simulation. Computer Graphics Forum, 37 (1), 389–402. doi:10.1111/cgf.13304
   Web of Science ®Google Scholar
• Lertzman, R., 2015. Environmental melancholia:. Psychoanalytic dimensions of engagement. London: Routledge.
   Google Scholar
• Li, C., et al., 2011. Modeling and generating moving trees from video. ACM Trans. Graph, 30 (6), 1–12. doi:10.1145/2070781.2024161
   Web of Science ®Google Scholar
• Li, H. and Reynolds, J.F., 1993. A new contagion index to quantify spatial patterns of landscapes. Landscape Ecology, 8 (3), 155–162. doi:10.1007/BF00125347
   Web of Science ®Google Scholar
• Liberman, N. and Trope, Y., 1998. The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. Journal of Personality and Social Psychology, 75 (1), 5–18. doi:10.1037/0022-3514.75.1.5
   Web of Science ®Google Scholar
• Lucash, M.S., et al., 2017. Spatial resilience of forested landscapes under climate change and management. Landscape Ecology, 32 (5), 953–969.
   Web of Science ®Google Scholar
• Lucash, M.S., et al., 2018. More than the sum of its parts. How disturbance interactions shape forest dynamics under climate change. Ecosphere, 9 (6), e02293. doi:10.1002/ecs2.2293
   Web of Science ®Google Scholar
• Maceachren, A.M. and Brewer, I., 2004. Developing a conceptual framework for visually-enabled geocollaboration. International Journal of Geographical Information Science, 18 (1), 1–34. doi:10.1080/13658810310001596094
   Web of Science ®Google Scholar
• Makowski, M., et al., 2019. Synthetic silviculture. multi-scale modeling of plant ecosystems. ACM Transactions on Graphics, 38 (4), 1–14. doi:10.1145/3306346.3323039
   Web of Science ®Google Scholar
• Marriott, K., et al., eds., 2018. Immersive Analytics. Cham: Springer International Publishing.
   Google Scholar
• Mladenoff, D.J., 2004. LANDIS and forest landscape models. Ecological Modelling, 180 (1), 7–19. doi:10.1016/j.ecolmodel.2004.03.016
   Web of Science ®Google Scholar
• Müller, P., et al., 2006. Procedural modeling of buildings. ACM Transactions on Graphics, 25 (3), 614. doi:10.1145/1141911.1141931
   Web of Science ®Google Scholar
• Neubert, B., et al., 2011. Improved model- and view-dependent pruning of large botanical scenes. Computer Graphics Forum, 30 (6), 1708–1718. doi:10.1111/j.1467-8659.2011.01897.x
   Web of Science ®Google Scholar
• Neubert, B., Franken, T., and Deussen, O., 2007. Approximate image-based tree-modeling using particle flows. ACM Transactions on Graphics, 26 (99), 88. doi:10.1145/1276377.1276487
   Google Scholar
• Nielsen, J., 1993. Iterative user-interface design. Computer, 26 (11), 32–41.
   Web of Science ®Google Scholar
• O’Neill, R.V., et al., 1988. Indices of landscape pattern. Landscape Ecology, 1 (3), 153–162. doi:10.1007/BF00162741
   Web of Science ®Google Scholar
• Perry, G.L.W. and Enright, N.J., 2006. Spatial modelling of vegetation change in dynamic landscapes: a review of methods and applications. Progress in Physical Geography, 30 (1), 47–72. doi:10.1191/0309133306pp469ra
   Web of Science ®Google Scholar
• Pettit, C., et al., 2012. Developing a multi-scale visualisation framework for use in climate change response. Landscape Ecology, 27 (4), 487–508.
   Web of Science ®Google Scholar
• Saito, K., et al., 2007. Historical forest landscape reconstruction using continuous forest management data from 1931 to 2001 at Tokyo university forest in Chichibu, central Japan. Journal of Forest Research, 12 (3), 193–200. doi:10.1007/s10310-007-0010-3
   Web of Science ®Google Scholar
• Scheller, R.M., et al., 2007. Design, development, and application of LANDIS-II, a spatial landscape simulation model with flexible temporal and spatial resolution. Ecological Modelling, 201 (3–4), 409–419. doi:10.1016/j.ecolmodel.2006.10.009
   Web of Science ®Google Scholar
• Scheller, R.M., et al., 2011. The effects of forest harvest intensity in combination with wind disturbance on carbon dynamics in lake states Mesic forests. Ecological Modelling, 222 (1), 144–153. doi:10.1016/j.ecolmodel.2010.09.009
   Web of Science ®Google Scholar
• Scheller, R.M. and Mladenoff, D.J., 2005. A spatially interactive simulation of climate change, harvesting, wind, and tree species migration and projected changes to forest composition and biomass in northern Wisconsin, USA. Global Change Biology, 11 (2), 307–321. doi:10.1111/j.1365-2486.2005.00906.x
   Web of Science ®Google Scholar
• Scheller, R.M. and Mladenoff, D.J., 2007. An ecological classification of forest landscape simulation models. Tools and strategies for understanding broad-scale forested ecosystems. Landscape Ecology, 22 (4), 491–505. doi:10.1007/s10980-006-9048-4
   Web of Science ®Google Scholar
• Schroth, O., Pond, E., and Sheppard, S.R.J., 2015. Evaluating presentation formats of local climate change in community planning with regard to process and outcomes. Landscape and Urban Planning, 142, 147–158. doi:10.1016/j.landurbplan.2015.03.011
   Web of Science ®Google Scholar
• 1962. Sensorama Patent.
   Google Scholar
• Shaw, A., et al., 2009. Making local futures tangible—synthesizing, downscaling, and visualizing climate change scenarios for participatory capacity building. Global Environmental Change, 19 (4), 447–463. doi:10.1016/j.gloenvcha.2009.04.002
   Web of Science ®Google Scholar
• Sheppard, S., 2012. Visualizing Climate Change. A guide to visual communication of climate change and developing local solutions. London: Earthscan.
   Google Scholar
• Sheppard, S.R.J., 2015. Making climate change visible. A critical role for landscape professionals. Landscape and Urban Planning, 142, 95–105. [Accessed 11 January 2017 doi:10.1016/j.landurbplan.2015.07.006
   Web of Science ®Google Scholar
• Singh, A.S., et al., 2017. The perceived psychological distance of climate change impacts and its influence on support for adaptation policy. Environmental Science & Policy, 73, 93–99. doi:10.1016/j.envsci.2017.04.011
   Web of Science ®Google Scholar
• Sutcliffe, A. and Gault, B., 2004. Heuristic evaluation of virtual reality applications. Interacting with Computers, 16 (4), 831–849. doi:10.1016/j.intcom.2004.05.001
   Web of Science ®Google Scholar
• Sutherland, I.E., 1968. A head-mounted three dimensional display. In: 1968.
   Google Scholar
• Swetnam, R.D. and Korenko, J., 2019. Can computer game landscapes target new audiences for landscape quality assessment? Applied Geography, 113, 102102. doi:10.1016/j.apgeog.2019.102102
   Web of Science ®Google Scholar
• Tan, P., et al., 2007. Image-based tree modeling. ACM Transactions on Graphics, 26 (99), 87. doi:10.1145/1276377.1276486
   Google Scholar
• Wang, X., et al., 2006. Visualizing forest landscapes using public data sources. Landscape and Urban Planning, 75 (1–2), 111–124. doi:10.1016/j.landurbplan.2004.12.010
   Web of Science ®Google Scholar
• Williams, J.W. and Jackson, S.T., 2007. Novel climates, no-analog communities, and ecological surprises. Paleoecology, 5 (9), 475–482.
   Google Scholar
• Wilson, B.T., Lister, A.J., and Riemann, R.I., 2012. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. Forest Ecology and Management, 271, 182–198. doi:10.1016/j.foreco.2012.02.002
   Web of Science ®Google Scholar
• Wither, J., et al., 2009. Structure from silhouettes: a new paradigm for fast sketch-based design of trees. EUROGRAPHICS, 30 March–3 April 2009 Munich. Oxford: Blackwell Publishing, 541–550.
   Google Scholar
• Xie, X., et al., 2011. Application of China’s national forest continuous inventory database. Environmental Management, 48 (6), 1095–1106. doi:10.1007/s00267-011-9716-2
   PubMed Web of Science ®Google Scholar
• Zamuda, A. and Brest, J., 2013. Environmental framework to visualize emergent artificial forest ecosystems. Information Sciences, 220, 522–540. doi:10.1016/j.ins.2012.07.031
   Web of Science ®Google Scholar
• Zhang, F., et al., 2012. Attributing carbon changes in conterminous U.S. forests to disturbance and non-disturbance factors from 1901 to 2010. Journal of Geophysical Research: Biogeosciences, 117 (G2), n/a-n/a.
   Google Scholar
• Zhang, X., et al., 2014. Data-driven synthetic modeling of trees. IEEE Transactions on Visualization and Computer Graphics, 20 (9), 1214–1226. doi:10.1109/TVCG.2014.2316001
   PubMed Web of Science ®Google Scholar
• Zhang, X., et al., 2017. Tree branch level of detail models for forest navigation. Computer Graphics Forum, 36 (8), 402–417. doi:10.1111/cgf.13088
   Web of Science ®Google Scholar