Digital data of the earth surface on elevation (digital Terrain models, DTM) and of land cover (satellite and aerial photographs) in combination with new software (Geographical Information Systems, GIS) offered new levels of detail, interactivity and dynamics in landscape visualisation.
Thus, it became a commonly used tool to support decision making and participatory approaches in landscape planning (Smith et al. 2009, http://www.mssanz.org.au/modsim09/F8/smith_el.pdf), to illustrate scenarios of landscape development, and last not least, to support tourism (Patterson 2009, http://www.shadedrelief.com/). Landscape visualisation thereby ranges from photorealistic images (Dey 2005, http://www.3dnworld.com/users/103/research/dey_research_paper.pdf) to highlighting or selecting certain relevant parameters, according to thematic mapping.
To receive information about the surface of the land there are conventional ground survey maps, satellite images and digital elevation data. Cognition is most fruitful, when combining these data sources (Donner 2008). Improving GIS capabilities, data storage capacity and data availability strongly supports the trend towards computer-based visualisation of geographical topics.
In pre digital age, most 3D-visualisations were drawn according to geographer’s imagination of morphographic reality. This practice gave 3D-visualisations the status of being the result of subjective personal efforts (with an unknown bias or error).
As nowadays combinations of terrain data with other information layers can be produced in an unbiased form by elaborated GIS methods, digitally generated 3D-visualisations became an objective derivation from reality and thus more reliable and inviting as a tool for subsequent cognition to discover spatial correlations, patterns, structures etc.
The cognitive potential of digitally generated 3D-visualisations increases if the presented parameters are offered with selective accentuation, highlighting certain parameters while suppressing and generalizing others.
Most research on visualisation of landscape and land surface is done to achieve photorealistic results of actual and possible future states for planning purposes (Wissen 2009). The planning alternatives or future scenarios refer in the majority of cases to land cover changes, development projects, impacts of infrastructure construction and mining (Buhmann 2002) or of power generation, e.g. dams or wind energy parks (Hehl-Lange and Lange 2005). Similar approaches are carried out for urban areas (Glander and Döllner 2008, Ross et al. 2009). As planning increasingly relies on participatory approaches, the interactive aspects of visualisation tools gain importance (Schroth 2009, Paar 2006).
These applications are also accompanied by theoretical considerations on the advantages of visual interpretation via comprehensive observation (“verstehendes Beobachten”, see Donner 2008). Besides, for engineering purposes and geo-ecological assessments, countless overlay operations including spatially comprehensive data collection of parameters relating to land surface qualities are carried out by standard GIS applications.
Methods of geographically oriented and earth surface related data processing for 2,5 dimensional representation (Trapp and Döllner 2009), generalisation procedures (Trapp and Döllner 2008) and scaling approaches (Gallant and Hutchinson 1997) provide substantial basics. One of the most important prerequisites to promote cognitive processes and better understanding of landscape structures and processes is an adequate level of detail (Gallant and Hutchinson 2008). This refers to the morphologic structure of the evolved terrain model (Reuter et al. 2006; Wolock 2000) as well as to the draped thematic layers (Glander and Döllner 2008).
Further links to history, options and techniques of landscape visualisation at ETH Zürich (http://www.reliefshading.com/design/flatarea.html,
http://www.terrainmodels.com/)
and at the company scilands (http://www.scilands.de/)
References:
Buhmann, E. 2002: Using GIS for Visualization of the Changing Landscape of the Brown Coal Mining Areas at the International Building Exhibition (IBA) Fürst Pückler Land. - In: Trends in GIS and Virtualization in Environmental Planning and Design. – Heidelberg, Wichmann Verlag
Donner, R.U. 2008: Die visuelle Interpretation von Fernerkundungsdaten. - Habilitationsschrift, TU Bergakademie Freiberg: 175 p.
Gallant, J. C. and Hutchinson, M.F. 2008: Digital terrain analysis. - In: McKenzie, N. J., Webster, R., Grundy, M.J. and A.J. Ringrose-Voase (eds.): Guidelines for surveying soil and land resources, 2nd ed.: 75-91
Glander, T. and J. Döllner 2008: Automated Cell-Based Generalization of Virtual 3D City Models with Dynamic Landmark Highlighting. - Online Proceedings of 11th ICA Workshop on Generalisation and Multiple Representations.
Hehl-Lange, S. and E. Lange 2005: Ein partizipativer Planungsansatz für ein Windenergieprojekt mit Hilfe eines virtuellen Landschaftsmodells. - Natur und Landschaft 80: 148-153
Paar, P. 2006: Landscape visualizations: Applications and requirements of 3D visualization software for environmental planning. - Computers, Environment and Urban Systems 30 (6): 815–839
Reuter, H.I., Wendroth, O. and K.C. Kersebaum 2006: Optimisation of relief classification for different levels of generalisation. - Geomorphology 77 (1-2): 79-89
Ross, L., Bolling, J., Döllner, J. and B. Kleinschmit 2009: Enhancing 3D City Models with Heterogeneous Spatial Information: Towards 3D Land Information Systems. – In: Sester, M., Bernard, L., and V. Paelke (eds.): 12th AGILE International Conference on GI Science, Lecture Notes in Geoinformation and Cartography. – Heidelberg, Springer: 113–133
Schroth, O. 2009: From Information to participation. Interactive landscape visualization as a tool for collaborative planning. - Bericht des Instituts für Raum- und Landschaftsentwicklung (IRL) der ETH Zürich 6: 232 p.
Trapp, M. and J. Döllner 2009: Dynamic Mapping of Raster-Data for 3D Geovirtual Environments. - 13th International Conference on IEEE Information Visualization, IEEE Computer Society Press: 387–392
Trapp, M. and J. Döllner 2008: Generalization of Single-Center Projections Using Projection Tile Screens. – In: Braz, J., Ranchordas, A.K., Pereira, J. M. and H.J. Aráujo (eds.): Advances in Computer Graphics and Computer Vision (VISIGRAPP), Communications in Computer and Information Science (CCIS) – Heidelberg, Springer.
Wolock, D.M. 2000: Differences in topographic characteristics computed from 100-and 1000-m resolution digital elevation model data. – Hydrological Processes 14: 987-1002
Wissen, U. 2009: Virtuelle Landschaften zur pertizipativen Planung. Optimierung von 3D Landschaftsvisulisierungen zur Informationsvermittlung. Publikationen des Instituts für Raum- und Landschaftsentwicklung (IRL) der ETH Zürich 5: 244 p.