Un exemple sobre la teoria del disseny de mecanismes

the right each newly growing tree is modelled

information. Aerial orthophotos, which are now available at a resolution of as little as 10 cm, provide the basis for highly realistic visualizations. Furthermore, satellite-based sensors such as the recently launched QuickBird (October 2001) or Ikonos (1999) are constantly improving and are already achieving resolutions of 61 cm in the case of QuickBird (see Chapter 3 for further details on the use of digital terrain and remote sensing data). In small areas, especially when the camera is close to ground level, even very high-resolution imagery can produce unrealistic foreground effects and individual elements, such as vegetation, may need to be modelled (Figure 1.12).

Vegetation

Because of its richness in geometry, vegetation is perhaps the most challenging landscape element. Real vegetation is very complex, as it consists of a large number of objects such as leafs, buds, flowers, twigs, bark, etc. Even more challenging is the diversity of

Communication, perception and visualization 15

vegetation elements in the landscape. Depending on neighbourhood relationships, competition for light and nutrients or any impact caused by human intervention or natural phenomena, one particular tree species can take quite different physical forms.

Vegetation is typically represented by either applying texture maps on simple rectangular polygons, so called billboards, or by detailed polygonbased modelling of the geometry of the vegetation (see Reffye et al. 1988). The problem with the latter approach is that even one single tree with leaves or needles can consist of thousands or even millions of polygons. Consequently, this has a considerable impact on the time it takes the computer to draw (render) the picture (‘so many polygons, so little time’). This can undermine the utility of the whole simulation process (see also Chapter 4, ‘Efficient modelling and rendering of landscapes’, p. 56).

Texture mapping is a very efficient and simple method of rendering vegetation structures. Relatively simple texture maps can replace complex three-dimensional geometries and microstructures. This allows high visual complexity without excess effort on geometric complexity. Convincing representation of vegetation can also be achieved through a combination of detailed geometry where needed (stem and large twigs) and detailed texture (e.g. small twigs, leafs and flowers) mapped on simple polygons where the geometry is incidental.

Prusinkiewicz and Lindenmayer (1996) developed a different polygon-based approach. Their L-System, which allows the rendering of photorealistic plants, is based on a formal language describing the natural growth of the plants. House et al. (1998) applied hierarchical level-of-detail modelling in order to create a very convincing computer-generated walkthrough of an existing forest. Deussen et al. (see p. 56) have shown that the complexity of a visual representation of plant ecosystems can be addressed efficiently by combining the use of different levels of abstraction at different stages of the modelling and rendering process (Deussen et al. 1998; Deussen 2003).

Animals and humans

For a long time, the visualization of animals and humans in a landscape context was either omitted, or a character animation was the sole purpose (e.g. Magnenat-Thalmann et al. 1987, 1989). From a broader ecological view, animals and humans need to be included as they are an important factor influencing human visual perception and shaping the landscape. The presence or absence of animals and humans in the real landscape can greatly influence an evaluation (Hull and McCarthy 1988).

In contrast to essentially static landscape elements such as vegetation, what is especially complicating in the representation of animals and human is their inevitable movement through space. The virtual oceanarium project, for example, permits exploration of several aquatic ecosystems from around the world in a moderate degree of realism, but in three-dimensional stereo projection (see Fröhlich 2000). It is also possible now to encounter virtual humans that deform themselves during motion (Magnenat-Thalmann and (Magnenat-Thalmann 2001). These simulated people—often called avatars—can be placed in a simulated environment and can then determine their own actions as independent agents (Farenc et al. 2000).

Technologies to capture the complex geometry of the bodies of animals and humans received a strong impulse from computer game development, automotive design, medical

and military applications. Nowadays, three-dimensional scanners allow scanning, and hence model building, of the shape and colour of a whole human body at once (e.g.

http://www.cyberware.com/). Optical motion tracking systems (e.g.

http://www.ascension-tech.com/) can capture up to 900 measurements per second. This may be combined with a stereo three-dimensional projection so users can directly interact with characters embedded within virtual worlds.

Virtual stewardesses now explain safety measures on aircrafts. Movies feature highly realistic virtual actors. Even the subtle expression of moods, age transitions or the gravitational behaviour of hair can be simulated. Walter et al. (2001) automatically produce mammalian models with individual bodies at different ages and their associated coat patterns.

Despite these prodigious developments, and although some experimental methods exist for specific environments (Cruz-Neira 2003), we are still some way from simulation and visualization of autonomous animal or human behaviour.

Water

Water is only occasionally static. Apart from a lake surface on a quiet morning, water is a very dynamic landscape element. It takes many forms—rushing streams, waterfalls, waves—which interact in complex ways with the terrain over which they are moving.

Fournier and Reeves (1986) modelled ocean waves where the disturbing force is wind and the restoring force is gravity. Even the foam generated by the breakers was modelled by particle systems. This concept is now integrated in standard software running on PCs allowing particles to react with dynamic fields such as gravity, wind and turbulences.

Built structures

In our cultural landscape, built structures play a significant role in creating a sense of place. Integrating built objects in a virtual environment can

be a very labour-intensive process. Nowadays, nearly all new architectural proposals are created using computer aided design (CAD). However, from a planning perspective it is equally important to include the surroundings as well. This raises two problems:

• typically these existing nearby buildings are not available digitally;

• CAD systems do not include georeferencing and so fitting new structures into existing models is imprecise.

Nevertheless, some cities, e.g. Basel, Switzerland, already work with detailed three-dimensional models on a citywide scale.

Recent software developments make it easier to build simple three-dimensional models which may be sufficient for many visualization needs. These programs use a library of common structural types (e.g. cuboid, cylinder, trapezium) and can model in a photorealistic way from digital photographs quickly and without extensive three-dimensional skills. Despite the simplified geometry, the models look realistic because of the extracted textures which are mapped on the surfaces.

Beck and Steidler (2001) present a new, efficient approach to record and visualize existing built objects. Their approach is based on a semiautomated generation of

three-Communication, perception and visualization 17

dimensional objects of the built environment, which allows the fitting of planar structures to a measured set of point clouds. The measurements are taken by an operator, and the structuring of the data is done by the computer.

Another promising alternative for detailed data collection for a small number of buildings is ground-based three-dimensional laser scanning (Manandhar and Shibasaki, 2001). This technology provides a very dense point cloud which can then be structured with specialized software.

Atmosphere and light

The appearance of all these landscape elements can vary greatly under different atmospheric conditions. Influences, including the position and the related intensity of the sun, objects obscuring the light source and general atmospheric or weather conditions, affect the hue, saturation and lightness of all surfaces. Simple fog models reduce the saturation of image pixels based on depth. Clouds can be texture mapped onto a sky dome. However, the atmosphere is often more complex than these simple approaches can effectively portray.

Early in computer graphics history, Reeves and Blau (1985) developed particle systems for the visualization of vegetation elements, water movement, fire, explosions and flocks of birds. Nowadays, several commercial software packages offer particle systems for the visualization of atmospheric effects, such as snow and rain. Based on the OpenGL library freely definable volumetric cloud layers, consisting of several different cloud types, can be rendered in real time, even reacting to wind speed and wind direction (Beck 2003).

The two most popular methods for calculating realistic images are radiosity and ray tracing. Both methods allow computation of the effects of lighting and can output images of photorealistic quality. With ray tracing, scenes that include specular reflections and transparency can be simulated very effectively. For scenes with a high number of light sources and only diffuse reflections, the radiosity approach has advantages: particularly it allows for real-time movement through the model. Kaneda et al. (1991) and Nakamae et al. (2001) provide good examples of landscapes under natural sunlight conditions.

Perceptual and societal issues

Research in landscape visualization has, to date, focused on technological issues and incredible advances have been made. It may not be long before we see photorealistic interactive virtual worlds where avatars (virtual humans) interact with each other and the environment in natural ways. However, in contrast to the technological challenges, perceptual and societal issues of visualization have hardly been touched (cf. Lange 1999;

Bishop et al. 2001) in landscape visualization research.

Because landscapes are highly complex, visualization can be incredibly laborious. On the other hand, the omission of details of the real landscape makes for a certain sterility of virtual landscapes (Ervin 2001). How much reality is needed for effective environmental planning? What is the difference in terms of perception between naturalism (‘looking like’) and realism? Daniel and Meitner (2000:69) suggest that:

more abstract representations appear to be inappropriate for determining landscape aesthetic/scenic beauty values. An important question for further research is to determine what representations are necessary and sufficient to achieve valid indications of the effects of particular environmental conditions and characteristics on specified behavioral, perceptual or valuation responses.

Meanwhile, Appleton and Lovett (2003) have undertaken perception studies using commercial software to determine how acceptance of GlS-based visualization varies with level of detail. While studies in landscape have tended to reinforce the importance of realism, Pietsch (2000) has argued the contrary in an architectural, urban design context.

This debate continues.

For many years now, digital visualizations have been available for planning. However, they are often regarded as the pretty pictures produced at the end of a linear planning process. The great potential of landscape visualization lies in its early integration in the planning process. Only if the (still pretty) pictures are an integrated and integrating part of the planning process we can expect better and more informed results. The major reasons why this change has not happened already appear to be:

• the lack of user-friendly and intuitive software tools for easy manipulation and design of the landscape (as proposed by ZALF 2002). Visualization has required a high level of specialized skills thereby limiting a widespread application in practice;

• the lack of coupling of visual representation with underlying landscape functions (Hehl-Lange 2001) or political processes which create the specific visual appearance of the landscape;

• the inability, in most cases, of the visualization to support interactive manipulation of design or planning elements.

Some emerging programs (e.g. Kwartler and Bernard 2001) allow alternative land development policies to be formulated as maps, through tables or as the output of scenario models. The alternatives are then directly translated into a three-dimensional model allowing interactive exploration of scenarios, with underlying functional models and minimal technological expertise (see also Chapter 10, p. 251).

Over the next decade, these developments will become widely accessible to people working in spatially relevant disciplines. It is then up to the professionals in landscape and environmental planning to take advantage of the opportunities offered.

Communication, perception and visualization 19

CHAPTER 2