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VISUALISING FUTURE LANDSCAPES IN THE EAST OF ENGLAND ANDREW LOVETT, KATY APPLETON,TRUDIE DOCKERTY AND GILLA S키NNENBERG Introduction The future form of the British countryside is currently the subject of considerable debate (e.g. Countryside Agency, 2003; Townshend et al., 2004).

These deliberations reflect a range of pressures, including economic difficulties in the agricultural sector, concerns regarding habitat loss, problems of social exclusion, and the possible impacts of climate change (DETR and MAFF, 2000; Countryside Agency 2002). Recent years have also seen an increasing emphasis on public consultation and participation within the planning system (Cullingworth & Nadin, 2002), but communicating different options in a manner that facilitates informed decisions by stakeholders can be far from straightforward. This is particularly true with more abstract and uncertain issues such as the potential impacts of climate change.

Developments in computing technology are now helping to tackle these issues by making it feasible to produce visualisations of current or possible future landscapes. Map information can be compiled in digital form within a Geographical Information System (GIS) and then processed within linked visualisation software to generate a variety of outputs including still images of the view from a particular point, animated sequences (such as a flythrough of an area), or a virtual world where a mouse or joystick can be used to interactively navigate around a landscape (Appleton et al., 2002). These types of visualisations can now be produced on a PC and make use of the digital map databases available from organisations such as the Ordnance Survey (www.ordnancesurvey.co.uk). The combination of these developments has also substantially reduced the cost of undertaking this type of work and a number of studies have begun to evaluate the benefits of using visualisation techniques to communicate policy options or facilitate stakeholder engagement in decision-making processes (e.g. Batty et al., 2001; Lovett et al., 2002).

The remainder of this paper presents two case studies of the use of visualisation techniques. Both studies were based within part of the area covered by the Norfolk Arable Land Management Initiative (NALMI). The first study was concerned with visualising the potential impacts of different climate change scenarios at the local scale, while the second examined several public access and restoration options along a section of the River Wissey.

Implications from these studies regarding the further use of GIS-based landscape visualisations are considered in the concluding section of the paper.

The Study Area NALMI is a pilot study within a national Countryside Agency programme (see www.countryside.gov.uk/LivingLandscapes/sustainable_land_management/ LMIs.asp), working with rural communities in several parts of Britain to identify means of facilitating more sustainable land management practices.

Previous research for NALMI has included some assessment of potential climate change impacts in the locality (e.g. Lorenzoni et al., 2001) and the Trans. Suffolk Nat. Soc. 40 (2004)

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area is also a good example of the types of agriculture and environmental conditions present in much of eastern England. The region covered by the NALMI project encompasses 13 parishes situated between the towns of Dereham and Watton in central Norfolk (see Figure 1). A 9 km by 5 km

Figure 1. The location of the NALMI project and study area

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rectangle (delimited by a dashed line in Figure 1) within this region was selected as the particular study area for the research. This rectangular area constitutes a small river valley with slightly higher elevations on the northern, eastern and southern sides. Large arable fields (at least 10 hectares in size) characterise much of the area, with smaller plots of land closer to the river.

The River Wissey flows west from its source north of Shipdham through the study area and subsequently joins the Great Ouse which drains into The Wash at King’s Lynn.

Ordnance Survey (OS) digital map data for the study area were obtained via the Digimap project web site at the University of Edinburgh (edina.ac.uk/ digimap). Variations in elevation were represented using the Panorama® 1:50,000 scale Digital Elevation Model (DEM) with a 50 m cell resolution. OS Landline® vector data were used as a framework for more detailed land use mapping. These data consist of point, line and annotation features.

Considerable editing was carried out in the Arc/Info GIS to produce a map layer that contained over 10,000 polygons. Attributes for features such as buildings, roads and water bodies were assigned from the Landline® information. Additional land use information (particularly on crop distributions) was obtained through a field survey during summer 2001. Over 30 different crop and other land use categories were recorded and these details were subsequently assigned as attributes to the vector polygons. Figure 2 shows a simplified map of the land use survey data and highlights the dominance of arable farming (mainly wheat, barley, sugar beet, oil seed rape and several types of fruit production) with little in the way of livestock husbandry.

Creating Landscape Scenarios from Climate Change Information There is now growing certainty that human activities are increasing concentrations of greenhouse gases in the atmosphere and causing global warming and climate change (Inter-governmental Panel on Climate Change, 2001). Making projections of climate change requires knowledge of past and current emissions of greenhouse gases and an estimation of likely future emissions. The level of future emissions depends on how society develops, a question that is subject to rather greater uncertainty than those which remain in understanding the science of the climate system (UKCIP, 2000). To provide a framework for climate change projections the IPCC have therefore produced a set of scenarios representing possible directions for human development over the next 100 years, encompassing different systems of governance and societal structures (Nakićenović et al., 2000).

Four equi-plausible scenarios have been formulated that together cover a significant proportion of the uncertainties in future demographic, economic and technological change. These ‘storylines’ are differentiated on two dimensions (see Figure 3), with the horizontal axis representing an emphasis on economic development at one end and sustainability at the other. The vertical axis differentiates systems of governance, with the lower extreme denoting a convergence in the policies of individual governments and globalised markets, while the upper one constitutes a future where national or

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Figure 2. Land use in the Wissey study area, summer 2001.

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regional policy differences are maintained. Combining the two dimensions defines a range of societal futures where levels of warming are likely to vary, as shown in Figure 3 by the predicted temperature changes from the latest UK projections prepared by the Tyndall Centre for Climate Change Research (Hulme et al., 2002).

A2 National Enterprise Local economic emphasis, some switch from fossil fuels, Temperature up 1.9°C by 2050s Consumerism

A1 World Markets Global economic emphasis, no switch from fossil fuels, Temperature up 2.2°C by 2050s

Governance

Autonomy

where we are now

Interdependence

B2 Local Stewardship Local sustainability emphasis, some new technology, Temperature up 1.6°C by 2050s Community

B1 Global Sustainability Global sustainability emphasis, clean technology, Temperature up 1.4°C by 2050s

Figure 3 Characteristics of future world development pathways (Sources: Nakićenović et al., 2000; SPRU, 1999; Hulme et al., 2002) A key factor mediating how these scenarios translate into changes in the British countryside is likely to be the economic return available from the land.

To take account of possible future changes in agricultural activities, particularly alterations in crop distributions, output from CLUAM (a climate and land use allocation model) was used. CLUAM incorporates the effects of global climate change on agriculture in England and Wales through links with a model of world food trade (the Basic Linked System or BLS) (Parry et al., 1999). The BLS is run for a particular socio-economic and climate change scenario and the outputs (yields, prices and demands for the modelled commodities – crops and grass-based livestock production) are subsequently input to CLUAM. Base data within CLUAM are structured around the ITE Land Classification System and total areas for each agricultural activity are calibrated to accord with Agricultural Census statistics, so providing a baseline against which changes suggested by individual scenarios can be compared.

The current baseline is centred on the mid-1990s. CLUAM essentially treats England and Wales as a single farm and, for a given scenario and associated constraints, employs linear programming techniques to allocate 1 km squares to uses in a manner that optimises the gross margin achievable from all the possible agricultural activities on the different parcels of land. CLUAM output indicates the percentage land use per 1 km square for each of the following; wheat, barley, oats, oilseeds, peas/beans, sugar beet, potatoes, maize, sunflowers, ley grassland, permanent grassland, and rough grassland.

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Climate change futures for the NALMI area in 2020 were investigated by considering two IPCC scenarios – A2 National Enterprise and B2 Local Stewardship. These are contrasting views in that the former emphasises economic development and the latter strong environmental policies. In order to represent the CLUAM outputs as landscape visualisations a downscaling exercise was carried out. This used a set of rules to assign land uses to fields in the GIS database in a manner that matched the percentage shares in the 1 km square CLUAM output and also resulted in a plausible crop pattern given the nature of the terrain, soils and farming history in the area. All attribute changes were made using reselection or other editing commands within the Arc/Info GIS, and it proved possible to approximate the required hectare targets without splitting fields. For further details see Dockerty et al. (2005).

A raster DEM and ArcView shapefiles of the land use distributions were imported into the Visual Nature Studio (VNS) software (www.3dnature.com) to produce landscape visualisations. This software simulates landscape scenes by generating photorealistic images of the view from camera positions defined by the user (Appleton et al., 2002). Within VNS, visual properties such as colour, texture and the presence of vegetation can be assigned to areas of the terrain surface as defined by polygon or grid cell attributes. Buildings and other structures can be linked to specified points, and other features such as water, sky and lighting can also be controlled. Externally produced 3-D objects can be imported to represent buildings or other structures, and may also be used for vegetation, although two-dimensional images are more commonly employed (as in this research) due to high processing loads when they are as numerous as trees and plants in a rural landscape. For processing reasons, it was also decided to represent most buildings by simple extrusions of their footprint polygon.

In this study, three sets of land use information were involved (for 2001 and the two climate change scenarios) and search queries in VNS were used to dynamically relate feature attributes (e.g. as defined in a shapefile) to the relevant visual properties. The software’s own ‘scenario’ capability was also used to link together and activate/deactivate different sets of visual properties depending on the landscape to be simulated. For example, in the National Enterprise scenario grass, trees and bushes were altered to appear more yellow/brown, additional gaps were added to hedges, dead trees were introduced, and the river level was reduced to reflect the effects of higher warming and poor water management.

Figure 4 shows three example visualisations from a bird’s-eye perspective looking south east across the Wissey valley (see arrow on Figure 2). The image in Figure 4a represents the 2001 landscape, with the River Wissey in the foreground, rough grassland to the left, sugar beet to the right, and cereal fields in the right middle distance. In Figure 4b, the National Enterprise scenario for 2020, conditions are drier and although wheat production remains important the CLUAM output suggests that some crops such as sugar beet will be replaced by others like sunflowers that can cope better with the climate.

This is depicted in Figure 4b by the change in land use to the right of the River Wissey. The model output also suggests an increase in ley grassland and a

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shift into livestock farming, as indicated by the cows on the ley grassland to the left of the river. A reduced river level and a decline in vigour of hedgerow trees and shrubs indicate the underlying water-stress, with gaps appearing where a lower water table leads to die-back. Fossil fuels are still widely used and the only evidence of adaptation in the built environment is the whitewashing of walls to reflect the heat.

In the Local Stewardship scenario there is more emphasis on environmental protection, with less intensive agricultural systems and better water conservation practices. As with the National Enterprise results, CLUAM predicts a shift into animal production and increased grassland cover.

However, there is also a marked change in crop composition, with wheat production moving to more favourable climatic conditions further north in England and being replaced by barley and maize. These developments are shown in Figure 4c with maize on the field to the right of the river and a lower density of cattle on the grassland to the left. The landscape also reflects a greater emphasis on mitigation and adaptation, with a move away from the use of fossil fuels being indicated by the wind generator and solar panels on the house in the right foreground and the windfarm on the horizon.

Viewed as a sequence, the images in Figure 4 provide more immediate illustrations of the potential local impacts of climate change than are possible with conventional maps. The variation between scenarios also conveys the point that the future is not fixed and that much will depend on how societies respond to the challenges posed by climate change. Disseminating such a message is important from the perspective of generating discussion about policy options. Nevertheless, it is important to recognise that the visualisations are no more than ‘plausible representations’ and not to interpret them too literally. A number of assumptions, and no little creative interpretation, were involved in downscaling and visualising the CLUAM output. Similar types of decision are inherent in the production of almost any landscape visualisation and it is therefore important for the data sources and production process to be as transparent as possible if the results are to be regarded as credible by their intended audiences (Sheppard, 2001). Possible extensions to the research discussed above are considered in the final section of this paper.

Landscape Planning at the Local Scale A second example of the use of landscape visualisation techniques examined several public access and restoration options along a section of the River Wissey. Previous local consultation by the NALMI project had identified a desire for greater access to the River Wissey, and concerns, both locally and downstream, over flooding. Three separate schemes were put forward by two landowners for initial consideration by the public and relevant statutory agencies (see Figure 5). They comprised: a riverside bridlepath (permissive rather than a full right-of-way); lakes, to be formed by gravel-digging; and an area of managed floodplain with associated earth bank and sluice. Existing GIS databases for the area (discussed in Section 2) were used as the basis for visualising the proposals, supplemented by field visits, ground and aerial photography as necessary.

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Figure 4. Landscape visualisations from a bird’s-eye perspective.

Figure 4a. Land use in summer 2001.

Figure 4b. Potential land use in 2020 (A2 scenario).

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Figure 4c. Potential land use in 2020 (B2 scenario).

As part of a consultation exercise, the VNS software was used to produce ‘before’ and after ‘images’ for each of the viewpoints on Figure 5. Examples of these images are shown in Figures 6 and 7. Both landowners reviewed preliminary versions of the visualisations and some small amendments were made. The images were then incorporated into a set of web pages, alongside additional written information and maps of the project area. These materials were subsequently used in several meetings arranged by the NALMI project officer, including separate discussions with 20 local residents (from Bradenham, Necton and Holme Hale), Breckland District Council planners and Environment Agency staff.

It should be noted that the main purpose of the discussions was to consider the proposals themselves and consequently the total amount of time spent considering the images was relatively short compared to the length of the meeting. Nevertheless, comments were made by both professionals and the general public supporting the view that the visualisations provided clear visual communication, which in turn helped with decision-making.

“Much better to have things out in the open … and understand how both sides are looking at it, and you’re much more likely to eventually arrive at a sensible sort of conclusion.” (village resident) “Clarity is so important, because it brings an honesty to the planning system. … The greater the accuracy, the greater the clarity of information we’re giving, I’m quite convinced in my mind that you’re going to get the best decisions out of it.” (planner)

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Figure 5. Map of the landowners’ proposals.

Overall, the visualisations were felt to be quite realistic and recognisable by those who knew the area. A degree of interactivity was mentioned several times as a desirable improvement, but participants found it hard to speculate on whether low-detail/interactive methods would be better than high-detail/ still or animated ones. The local authority planners and Environment Agency staff also appreciated the GIS base of the images, feeling that it gave them a high level of defensibility. More generally, it was recognised that much discussion is required between those proposing a landscape change and the individual creating the visualisations. It is vital that the information is presented correctly, particularly if it is to be made available on demand (such as via the Internet) without the means to question the proposers.

Challenges in Landscape Visualisation Although the ability to create landscape visualisations from GIS databases has improved enormously in recent years there are still a number of challenges associated with such work. Some of these are essentially practical matters and, for example, the degree of realism in the representation of buildings and

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Figure 6. Viewpoint 1 images.

Figure 6a. Before.

Figure 6b. After.

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Figure 7. Viewpoint 4 images.

Figure 7a. Before.

Figure 7b. After.

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Figure 7c. Ground level view of lake. vegetation is likely to improve through a combination of greater processing power, more extensive libraries of 3-D objects and better tools to recreate features from digital photographs (e.g. see www.photomodeler.com).

A more challenging problem is the representation of uncertainty. This occurs in several different forms, one aspect being the visualisation of boundary zones. Images such as Figure 4 tend to show quite abrupt and clearly defined changes in land use, whereas in the real world there is frequently a visible degree of transition or fuzziness at a boundary. For instance, several viewers have noted that showing crops right up to the edge of a field is unrealistic, as even without managed field margins there is a certain amount of uncropped space due to machinery limitations and the impact of hedgerow wildlife on peripheral seeds and young crops. Transitions are also important in less heavily managed landscapes, where there may be a need to show different types of vegetation merging gradually into one another (e.g. from open heath into varying densities of scrub, and then into more mature woodland).

Facilities do exist in some visualisation packages (e.g. Terraffectors in VNS) to define symmetrical variations in land cover either side of a line (e.g. to represent roadside verges or river banks), but creating asymmetrical transitions across polygon boundaries is likely to require a more complete integration of GIS analysis tools and visualisation software than has been achieved to date.

Visualising uncertainty is also a key issue when the probability of particular features being present in a landscape is likely to vary. Sets of images, such as those in Figure 4, can illustrate a range of possible outcomes, but in each view all the landscape elements appear equally definite. In reality,

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some components of the scene (e.g. buildings and field boundaries) are more likely to be present in the 2020s than others (e.g. particular crops in fields).

Refining this aspect of the visualisations obviously requires better understanding and prediction of climate change impacts, but assuming such scientific knowledge does become available there is still the question of how to best convey these differences in likelihood. The use of paired images, with one depicting a landscape view and the other the likelihood of different features being present, could be one way forward, and the scope for employing animations (where duration in a time sequence is related to probability of occurrence) also merits investigation. At present, however, it seems likely that considerable research will be necessary before operational solutions become available.

Other audience comments have mentioned the possibility of exploring the displayed landscape in an interactive manner. Navigating through a virtual landscape is a common example of such interactivity and while this is not possible with the conventional VNS output discussed in this paper, a newly released extension, SceneExpress, allows visualisations to be output to various real-time display formats including Virtual Reality Modelling Language (VRML). Several other research projects have converted GIS databases to VRML as a means of generating navigable virtual environments (e.g. Lovett et al., 2002) and software products such as TerraVista (www.terrex.com) now provide more sophisticated means of creating large terrain databases optimised for interactive landscape display. Researchers are also beginning to address interactivity in the sense of allowing users to move or alter landscape elements in real-time and this enhanced ability to pose ‘what if’ questions is important in facilitating greater stakeholder engagement in decision-making processes.

There is still some tradeoff between providing interactivity and the degree of realism with which features are represented, but the gap is narrowing all the time. As the costs of high-end image generation and projection systems (e.g. www.uea.ac.uk/zicer/ssevrel) continue to fall the use of real-time landscape display methods seems likely to become much more widespread.

A final issue concerns the way in which landscape visualisations are used.

It is evident that the techniques for generating 3-D visualisations from GIS databases have advanced enormously in the past few years, but it is also increasingly recognised that there is still much to learn about how best to apply such tools for environmental management and planning purposes (e.g.

Orland et al., 2001). Research is now starting to provide insights as to how the public perception of virtual landscapes is influenced by the realism with which different features are represented (e.g. Appleton & Lovett, 2003) and the further development of practical guidelines (e.g. Sheppard, 2001; Appleton, 2004) will be very important for the application of visualisation methods in landscape planning.

Acknowledgements The initial phase of this research was primarily funded by the Jackson Foundation, with additional resources provided by the NALMI project. Katy Appleton was also supported by a PhD studentship from the University of East

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Anglia. More recent work has been funded by the Economic and Social Research Council as part of the Programme on Environmental Decision Making based at the Centre for Social and Economic Research on the Global Environment (CSERGE) at the University of East Anglia. We would like to thank Martin Parry for the provision of CLUAM output and John Terry (NALMI Project Officer) for assistance in organising fieldwork and meetings.

All maps are derived from Ordnance Survey digital data,  Crown copyright Ordnance Survey. The provision of these data is an EDINA Digimap/JISC supplied service.

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