This example illustrates the possible use of LUCC models to support the discussion on land use policies and its effects for agriculture and future land use patterns. A representative LUCC model that allows the exploration of future land use patterns under different scenarios is applied to the Randstad region in the Netherlands. The term 'Randstad Holland' was launched to denote a group of towns and cities located relatively close together in the west of the Netherlands (see Figure 2.1 A for the location). Surrounding by these cities is a rural area predominantly consisting of meadows, dairy farming, scattered villages and nature reserves. This area is commonly called 'the Green Heart' of the Randstad region and has important functions for agriculture, recreation and nature/landscape preservation. In the 90's emphasis was given to promoting compact urbanization by developing sites within and directly adjacent to cities. New business locations and residential areas were encouraged to be close to existing cities. This policy aimed at providing opportunities to keep the Green Heart open and green (Dieleman et al., 1999). These policies formed the basis for the so-called VINEX locations, designated areas for most of the Randstad's new housing up to 2005.
The Green Heart policy is an important part of the Dutch spatial planning doctrine, and in 1990 the area was given official borders by the Ministry responsible for land use planning. The Green Heart was appointed as national landscape to preserve and strengthen the cultural historic and ecological aspects and improve the visual coherence of built-up area and environment. Spatial policies have been relatively successful in keeping the Green Heart as a central open space surrounded by urban development. However, protection is no longer the sole objective of land use planning for the Green Heart. Apart from restrictive measures - in relation, for example, to busi,nesses and new housing - policy largely focuses on developing the Green Heart's potential. It will be obvious from the above overview that a shift has occurred from a largely defensive approach to policies in which incentives play a key role.
The dynamic, spatially explicit, land use change model CLUE-S (Verburg et al., 2002, Verburg and Veldkamp, 2004) was used for the simulation of potential future land use changes in the Randstad region. The model structure is based on systems theory to allow the integrated analysis of land use change in relation to socio-economic and biophysical driving factors. In the CLUE-S model the complexity of land use systems is captured by a combination of dynamic modelling and empirical quantification of the relations between land use and its driving factors. The model allocates predefined demands to different locations within the study area. For each location, the possibilities for change are evaluated based on the actual land use and the competitive strength of the different land uses. Furthermore, areas where spatial land use policies apply can be indicated. Scenarios can be used to evaluate different land use change situations caused by differences in demographic change, land use requirements and spatial policies.
A data set of maps representing land use, biophysical characteristics and socioeconomic conditions at a resolution of 500 meter was used for the simulation (Verburg et al., 2004).
The model was run for two different scenarios for the period from 1996 to 2015 to explore the potential future changes of land use in the region. Two different scenarios are created based on different spatial policies. Both scenarios use the same claims for the different land use types, based upon the observed trends for the period 1989-1996. The claim for urban land uses (residential, industrial, commercial and recreational areas) increases by about 1.2% per year, while the claims for agricultural land use are expected to decrease 0.7% per year. In the base scenario, the model is run without specification of any spatial policy, which means that a certain land use will be allocated as a result of the 'preference' that the decision makers have for a certain location based on its biophysical, socio-economic, accessibility and other characteristics as well as on the competition between the land use types. The second scenario assumes a strict implementation of spatial policies aimed at the protection of the agricultural and natural areas within the Green Heart area. Expansion of urban land uses is under these conditions not allowed within the Green Heart area.
The maps of predicted land use in 2015 are given in Figure 2.1 for both scenarios. In the base scenario, without protection of the Green Heart, the small towns inside the Green Heart face a large expansion in residential areas. These towns are especially attractive for housing because of the rural environment and their proximity to the main cities. New industries (including greenhouses) and recreational areas arise at the outskirts of the cities and along highways. In the second scenario the Green Heart mainly remains under agricultural use (grassland and some arable land), while urban growth occurs mostly near to the four main cities. Especially the area between The Hague and Rotterdam almost completely changes into urban area.
The results of the two scenarios show the relevance of the ongoing discussion in the Netherlands about the implementation of spatial policies that restrict urban development in the Green Heart. Different implementations of spatial policies clearly result in different spatial patterns of land use with consequences for urban structure, openness of the rural hinterland and the role of agriculture within the landscape. The model results help to visualize these different policy options and structure the discussion by showing the potential consequences of land use planning decisions. This case study is a relatively simple representation of the changes the area might be facing in the future. Further analysis might include the land requirements for water retention and coastal protection under conditions of climate change as well as an in-depth analysis of the effects of changing agricultural policies on the future of agricultural practices in the area.
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