The CLUE model
Hands-on exercises
Course material
Peter Verburg
January 2010
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CLUE model - background
Introduction
The Conversion of Land Use and its Effects modelling framework (CLUE) was
developed to simulate land use change using empirically quantified relations between
land use and its driving factors in combination with dynamic modelling of competition
between land use types. The model was developed for the national and continental level
and applications for Central America, Ecuador, China and Java, Indonesia are available.
For study areas with such a large extent the spatial resolution for analysis was coarse
and, as a result, each land use is represented by assigning the relative cover of each
land use type to the pixels.
Land use data for study areas with a relatively small spatial extent is often based on land
use maps or remote sensing images that denote land use types respectively by
homogeneous polygons or classified pixels. This results in only one dominant land use
type occupying one unit of analysis. Because of the differences in data representation
and other features that are typical for regional applications, the CLUE model cannot
directly be applied at the regional scale. Therefore the modelling approach has been
modified and is now called CLUE-S (the Conversion of Land Use and its Effects at Small
regional extent). CLUE-S is specifically developed for the spatially explicit simulation of
land use change based on an empirical analysis of location suitability combined with the
dynamic simulation of competition and interactions between the spatial and temporal
dynamics of land use systems. More information on the development of the CLUE-S
model can be found in Verburg et al. (2002) and Verburg and Veldkamp (2003).
The more recent versions of the CLUE model: Dyna-CLUE (Verburg and Overmars,
2009) and CLUE-Scanner include new methodological advances.
Model structure
The model is sub-divided into two distinct modules, namely a non-spatial demand
module and a spatially explicit allocation procedure (Figure 1). The non-spatial module
calculates the area change for all land use types at the aggregate level. Within the
second part of the model these demands are translated into land use changes at
different locations within the study region using a raster-based system. The user-
interface of the CLUE-S model only supports the spatial allocation of land use change.
For the land use demand module different model specifications are possible ranging
from simple trend extrapolations to complex economic models. The choice for a specific
model is very much dependent on the nature of the most important land use conversions
taking place within the study area and the scenarios that need to be considered. The
results from the demand module need to specify, on a yearly basis, the area covered by
the different land use types, which is a direct input for the allocation module.
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Figure 1. Overview of the modelling procedure
The allocation is based upon a combination of empirical, spatial analysis and dynamic
modelling. Figure 2 gives an overview of the information needed to run the CLUE-S
model. This information is subdivided into four categories that together create a set of
conditions and possibilities for which the model calculates the best solution in an
iterative procedure. The next sections discuss each of the boxes: spatial policies and
restrictions, land use type specific conversion settings, land use requirements (demand)
and location characteristics.
Spatial policies and restrictions
Spatial policies and land tenure can influence the pattern of land use change. Spatial
policies and restrictions mostly indicate areas where land use changes are restricted
through policies or tenure status. For the simulation maps that indicate the areas for
which the spatial policy is implemented must be supplied. Some spatial policies restrict
all land use change in a certain area, e.g., a log-ban within a forest reserve. Other land
use policies restrict a set of specific land use conversions, e.g., residential construction
in designated agricultural areas or permanent agriculture in the buffer zone of a nature
reserve. The conversions that are restricted by a certain spatial policy can be indicated
in a land use conversion matrix: for all possible land use conversions it is indicated if the
spatial policy applies.
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Figure 2. Overview of the information flow in the CLUE-S model
Land use type specific conversion settings
Land use type specific conversion settings determine the temporal dynamics of the
simulations. Two sets of parameters are needed to characterize the individual land use
types: conversion elasticities and land use transition sequences. The first parameter set,
the conversion elasticities, is related to the reversibility of land use change. Land use
types with high capital investment will not easily be converted in other uses as long as
there is sufficient demand. Examples are residential locations but also plantations with
permanent crops (e.g., fruit trees). Other land use types easily shift location when the
location becomes more suitable for other land use types. Arable land often makes place
for urban development while expansion of agricultural land occurs at the forest frontier.
An extreme example is shifting cultivation: for this land use system the same location is
mostly not used for periods exceeding two seasons as a consequence of nutrient
depletion of the soil. These differences in behaviour towards conversion can be
approximated by conversion costs. However, costs cannot represent all factors that
influence the decisions towards conversion such as nutrient depletion, esthetical values
etc. Therefore, for each land use type a value needs to be specified that represents the
relative elasticity to change, ranging from 0 (easy conversion) to 1 (irreversible change).
The user should decide on this factor based on expert knowledge or observed behaviour
in the recent past.
The second set of land use type characteristics that needs to be specified are the land
use type specific conversion settings and their temporal characteristics. These settings
are specified in a conversion matrix. This matrix defines:
To what other land use types the present land use type can be converted or not (Figure
3).
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In which regions a specific conversion is allowed to occur and in which regions it is not
allowed.
How many years (or time steps) the land use type at a location should remain the same
before it can change into another land use type. This can be relevant in case of the re-
growth of forest. Open forest cannot change directly into closed forest. However, after a
number of years it is possible that an undisturbed open forest will change into closed
forest because of re-growth.
The maximum number of years that a land use type can remain the same. This setting is
particularly suitable for arable cropping within a shifting cultivation system. In these
systems the number of years a piece of land can be used is commonly limited due to soil
nutrient depletion and weed infestation.
It is important to note that only the minimum and maximum number of years before a
conversion can or should happen is indicated in the conversion table. The exact number
of years depends on the land use pressure and location specific conditions. The
simulation of these interactions combined with the constraints set in the conversion
matrix will determine the length of the period before a conversion occurs. Figure 4
provides an example of the use of a conversion matrix for a simplified situation with only
three land use types.
Figure 3. Illustration of the translation of a hypothetical land use change sequence into a
land use conversion matrix
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