Download - A qualitative method for the spatial and thematic downscaling of land-use change scenarios

A qualitative method for the spatial and thematicdownscaling of land-use change scenarios

Sophie Rickebusch a,*, Marc J. Metzger a, Guangcai Xu a,b, Ioannis N. Vogiatzakis c,Simon G. Potts c, Maria Teresa Stirpe c, Mark D.A. Rounsevell a

aCentre for the Study of Environmental Change and Sustainability (CECS), School of GeoSciences, The University of Edinburgh,

Drummond Street, Edinburgh EH8 9XP, UKbCollege of Resources Science and Technology, Beijing Normal University, Beijing 100875, ChinacCentre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, RG6 6AR, UK

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 2 6 8 – 2 7 8

a r t i c l e i n f o

Published on line 8 December 2010

Keywords:

Rule-based approach

Bio-energy crops

Land abandonment

Local knowledge

United Kingdom

a b s t r a c t

Assessing the potential impact of future land-cover changes on habitat quality requires

projections with a fine spatial and thematic resolution. The former is usually addressed by

downscaling methods, often at the expense of the latter. We present a new, rule-based

method to downscale land-use change scenarios to the landscape level while keeping a large

number of land-cover classes (CORINE level 3). The method relies on the interpretation of

European scenario storylines, the observation of past land-use changes, high-resolution

regional data and expert knowledge.

The results give a landscape-level transposition of the scenario storylines which reflects

the local conditions. The method has a number of advantages, such as its potential

application in dialogues with policy-makers and stakeholders. Possible further develop-

ments include automating the rule-based selection to overcome the current limitations of

this method in terms of spatial extent.

# 2010 Elsevier Ltd. All rights reserved.

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1. Introduction

Land-use/-cover change has increased dramatically in recent

decades due to rapid demographic and societal changes; this is

expected to continue in many European regions (Busch, 2006;

Lambin et al., 2001). Agricultural and urban lands are

particularly susceptible to these changes and affect habitat

quality for both flora and fauna. Increased urbanisation and

peri-urban settlement results in fragmentation, which affects

various ecosystem processes (Lambin et al., 2001). Assessing

the impact of land-cover changes on ecosystem processes or

habitat quality requires data with a fine spatial and thematic

resolution (Conway, 2009). Spatial resolution is necessary to

calculate the degree of fragmentation (patch size, connectivi-

ty) precisely (Fahrig, 2003; Reidsma et al., 2006) and to preserve

* Corresponding author. Tel.: +44 131 651 4449; fax: +44 131 650 2524.E-mail address: [email protected] (S. Rickebusch).

1462-9011/$ – see front matter # 2010 Elsevier Ltd. All rights reservedoi:10.1016/j.envsci.2010.11.003

the representation of land-use types with typically small

patches, such as grasslands, which may be lost at coarser

resolutions (Schmit et al., 2006). Using a finer resolution also

reduces the errors inherent to mapping methods, such as

representing continuous data in a raster (grid) format

(Dendoncker et al., 2008). Without sufficient thematic resolu-

tion it is impossible to assess habitat quality correctly:

‘‘agricultural land’’ may include very different land-cover

types, such as highly intensive monocultures or low intensity,

species-rich pastures (de Chazal and Rounsevell, 2009). This

level of precision is often available for recent land-cover

changes (post-1990), for instance in the CORINE land-cover

(CLC) datasets (Buttner et al., 2002). Even the finest CLC

classification (level 3, 44 classes) is arguably not sufficient for

some ecological applications, especially as the more ‘‘natural’’

d.

http://dx.doi.org/10.1016/j.envsci.2010.11.003

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e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 2 6 8 – 2 7 8 269

land-cover types, such as forest or shrub, are not very well

differentiated in CLC (Neumann et al., 2007; Waser and

Schwarz, 2006). However, high thematic resolution is less

common for older changes, which makes the calibration of

land-use/-cover change models very difficult. One exception is

the BIOPRESS transect data, which details CLC level 3 land-

cover in 1950, 1990 and 2000 (Gerard et al., 2010; Kohler et al.,

2006; Thomson et al., 2007).

Scenarios providing alternative images of how the future

may unfold are a popular tool for exploring alternative

plausible land-use futures (Ewert et al., 2005; Rounsevell et al.,

2005, 2006; Rounsevell and Metzger, 2010; Verburg et al., 2006).

Rounsevell et al. (2006) describe how the Intergovernmental

Panel on Climate Change (IPCC) Special Report on Emissions

Scenarios (SRES) (Nakicenovic and Swart, 2000) can be

interpreted to derive European land-use change scenarios.

The same methodology was used to develop the ALARM

scenarios (Settele et al., 2005), which we use in this study. At

the European level, the ALARM scenarios show a continuation

of current trends in land-use change, but with differing

magnitudes between the scenarios (Reginster et al., 2010;

Spangenberg et al., in press). Unfortunately, the 100 spatial

resolution (approximately 16 km � 16 km) of these projec-

tions is too coarse for studies relating to biodiversity or

ecosystem services, for instance. A number of quantitative

downscaling methods have been used to refine the spatial

resolution of land-use change projections (Dendoncker et al.,

2006, 2008; Verburg et al., 2008, 2006). However, automated

quantitative downscaling has limitations, such as the

inability to adjust to the local context and the lack of control

over the results. With fewer than 10 land-use categories, the

thematic resolution of many current land-use scenarios also

remains too coarse. Some studies use a finer thematic

resolution in their particular field of interest, e.g. agriculture,

but group all other land-uses into a single ‘‘other’’ category

(Chakir, 2009). As observed by Conway (2009), increasing the

number of classes in land-use change models may in some

cases be detrimental to model accuracy, especially where less

frequent types of conversions are concerned. This may

explain why so little attention has been paid to improving

thematic resolution, despite its importance for ecological and

other applications.

For planners and policy-makers, it is essential to under-

stand how land-use change might affect biodiversity and

other ecosystem services, so that these services can be

preserved and potential synergies or conflicts identified

(Verburg et al., 2009). In turn, planners and policy-makers

can also inform research by bringing local knowledge into the

interpretation of scenario storylines for their region. This

interaction is often overlooked when developing models and

decision-making tools for management purposes (Jakeman

et al., 2006).

We describe here a rule-based downscaling method which

provides plausible projections of land-cover change at the

landscape level, with fine spatial and, more importantly,

thematic resolution. By using a qualitative method, we

circumvent the main problem with calibrating models for

rare events, namely insufficient data. As it relies on visualising

the data, this method enables us to check during the

downscaling process that the results are representative of

local conditions and has the potential to be used for policy

dialogue. Finally, working at the local level allows the use of

detailed data and ancillary knowledge, which are often not

available for large-scale data.

2. Materials and methods

Projected land-use changes from the pan-European ALARM

scenarios (Reginster et al., 2010; Spangenberg, 2007; Spangen-

berg et al., in press) were thematically disaggregated and

spatially allocated in a three-step process. Firstly, we derived

the relative changes in four aggregated land-use categories

from the ALARM scenarios and converted them to absolute

increases or decreases in number of 100 m � 100 m (1 ha) grid

cells within seven transects from the BIOPRESS project (Gerard

et al., 2010; Kohler et al., 2006; Thomson et al., 2007). Secondly,

we disaggregated these changes to the more detailed CLC

categories used in the transects, to obtain the number of 1 ha

grid cells by which each land-cover category will increase or

decrease under each scenario. Finally, we allocated these

changes spatially within the transect using a combination of

scenario-specific rules, regional information and expert

judgement (by the author team at this stage). These three

steps are detailed in Sections 2.2.–2.4.

2.1. Land-cover data and scenarios

We used land-cover data from seven transects in the United

Kingdom (Fig. 1), taken from the BIOPRESS project (Gerard

et al., 2010; Kohler et al., 2006; Thomson et al., 2007). The

transects in that project were chosen to intersect with Natura

2000 sites and are 15 km � 2 km rectangles, though some have

missing sections due to lack of coverage by the aerial

photographs from which the data is derived. The data follows

CLC level 3 (44 classes). Each transect consists of a mosaic of

polygons representing contiguous areas with a common land-

cover history.

For our purpose, the polygon data for each transect was

converted to 100 m (1 ha) grids, one per time step (1950, 1990,

2000). These were then converted to point data, with the points

in the centres of the grid cells. The associated attribute table

contained the land-cover classes for the three past time steps,

to which we added three fields for 2030, one per scenario.

These were initially filled with the land-cover class for 2000.

The ALARM land-use change scenarios (Reginster et al.,

2010; Spangenberg, 2007; Spangenberg et al., in press) give

projections of the percentage cover per 100 grid cell in 2030 for

seven land-use categories, following the three storylines in

Table 1. The land-use categories are: urban, cropland,

grassland, permanent crops, biofuels, forests and land in

succession (i.e. abandoned agricultural land). The construc-

tion of the ALARM scenarios involved three steps: (1)

construction of three alternative scenario storylines (Table

1), (2) estimation of the aggregate totals of land-use change

using a supply/demand model and (3) spatial allocation of

these aggregate quantities using spatially explicit rules

(Rounsevell et al., 2006). The methodology is described in

detail by Reginster et al. (2010) and Spangenberg et al. (in

press).

Fig. 1 – Location of the seven United Kingdom transects used and example of past land-cover changes (1950–1990–2000) in

BIOPRESS transect UK1 (Gerard et al., 2010; Kohler et al., 2006).


2.2. Change in aggregated land-use categories

To quantify the land-cover changes in the transects, we need

to know the relative changes compared to the current

situation. This is done for five aggregated categories (cropland,

forest, grassland, built-up areas and surplus land). As some

Table 1 – Summary of the three ALARM scenario storylines: GR2007; Spangenberg et al., in press).

In the GRAS (GRowth Applied Strategy) storyline, deregulation and free m

regions. The guidelines of the European planning policies (The Europe

patterns for new urbanisation are urban sprawl and diffuse peri-urban

the free market policy and regional subsidies and compensation withi

areas are only maintained in optimal locations with a comparative ad

areas are preserved, but the NATURA 2000 site network is not enforce

In the BAMBU (Business-As-Might-Be-Usual) storyline, the European plan

Perspective, ESDP) are applied. Compact city development and limits o

agricultural policy (CAP) is maintained, but adapted to avoid overprodu

comparative advantage and a minimum level of agriculture is preserv

current European afforestation policy is maintained. Current protected

In the SEDG (Sustainable European Development Goal) storyline, integra

extensification of agriculture and the encouragement of organic farmi

biodiversity by, for example, limiting nitrogen and pesticide use. Small

rural areas. Planning policies are strict and favour compact settlement

public transport and other energy savings. Current protected areas are

ALARM categories such as ‘‘bio-energy crops’’ and ‘‘surplus

land’’ are not currently present, we cannot calculate relative

change figures for these. For bio-energy crops, we solved this

by calculating the relative change for aggregated categories

(‘‘total crops’’ and ‘‘total forests’’, Table 2) which incorporate

the various bio-energy crop types. These relative changes were

AS, BAMBU and SEDG (Reginster et al., 2010; Spangenberg,

arket objectives lead to the reduction or abolition of zoning in

an Spatial Development Perspective, ESDP) are not applied and the

isation. Liberalisation of food trade is one of the consequences of

n the common agricultural policy (CAP) are abolished. Agricultural

vantage, where the agricultural rent is positive. Current protected

d.

ning policy guidelines (The European Spatial Development

n peri-urbanisation are implemented and enforced. The common

ction. Agricultural areas are maintained in optimal locations with a

ed in traditional landscape for rural development objectives. The

areas are preserved and the NATURA 2000 site network is enforced.

ted social, environmental and economic policies lead to the

ng. This reduces the effect of agricultural intensification on

decreases in agricultural areas help to reduce unemployment in

to reduce travel needs. This provides opportunities for efficient

preserved and the NATURA 2000 site network is enforced.

Table 2 – Correspondence of the aggregated land-covercategories with ALARM and CORINE. There is notnecessarily a direct correspondence between ALARM andCORINE, hence the need for aggregation.

Aggregated ALARM CORINE

Total crops Cropland permanent

crops liquid bio-energy

crops non-woody

bio-energy crops

Arable land (2.1.x)

heterogeneous

agricultural areas

(2.4.x) permanent

crop (2.2.x)

Total forest Forest woody

bio-energy crops

Forest (3.1.x)

Grasslands Grasslands Pastures (2.3.x)

natural grasslands

(3.2.1)

Built-up area Built-up area All artificial

surfaces (1.x.x)


then used to calculate the absolute change in 1 ha grid cells per

transect, which was then disaggregated to CLC level 3 (plus the

new bio-energy categories).

Two other land-cover category groups can be discerned.

First, categories which are assumed to be uninfluenced by the

main land-use change drivers (Rounsevell et al., 2005). These

include bare rock, water bodies, glaciers and natural areas,

which were left out of this analysis as they do not change

under the ALARM scenario assumptions. Second, the ‘‘sur-

plus land’’ category, which consists mainly of abandoned

agricultural land. It is defined as land remaining after the

demand for urban uses, agriculture and forestry has been

met. It can therefore be calculated as the difference between

total land-use change and the sum of changes in all other

categories.

The ALARM scenarios were constructed using the relatively

coarse 1 km resolution PELCOM land-cover map, which can

deviate considerably from detailed regional land-use patterns

(Dendoncker et al., 2008; Schmit et al., 2006), especially for

land-uses with small spatial extents. Consequently, using

individual 100 ALARM grid cells to derive future change may

cause problems. For example, the ALARM grid cell might not

contain any urban land-cover, resulting in no change for that

category, while in reality there are small settlements in the

region. This problem can be overcome by calculating average

trends over a wider region. In order to develop a methodology

which could be rolled out across Europe, we chose to calculate

average regional trends for the Atlantic Central and Atlantic

North European environmental zones (Metzger et al., 2005).

For the United Kingdom, these distinguish between the

English lowlands (transects 1, 2, 5 and 6) and the uplands

plus Scotland (transects 3, 8 and 9; Fig. 1).

2.3. Disaggregation to CORINE level 3 categories

The relative proportions of bio-energy to conventional

cropland or forest in the ALARM scenarios were used to

disaggregate the ‘‘total crops’’ and ‘‘total forest’’ categories,

respectively. For example, if under a given scenario the ‘‘total

forest’’ category increased by 50 grid cells from 1500 to 1550

and the fraction of woody bio-energy crops increased from 0 to

10%, then conventional forestry would decrease by 105 cells to

1395 [=1550–(1550 � 0.1)] and woody bio-energy crops would

increase by 155 cells. In this way, it is possible to obtain

absolute changes for the three types of bio-energy crops as

well as arable land, permanent crops and forests.

Since some future land-use categories do not yet exist in

the CLC classification, we created a new level 2 class (2.5) for

bio-energy crops, which is split into three level 3 classes: liquid

bio-energy crops (2.5.1; e.g. oilseed rape), non-woody bio-

energy crops (2.5.2; e.g. Miscanthus grass) and woody bio-

energy crops (2.5.3; e.g. willow). Additionally, we created two

categories for surplus land (abandoned agricultural land)

within the relevant level 2 groups: abandoned arable land

(2.1.4) and abandoned pastures (2.3.2).

2.4. Rule-based spatial allocation

Within each transect, we re-allocated the necessary number of

1 ha grid cells of each type to match the downscaled ALARM

values for each scenario, following a set of rules. Technically,

this was done using GIS (Geographical Information System)

software and working on the point files created from the

transect grids. The points to be changed were selected

manually and a new land-cover category assigned to the

corresponding field in the attribute table. This procedure is

illustrated in Fig. 2, with more details available in Appendix A

(online supplementary material). Once all the changes had

been made, following the allocation rules below, we created

grids of the projected land-cover for 2030 according to each

scenario.

The allocation rules come primarily from interpreting the

ALARM scenario storylines. Additionally, we also took into

account past trends in the BIOPRESS transects, e.g. most

frequent conversions between land-cover classes, polygons

previously subjected to change. Finally, we added expert and

local knowledge (from within the author team), e.g. require-

ments for a given crop type, presence of small landscape

features within other land-use types (heterogeneity). The

changes followed this order of precedence: (1) protected areas,

(2) urban areas, (3) agricultural land, (4) forests and (5)

abandoned land. An example of the detailed allocation rules

for transect UK1 is given in Table 3 and the rules for all

transects and scenarios are provided in Appendix B (online

supplementary material). In general terms, protected areas

were preserved (with an additional 100-m buffer zone in the

SEDG scenario), so no changes were allowed. Urban areas

expanded mostly into agricultural land-use types, with

locations varying according to the scenarios. Agricultural land

tended to decrease with, aside from the conversion to urban

land, some conversion to biofuels (woody biofuels could also

be grown on land converted from forest) and some to forest.

Surplus land (mostly from agriculture) was considered to be

abandoned. Cells were preferably changed in clusters corre-

sponding to the original BIOPRESS polygons, i.e. areas which

had a common history of change. This common history is

likely to be the result of other factors, such as land-ownership,

which also affect future changes.

The results are presented as maps for the various

scenarios. For transect UK1, we also plotted the total area

per CORINE level 3 class for the six most abundant classes in

2000 (i.e. in the baseline data).

LUC allocation

1. Identify protected areas & 100 m buffer zones

BAMBU

No change within protected

area

As GRAS + avoid allocating urban

areas & intensive agriculture next

to protected areas

As BAMBU + extend

protection to 100 m buffer

zone

GRAS SEDG

landAllocate urban

BAMBU

2.

Create new settlements or

expand small ones by converting

(extensive) arable land

Expand small

settlements by

converting arable land

Expand or fill in large

settlements by converting

(intensive) arable land

SEDGGRAS

3. Allocate agricultural land (arable, liquid/non-woody bio-fuels & grass)

Identify unfavourable land, e.g. steep terrain, river banks, & historic trends

BAMBU

Convert arable land &

grassland to liquid & non-

woody bio-fuels, preferably

near industrial zones

Convert arable land to liquid

& non-woody bio-fuels,

preferably near industrial

zones

Convert (intensive) arable

land to liquid & non-woody

bio-fuels, preferably near

industrial zones

GRAS SEDG

bio-fuels)woody(incl.forestsAllocate4.

BAMBU

Convert forest & grassland

to woody bio-fuels

Convert grassland to woody

bio-fuels & forest

Convert arable & grassland

to woody bio-fuels & forest,

expand protected forests

SEDGGRAS

5. Allocate abandoned land (former arable & grassland)

BAMBU

Convert remaining arable &

grassland to abandoned land


grassland to abandoned land,

esp. around protected areas


grassland to abandoned land,

esp. around protected areas

GRAS SEDG

End

Fig. 2 – Flow chart illustrating the steps in the land-use change allocation method. Blue, dotted arrows indicate surplus land

which is re-allocated outside the initial category. (For interpretation of the references to colour in this figure legend, the

reader is referred to the web version of the article.)


3. Results

Fig. 3 shows an example of land-cover projections for transect

UK1 (Kennet Valley Alderwoods) according to the three

ALARM scenarios. Urban areas (categories 1.1.2–1.4.2) increase

by similar amounts in all scenarios (24–27 ha), but locations

are scattered with GRAS, whereas with SEDG they are close to

existing urban centres. The GRAS scenario has the lowest

conversion to biofuel crops (cat. 2.5.1–2.5.3; 120 ha) and the

most abandonment of pasture land (cat. 2.3.2; �302 ha), while

the SEDG scenario is the opposite (235 ha biofuels, �91 ha

pastures). Results for the BAMBU scenario are generally

somewhere in between, though often closer to GRAS scenario

results. Abandoned land is mostly situated along the Kennet

River, which runs the length of the transect. These areas were

considered more difficult to access, making them less

profitable for agriculture. The historical data (Fig. 1), which

shows some conversion from pasture to forest along the

Table 3 – Example of land-cover allocation rules and local decisions (in italics) for the three ALARM scenarios (GRAS,BAMBU, SEDG) in transect UK1. The results are shown in Fig. 3.

GRAS BAMBU SEDG

1. Protected Current protected

areas maintained

Current protected

areas maintained

Current protected

areas maintained

Expand protection

with 100 m buffer

2. Urban Dispersed urban

sprawl, new urban

centres can appear

Compact city

development and

peri-urbanisation

around existing

smaller centres

Compact city

development and

peri-urbanisation

around existing cities

Small new settlements,

some expansion of

existing small town;

existing industrial sites

expand, new site near

small town

Urban expansion on

agricultural land around

existing towns; new

industrial sites on edges

of towns; bypass extended

to industrial estate; urban

green in arable field in

town; new leisure facility

next to village

Urban expansion on

agricultural land around

existing towns; new

industrial sites on edges

of towns; bypass extended

to industrial estate; urban

green in arable field in town;

new leisure facility next

to village

3. Agriculture Agriculture shifts to

most favourable

areas

Limited shifts in

agriculture to most

favourable areas

Extensification—little

change in agriculture

distribution

First allocate crops, then

bio-energy crops, then

grassland; less intensive

agriculture categories

disappear first




agriculture categories

reduced by 50%; liquid

biofuels located on arable

land near towns/industry




agriculture categories are

maintained; liquid biofuels

located on arable land near

towns/industry

4. Forest General support for

afforestation

General support for

afforestation

General support for

afforestation

Existing patches expand

or contract around the

periphery. Some conversion

of woodland to woody

bio-energy crops; bio-energy

crops also expand on wet

grasslands and arable land

along river

Existing patches expand or

contract around the periphery;

expansion of smaller patches;

woody biofuels on grasslands

and arable land along river

Existing patches expand or

contract around the periphery;

expansion of smaller patches;

woody biofuels on grasslands

and arable land along river

5. Abandoned Abandoned land goes

into succession

Abandoned land goes into

succession

Abandoned land goes into

succession

Abandoned grasslands

mainly along the river

(too wet or remote) and

around nature reserve

Abandoned grasslands mainly

along the river (too wet or remote)

Very limited abandonment of

grasslands along the river


Kennet River between 1950 and 1990, supports this reasoning.

Details of the surfaces gained/lost per category and maps for

the all transects can be found in Appendix B.

The evolution over time of the total area occupied by the

main land-cover categories in transect UK1 (Fig. 4) shows

the largest differences between scenarios for the three

categories of agricultural land. For non-irrigated arable land

(2.1.1), GRAS shows a slight increase, similar to the 1950–

1990 period, whereas BAMBU shows a stabilising situation

and SEDG a continuation of the 1990–2000 trend. For

pastures (2.3.1), GRAS again shows a similar trend to

1950–1990 (marked decrease), while SEDG results in a much

smaller decrease. For agricultural land with natural areas

(2.4.3), SEDG shows a continuation of the 1990–2000 trend

(slight increase), while GRAS results in the disappearance of

that land-cover category. Results for BAMBU are intermedi-

ate between the other two scenarios, but often closer to

GRAS. For urban fabric (1.1.2) and forests (3.1.1, 3.1.3), the

future changes are much smaller than in the past 50 years

and there is little difference between scenarios in terms of

total area.

4. Discussion

Our results are plausible according to scenario terminology,

i.e. they may be radical but are ultimately possible (Rounsevell

and Metzger, 2010), in the opinion of the authors. In fact, the

projected changes are no more radical than those observed in

the past (Fig. 4). These results raise general policy questions,

Fig. 3 – Transect UK1 (Kennet Valley Alderwoods). Current (2000) land-cover and projections for 2030 according to three

scenarios. The categories correspond to CORINE land-cover level 3 (1.x.x artificial surfaces, 2.x.x agricultural areas, 3.x.x

forest and semi-natural areas, 5.x.x water bodies; see Appendix B for full nomenclature), except for the new categories:

2.1.4 (abandoned arable land), 2.3.2 (abandoned pastures), 2.5.1 (liquid biofuels), 2.5.2 (non-woody biofuels) and 2.5.3

(woody biofuels). The black lines are the perimeters of the protected areas.


such as the impact of a biofuel-oriented ‘‘sustainable’’

scenario. However, this work is mainly policy-relevant

through the method’s potential for exploring local-level

policies by means of stakeholder dialogue.

4.1. Land-use projections

In our example transect (UK1-Kennet Valley Alderwoods),

there is little difference between the scenarios in the quantity

of new urban land-use by 2030. However, in accordance with

the scenario storylines, the new urban areas vary in location

from scattered new settlements (GRAS) to compact city

development (SEDG). The land-use categories which show

the most differences between scenarios are arable and

grassland categories (2.x.x). These also happen to be the most

interesting categories in terms of biodiversity and ecosystem

services, as agricultural land-uses are more heterogeneous in

this respect than urban or forest types.

The GRAS scenario shows a small increase in intensively

cultivated land (2.1.1) and some conversion to biofuel

production (mostly liquid biofuels, often intensive monocul-

tures). Simultaneously, extensive agricultural land-covers (e.g.

2.4.3) decrease dramatically. Pastures (2.3.1) also decrease

quite strongly in the GRAS projection, as a lot of pasture-land

is abandoned. This will eventually revert to (semi-)natural

vegetation types, such as scrub or forest, which will favour

some species (e.g. forest dwellers) but be detrimental to others

(Falcucci et al., 2007). This scenario therefore shows two

contrary trends: intensification of agriculture and a potential

increase in natural vegetation. Consequently, the land-cover

type most likely to decrease drastically in the long term is

open, semi-natural land, such as pastures with low grazing

intensities.

The high uptake of biofuel production inherent to the SEDG

scenario (Reginster et al., 2010; Spangenberg et al., in press)

raises an interesting paradox in terms of natural habitat

quality. Although this scenario is supposedly more ‘‘sustain-

able’’, large areas of biofuel monocultures may in fact be less

favourable for biodiversity, either directly through land-use

conversion or indirectly through invasive species than the

abandoned agricultural land and pastures found in the GRAS

scenario (Barney and DiTomaso, 2010). However, some biofuel

types such as short rotation forestry stands seem more

biodiversity-friendly than others (Giordano and Meriggi, 2009;

Rowe et al., 2009). In general though, this scenario does show a

shift from intensive to more extensive agriculture, which is

associated with valued semi-natural habitats, favourable for

many species, of plants and birds in particular (Falcucci et al.,

2007; MacDonald et al., 2000; Uematsu et al., 2010; Wolff et al.,

2001).

Fig. 4 – Area change (ha) in transect UK1 between 1950 and 2030 for six of the most common CORINE level 3 categories.

Scenarios GRAS, BAMBU and SEDG are represented by full, dashed and dotted lines, respectively.


The BAMBU scenario gave intermediate results between

the two extremes of GRAS and SEDG for both urban and

agricultural land-uses. It is interesting to note that the SEDG

scenario results, for agriculture at least, seem closer to a

continuation of recent trends (1990–2000) than BAMBU, which

is supposed to represent ‘‘business as usual’’. This may be a

result of more environmentally conscious ‘‘SEDG-like’’ poli-

cies in the more recent time step. On the other hand, the GRAS

scenario gives similar trends to the earlier time-step (1950–

1990). BAMBU, which gives intermediate results, therefore

shows a continuation of long-term trends.

The small changes observed in urban and forest land-use

types for all scenarios come from the ALARM scenarios and

the quantities of change in the large-scale ALARM projections.


If these changes are small, the downscaled ones will be small

too. For discontinuous urban fabric (1.1.2, Fig. 4), this small

change represents a continuation of the levelling-off of growth

which already started in the 1990–2000 period. For broad-

leaved forests (3.1.1), previous changes have been more

variable (slow increase between 1950 and 1990, followed by

a sharp decrease), so the absence of change in the scenarios

may simply reflect the absence of a clear trend.

4.2. New downscaling method

Downscaling of land-use change projections often addresses

the spatial resolution and ignores the thematic disaggre-

gation, which makes the results unsuitable for many ecologi-

cal purposes. However, using a finer thematic resolution can

be difficult, as it will highlight any disparities in the original

data from different time steps or countries (e.g. CORINE vs.

PELCOM classification) (Schmit et al., 2006). This is generally

overcome by grouping the land-cover types into broader

categories, at the expense of the thematic resolution. In

contrast, the downscaling and allocation method described

here gives plausible, scenario-specific projections when a finer

resolution is necessary. Another advantage of this method is

that it incorporates an element of informed, but also partly

arbitrary, decision-making. This mimics local decision-mak-

ing by planners, farmers and other agents, such as switching

from food to biofuel crop production. This is partly driven by

market rules and partly by less predictable events, such as

land-ownership changing through succession or local atti-

tudes (Geoghegan et al., 2001; Liverman and Roman Cuesta,

2008). This method therefore results in projections which are

more representative of real life in terms of patterns, though

exact locations may vary. It has greater flexibility than large-

scale statistical downscaling and allocation, which cannot

take into account some human aspects of decision-making or

local scale socio-economic factors, which may affect land-

cover patterns significantly (Gellrich et al., 2007; Gellrich and

Zimmermann, 2007; Rickebusch et al., 2007). It can also use

ancillary knowledge which is only available at the local scale,

though we did not include any external experts in the present

study, which focussed on method development. Local expert

knowledge can prove a useful complement to large-scale

scientific knowledge in understanding land-cover patterns

and changes (Chalmers and Fabricius, 2007). Finally, the user

can check that the results reflect local conditions throughout

the process.

While the downscaling method was developed as an

academic desk study, it could be used as a participatory

planning tool. Land-use scenarios act as a stimulus for critical

thinking, providing a heuristic and rhetorical guide in the

planning process, both for organizational learning and for

option searching (Xiang and Clarke, 2003). While the thematic

downscaling of existing scenarios provides boundary condi-

tions for change, regional stakeholders can help interpret the

impacts of these changes for the region, thus helping planners

to minimise potential conflicts and maximise opportunities.

For example, if a scenario indicates a large increase in bio-

energy production or forest expansion, local expertise can be

used to identify the most likely regions of conversion based on

suitability and assess potential consequences for biodiversity

targets. This could lead to additional protection policies for

biodiversity or incentives for stimulating conversion in parts

of the region. In this way the method presented here allows for

strategic conversations (Van der Heijden, 2005) about possible

future changes to be framed in a credible scenario framework

and to be graphically illustrated and communicated in maps.

From a methodological point of view, different stake-

holders’ perspectives could also provide a basis for assigning

probabilities of change for each grid cell, using the level of

agreement between stakeholders as a proxy. Furthermore, it

would be possible to explore the differences in preference

between stakeholder groups (e.g. planners and land-owners)

or to get them to negotiate the most optimal compromise.

Additionally, stakeholder dialogue could be used to refine the

allocation rules (Table 3 and Appendix B) and give them more

transparency. Both sides benefit from this type of approach, as

decision-makers are then more likely to find the results

credible and relevant, therefore useful (Cash et al., 2003;

Olsson and Andersson, 2007).

Every land-cover change analysis method has its limita-

tions and is more suited to specific uses (Lambin, 1997).

Despite its relatively fine thematic resolution, CLC remains

unable to detect very small, key ecological features. This is

why it is important to add local expert knowledge when

applying this method in future, for instance to point out

which agricultural areas within the landscape contain the

most patches of other habitats, which might influence the

allocation. The method presented here could also be used to

apply scenarios to a smaller field study site, if a finer land-

cover map were available. This method is definitely most

suitable for small spatial extents, up to the landscape level,

as it would be unpractical to select and change manually

more than a few hundred points. It is therefore not easily

applicable as such to areas much larger than the transects

used here (3000 grid cells). One possibility would be to

combine this with other downscaling methods to focus on

areas of particular interest. Alternatively, the rule-based

method could be automated, in part at least. This could

include selecting areas with a particular land-cover history

or certain spatial attributes, such as distance to urban areas.

Finally, it would be interesting to explore the possibilities of

machine learning, whereby a computer could automate the

rules based on the responses given by experts or stake-

holders.

5. Conclusions

The qualitative, rule-based method for downscaling land-use

change scenarios described here provides a fine spatial

resolution and a detailed thematic disaggregation for small

areas up to the landscape level. It gives results which reflect

local conditions more closely than automated statistical

methods and provides opportunities for direct stakeholder

involvement in the downscaling process. It is therefore

particularly relevant for stakeholders and policy-makers

working at the local level. To overcome the current limitations

in the size of the study region, it would be useful to automate

parts of the rule base, while maintaining the flexibility of the

approach.


Acknowledgements

This research was funded by the EU in the 6th framework

project ‘‘COCONUT - Understanding effects of land use

changes on ecosystems to halt loss of biodiversity’’ (SSPI-

CT-2006-044343). We would like to thank Dr. France Gerard

from the Centre for Ecology and Hydrology for providing the

BIOPRESS dataset. Our thanks also go to Prof. David Sugden

from the University of Edinburgh and two anonymous

reviewers for their helpful comments.

Appendix A. Supplementary data

Supplementary data associated with this article can

be found, in the online version, at doi:10.1016/j.envsci.

2010.11.003.

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Sophie Rickebusch is a post-doctoral researcher at the Centre forthe study of Environmental Change and Sustainability (CECS),University of Edinburgh. An ecologist by training, her currentresearch concerns various aspects of land-use change modelling.

Marc Metzger is a senior research fellow at the Centre for the studyof Environmental Change and Sustainability (CECS), University ofEdinburgh. His research interests include global change foresightanalysis and the development of assessment techniques for globalchange impacts, across scales.

Guangcai Xu is a doctoral student majoring in land resourcemanagement at the College of Resources Science and Technology,Beijing Normal University. His current research interest is focusedon regional environmental change and human adaptation, partic-ularly in China’s northern dryland area.

Ioannis Vogiatzakis is a research fellow at the Centre for Agri-Environmental Research (CAER) at the University of Reading. He isa landscape ecologist and biogeographer with research interests invegetation and habitat modelling, particularly in Mediterraneanislands and mountain environments.

Simon G. Potts is a principal research fellow in the School ofAgriculture, Policy and Development at the University of Reading.His interests include developing evidence-based tools to underpinthe management of protected areas, agro-ecosystems and associ-ated ecosystem services.

Maria Teresa Stirpe is a research assistant at the Centre for Agri-Environmental Research (CAER) at the University of Reading. Herresearch interests include the effects of land cover change onspecies and habitat distribution.

Mark Rounsevell is a professor of Rural Economy and Sustainabil-ity at the University of Edinburgh and director of the University’sCentre for the study of Environmental Change and Sustainability(CECS). His research focuses on the effects of environmentalchange on rural and urban landscapes with particular emphasison land-use change modelling. Model applications include theexploration of alternative futures of climate and other environ-mental change drivers and the response of individuals and societyto these changes. He was a lead author to Working Group II of theIntergovernmental Panel on Climate Change (IPCC).

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