ARTICLE IN PRESS
Land Use Policy 24 (2007) 234–247
www.elsevier.com/locate/landusepol
Corine land cover change detection in Europe
(case studies of the Netherlands and Slovakia)
Jan Feraneca,, Gerard Hazeub, Susan Christensenc, Gabriel Jaffraind
a
Institute of Geography, Slovak Academy of Sciences, Stefanikova 49, 81473 Bratislava, Slovak Republic
b
Alterra b.v., P.O. 47, NL-6700 AA Wageningen, The Netherlands
c
Via Marconi 38, 21020 Ispra (VA), Italy
d
IGN France International, 39 ter, rue Gay-Lussac, 75005 Paris, France
Received 1 February 2005; received in revised form 19 January 2006; accepted 17 February 2006
Abstract
We present a land cover change detection methodology in the framework of the IMAGE and CORINE Land Cover 2000 (I&CLC2000)
project managed jointly by the European Environment Agency in Copenhagen, Denmark and the Joint Research Centre of the European
Commission in Ispra, Italy. The generated data layers CLC2000 (land cover for the year 2000) and CLC90/2000-changes (land cover changes
between the years 1990–2000) cover 29 European countries with a total area of about 4.5 million square kilometers at scale 1:100 000. The
variants of computer aided visual interpretation of satellite images referred to as updating and backdating were applied in the I&CLC2000
project. This makes use of the revised CLC90 data layer and the Landsat ETM satellite images from 2000 (71 year) for generation of the
CLC 2000 data layer. The CLC90/2000-changes data layer is generated by the overlay of the CLC90 and CLC2000 data layers with the
change area of minimum 5 ha. This approach may overestimate and underestimate identified land cover changes in some specific situations
described in the paper. As an example of land cover change, identification obtained by applying the updating method in the case of the
Netherlands is presented. An area of 1681 km2 of land cover change was identified for the period 1986–2000. Backdating was a suitable
methodological tool applied to the land cover inventory in Slovakia for the years 1970–2000 (3156 km2 of land cover changes were identified).
Thematic accuracy of derived data layers is X85% and the geometric accuracy is better than 100 m. The CLC methodology and results are
widely used in several other projects and are of relevance to policies in land management, nature conservation and water management.
r 2006 Elsevier Ltd. All rights reserved.
Keywords: Land cover; Land cover changes; Land cover detection; Computer aided visual interpretation; Updating; Backdating; CORINE Land Cover
Project; The Netherlands; Slovakia
Introduction
Environmental protection has become a major challenge
and concern within the European Community. The
European Community/Commission introduced environmental action programmes in 1972 (Statement from the
Paris Summit, 1972). However, the real breakthrough
occurred with the Treaty of Amsterdam in 1992, where
environmental protection was set as a priority.
In support of environmental assessment, the need for
updated information on land cover (LC) has become
important both at the European and national levels. The
Corresponding author. Tel.: +421 2 52495587; fax: +421 2 52491340.
E-mail addresses: feranec@savba.sk (J. Feranec), Gerard.Hazeu@wur.nl
(G. Hazeu), suc@terma.it (S. Christensen), gjaffrain@ignfi.fr (G. Jaffrain).
0264-8377/$ - see front matter r 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.landusepol.2006.02.002
growing interest in such information can be ascribed to the
important role of LC in processes taking place on the
Earth’s surface, such as absorption of solar radiation,
utilization of carbon dioxide by plant associations and
evaporation. Landscape changes at the national and global
levels are becoming even more topical, and their importance acquires new dimensions not only in research, but
also in environmental management. That is why harmonized and standardized spatial reference data are considered mandatory in support to the following environmental
management in the European Union policies:
Environmental assessment and sustainable development
(European Union Strategy for Sustainable Development
COM (European Commission, 2001)).
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Territorial impact assessment and regional planning
(Structural Funds, European Spatial Development
Perspective).
Impact of agricultural policies on the environment.
Nature Conservation and Environmental Protection
(the Natura 2000 network and LIFE programme).
Water Framework Directive.
Integrated coastal management.
Strategic environmental assessment of the TransEuropean network.
In response to the need for environmental assessment,
the IMAGE 2000 & CORINE Land Cover 2000
(I&CLC2000) project was launched by the European
Environmental Agency (EEA) and the Joint Research
Centre of the European Commission (JRC). The
I&CLC2000 project aims to provide a satellite image
mosaic of Europe (IMAGE2000), an up-to-date LC
database for the year 2000 (CLC2000), and information
on general LC changes in Europe between 1990 and 2000
(Steenmans and Perdigao, 2001). The I&CLC2000 is a joint
3-year project between the EEA and the JRC, co-funded by
the European Commission and the participating countries.
Initiated in 2000 in Member States, the project was
extended in 2001 to accession countries and currently
covers 29 countries (Büttner et al., 2004).
As the stated aim suggests, its solution will bring
information on LC changes in Europe for the 1990–2000
period. This study addresses: (1) the methodology of LC
change detection in the context of I&CLC2000 project and,
(2) presents results obtained in the Netherlands and the
Slovak Republic, and (3) applications of CLC methodology and data.
Approaches to LC change detection: a brief literature review
The basic prerequisite of LC change detection by
application of remote sensing (RS) is the existence of
changes in spectral response registered by airborne and/or
spaceborne sensors (Lillesand and Kiefer, 1979). LC
change can be identified by the comparison of a minimum
of two images from different time periods. The results of
comparison are, for example, polygon, line or point
features of LC which represent the size, shape or
occurrence of changes in the framework of the temporal
horizons in question. In this context the spatial resolution
of the images is important, because it determines to which
level of detail changes can be detected.
Several approaches have been developed for LC change
identification by RS data application (Coppin et al., 2004).
The quoted study reviews the state-of-the-art of digital
change detection—confined to bi-temporal change detection methodologies (comparing the same area at two points
in time) and multi-temporal trend analysis (comparing the
same area over longer time intervals with multiple
imagery). CLC change detection is one of the bi-temporal
methods.
235
Treitz and Rogan (2004) and Rogan and Chen (2004)
also provide a comprehensive view of the remote sensing
technology for mapping and monitoring of LC and land
use change. These studies are important for cognition of so
far used applications, above all digital approaches related
to change detection. The computer aided visual interpretation treated in this study is their counterpart.
Despite the wide use of the digital change detection
approaches, the method of the computer aided visual
interpretation of satellite images (Büttner et al., 2004;
Steenmans and Perdigao, 2001) was applied in the process
of updating the 1990 European LC to 2000 (71 year) and
the LC change detection for the interval of 1990–2000. This
method was applied because while mapping the heterogeneous LC classes (example of CLC nomenclature classes,
Heymann et al., 1994; Bossard et al., 2000) by application
of satellite images, it must be borne in mind that:
CLC classes are very heterogeneous regarding their
spectral characteristics. Objects that fall under one class
can be fairly different (e.g. artificial surfaces, urban
greens, small water bodies, etc.) and such varied objects
cannot be classified by computer approach into one
class only on basis of spectral signature or texture.
Translation of interpretation element association
(Feranec, 1999) as a classifying criterion in the framework of the corresponding algorithm of image processing is difficult.
Natural conditions can modify the spectral properties of
objects. The identification of LC classes is affected by
those conditions. The same LC class can have a different
spectral signature due to, for example, a different level
of ground water.
The LACOAST (LAnd cover changes in COASTal
zones) project determined the past LC changes of the
European coastal zones from 1975 to 1990 at 3–5 historic
dates using the CORINE land cover 90 (CLC 90), historic
aerial photographs and Landsat MSS data (Perdigao and
Christensen, 2000).
Historical CLC data for the 1970s were also produced
for Hungary, the Czech Republic, Romania and Slovakia
by backdating (see below) CLC 90 using Landsat MSS
imagery (Feranec et al., 2000, 2001). The results obtained
allowed the expansion of possibilities of landscape change
analyses that applied the traditional statistical data
collected from the territorial-administrative units. The use
of satellite data proved invaluable for providing more
accurate spatial information with respect to the morphological and biophysical characteristics of landscape types,
as they also show spatial distribution of the individual LC
classes in contrast to the classical statistical data that only
refer to administrative units (the area of forest, arable land,
etc. is quoted only in, for instance, a district).
The study of Kalensky et al. (2003) presents an extensive
overview and comparison of all studies focused on LC
mapping. Most of the studies are based, unlike the
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approach applied in this study, on automated processing of
satellite data. The Earth Observation for Sustainable
Development of Forest (EOSD) Project which aimed to
produce a LC map of the forested area of Canada, is
another one that applies automated processing of satellite
data. Nine out of the 23 classes characterize the forest
landscape (Wulder, 2002). A second-generation 2001
National Land Cover Database (NLCD, 2001) for the
United States contains 29 classes of land cover data derived
from the Landsat 5 and 7 imagery (processing methods
include: spectral clustering, expert systems, neural networks, and decision tree classifiers), ancillary data and
derivatives. The database should be completed by 2006
(Homer et al., 2004).
In spite of the fact that visual change detection relies on
subjective aspects of analysis, it is a reliable methodical
tool for change identification of inherently highly
heterogeneous LC classes with thematic accuracy X85%
(EEA-ETC/TE, 2002; Büttner et al., 2004).
Table 1
Physiognomic attributes relevant for identification of CLC classes
Urban fabric areas
Agricultural areas
Forest and semi-natural
areas
CORINE land cover nomenclature
Although the comprehensive characteristics of the CLC
nomenclature were published already 10 years ago
(Heymann et al., 1994), we consider it necessary to
emphasize its dominant attributes which should be taken
into account in its application. The CLC nomenclature is
based mainly upon physiognomic attributes (shape, size,
colour, texture and pattern) of landscape objects (natural,
modified–cultivated and artificial) and spatial relationships
of the landscape objects (association, Feranec, 1999). These
attributes are crucial for identification of LC classes on
satellite images. Artificial surfaces and agricultural areas
are also discerned by functional (user) attributes and are
related to land use (LU). Please note the principal
differences between LC and LU.
Humans perceive landscape as a combination of physical
objects of natural character and objects (re-)created by
them (agricultural land, artificial urban objects). LC
represents the biophysical state of the real landscape which
means that it consists of natural but also modified
(cultivated) and artificial objects (see Feranec and Otahel,
2001; cf. Barnsley et al., 2001), whereas LU refers to the
purpose for which land is used (function). On one hand
urbanized objects (artificial surfaces) or intensively used
agricultural objects (arable land, permanent crops) are LC,
but the terms also indicates their LU, their societal
function. On the other hand, the nature and appearance
of the natural or semi-natural objects do not mean that
they are not used or that they do not have a function. For
instance, coniferous forest can be used in the framework of
forest management, recreation and nature conservation or
for military purposes (Feranec et al., 2004). These reasons
explain why the CLC nomenclature does not consistently
discern LC from LU. However, the mentioned functions
do not have to be, and often are not, visually identifiable,
especially from remote sensing data. Satellite images are
Wetlands
Water bodies
Size, shape and density of the buildings,
share of supplementing parts of the class
(e.g. square, width of the streets, gardens,
urban greenery parking lots), character of
transport network, size and character of
neighbouring water bodies, arrangement of
infrastructure, size of quays, character of the
runway surfaces, state of the dumps and
arrangement and share of playgrounds and
sport halls
Share of dispersed greenery within
agricultural land, arrangement and share of
areas of permanent crops, relationships of
grasslands with urban fabric, occurrence of
dispersed houses (cottages), arrangement
and share of agricultural land (arable land),
grasslands, permanent crops and natural
vegetation (mainly trees and bushes),
irrigation channel network
Character (composition), developmental
stage and arrangement of vegetation (mainly
trees and bushes), share of grass and
dispersed greenery (composition density)
Character of substrate, water and vegetation
Character (shape) of water bodies
sources of information on the physiognomic attributes
(Table 1) of landscape objects, by means of which CLC
classes are identifiable.
The CLC nomenclature (Table 2) comprises three class
levels characterized by quoted attributes:
the first level (5 items) indicates the major categories of
LC on the planet,
the second level (15 items) is for use on scales 1:500 000
and 1:000 000,
the third level (44 items) is for use on scale 1:100 000
(Heymann et al., 1994).
CORINE land cover change identification methodology
A LC change is interpreted as a categorical change, when
one LC class or its part(s) is a replaced by another LC
class(es) (cf. Coppin et al., 2004). An example of
conversion is the change of a broad-leaved forest area into
a mineral extraction site area, or a pasture area into a
discontinuous urban fabric area.
The basic condition for identification of LC changes by
application of satellite images is the existence of changes
in spectral reflectance. Such changes are manifest on
images due to changes of characteristics of interpretation
elements (shape, colour, texture, pattern etc.). From the
methodological point of view it means that images
acquired in two or more time horizons are used in
identification of LC changes. Three basic approaches are
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211
Table 2
The CLC nomenclature contains 44 classes (Heymann et al., 1994;
Bossard et al., 2000)
112
3. Forest and semi-natural areas
3.1. Forests
3.1.1. Broad-leaved
forests
3.1.2. Coniferous forests
3.1.3. Mixed forests
1.2. Industrial, commercial
and transport units
1.2.1. Industrial or
commercial units
1.2.2. Road and rail
networks and
associated land
1.2.3. Port areas
1.2.4. Airports
3.2. Scrub and/or herbaceous
vegetation associations
3.2.1. Natural grasslands
3.2.2. Moors and
heathland
3.2.3. Sclerophyllous
vegetation
3.2.4. Transitional
woodland-scrub
1.3. Mine, dump and
constructions sites
1.3.1. Mineral extraction
sites
1.3.2. Dump sites
1.3.3. Construction sites
3.3. Open spaces with little or
no vegetation
3.3.1. Beaches, dunes,
sands
3.3.2. Bare rocks
3.3.3. Sparsely vegetated
areas
3.3.4. Burnt areas
3.3.5. Glaciers and
perpetual snow
1.4. Artificial, non-agricultural
vegetated areas
1.4.1. Green urban areas
1.4.2. Sport and leisure
facilities
2. Agricultural areas
2.1. Arable land
2.1.1. Non-irrigated
arable land
2.1.2. Permanently
irrigated land
2.1.3. Rice fields
2.2. Permanent crops
2.2.1. Vineyards
2.2.2. Fruit trees and
berry plantations
2.2.3. Olive groves
2.3. Pastures
2.3.1. Pastures
2.4. Heterogeneous
agricultural areas
2.4.1. Annual crops
associated with
permanent crops
2.4.2. Complex
cultivation patterns
2.4.3. Land principally
occupied by
agriculture, with
significant areas of
natural vegetation
2.4.4. Agro-forestry areas
4. Wetlands
4.1. Inland wetlands
4.1.1. Inland marshes
4.1.2. Peat bogs
4.2. Maritime wetlands
4.2.1. Salt marshes
4.2.2. Salines
4.2.3. Intertidal flats
5. Water bodies
5.1. Inland waters
5.1.1. Water courses
5.1.2. Water bodies
5.2. Marine waters
5.2.1. Coastal lagoons
5.2.2. Estuaries
5.2.3. Sea and ocean
211
112
211
112
211
211
112
111
112
Time
T-n
1. Artificial surfaces
1.1. Urban fabric
1.1.1. Continuous urban
fabric
1.1.2. Discontinuous
urban fabric
237
T-2
T-1
backdating
T
T+1
T+2
T+n
updating
Fig. 1. Basic principle of updating and backdating (Feranec et al., 2005).
The referential layer, copy of which—the template—is modified in
updating or backdating subject to the changes of LC shape, for instance
in the T þ 1 time horizon or T 1, etc. is in red.
known, by which information on LC changes can be
obtained from images:
visual and computer aided visual interpretation,
digital methods of change identification (Jensen, 1986;
Rogan and Chen, 2004),
use of a combination of these two methods is frequent
(e.g. the Finnish CLC methodology approach, Sucksdorff and Teiniranta, 2001).
The approach of computer aided visual interpretation of
satellite images is applied in the I&CLC2000 Project. The
method makes use of the CLC90 data layer and the
Landsat ETM satellite images from 2000 (71 year)
referred to as Image 2000 (I2000) (Steenmans and
Perdigao, 2001, I&CLC2000–Technical Reference Document 2002). The CLC90 data layer represents the LC of a
substantial part of the European countries in the 1990s,
which was produced by the method of visual interpretation
of the Landsat TM images.
Generation of the CLC2000 data layer is based on the
updating approach (an opposite of backdating, see Fig. 1).
The primary purpose of updating is to minimize the chance
of introducing inaccuracies into the data layer of changes,
which are common to the independent generation of data
layers (e.g. the inaccurate drawing of the same LC class
borders in two related data layers). Instead of generating a
completely new data layer, the adopted approach uses
the copy of the corrected (revised) CLC90 template
(reference data layer) as the initial CLC2000 data layer
for updating or in case of, for example CLC70 for
backdating (see Feranec et al., 2000).
All needed modifications are performed on this initial
CLC2000 data layer which is altered only locally in areas of
the identified LC changes. The common boundaries of all
unchanged areas (polygons1) are maintained without any
modifications. This approach is known in the GIS
terminology as an update. The basic principle of this
approach is shown in Fig. 1.
It is evident from Fig. 2 showing an example of the
CLC2000 generation that the updating method reduces to
a minimum the possibilities of generation of spatial
discrepancies during identification and interpretation of
CLC classes.
1
Polygons (also called faces in topological terminology) describe areas
bounded by lists of arcs (sometimes called links) (Jones 1997, p. 34).
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Fig. 2. Generation of the CLC 2000 by the computer aided visual interpretation method: two different approaches–A, B.
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In addition to the facts mentioned above, as only the
areas representing CLC changes are the subject of
identification and interpretation, the updating method
saves time as well. It minimizes the time needed by an
interpreter to generate a new data layer. This approach
(Fig. 2A) makes it possible to check the minimum mapping
unit and amalgamation of the residual parts of polygons
smaller than 25 ha with neighbouring polygons (by using
the priority table, see EEA-ETC/TE, 2002). The approach
(Fig. 2B) when the CLC2000 data layer is completed
according to the formula: CLC2000 ¼ CLC90+CLC
change is characterized in: CORINE land cover update.
I&CLC2000 project (EEA-ETC/TE, 2002).
It should be emphasized that after adding the residual
parts of polygons to the neighbouring polygons, their
shape and area change, not in the consequence of e.g.
human impact, natural development, etc. but as the result
of amalgamation (technical–unreal change). Technical
changes, which overestimate the actually identified changes
constitute a part of the new thematic layer of CLC
changes–the result of the CLC90–CLC2000 overlay.
Labelling of technical changes may result in the possibility
of distinguishing them from real changes and to eliminate
them in final statistics. Refer to Büttner et al. (2004) and
Hazeu (2003), as also underestimation is possible (because
an isolated polygon appears as a change but smaller
than 25 ha and it is not incorporated into CLC2000;
see Fig. 3–the last example: appearance of a new polygon).
Spatial characteristics of areas
Identification respects the requirement of spatial characteristics of the generated data layer in terms of the CORINE
LC project methodology (Heymann et al., 1994), e.g., the
resulting new area will comply with the criteria of the
minimum area of 25 ha and the minimum width of 100 m.
The identified change in the CLC2000 data layer can be
accepted only if the area of the change area is larger than
5 ha and the width of change is X100 m. Possible changes
between the initial data layer and that of CLC2000 are
shown in Figs. 3 and 4 (EEA-ETC/TE, 2002; Büttner et al.,
2004).
Methodological procedure
The methodological procedure A (see Fig. 2) of updating
is characterized by the following steps:
preparation of the IMAGE 2000 (satellite images) for
identification of CLC classes of 2000 at scale 1:100 000,
creation of the initial CLC2000 data layer by copying
the corrected (revised) CLC90 template,
identification of CLC2000 data layer by modification of
the initial CLC90 data layer using the IMAGE 2000,
detection of LC changes by overlay of the CLC2000,
CLC90 data layers (the minimal area of polygon change
is 5 ha).
239
Note that the polygon smaller than 25 ha arises after the
change delineation is amalgamated to the neighbouring
polygons by applying rules of the priority table (EEAETC/TE, 2002).
General quality control of CLC data
Quality assessment of the CLC data was essential to
ensure an integrated, harmonized and consistent European
database. Quality control was realized at national and
European levels. At the national level, internal quality
checks were performed under the responsibility of each
National Team leader. At European level, a CLC2000
Technical Team was mandated by the EEA to organize
regular visits (verification missions) to the countries in
support to the National Teams and to carry out external
quality control on all CLC deliverables.
The first visit consisted of training of the National Teams
and focused on nomenclature explanation, change detection and specific interpretation problems related to the
country. This allowed for a common understanding of the
methodology and the interpretation rules for updating,
specific to the project.
Two verification missions were carried out in each
country to harmonize the European CLC products. The
first verification mission was realized during the interpretation phase, when about 50% of the coverage was realized.
The goal of the first quality control was ‘‘corrective,’’ with
the objective to detect errors at an early stage of the
interpretation phase in order to avoid systematic errors
throughout the data and hereby enhance the overall
accuracy of the datasets. The second quality control
mission was carried out on the completion of the
interpretation with the objective to assure that both the
thematic accuracy X85% and the geometric accuracy
better than 100 m (EEA-ETC/TE, 2002; Büttner et al.,
2004) were reached by each country.
The external quality control was carried out on a
randomly selected sample covering around 8% of the total
territory. An overview of verification scheme per country is
shown in Table 3.
From a grid of a 10 10 km a minimum of one
verification unit was selected per working unit (see Fig. 5).
The quality assessment focused on both the qualitative
and quantitative information of CLC2000 data layer and
CLC change data layer. The verification units were selected
from both the CLC2000 data layer and the CLC change
data layer based on following criteria:
amount of the LC changes (important changes, few
changes or no changes),
different CLC class changes and taking into account any
unusual or specific changes,
landscape features and the complexity of the landscape
within the main landscape types: agriculture, urban,
natural, etc. (type of CLC).
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240
CLC90
CLC2000
243
CLC Changes
243
311
311
222
211
231
211
231
211
211
211
222
112
112
Change of the CLC code: the polygon with 211 code has changed into 231
311
211
311
211
112
211
112
112
Area > 5 ha
242
242
Area exchange between two polygons: 112 has increased, 211 decreased (change > 5 ha)
211
311
211
311
324
311
324
243
243
Disappearance of a polygon: 311 has increased, 324 disappeared
211
211
243
243
311
222
222
243
311
Area > 25 ha
Appearance of a new polygon: 311 appeared inside 243 (area must be >25 ha)
Fig. 3. Examples of simple LC changes (EEA-ETC/TE, 2002).
CLC90
CLC2000
311
243
112
CLC Changes
311
243
311-112
13 ha
211- 112
4 ha
112
211
243-112
3 ha
211
Fig. 4. Examples of complex LC changes (EEA-ETC/TE, 2002). Total increase of a polygon (45 ha) can include several contiguous elementary changes,
some of them smaller than 5 ha. The example illustrates the enlargement of a settlement. A similar process is the diminution of polygon.
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Examples of landscape/land cover change detection by
application of CLC70, CLC90 and CLC2000
Table 3
Overview of the first verification step by countries
Countries
Belgium
Czech Republic
Denmark
France
Germany
Ireland
Italy
Latvia
Luxembourg
Netherlands
Sweden
United
Kingdom
Austria
Bulgaria
Estonia
Finland
Hungary
Lithuania
Poland
Portugal
Romania
Slovak Republic
Slovenia
Spain
Total
Country area Real area to be
verified for the
(km2)
first verification
mission (km2)
Area
No. of
checked
checked
verification (%)
units
30 520
78 860
43 090
551 500
356 755
70 229
301 270
63 700
2600
33 940
449 960
244 880
13 730
42 510
11 565
132 037
104 728
37 285
152 909
34 194
2600
21 600
192 784
74 855
14
35
15
62
63
33
105
22
3
23
115
50
10.19
8.23
12.97
4.69
6.01
8.85
6.86
6.43
11.50
10.64
5.96
6.68
83 850
110 993
45 226
338 130
93 036
65 200
312 685
92 300
237 500
49 035
20 273
504 780
37 000
30 364
23 000
27 660
29 973
37 572
143 565
33 040
106 000
26 720
10 000
321 600
30
24
24
22
24
32
115
24
79
17
12
111
8.10
7.90
10.40
7.95
8.01
5.52
8.01
7.26
7.45
6.36
12.00
3.45
4 180 312
1 647 291
1054
6.39
The quality control consisted in verification of:
241
consistent application of nomenclature: right codes,
enough details, right application of generalization rules,
that the minimum area limit of 25 ha is respected,
accurate delineation of changes: delineation of changes
should have an accuracy within a pixel size (25 m),
consistent delineation of changes: only changes larger
than the limits (5 ha, 100 m),
topological consistency of data layers: no invalid
codes, all polygons are closed, only a single code
per polygon, no neighbour polygons with the same
code,
control of meta-data.
The thematic verification has been done on screen
comparing the satellite images from 1990 to 2000 overlaid
with CLC2000 and the CLC change data layer as the main
information source. Beside these basic data, other reference
data such as topographic maps, aerial photographs and
thematic maps in digital or paper format were used
according to the availability, but in some countries also
results of field checking were available. A verification
report was produced for each country.
In order to the demonstrate applications of the computer
aided visual interpretation, two examples have been
chosen. Results obtained by applying the updating method
are presented on the example of LC change identification in
the Netherlands for the years 1986–2000 as obtained in the
framework of the CLC2000 the Netherlands Project
(Hazeu, 2003; Hazeu and de Wit, 2004). Backdating was
a suitable methodological tool applied to the landscape
change inventory in Czechia, Hungary, Romania and
Slovakia for the years 1970–1990. This task has been
accomplished as part of the Phare Topic Link on Land
Cover Project (Feranec et al., 2000).
CLC90/2000 changes in the Netherlands
The total area of LC changes in the Netherlands, identified
by using CLC90 and CLC2000 was 1681.2 km2 (4.8% of all
country surface). Table 4 shows the LC changes at CORINE
level 2. The most important LC change was the conversion
of agricultural land into artificial surfaces (11, 12, 13 and
14–mainly sport areas). This process of urbanization has
been mainly restricted to the class 211, 231 and 242 which
had changed into classes 112, 121, 133 and 142, also the
conversion of almost 75% of the construction sites (133) into
the classes 112, 121 and 123 in 1986 was part of this
urbanization process. In the Netherlands there exists a large
demand for housing and industrial/commercial units.
Various governmental plans (VROM, 2003) are trying to
regulate the expansion of urban fabric and industry. The
conversion of agricultural land into artificial land is
concentrated in VINEX locations which are the main
reasons for the large concentrated development of new
urban or industrial areas (VROM, 2003). Some examples
include the creation of the Almere city and the VINEX
locations in the provinces of Utrecht (Amersfoort, Leidsche
Rijn), Zuid-Holland (Ypenburg, Pynacker, Zoetermeer) and
Gelderland (around Arnhem & Nijmegen). The transformation within cities from old to new housing is also important
(VROM, 2003). However, this is difficult/impossible to
monitor by the CLC approach.
The change of pastures (23) into arable land (21) mainly
represented by the conversion of pastures into greenhouses,
was also a very typical change in the period 1986–2000.
One of the reasons for the conversion of pastures into
greenhouses is the transformation of land with pastures
and relatively isolated greenhouses into artificial surfaces.
The greenhouses are replaced and concentrated into new
areas. The scale of enlargement/intensification is another
reason to increase the area occupied by greenhouses
(profits are higher) (VROM, 2003).
Other important changes for the Netherlands were the
conversions of agricultural land into forests (31), shrub
and/or herbaceous vegetation (32) and inland wetlands
(41). The conversion of agricultural land into forest or
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J. Feranec et al. / Land Use Policy 24 (2007) 234–247
Fig. 5. The example of the verification scheme (Romania).
other semi-natural CLC classes is an important issue in
spatial planning. Accessible greenery (recreation areas/
forest) in the neighbourhood of cities and the construction
of green corridors are the main issues taken into account in
the development of new urban areas (‘‘nature compensation’’) (VROM, 2003, LNV, 2003).
The Netherlands has to fulfil its biodiversity goals as
defined in the Rio Convention. One of the most important
concepts to reach these targets is the construction of the main
ecological structure (EHS) in the Netherlands (Opdam, 2002).
The idea is to connect ‘isolated’ areas of high natural value by
conversion of agricultural land into forest and/or seminatural land (LNV, 2000). The use of land for agricultural
purposes is no longer stimulated in the areas designated to the
EHS. Farmers are subsidized to convert agricultural land into
forest and semi-natural areas and to participate in management of their semi-natural areas (DLG, 2003).
The area of these changes is slightly overestimated (by 9%).
The accuracy assessment carried out indicates a producer’s
accuracy of 91% (Hazeu, 2003). The reason is explained in
part 4 of this contribution. Nevertheless, the total amount of
CLC changes is comparable with the number of changes
found in the National Land Cover database (LGN) of the
Netherlands. Changes in LC calculated between 1996 and
2000 (of 41 500 km2) were nearly 390 km2 (equalling nearly
1%, De Wit, 2003). Extrapolation of this to the period
1986–2000 shows that the figures are similar. A small
overestimation of changes can be expected.
CLC70/1990 changes in Slovakia
The area of the identified LC changes for the abovementioned period in Slovakia is 3156.4 km2 representing
6.4% of the total area of the country (see Table 5).
Collectivization of agriculture was initiated first in the
lowlands in the 1950s. It continued into the basin and
mountain localities until the 1970s. The structure of smallsize plots of fields and meadows was replaced by large
fields, mainly arable land, while the proportion of
meadows and natural vegetation on the meadows or balks
dropped. Table 5 confirms changes of pastures (23) into
arable land (21) and heterogeneous agricultural areas (24),
as well as diminishing of heterogeneous agricultural areas
(24) and pastures (23) in favour of arable land (21). They
are the most extensive LC changes identified in Slovakia in
the study period.
Changes of forest (31) into transitional woodland shrub
(32) were the second most extended ones. This type of
change is the result of the anthropogenic impact–timber
extraction and various calamities in forest. Changes,
mainly those of arable land (21) and heterogeneous
agricultural areas (24) into urban fabric (11), industrial,
commercial and transport units (12) and construction sites
(13) were identified in favour of urbanization and
industrialization. Their occurrence is linked above all to
the principal urbanizing axes of Slovakia (Váh, Nitra,
Hron valleys and the construction of water works in
Gabčı́kovo) (Feranec et al., 2000). These results were not
compared with National Statistics.
Applications and policy issues of the CLC data layers
EEA (Steenmans and Perdigao, 2001, EEA-ETC/TE,
2002) identified the main areas of CLC data application
among users at the European level. The results indicate
that the data can be used especially in land management,
nature conservation and water management (e.g. for
various analyses in agriculture, forestry, spatial planning
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243
Table 4
Land cover changes in The Netherlands for the period 1986–2000 (in km2)
The Netherlands
1986’s classes
11
12
13
14
21
22
23
24
31
32
33
41
42
51
52
Total 2000
2000’s classes
Total 1986
11
12
13
14
21
22
23
24
31
32
33
41
42
51
52
—
0.2
36.7
5.9
112.5
2.0
175.8
111.7
2.8
0.1
0.6
0.2
0.1
0.4
—
448.9
6.7
—
47.2
1.6
65.9
0.7
80.7
58.1
3.5
2.5
0.7
—
—
1.6
0.1
269.4
—
2.2
—
0.3
38.4
0.1
45.3
14.8
3.2
1.6
1.0
0.8
—
6.2
1.5
115.5
0.4
—
3.4
—
40.3
1.3
42.8
23.5
8.9
1.3
0.5
1.6
—
0.3
—
124.4
—
0.4
2.3
—
—
2.7
203.3
13.0
0.8
—
—
1.1
—
2.4
—
225.9
—
—
—
—
9.9
—
3.4
0.3
—
—
—
—
—
—
—
13.6
—
0.1
—
—
18.6
—
—
0.2
0.8
—
—
0.2
—
0.2
—
20.1
0.1
—
0.2
—
23.4
0.3
45.7
—
1.3
—
—
1.5
—
0.3
—
72.7
0.1
—
1.9
0.3
58.9
0.3
23.9
9.4
—
12.1
—
6.0
—
—
—
112.9
—
0.3
7.4
—
37.3
—
13.9
5.0
9.8
—
3.8
—
14.5
0.2
—
92.3
—
—
—
—
0.3
—
0.9
—
—
0.0
—
—
10.4
—
4.8
16.4
—
0.2
2.3
—
12.6
0.6
25.4
0.0
—
0.8
—
—
6.3
6.8
—
55.1
—
0.4
—
—
—
—
6.2
—
—
—
20.0
—
—
—
14.1
40.7
—
0.3
3.5
0.3
7.5
0.1
15.6
8.8
1.1
0.7
0.6
0.3
12.7
—
2.0
53.5
—
0.1
0.5
—
0.4
—
—
—
—
—
11.6
—
7.2
—
—
19.8
7.4
4.2
105.5
8.5
426.1
8.2
682.9
244.8
32.2
19.1
38.7
11.6
51.2
18.4
22.6
1681.2
Classes 423, 522 and 523 were not taken into consideration.
and in lesser extent in transport, tourism, coastal management and development of spatial indicators based on LC
changes). CLC data are also used in several research and
applied projects. Some of them are presented here.
CLC and BIOPRESS
The CORINE Land Cover database and methodology
(Feranec et al., 2005) has been used in the BIOPRESS
(BIOPRESS Project, 2005)–Linking pan-European LC
change to pressures on biodiversity. This is a European
Community Framework 5 Project (http://www.creaf.uab.
es/biopress) funded under the framework of the GMES
programme ‘Global Monitoring for Environment and
Security’ (http://gmes.info). The aim of the project was to
assess the impact of LC changes on biodiversity by linking
these changes to pressures on biodiversity (e.g. urbanization, land abandonment, deforestation). The project has
produced LC change information for the period between
1950, 1990 and 2000 from 73 windows 900 km2 and 59
transects of 30 km2 containing Natura 2000 sites and
distributed across 17 countries to represent the major
biogeographical regions of Europe (see an example of the
Netherlands, Hazeu and Mucher, 2005).
graphical regions, boreal, temperate and Mediterranean
countries.
The two first levels of CLC nomenclature indicate the
major and common classes of LC on the Earth surface,
which are detailed at the third level. Several practical
projects of LC data base implementation have been set up
since 2000 by the French National Geographic Institute
(IGN FI) and concerned some tropical, subtropical and
Sahelian countries (e.g. Guadeloupe, San Salvador Honduras, Guatemala, Colombia, Burkina Faso).
These projects have proved the perfect adaptability of
the CLC nomenclature to these different LC types. After
workshops and discussions with local experts, some classes
have been added and other classes have been eliminated at
the third level (according to the national landscape
specificity) such as, in the agricultural areas, olive groves,
vineyards were replaced by coffee, cocoa trees, palm trees,
etc.
In the natural domain, the aspects of savannah, steppe,
evergreen forests, mangrove swamps should be more
explicit and detailed than in the European standard
nomenclature. Today, standard LC information is recognised by decision makers as a reference dataset for spatial
and territorial analysis and CLC data at the second level
could be a core design for the whole world.
Extension of CLC concept in other biogeographical regions
LC accounts
The particular values of CLC methodology and data
layers come from the merging of general with local topdown vision summarized in a simple and standard
methodology, single detailed and hierarchic nomenclature,
precise definition of geographical object and scale. The
European CLC nomenclature includes 3 levels and 44
classes at the third level and covers 3 different biogeo-
LC accounts are currently being produced at the EEA on
the basis of CLC data layers 1990–2000 (EUROSTAT
integration of geographical and statistical data in the
environmental accounting framework, Weber et al. (2003)
and an implementation by IGN-France International
within a framework of Burkina Faso study; Jaffrain
et al., 2005).
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244
Table 5
Land cover changes in the Slovak Republic for the period 1970–1990 (in km2)
Slovak Republic
1970’s classes
Total 1990s
1990’s classes
11
12
13
14
21
22
23
24
31
32
33
41
51
Total 1970s
11
12
13
14
21
22
23
24
31
32
33
41
51
—
0.1
12.6
0.1
38.9
0.2
1.5
20.7
0.6
—
—
0.2
—
74.8
1.3
—
3.1
—
18.2
—
0.2
4.2
1.8
0.7
—
0.2
0.1
29.8
0.3
0.7
—
—
25.5
—
2.0
4.1
31.2
1.3
0.1
0.6
3.9
69.6
—
—
0.0
—
0.5
—
—
1.2
1.4
0.5
—
—
0.4
4.0
0.1
0.1
1.9
—
—
15.3
157.5
371.5
14.1
3.4
—
1.8
0.1
565.8
—
—
0.0
—
65.4
—
1.1
22.4
0.1
—
—
—
—
89.0
—
—
0.9
—
127.7
0.2
—
417.1
10.1
2.8
—
1.4
0.2
560.5
1.3
0.1
3.4
0.1
183.0
4.3
431.9
—
41.3
22.2
0.1
1.9
3.1
692.7
—
—
0.3
—
3.6
—
2.7
14.7
—
54.8
—
2.3
3.8
82.1
—
0.2
1.4
—
1.5
—
25.1
11.4
871.6
—
6.6
3.3
1.3
922.4
—
—
—
—
—
—
—
—
0.1
9.9
—
—
0.0
10.1
—
—
0.5
0.1
0.4
—
—
1.2
2.1
0.1
—
—
16.4
20.8
0.5
0.1
6.3
0.4
6.4
—
3.4
8.7
3.5
1.0
0.9
3.5
—
34.8
The main goal of LC account is to provide an easy and
comprehensive access to LC data showing the ‘stock’
available for each LC class in the different LC data, and
providing also the changes occurred in the periods between
different LC works. LC accounts interpret LC changes and
convert them (group them) into flows that explain the
processes, driven either by human activities or by natural
conditions that take place.
They measure on the one hand the consumption of LC
of the initial year and, on the other hand, the formation of
new LC. The nomenclature of flows built on CLC for
Europe has 9 top level groups, 37 classes at second level
and 24 additional subdivisions at the third level (the most
detailed list being of 51 items).
LC flows are subsequently linked to the social and
economic processes that have generated them in land use
accounts that analyze functions such as housing, transport,
food production, forestry, tourism and recreation. In land
use accounts, data on surfaces are supplemented with data
on other variables, including in monetary terms.
LC accounts are as well interpreted as one group of
stress factors on ecosystems, in the third dimension of the
framework, so-called ecosystem accounts. In ecosystem
accounts, data on surfaces and spatial patterns are
supplemented with data on fauna and flora, nutrient
cycling and pollution.
The LC and ecosystem accounting framework creates
novel statistics that help describing the interactions
between human activities and their environment, taking
into consideration the place where things happen and
possible conflicts.
Discussion
The European landscape has been monitored by means
of the CORINE Land Cover project and its two branches,
3.5
1.2
30.4
0.7
471.1
20.0
625.5
877.2
977.8
96.6
7.7
15.3
29.5
3156.4
one of them referred to as CLC90 and the other, almost
finished, referred to as I&CLC 2000, since 1985. The
I&CLC 2000 projects demonstrate the possibilities of LC
mapping by satellite images at scale 1:100 000, but also
those of detection of LC changes applying the CLC data
layers of two time horizons (the 1990s and the year 2000
71 year, or also the 1970s in four countries of Central and
Eastern Europe). The generated data layers concerning LC
changes offer the possibility to analyze and assess the
changes that have occurred in Europe over the last decade.
CLC90, CLC2000 and CLC90/2000-changes data users
should be informed about some aspects that ought to be
taken into account in further use or refinement of the data.
CLC nomenclature is based on physiognomic characteristics of landscape objects discernible on satellite images
with thematic accuracy X85% (EEA-ETC/TE, 2002,
Büttner et al., 2004). These cannot be quite separated
from their functional properties. For instance, urbanized
objects (artificial surfaces) or intensively used agricultural
objects (arable land, permanent crops) also indicate the
land use–their social function. This is the reason why it is
considered desirable to take into account physiognomic
and functional attributes of landscape objects together
(above all in the more detailed itemization of the CLC
nomenclature) and to choose an adequate name/designation for them. The applied methodology facilitated
classification of all LC objects of Europe in one of 44
classes of the CLC nomenclature. Table 6 lists the
inaccuracies and problems found.
Identification of heterogeneous LC classes, such as those
that are parts of the CLC nomenclature require the
preference of computer aided visual interpretation of
satellite images. This approach differs from Land-Use
and Land-Cover Changes (LUCC) studied in the framework of the International Geosphere–Biosphere Programme (IGBP) and International Human Dimensions
ARTICLE IN PRESS
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245
Table 6
Inaccurancies in identification, classification and delimitation of CLC classes found during the verification missions in 29 European countries
CLC classes
Problems
Continuous urban fabric (111)
Discontinuous urban fabric (112)
Overestimation; occurrence more than 20% of urban greens within calss 111
Missing some areas of this calss; reasons: 300 m distance between houses; rural settlements with
area around 25 ha (71–3 ha) missing
Misinterpretation with classes 31x or 324
Distinguishing from the class 231 (or abandoned land)—serious problem because both classes
have very similar spectral characteristic
Frequent confusion with natural grasslands (321); not considering human impact and specific
natural conditions (e.g. steep slopes, high mountains, etc.)
Overestimation caused by inaccurate discerning of the class 243 (presence of small parts of tree,
shrub vegetation, swamps); areas of annual or permanent crops larger than 25 ha
Overestimation caused by a presence of parts (e.g. arable land, natural vegetation, etc.) larger
than 25 ha
Confusion or erroneous determination of these classes represented by bushy vegetation;
separation of the climax stage of bushy vegetation (e.g. Atlantic and Alpine heaths, sub-Alpin
bushes—322; Mediterranean and sub-Mediterranean evergreen sclerophyllous bushy and scrub
vegetation—maquis, garrig, mattoral, etc—323) and vegetation representing either woodland
regeneration or degradation, e.g. clear-cuts, forest calamities by air pollution, etc.—324 is
problematic
Underestimation in some countries mainly in the CLC90
Ambiguities in distinguishing of these classes; they are very similar in terms of spectral
characteristics
Green urban area (141)
Non-irrigated arable land (211)
Pastures (231)
Complex cultivation patterns (242)
Land principally occupied by agriculture with
significant areas of natural vegetation (243)
Moors and heathlands (322), sclerophyllous
vegetation (323), transitional woodlands/shrub
(324)
Transitional woodland shrub (324)
Inland marshes (411) and peatbogs (412)
Programme on Global Environmental Change (IHDP)
(Lambin et al., 2003), which apply the digital methods of
change detection. The grounds for applying the computer
aided visual interpretation in the CLC methodology were
stated earlier in this paper. The question of overestimation of
identified changes was commented in the fourth part.
Analysis of its possible solution will require a separate study.
The quality assessment process concerning the obtained
data goes on. It is based on the use of field photographs
taken in the framework of the LUCAS (Land Use/Land
Cover Area frame Statistical survey) Project.
Conclusions
The generated data layers cover 29 European countries
with a total area of about 4.5 million square kilometres at
the original scale 1:100 000, which is a unique information
source on European LC. The data layers are accessible at
the web site: http://dataservice.eea.eu.int and represent an
important contribution to the existing trend of global
monitoring of LC and its changes. The dissemination and
use of mentioned products is defined in an agreement
between the EEA, the European Commission, and the
participating countries. Several policy-related applications
were foreseen by the European Commission Services, as
well as in EEA and its European Topic Centres (Büttner
et al., 2004). CLC data layers have also been used in
various research and applied projects. Application of the
described CLC methodology in 26 European countries
(and digital methods of change detection were applied in
two countries and the combined method was used in one
country) confirmed that the use of computer aided visual
interpretation of satellite images in mapping of hetero-
geneous LC classes at scale 1:100 000 is justified. The
characterized methodology should contribute to deeper
insight into the nature of data that were derived by it. The
use of the quoted data in the examples of LC change
identification in Netherlands and Slovakia showed that
they help to identify such trends as enlargement of
urbanized areas, structural changes in agricultural landscape (changes between arable land, grassland and complex
cultivation pattern) but also those in forestation or
deforestation. Consequently, the existence of CLC data
layers is the tool for landscape assessment and identification of landscape changes at national and European levels.
The acquired accessibility to the CLC90, CLC2000, and
CLC90/2000-changes has in addition created new conditions for analysis of trends, causes and consequences of
natural and social processes ongoing in the landscape, and
for the assessment of its ecological stability at the
European, national or regional levels. Such information
can comprise inputs for varied environmentally oriented
projects and will certainly represent an essential component
of the landscape management.
Acknowledgements
The CLC2000 project was part of the work programme
of the European Topic Centre on Terrestrial Environment
(ETC-TE) working under the European Environment
Agency between 2001 and 2004. The authors express
their gratitude to Chris Steenmans EEA project manager,
Adriana Gheorge EEA-EIONET coordinator and Stefan
Kleeschulte ETC-TE manager for their continuous cooperation and support.
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The study is also one of the outputs attained in the
framework of the project Identification and assessment of
landscape changes by application of remote sensing data,
CORINE land cover databases and the GIS, No. 2/4189/24
at the Institute of Geography of the Slovak Academy of
Sciences in 2004 supported by the VEGA Grant Agency.
Authors are grateful to Hana Contrerasová for linguistic
revision of this contribution and to Magda Nováková for
help with its technical aspect.
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