Inventory of major landscape changes in the Czech Republic, Hungary, Romania and Slovak Republic...

JAG l Volume 2 - Issue 2 - 2000

COMMUNICATION

Inventory of major landscape changes in the Czech Republic, Hungary, Romania and Slovak Republic 1970s - 1990s

J2n Feranecl, Marcel %ril, J2n Ot’ahel’l, Tom25 Cebecauerl, Jan Kol32, Tom% Soukupz, Dagmar Zdetikovd2, Jifi Waszmuthz, Vasile Vdjdea3, Anca-Marina Vijdea3 and Constantin Nitica3

1 institute of Geography, Slovak Academy of Sciences, Stefanikova 49, 814 73 Bratislava, Slovak Republic (e-mail: [email protected])

* GISAT, Charkovska 7, 101 00 Praha 10, Czech Republic

3 Geological Institute of Romania, 1 Caransebes Street, 783 44 Bucharest, Romania

KEYWORDS: Afforestation, change detection, computer-

aided visual interpretation, CORINE land cover database,

deforestation, extensification of agriculture, intensifica-

tion of agriculture, land cover changes, urbanisation

ABSTRACT

One of the most important achievements in 1998-1999 of Phare

Topic Link on Land Cover has been the development and practical

application of a methodological approach to landscape change iden-

tification and analysis in the territories of four Phare countries (the

Czech Republic, Hungary, Romania, and the Slovak Republic). The

changes were identified on a national level from Landsat TM and

MSS satellite images by application of the CORINE Land Cover data-

bases for two time horizons (the late 1970s and early 1990s) at the

second hierarchic level. Based on identified causality, the landscape

changes were grouped into 7 types: intensification of agriculture,

extensification of agriculture, urbanisation-industrialisation, enlarge-

ment (exhaustion) of natural resources, afforestation, deforestation

and other anthropogenic causes. The results of the groupings are

presented in the form of contingency tables and maps showing the

spatial distribution of the changes. From the point of view of total

extent, forest landscape changed the most in the Czech Republic.

This change represents a reduction of forest by 167,702 ha and an

enlargement of transitional woodland-scrub by about 26,339 ha. In

Hungary the most pronounced changes were decrease of forests by

66,622 ha and decrease of arable land, orchards and vineyards by

21,529 ha. The most remarkable changes identified in Romania were

decrease of arable land, forests and wetlands by 366,817 ha,

285,887 ha, and 59,967 ha, respectively, as well as enlargement of

areas of complex cultivation pattern by almost 347,220 ha. The most

pronounced changes in Slovakia were represented by diminution of

forest by 94,935 ha and that of heterogeneous agricultural areas by

18,451 ha; enlargement of transitional woodland-scrub areas and

urbanised area were about 13,107 ha and 14,990 ha, respectively.

INTRODUCTION

Changes of landscape objects represent in the most gen-

eral sense a sequence of linked states of these objects,

characterised by the particular time horizon and by a set

physiognomic, physical, chemical and biological proper-

ties, as well as functions and relations to its environs

(Figure 1). Changes of landscape objects can be identified

by analysis of the selected states the objects pass

through during a period of study [Feranec et al, 19991.

Analysis of landscape changes is equally important from

the point of view of assessing natural and socio-economic

processes, their dynamics and causes, as well as possible

trends of future development. Direct observation of land

cover changes by multispectral remote sensing data

allows identification of major processes of change and,

by inference, the characterisation of landscape dynamics

[Martens & Lambin, 19991. Therefore, land cover is

important in terms of gaining better understanding of the

landscape at any level.

CORINE Land Cover (CLC) is one of the most important

databases managed by the European Environmental

Agency, and it aims at collecting comparable and consis-

tent land cover data across Europe by application of

satellite images. This information base combined with

other relevant data (eg, thematic maps, aerial pho-

tographs, statistical data) offers an essential tool for

inventory analysis and assessment of landscape changes,

in addition to providing useful long-term monitoring

tools.

m-7 /

0 4. /

/ / *’ FIGURE 1 Changes of the IL) I landscaoi obiect in the

/ 4

/ course ‘of three temporal horizons. Tl, T2, T3 -

129

inventory of major landscape changes JAG l Volume 2 - Issue 2 - 2000

The importance of studying landscape changes at a local and regional level has also been highlighted in other EC

projects. The LACOAST Project was one of the first,

applying CLC data from the period 1975-1999 of a

10 km coastal strip of 9 EU Member States with the aim

of measuring land cover changes in coastal areas.

Another EC project based on the application of CLC -

MURBANDY - aims to study land cover/land use changes

and to develop a set of indicators to monitor the urban

dynamics associated with the change.

CLC70) was created from CLC90 by computer aided visu-

al interpretation of the Landsat MSS images [Perdigao &

Annoni, 19971. The land cover changes database for the

period 197Os-1990s (hereafter CLC70-90) is the result of

GIS overlaying and postprocessing the CLC70 and CLC90

databases (see Figure 2).

Within the framework of EEA, Phare Topic Link on Land

Cover (PTULC) is a consortium co-ordinated by the GISAT

company (Czech Republic) and its members include the

Institute of Geography SAS (Slovak Republic), the

Geological Institute (Romania) and HNIT-Baltic (Lithuania).

Its seven main tasks were identified under the Phare pro-

ject No. ZZ-9512. The contract among the respective par-

ties was signed in July 1997 for a duration of 24 months.

The aim of the task “Development of the application

using the CLC database” was landscape changes identifi-

cation and analysis. This introduces the practical case of

the large-scale application of the CLC database and the

methodology of landscape changes inventory (the territory of the Czech Republic, Hungary, Romania and the

Slovak Republic was mapped, a total area of almost half

million square kilometres). Extension of this approach to other European countries will it possible to compare land-

scape changes, which has taken place on the continent in

different geographical regions and under different social

and economic conditions.

The aim of this paper is to describe the basic characteris-

tics of the methodology of a newly created land cover

changes database and to evaluate the results of a land-

scape changes inventory in the four Phare countries men-

tioned for the period 197Os-1990s.

FIGURE 2: Creation of the land cover change database CLC70- 90 (example of Starina valley, East Slovakia)

The applied methodological procedure is summarised in the following steps:

METHODOLOGY OF LANDSCAPE CHANGE ANALYSIS

The methodology was based on the basic assumption

that changes in land cover can be used to infer changes

in landscape. It consists of two parts - creation of a land

cover changes database and landscape change analysis.

CREATION OF A LAND COVER CHANGES DATABASE

The basic input data for identification of land cover

changes was a vector database 1 :I 00 000 representing

the beginning of the 1990s (1989-1992, hereafter CLC90) that was mapped under the framework of a CLC project realised in 6 Phare countries in the period 1994-

1996. From a methodological point of view, the CLC90 database was created by the visual interpretation of Landsat TM images and subsequent digitising of the results obtained. A land cover database for the period of the second half of the 1970s (1976-1980, hereafter

preparation of Landsat TM and Landsat MSS satellite

imagery for mapping the land cover classes of the

1970s (CLC70); aggregation of the 3rd level classes to the 2nd hierar-

chic level of the CLC nomenclature (Table 1; Heymann

et a/, 1994) and segmentation of the CLC90 vector

database; identification of CLC70 classes by re-interpretation of

the aggregated CLC90 database; mosaicking of the single CLC70 database segments, checking the physical and logical integrity; creation of the CLC70-90 changes database by over-

laying CLC70 and CLC90; elimination of sliver polygons by application of cleaning procedures and sepa- ration of revised CLC70 database.

The individual steps of the methodology were realised using the GIS software Easi/Pace, ERDAS Imagine (prepa-

130

Inventory of major landscape changes JAG l Volume 2 - Issue 2 - 2000

TABLE 1: CLC nomenclature

1. Artificial surfaces

1.1. Urban fabric 1 .I .I. Continuous urban fabric 1 .1.2. Discontinuous urban fabric

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

1.3. Mine, dump and constructions sites 1.3.1. Mineral extraction sites 1.3.2. Dump sites 1.3.3. Construction sites

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 .I. 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

3. Forest and semi-natural areas

3.1. Forests 3.1 .l. Broad-leaved forests 3.1.2. Coniferous forests 3.1.3. Mixed forests

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

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

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 .l. 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

ration of the satellite data for interpretation), ArcView

GIS, Arc/INFO, COPILOT and GenaMap GIS (computer

aided visual interpretation, database processing and final

checking).

In the preparatory stage the source data were segmented

according to the sheets of 1 :I 00 000 topographic maps

as resulting from the system of S-42 Gauss-Kruger map

projection in the Czech Republic, Romania and Slovakia

and EOV Reduced Conformal Cylindrical projection in

Hungary. Coloured, contrasted compositions (spectral

bands TM 542 and MSS 421) were created from satellite

data and prepared for the visual interpretation. The RMS

(root mean square) error of transformed (geocoded)

Landsat TM data is about 1 pixel (30 m). It was not possi-

ble to maintain this 30-m accuracy for MSS data as well,

due to its pixel size of 80 m. Nevertheless, the relative

accuracy of the transformation was kept to less than 2

pixels. A mean value of RMS error on GCPs of trans-

formed MSS scenes was 0.712 pixels (49.8 m).

Because of the coarse resolution of the MSS data (spatial

resolution 80 m, radiometric resolution 6 bits, 4 spectral

bands) it was reasonable to work only with the second

hierarchic level of the CLC nomenclature. Hence the pri-

mary CLC data set, as the source for identification of the

land cover from the 197Os, was derived from the original

CLC90 segments aggregated at the 2nd level (which

reduced the number of land cover classes from 36 at the

third level to 15 at the second level).

Creation of the CLC70 database was based on the retro-

spective analysis (downdating), of comparison of original

CLC90 polygons with the Landsat MSS images from the

1970s and their modification. The computer-aided visual

interpretation was carried out using two parallel win-

dows. The CLC90 data overlaid on the Landsat TM image

in the left window represented the auxiliary etalon. The

aggregated CLC90 data with the Landsat MSS were visu-

alised in the right window. In the right window, the

CLC70 database was created gradually, polygon by poly-

gon, based on the identified shape and size changes of

the corresponding land cover classes. Identified change in

CLC70 database was accepted only if the area of the

changed polygon was larger than 4 ha. Identification also

respected the requirement of the spatial characteristics of

the generated database in terms of the CLC project

methodology, ie, the resulting polygon had to comply

with the criteria of a minimum area of 25 ha and a mini-

mum width of 100 m.

131

Inventory of major landscape changes

More difficulties arose during change detection. From a

spectral point of view, discernability of changes in CLC

classes (on the 2nd level) using MSS data was feasible,

although correct identification and delineation of certain

changes (eg, classes 23121, 22121 - see Table 1) was

problematic without support of additional information

(eg, topographic maps at various scales, aerial photos).

The most sensitive types of change for support of ancil-

lary data were those that took place within the main land

cover classes on the 1st level. Above all, this was the case

for changes in agricultural areas. Detection and delin-

eation of changes connected to urban fabric and forested

areas were generally much more feasible with the help of

additional image processing. There was no problem with

detecting and delineating changes connected with mining

activities or water bodies. From a geometrical point of

view, on the other hand, delineation of detected changes

was problematic, especially in heterogeneous classes 24

and 32 (see Table I), and the help of original TM data or

a topographic map was necessary. On particular locations

the lower geometrical resolution and the positional accu-

racy of the geocoded MSS data interfered with the criteri-

on of a minimum width of 100 m for delineating the

change.

Mosaicking of the individual CLC70 map segments result-

ed in a single data layer at a national level. While aggre-

gating the segments, control procedures and correction

of the physical and logical integrity of the data were car-

ried out. This included the topology-related quality

checks, elimination of polygons not complying with the

area requirement, correction of aggregation inconsisten-

cies, identification of thematic errors, and edge-matching

on national boundaries.

In the next stage, the CLC70-90 land cover changes data-

base was created using overlay (intersect) GIS procedures

for the two CLC70 and CLC90 data sets. The resulting

database was tested on presence of polygons not com-

plying with criterion of at least 4 ha area. Polygons small-

TABLE 2: Conversion table


er than 4 ha were excluded using a set of border tests

under the condition to preserve the original structure of

CLC90. The only polygons that did not have to meet the

criterion were those artificially limited by the state border.

The final CLC70 database was generated from the

cleaned CLC70-90 changes database.

METHODOLOGY OF THE LANDSCAPE CHANGE ANALYSIS

This approach consists of the three steps [Feranec et al,

19991:

1.

2.

3.

The classes of the CLC70-90 land cover changes

databases were reclassified into 7 types of land-

scape changes according to the conversion table

(Table 2).

Statistics - contingency tables (Tables 3, 4, 5 & 6) -

were calculated using standard GIS cross-tabulation

procedures; they express thematic re-distribution of

individual classes within the study period.

Comparisons of the area of changes are presented

in Figure 3 and Table 7.

Spatial intensity of landscape changes was

expressed by a set of 8 maps (7 maps show individ-

ual type of changes, and the 8th map depicts the

sum of all 7 types of changes), at original scale

1:3,000,000. The maps show the spatial extent of

changes recalculated to a grid with resolution 1.5

km x 1.5 km. The selected examples are presented

in Figures 4-7. The four intervals of the landscape

changes spatial intensity were considered:

76-100 % of the grid cell area

was changed - full change,

25-75 % of the grid cell area

was changed - substantial change,

l-24 % of the grid cell area

was changed - minor change,

0 % of the grid cell area

was changed - no change.

132

Inventory of major landscape changes JAG • Vo lume 2 - Issue 2 - 2000

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133

inventory of major landscape changes

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AREA OF CHANGES 400000

350000

3ooooo

250000

200000

150000

100000

5oooo

0

X OF TOTAL AREA

% OF CHANGED AREA

UC2 q HU q BRO q SK

FIGURE 3 Comparison of area of identified landscape changes in the Czech Republic, Hungary, Romania and the Slovak Republic


Definitions of types of landscape change are based on

land cover information, which is the attribute of the CLC

database [Feranec et al, 19991:

Intensification of agriculture

Change of meadow-pasture or forest to arable land, as

well as the change of arable land to vineyards, orchards,

berry plantations, greenhouse management, etc. This

definition accepts general principles of agricultural pro-

duction within the context of the analysed land cover

classes.

Extensification of agriculture

Change of vineyards, orchards and berry plantations to

arable land or grassland, and change of arable land to

grassland. For this type of landscape change it is impor-

tant also to take into account abandoned agricultural

land - land not used for three or more years: it can

change later into the class 231 Pastures and thereafter to

324 Transitional woodland shrub.

Urbanisa tion (industrialisa tion)

Change of mainly agricultural and forest land to

urbanised land (construction of buildings destined for liv-

ing, education, health care, recreation and sport) and

industrialised land (construction of facilities for produc-

tion, all forms of transports, electric power generation,

construction connected with the enlargement of water

reservoirs and ponds).

Enlargement or exhaustion of natural resources

Change of agricultural, forest and other types of land to

areas for mineral mining, and their enlargement or

exhaustion in corresponding localities.

Afforestation

Natural or man-induced changes of areas after tree

felling, changes of meadow-pasture or arable land to

forests in various stages of growth.

TABLE 7: Comparison of area for individual types of changes - a) area in hectares, b) proportion (%) of total area, c) proportion (%) of changed area

%oftomlanxi 0 1 2 3 4 5 6 7 Tatwluw TocJdNnfm

-Rapublic 96.59 0.27 0.22 0.14 0.10 0.33 2.12 0.22 ml.09 3.41

itiz 9aO5 94.24 0.22 1.24 0.23 1.51 0.06 0.12 0.04 0.03 0.52 1.14 0.72 1.18 0.54 0.17 lUb.00 100.00 1.95 5.76 SlovdRumblk 93.56 226 1.52 0.31 0.02 0.27 1.94 0.19 l~.OO 6.44

%dchimaedarea 0 1 2 9 4 5 6 7 To#mm rotddmnm

czecilRqnlblk 7.93 634 4.18 294 9.76 6z26 6s 100.00 HMpry 11.20 11.67 3.19 1.94 26.56 36b74 a50 1OlMO

21.60 26.20 2.07 0.45 19.% 20.42 9.41 100.00 SbmkRwublk 3426 23.55 4.75 0.33 4.15 30.67 2% 100.00

135


Deforestation

Clear-cut, devastation of forest by anthropogenic activity,

natural disasters, etc.

Other anthropogenic causes

Re-cultivation of former mining areas, dump sites, etc.

ANALYSIS OF LANDSCAPE CHANGES

Land cover changes generally reflect socio-economic and

political development of the state. The largest changes in

period 1970s1990s were determined by continuing

industrialisation, accompanied by urbanisation, as well as

anthropogenic pressure on arable land, forests and wet-

lands (see Tables 3, 4, 5 & 6).

The greatest volume of landscape changes by country

was caused by extensification processes in Romanian agri-

culture (Figure 4): 366,817 ha, or 26 percent of the total

area of change in the country, were affected. This large

change was a consequence of Land Use Act No. 18,

enacted in 1991. Agricultural land returned to former

owners was subsequently divided into parcels for complex

cultivation with a lower proportion of arable land.

Similarly large changes - the result of extensification of

agriculture - on 74,331 ha (24 percent of the total area

of change) were identified in Slovakia. The effect here is

related to a lower production efficiency of arable land on

steeper hilly slopes and its transition to meadow and pas-

ture, in addition to the process of privatisation of agricul-

tural land. This type of change was not so distinct in the

Czech republic; and Hungary (17,090 ha, or 6 percent,

and 2 1,529 ha, or 12 percent, respectively).

The second greatest change identified was intensification

of agriculture (Figure 5). This type of change was most apparent in Slovakia (IO&l 53 ha, which accounts for 34

percent of the total changed area), where it was connect-

ed with amelioration and other water-management mea-

sures undertaken on agricultural land, especially on

meadows and pastures in water-logged areas of alluvial

plains. In Romania it covers 302,448 ha, which accounts

for 22 percent of the total changed area. The scope of intensification of agriculture in the Czech Republic and

Hungary was balanced by the extensification process

(21,359 ha, which accounts for 8 percent of the total

changed area in the Czech Republic and 20,315 ha,

which accounts for 11 percent of the total changed area

in Hungary).

The amount of afforestation and deforestation also varied

to large extent among the countries. Deforestation (Figure 6) reached 285,887 ha (20 percent of the total

changed area) in Romania and 167,702 ha (62 percent of

the total changed area) in the Czech Republic. Thus,

deforestation was the most noticeable change in the

Germany Extenstticatton of Agriculture

Poland

Ukraine

Austria

Moldova

Italy

Slovenia

Croatia

Bosnia/Herzegovina Yugoslavia

taly Bulgaria

FIGURE 4 Extensification of agriculture

136


Germany 1 ,I ~. ) -

_,’ / .’ Poland

j :, j, ~ ., .”

./

.’ .-

: ,, ‘\

: ‘_,“’

Ukraine

, Austria : :

,I Moldova

._ .;

-. .._ -.. I _ I

Italy i / _’ Siovenia

;\ r ,.. Croatia

FIGURE 5: Intensification of agriculture

Ukraine

Bulgaria

Ukraine

Austria

Ukrain

FIGURE 6: Deforestation


.

Poland

Ukraine

Austria :’

W

FIGURE 7: Total changes

Bulgaria -~~ --..---.-. ----~ .--..-..-- ----~~~-~ -..-...--.-....... ..~.-~._...- ..--..-. ~~~~~~..._...__.._._~_~_~_ .._.._.. _

Czech Republic. These changes stemmed from both forest

management (regulated logging and planting) and air pol-

lution, as well as the devastating impact of snow, wind

and insects on the forests. Such extensive areas of defor-

estation were not observed in Slovakia or Hungary

(94,935 ha, which accounts for 30 percent of the total

changed area, and 66,622 ha, or 37 percent of the total

changed area, respectively). The most extensive process of

afforestation (natural and man-induced) was observed in

Romania (278,119 ha or 20 percent of the total changed

area), while in Slovakia it was the least 13,107 ha

(accounting for 4 percent of the total changed area).

The remaining types of change (urbanisation, enlarge-

ment of natural resources and other anthropogenic caus-

es) represented only several percent of changed areas.

Figure 7 presents the total share of changes within the

four countries.

CONCLUSIONS

The applications of CLC70-90, remote sensing data and

methodological tools of GIS for identification and analysis

of landscape changes in the Czech Republic, Hungary,

Romania and the Slovak Republic revealed the following

principal findings concerning the methodology and

results:

- Coarse spatial and spectral resolution of Landsat MSS

data represents a limitation in observation of land cover

changes in the CORINE land cover database. Further, it

causes problems with distinguishing CLC classes on the

2nd level (for instance 21 and 24, 31 and 32).

- Computer-aided visual interpretation is a more effec-

tive and precise approach for identification of land cover

changes than traditional techniques.

- Causes of detected changes cannot be determined

from satellite images. They can be only determined by

means of ancillary data (particularly for identification of

natural causes - floods, blizzards, landslides, etc.). The

use of topographic and thematic maps, aerial pho-

tographs and statistical yearbooks for analysis was

inevitable.

- Analyses confirmed that land cover data from various

time horizons are an indispensable condition for suc-

cessful detection of landscape changes. It is one of the

rationales for regular CLC database updating.

- GIS technology makes it possible to produce informa-

tion on landscape changes, determining trends in nat-

ural and social development, and documenting the

rate and scope of the changes.

- Ongoing changes in landscape are permanent and

depend on natural, social, economic and political con-

ditions The structure and character of landscape

changes are variable in a wide scope and depend on

particular driving forces. It is the human factor that is

predominant, at least in the countries studied. For

instance, application of Land Use Act 18/l 991 brought

138

Inventory of major landscape changes JAG l Volume 2 - issue 2 - 2000

about the most extensive changes in Romanian agri-

culture.

- The output maps (Figures 4-7) demonstrate the unique

feature of spatial data. The geographical distribution

of those areas in the Czech Republic, Hungary,

Romania and Slovakia where the greatest landscape

changes occurred are easily discernable. For instance,

areas of the Northwest of the Czech Republic in which

more than 75 percent land surface has changed over

the last dozen years, and a construction site for the

Gabcikovo hydroelectric power plant in Slovakia - are

clearly identifiable.

under different social and economic conditions.

Results obtained demonstrate the significance of peri-

odical up-dating of the CLC database. The up-dated

CLC database will become an irreplaceable informa-

- The PTULC project is one of the first practical large-

scale applications of land cover change identification

(a territory of four European countries with total area

of almost one half million square kilometres was

analysed). Extension of this approach to other

European countries will make it possible to compare

landscape changes that take place in different regions

sage dans les territoires de quatre pays Phare (la Republique Tcheque, la Hongrie, la Roumanie et la Republique Slovaque).

Les changements les plus marques en Slovaquie ont ete la dimi-

Les changements ont ete identifies au niveau national a partir d’images satellite Landsat TM et MSS en appliquant les bases de

nution des for&s de 94.935 ha et des zones d’agriculture hete-

donnees de couverture des terres CORINE pour deux epoques (fin 1970 et debut 1990) au deuxieme niveau hierarchique.

rogene de 18.451 ha ; I’augmentation des zones de transition

Bases sur une causalite identifiee, les changements de paysage ont ete group& en 7 types : agriculture intensive, agriculture

for&- buisson et de zones urbanisees a ete respectivement de

extensive, urbanisation-industrialisation, accroissement (Ppuise-

13.107 ha environ et de 14.990 ha.

ment) des ressources naturelles, boisement, deboisement et autres causes anthropogeniques. Les resultats des groupements sont present& sous la forme de tables de contingence et de cartes montrant la distribution spatiale des changements. Du point de vue de I’etendue totale, le paysage forestier changeait le plus en Republique Tcheque. Ce changement represente une reduction de la for& de 167.702 ha et une augmentation des zones de transition for&-buisson d’environ 26.339 ha. En Hongrie les changements les plus marques ont Pte la diminution des forets de 66.622 ha et la diminution de terres cultivables, de vergers et de vignes de 21.529 ha. Les changements les plus frappants identifies en Roumanie ont ete la diminution des terres cultivables, des for&s, et des marais respectivement de 366.817 ha, 285.887 ha et 59.967 ha ainsi qu’une augmentation des zones de forme de culture comolexe de 347.220 ha.

tion source for landscape change observation in the

whole Europe.

ACKNOWLEDGEMENTS

This paper presents part of the results of the project EEA Phare Topic Link on Land Cover, which is supported by

the Phare Programme. The authors wish to express their

thanks to H. Contrerasova for the revision of English text.

REFERENCES

Feranec, J., M. Suri, J. Otahel, T. Cebecauer, J. Kolar, T. Soukup, D. Zdenkova & V. Vajdea, 1999. inventory and analysis of major land cover changes in Central and Eastern Europe over the past 20 years. PTULC Topic Report, Gisat Praha, 37 pp.

Heymann, Y., Ch. Steenmans, G. Croisille & M. Bossard, 1994. CORINE Land Cover. Technical Guide. Office for Official Publications of the European Communities, Luxembourg, 136 PP.

Martens, B. & E. Lambin, 1999. Modelling land cover dynamics: inte- gration of fine scale land cover data with landscape attributes, JAG 1: 48-52.

Perdigao, V. & A. Annoni, 1997. Technical and Methodological Guide for Updating CORINE Land Cover Data Base. Joint Research Centre, European Environmental Agency, Luxembourg, 124 pp.

RESUME

Une des realisations les plus importantes en 1998-1999 de ’ Phare Topic Link’ sur la couverture des terres a ete le develop- pement et I’application pratique d’une approche methodolo- gique pour I’identification et I’analyse de changements de pay-

RESUMEN

Una de las realizaciones mds importantes del Phare Topic Link sobre Cobertura Vegetal en 1998-l 999 ha sido el desarrollo y la aplicacion practica de un enfoque metodologico para la identifi- caci6n y el analisis de cambios de paisaje en 10s territorios de cuatro paises pertenecientes a Phare (la Republica Checa, Hungria, Rumania, y la Republica Eslovaca). Se identificaron 10s cambios a nivel national a partir de imagenes satelitdrias de Landsat TM y MSS mediante aplicaci6n de las bases de datos de Cobertura Vegetal de CORINE al Segundo nivel jerarquico para dos horizontes temporales (finales de 10s atios 1970 y principios de 10s arias 1990). Los cambios,de paisaje fueron agrupados en siete tipos en base a causas identificadas: intensificaci6n de la agricultura, extensificaci6n de la agricultura, urbanizacion-indus- trializacion, ampliacidn (agotamiento) de 10s recursos naturales, plantaci6n de bosques, deforestaci6n y otras causas antropoge- nicas. Los resultados del agrupamiento se presentan en la forma de tablas de contingencia y de mapas que muestran la distribu- cidn espacial de 10s cambios. Desde el punto de vista de la extensi6n total, el paisaje de bosque es el que mds cambi6 en la Republica Checa. Este cambio representa una reduction de 10s bosques por 167.702 ha y una ampliaci6n de la formaci6n tran- sicional de monte-chaparral por 26.339 ha. En Hungria, 10s cambios mas pronunciados fueron una disminucidn de 10s bosques por 66.622 ha y una disminucion de las tierras arables, 10s huer- tos y 10s viriedos por 21.529 ha. Los cambios mas notables iden- tificados en Rumania fueron una disminuci6n de las tierras arables, 10s bosques y 10s humedales por 366.817 ha, 285.887 ha y 59.967 ha, respectivamente, y una ampliacion de las areas con un complejo patr6n de agricultura por aproximadamente 347.220 ha. Los cambios mas marcados en Eslovaquia corres- ponden a una disminuci6n de 10s bosques por 94.935 ha y de las areas agricolas heterogeneas por 18.451 ha; la expansion de las areas transicionales de monte-chaparral y de las areas urbani- zadas fueron de 13.107 ha y 14.990 ha, respectivamente.

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Inventory of major landscape changes in the Czech Republic, Hungary, Romania and Slovak Republic...

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