Landscape c�anges: anal�ysis and cl�assi��cation
�an �ťa�eľ, �an Feranec, ��raj Betak, Karol� H�sar, Monika Kopecka
Institute of Geography of the Slovak �cade�y of Sciences Stefanikova 49, 814 73 Bratislava, Slovak Republic, e-�ail: Oťaheľ@savba.sk
_________________________________________________________________________________
Abstract. �he paper e�phasizes the significance of a correct identification and classification of the selected conditions of the landscape as natural (original) landscape and present land use, which are representative for landscape change analysis.
�he natural landscape is reconstructed as a hypothetic state, which existed before the hu�ans entered it. �he present land use is identified by CORINE land cover (CLC) �ethod in 1990 (CLC90) an 2000 (CLC2000). Long-ter� changes were identified by the co�parison of the natural landscape and CLC2000 and classified as urban develop�ent, far�ing expansion, forest and water �anage�ent. Co�parison of the CLC90 and CLC2000 data layers was used for the analysis of short-ter� landscape changes and classified in the context of i�portant driving forces as urbanization, intensification and extensification of agriculture, forestation, deforestation and other changes. �he procedures of identification and classification of the conditions and changes of the landscape were presented on the exa�ple of �rnava region (Slovakia).
Key words: natural (reconstructed) landscape, CORINE land cover, landscape develop�ent, landscape change classification
Introduction
Landscape changes and their intensity is deter�ined both by natural assets in the sense of the landscape potential and social, econo�ic and political conditions as the driving forces of the regional develop�ent.
Identification and classification of selected states of the landscape recorded in representative ti�e horizons is essential for the landscape change analysis. Land cover is applicable as a suitable tool for landscape change assess�ent. It is appropriate to analyse, identify and classify the natural landscape (original land cover) as the pri�ary state (referential layer) by geosyste�ic and environ�ental approaches. �he long-ter� i�pact of hu�an activities deter�ined the present state of the landscape which changes ever �ore frequently and intensively due to the dyna�ics of econo�ic driving forces.
Landscape change assess�ent and analysis by �eans of the CORINE land cover (CLC) data layers are efficient in ter�s of data co�patibility and result co�parability in the fra�ework of the European countries. CLC data are used in nu�erous international pro��ects BIOPRESS, L�CO�S�, Integrated Environ�ental and Econo�ic
�ccounting (Feranec et al. 2007) or in the fra�ework of research involved with selected countries or regions (Coppin et al. 2004, Feranec et al. 1997, 2000, 2006, 2007, Haines-Young R., Weber J.-L. 2006, �ue��erle et al. 2006, Oťaheľ et al. 2000, 2004a, 2004b in print, Wille�s et al. 2005, and others).
�he ai� of the contribution is to e�phasize the significance of a correct identification and classification of the representative state of the landscape suitable for landscape analysis and assess�ent of landscape changes.
�hese data about the states of the landscape that present its original hypothesis to the conte�porary land use also constitute the base for an efficient presentation and classification of landscape changes. �he procedures of identification and classification of the relevant conditions and changes of the landscape will be introduced using Klasyfikacja krajobrazu. Teoria i praktyka. Problemy Ekologii Krajobrazu. 2008, t. XX. 45-56.
region �rnava as exa�ple, presented also in the articles of Feranec et al. (2006) and Oťaheľ et al. (in print).
Landscape identi��cation and cl�assi��cation
�efining and univocal interpreting of the concept landscape is the first condition of correct landscape analysis and identification. �he holistic interpretation of the landscape is the scientific hypothesis, which �ust be verified.
�his hypothesis presents the landscape as a “co�prehensive integrated syste� for�ed by interaction of basic ele�ents (rocks, water, air, soil, plants, ani�als and hu�an activities) and their properties, physiogno�y of which for�s one recognizable whole” (Zonneveld 1988). It is not si�ple to analyse and identify this syste�.
Possible ways are those that apply analysis of ele�ents within this syste� and their relevant relationships through the analysis of subsyste�s and decisive funda�ents (layers) of the landscape and finally the analysis of the landscape as a whole, or perception (i�age), recorded by sub��ective perception or by technical tools.
�nalysis of ele�ents, subsyste�s and perceptions requires the data and necessary records. Quality and correctness of the analysis depends on these data and records. �ata consist of different for�s of available records and docu�ents about the landscape and its ob��ects (statistical and cartographic data, photographs, aerial photographs, satellite i�ages).
�he holistic concept of the landscape was also applied to landscape research in a various consistency (Richling, Solon 1996, Oťaheľ 1996). �he accent on cognition of the content of the landscape is connected with the develop�ent of analytical geoscientific disciplines where a detailed spatial (regional) research of its ele�ents and properties do�inates. However, the holistic cognition of the landscape was �ainly sti�ulated by a syste�
approach, synthesising attribute and the environ�ental concept of the landscape. Syste� and environ�ental approach si�ultaneously brought an e�phasis on landscape structure research. Landscape research, above all in geography, leans on the diagnosis of natural and anthropogenic (cultural) structure (cf. �rcho 1968), for�ing decisive subsyste�s in ter�s of landscape functioning as the living environ�ent.
Identified spatial landscape units (ob��ects) have to be de�arcated, na�ed and presented in certain logical and syste�ised for�. Classification as a general clustering of landscape units (ob��ects) into classes based on co��on properties (traits) and �utual relationships see�s to be a suitable process of presentation (Grigg 1965).
�s the landscape units possess a spatial trait, their classification is called regionalization or regional taxono�y (Bezák 1993). �s Grigg (1965) asserts, the concept region is attributed the sa�e �eaning in geography as the class in other e�pirical sciences. Hence, regionalization can be referred to as the spatial for� of classification.
�he ob��ect of landscape research deter�ines identification of spatial units. It is not a si�ple proble� regarding the definition of the landscape. �he quoted two traits insinuate the co�plexity of the landscape as an integrated syste� with one recognizable whole (appearance). Precisely the syste�ic approach to landscape research
�akes it possible to identify and then classify the whole in the fra�ework of a hierarchic syste� at several levels. �he geosyste�ic �odel offers the opportunity of differentiated cognition, while the environ�ental �odel e�phasises the analysis of vertical and horizontal relationships of the natural and anthropic subsyste�s.
However, according to Grigg (1965), the identified units should fulfil several basic criteria. Each unit should be continuous, neither two of the� should spatially overlap and all identified units should cover the study area.
Identi��cation and cl�assi��cation o� t�e nat�ral� l�andscape str�ct�re
�nalysis of the natural subsyste� is base of the landscape knowledge. Cognition of the natural landscape in cultural landscape conditions is a hypothesis about the state of the landscape functioning free fro� social influences and regulations. Principles of the landscape analysis reveal that the synthesis of the natural landscape layer is the reconstruction of the original landscape as it was before intervention of the hu�an, but in present cli�atic conditions. Reconstruction of the natural landscape is based in an analysis of key co�ponents, properties and relationships decisive for the functioning of the self-regulating and self-regenerating �echanis�
of the landscape. Cognition of the key properties and relationships in the landscape is especially i�portant precisely fro� the point of view of hu�an activities. �apping of potential natural vegetation (�ichalko et al.
1986) is �ethodologically close to landscape reconstruction.
Spatial cognition of key properties can be efficiently expressed by �eans of regional taxono�y. Regional types of the natural landscape represent spatial units with relatively ho�ogeneous properties. �heir identification requires analysis of synergic vertical relationships of the relevant interacting landscape ele�ents (substrate, georelief, water, soil and potential vegetation) and their properties. �he �ethod of overlaying of individual spatial (�ap) analysis of the relevant ele�ents and properties facilitates vertical verification of these relationships and si�ultaneously deli�itation of borders to their horizontal ho�ogeneity.
�he basic regional typology of Slovakia as presented in Geoecological – natural landscape – types by �azúr et al. 1977 at scale 1:500 000 was starting-point of the natural landscape identification and classification. �he adapted version of natural landscape �ap (Oťaheľ 2000) respect a relevant landscape properties regarding of the land use, types of relief, soils and potential natural vegetation. �he adapted �ap of natural landscape of Slovakia contains 27 classes at four hierarchic levels (Oťaheľ et al. 2000). �he �ap was used for the assess�ent of the landscape in the context of the natural base and land use co�patibility (Oťaheľ et al. 2000). Classification of the natural landscape of �rnava region is docu�ented as an exa�ple at regional scale (tab. 1, fig. 1).
Fig. 1. Natural (reconstructed) landscape (Oťaheľ et al., in print)
�able 1. Natural (reconstructed) landscape (Oťaheľ et al., in print)
1 IN�R�-�OUN��IN LOWL�N�
L�N�SC�PE 2 �OUN��INOUS L�N�SC�PE
11 AccumulAtion plAin lAndscApe with porous
groundwAters 21 BAsin, furrow, And vAlley erosion-
AccumulAtion lAndscApe with cApillAry And porousgroundwAters
111 Fluvial to aeolian-fluvial plains 211 War� basins with oak to oak-hornbea�
forests 1111 Holocene flood plains with floodplain forests
on Fluvisols to �ollic Fluvisols 2113 Subberglands and intra�ontane furrows with oak-hornbea� forests on Ca�bisols 1112 Pleistocene flood plains covered by
prevailingly carbonate (aeolian) sedi�ents with xero- ther�ophilous oak forests on Chernoze�s
212 �oderately war� basins with oak-hornbea�
forests
112 Undulated fluvial to aeolian-fluvial plains 2133 Subberglands and intra�ontane furrows with oak forests on Ca�bisols
1121 Low terraces and cones with el� floodplain forests to oak-hornbea� forests on Ca�bisols
22 mountAinous erosion-denudAtion lAndscApe with fissured-lAyered to fissured-kArstic groundwAters
1122 Fluvial-eolian terraces to neogene plains with oak-hornbean to oak-pine forests on Ca�bisols
1123 �unes with pine forests on �renosols 221 Pro�ontories to plateaux
12 erosion-AccumulAtion hilly lAndscApe with
cApillArygroundwAters 2211 War� to �oderately war� pro�ontories and plateaux with oak-hornbea� forests on Ca�bisols to Rendzinas
121 Loess erosion-accu�ulation landscape 222 Uplands to highlands 1211 Loess tables with war� oak forests on
Chernoze�s
2221 War� to �oderately war� uplands to highlands with oak-hornbea� forests on Ca�bisols to Rendzinas
1212 Loess hilly lands with oak to oak-hornbea�
forests on Chernoze�s, Haplic Luvisols and Luvisols
122 Polygenetic hilly lands with oak to oak-
hornbea� foersts on Ca�bisols 2222 �oderately cold uplands to highlands with beech to spruce forests on Ca�bisols to Rendzinas
Identi��cation and cl�assi��cation o� t�e present l�andscape str�ct�re – l�and cover
Natural conditions and social develop�ent have deter�ined the character of the present landscape where
�an-�ade surfaces alternate with distinctly �odified and cultivated areas and parts close to the original natural landscape. Spatial knowledge about individual �aterial landscape ele�ents has greatly contributed to research of the natural landscape. �aterial-energetic content of the landscape is pro��ected in individual landscape seg�ents (ob��ects), which possess their physiogno�ic �anifestation. Hu�ans perceive and identify these ob��ects through physiogno�ic traits. Perception of the landscape in geography is naturally connected with cognition of its content, it �eans the geosyste�ic and spatial properties of the landscape.
�erial photographs and satellite i�ages have contributed to ob��ectification of the landscape research procedures. �hese tools record landscape by physiogno�ic traits and contribute to the identification of ob��ects with biophysical substance covered by the ter� land cover. Land cover represents the �aterialised pro��ection of the natural spatial assets (�orphopositional and bioenergetic) and si�ultaneously that of the conte�porary land
use – it �eans by society recreated (cultivated) or created ob��ects of the conte�porary landscape (Feranec, Oťaheľ, 2001). Land cover integrates natural conditions and social i�pact and is interpreted by synthesised both visual and landscape content signs. �he European CORINE (Coordination of Infor�ation on the Environ�ent) Land Cover Progra��e (Hey�ann et al., 1994, Feranec, Oťaheľ, 2001) produced the �ethod for �apping of land cover as the real physical state of the conte�porary landscape. �he CORINE land cover (CLC) �ethod applying the Landsat satellite i�ages was used to generate individual data layers for land cover of Slovakia and the prevailing part of Europe at scale 1:100 000 for the years 1990 (CLC90) and 2000 (CLC2000). Land cover data layer of �rnava region (Feranec et al. 2006 and Oťaheľ et al., in print) fro� the year 2000 (CLC2000) is docu�ented as an exa�ple (tab. 2, fig. 2).
�able 2. Land cover 1990 (CLC90) and 2000 (CLC2000) (Oťaheľ et al., in print) 1 �rtificial surfaces
11 Urban fabric
111 Continuous urban fabric 112 Discontinuous urban fabric 12 Industrial, commercial and transport units
121 Industrial or commercial units
122 Road and rail networks and associated land
124 Airports
13 Mine, dump and constructions sites 131 Mineral� extraction sites 132 Dump sites
133 Construction sites
14 Artificial, non-agricultural vegetated areas 141 Green urban areas
142 Sport and leisure facilities 2 �gricultural areas
21 Arable land
211 Non-irrigated arable land 22 Permanent crops
221 Vineyards
222 Fr�it trees and berry pl�antations 23 Pastures
231 Pastures
24 Heterogeneous agricultural areas 242 Complex cultivation patterns 243 Land principally occupied by agric�l�t�re, wit� signi��cant areas o�
natural vegetation 3 Forest and se�i-natural areas 31 Forests
311 Broad-leaved forests 312 Coniferous forests 313 Mixed �orests
32 Scrub and/or herbaceous vegetation associations 321 Natural grasslands
324 Transitional woodland-scrub 33 Open spaces with little or no vegetation
331 Beac�es, d�nes, sands 4 Wetlands
41 Inland wetlands
411 Inland marshes 5 Water bodies
51 Inland waters
511 Water courses 512 Water bodies
Identi��cation and cl�assi��cation o� l�ong-term l�andscape c�anges
Landscape changes are interpreted as a sequence of �utually linked different states (conditions) of physical nature that occur at certain ti�e horizons. In the context of environ�ental landscape assess�ent it is appropriate to relate the conte�porary state to an opti�al one which represents the hypothetic (reconstructed) natural landscape. �his condition is close to the original natural landscape and co�parison of both states facilitates analysis of the landscape develop�ent or long-ter� landscape changes. �he �eaning of the co�parison is obvious above all for the assess�ent of long-ter� functioning of self-regulating capacities, stability of forest (vegetation) cover, hence the rate of its persistency.
Long-ter� landscape changes were identified by co�parison the data layer corresponding to the natural reconstructed landscape (fig. 1) with the LC data layer of 2000 (CLC2000). Integration of the quoted data layers in one �ap (fig. 2) offers the option to interpret efficiently the develop�ent of the landscape. Overlay of databases in the GIS �rcView environ�ent was used for identification and location of long-ter� changes in the classes of the natural landscape. Statistical processing of land use fro� 2000 by natural landscape classes �ade it possible to interpret long-ter� changes and their classification according to the principal LC classes while the representation of urbanized areas (LC classes cover 1xx) in natural landscape classes was
classified as urbanization (urban develop�ent); representation of agricultural areas (LC classes 2xx) is under far�ing expansion and representation of forest, wetland and water areas (LC classes 3xx to 5xx) is under forest and water �anage�ent. Regarding natural landscape potential analysis, representation of sport and leisure facilities (LC class 142) were identified and classified separately as the develop�ent of recreation (leisure) areas to discern the� fro� urbanized and technicised areas (urban develop�ent). Likewise, representation of forest, se�i-natural areas and wetlands (LC class 3xx and 4xx) was classified as forest �anage�ent while water bodies (LC classes 5xx) were classified as water �anage�ent (fig. 3).
Fig. 2. Natural (reconstructed) landscape and CLC2000 (Oťaheľ et al. in print)
Fig. 3. Long-ter� changes (urbanization - 1xx, recreation area develop�ent - 142, far�ing expansion – 2xx, forest
�anage�ent – 3xx and 4xx, water �anage�ent – 5xx) expressed by percentage of the selected land cover classes (CLC 2000) in natural landscape classes
Identi��cation and cl�assi��cation o� s�ort-term c�anges
Co�parison of physical states of the conte�porary landscape structure represented by land cover layers fro� shorter ti�e horizons facilitates analysis of short-ter� landscape changes and assess�ent of landscape dyna�ics in ter�s of de�ographic, socio-econo�ic and political incentives. In this sense, the available statistical and cartographic data and above all spatially and te�porally precise aerial photographs and satellite i�ages fro� the last 50 years are i�portant (Feranec et al., 1997, Oťaheľ et al., 2004). �s far as the landscape change interpretation is concerned, it is possible and appropriate to choose the quoted data corresponding to ti�e horizons when social and political events took place (socialist industrialization and collectivisation of agriculture in the 1950s, political transition in the 1990s, Feranec et al. 1997, Cebecauerová, Cebecauer 2004). �he land cover data for Slovakia at scale 1:100 000 for 1990 (CLC90) and 2000 (CLC2000) are especially convenient.
�pplying the �ethodological approaches of Feranec et al. (2002) and Feranec et al. (2006) land cover changes can be interpreted with regard to socio-econo�ic processes that deter�ined the�, such as urbanization (urban develop�ent), intensification and extensification of agriculture, forestation and deforestation and other landscape changes.
�he first type – urbanization (urban develop�ent) – was classified by changes of agricultural, forest and se�i- natural LC classes (21x–32x) into classes of artificial surfaces (11x–14x) and classes of �ining, du�ping and construction sites (13x) into industrial, co��ercial and transport units (12x). Four subtypes fall under this type:
U1 – enlarge�ent of urban fabric, U2 – enlarge�ent of industrial, co��ercial and transport built-up area, U3 – enlarge�ent of natural resources exploitation, U4 – enlarge�ent of sport and leisure facilities area (tab. 3 and 4). �he second type – intensification of agriculture – was classified by changes of �ining and construction sites, classes of less intensive agricultural use and forest classes (13x, 231 and 243) into classes of a �ore intensive agricultural use (211, 221, 222 and 242). Under intensification of agriculture the following subtypes were identified: I1 – enlarge�ent of arable land, I2 – enlarge�ent of vineyards, I3 – enlarge�ent of orchards and berry plantations, I4 – enlarge�ent of co�plex cultivation pattern area. �he third type – extensification of agriculture – was classified by changes of classes of �ore intensive agricultural land use (211) into classes of an extensive agricultural use (e.g. changes of class 211 into 231 and 243 and classes 221, 222 into 211, 231 and 243). Under extensification of agriculture the following subtypes were deter�ined: E1 – reduction of arable land
area, E2– reduction of vineyards, E3– reduction of orchards and berry plantations, E4 – reduction of co�plex cultivation pattern area. �he fourth type – deforestation after felling or cala�ities (caused by wind, e�ission, forest fires, etc.) – was classified by changes of classes 31x into class 324, or classes 31x and 324 into 211 and 243. It contains two subtypes: �1 – felling or cala�ities in forest, �2 – deforestation and enlarge�ent of agricultural land. �he fifth type – forestation (natural overgrowing and cultivation of forest) – represents changes of classes 131, 132, 211, 231, 243, 321 into classes 324 and classes 324 into 311, 312, 313. �wo subtypes fall under this type: F1 – natural develop�ent of forest, F2 – econo�ic growing of forest. �he sixth type – other changes – contains the following subtypes: O1 – enlarge�ent of water areas, O2 – reduction of water areas, O3 – forest co�position changes (tab. 3 and 4, fig. 4, 5).
Fig. 4. Landscape change types expressed by area (in %, codes are explained in text) (Feranec et al., 2006).
Conclusion
Identification and classification of representative states of the landscape are a�ong the basic outputs of landscape learning. �he produced data layers of the natural landscape and land cover (fig. 1 and 2) are the key source �aterials for the decision-�aking process and environ�ental planning. �s regards the level of basic research, relevant source �aterial for identification and classification of landscape changes was drawn.
Co�parison of the natural and the present landscape structure (LC) and processing in �rcView was followed by identification and classification of long-ter� changes in region �rnava. Relatively least changed were the dunes (1123) in the lowland landscape of Gbelský bor in the lowland Záhorská nížina. Prevailingly little fertile
�erosols are coated by �ixed beech-pine and pine forest (68.1 %, fig. 3). Loess tables (1211) appeared to be
�ost changed. Fertile Chernoze�s are used for agriculture (87.7 %). �he �ountainous landscape is the least changed in the region. Oak-hornbea� and beech forests (78,4 %) prevail in �oderately war� uplands (2221), or in �oderately cold highlands (2222, 99.8 %). Recreation areas are the �ost located in the war� uplands (2221, 0.9 %, fig. 3). Representation of forest points to relatively long ter� (ecological) stability of land use and forest above all.
�yna�ics of short-ter� landscape changes in the context of significant driving forces was identified after co�parison of states of the landscape in 1990 and 2000 (CLC90 and 2000). �he greatest change in region
�rnava was forestation connected with forest growing (F2 – 28,5 %), or natural succession (F1 - 8.4%), above all on abandoned pastures (fig. 4, 5). Enlarge�ent of water bodies following the construction of the water works Gabčíkovo is also an i�portant change (18.7% of the total change area). For �ore details refer to studies Feranec a et al. 2006 and Oťaheľ et al. (in print).
Fig. 5. Landscape change types in south part of the �rnava region (codes are explained in text).
Acknowledgement: �he paper is one of the outputs of the Pro��ect No. 2/7021/27: „Structure of the rural landscape: analysis of the develop�ent, changes and spatial organization by application of the CORINE land cover databases and the geographical infor�ation syste�s” pursued at the Institute of Geography of the Slovak
�cade�y of Sciences in 2007, supported by the VEG� Grant �gency.
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�able 3. LC changes 1990-2000 (area – in hectares) (Feranec et al., 2006) CLC 2000 112121122131142211221222231242243311312313324331511512total
CLC 1990
131-6.2---5.8--------36.9--63.7112.5 132--------------148.5---148.5 133-159.9---33.6--------359.8-1563.11832.03948.3 211648.5220.531.5---140.560.920.5949.5323.915.7-5.037.4--32.52486.4 22172.711.0---1516.6----5.4-------1605.8 22257.8----643.5---56.125.7-------783.1 231-----117.4---22.0--61.2-150.7---351.3 242-----35.7----59.6-------95.3 24345.8---66.7914.1--84.262.0-461.5-65.3260.3--145.62105.4 31120.55.3---6.1----101.2--225.0761.5-5.9-1125.6 312---17.5---------55.7402.7---475.9 313-6.0-16.0-------75.4174.3-542.0---813.7 321--------------119.0---119.0 3247.7----19.8-----4286.3566.8932.6----5813.2 411----------118.7------95.1213.8 511---------------25.6-45.971.5 total853.0408.931.533.566.73292.8140.560.9104.71089.7634.54838.8802.31283.62818.725.61569.02214.720269.3
�able 4. Conversation table of landscape change types (codes are explained in text) (Feranec et al. 2006) CLC 2000 112121122131142211221222231242243311312313324331511512
CLC 1990
131-U2---I1--------F1--O1 132--------------F1--- 133-U2---I1--------F1-O1O1 142------------------ 211U1U2U2---I2I3E1I4E1F2-F2F2--O1 213-----E5------------ 221U1U2---E2----E2------- 222U1----E3---E3E3------- 231-----I1---I4--F2-F1--- 242-----E4----E4------- 243U1---U4I1--E1I4-F1-F1F1--O1 311U1U2---�2----I1--O3�1-O1- 312---U3---------O3�1--- 313-U2-U3-------O3O3-�1--- 321--------------F1--- 324U1----�2-----F2F2F2---- 411----------I1------O1 511---------------O2-O1
