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Landscape Continuity Analysis as a Toolfor Landscape Planning: A Case Study inIstanbulSimay Kircaa, Hakan Altinçekiça & Noam Levinb

a Department of Landscape Planning and Design, IstanbulUniversity, Turkey.b Department of Geography, The Hebrew University of Jerusalem,Israel.Published online: 21 Oct 2013.

To cite this article: Simay Kirca, Hakan Altinçekiç & Noam Levin (2013): Landscape ContinuityAnalysis as a Tool for Landscape Planning: A Case Study in Istanbul, Landscape Research, DOI:10.1080/01426397.2013.824561

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Landscape Continuity Analysis as a Toolfor Landscape Planning: A Case Study inIstanbul

SIMAY KIRCA*, HAKAN ALTINÇEKIÇ* & NOAM LEVIN***Department of Landscape Planning and Design, Istanbul University, Turkey **Department of Geography, TheHebrew University of Jerusalem, Israel

ABSTRACT In the last decade the city of Istanbul has suffered a gradual decrease in green areasbecause of the growing need for space for new settlements, trade and industry. Landscape conti-nuity analysis takes its starting point from the analysis of built areas rather than the landscape inbetween. This study aimed to demonstrate the applicability of landscape continuity analysis withina region adjacent to a large metropolis—Istanbul—that is utilised for multiple purposes and com-posed of an important amount of green areas including Belgrade Forest. The general frameworkof this research consists of two major steps: 1) quantification of the inferred influence of humanactivities on green areas; and 2) evaluation of the compatibility between the inferred influence ofhuman activities, and implemented planning decisions (particularly the Forest Management Plan).The results support the use of landscape continuity analysis as a timesaving and cost-effectivesupplementary tool in decision-making processes. This is achieved by the development and com-parison of alternative land-use options, aiming where possible to prevent the fragmentation andalteration of green areas.

KEY WORDS: Landscape continuity, habitat fragmentation, Istanbul, landscape planning, ForestManagement Plan

1. Introduction

An acceleration in landscape alteration over the past century has resulted in loss ofhabitats and associated species. About 140 years ago Marsh (1869) observed this threatand described disruptive human activities as the most powerful driver of landscapechange: “Man has too long forgotten that the earth was given to him for usufruct alone,not for consumption, still less for profligate waste.” At the 1992 United Nations Confer-ence on Environment and Development in Rio de Janeiro (Birnie et al., 2009; Glowkaet al., 1994) and other meetings held afterwards, the renewed relevance of Marsh’sobservations to the post-industrial period, and the urgent necessity to save remainingbiological diversity were clearly emphasised. Certainly, natural processes like volcaniceruptions, long-term climate changes and earthquakes may alter landscapes or destroy

Correspondence Address: Simay Kırca, Department of Landscape Planning and Design, Istanbul University,Istanbul Universitesi Orman Fakultesi, Peyzaj Planlama ve Tasarımı Anabilim Dalı, Bahcekoy-Sarıyer, Istanbul34473, Turkey. Email: simay@istanbul.edu.tr

Landscape Research, 2013http://dx.doi.org/10.1080/01426397.2013.824561

� 2013 Landscape Research Group Ltd

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the habitats of some species (Bastian & Steinhardt, 2002). However, today it is anaccepted fact that habitat fragmentation and loss are due mainly to human interventions(Antrop, 1998; Meyer & Turner, 1994). In this context, the major drivers of landscapechange could be addressed under five topics as: 1) socio-economic, 2) political, 3) tech-nological, 4) natural and 5) cultural, respectively (Brandt et al., 1999). These alsoappear to be the basic components of urbanisation paralleling rapid population growthin many developing countries (after Aydemir et al., 2004; Castells, 1977).

In the last decade, as a still growing megacity with a dynamic physical and socialstructure, Istanbul has also become a battleground between conservation and develop-ment pressures. The city has experienced a gradual decrease of green space because ofthe growing expansion of new settlements, commercial and industrial areas. The impor-tance of forests, agricultural and public green areas for urban texture, for the connectiv-ity of habitats they contain, as well as for the benefits they provide to the inhabitants ofthe city have rarely been considered by governmental planning institutions in Turkey(Altınçekiç, 1991). However, the ecological and functional importance of BelgradeForest had already been recognised by the end of the sixteenth century, when an organi-sation was set up for nature conservation in the forest by order of the Ministry of Waterin 1575 (Yaltırık, 1994). Unfortunately, this attempt was short-lived, particularlybecause of the establishment of new settlements in the forest for the captives broughtfrom the Balkans after 1521 (Montagu, 1962). The total area of Belgrade Forest wasreduced from 13 000 to 12 000 ha in the 1840s, followed by a sudden further reductionin the 1870s to 7500 ha. The negative effects of this policy on nature were thenrecognised and the settlements located in the Belgrade Forest were moved to moreappropriate parts of Istanbul in 1894, and strict nature conservation measures wereapplied (Yaltırık, 1994). In the 1980s, these former recreation and hunting areas ofIstanbul started to be developed in response to rapid population growth (Cecener,1995). Today the Black Sea coast, as well as the western shore of the Bosphorus,continue to suffer from the construction of luxury homes, summer houses and touristdevelopment (Gülersoy, 1998). This has resulted in the severe decline of the greenareas, which serve as buffers near the Belgrade Forest. Currently, Belgrade Forest isofficially under the control of the Ministry of Forest and Water Management, and arevised Forest Management Plan (Anonymous, 2003) is in effect until 2022, in whichforest use types and their management strategies have been put forward.

Situated at the crossroads of two continents, Istanbul represents many interestinglandscape characteristics with its topographic, geologic, climatic and cultural diversity.Belgrade Forest (Figure 1) and its close environs are a typical prototype of this richnesscontaining about 415 plant taxa and many historical remnants and landmarks inheritedparticularly from the Byzantines and Ottomans (i.e. water reservoirs and aqueducts).Unfortunately, this unique landscape is threatened by development, and there is anurgent need for a multi-layered planning process to address the sustainable use of greenareas (i.e. Belgrade Forest, forested areas around Belgrade Forest, groves, agriculturalareas, non-forest green areas and habitat fragmentation).

The term ‘landscape connectivity’ has been widely put forward as one of the majorissues in landscape planning studies worldwide (Merriam, 1984). According to Tayloret al. (1993), landscape connectivity is the degree to which the landscape facilitates orimpedes movement among resource patches, described by With et al. (1997) as thefunctional relationship between landscape patches. Transitions between these patches

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are also related to the landscape structure within which organisms subsist (Tischendorf& Fahrig, 2000). Forman (1995) formulated this as the degree of connectivity or spatialcontinuity of a corridor, network or matrix. Consequently, many analytical tools weredeveloped in order to determine landscape structure and the connectivity of habitats asa basis for landscape plans, such as: the key patch approach (Verboom et al., 2001),graph-theory (Minor & Urban, 2007) and least-cost path function (Fall et al., 2007;Rouget et al., 2006).

While the former approaches for modelling human impact focus on the analysis ofecological core areas, landscape continuity analysis (Levin et al., 2007) takes its startingpoint from the analysis of built areas. The basic principles for modelling humaninfluence on the environment were mainly developed by GLOBIO: The GlobalMethodology for Mapping Human Impacts on the Biosphere (UNEP, 2001), in order to

Figure 1. Location of the study area in Istanbul.

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estimate the amount of disturbance on flora and fauna according to their distance frombuilt areas on a global scale (UNEP/RIVM, 2004). CIESIN-International Earth ScienceInformation Network also focused on mapping the direct measures of human infrastruc-ture and population that have the most immediate impact on wildlife and intact wildnatural areas. In the resulting Global Human Footprint Map, population density, landtransformation, accessibility and electrical power infrastructure were used as proxies forhuman influence at a resolution of 1 km (Sanderson et al., 2002). These were combinedin nine datasets, each coded into standardised scores that reflected their estimatedcontribution to human influence on a scale of 0–10 (0: low human influence, 10: highhuman influence). The scores were then combined to generate the human influenceindex (HII), which indicates that 83% of Earth’s land surface is influenced by theabove-mentioned factors. The human footprint approach has also been rescaled for useat regional scales (Woolmer et al., 2008).

Lastly, the Open Landscape Institute of the Society for the Protection of Nature in Israeladded another approach, which is the continuity of open landscape areas, to the evaluationprocess of natural landscape resources for land use planning. According to this analysisgreen (non-built) areas were evaluated as a whole entity, even if they have a low value fortheir ecological interest (e.g. agricultural areas, deforested open spaces) (Levin et al.,2007). The basic principles put forward by Levin et al. (2007) were also used in this study,since their landscape continuity analysis provides much more detail than previous globalscaled analyses. The shortest distance of a green area (forests, groves, agricultural areas,non-forest green areas) from built-up areas is expressed by the landscape continuity value,while different categories of built-up areas exert different influences (e.g. a highway willhave greater effects upon its surroundings compared with a local road). In this context,there are two points to be emphasised: 1) the influence of built areas upon their surround-ings will have different forms of distance decay functions (the influence of certainimpacts, e.g. road construction, may decay at a uniform rate with distance from builtareas, while the distance decay function of other environmental impacts such as air pollu-tion might change its rate as distance increases) on different environmental aspects; and 2)landscape continuity value may be described as a growth function, whose value increaseswith distance from built-up areas (landscape continuity value would be higher in an areawith green areas far from built areas than an area containing green areas closer to the builtareas; see Levin et al., 2007 for details of the methodology). It should also be noted thattheoretical and empirical data on environmental impact distance decay functions is stillrelatively scarce. However, important contributions have been made on the effects of builtareas such as roads, human population density, power infrastructure and settlements(Arroyo & Razin, 2006; Forman & Deblinger, 2000; Karanth et al., 2006; Landenberger& Ostergren, 2002; Leu et al., 2008; Liu et al., 1993; Reijnen et al., 1996; Sandersonet al., 2002; Stein et al., 2002; UNEP/RIVM, 2004).

The determination of landscape continuity around Belgrade Forest and the state ofbuilt-up areas and their close environs is instructive for future planning as well as forthe evaluation of planning decisions. Other commonly used parameters (such as soilstructure, climatic variables, plant and animal diversity) are important. However, inpreliminary analyses of the landscape planning process, landscape continuity analysiscan be a practical and time-saving approach. In this context, the aim was to demonstratethe application of landscape continuity analysis within a small region composed of animportant amount of green areas adjacent to a large metropolis. Therefore quantification

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of the inferred influence of human activities on green areas and evaluation of the con-sistence between the inferred influence of human activities, and implemented planningdecisions (particularly the Forest Management Plan), constitute the general frameworkof this research.

2. Methodology

2.1. Study Area

The study area is located in the north-eastern part of the European side of Istanbul,between 28o55′ and 29o06′ East and 41o15′ and 41o05′ North. It contains BelgradeForest, the whole Sarıyer district, and part of the Eyüp, Şişli and Beşiktaş districts,which became important centres for settlement, trade and production. The northern andeastern parts of the area are bordered by the Black Sea and Bosphorus, while theAlibeykoy Reservoir is an important feature in the south-west. Additionally, FatihSultan Mehmet Bridge is connected to the O-2 Highway in the south (Figure 1). Thestudy area covers 24 032 ha, of which 5408 ha comprises the Belgrade Forest.

The study area has a total population of approximately 300 000, and is a minor urbancentre with various industrial and commercial enterprises including chemical manufac-ture, building trades and service sector businesses (Istanbul Municipality EnvironmentalState Report, 2006). Eight main land use types are present in the study area classifiedas settlements, industrial and commercial areas, military zones, agricultural facilities/buildings, waste disposal areas, quarries, infrastructure (roads, power lines) and greenareas (Figure 2a). Amongst these features, the Belgrade Forest comes into prominence,in part because of the water features it contains. The forest has been utilised for high-quality water production for the city since the Byzantine Period and many waterwaysand small dams were constructed by both the Byzantine and Ottoman Empires (Cecen,1999). However, today the amount of water produced in these watersheds cannot meetthe demands of the city. The dominant vegetation is maquis on the Black Sea coast ofthe study area and shallow-soiled hills and ridges of the Bosphorus (Yaltırık & Uluocak,1973), while deciduous forest is found in Belgrade Forest. This forest is surrounded bya woody and herbaceous shrub community, described as pseudo-maquis (Yaltırık,1966). Furthermore, three of Turkey’s 122 Important Plant Areas (IPA), as determinedby WWF-Turkey (World Wildlife Foundation), are located in the study area. Theseareas—Kilyos Dunes, Pastures of Western Istanbul and Northern Bosphorus—contain atotal number of 70 rare and threatened taxa (Ozhatay et al., 2005). The research areaalso offers shelter and food for wildlife as well as birds; 146 bird species from 41 fami-lies were recorded in Belgrade Forest, which is located on the route of many migratorybirds (Arslangündogdu, 2005).

2.2. Methods

The research was carried out in two stages: 1) determination of landscape continuityvalue around Belgrade Forest; and 2) evaluation of forest use types determined in theforest management plan (Anonymous, 2003) according to the results of landscapecontinuity analysis.

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Figure

2.(a)Landcovermap

ofthestud

yarea

representin

gdifferentland

-use

types.(b)Landscape

continuity

map

with

amaxim

umeffectivedistance

of3km

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2.2.1. Determination of landscape continuity value. In the first stage of the research, theassumptions and simplifications on which the landscape continuity analysis is based canbe summarised under three main points: 1) potential focal or continued disturbancescaused by topographical, meteorological, hydrological, social or socio-economic factorsare not considered; 2) the distance decay function of the built and disturbed area is lin-ear, assuming no asymptote (this can be justified due to the relatively small distancesconsidered); 3) the slope of that function depends upon the type of the built-up area.Therefore different weights were assigned to each category of built-up area according toexpert estimates: weighting was performed relative to the impact of urban areas that areconsidered as having a relative human impact of 100%. For example, if an urban areahas an environmental impact four times greater than a village, an effective distance of 1km from a village would be equal to that of 4 km from an urban area. Thus a weightof 25% was assigned to the villages (after Levin et al., 2007) (Table 1). The weightsassigned to the different built-up area categories were mainly based on the estimationsof Liu et al. (1993), Forman and Deblinger (2000), Sanderson et al. (2002), Stein et al.(2002), Kuitunen, Viljanen, Rossi, and Stenroos (2003), Arroyo and Razin (2006)and Levin et al. (2007). The following steps were taken to determine the landscapecontinuity value:

(1) Land-cover mapping at the relevant resolution, using IKONOS satellite images,forest management plans at a scale of 1:25 000 and the Istanbul MunicipalityEnvironmental Plan at a scale of 1:100 000.

(2) Classification of built-up areas into categories exerting impacts (Table 1).(3) Different impact weights were assigned to each category of built-up area in

accordance with their relative environmental and ecological impacts (Table 1).

Table 1. Weights for different built-up area categories as applied in the landscape continuityanalysis (population of built-up residential area classes were identified after Aydemir et al., 2004as: city >10 000; town: 2000–10 000; village: <2000 and isolated buildings: residents foundwithin the administrative borders of villages in a relative isolated way) (modified after Levinet al., 2007)

Weight

Equivalent distance of 1 km from alarge city (km) = Reciprocal of the

respective weight

Built-upresidentialareas Roads

Other built-up areacategories

100% 1 City Motorway Industrial andcommercial areas,quarries

75% 1.333 Town Dualcarriageway

-

50% 2 - Busy two-lane road

Military trainingfacilities

33% 3 - - Waste disposal areas25% 4 Village Quiet two-

lane road-

10% 10 Isolatedbuildings

Single laneroadUnmaderoad

Agricultural facilities/buildings, electricitytransmission lines

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(4) A distance surface was created for each category of built-up area by assigningdistance values to each pixel from the nearest green area. Maximum distance(maximum pixel value in the surface) of each built-up area to the nearest greenarea was calculated by Euclidean distance function.

(5) Each of the distance surfaces (pixel values) were multiplied by the reciprocal ofthe weight of the built-area category, thus effective-distance maps were obtained.

(6) Effective-distance maps were superposed in order to assign the minimum value toeach pixel.

(7) A continuous colour palette was used for the presentation of landscape continuityvalue surface (landscape continuity map).

These steps were easily automated by using ArcGIS 9.2 and the model builder avail-able in ERDAS Imagine 9.1. In accordance, the determination of landscape continuityvalue at a certain pixel may be expressed by the following equation (Equation 1):

LCV ¼ minimum½1=W1 � D1; 1=W2 � D2; . . . ; 1=Wn � Dn� ð1Þ

Where: LCV = landscape continuity value; Wn = weight assigned to the category n of abuilt-up area; Dn = distance of a certain pixel from the nearest feature belonging to thecategory n of a built-up area.

According to Equation 1, the units of landscape continuity value are referred asweighted distance or effective-distance. However, total landscape continuity value overan area also depends on the spatial resolution used. Therefore, pixel values weresummed in order to obtain the total landscape continuity value, while the spatialresolution was also taken into consideration (Equation 2):

Total LCV ¼X

LCV � R2 ð2Þ

Where:P

LCV = sum of landscape continuity values over an area; R = spatialresolution (pixel size).

The determination of human effect on green areas was applied to a relatively smallarea compared to previous studies (Levin et al., 2007; Sanderson et al., 2002; UNEP/RIVM, 2004; Woolmer et al., 2008). Therefore a spatial resolution of 10 x 10 m wasused to derive more sensitive results. As a result, LCV is expressed as cubic weighted(or effective) distance.

2.2.2. Evaluation of forest management plans according to landscape continuityanalysis. Different types of green areas (e.g. forest, agriculture) were treated equally inthe application of landscape continuity analysis; however, the distribution and quality ofthese areas plays an important role in evaluating the effective-distances of built-upareas. Furthermore, according to UNEP/RIVM (2004) the extent of the impact zonesurrounding a given infrastructural element will be different for different vegetationtypes (e.g. croplands, grasslands, deciduous forests, etc.). Therefore, they have inte-grated some major vegetation types with distance from infrastructure in order to esti-mate the impact on species richness due to disturbance from infrastructure (UNEP/RIVM, 2004). On the basis of the responses of species to disturbance arising frominfrastructural development, four impact zones (high impact, medium–high impact,

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medium–low impact, low impact) were statistically determined within a certain distanceof a particular infrastructure in UNEP/RIVM (2004). In the current study the effects ofbuilt-up areas on forest areas in the study area (containing Belgrade Forest) were evalu-ated by superposing the forest use types determined in Forest Management Plan andlandscape continuity map. This second stage of the study is summarised in Figure 3.

3. Results

3.1. Landscape Continuity Value and Its Relation with the Distribution of Land-useTypes

The distribution and spatial extent of different land-use types including green areas,settlements, industrial areas and infrastructure are the major determinants of LCV. As amatter of fact 73.6% of the study area was covered with green areas, of which forest areastook the greatest part (86.6%). Settlements were found to be the second main land-usetype (18.5%) and were mostly concentrated along the Bosphorus, expanding through theBlack Sea coast to the north, and into the Eyüp district in the western part of the studyarea. Industrial and commercial facilities were mainly found in the south, adjacent to theO-2 Motorway and motorways, and only cover 1.7% of the study area. Dual carriage-ways and unmade roads were the most common infrastructural elements and there wasalso a main electricity transmission line passing through the forest area (Figure 2a).Built-up area categories were classified according to their weight categories in Table 1,while their distribution and effective distances in the study area were given in Table 2.

A direct relationship between the LCV and distribution-spatial extent of built-up areaswas also confirmed by the effective-distance of each built-up area category. For

Figure 3. General framework of the second phase of the study (prepared according to UNEP/RIVM, 2004).

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instance, settlements classified as cities had a maximum distance of 14.7 km, whiletowns had 8 km. The main reason is that the first land-use type is mostly concentratedon the coast of the Bosphorus, relatively far from the green areas, whereas towns arescattered throughout the study area. Another example is the motorways (6.2 km) foundin the southern part of the study area, which had a maximum distance of 19.2 km. Onthe other hand, unmade roads (total length 309.5 km), which were scattered through thestudy area, had a maximum distance of 6.0 km. In both situations, it was found thatareas classified as cities and motorways have higher effective distances, although higherweights are assigned to these built-up areas. These are not scattered throughout thestudy area and concentrated on the locations that are relatively far from the green areas.On the other hand, settlements and infrastructure like towns and unmade roads(assigned weights are 75% and 10%, respectively) are fragmenting the study area morethan cities and motorways because they are dispersed spatially and situated closer to thegreen areas. Built-up area categories with assigned weights of 10% and 25% had rela-tively short maximum distances, which were under 11 km. On the other hand, the mosteffective built-up area categories with assigned weights of 75% and 100% mostly hadmaximum distances over 12 km, since they were generally located on the coast or moreperipheral parts of the study area (e.g. city, motorway) (Table 2).

After assigning weights to the aforementioned built-up area categories the total LCVof the study area was found as 68.95 km3 with the use of 10 � 10 m spatial resolution.According to the landscape continuity map (Figure 2b), the maximum effective-distancefrom a built-up area was 3.0 km. As the results of the analysis were assessed in 1 kmclasses, it was seen that 67.3% of total LCV is composed of the 0.0–1.0 kmclass, which covered 92.3% of the study area. The 1–2 km class constituted 7.3%, andthe 2–3 km class 0.4% of the total area, while their contribution to the total LCV was

Table 2. Built-up area categories, their distribution and effective distances in the study area

Built-up area categoryWeight(%)

Size(ha/km)a

Maximum distance of eachbuilt-up area category to the nearest

green area (km)

City 100 1801.0 14.7Highway 6.2 19.2Industrial and commercial

areas400.9 17.0

Quarries 694.9 9.8Town 75 1876 8.0High density multi-lane road 67.1 12.0High density two-lane road 50 71.4 9.0Military training facility 524.9 15.0Waste disposal area 33 64.9 20.0Village 25 667.0 10.0Low density two-lane road 123.6 5.5Isolated buildings 10 81.0 9.2Single lane road/Unmade road 309.5 6.0Agricultural facilities/buildings 139.6 10.0Electricity transmission line 26.1 11.0

aSize of roads and electricity transmission line are given in km, while area of other land use typesare given in ha.

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29.2% and 3.5%, respectively. Although the effective-distance of >1 km covered only7.7% of the total area, its contribution to the total LCV was 32.7%. Results of theanalysis are summarised in Table 3.

3.2. Evaluation of the Forest Management Plan According to Landscape ContinuityAnalysis

In the first phase of determining the LCV forest areas were evaluated as a whole, as ifhaving similar qualities and functions. However, forests in the study area were classifiedfor different ecosystem services (Figure 4) in the Forest Management Plan, such as pro-duction forest, soil protection, water protection, landscape protection. In this context,three main impact degrees were determined by superposing the landscape continuitymap and the forest use types in the Forest Management Plan (Figure 3). These impactdegrees were: 1) high, 2) medium–high and 3) medium–low impact.

There was a total of 13 492 ha of forest in the study area. Soil protection coverednearly a quarter of this; other functions were wood production, water protection andlandscape protection (11.6–14.0%). Recreation and scientific research forests coverednearly 4.3%, while national defence function had a percentage of 8.4 in the forest areas.Nature protection represented only 1.0%. In the study area, 19.2% of forest areas weredesignated as conversion forest (change from one silvicultural system to another; Colak& Asan, 2010; IUFRO, 2005) with associated functions of wood production, soilprotection, water protection and recreation.

3.2.1. Forest areas subject to high impact. Forest areas located within an effective-dis-tance of 0.0–0.3 km from the nearest built-up area were evaluated as highly impactedareas. Nearly half of the total forest area (44.4%) was found to be subject to highimpact, while more than 1200 ha of both soil protection and landscape protectionforests also fell under this impact category. Although water protection was one of themain functions in the study area (1718 ha) after soil protection and landscape protec-tion, 37.0% of this protected area was subject to high impact, and was particularlyfragmented with busy/quiet two lane roads. Pressure of built-up areas was less over pro-duction forests (2715.5 ha) than other forest use types, though 35.6–39.4% of their areawas found as subject to high impact (Figure 4a). Theoretically this suggests that morethan 50% of all species in forest areas with different functions determined in the forestmanagement plan (water protection, soil protection, landscape protection, etc.) locatedwithin an effective-distance of 0.0–0.3 km from the nearest built-up area will declineby over 50% (UNEP/RIVM, 2004). Detailed results for forest areas subject to highimpact are given in Table 4.

Table 3. Total LCV and its range in 1 km classes

Effective distance of built up areas (km) Total LCV in 1 km classes (km3) Total LCV (km3)

0–1 46.36 68.951–2 20.132–3 2.41

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Figure

4.Forests

subjectto

high

,medium-highandmedium

low

impact

show

ndu

eto

differentforest

usetypes:

(a)44

.4%

oftotalforest

area

was

foun

das

subjectto

high

impact,(b)39

.6%

oftotalforest

area

was

foun

das

subjectto

medium-highim

pact

(c)and16

.0%

oftotalforest

area

was

foun

das

subjectto

medium-low

impact.

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3.2.2. Forest areas subject to medium-high impact. Forest areas located within an effec-tive-distance of 0.3–0.9 km from the nearest built-up area were evaluated as medium-high impacted areas. In this context, 39.6% of the total forest area was found as subjectto medium-high impact, and half of the nature protection forests were found under thiscategory. Over 40% of forest with production, soil protection and scientific researchfunctions and conversion forests, both for production and water protection, were deter-mined to be under medium–high impact. Water protection, landscape protection and rec-reation forests were less likely to be subject to medium–high impact, since these areaswere mostly subject to high impact (Table 4; Figure 4b). The results indicate the poten-tial risk of a decline in the abundance of 25–50% of all species within this distanceaccording to UNEP/RIVM (2004).

3.2.3. Forest areas subject to medium–low impact. Forest areas located within an effec-tive-distance of 0.9–3.0 km from the nearest built-up area were considered as subject tomedium–low impact. As a result, 15.9% of total forest area was found under thisimpact category. Although 25% of recorded species are predicted to decline by over50% (UNEP/RIVM, 2004) within the specified distance, less than 25% of the area of

Table 4. Forest use types subject to different levels of impact

Forest usetype

Highimpact(ha)

Highimpact(%)

Medium–highimpact(ha)

Medium–highimpact(%)

Medium–low

impact(ha)

Medium–low

impact(%)

Totalarea (ha)

Production 623.3 39.5 675.8 42.9 279.3 17.6 1578.4Soil protection 1315.3 41.7 1401.9 43.2 524.4 16.1 3241.6Water

protection636.4 37.0 644.7 37.5 437.4 25.5 1718.5

Landscapeprotection

1281.7 67.7 552.4 29.2 57.4 3.1 1891.5

Recreation 336.4 56.6 214.1 36.0 43.3 7.2 593.8National

defence503.7 44.1 383.1 33.5 254.6 22.4 1141.4

Scientificresearch

269.2 45.6 249.6 42.3 71.1 12.1 589.9

Natureprotection

50.1 35.3 71.2 50.2 20.6 14.5 141.9

Conversionforest(production)

405.0 35.7 511.2 44.9 220.9 19.4 1137.1

Conversionforest(soilprotection)

209.0 59.3 131.4 37.3 11.7 3.4 352.1

Conversionforest(waterprotection)

341.5 31.6 510.6 47.1 213.9 21.3 1066.0

Conversionforest(recreation)

21 97.3 0.6 2.7 - - 21.6

Sum 5992.6 5346.6 2134.6 13 473.8

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each forest use type was found to be under medium–low impact. For instance, only 3%of landscape protection forests were determined under this impact category, since theseareas were particularly close to residential areas and fragmented with roads (Figure 4c).Results related to all forest use types under medium low impact are given in Table 4.

4. Discussion

In urban ecology land use types are characterised by different anthropogenic uses (e.g.residential areas, industrial and commercial functions, infrastructure). The interactionbetween these different land use types and natural areas has increasingly become a focalpoint in remote sensing and GIS based landscape planning studies in recent years(Cozzi et al., 2008; Leitäo & Ahern, 2002). According to Niemelä (1999) and Dearbornand Kark (2010), urban areas harbour diverse nature areas ranging from semi-naturalhabitats to wastelands, parks and other highly human-influenced biotopes with theirassociated species assemblages. Therefore different tools should be developed for theeffective use and maintenance of urban biodiversity in the face of expanding cities andintensive use of urban land. Thus, many studies have been carried out on the urbangrowth of Istanbul and shrinkage of green areas (Cakır et al., 2008; Geymen & Baz,2008; Kaya, 2007; Terzi & Bölen, 2009). However, the effects of built-up areas on nat-ural and open spaces in Istanbul were not evaluated in detail in these studies. In thiscontext, this study tried to apply the basic principles of recent studies on thedetermination of human impacts on different habitats (Levin et al., 2007; Sandersonet al., 2002; UNEP/RIVM, 2004) in order to evaluate the use of such an analysis as aneffective tool in landscape planning and management implementations at differentscales.

4.1. Landscape Continuity Value and Its Relationship with the Distribution ofLand-use Types

Landscape continuity analysis was applied by assigning different weights to differentbuilt-up area categories, considering their environmental impacts and depending on theirdistance from the nearest green area, generating results that help us understand thetheoretical impact of these areas. In addition, it created an opportunity to evaluate thequantity and partly the quality of green areas under anthropogenic pressure.

The distance surface of each built-up area category is an important component oflandscape continuity analysis, since distance surfaces vary due to the distribution ofland use types in the study area. For instance, low density two-lane roads and unmaderoads have relative low environmental impacts with the assigned weights of 25% and10%, respectively. They also have the lowest distance surfaces compared to other land-use types. However, due to their wide spatial distribution the contribution of these twoland-use types to the total LCV was higher than motorways. This indicates that threemain factors constitute the total LCV, namely: size, distribution and assigned weight ofthe built-up area. There are also two general parameters effecting the total LCV: size ofthe study area and spatial resolution used for the analysis. The differences between theresults of the studies carried out around Belgrade Forest and throughout Israel (Levinet al., 2007) are both due to these parameters. As indicated earlier, the chosen spatial

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resolution (R) of the analysis directly affects the results. It has been shown in this study,that higher spatial resolution values give more realistic results. Size of the study area isanother parameter which affects the total LCV, since Israel is 90 times bigger than ourstudy area. Therefore, total LCV is higher in Israel than in our study area.

As a result, it is concluded that landscape continuity analysis might be used as asupportive tool for determination of land-use types, their location, capacity and potentialenvironmental effects in the landscape planning process, as well as other natural,cultural and aesthetic determinants (Antrop, 2000) such as soil, flora, fauna, scenicviews and historical values. However, more empirical knowledge is required in order toaccurately calculate distance-decay functions of built-up areas to obtain more realisticresults regarding their environmental effects. Application of the analysis at differentscales from an urban to regional level would help to develop an index and compareresults from various landscapes.

4.2. Gaps and Conflicts between Planning Targets

In recent years many planners have increasingly emphasised the role of ecologicalapproaches in the landscape planning process, rather than just adopting human orienteduser-function relationships (Debes et al., 2001; Leitão & Ahern, 2002; Opdam et al.,2002). This was followed by the idea of using the environmental effects of anthropo-genic activities as a tool in order to determine nature protection opportunities and priori-ties. Thus, globally applied regional human impact/wilderness models (UNEP/RIVM,2004) and a human influence index (Sanderson et al., 2002) indicated dramatic anthro-pogenic impacts on nature. In recent years, Belgrade Forest and its close environs havealso been suffering from increasing anthropogenic pressure mostly caused by unsuitableplans, which contain contradictory land use types ignoring ecological processes anddynamics. Based on the methodology of Levin et al. (2007), our study area was foundto be under high human impact, while low impact was not present. These impact zoneswere determined according to the classification of UNEP/RIVM (2004) for deciduousforest, the dominant vegetation type in the study area. However, there are also someparts covered with conifer and mixed forests, which were not evaluated in the analysisof UNEP (only nine main vegetation types were determined). Therefore, empirical dataon impact zones surrounding a given infrastructural element for further vegetation typesneeds to be developed.

Considering the forest use types in the Forest Management Plan, an inconsistency isevident between the actual situation and management targets in most cases. Forinstance, the main management objective of forests for soil protection was defined toprevent water and wind erosion as well as rock and stone fall (Anonymous, 2003).However, 41.7% of these forests were found to be subject to high impact. Likewise74.5% of water protection forests were found to be under high and medium–highhuman impact, while their main management objectives are to protect drinking watersources and basins, and to provide high quality drinking water. Relevant to theseresults, Serengil (2002) indicated that water quality has been reduced (i.e. acidificationof water) over the years in Belgrade Forest as well. Another dramatic result obtainedfrom the analysis is the impact rate of nature protection forest (which was generated inorder to protect wildlife and to host a wildlife production unit). The research shows that85.4% is subject to high and medium–high impact. The results indicate conflicts

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between the land-use types and current plans, in which connectivity between habitatswas not sufficiently considered. Although there are many regulations and conventionsparticularly among European Union countries like Global Biodiversity Strategy, HabitatsDirective, Pan-European Biological and Landscape Diversity Strategy (Fricke, 2001;Golub, 2003; Knill, 2000), a parallel development could not be achieved in practicein Turkey. Nevertheless, the European Landscape Convention signed by the TurkishGovernment in 2003 could be accepted as an important step on this issue.

The results indicate the compatibility of landscape continuity analysis as asupplementary tool in decision-making processes by the development and comparisonof alternative land-use options in landscape planning implementations, aiming to pre-vent the fragmentation and alteration of as many green areas as possible. The methodcould be a time-saving and cost-effective tool to compare alternative land use planswith the overall aim of maintaining maximum landscape continuity of green areas inplanning studies, since it is easily applied following the preparation of a land-covermap at relevant resolution. Furthermore, the application of this method at differentscales of planning studies (preparation of forest management plans, planning of natureconservation areas, etc.) would play an important role in the assessment of decisionstaken by planning bodies.

Landscape continuity analysis could be supported by further models like ‘landscapecontour model’, ‘island model’, ‘patch-matrix-corridor model’ and ‘variegation model’(Bastian & Steinhardt, 2002; Fischer et al., 2004; Forman, 1995; McIntyre & Barrett,1992) in order to reach planning targets and minimise habitat fragmentation. Further-more, the efficiency and suitability of habitat connectivity measures such as wildlifecorridors, stepping stones, soft matrix, etc. could be tested with landscape continuityanalysis. Consequently, there is scope for the basic principles of this method to be usedin a wide variety of different planning applications.

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