inventory of quantitative and qualitative...

Report Nr.: MS 25

INVENTORY OF QUANTITATIVE AND

QUALITATIVE FUNCTIONAL LINKAGES BETWEEN

UGI COMPONENTS, BCD AND IMPACT

MILESTONE NR MS 25

Work package: 3 Partners involved: Humboldt-Universität zu Berlin, University of Ljubljana

Researchers: Dagmar Haase, Nadja Kabisch, Michael Strohbach, Eler Klemen, Špela

Železnikar, Rozalija Cvejić, Marina Pintar

Description:

INVENTORY OF QUANTITATIVE AND QUALITATIVE FUNCTIONAL LINKAGES BETWEEN UGI COMPONENTS, BCD AND IMPACT • Report Milestone

NR MS 25 • WP3 • Page 1

CONTENTS

1 Introduction 6

2 Methods 9

2.1 Landscape metrics 10

2.2 Climatic and land variables 11

2.3 European Planning Families 12

3 REsults 15

3.1 Landscape metrics analysis 15

3.2 Metrics analysis 25

3.3 Land use and planning families 27

3.4 Resilience to climate change: Examples of the ULLs Berlin and Ljubljana 29

4 Discussion and conclusions 32

5 References 33

6 APPENDIX 37

6.1 Appendix 1: Input data used in the analysis 37

6.2 Appendix 2: Definition of the land use variables used in the analysis 39



LIST OF FIGURES

Figure 1: The European Urban Audit cities and their planning families (adapted from Hansen et

al., 2015). 14

Figure 2 Mean Patch Size of green urban areas in European cities (bottom 20 and top 20). 15

Figure 3. Mean Patch Size of urban forest areas in European cities (bottom 20 and top 20). 16

Figure 4. Mean Patch Size of urban waters in European cities (bottom 20 and top 20). 17

Figure 5. Total Edge of green urban areas in European cities (bottom 20 and top 20). 18

Figure 6. Total Edge of urban forest areas in European cities (bottom 20 and top 20). 19

Figure 7. Total Edge of urban waters in European cities (bottom 20 and top 20). 20

Figure 8. Shape Index of green urban areas in European cities (bottom 20 and top 20). 21

Figure 9. Shape Index of urban forest areas in European cities (bottom 20 and top 20). 22

Figure 10. Shape Index of urban waters in European cities (bottom 20 and top 20). 23

Figure 11. Distribution of the three landscape metrics Mean Patch Size, Total Edge and Shape

Index across European cities (breaks in the distribution are 33.3 percentile, 66.6 and 99.9

percentile). 24

Figure 12. Distribution of the variable “per capita green space” across European cities (breaks in

the distribution are 33.3 percentile, 66.6 and 99.9 percentile). 25

Figure 13: Principal component analysis (PCA) analysis: similarity of cities in Europe from Urban

Atlas with regard to their climatic characteristics and their belonging to one of the five planning

traditions. 26

Figure 14: Correlations (Pearson product moment - r) depicted in a correlation plot between the

percentages of some land-uses in a defined area and various climatic characteristics of the city

area. N= 295 European cities from Urban atlas. 27

Figure 15: Differences in percentages of different land uses in EU cities between European

planning traditions (N=295 cities from Urban Atlas). 29

Figure 16: Heat island effect of Ljubljana based on mean temperature of the warmest yearly

quarter. 31



LIST OF TABLES

Table 1 Landscape Metrics used for the calculation of green infrastructure component diversity

across European cities. .............................................................................................................................................................. 11

Table 2 Overview of the land use variables used in the analysis ............................................................................. 12

Table 3. Planning families and respectve EU countries grouped (adapted from Hansen et al.

2015)................................................................................................................................................................................................. 13

INVENTORY OF QUANTITATIVE AND QUALITATIVE FUNCTIONAL LINKAGES BETWEEN UGI COMPONENTS, BCD AND IMPACT • Report

Milestone NR MS 25 • WP3 • Page 6

1 INTRODUCTION

This milestone introduces an inventory of quantitative and qualitative functional linkages

between the components of urban green infrastructure in European cities, the green

infrastructure diversity and shape and potential impacts for green infrastructure planning. We

want to get a picture of the green infrastructure distribution across the cities of Europe and its

pattern. This pattern, respectively, gives a deeper insight into the diversity of GI and allows

conclusions for its management. Further, the purpose was to explore the interdependence of

biophysical characteristic and urban land use and its relation to the planning culture in

European cities.

The approach behind this pilot study bases on the assumption that green infrastructure

components (such as green spaces, forests, waters, see Deliverable D3.1) are not equally

distributed across European cities and planning families (Hansen et al., 2015) and, what is more,

that the populations of European cities do not all benefit from large amounts of green spaces,

forests and waters within their cities. Literature suggests that history and geographical location

play a major role in current green space extension and patterns so that Northern, Southern,

Western and Eastern European cities must differ as well as western wellfare-capitalist and

eastern post-socialist cities. Urban land use is complex interaction both with biophysical

characteristic and planning culture. Land use data is therefore crucial to explore processes

behind urban land use and its function. Politicians and planers need information on changing

proportions of areas and distribution of areas. Only in this way they are able to project

transportation and utility demand, better determine land use policy, identify future development

pressure points and areas and implement effective plans for regional development (Anderson et

al., 1976).

In order to analyse the diversity of different green infrastructure components across a large

sample of European cities, in this milestone:

• cities have been ranked according to the area the different green infrastructure

components cover;

• a set of landscape metrics have been calculated describing the diversity of the three

green infrastructure components within the city;

• the relation between urban green spaces in cities and their biophysical characteristics

based on temperature related variables were calculated and

• all calculations were used to for conclusions concerning future planning and

management of green spaces in European cities.



Temperature and the urban heat island effect

Vegetation and trees in an urban environment can mitigate urban heat island development by

reducing summer air temperature and improve microclimates (Dimoundi et al., 2003). There are

two processes that vegetation can provide for - (a) cooling urban climates through shading and

(b) evapotranspiration (Santamouris, 2001). Studies in Australia showed that tree shade reduce

external air temperature by up to 1°C and wall surface temperature up to 9°C (Berry et al.,

2013). Also, a study in Taipei, surveyed the cool island intensity of 61 city parks, and found that

during the summer the cooling effect was stronger than in the winter (Chang et al., 2007).

Schwarz et al. (2012) conducted a study in Germany and found that the land cover classes

showed statistically significant influences for air temperature and land surface temperature:

Moreover the post-hoc test indicated same patterns for air and land surface temperature. In land

cover types discontinuous urban fabric and green urban areas (category artificial surfaces)

showed significantly different values from each other and from all other land cover types.

Differences between the agricultural and urban temperature showed a small range across the

measurements and points in time (1-1.5 K). Differences between urban and water temperature

according to land surface temperatures showed a cooling effect of -1 K and -4K for the evening

and morning (Schwarz et al., 2012). Cao et al. (2010) found that the cooling of urban green areas

depends on park size.

Therefore, larger parks result in greater surface temperature differences between urban and

park areas. Moreover, the configuration of the urban green area significantly affect cooling in the

cities. Woody vegetation results in highest temperature differences. Also, evenly distributed

woody vegetation reduces surface temperature more than clustered vegetation because the form

provides more shade for surrounding non-vegetated surfaces (Zhou et al., 2011). In the study of

Hamada et al. (2013) the results showed that the surface temperatures of green areas

(agricultural land, forests and lawns) in the daytime were lower than all other categories, except

ponds. Among green areas the temperatures were lower in the forest than above the lawn and

agricultural lands. The researchers concluded that the cooling effect of green areas can extend to

surrounding urban areas and improve the thermal environment. Moreover, older studies show

the following findings. The cooling impact of plants on air and surface temperature vary with

environmental factors and plant specific thermal and optical characteristics. Vegetation with

highly reflective surface can reduce surface temperature by reducing the amount and intensity of

thermal radiation. This may also lower local and downwind heat fluxes from cooler surfaces

(Taha, 1997).

There are also some critics about the research on the links between temperature and land use

types in cities. Kershaw et al. (2010) found a lack in gathering detailed information on land cover

and computing power, because most climate models generally do not include a representation of

urban areas. Therefore, their climate projections are likely to underestimate the heat island

phenomenon. Rohinton et al. (2012) found that, during summer, vegetation and urban land



cover produces the largest proportionate sensible heat, while urban land cover produces the

second largest proportionate latent heat flux.

Fujibe (2011) found that there is a warming trend of 0.3-0.4°C/decade for locations with low

population density (<100 people per km2). For areas with larger population density (100-300 /

km2) the trend is 0.03-0.05°C/decade. This indicates that recent temperature increase is largely

contributed by background climate change. Moreover, Thorsson et al. (2011) found that, urban

geometry could cause large intra-urban differences in mean radiant temperature on yearly,

daytime and hourly time scales. They also found, densely built urban structure can mitigate

extreme swings in mean radiant temperature and in the generally adopted thermal comfort

index by improving outdoor comfort conditions both in winter and summer. Although urban

areas of the world only make up 2.8% of Earth`s land area cities drive global climate change

mainly because of their high demand for resources for energy use and material (UNFPA, 2007).



2 METHODS

Now days, land use mapping and conditions mainly stand on Urban Atlas standards and

classifications that offer a high-resolution land use map of urban areas. Urban Atlas parts land

use in two major categories, land and water. Following the division of land use, we found that

areas of our interest, such as green urban areas, land without current use and sport and leisure

facilities fall within the category artificial surfaces. This is a part of the human activity non-

agricultural land category. Urban parks, trees, sport fields and areas without current use are all

part of the urban green space network in cities. These types of land use are all connected to

different biophysical characteristic.

In a first step, the different land cover classes have been extracted from the European Urban

Atlas land cover data set using GIS (Geographical Information System) technology. Land cover

data stem from the European Urban Atlas land cover dataset obtained from the European

Environmental Agency (EEA, http://www.eea.europa.eu/data-and-maps/data/urban-atlas). The

data in the Urban Atlas data which refer to 305 larger urban zones which refer to commuting

zones around cities. Urban Atlas data are based on Earth observation satellite images with a

2.5 m spatial resolution and, thus provides comparable land use data for all of the European core

cities and respective larger urban zones with more than 100,000 inhabitants.

The analyses in this Milestone focus on the core cities. Core cities were delineated using the core

city layer from the Urban Audit (European Commission, 2004), which refers to current

administrative city boundaries. In this analysis, about 290 cities have been finally included due

to data availability reasons.

In the Urban Atlas, 21 thematic classes, including diverse urban fabric, transportation, industrial

and environmental classes, are distinguished (European Commission, 2011). This classification

of urban land cover and land use is, thus, finer than commonly used datasets of land-cover/land

use. In particular, the Urban Atlas includes groups of different urban fabric classes according to

density and a ‘‘land without current use’’ class, which represents brown fields. For green

assessments the classes “green urban areas” and “forest areas” are used. Unfortunately, the class

“agricultural areas, semi-natural areas and wetlands” combines land that can potentially serves

as green space (i.e. grasslands, semi-natural land) with land of low recreational value (i.e.

cropland). We therefore excluded it from our analysis. We also decided not to include Urban

Atlas class 142 “sports and leisure facilities” in our definition of urban green spaces because

“sports and leisure facilities” includes race courses and areas of sport compounds (e.g., football

stadiums, tennis courts, golf courses), which can be sealed to a high degree. In addition, nearly all

of them are not publicly available and thus not accessible to all urban residents. Both restrictions

might lead to an underestimation of both per capita green space values and its accessibility. The

Urban Atlas land use data refer to the year 2006, but a follow-up version that should be based on

2012 imagery is in preparation and expected to be published in 2015.



2.1 Landscape metrics

Landscape metrics have been successfully used for quantitative spatial model building in

biological, habitat and landscape ecological contexts (McKenzie, Cooper, McCann, & Rogers,

2011; Uuemaa et al., 2009) and in connection with land use change analysis (DiBari, 2007), but

in most cases, have been used to characterise open and natural landscapes (Walz, 2011). For

quantification of the diversity of green infrastructure components in cities, based on a literature

review three landscape metrics (Table 1) were calculated using the FRAGSTATS manual,

formula presented by Farina (200&) in “Principles and Methods in Landscape Ecology” and

ArcGIS 10.0 (McGarigal, Cushman, & Ene, 2002):

a) the Mean Patch Size (MPS as average area in ha) of each green infrastructure component

class (equation 1),

ii

ij

n

AMPS ∑= (1)

b) the Total Edge (TE as Sum of perimeter in m) of each green infrastructure component

class, and, finally (equation 2),

∑=

=m

kiklTE

1

(2)

c) the Shape Index (calculated as average patch perimeter/patch size) (equation 3).

∑=i

i

A

L

nSI

1 (3)



Table 1 Landscape Metrics used for the calculation of green infrastructure component diversity across European cities.

Landscape Metric Calculation Source

Mean Patch Size

ii

ij

n

AMPS ∑=

where Ai is the area of patches of category i in a map.

Farina, A. (2006) Principles and Methods in Landscape Ecology: Towards a Science of the Landscape. Springer, Dordrecht.

Total Edge ∑

=

=m

kiklTE

1

where lik = total length (m) of edge in landscape involving patch type (class) i; includes landscape boundary and background segments involving patch type i. E equals the sum of the lengths (perimeter, m) of all edge segments involving the corresponding patch type.

fragstats.help.4.2.pdf

Shape Index ∑=

i

i

i A

L

nSI

1

where ni is the number of patches of category i in a map, Li is the perimeter and Ai is the area of each patch in category i. High values indicate the presence of many patches with small interiors.


2.2 Climatic and land variables

The study includes 67 climatic variables. Appendix 1 shows the considered climatic variables

which were derived from surface temperature and precipitation data series of 1km x 1km

resolution for period 1995-2000 (WorldClim, 2015; Hijmans et al., 2005.). They were used as an

input data for the analysis. The climate variables are defined for the core city boundaries by

using licensed Arc GIS Spatial analyst extension. Further data analysis was performed using open

source programme R. The study includes 13 land use variables. Table 1 shows an overview of

land use variables. Appendix 2 shows the considered land use variables derived from European

Urban Atlas (EU, 2011) based on Urban Atlas mapping units.



Table 2 Overview of the land use variables used in the analysis

Land use variable no. Labelling used in the correlation plot (Fig. 14)

1 Green urban areas

2 Forests

3 Forests + Green urban areas

4 Agricultural + Semi-natural + Wetlands

5 Agric., Semi-nat., Forests, Wetlands, Green urban areas

6 Water bodies

7 Construction sites

8 Ruderal areas

9 All green.

10 Discontinuous Very Low Density Urban Fabric

11 Discontinuous Medium Density Urban Fabric

12 Ratio Urban green + Forest vs. Urban Fabric

13 All green vs. Urban Fabric

A principle component analysis (PCA) was used to assess possible relations between land use

and climate variables for the EU Urban Atlas cities. The PCA is a multivariate method that

analyses a data table in which observations are described by several inter-correlated

quantitative dependent variables. The main goal of the method is to extract the important

information from the table, to represent it as a set of new orthogonal variables (principal

components) and to display the pattern of similarity of the observations and of the variables as

points in maps. The quality of the PCA model can be evaluated using cross-validation techniques

(Abdi and Williams, 2010).

2.3 European Planning Families

The results of the climate variables and their relation to land use were discussed against the framework of different European Planning Families (PF) which is one basis for a number of different other (planning) analyses in the GREEN SURGE project. The framework was developed by Hanson et all. (2015) and bases on a review of existing European classification frameworks for planning systems. Authors refer to a review of existing planning system typologies which were done by Nadin and Stead (2008). The framework is based on different aspects of spatial, legal and social planning issues and integrates the commonalities of the systems. It is further based on scientific studies and European projects with reflections on the ESPON (2007) and EU Compendium (COM 1997) classifications. Five main planning families were identified:

• Nordic/Comprehensive integrated, • British/Land use management, • Mediterranean/Urbanism



• Central/Regional economic planning and • New Member States.

Table 3. Planning families and respective EU countries grouped (adapted from Hansen et al. 2015).

Planning family Description Countries

Nordic Comprehensive integrated: coordination of spatial impacts of public policies by the frame of strategic documents and plans

Denmark, Finland, Sweden

British Land use management: regulation of functional land use by plans and decisions about the conflicts

United Kingdom, Ireland

Mediterranean Urbanism: structural planning, urban design through rigid building regulations, zoning and codes

Greece, Italy, Spain, Portugal, Malta, Cyprus

Central Regional economic planning: management of regional economy by public interventions into the infrastructure and development

Austria, Germany, France, Nether-lands, Belgium, Luxembourg

New Member States

Post-socialist: in the process of change

Estonia, Latvia, Lithuania, Poland, Czech republic, Slovakia, Hungary, Slovenia, Croatia, Romania, Bulgaria



Figure 1: The European Urban Audit cities and their planning families (adapted from Hansen et al., 2015).



3 RESULTS

3.1 Landscape metrics analysis

For displaying the results of the complex analysis of almost 300 European cities, we have chosen

the bottom and top 20 cities in terms of the three landscape metrics, MPS, TE and Shape Index.

The results are shown within cities’ boundaries for “green urban areas” in Figures 1, 4 and 7, for

forest areas in Figure 2, 5 and 8 and for urban waters in Figures 3, 6 and 9.

Starting with the MPS, Figures 1-3 show a bias across Europe’s cities: Among the top 20 of cities

with the highest mean patch size of green urban areas are exclusively central and northern as

well as north-eastern European cities, and almost no capital cities. The only exception is Alba

Iulia in Romania. The highest mean patch size shows Karlovy Vary in Czech Republic followed by

the West Midlands (UK), Kaunas (Lithuania) and Antwerp (Belgium). Also five German cities are

among the top 20: Köln, Bonn, Weimar, Berlin and Leipzig. Particularly Berlin is also seen and

sold as the “green capital of Germany” and thus the metric justifies this image.

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Figure 2 Mean Patch Size (ha) of green urban areas in European cities (bottom 20 and top 20).

Interestingly, quite a number of (south)eastern European cities is among the top 20 which is in

accordance with a study be Larondelle et al. (2014) which found a comparatively high

contribution of inner-city areas to the overall UES performance for eastern European cities

compared to other regions in Europe. Among the bottom 20, most of the cities are from southern



Europe, namely Greece, Italy, Mallorca (Spain) and Romania. This is not surprising as we know

that green space in southern Europe is mostly situated outside the city boundaries but it

underlines that urban planning might have little influence on the improvement and protection of

green urban area in these countries and that the city scale might not be the right/only one to be

addressed when publishing and discussing the findings of GREENSURGE.

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Figure 3. Mean Patch Size (ha) of urban forest areas in European cities (bottom 20 and top 20).

When looking at the bottom and top 20 of mean patch size of forest areas within city boundaries

we see almost the same picture as for green urban areas: Central European cities dominate, but

also the new member states of the EU such as Romanian cities with Piatra Neamt (highest MPS of

urban forest area), Arad, Targu Mures, Timisoara and Sibiu as well as Bulgarian cities with Sofia

(capital), Slovakian cities with Bratislava, Presov and Kosice or Czech Republic with Banska

Bytrica. Interestingly, again a number of German cities are among the top 20 of forest mean

patch size: Freiburg, Augsburg, Gottingen and Wiesbaden (all in western Germany). Also Vienna

is among them. Surprisingly, only two Scandinavian cities is among the top 20, Umea and

Uppsala (both in Sweden).

In terms of the bottom 20, the picture is a bit more diverse than for the mean patch size of green

urban area: We have again southern European cities here but also cities in the sparsely-wooded,



coastal Netherlands (Amsterdam, Rotterdam), and in the agricultural east of Poland (Rzeszów)

as well as in coastal UK (Bristol, Leicester, Portsmouth). Overall, cities in vicinity to the coast

exhibit low patch sizes of forest area.

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Figure 4. Mean Patch Size (ha) of urban waters in European cities (bottom 20 and top 20).

Concerning the water areas, among the top 20 are the coastal cities across Europe be they along

the Baltic Sea, the North Sea, the Black Sea or the Mediterranean. Conversely, continental

settings characterise the bottom 20 cities for mean patch size of urban waters.

When comparing the top and bottom 20 list of the total edge (TE) of the green urban area across

European cities with those for the mean patch size discussed above, we find a completely

different picture: Here, among the top 20 many (large) capital cities can be found such as

Warsaw, Roma, Stockholm, Madrid, Vilnius, Budapest and Vienna which means TE is clearly

depending on space/area and thus larger cities have more niche and ecotone habitat potential

than smaller ones. This also might mean that these cities have more niches for species in these

transition spaces. But this remains an hypothesis so far. In terms of green space provision and

planning we hypothesize that capital cities are often touristic cities and smaller green spaces

have been created to give the people places close to the sites to sit and to relax.



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Concerning the bottom 20, again southern European cities dominate among them the city with

the highest MPS of urban green areas, Alba Iulia in Romania. Having a low TE value might mean

two things: a) the city has an overall low amount of urban green area according to a smaller size

or b) the green space is clumped into single large patches and thus the “contact zone” between

green space and others land uses is small.



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p 2

0 E

U C

ore

cit

ies

Figure 6. Total Edge (m) of urban forest areas in European cities (bottom 20 and top 20).

When looking the ranking of the TE of forest areas within the city borders we find the expected

picture of having many Northern European cities in the top 20 (mainly from Sweden) but also

southern European cities such as Roma, Nice, Coimbra or Perugia. Among those with the lowest

TE are many southern European cities, too, which either miss forest area overall or have it

packed into one or two patches (like a “city forest”).



0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

Wei

mar

Pal

erm

oC

aser

taZ

ielo

na G

óra

Göt

tinge

nB

ari

Trie

ste

Trn

ava

Kos

zalin

Rad

omIr

akle

ioLi

bere

cA

thin

aT

hess

alon

iki

San

ta C

ruz

de T

ener

ifeF

unch

alK

ielc

eP

átra

Dar

mst

adt

Nap

oli

Göt

ebor

gS

zcze

cin

Nan

tes

Bor

deau

xR

iga

Cór

doba

Mon

tpel

lier

Str

asbo

urg

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on (

Bal

lung

srau

m)

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terd

amB

erlin

Ven

ezia

Ham

burg

Upp

sala

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bro

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öpin

gJö

nköp

ing

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pere

/ T

amm

erfo

rsA

mst

erda

mU

meå

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tal

Ed

ge

(m)

Wat

er

Are

as B

ott

om

20

and

To

p 2

0 E

U C

ore

cit

ies

Figure 7. Total Edge (m) of urban waters in European cities (bottom 20 and top 20).

The TE of the urban water areas provides a nice picture for all landscape ecologists and

landscape scientists: Here those cities in regions with glacial past and respective channel lakes

show the highest values regardless their belonging to Northern or Eastern Europe as well as

coastal cities in Southern Europe. Conversely, inland cities show low values. There are some

exceptions like Córdoba or Strasbourg which are situated along large rivers.

Coming finally to the shape index of green urban areas, urban forest areas and waters, we overall

find less differences in the values for all three green and blue space classes except the highest

values which are extreme in all three cases: Thessaloniki in terms of green urban area, Nijmegen

in terms of urban forest and Stockholm in terms of urban waters. As the shape index equals the

average area divided by patch for the respective green and blue infrastructure classes, a high

shape index means that the form of the respective land use class is spread/fragmented and less

compact compared to cities with low shape indices.



0

0.05

0.1

0.15

0.2

0.25

0.3

Ant

wer

pen

Gor

zów

Wie

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olsk

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leni

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aja

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Mön

chen

glad

bach

Um

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ydgo

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s P

alm

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aC

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ncol

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anta

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Ten

erife

Hee

rlen

Sal

erno

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evez

ysK

aval

aS

heff

ield

Cat

anza

ro's

-Gra

venh

age

Mad

ridLo

groñ

oN

ijmeg

enT

hess

alon

iki

Sh

ape

Ind

ex G

reen

Urb

an A

reas

Bo

tto

m 2

0 an

d T

op

20

EU

Co

re c

itie

s

Figure 8. Shape Index of green urban areas in European cities (bottom 20 and top 20).



0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Tim

isoa

raA

rad

Fog

gia

Târ

gu M

ures

Live

rpoo

lC

alar

asi

Lefk

osia

Ape

ldoo

rnF

aro

Kal

amat

aP

átra

Bur

gas

Sch

wer

inT

oulo

nC

atan

iaP

lovd

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luj-N

apoc

aZ

ilina

Sze

ged

Wie

sbad

enN

owy

Sac

zO

pole

Stu

ttga

rtT

allin

nH

elsi

nki /

Hel

sing

fors

Rei

ms

Bia

lyst

okJe

leni

a G

óra

Kie

lK

ings

ton-

upon

-Hul

lK

ecsk

emét

Linc

oln

Pad

ova

Bel

fast

Ces

ké B

udej

ovic

eC

oim

bra

Gla

sgow

Plo

ckN

ijmeg

en

Sh

ape

Ind

ex F

ore

st A

reas

Bo

tto

m 2

0 an

d T

op

20

EU

Co

re c

itie

s

Figure 9. Shape Index of urban forest areas in European cities (bottom 20 and top 20).



0

1

2

3

4

5

6

Ioan

nina

Kos

zalin

Suw

alki

Trn

ava

Giu

rgiu

Szé

kesf

ehér

vár

Wie

sbad

enR

ouen

Düs

seld

orf

Bud

apes

tN

yíre

gyhá

zaM

ainz

l'Aqu

ilaP

écs

Kat

owic

eT

ampe

re /

Tam

mer

fors

Göt

tinge

nK

ielc

eM

arib

orS

chw

erin

Aja

ccio

Por

toW

est

Mid

land

s ur

ban

area

Pot

enza

Tal

linn

Fun

chal

San

tand

erM

önch

engl

adba

chT

orin

oK

oble

nzM

ontp

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rLo

groñ

oP

aler

mo

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oaS

ofia

Nap

oli

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ante

/Ala

cant

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dS

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holm

Sh

ape

Ind

ex

Wat

er

Are

as B

ott

om

20

and

To

p 2

0 E

U C

ore

cit

ies

Figure 10. Shape Index of urban waters in European cities (bottom 20 and top 20).

The three landscape metrics are displayed again in their spatial patterns for all European cities involved

in the calculation in Figure 10.



Mean Patch Size Total Edge Shape index

Forest Areas

Water areas

Figure 11. Distribution of the three landscape metrics Mean Patch Size, Total Edge and Shape Index across European cities

(breaks in the distribution are 33.3 percentile, 66.6 and 99.9 percentile).



When comparing the picture of diversity of green and blue urban infrastructure across European cities with the variable of “per capita green space” (Fig. 11) the patterns of urban green areas fit best meaning that there where we have low MPS of green space also the per capita values are low and vice versa. This might be an indication that biological and socio-cultural diversity could be spatially linked.

Figure 12. Distribution of the variable “per capita green space” across European cities (breaks in the distribution are 33.3

percentile, 66.6 and 99.9 percentile).

3.2 Metrics analysis The results of the analysis of climatic variables an land use are discussed around the following themes:

• similarity of the cities based on their climate characteristics,

• correlation between the land use and the climate characteristics,

• relation between the land use and the planning families, and

• resilience to climate change: examples of Berlin and Ljubljana.

Similarity of the cities based on their climate characteristics

In this chapter we represent the similarity of the European cities based on their climate characteristics.

The city sample is represented by 295 European cities. Figure 2 visualises how cities are similar with



regard to their climatic characteristics. The x-axis (PC1) shows cities with high average temperatures. The

ones with lower average temperatures are shown at the right part of the figure and are generally northern

European cities such as Helsinki, Uppsala and Stockholm. At the left part of the figure we can observe cities

have higher average temperature, which are generally southern cities such as Sevilla, Cordoba and Bari.

There are no extreme clusters among the sample of the cities, although extremes are less numerous and

most cities resemble semi high temperatures. The y-axis (PC2) axis distributes cities with regard to their

precipitation. Continental climate cities are drier and are show at the bottom part of Figure 2 while wetter,

ocean climate cities at the upper part of PC2 axis of the Figure 2.

-15 -10 -5 0 5 10 15

-50

510

PC1

PC

2

Wien

GrazLinz

Salzburg

Innsbruck

Bruxelles/BrusselAntwerpenGent

CharleroiLiege

Brugge Namur

Sof ia

Plov div

VarnaBurgas

Plev enRuseVidin

Lef kosia

PrahaBrno

Ostrav aPlzenUsti nad Labem

Olomouc

LiberecCeske Budejov ice

Hradec Kralov ePardubice

Zlin

Jihlav a

Berlin

Hamburg

Munchen

Koln

Frankf urt am Main

Stuttgart

Leipzig

Dresden

DusseldorfBremen

Hannov er

Nurnberg

Wuppertal

Bielef eld

Halle an der SaaleMagdeburg

Wiesbaden

Gottingen

Darmstadt

Trier

Freiburg im Breisgau

Regensburg

Frankf urt (Oder)

Weimar

Schwerin

Erf urt

AugsburgBonn

Karlsruhe

Monchengladbach

Mainz

Kiel

KoblenzKobenhav n

AarhusOdense

Aalborg

Tallinn

Tartu

Madrid

Barcelona

Valencia

Sev illa

Malaga

Murcia

Las PalmasValladolid

Palma di Mallorca

Santiago de Compostela

Vitoria/Gasteiz

Ov iedoPamplona/Iruna

Santander

Toledo

Badajoz

Logrono

Cordoba

Alicante/Alacant

Vigo

Gijon

Santa Cruz de Tenerif e Helsinki

Tampere

Turku

Oulu

Ly onToulouse

Strasbourg

BordeauxNantes

Lille

Montpellier

Saint-Etienne

Le Hav reRennes

Amiens

Rouen

NancyMetz

Reims

Orleans

Dijon

Poitiers

Clermont-Ferrand

Caen

Limoges

Besancon

Grenoble

Ajaccio

Toulon Tours

Aix-en-Prov ence

Marseille

Nice

Lens-Liev in

Thessaloniki

Patra

Irakleio

Larisa

Volos

Ioannina

Kav ala

Kalamata

Budapest

MiskolcNy iregy haza

Pecs

Debrecen

Szeged

Gy orKecskemetSzekesf eherv ar

Dublin

Cork

Limerick

Galway

Waterf ord

RomaMilano

Napoli

TorinoPalermo

Genov a

Firenze

Bari Bologna

Catania

Venezia

VeronaCremona

Trento

Trieste

Perugia

Ancona

l'Aquila

Pescara Campobasso

Caserta

Taranto

Potenza

Catanzaro Reggio di Calabria

Sassari

Cagliari

Padov a BresciaModena

Foggia

Salerno

VilniusKaunasPanev ezy s

Luxembourg

Riga

Liepaja

VallettaGozo

s' Grav enhageAmsterdam

RotterdamUtrecht

Eindhov enTilburg Groningen

EnschedeArnhemHeerlen

Breda

Nijmegen

Apeldoorn

Leeuwarden

WarszawaLodz

Krakow

WroclawPoznan

Gdansk

Szczecin

By dgoszcz

Lublin

KatowiceBialy stokKielce

Torun

Olszty n

Rzeszow

Opole

Gorzow Wielkopolski

Zielona Gora

Jelenia Gora

Nowy SaczSuwalki

Konin

Czestochowa

Radom

Plock

Koszalin

Lisboa

OportoBraga

FunchalCoimbra

Setubal

Ponta Delgada

Av eiro

Faro

Bucuresti

Cluj-Napoca

Timisoara

Craiov a

Braila

Oradea

Bacau

Arad

SibiuTargu Mures Piatra Neamt

CalarasiGiurgiu

Alba Iulia

Stockholm

Goteborg

Malmo Jonkoping

UmeaUppsala

LinkopingOrebro

Ljubljana

Maribor

Bratislav a Kosice

Banska By strica

Nitra

Presov

Zilina

Trnav a

Trencin

London

Birmingham

Glasgow

Liv erpoolEdinburgh

Manchester

Cardif f

Shef f ield

Bristol

Belf ast

Newcastle upon Ty ne

Leicester

Derry

Aberdeen

Cambridge

Exeter

Lincoln

Portsmouth

WorcesterCov entry

Kingston-upon-Hull

Stoke-on-trent

Wolv erhampton

Nottingham

Planning families / traditions

CentralNew memberMediterraneanNordicBritish

Scores

BerlinBari

Malmo

LjubljanaEdinburgh

Figure 13: Principal component analysis (PCA) analysis: similarity of cities in Europe from Urban Atlas with regard to their

climatic characteristics and their belonging to one of the five planning traditions.

Correlation between land use and climate characteristics

After exploring the similarities of cities in Europe to their climatic characteristics we explored

the corresponding variabilities of the PCA between them. As shown in Figure 3 the most

important variability was annual precipitation. As shown in Figure 4 the correlation between

selected climatic variables and selected land use variables for the Urban Atlas sample cities are

not very high ranging between 0.4 and -0.6. Correlations are higher for temperature-related



variables and lower for precipitation-related variables. Relatively high negative correlations

between percentage of forests and green urban areas and the temperatures - meaning that the

larger the percentage of these areas is the lower are the temperatures. Moreover relatively high

positive correlation could be observed between the percentages of agricultural areas and

construction sites and the temperatures – larger percentage of these areas, higher the

temperatures. Some correlations, e.g. for forests and urban green areas with temperatures have

yearly dynamics – lower correlations for summer and higher for winter.

Correlation plot

Ratio All green vs. Urb. Fabr.Ratio Urban green+Forest vs. Urb. Fabr.

Discont. Medium Dens. Urb. Fabr.Discont. Very Low Dens. Urb. Fabr.

All greenRuderal areas

Construction sitesWater bodies

Agric., Semi-nat., Wetl., Forests, Green urbanAgricult. + Semi-nat. + Wetlands

Forests + Green urban areasForests

Green urban areas

Ann

ual M

ean

Tem

p.M

ean

Diu

rnal

Ran

geIs

othe

rmal

ityT

empe

ratu

re S

easo

nalit

yM

ax T

emp.

of

War

mes

t M

onth

Min

Tem

p. o

f C

olde

st M

onth

Tem

p. A

nnua

l Ran

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ean

Tem

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f W

ette

st Q

uart

.M

ean

Tem

p. o

f D

riest

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rt.

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n T

emp.

of

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mes

t Q

uar.

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n T

emp.

of

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Qua

rt.

Ave

r. M

in.

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p. J

anA

ver.

Min

. T

emp.

Feb

Ave

r. M

in.

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p. M

arA

ver.

Min

. T

emp.

Apr

Ave

r. M

in.

Tem

p. M

ayA

ver.

Min

. T

emp.

Jun

Ave

r. M

in.

Tem

p. J

ulA

ver.

Min

. T

emp.

Aug

Ave

r. M

in.

Tem

p. S

epA

ver.

Min

. T

emp.

Oct

Ave

r. M

in.

Tem

p. N

ovA

ver.

Min

. T

emp.

Dec

Ave

r. M

ax.

Tem

p. J

anA

ver.

Max

. T

emp.

Feb

Ave

r. M

ax.

Tem

p. M

arA

ver.

Max

. T

emp.

Apr

Ave

r. M

ax.

Tem

p. M

ayA

ver.

Max

. T

emp.

Jun

Ave

r. M

ax.

Tem

p. J

ulA

ver.

Max

. T

emp.

Aug

Ave

r. M

ax.

Tem

p. S

epA

ver.

Max

. T

emp.

Oct

Ave

r. M

ax.

Tem

p. N

ovA

ver.

Max

. T

emp.

Dec

Ave

r. M

ean.

Tem

p. J

anA

ver.

Mea

n. T

emp.

Feb

Ave

r. M

ean.

Tem

p. M

arA

ver.

Mea

n. T

emp.

Apr

Ave

r. M

ean.

Tem

p. M

ayA

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Mea

n. T

emp.

Jun

Ave

r. M

ean.

Tem

p. J

ulA

ver.

Mea

n. T

emp.

Aug

Ave

r. M

ean.

Tem

p. S

epA

ver.

Mea

n. T

emp.

Oct

Ave

r. M

ean.

Tem

p. N

ovA

ver.

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n. T

emp.

Dec

Ann

ual P

reci

p.P

reci

p. o

f W

ette

st M

onth

Pre

cip.

of

Drie

st M

onth

Pre

cip.

Sea

sona

lity

Pre

cip.

of

Wet

test

Qua

rt.

Pre

cip.

of

Drie

st Q

uart

.P

reci

p. o

f W

arm

est

Qua

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cip.

of

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dest

Qua

r.P

reci

p. J

anP

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p. F

ebP

reci

p. M

arP

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p. A

prP

reci

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ayP

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p. J

unP

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ulP

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ctP

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p. N

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ec

-0.6

-0.4

-0.2

0.0

0.2

0.4

Figure 14: Correlations (Pearson product moment - r) depicted in a correlation plot between the percentages of some land-

uses in a defined area and various climatic characteristics of the city area. N= 295 European cities from Urban atlas.

3.3 Land use and planning families

The relation between the different shares of land use in the European cities concerning their

related planning families (PF) of respective countries is displayed in Figure 5. The share of green

urban areas is larger in cities related to the Nordic and British PF within cities (>5%) compared

to other planning families (<5%). A quarter of Nordic PF cities reach green urban area cover of

about 10-20%, which is more than in British PF (10-15%) and others (5-12%). Concerning share

of forest areas, cities grouped to the Nordic PF have larger average percent of forest areas within

cities (>12%) compared to the other PF (<8%). A quarter of Nordic PF cities areas of urban

forests cover 35-78%. The lowest values can be found for cities of the British PF. Here, average

percent of urban forests in cities is less than 2%. The highest variability in percent of forest areas

has the Nordic PF, followed by New Member PF and Central PF.

For the agricultural, semi-natural areas and wetlands, notably cities of the Mediterranean PF

have larger average percent of agricultural and semi-natural areas within cities (appr. 30%)

compared to other PF (15-20%). In a quarter of Mediterranean PF cities agricultural, semi-

natural areas and wetlands cover 58-85%, which is more than in New Member PF (38-70%) and



British PF that has the lowest (20-38%). The highest variability in percent of agricultural and

semi-natural areas has the Mediterranean PF, followed by New Member PF.

The share of construction area is in all PF have approx. 1%. A quarter of Mediterranean PF

construction areas cover 1.5-3%, which is more than in all other PF (approx. 2%). There are no

considerable differences between variability among all PF.

All PF have an average share of ruderal areas within cities <2%. In a quarter of Mediterranean

PF cities ruderal areas cover 3-6%, this is more than in all other PF (approx. 4%). There are no

considerable differences between variability among all PF.

Concerning all green areas, notably cities of Central, Mediterranean, New Member and Noric PF

have a similar average share (appr. 30%) compared to a lower share in cities of British PF

(20%). Cities from the British PF show the lowest share of all green areas (35-50%). There are

no considerable differences between variability among all PF, except for British PF.

Concerning residential space in cities expressed as continuous urban fabric in Urban Atlas land

cover classes, cities in Central, Mediterranean and New Member PF cities have a similar average

percent of continuous urban fabric within cities (8-11%) and British and Nordic PF cities have a

similar average percent of continuous urban fabric within cities (approx.2%). In a quarter of

New Member PF cities areas of continuous urban fabric cover 18-38%, which is more than in all

other PFs, where the highest is the Mediterranean PF with approx. 30%. There are considerable

differences between New Member and Mediterranean PF compared to others PF.

In less dense urban fabric classes such as the discontinuous dense urban fabric cities from

Central, Mediterranean, New Member and Nordic PF have smaller average shares of (<15%)

compared to British PF cities where the share is approx. 25%. In a quarter of British PF cities

discontinuous dense urban fabric covers 30-45%, which is more than in all other PF where the

highest shares have Mediterranean and New Member PF cities with approx. 18-30%. There are

considerable differences between British PF compared to others PF.

Finally, in the low dense urban fabric land cover - the discontinuous dense urban fabric

(medium, low, very low) it is cities from the British and Nordic PF which have a similar shares of

appr. 15% compared to Central and Mediterranean PF cities with approx. 5% and New Member

with approx. 2%. In a quarter of British PF cities discontinuous dense urban fabric covers 18-

33%, followed by Nordic PF with 23-32% cover.



Brit

ish

Cen

tral

Med

iterr

anea

n

New

mem

ber

Nor

dic

0

5

10

15

20

% of urban green areas

% a

rea

Brit

ish

Cen

tral

Med

iterr

anea

n

New

mem

ber

Nor

dic

0

20

40

60

80

% of forest areas

% a

rea

Brit

ish

Cen

tral

Med

iterr

anea

n

New

mem

ber

Nor

dic

0

20

40

60

80

% of agricultural and seminatural areas

% a

rea

Brit

ish

Cen

tral

Med

iterr

anea

n

New

mem

ber

Nor

dic

0

1

2

34

5

6

7

% of construction areas

% a

rea

Brit

ish

Cen

tral

Med

iterr

anea

n

New

mem

ber

Nor

dic

0

2

4

6

8

10

% of ruderal areas

% a

rea

Brit

ish

Cen

tral

Med

iterr

anea

n

New

mem

ber

Nor

dic

20

40

60

80

% of all green areas

% a

rea

Brit

ish

Cen

tral

Med

iterr

anea

n

New

mem

ber

Nor

dic

0

10

20

30

40

% of continuous urban fabric

% a

rea

Brit

ish

Cen

tral

Med

iterr

anea

n

New

mem

ber

Nor

dic

0

10

20

30

40

% of discontinuous dense urban fabric

% a

rea

Brit

ish

Cen

tral

Med

iterr

anea

n

New

mem

ber

Nor

dic

0

510

15

2025

3035

% of discontinuous urban fabric (medium, low , very low )

% a

rea

Figure 15: Differences in percentages of different land uses in EU cities between European planning traditions (N=295 cities

from Urban Atlas).

3.4 Resilience to climate change: Examples of the ULLs Berlin and Ljubljana

We discuss the predicted climate changes from Table 2 only for the city relevant climate change

predictions – surface temperature and precipitation change. The two climatic variables are dis-

cussed through perspective of planning families and land use categories.



Overall the results show the surface temperature has a positive correlation with the share of

agricultural, semi-natural areas and wetlands (higher share higher temperature), while share of

forest has a negative correlation (higher share lower temperature). However these results are

not conclusive to understand certain agricultural plants lower local surface temperature as

explained in other scientific literature (Hamada et al., 2013; Berry et al., 2013; Zhou et al., 2011).

This should be further discussed in the forthcoming GREEN SURGE deliverables.

Ljubljana and Berlin have similar average temperatures, although Ljubljana receives more

annual precipitation.

Berlin has approx. 15% of forest within the city, which is close to the average share of forests in

the cities within the Central PF. Based on the share of agricultural, semi-natural areas and wet-

lands (less than 7%) Berlin falls within the first quarter of the Central PF. Berlin has a low share

of agricultural and semi-natural land within city boundaries. Therefore it is expected that the

influence of surface temperature rise will be less expressed as would be with lower share of

agricultural, semi-natural areas and wetlands. It is expected that the average local temperature

rise will not be as high as predicted for entire Central and Eastern Europe. As above, the results

are not conclusive and should be developed further.

Ljubljana has a high share of forest (40%) within the city in comparison to other cities in the

New Member PF and is therefore comparable with some of higher ranked cities from the Nordic

PF. Although Ljubljana has a high share of agricultural and semi-natural land (approx. 27%)

within city boundaries the predicted increases in warm temperature extremes might in future be

compensated by its extremely high share of forest within the city. Nevertheless, we should not

neglect the fact the predicted decreases in summer precipitation might influence the vitality of

forests and decrease their ecosystem value. The predicted decrease of economic value of forest

in Ljubljana is negligible as the forests within the city area are not used as economic asset.



Planning family Land use Heath island

Central/ Regional economic planning

BE

RLI

N

New member states in the process of change

LJU

BLJA

NA

Figure 16: Heat island effect of Ljubljana based on mean temperature of the warmest yearly quarter.



4 DISCUSSION AND CONCLUSIONS

The results of this study about the diversity of green and blue infrastructure across a large

number of European cities have clearly shown that there is not “the one parameter/variable”

that explains this diversity and its spatial patterns. Using the three landscape metrics of mean

patch size, total edge and shape index we were able to uncover that there is a number of factors

that determines green infrastructure diversity: Situation within Europe, glacial landscape

formation, history of land use and last but not least current planning. All these factors contribute

to either positive or negative consequences for urban green infrastructure provision in the cities

itself. A more detailed analysis of the single green space classes below the current separation

level between “green urban areas”, “forest area” and “urban waters” would permit to distinguish

the importance of the single factors for each class/type of green infrastructure. Thus, the glacial

past is more important for the total edge and contact zone of blue infrastructure than current

planning. The latter, vice versa, determines the total edge of urban parks in capital cities which is

clearly a man-made/induced setting. For green infrastructures like gardens, cemeteries, green

roof tops or wall green such a patterns and diversity analysis is still pending.

Similarity of the cities based on their climate characteristics show there here are no extreme

clusters among the sample of the cities. Although extremes are less numerous and most cities

resemble semi high temperatures and are significantly distributed with regard to their annual

precipitation. Correlation between the land use and the climate characteristics is not very high.

Relatively high positive correlations between percentage of forests and green urban areas and

the temperatures - meaning that the larger the percentage of these areas the lower are the

temperatures. Moreover relatively high positive correlation is observed between the

percentages of agricultural areas, semi-natural areas and wetlands and the temperatures – larger

percentage of these areas, higher the temperatures. However this correlation should be

discussed further also regarding the available literature. Cities belonging to Nordic PF has the

largest average share of forest areas within cities (>12%) compared to the other PF but also the

highest variability share of forest areas, followed by New Member PF and Central PF. Cities of

Mediterranean PF have on average larger shares of agricultural areas, semi-natural areas and

wetlands (appr. 30%) to other PF (15-20%).

Ljubljana and Berlin have similar average temperatures, although Ljubljana receives more

annual precipitation. Berlin has a relatively high share of forest and a low share of agricultural

areas, semi-natural areas and wetlands within the city. Therefore the capacity on lowering

surface temperatures by agricultural areas, semi-natural areas and wetlands lowering is

expected to be small. Ljubljana has a high share of forest and agricultural areas, semi-natural

areas and wetlands within the city. Therefore the influence of forest on lowering the surface

temperature and the influence of agricultural areas, semi-natural areas and wetlands on

increasing the surface temperature, is expected to be higher, but mutually compensated.



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6 APPENDIX

6.1 Appendix 1: Input data used in the analysis

Appendix 1: Input data used in the analysis.

BIO1 Annual Mean Temperature

BIO2 Mean Diurnal Range (Mean of monthly (max temp - min temp))

BIO3 Isothermality (BIO2/BIO7) (* 100)

BIO4 Temperature Seasonality (standard deviation *100)

BIO5 Max Temperature of Warmest Month

BIO6 Min Temperature of Coldest Month

BIO7 Temperature Annual Range (BIO5-BIO6)

BIO8 Mean Temperature of Wettest Quarter

BIO9 Mean Temperature of Driest Quarter

BIO10 Mean Temperature of Warmest Quarter

BIO11 Mean Temperature of Coldest Quarter

BIO12 Annual Precipitation

BIO13 Precipitation of Wettest Month

BIO14 Precipitation of Driest Month

BIO15 Precipitation Seasonality (Coefficient of Variation)

BIO16 Precipitation of Wettest Quarter

BIO17 Precipitation of Driest Quarter

BIO18 Precipitation of Warmest Quarter

BIO19 Precipitation of Coldest Quarter

PREC1 Average monthly precipitation (mm)jan

PREC2 Average monthly precipitation (mm)feb

PREC3 Average monthly precipitation (mm)mar

PREC4 Average monthly precipitation (mm)apr

PREC5 Average monthly precipitation (mm)maj

PREC6 Average monthly precipitation (mm)jun

PREC7 Average monthly precipitation (mm)jul

PREC8 Average monthly precipitation (mm)avg

PREC9 Average monthly precipitation (mm)sep

PREC10 Average monthly precipitation (mm)okt

PREC11 Average monthly precipitation (mm)nov

PREC12 Average monthly precipitation (mm)dec

TMIN1 tmin = average monthly minimum temperature (°C * 10)jan

TMIN2 tmin = average monthly minimum temperature (°C * 10)feb



Appendix 1: Input data used in the analysis.

BIO1 Annual Mean Temperature

TMIN3 tmin = average monthly minimum temperature (°C * 10)mar

TMIN4 tmin = average monthly minimum temperature (°C * 10)apr

TMIN5 tmin = average monthly minimum temperature (°C * 10)maj

TMIN6 tmin = average monthly minimum temperature (°C * 10)jun

TMIN7 tmin = average monthly minimum temperature (°C * 10)jul

TMIN8 tmin = average monthly minimum temperature (°C * 10)avg

TMIN9 tmin = average monthly minimum temperature (°C * 10)sep

TMIN10 tmin = average monthly minimum temperature (°C * 10)okt

TMIN11 tmin = average monthly minimum temperature (°C * 10)nov

TMIN12 tmin = average monthly minimum temperature (°C * 10)dec

TMAX1 tmax = average monthly maximum temperature (°C * 10)jan

TMAX2 tmax = average monthly maximum temperature (°C * 10)feb

TMAX3 tmax = average monthly maximum temperature (°C * 10)mar

TMAX4 tmax = average monthly maximum temperature (°C * 10)apr

TMAX5 tmax = average monthly maximum temperature (°C * 10)maj

TMAX6 tmax = average monthly maximum temperature (°C * 10)jun

TMAX7 tmax = average monthly maximum temperature (°C * 10)jul

TMAX8 tmax = average monthly maximum temperature (°C * 10)avg

TMAX9 tmax = average monthly maximum temperature (°C * 10)sep

TMAX10 tmax = average monthly maximum temperature (°C * 10)okt

TMAX11 tmax = average monthly maximum temperature (°C * 10)nov

TMAX12 tmax = average monthly maximum temperature (°C * 10)dec

TMEAN1 tmean = average monthly mean temperature (°C * 10)jan

TMEAN2 tmean = average monthly mean temperature (°C * 10)feb

TMEAN3 tmean = average monthly mean temperature (°C * 10)mar

TMEAN4 tmean = average monthly mean temperature (°C * 10)apr

TMEAN5 tmean = average monthly mean temperature (°C * 10)maj

TMEAN6 tmean = average monthly mean temperature (°C * 10)jun

TMEAN7 tmean = average monthly mean temperature (°C * 10)jul

TMEAN8 tmean = average monthly mean temperature (°C * 10)avg

TMEAN9 tmean = average monthly mean temperature (°C * 10)sep

TMEAN10 tmean = average monthly mean temperature (°C * 10)okt

TMEAN11 tmean = average monthly mean temperature (°C * 10)nov

TMEAN12 tmean = average monthly mean temperature (°C * 10)dec



6.2 Appendix 2: Definition of the land use variables used in the analysis

Land use

variable no.

Labelling used in the

correlation plot (Fig. 5)

Urban Atlas

No.

Vector data

code

Urban Atlas mapping unit

nomenclature

1 Green urban areas 1.4.1 14100 Green urban areas

2 Forests 3 30000 Forests

3 Forests + Green urban

areas COMPOSED COMPOSED COMPOSED

4 Agricult. + Semi-nat. +

Wetlands 2 20000

Agricultural areas, semi-natural

areas and wetlands

5

Agric., Semi-nat., Forests,

Wetlands, Green urban

areas

COMPOSED COMPOSED

Agricultural areas, semi-natural

areas and wetlands + Forests +

Green urban areas

6 Water bodies 5 50000 Water bodies

7 Construction sites 1.3.3 13300 Construction sites

8 Ruderal areas 1.3 / Mine, dump and construction sites

9 All green. COMPOSED COMPOSED COMPOSED

10 Discont. Very Low Dens.

Urb Fabr. 1.1.2.4 11240

Discontinuous Very Low Density

Urban Fabric (S.L. < 10%)

11 Discont. Medioum Dens.

Urb. Fabr. 1.1.2.2 11220

Discontinuous Medium Density

Urban Fabric (S.L.: 30% - 50%)

12 Ratio Urban green +

Forest vs. Urb. Fabr. COMPOSED COMPOSED COMPOSED

13 All green vs. Urb. Fabr. COMPOSED COMPOSED COMPOSED

inventory of quantitative and qualitative...

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