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TeMA Journal of Land Use Mobility and Environment 1 (2016)
EXTREME WEATHER EVENTS CAUSED BY
CLIMATE CHANGE
1 (2016)
Published by
Laboratory of Land Use Mobility and Environment
DICEA - Department of Civil, Architectural and Environmental Engineering
University of Naples "Federico II"
TeMA is realized by CAB - Center for Libraries at “Federico II” University of Naples using Open Journal System
Editor-in-chief: Rocco Papa
print ISSN 1970-9889 | on line ISSN 1970-9870
Licence: Cancelleria del Tribunale di Napoli, n° 6 of 29/01/2008
Editorial correspondence
Laboratory of Land Use Mobility and Environment
DICEA - Department of Civil, Architectural and Environmental Engineering
University of Naples "Federico II"
Piazzale Tecchio, 80
80125 Naples
web: www.tema.unina.it
e-mail: redazione.tema@unina.it
Cover Image: Wind travels across Lake Washington, buffeting the 520 floating bridge as the storm grows in strength. (Steve Ringman / The Seattle Times).
TeM
A
Journal of
Land Use, Mobility and Environment
TeMA Journal of Land Use Mobility and Environment 1 (2016)
TeMA. Journal of Land Use, Mobility and Environment offers researches, applications and contributions with a unified approach to planning and
mobility and publishes original inter-disciplinary papers on the interaction of transport, land use and environment. Domains include: engineering,
planning, modeling, behavior, economics, geography, regional science, sociology, architecture and design, network science and complex
systems.
The Italian National Agency for the Evaluation of Universities and Research Institutes (ANVUR) classified TeMA as scientific journal in the Area
08. TeMA has also received the Sparc Europe Seal for Open Access Journals released by Scholarly Publishing and Academic Resources
Coalition (SPARC Europe) and the Directory of Open Access Journals (DOAJ). TeMA is published under a Creative Commons Attribution 3.0
License and is blind peer reviewed at least by two referees selected among high-profile scientists. TeMA has been published since 2007 and is
indexed in the main bibliographical databases and it is present in the catalogues of hundreds of academic and research libraries worldwide.
EDITOR IN-CHIEF
Rocco Papa, University of Naples Federico II, Italy
EDITORIAL ADVISORY BOARD
Mir Ali, University of Illinois, USA
Luca Bertolini, University of Amsterdam, Netherlands
Luuk Boelens, Ghent University, Belgium
Dino Borri, Polytechnic University of Bari, Italy
Enrique Calderon, Polytechnic University of Madrid, Spain
Roberto Camagni, Polytechnic University of Milan, Italy
Derrick De Kerckhove, University of Toronto, Canada
Mark Deakin, Edinburgh Napier University, Scotland
Aharon Kellerman, University of Haifa, Israel
Nicos Komninos, Aristotle University of Thessaloniki, Greece
David Matthew Levinson, University of Minnesota, USA
Paolo Malanima, Magna Græcia University of Catanzaro, Italy
Agostino Nuzzolo, Tor Vergata University of Rome, Italy
Rocco Papa, University of Naples Federico II, Italy
Serge Salat, Urban Morphology and Complex Systems Institute, France
Mattheos Santamouris, National Kapodistrian University of Athens, Greece
Ali Soltani, Shiraz University, Iran
ASSOCIATE EDITORS
Rosaria Battarra, National Research Council Institute of Studies on Mediterranean Societies, Italy
Luigi dell'Olio, University of Cantabria, Spain
Romano Fistola, University of Sannio, Italy
Adriana Galderisi, University of Naples Federico II, Italy
Carmela Gargiulo, University of Naples Federico II, Italy
Thomas Hartmann, Utrecht University, Netherlands
Markus Hesse, University of Luxemburg, Luxemburg
Seda Kundak, Technical University of Istanbul, Turkey
Rosa Anna La Rocca, University of Naples Federico II, Italy
Houshmand Ebrahimpour Masoumi, Technical University of Berlin, Germany
Giuseppe Mazzeo, National Research Council Institute of Studies on Mediterranean Societies, Italy
Nicola Morelli, Aalborg University, Denmark
Enrica Papa, University of Westminster, United Kingdom
Dorina Pojani, University of Queensland, Australia
Floriana Zucaro, University of Naples Federico II, Italy
EDITORIAL STAFF
Gennaro Angiello, PhD student at University of Naples Federico II, Italy
Gerardo Carpentieri, PhD student at University of Naples Federico II, Italy
Stefano Franco, PhD student at Luiss University Rome, Italy
Chiara Lombardi, Architect, University of Naples Federico II, Italy
Marco Raimondo, Engineer, University of Naples Federico II, Italy
Laura Russo, PhD student at University of Naples Federico II, Italy
Maria Rosa Tremiterra, PhD student at University of Naples Federico II, Italy
Andrea Tulisi, PhD at Second University of Naples, Italy
TeMA Journal of Land Use Mobility and Environment 1 (2016)
TeM
A
Journal of
Land Use, Mobility and Environment
EXTREME WEATHER EVENTS CAUSED BY CLIMATE
CHANGE 1 (2016)
Contents
3
EDITORIAL PREFACE
Mir Ali
FOCUS
7 Green Infrastructure and climate change adaptation
Konstantina Salata, Athena Yiannakou
25 "Natural" disasters as (neo-liberal) opportunity? Discussing post-hurricane Katrina urban
regeneration in New Orleans
Cecilia Scoppetta
43 Cities at risk: status of Italian planning system in reducing seismic and
hydrogeological risks
Grazia Di Giovanni
63 Surface thermal analysis of North Brabant cities and neighbourhoods
during heat waves
Leyre Echevarria Icaza, Andy Van den Dobbelsteen, Frank Van der Hoeven
LAND USE, MOBILITY AND ENVIRONMENT
89 Aspects of land take in the Metropolitan Area of Naples
Giuseppe Mazzeo, Laura Russo
TeMA Journal of Land Use Mobility and Environment 1 (2016)
109 REVIEW PAGES
Gennaro Angiello, Gerardo Carpentieri,
Chiara Lombardi, Laura Russo, Andrea Tulisi
TeMA
Journal of
Land Use, Mobility and Environment
TeMA 1 (2016) 63-87
print ISSN 1970-9889, e- ISSN 1970-9870
DOI: 10.6092/1970-9870/3741
review paper received 26 January 2016, accepted 23 March 2016
Licensed under the Creative Commons Attribution – Non Commercial License 3.0
www.tema.unina.it
How to cite item in APA format:
Icaza, L. E., Van den Dobbelsteen, A., Van der Hoeven, F. (2016). Surface thermal analysis of North Brabant cities and neighbourhoods during heat
waves. Tema. Journal of Land Use, Mobility and Environment, Issue Volume (Issue Number), 63-87. doi: http://10.6092/1970-9870/3741
SURFACE THERMAL ANALYSIS
OF NORTH BRABANT CITIES
AND NEIGHBOURHOODS
DURING HEAT WAVES
ABSTRACT
The urban heat island effect is often associated with
large metropolises. However, in the Netherlands even
small cities will be affected by the phenomenon in the
future (Hove et al., 2011), due to the dispersed or
mosaic urbanisation patterns in particularly the
southern part of the country: the province of North
Brabant. This study analyses the average night time
land surface temperature (LST) of 21 North-Brabant
urban areas through 22 satellite images retrieved by
Modis 11A1 during the 2006 heat wave and uses
Landsat 5 Thematic Mapper to map albedo and
normalized difference temperature index (NDVI)
values. Albedo, NDVI and imperviousness are found to
play the most relevant role in the increase of night-
time LST. The surface cover cluster analysis of these
three parameters reveals that the 12 “urban living
environment” categories used in the region of North
Brabant can actually be reduced to 7 categories, which
simplifies the design guidelines to improve the surface
thermal behaviour of the different neighbourhoods
thus reducing the Urban Heat Island (UHI) effect in
existing medium size cities and future developments
adjacent to those cities.
KEYWORDS:
Urban Heat Island; Climate Change; Sustainable Urban
Planning; Remote Sensing.
LEYRE ECHEVARRIA ICAZAA, ANDY VAN DEN DOBBELSTEENB,
FRANK VAN DER HOEVENC
a, b, c Delft University of Technology
Faculty of Architecture and the Built Environment
ae-mail: L.EchevarriaIcaza@tudelft.nl
be-mail: a.a.j.f.vandendobbelsteen@tudelft.nl
ce-mail: F.D.vanderHoeven@tudelft.nl
TeMA有关土地使用、交通和环境的杂志
TeMA 1 (2016) 63-87
print ISSN 1970-9889, e- ISSN 1970-9870
DOI: 10.6092/1970-9870/3741
review paper received 26 January 2016, accepted 23 March 2016
Licensed under the Creative Commons Attribution – Non Commercial License 3.0
www.tema.unina.it
How to cite item in APA format:
Icaza, L. E., Van den Dobbelsteen, A., Van der Hoeven, F. (2016). Surface thermal analysis of North Brabant cities and neighbourhoods during heat
waves. Tema. Journal of Land Use, Mobility and Environment, Issue Volume (Issue Number), 63-87. doi: http://10.6092/1970-9870/3741
对遭遇热浪袭击的布拉班特
省北部城市及其周边
地区进行的地表热力分析
LEYRE ECHEVARRIA ICAZAA, ANDY VAN DEN DOBBELSTEENB,
FRANK VAN DER HOEVENC
a, b, c Delft University of Technology
Faculty of Architecture and the Built Environment
ae-mail: L.EchevarriaIcaza@tudelft.nl
be-mail: a.a.j.f.vandendobbelsteen@tudelft.nl
ce-mail: F.D.vanderHoeven@tudelft.nl
摘要
城市热岛效应经常与大城市密切相关。然而，荷兰的城市
大多是像南部的布拉班特省一样分散式或马赛克式的，这
导致一些小城市都将在未来受到这种热岛效应的影响（Ho
ve et al., 2011）。本研究通过 MODIS
11A1（搭载在terra和aqua卫星上的一个重要的传感器）
传回的22幅关于2006年热浪的卫星图像和陆地卫星5号（L
andsat 5）专题测图仪（Thematic
Mapper）绘制的反射率和归一化温度指数（NDVI）值，研
究分析了布拉班特省北部21个地区的夜间平均地表温度（
LST）。研究发现，反射率、归一化温度指数（NDVI）值
和不渗透性是导致夜间平均地表温度上升最有关联性的因
素。通过上三个参数对地表覆盖聚丛的分析，体现出曾用
于布拉班特省北部地区的12个“城市居住环境”类型可以
减少到7类。这有利于简化设计指导方，提高周边地区的
地表热力体现，减少中等城市的城市热岛效应，并促进毗
邻城市未来的发展。
关键词:
城市热岛效应；气候变化；可持续的城市规划；遥感
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
65 - TeMA Journal of Land Use Mobility and Environment 1 (2016)
1 INTRODUCTION
1.2 HEAT ISLANDS AND MEDIUM SIZE CITIES
The urban heat island (UHI) refers to the temperature difference between urban areas and their rural and/or
natural surroundings. This temperature difference may affect the air temperature, the land surface
temperature (LST) or both. Although the two are related, the difference is that while land surface
temperature´s peak takes place during the day, the air temperatures differences are largest after sunset.
The air temperature difference peak that is typically reached after sunset can reach up to 12ºC for a city of
1 million inhabitants (United Stated Environmental Agency). These relative high temperatures are especially
problematic during heat waves and can easily result in heat stress among vulnerable segments of the urban
population, leading to widespread mortality. Several climate studies show that even though The Netherlands
may seem relatively safe from heat events due to its moderate maritime climate and its polycentric urban
structure, it is actually also affected by heat events like those that took place in France in 2003 or in Russia
in 2010 (Hove et al. 2011, Van der Hoeven F. and Wandl A. 2014, Albers et al., 2015). Many studies
highlight the importance of developing and implementing urban planning measures to adapt our cities to
climate change (Galderisi A. and Ferrara F.F., 2012; Papa R., Galderisi A., Vigo Majello M.C., Saretta E.,
2015; Deppisch S., Dittmer D., 2015; Balaban O., Balaban M.S., 2015). The impact that heat islands can
have on society has been studied in the last decade by several research groups (Stone B., 2012). A link was
found between the night-time urban heat island as observed by satellites and the excess mortality in Paris
during the heat wave of 2003 (Dousset et al., 2011). Other investigations showed that the urban heat island
did have a measurable effect on aggravating the impact of the same heat wave event in Paris (Vandentorren
et al., 2006). Similar conclusions were drawn in the case of London (Mavrogianni et al., 2011). In all of these
cases the object of research is the large metropolis. Similar investigations into dispersed regional
urbanization patterns are lacking.
North Brabant is a province located in the South and Center of the Netherlands. It is one of the biggest and
most populated Dutch provinces. Due to its polycentric urban structure the Netherlands still has a relative
high population density. The population that inhabits the Dutch towns and cities is ageing and becomes
more vulnerable to heat. The four climate scenarios that are drawn up by the Royal Netherlands
Meteorological Institute (KNMI) predict an increase of the global temperatures of at least 1 ºC (Van den
Hurk B. et al., 2006) and predictions foresee an increased probability of summer heat waves (Sterl et al,
2008). The definition of a heat wave differs from country to country. In the Netherlands each period of at
least five consecutive days with a maximum temperature above 25˚C, of which at least three days peak
above 30˚C is registered as an official heat wave.
1.3 NORTH BRABANT: PARTICULAR MOSAIC URBAN STRUCTURE
In the context of urban heat the province of North Brabant is particularly interesting. The urban structure of
NorthBrabant (2.5 million inhabitants, 500,000 ha) consists of a network of almost 300 small-size cities
(urban cores in rural areas with surfaces below 900 ha) and some 60 midsize cities (urban concentration
areas with surfaces below 8,000 ha) interleaved with rural and natural park areas. The overall percentage of
urbanized land represents 15.4%. The future spatial vision of North Brabant region (Provincie Noord-
Brabant, 2010) organizes the midsize cities in three clusters: the first one comprises the two most important
agglomerations Tilburg (12,000 ha and 550,000 inhabitants) and Eindhoven (750,000 inhabitants), the
second one which comprises a group of cities to the west: Bergen op Zoom, Roosendaal, Etten-Leur, Breda,
Oosterhout, Waalwijk, ´s-Hertogenbosch and Oss (with sizes ranging from 12,900 ha in Breda to 5,590 ha in
Etten-Leur) and the third category which consists of Uden (6,700 ha) and Veghel (7,900 ha), two former
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
66 - TeMA Journal of Land Use Mobility and Environment 1 (2016)
villages that have strongly grown in the last decade and that have a marked suburban and industrial
character. The urban structure is considered as a network up to the point that the five most important cities
of the region - Tilburg, Breda, ´s-Hertogenbosch, Eindhoven and Helmond - receive the name of
Brabantstad, which means in Dutch as much as: Brabant City.
Fig. 1 Growth areas adjacent to medium-size cities
1.4 FUTURE EXPANSION PLANS OF THE REGION
The future spatial vision of North-Brabant (Provincie Noord-Brabant, 2010) foresees to enhance the spatial
structure of the urban network through two different development prospects for small cities and for
medium-size cities. The intention is to have midsize cities host regional urban infrastructures, as opposed to
the small cities, which will inevitably play a role at a more local scale. In order to materialise the
reinforcement of the urban network, the region of North Brabant has identified growth areas (in Dutch
“zoekgebieden voor verstedelijking”) connected both to small and midsize cities (Provincie Noord-Brabant,
2014). These are rural areas, which will be converted into urban plots, connected to existing cities (figure 1).
The Province plans to urbanise over 25,000 hectares of which roughly 17,000 ha are adjacent to midsize
cities. Overall the future percentage of urbanised land will increase from 15.4% to 20.4%. The province of
North Brabant is aware of the implications of increasing the urbanised areas, and has used the “ladder for
sustainable urbanisation” developed by the Dutch Ministry of Infrastructure and Environment to compare the
urban development needs with the options to restructure nearby derelict areas prior to delimiting the
“growth areas”. However, the potential urban heat island aggravation produced by the growth of urban
areas has not been taken into consideration.
1.5 RESEARCH QUESTIONS:
How can we ensure that future development plans do not aggravate the urban heat island effect in the
province of North Brabant?
In order to answer this question we have formulated four sub questions:
− What is the extend of the current heat island problem in North Brabant?
− How does albedo, normalized difference vegetation index (NDVI), imperviousness, city size and
proximity to other urban areas influence the phenomenon?
− Which of these play the most relevant UHI role?
− Can we establish a surface thermal urban classification to provide design guidelines to ensure that
future developments do not aggravate the UHI phenomenon?
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
67 - TeMA Journal of Land Use Mobility and Environment 1 (2016)
2 METHODOLOGY
2.1 DETERMINING THE ROLE OF DIFFERENT PARAMETERS IN THE FORMATION OF THE UHI
IN THE REGION OF NORTH BRABANT
In the first section of the study we have mapped and calculated the average night-time land surface (LST)
temperature (which has been calculated for 21 medium-size cities in the region of North Brabant with
MODIS 11A1 images retrieved during the heat wave of 2006 in The Netherlands), albedo (calculated with
Landsat 5TM imagery retrieved during the 2006 heat wave), NDVI (calculated with Landsat 5TM imagery
retrieved during the 2006 heat wave), imperviousness coefficient (calculated using official Netherlands
ArcGis files) and surface, and we have completed a multiple regression analysis to understand how each of
these parameters affected the average night-time LST, and which of them played the most important role in
the region of North Brabant. We have also used Excel´s dynamic charts to establish thresholds and
reference figures for each of the analysed parameters.
2.1.1 NIGHT-TIME LAND SURFACE TEMPERATURE FROM JULY 2006 AS A KEY UHI INDICATOR
The spatial pattern of the daytime (LST) urban heat island differs often significantly from the spatial pattern
of the night time (air temperature) urban heat island. However, the night-time air temperature and LST heat
islands have strong correlations (Nichol J. 2005). The main exceptions are water surfaces. Because the cities
in the province North-Brabant have relatively little open water, we can use night-time satellite imagery as a
source of data for determining for the overall UHI effect. In this context we analysed the average night-time
surface temperature of 21 midsize cities (with surfaces ranging from 117 ha to 7,700 ha) using 22 satellite
images retrieved by Modis 11A1 during July 2006. July 2006 was the warmest month on record since
systematic measurements started some 300 years ago in the Netherlands. The mean daily temperature in
July 2006 was 22.3ºC, almost 5ºC higher than the average over the period of 1971-2000: 17.4ºC according
to the Royal Netherlands Meteorological Institute (KNMI, 2006). Temperatures reached on July 19th 2006 a
maximum of 35.7 ºC (KNMI, 2013). Statistics Netherlands published an article in its web magazine that
states that 1,000 inhabitants died in July 2006 above the average mortality in a July month.Predeominatly in
the western part of the country, by the way. Topography plays a significant role in many regions in the
world when it comes to climate. Not so in the case of North-Brabant. North-Brabant is like much of the rest
of the Netherlands: flat. The lowest parts in the western part of the province measure 1-2 metres above sea
level. The highest parts in the east at a distance of 100 kilometres are 45 metres above sea level.
There is a lack of prevailing winds during heat waves. Heat waves emerge in the Netherlands predominantly
under the condition of low or even lacking wind speeds. Problems with urban heat occur especially when
there is no or little wind. For example, during the temperature peak on July 19th the KNMI measured wind
speeds between 2.0-3.0 m/s2, while in urban areas these wind speeds would be significantly lower due to
the many buildings, trees and other obstacles. Temperature is the dominant factor here.
The data we used, MOD11A1, is a satellite imagery product issued by the Moderate Resolution Imaging
Spectro-radiometer (MODIS), which has a resolution of 1,000 m and a daily temporal frequency. The images
have been downloaded from the United States Geological Survey webpage. First, the average night time
surface temperature for each of the medium size cities was calculated for each of the satellite images and
afterwards the average value of all the heat wave satellite images retrieved during the heat wave. These
two operations were performed using ArcGis.
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
68 - TeMA Journal of Land Use Mobility and Environment 1 (2016)
Fig. 2 Average night time LST urban areas of the province of North Brabant. LST values retrieved from Modis 11A1 imagery of the
23rd of July 2006
2.1.2 UHI-RELATED PARAMETERS ANALYSED
PARAMETERS RELATED THE URBAN DESIGN
Albedo, imperviousness and vegetation seem to be relevant parameters influencing the UHI. Several works
have investigated the role of surface albedo in the UHI formation (Gao et al. 2014; Prado & Ferreira, 2005;
Akbari et al. 2001; Taha 1997; Sailor 1995; Taha et al. 1992; Taha et al. 1988). Other studies have found a
strong linear relationship between the land surface temperature (LST) and the imperviousness percentage
and an inverse linear relationship between LST and the NDVI during the summer seasons (Nie & Xy 2014,
Yu & Lu 2014; Heldens et al. 2013; Xu et al. 2012; Zhang et al. 2009; Weng & Lu 2008; Xiao et al. 2007;
Yuan & Bauer 2007).
In this study we have used Landsat 5 TM satellite imagery from the 25th of July 2006 to calculate albedo and
NDVI. We have downloaded the raw satellite images from the US Geological Survey (USGS) webpage, Earth
Resources Observation and Science Center (EROS). For the albedo calculation, we have used software for
satellite imagery atmospheric topographic correction called ATCOR 2/3 which allows not only to correct
atmospherically the images but also to generate the corresponding albedo distribution image (Richter &
Schlapfer, 2013) (figure 3). For the NDVI calculation we have first corrected Landsat 5 TM spectral bands 3
(visible) and 4 (near-infrared) – both with a 30 m resolution - in ATCOR 2/3 and we have further used a
geospatial imagery treatment software called ENVI 4.7 to map the actual index, which is defined as (NIR-
VIS)/(NIR+VIS), where VIS (visible radiation) is the surface reflectance in the red region (650 nm) and NIR
(near-infrared radiation) is the surface reflectance in the near-infrared region (850 nm) (figure 4). The final
average calculation of the average albedo and NDVI values for each of the 21 analysed cities has been done
in ArcGis.
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
69 - TeMA Journal of Land Use Mobility and Environment 1 (2016)
Fig. 3 Average albedo in urban areas of the province of North Brabant
Fig. 4 Average NDVI in urban areas of the province of North Brabant.
The mapping and calculation of the average imperviousness was done calculating for each of the 21 midsize
cities, the surface occupied by buildings and roads. We have processed in ArcGis the TOP10NL file to obtain
the percentage of imperviousness for each city.
PARAMETERS RELATED TO CITY SIZE
The accumulation of urban heat is correlated with the size of cities. Several studies have made an effort to
quantify the relationship between city size and the UHI effect. Oke (1973) found a linear relationship
between the maximum urban heat island intensity (max UHI) and the logarithm of the population of cities in
North America and in Europe: equations 1 and 2 that were obtained using data from the 1970s and 1980s.
North America
[max UHI = 2.96 log (P) - 6.41] (1 )
Europe
[max UHI=2.01 log (P) - 4.06] (2)
Park and Kufuoka have attempted to find the relationships for South Korea and Japan (Park, 1986; Fujuoka,
1983). Hove(2011) highlights that the studies carried out with results from 1987 to 2008 reveal that there is
a steeper relationship between the maximal UHI intensity and the population, and that the maximal UHI for
Dutch cities with a population between 100,000 and 800,000 inhabitants would range from 4 to 8ºC. In
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
70 - TeMA Journal of Land Use Mobility and Environment 1 (2016)
North-Brabant the vast majority of the midsize cities have less than 100,000 inhabitants (except for Tilburg,
Breda,’s-Hertogenbosch and Eindhoven). This study aims at analysing how the size of smaller Dutch cities
affects the UHI effect. For the analysis of the size of the cities we have chosen to analyse the city surface
instead of the city population, since we have found a very high correlation (r2= 0.99) between the number
of inhabitants (2006, PBL Netherlands Environmental Assessment Agency) and the surface of the cities
(Graph 1).
0
50.000
100.000
150.000
200.000
250.000
300.000
0
10.000.000
20.000.000
30.000.000
40.000.000
50.000.000
60.000.000
70.000.000
80.000.000
Halder
Schietberg
Zonnehof
Holthees
DeHeen
Molenheide
Gastel
Walik
Heijningen
Helenaveen
Molenschot
Lithoijen
Biezenmortel
Keldonk
Nederwetten
Maashees
Sterksel
Esbeek
Gassel
Boskant(NB.)
Oploo
Venhorst
Eerde(NB.)
Haarsteeg
Lepelstraat
Mariahout
LageMierde
Heusden(NB.)
Genderen
Middelrode
Bosschenhoofd
Westerhoven
Standdaarbuiten
Riel(NB.)
Luyksgestel
Chaam
Oud‐Heusden
Putte
Nuland
Erp
Woudrichem
Breugel
Hapert
Moergestel
Klundert
Heeswijk/Dinther
Berlicum
Budel
Grave
SintMichielsgestel
Berkel‐Enschot
Made
Asten
Vlijmen/Nieuwkuijk
Nuenen
Dongen
Waalwijk
Oss
Tilburg
SURFACE_SQM
POPULATION
Graph 1 Analysis of surface and population of North Brabant medium-size cities
2.2 THERMAL URBAN CLASSIFICATION OF MEDIUM-SIZE CITIES IN THE REGION OF NORTH BRABANT
In the second part of our study we have created a surface thermal classification map of the different
neighbourhood typologies present in the analysed medium-size cities of the region of North Brabant. Urban
climate classification maps provide practical information on the behaviour of different urban structures and
climate, thus connecting climatological studies to urban planner´s reality. There is a first group of
investigations that have been completed based on site measurements and available urban morphology
documentation. It is the case of several studies carried out before. Chandler (1965) used climate,
physiography and built form to classify Greater London in four zones. Auer (1978) analysed vegetation and
building characteristics to create 12 “meteorologically significant” land uses in the city of St. Louis. Ellefsen
(1990) analysed geometry, street configuration and construction material for the creation of “urban terrain
zones”. Wilmers (1991) worked on urban and rural structures, use and vegetation to identify the main
“climatotopes” in Metropolitan Hannover. Scherer (1999) analysed land use and topography for the
generation of a refined “climatope” classification of the region of Basel. Oke (2004) studied urban structure,
cover, fabric, metabolism and potential to generate “urban climate zones”. Stewart & Oke (2012), finally,
researched “local climate zones” for urban heat island observation. There is a second group of papers that
have produced urban climate classifications based on remote sensing analysis, which systematizes and
makes it more cost effective. It is the case of the semi-automatic classification carried out for the city of
Toulouse (Houet & Pigeon, 2011) to classify sample areas in “urban climate zones”, the surface material
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
71 - TeMA Journal of Land Use Mobility and Environment 1 (2016)
assessment of urban zones for the generation of “urban structure types” in the city of Munich (Heiden et al.,
2012), the socio-economic and environmental impacts of the different urban structures in the same city
(Pauleit & Duhme,2000), the object-based image classifiation used to map urban structure typologies also in
Munich (Wurm et al. 2010) and the land-use classification produced for metropolitan Atlanta (Tang, 2007).
In the Netherlands there are two main “urban living environment” classification systems. One is the one
developed by ABF (ABF research, 2005) and the other one is RIGO-typology for neighbourhoods built before
and after the war (RIGO 1995; RIGO 1997). Both analyse physical characteristics of housing and urban
equipment but Rigo classification also takes into consideration socio-economic factors (Planbureau voor de
leefomgeving, 2006). Even though these “urban living environment” typologies are consistent throughout
the country, since 2004 the role of these classification systems has considerably been reduced since the
approval of the Spatial Strategy of 2004 - Nota Ruimte 2004 (VROM, 2004) - which conferred most of the
spatial policy competences to provinces and municipalities. Most provinces and municipalities have used
these as a basis to develop their own classification systems to analyse the existing built environment and to
create design guidelines for future developments from different angles. In the case of the province of North
Brabant, an “urban living environment” classification was carried out based on physical characteristics of the
neighbourhoods (location, density, housing typology and mix of uses) (figure 5) in the context of a housing
survey carried out in 1998 (WBO, 1998) and further been used in other housing surveys of the region
(Poulus & Heida, 2002). This classification establishes twelve main categories: high-density city centre, city
centre, pre-war neighbourhood, post-war compact neighbourhood, post-war soil bound neighbourhood,
urban green, small urban centre, small urban, small urban green, village centre, village, rural accessible.
Fig. 5 Analysis of surface and population of North Brabant medium-size cities
In this study we have created a 6-cluster surface cover thermal classification of the urban cores of the
region of North Brabant using the three most relevant parameters (identified in the first part of the study)
influencing night time urban LST to complete an unsupervised cluster classification in GIS. Further we have
overlapped the obtained surface cluster classification with the “urban living environment” classification of the
region of North Brabant, in order to review this official “urban living environment” classification with surface
cover thermal criteria.
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3. RESULTS AND DISCUSSION
3.1 THE ROLE OF DIFFERENT PARAMETERS IN THE FORMATION OF THE UHI IN THE REGION
OF NORTH BRABANT
The multiple regression analysis of the average values obtained for Albedo, NDVI, imperviousness, distance
to the nearest urban area and town size, shows that there is a multiple correlation coefficient of R=0.7 and
R2=0.5 that relates these parameters with the average night time surface temperature (chart 1). We have
obtained the following parameter coefficients:
LST (average night) = 27.7 – 34,8*A + 2.3E-08*S - 0,1*NDVI
Where A = Albedo, S = surface and NDVI = Normalized Difference Vegetation Index
It seems that the most relevant indicators in this case are albedo and NDVI (Graph 2). Imperviousness, the
distance to nearest town and the surface of the analysed cities do not seem to play a significant role in the
LST night values for the medium-size cities analysed in the region of North Brabant, which do not exceed
7,700 ha in any case. The maximum calculated average city night time LST difference is 2.9ºC. The average
city albedo values are pretty similar for all cities and range from 0.20 to 0.23. NDVI variations vary from
0.31 till 0.50 and imperviousness coefficient ranges from 23% to 37.4%. The future growth of most
medium-size cities of the regions will not per se aggravate the UHI phenomenon, in turn it will be the design
of the new neighbourhoods, which will impact or not the formation of urban heat in the province.
Average Modis
night time lst.
July 2006.
Albedo NDVI % impervious
surface distance to
nearest town
(in m)
Surface (in
sqm)
19,1 0,23 0,40 23,0 1051 1.731.927
19,3 0,22 0,36 31,0 1175 1.185.046
19,5 0,22 0,35 32,0 445 5.344.222
19,6 0,22 0,33 31,7 1472 7.077.232
19,6 0,22 0,32 34,2 306 4.523.848
19,8 0,21 0,50 28,8 1101 1.699.638
20,1 0,21 0,41 28,0 154 1.815.197
20,4 0,21 0,40 30,5 419 5.880.326
20,5 0,21 0,46 28,4 220 6.569.954
20,6 0,21 0,30 35,4 135 42.534.733
20,6 0,22 0,35 31,0 264 4.112.599
20,7 0,22 0,39 37,2 509 8.318.652
20,9 0,21 0,36 30,5 0 1.328.614
20,9 0,22 0,31 31,9 532 12.355.689
21,0 0,20 0,50 24,9 225 229.409
21,0 0,21 0,33 31,1 0 40.850.073
21,0 0,22 0,33 36,9 365 43.619.598
21,0 0,22 0,34 33,0 733 21.948.146
21,2 0,21 0,33 32,9 0 26.512.601
21,3 0,22 0,34 30,2 2500 13.259.052
Tab.1 Data list of the analysed medium size cities of the region of North Brabant. Parameters analysed: night time LST, albedo,
NDVI, imperviousness, distance to nearest town and surface.
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
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Graph 2 Analysis of the relationship between the different parameters and night time average LST, for each of the analysed medium
size cities in the region of North Brabant
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
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3.2 REDEFINING THE “URBAN LIVING ENVIRONMENT” CLASSIFICATION BASED ON
THERMAL SURFACE COVER CRITERIA
3.2.1 UNSUPERVISED SURFACE THERMAL CLUSTERING
Even though the average LST presents maximum variations of 3ºC (section 3.1), we have completed an
unsupervised cluster classification of the three most relevant surface cover parameters (albedo, NDVI and
imperviousness), in order to understand the different surface behaviours within each town. We have
obtained 6 different clusters.
Even though the average night time LST of these clusters are pretty similar, each of these clusters has a
singular albedo, NDVI and imperviousness combination (graph 3).
The thermal surface cover assessment is more accurate when performed through the unsupervised
classification of albedo, NDVI and imperviousness than through the calculation of the night time LST, due to
the tools used in this study for these calculations.
Albedo and NDVI are calculated based on Landsat satellite imagery which has a resolution of 30 m and
imperviousness is calculated based on a GIS model whereas night LST is calculated based on Modis 11A1
satellite imagery which has a 1km resolution.
Modis 11A1 has a resolution appropriate for average city LST calculations, but not for surface cover
discrimination.
The combined analysis of these three parameters allows classifying different surface typologies. The
scatterplots analysis (graph 4, 5 and 6) highlights the importance of the combined analysis.
There are many areas from different clusters sharing identical values for each parameter separately;
however they present different albedo, NDVI and imperviousness combinations.
Even though the average city values for albedo, NDVI and imperviousness did not differ considerably from
one city to the next (graph 2), the surface cover cluster analysis presents average albedo ranging from 0.11
till 0.30, NDVI varying from 0.18 till 0.55 and imperviousness coefficients going from 0.21% till 0.41%.
The spatial distribution of each of these clusters reveals that three of these clusters (clusters 1 to 3)
correspond to clusters of built area surface cover, and three of these clusters (clusters 4 to 6) correspond to
non-built areas surface cover clusters (figure 6).
Cluster 1 corresponds to specific urban areas with the poorest surface thermal behaviour, mainly present in
small specific areas of the city centres or of industrial areas.
They have very low albedo (0,11), high imperviousness (39%), and low NDVI (0,17) ).
Cluster 2 presents a similar average NDVI value (0,19), slightly higher imperviousness (0,42) and
considerably higher albedo (0,2) than cluster 1. The main difference between cluster 1 and cluster 2 is the
albedo.
The majority of the city centre surfaces belong to cluster 2.
Cluster 3 seems to correspond to urban residential areas (row houses) with interspersed green areas,
presenting a slightly higher albedo (0,24), higher NDVI (0,28) and lower imperviousness (0,31).
Cluster 4 can be identified with low density residential areas (detached houses) areas of urban parks with
trees with higher NDVI, lower albedo (due to the presence of greenery) and slightly higher imperviousness.
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
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Cluster 5 corresponds to urban trees and water areas with the highest NDVI (0,55), the lowest
imperviousness 22% and a relatively low albedo (0,21 due to the presence of vegetation) and cluster 6
corresponds to bare soil areas with the highest albedo (0,31), considerably low NDVI (0,20) and small
imperviousness 26% (Graph 3).
Graph 3 Average albedo, NDVI, imperviousness and night LST values for each of the 6 clusters resulting from the unsupervised
classification of the albedo, NDVI and imperviousness maps of the analysed medium-size cities of North Brabant.
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
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Graph 4 Scatterplots of night LST and albedo, for each of the different surface cover clusters
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Graph 5 Scatterplots of night LST and NDVI, for each of the different surface cover clusters
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Graph 6 Scatterplots of night LST and imperviousness, for each of the different surface cover clusters
If we analyse the case Eindhoven metropolitan area we can see that the city centre is mostly covered with
cluster 2 surfaces, and that in turn, cluster 4 has more presence in areas outside the city centre. Cluster 1 is
only present in very specific, heat absorbing surface areas, whereas cluster 6 is hardly present in the city
area (this is why it was not included in the analysed figure) (figure 6 and 7).
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Fig. 6 Spatial distribution of surface cover clusters in Eindhoven metropolitan area
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3.2.2 ANALYSIS OF THE PRESENCE OF SURFACE THERMAL CLUSTERS IN “URBAN LIVING
ENVIRONMENT” CATEGORIES
The maps of Eindhoven metropolitan area illustrate the different spatial distribution of the clusters and the
“urban living environment” maps. Each “urban living environment” map comprises a specific surface cluster
mix (figure 7). In order to analyse how the surface thermal clusters match with the “urban living
environment” categories of the region of North Brabant, we have calculated the proportion of clusters found
in each of the “ urban living environment” categories (graph 7). This analysis reveals that “urban living
environment” classes 3, and 5 (pre-war neighbourhood and post-war ground based) present similar surface
covers where clusters 2 cover more than 35% of the surface and where the proportion of urban clusters (1,
2 and 3) is in all cases above 60%.
The cluster mix analysis, also reveals that “urban living environments” 4, 6, 7, 8 and 9 (post-war compact,
green, small urban centre, small urban and small urban green) present similar surface cover mixes, with
similar cluster 2 and 4 presence, and where the proportion of urban surface cover clusters (1,2 and 3) is
around 50%. We can establish that the 12 category “urban living environment” classification applied in North
Brabant, could be reduced to a 7 surface cover classification.
Fig. 7 Compilation of LST-related maps for Eindhoven metropolitan area: Albedo, NDVI, imperviousness, surface cover clustering
and “urban living environment” categories
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
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Graph 7 Surface cover cluster proportions for each of the “urban living environment” categories in the analysed medium-size cities
of the North Brabant region. Neighbourhoods 3 and 5 present similar cluster proportions, and thus could be grouped.
Neighbourhoods 4, 6, 7, 8 and 9 present similar cluster proportions, and thus could be grouped.
3.2.3 ANALYSIS OF AVERAGE NIGHT LST OF THE DIFFERENT “URBAN LIVING
ENVIRONMENT” CATEGORIES
The analysis of the average night time LST retrieved by Modis 11A1 in 16 satellite images during the heat
wave experienced in the month of July 2006 (Graph 8) reveals that 12 “urban living environment” categories
could actually be grouped in 7, since categories 3, 4 and 5 (pre-war neighbourhood, post-war compact and
post-war ground based) could be grouped into one single category since they present similar average LST
(around 21ºC) and categories 6,7, 8 and 9 could be grouped into another category because they present
similar average LST (around 20.3ºC).
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
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Graph 8. Average night LST for each of the “urban living categories”. Neighbourhoods 3, 4 and 5 present similar night LST, and thus
could be grouped. Neighbourhoods 6, 7, 8 and 9 present similar night LST , and thus could be grouped.
3.2.4 PROPOSED “URBAN LIVING THERMAL CATEGORIES” FOR THE REGION OF NORTH
BRABANT
The surface cluster analysis of the different “urban living environment categories” suggests that these can
be grouped into 7 categories. 1/ High density city centre 2/ City centre 3/ pre-war neighbourhood & post-
war soil bound 4/ post-war compact & urban green & small urban centre & small urban & small urban green
5/Village centre 6/Village 7/Rural accessible. The average night time land surface temperature analysis of
the “urban living environment categories” suggests the same groups except for the post-war compact
neighbourhood’s category which has a night LST similar to pre-war and post war soil bound. The main
reason is that post-war compact neighbourhoods is a category that consists of scattered high rise dwelling
blocks, interleaved with green areas and large infrastructural roads. The proportion of green areas that can
be found in these neighbourhoods is similar that the ones of small urban areas, however the overall night
LST is higher in these post-war areas.
4 CONCLUSIONS
This paper addressed the main question how to ensure that the future development plans do not aggravate
the urban heat island (UHI) effect in the North-Brabant urban areas, by focusing on three sub-questions:
How bad is the urban heat island problem currently? How does albedo, normalized difference vegetation
index (NDVI), imperviousness, city size and proximity to other urban areas influence the phenomenon?
Which of these play the most relevant UHI role? Can we establish a surface thermal urban classification to
provide design guidelines to ensure that future developments do not aggravate the UHI phenomenon?
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The answer to the main question is found in adjusting the design of the growth areas that are designated by
the province North-Brabant. The growth areas are based on the “ladder for sustainable urbanisation”
developed by the Dutch Ministry of Infrastructure and Environment. The aspect of urban heat islands is not
included in this methodology. We propose to include considerations about albedo, greenness (NDVI) and
imperviousness, in the design of these future developments. Our study has revealed that albedo and NDVI
are the most relevant parameters influencing the average night time LST for the analysed North Brabant
medium-size cities. Correlation coefficients extracted from the multiple regression analysis are:
LST (average night) = 27.7 – 34,8*A + 2.3E-08*S - 0,1*NDVI
Where A = Albedo, S = surface and NDVI = Normalized Difference Vegetation Index
The surface cover cluster analysis of these three parameters reveals that the 12 “urban living environment”
categories used in the region of North Brabant (high-density city centre, city centre, pre-war neighbourhood,
post-war compact neighbourhood, post-war soil-bound neighbourhood, urban green, small urban centre,
small urban, small urban green, village centre, village, rural accessible) can actually be reduced to 7
categories, since classes 3, 4 and 5 (pre-war neighbourhood, post-war compact and post-war ground based)
present similar surface covers (and could thus be grouped) and the “urban living environments” 6, 7, 8 and
9 (green, small urban centre, small urban and small urban green) also present similar surface cover mixes
(and could thus be grouped). This surface cover classification provides guidelines to improve the surface
behaviour of the most common urban typologies that can be found in the province of North Brabant and to
guide the urban design of the planned future urban developments. All of these conclusions could be
integrated in a climate-robust growth areas policy.
5 DISCUSSION
The purpose of using the surface cover cluster analysis for the thermal assessment of the different “urban
living environment” assessment (instead of calculating directly the average night time LST of each of these
neighbourhood typologies) is to actually map and quantify parameters that can be addressed and improved.
Measures to improve albedo, NDVI and imperviousness can be simulated and quantified. Mapping surface
cover categories allows designing specific mitigation solutions, instead of only assessing on the intensity of
the problem (night LST temperature).
The intention of the study is to analyse the thermal surface cover behaviour of the different “urban living
environment” categories in order to design UHI adaptation measures in the existing neighbourhoods and to
produce some surface adaptation guidelines for the future developments that will grow adjacent to the
existing medium-size cities. The same urban structures can considerably improve their thermal behaviour
though the implementation of measures that only affect their surface covers. We understand that
parameters related to the neighbourhood structure (sky view factor, wind, shadow, …) as well as factors
such as anthropogenic heat emissions should be the object of another study to determine to what extent
they influence the formation of the UHI in the province of North Brabant, and to explore and design the
development of design guidelines concerning the urban structure for the design of future urban
developments.
ACKNOWLEDGMENTS
This research was funded by the Climate Proof Cities project of the Dutch national Knowledge for Climate
research programme.
L.E. Icaza, A. Van Den Dobbelsteen, F. Van Der Hoeven – Surface Thermal Analysis of North Brabant Cities and Neighbourhoods During Heat Waves
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IMAGE SOURCES
Cover image: Courtesy of the U.S.Geological Survey.USGS/NASA Landsat.Further processed
Fig. 1: Province North Brabant. Spatial vision, 2010
Fig. 2: Courtesy of the U.S. Geological Survey. USGS/NASA Modis
Fig. 3: Landsat image (Courtesy of the U.S. Geological Survey. USGS/NASA Landsat) further processed with ENVI 4.7 and
Atcor 2.3.
Fig. 4: Landsat image (Courtesy of the U.S. Geological Survey. USGS/NASA Landsat) further processed with ENVI 4.7 and
Atcor 2.3.
Fig. 5: ABF research, 2005
AUTHOR’S PROFILE
Leyre Echevarria Icaza
PhD student at TU Delft University of Technology, Faculty of Architecture and the Built Environment.
Frank Van der Hoeven
Director of Research at Faculty of Architecture and the Built Environment. He is Associate Professor, Chair of Urban
Design at TU Delft University of Technology.
Andy Van den Dobbelsteen
Professor of Climate Design & Sustainability. Head of Department of Architectural Engineering + Technology. Theme
leader for the Delft Energy Initiative Principal Investigator for the AMS Institute.
