International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August-01 September 2012, Melbourne, Australia 
CLASSIFICATION AND MODELLING OF URBAN MICRO CLIMATES USING 
MULTISENSORAL AND MULTITEMPORAL REMOTE SENSING DATA 
B. Bechtel a ’ *, T. Langkamp“, J. Bôhner a , C. Daneke a , J. OBenbrügge a , S. Schempp a 
a KlimaCampus, University of Hamburg, BundesstraBc 55, 20146 Hamburg, Germany - 
benjamin.bechtel@uni-hamburg.de 
Commission VII1/8: Land 
KEY WORDS: Urban, Climate, Human Settlement, Classification, Thermal, DEM/DTM, Landsat 
ABSTRACT: 
Remote sensing has widely been used in urban climatology since it has the advantage of a simultaneous synoptic view of the full 
urban surface. Methods include the analysis of surface temperature patterns, spatial (biophysical) indicators for urban heat island 
modelling, and flux measurements. Another approach is the automated classification of urban morphologies or structural types. 
In this study it was tested, whether Local Climate Zones (a new typology of thermally 'rather' homogenous urban morphologies) can 
be automatically classified from multisensor and multitemporal earth observation data. Therefore, a large number of parameters 
were derived from different datasets, including multitemporal Landsat data and morphological profiles as well as windowed 
multiband signatures from an airborne IFSAR-DHM. 
The results for Hamburg, Germany, show that different datasets have high potential for the differentiation of urban morphologies. 
Multitemporal thermal data performed very well with up to 96.3 % overall classification accuracy with a neuronal network 
classifier. The multispectral data reached 95.1 % and the morphological profiles 83.2 %.The multisensor feature sets reached up to 
97.4% with 100 selected features, but also small multisensoral feature sets reached good results. This shows that microclimatic 
meaningful urban structures can be classified from different remote sensing datasets. 
Further, the potential of the parameters for spatiotemporal modelling of the mean urban heat island was tested. Therefore, a 
comprehensive mobile measurement campaign with GPS loggers and temperature sensors on public buses was conducted in order to 
gain in situ data in high spatial and temporal resolution. 
1. INTRODUCTION 
Urban climatology is an important application for remote 
sensing of urban areas. The growing interest in urban climatic 
phenomena like the urban heat island (UHI) is motivated by 
increasing vulnerability to health risks due to rapid urbanization 
in developing countries and climate change. 
The urban heat island indicates increased air temperatures in the 
urban atmosphere compared to a preurban state (Lowry, 1977) 
or (more often) to a rural reference station of identical regional 
and topo-climate. It is the most prominent effect of urban 
climate (Oke, 1982; Amfield, 2003; Yow, 2007) and has been 
studied more intensely than any other effect. The UHI varies 
both diumally and seasonally and depends on the prevailing 
synoptic conditions. It is particularly pronounced at low wind 
speeds and high air pressure (Oke, 1973) and has been 
documented for many towns and cities of different sizes and on 
different continents (Oke, 1982). Further, it depends on the size 
of the city (Oke, 1973). Within a city the UHI greatly varies 
depending on the urban structure and vegetation and is therefore 
also referred to as urban heat archipelago. 
Remotely sensed data can contribute to descriptive urban 
climatic studies and a better understanding of the underlying 
climatic processes. The assessment of surface atmosphere 
exchanges (Rigo and Parlow, 2007) was fostered by recent 
advances in earth observation technologies. Further, remote 
sensing has widely been applied to characterise the urban 
surface and to determine parameters for model and 
experimental studies. Thus, fundamental physical attributes of 
measurement sites and key parameters for urban climate models 
can be derived from remotely sensed data (Grimmond, 2006). 
This includes parameters of the urban energy balance like 
albedo and emissivity (Frey et al., 2007; Frey and Parlow, 
2009), the urban canyon geometry, and aerodynamic 
characteristics like roughness parameters (Bechtel et al., 201 1). 
Further, automated classification of urban structures in respect 
to their microclimatic properties can contribute to urban climate 
studies. A typology of such thermally defined urban structures 
was recently introduced with the Local Climate Zones (LCZ) 
scheme (Stewart and Oke, 2009). 
Great attention has been devoted to the application of thermal 
infrared data for UHI assessment (Roth et al., 1989; Eliasson, 
1992; Gallo and Owen, 1999; Voogt and Oke, 2003; Nichol et 
al., 2009; Wong et al., 2009; Fabrizi et al., 2011). Thermal 
imagery offers the chance to directly measure the surface 
temperature which is crucial for the urban energy balance and 
modulates the air temperature of the lowest atmospheric layer. 
Therefore, the spatial structure of the surface temperature 
patterns is not only directly related to surface characteristics but 
also used to study the energy balance and the relation between 
atmospheric heat island and surface temperature heat island 
(Voogt and Oke, 2003). However, there are various problems 
involved in linking thermal imagery to air temperatures which 
mostly remain unsolved (Roth et al., 1989; Voogt and Oke, 
1997; Voogt and Oke, 2003). 
Beside recent advances in the development of surface 
parameters for assessing the urban thermal response, “«evv 
methods for estimation of UHI parameters from multi-temporal 
Corresponding author.