Urban Morphology, Surface Characterisation and ...
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Urban Morphology Urban Morphology, Surface Characterisation and Anthropogenic Heat Urban Morphology, Surface Characterisation and Anthropogenic Heat Fredrik Lindberg, Thomas Smith, Sue Grimmond, Sean Beevers, Mario Iamarino, Martin Wooster and RoshniHarkishini King’s College London, Department of Geography, Environmental Monitoring and Modelling Group [email protected], [email protected], [email protected] Analysis of the urban morphology at its different scales is used to improve urban parameterisation in numerical meteorological modelling. Knowledge of urban morphology parameters also helps the analysis of energy fluxes between the surface and the atmosphere in urban areas. Examples of parameters derived: * Morphological characteristics * Surface roughness lengths z Continous image of Sky view factors derived from ground and building heights. This information could be used to investigate the openess and solar access on a city scale. Frontal area aspect ratio from two different directions for central parts of London. These images could be used Introduction The urban environment is one the most complex and diversified on the Earth’s surface. By using Geographical Information Systems (GIS) and remote sensing the city can be characterised with respect to diversity and spatial variability: How could one make use of morphological parameters within the concept of urban planning in order to improve the urban environment? Can measuring the radiative properties of urban surfaces improve our understanding of city-wide phenomena, such as the urban heat island? Measuring the emissivity of urban surfaces * Surface roughness lengths z 0 * Zero plane displacement height z d * Plan area aspect ratio λ p * Frontal area aspect ratioλ f * Sky view factor Ψ We make use of the VIRTUAL LONDON dataset which is a 3D building structures vector dataset covering Greater London to derive the different morphological parameters. to examine the ventialtion within the city. W d =East W d =North The amount of energy radiated by a surface at any given wavelength is a function of both its kinetic temperature and its spectral emissivity (ε). Measuring the emissivity of urban surfaces can improve the monitoring and modelling of urban landscapes, knowledge of surface emissivity can: * Enable the accurate determination of surface temperatures by remote methods (i.e. airborne/satellite thermal remote sensing. * Improve the parameterisation of urban energy balance models. * Lead to the possibility of classifying different urban surfaces from remotely sensed hyper-spectral images. * Help develop techniques to detect tarmac deterioration (Pascucciet al., 2008) and hazardous materials, such as asbestos-cement roofing (Bassaniet al., 2007). KCL’s FTIR emissivity kit (left) can measure the thermal radiance of surfaces (below). By calibrating the spectrometer using target surfaces of known radiance (blackbodies) and eliminating background radiation (using a gold plate), emissivity spectra (right) can be computed. (Open) European Commission under the seventh framework: BRIDGE, Grant agreement no.: 211345 THEME FP7-ENV-2007.1.1.2.1: MEGAPOLI project, Grant agreement no.: 212520 Dr Fredrik Lindberg is financially supported by FORMAS, The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning Thomas Smith is financially supported by the Natural Environment Research Council and the Economic and Social Research Council Ms RoshniHarkishini is financially supported by the Nuffield Foundation References Acknowledgements Anthropogenic Heat Flux (Inventory) Estimation of the heat flux generated by anthropogenic activities in the Greater London area classified by source (buildings, transportation, human metabolism). Energy flux is allocated over the Greater London area according to a fine spatial mesh based on the NeSS Geography Hierarchy, as adopted by the Office for National Statistics, and taking into account temporal variations (seasonal, daily, hourly). Results are correlated with residents and daytime density, consumer typology, temperature, etc., in order to obtain a predictive model for forecasting purposes. Data sources: DECC (Department of Energy & Climate Change), UK Office for National Statistics, Greater London Authority. Bassani, C., Cavalli, R.M., Cavalcante, F., Cuomo, V., Palombo, A., Pascucci, S. and Pignatti, S. (2007) Deterioration status of asbestos- cement roofing sheets assessed by analyzing hyperspectraldata, Remote Sensing of Environment, 109: 361-378. DECC (Department of Energy & Climate Change), UK Office for National Statistics, Greater London Authority. Pascucci, S., Bassani, C., Palombo, A., Poscolieri, M. and Cavalli, R. (2008) Road Asphalt Pavements Analyzed by Airborne Thermal Remote Sensing: Preliminary Results of the Venice Highway, Sensors 8: 1278-1296. Smith, T.E.L. (2008) Towards a field method for retrieving the spectral emissivity of urban surfaces, MSc dissertation, King’s College London. Virtual London, Centre for advanced spatial analysis (CASA), University College London 0 2 4 6 8 10 12 14 16 18 20 0.00 4.00 8.00 12.00 16.00 20.00 0.00 Q F [W m -2 ] Time [h] Winter Summer Daily and seasonal variation of Q f Q f for Greater London, in 2004. The very large Q f values (up to 1500 W m -2 ) refer to some very small areas that cannot be easily individuated. The dark red areas you can see by eyes in central London are typically less than 300 W m -2 . Domestic Industry Transportation y = 4E+06x 6 -2E+07x 5 + 5E+07x 4 - 6E+07x 3 + 4E+07x 2 -1E+07x + 2E+06 R² = 0.8552 0 20 40 60 80 100 120 140 160 180 0.7 0.8 0.9 1 Antropogenic Heat Flux (Wm -2 ) Sky View Factor There is a close connection between urban morphology and Q f .
Transcript of Urban Morphology, Surface Characterisation and ...
Urban Morphology
Urban Morphology, Surface Characterisation and Anthropogenic HeatUrban Morphology, Surface Characterisation and Anthropogenic HeatFredrik Lindberg, Thomas Smith, Sue Grimmond, Sean Beevers, Mario Iamarino, Martin Wooster and Roshni Harkishini
King’s College London, Department of Geography, Environmental Monitoring and Modelling Group
[email protected], [email protected], [email protected]
Analysis of the urban morphology at its different scales is used to improve urban
parameterisation in numerical meteorological modelling. Knowledge of urban morphology
parameters also helps the analysis of energy fluxes between the surface and the atmosphere
in urban areas.
Examples of parameters derived:
* Morphological characteristics
* Surface roughness lengths z
Continous image of Sky view factors derived from ground
and building heights. This information could be used to
investigate the openess and solar access on a city scale.
Frontal area aspect
ratio from two different
directions for central
parts of London. These
images could be used
Introduction
The urban environment is one the most complex and diversified on the Earth’s surface. By using Geographical Information Systems (GIS) and remote sensing the city
can be characterised with respect to diversity and spatial variability:
How could one make use of morphological parameters within the concept of urban planning in order to improve the urban environment?
Can measuring the radiative properties of urban surfaces improve our understanding of city-wide phenomena, such as the urban heat island?
Measuring the emissivity of urban surfaces
* Surface roughness lengths z0
* Zero plane displacement height zd
* Plan area aspect ratio λp
* Frontal area aspect ratio λf
* Sky view factor Ψ
We make use of the VIRTUAL LONDON dataset which
is a 3D building structures vector dataset covering
Greater London to derive the different morphological
parameters.
images could be used
to examine the
ventialtion within the
city.
Wd=East
Wd=North
The amount of energy radiated by a surface at any given
wavelength is a function of both its kinetic temperature and its
spectral emissivity (ε). Measuring the emissivity of urban surfaces
can improve the monitoring and modelling of urban landscapes,
knowledge of surface emissivity can:
* Enable the accurate determination of surface temperatures by
remote methods (i.e. airborne/satellite thermal remote sensing.
* Improve the parameterisation of urban energy balance models.
* Lead to the possibility of classifying different urban surfaces
from remotely sensed hyper-spectral images.
* Help develop techniques to detect tarmac deterioration
(Pascucci et al., 2008) and hazardous materials, such as
asbestos-cement roofing (Bassani et al., 2007).
KCL’s FTIR emissivity kit (left)
can measure the thermal
radiance of surfaces
(below). By calibrating the
spectrometer using target
surfaces of known radiance
(blackbodies) and
eliminating background
radiation (using a gold
plate), emissivity spectra
(right) can be computed.
(Open)
European Commission under the seventh framework: BRIDGE, Grant agreement
no.: 211345
THEME FP7-ENV-2007.1.1.2.1: MEGAPOLI project, Grant agreement no.: 212520
Dr Fredrik Lindberg is financially supported by FORMAS, The Swedish Research
Council for Environment, Agricultural Sciences and Spatial Planning
Thomas Smith is financially supported by the Natural Environment Research Council
and the Economic and Social Research Council
Ms Roshni Harkishini is financially supported by the Nuffield Foundation
References Acknowledgements
Anthropogenic Heat Flux (Inventory)
Estimation of the heat flux generated by anthropogenic
activities in the Greater London area classified by source
(buildings, transportation, human metabolism). Energy flux
is allocated over the Greater London area according to a
fine spatial mesh based on the NeSS Geography Hierarchy,
as adopted by the Office for National Statistics, and taking
into account temporal variations (seasonal, daily, hourly).
Results are correlated with residents and daytime density,
consumer typology, temperature, etc., in order to obtain a
predictive model for forecasting purposes.
Data sources: DECC (Department of Energy & Climate
Change), UK Office for National Statistics, Greater London
Authority.
Bassani, C., Cavalli, R.M., Cavalcante, F., Cuomo, V., Palombo, A., Pascucci, S. and Pignatti, S. (2007) Deterioration status of asbestos-
cement roofing sheets assessed by analyzing hyperspectral data, Remote Sensing of Environment, 109: 361-378.
DECC (Department of Energy & Climate Change), UK Office for National Statistics, Greater London Authority.
Pascucci, S., Bassani, C., Palombo, A., Poscolieri, M. and Cavalli, R. (2008) Road Asphalt Pavements Analyzed by Airborne Thermal
Remote Sensing: Preliminary Results of the Venice Highway, Sensors 8: 1278-1296.
Smith, T.E.L. (2008) Towards a field method for retrieving the spectral emissivity of urban surfaces, MSc dissertation, King’s College
London.
Virtual London, Centre for advanced spatial analysis (CASA), University College London
0
2
4
6
8
10
12
14
16
18
20
0.00 4.00 8.00 12.00 16.00 20.00 0.00
QF
[W m
-2]
Time [h]
Winter
Summer
Daily and seasonal variation of QfQf for Greater London, in 2004. The very large Qf
values (up to 1500 W m-2) refer to some
very small areas that cannot be easily individuated.
The dark red areas you can see by eyes in central
London are typically less than 300 W m-2.
Domestic Industry Transportation
y = 4E+06x6 - 2E+07x5 + 5E+07x4 -
6E+07x3 + 4E+07x2 - 1E+07x +
2E+06
R² = 0.8552
0
20
40
60
80
100
120
140
160
180
0.7 0.8 0.9 1An
tro
po
ge
nic
He
at
Flu
x (W
m-2
)
Sky View Factor
There is a close connection
between urban morphology and Qf.
