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5. Urban Remote Sensing
URBAN CLIMATOLOGY
Digital Numbers
DN = f(LST)
5.1 Remote Sensing Principles
5.1 Remote Sensing Principle
http://www.remote-sensing.net/concepts.html
5.1 Remote Sensing Principle
Stefan-Boltzmann law: The thermal energy radiated by a blackbody is
proportional to the fourth power of the absolute temperature:
4
TM σ=
M - thermal energy
T - absolute temperature
σ - the Stefan–Boltzmann constant
4
TM εσ=
Real surfaces
ε - emissivity
There are at least two problems in urban remote sensing:
1) How to determine emissivity of real surfaces in highly
heterogeneous urban environment
2) How to recalculate LST - Land Surface Temperature to air
temperature
• LANDSAT 7 satellite
• ETM+ imagery
• Date 24. 5. 2001
• Time 9:35:02 GMT
• Thermal band 10.4 – 12.5 µm
• Spatial resolution 60 m
Day with the clear sky, no wind,
anticyclonic weather type
Tmin 8.4°C
Tmax 23.3°C
Tavg 17.6°C
Tground 5,0°C
5.2 LST derivation from LANDSAT
ETM+ band
6.1
TOA
radiance
ETM+
1,2,3,4
Land cover
types
Emissivity
Land surface Temperature
Atmos.
corrected
values
MODTRAN
• transmissivity
• upwelling radiance
• downwelling radiance
(Barsi et al. 2005)
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This is a weak point
of mono window
algorithms
Emissivity map of basic land cover types
water 0,98
bare ground 0,97
vegetation 0,94
built-up area 0,925
Emissivity values
(Snyder et al. 1998)
Land Surface Temperatures Emissivity at bands 10-14
LST derivation from ASTER data
(more thermal images)
ASTER
bands 10-
14
TOA
radiance
10-14
Atmos.
Corrected
values
10-14
LST, Brno, 15 June 2006 Intensity of SUHI
Examples of SUHI analysis
Spatial distribution of land classification (left) and SUHI magnitude (right) within
Birmingham city extents for heatwave event at 18 July 2006 (Tomlinson et al., 2010)
Another useful Remotely Sensed variables for UC
1. Spatial distribution of the Land Surface Temperature
2. Terrain model with 3D building model (laser scanning)
3. Land use mapping, vegetation mapping
Various parameters derived from 3D model of buildings and from Digital
Elevation Model explain spatial variability of land surface temperatures.
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Precipitation and weather RADAR
Spatial distribution of radar reflectivity
(maximum values in vertical direction)
measured at meteorological radars
Skalky and Brdy at 15 July 2009,
19:25 hours of central European
summer time
Spatial distribution of daily precipitation
totals (mm) computed as a combination of
radar-based precipitation estimate and raingauge
measurements from 15 July 2009
(measured at 16 July 2009, 08 h central
European summer time). Stations with
higher precipitation totals are preferred in
the map. Spatial distribution of precipitation
totals is given in 1 x 1 km grid
Frequency of the above-average maximum radar reflectivity in Brno region composed
from 26 situations with extreme convection at Tuřany station in the period 2000–2007
Precipitation and weather RADAR
5.5 Final remarks and questions
1. What are limitations of URS in terms of spectral, spatial
and temporal resolution?
2. What are the main benefits of URS for heat wave studies
compared to air temperature analysis?
3. How can be URS used for practical urban planning, regional
development and for better adaptation to climate change?