10/24/2022
URBAN CLIMATOLOGY
5. Urban Remote Sensing
Paper to read
Author manuscript, published in "Urban Remote Sensing Event 2007, Paris : France (2007)"
2007 Urban Remote Sensing Joint Event
Application of satellite Remote Sensing for Urban Risk Analysis: a case study of the 2003 extreme heat
wave in Paris
B6n6dicte Dousset Hawaii Institute of Geophysics and Planetology University of Hawaii, Honolulu, USA bdou5set@ha\Yaii,edu
Francoise Gourmelon
Laboratoire Geomer CNRS UMR-G554, Institut UniversitaLre Européen de la Mer Plouzané. France Fi^coije,Gc:ii-r.glc;iť^urLÍv-brest.fi
Elena Mauri Istituto Nazionale di Oceanografica e di Geofisica Sperimentale Trieste, Italy emauri @ ogs .trie sie, it
https://is.muni.cz/auth/el/sci/podzim2022/ZX601/um/67875456/05_Dousset-URS-07.pdf
1
ĎN = f (LST)
5.1 Remote Sensing Principle
0.4     0.5     0.6 0.7
UNITS
1 micrometer (Urn) = 1 x 10$ meters 1 millimeter (mm} - 1 x 10"3 meters 1 centimeter (cm) = 1 x 10'* meters
]f \ Incoming from Sun
INFRARED
Emitted by Earth
MICROWAVE (RADAR)
I IS	111	71	B7	74
111	66	52	77	95
■:!	et	14	S7	ť.
89	...i	102	125	....
70	■""	Ml	■It	1 1
i r
0.1 llm     1 urn     10 urn    100 urn    1 mm       1 cm      10 cm 1m Wavelength (logarithmic scale)
http://www.remote-sensing.net/concepts.htrril
5.1 Remote Sensing Principle
Stefan-BoItzmann law: The thermal energy radiated by a blackbody is proportional to the fourth power of the absolute temperature:
M - thermal energy ]^/[ — q-J1^ T - absolute temperature
a - the Stefan-Boltzmann constant
Real surfaces
Emissivity is the measure of an object's ability to emit infrared energy. Emitted M — cvtT4 £ -emiSSivity     energy indicates the temperature of
±V±   — CrKJ 1 the object. Emissivity can have a value
from 0 (shiny mirror) to 1.0 (blackbody). Most organic, painted, or oxidized surfaces have emissivity values close to 0.95.
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
5.2 1ST derivation from LANDSAT
zxBA(Ts) + (1 - £A)L±
T^ + ^atoU
ETM+ band
TOA
radiance
Atmos. corrected
MODTRAN
• transmissivity
• upwelling radiance
• downwelling radiance
(Barsi et al. 2005)
I I
1,2,3,4
|a.4dc^
/
Land surface Temperature
(Snyder et al. 1998)
LST derivation from ASTER data
(more thermal images)
Land Surface Temperatures     Emissivity at bands 10-14
0 5 10 15 20 25 30 35 40 45 50 _LST [°C]_
Modification of LST field due to anthropic activities in big CZ. cities
Data: eight-day composites of mean surface temperatures from the AAODIS scanner with 1 x 1 km spatial resolution.
PRŮMĚRNÉ LST V PRAZE A JEJÍM PŘILEHLÉM OKOLÍ OD 1.1. 2008 DO 1.1. 2018
Spatial differentiation of surface temperatures (LST) in daytime (left),
nighttime (middle) hours and their average (right) in Prague and its _surroundings in the period 2008-2018_
Modification of LST field due to anthropic activities in big CZ. cities
DAY
NIGHT
J  ^  * «/ J-
.    • • •
,*°  # ^ « <?>
</ <f «/ y    y / / ^
hofr': prXrr£r"\f tepiots vzsitc-vSie oblsst    Ncirl p'iirr£rrj teplot = zazenii
Average LST of the ten largest cities of the Czech Republic in built-up and rural areas in the period 2008-2018.
Examples of SUHI analysis
• Birmingam City Extents
1: villages/farms
2: suburban
3: light suburban I 4. dense suburban I 5: urban/transport | 6: urban
I 7: light urban/open water I 8: woodland/open land
mm mm ljaB™'-x
m-.-w
Birmingham C ty Extort
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)
Examples of SUHI analysis
I
'J	*
	i-
Remote Sensing for Urban Risk Analysis: a case study of the 2003 extreme heat wave in Paris
I
0:
Ü
u
Average Land Surface Temperature infrared images over the heat wave event of August 4 to 13, 2003, for each of the diurnal time intervals. The color scales are in degrees Celsius. Analysis was based on 50 images sensed by NOAA satellites 12,16 and 17, over the Paris basin, from 4 to 13 August 2003.
The areas of the Paris region most vulnerable to heat stress were identified.
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. Vegetation mapping (NDVI), ...
Various parameters derived from 3D model of buildings and from Digital Elevation Model explain spatial variability of land surface temperatures.
Another useful Remotely Sensed variables for UC
4. Land use mapping - changes in time, UHI growth
V30 LETECH 19.STOLETÍ ,     2     ,     ,     ,    ,„„ V70. LETECH 19. STOLETÍ
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 summertime
Spatial distribution of daily precipitation totals (mm) computed as a combination of radar-based precipitation estimate and rain-gauge measurements from 15 July 2009 (measured at 16 July 2009, 08 h central European summertime). Stations with higher precipitation totals are preferred in the map. Spatial distribution of precipitation _totals is given in 1 x 1 km grid_
10/24/2022
Precipitation and weather RADAR
Frequency of the above-average maximum radar reflectivity in Brno region composed from 26 situations with extreme convection at Tufany station in the period 2000-2007
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?
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