Pinellas Land Surface Temperature and remote sensing

1. Bruce C. Mitchell
A thesis proposal submitted in partial fulfillment
of the requirements for a degree of
Master of Arts
Department of Geography
College of Arts and Sciences
University of South Florida

2.  Introduction – Urbanization and UHI
 Literature Review
 Research Questions
 Study Area - Pinellas County
 Methods
 Results
 Mitigation Strategies - cool roofs/urban
forestry
 Conclusions

3. • Half of the world’s population now live in urban areas and this
is projected to increase to 61% by 2035. Tropical regions
show greatest increase.
• Urbanization: decreased vegetation, increased
impervious surface, growing population
• Environmental consequences: greater storm water run-off,
increased air pollution and reduced CO2 filtration.2 Also
changes to urban micro-climate, including the Urban Heat
Island (UHI)phenomenon which has direct and indirect
effects.
• Several studies have correlated the elements of urbanization
with increases in land surface temperature (LST), a key
factor in the urban heat island (UHI)
1 http://esa.un.org/unpd/wup/index.htm Oct., 30, 2010, U.N. Department of Economic and Social Affairs
2 http://nrs.fs.fed.us/units/urban/ Oct., 30, 2010, USDA, Forest Service

4. Think of a square meter of grass, and of
asphalt in the summer sun.
Which would you prefer to stand on?
Why?

5. The radiative properties of a substance
determine what happens to the Sun’s energy.
Is it reflected? Albedo
grass – more reflective
asphalt – less reflective
Is it transmitted?
Emissivity
Is it absorbed? grass – higher emissivity
asphalt - slightly lower
emissivity

6. What is the heat capacity?
grass – low
asphalt – high
What is the thermal conductivity?
grass – low
asphalt - high

7. Heat balance equation -
Rn + F = H + G + A + LE
Rn is net all-wave radiation
F is artificial and anthropogenic heat
generated within the urban area
H is the convective sensible heat transfer
G is net heat storage within the urban fabric
(buildings, roads, soil, etc.)
A is net advected energy
LE is the latent heat transfer
From Chandler, T.J., (1976) Urban Climatology and its
Relevance to Urban Design, WMO publication

8.  Urban Heat Island (UHI)– General term for the difference in air
temperature between rural and urban areas. Usually measured at
“screen-level”
 Urban Canopy Layer Heat Island – Increased air temperature
between the ground to about building height
 Urban Boundary Layer Heat Island – Increased urban air
temperature of the planetary boundary layer above the canopy
layer
 Surface Urban Heat Island (SUHI) – Urban to rural difference in the
land surface temperature. This is the focus of the thesis
 Micro Urban Heat Island (MUHI) – Small urban heat islands which
exist below the local scale. Associated with individual structures
or groups of structures

9. Luke Howard,1833 The Climate of London: Deduced from
Meteorological Observations Made in the Metropolis and
at Various Places Around It. In Three Volumes.
Quantified temperature differences between
metropolitan London and surrounding rural areas.
Describing the basic mechanisms of the UHI, he noted:
Royal Meteorological Society
http://www.rmets.org/cloudb
• Differences in materials of built urban areas which
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retain and reradiate thermal energy more slowly than
vegetated rural areas
• Absorption and reflection of thermal energy by vertical surfaces
of the city
• Domestic and industrial processes in urban areas produce heat
• Diminished evapotranspiration in urban areas

10.  Wilhelm Schmidt – first use of
thermometers attached to
automobiles 1920’s Austria
 Middleton & Millar – 1936
Automobile measurements to do
transects of rural to urban temperature
differences in Toronto
 Ake Sundborg – 1950 Automobile transects with point
measurements and isoline mapping. Use of statistics.
Uppsala Sweden
 J.M. Mitchell and then T.J. Chandler 1950’s & early 60’s

11.  Automobile
transects, and point
measurement on a
large scale.
Comprehensive
statistical analysis.

12. Columbia, MD study
documented growth of
an urban heat island as
a rural landscape was
developed. Used point 1968 population 1,000
data collection.
The Urban Climate, 1981
1974 population 20,000

13. T.R. Oke, 1968 – present Boundary Layer Climates, 1978
• Population dynamics and the UHI
• Energy dynamics of the UHI
http://www.geog.ubc.ca/~toke/
• Describes its relation to land surface temperature (LST) through
the surface urban heat island (SUHI)
• Remote sensing of LST ( Voogt & Oke. 2003, Thermal Remote
Sensing of Urban Climates). Use of satellite imagery to assess the
SUHI

14. • LST is an indicator of the SUHI
• Synoptic view – Captures data over a large area
simultaneously
• Satellite remote sensing data is comprehensive-
extensive archive of images
• LANDSAT 5 TM and TERRA ASTER have good enough
resolution for urban studies at 120 m2 and 90 m2

15.  Deficient analysis of (sub)tropical regions
 Methodology has traditionally relied on
transects and point data collection. RS is
coming into its own in this area, however it
can only evaluate LST
 Need for enhanced surveying, efficient, low-
cost methods of evaluating the SUHI at small
scales (MUHI) – link to mitigation and urban
planning

16. 1. Is there a discernable LST
pattern in Pinellas? If so,
what are its spatio-temporal
characteristics?
2. How do the spatio-temporal
characteristics of the LST pattern in
Pinellas correlate with impervious
surface area (ISA), vegetation (NDVI),
and land use/land cover (LULC)?

17. 3. How effective are remote sensing
techniques at assessing the LST pattern
within the study area, and can they
provide an efficient method of analyzing
spatial patterns indicative of the surface
urban heat island (SUHI)?

18. • Subtropical climate (Koppen
Cfa) areas with this climate
type have been understudied.
(Roth, 2008)
• Densely populated -
underwent a process of rapid Madeira Beach
urbanization in the last
century
• With its flat local terrain and
urbanized area, Pinellas
County is an ideal subject for
remote sensing techniques.
Downtown St. Petersburg

19.  Usedremote sensing data to create LST
images using mono-window algorithm
 Validated
with water temperature data and
normalized
 Created NDVI, ISA, and LULC images
 Statistical analysis
 Comparative analysis

20. Remote Sensing and Land Surface Temperature
• One of the most extensive archives of remote sensing imagery, Landsat Thematic
Mapper or TM has not used more due to the difficulty in completing atmospheric
correction with a single thermal band.
• Technique used by Qin, Karnieli, & Berliner. 2001, A mono-window algorithm for
retrieving land surface temperature from Landsat TM data an its application to the
Israeli-Egypt border region
• Utilizes Landsat at-sensor radiance image and parameters of land cover emissivity,
atmospheric transmittance, and mean atmospheric temperature to calculate a LST
image
Ts = [a6(1- C6- D6)+(b6(1- C6- D6)+C6+D6)T 6- D6 Ta]
Ts is surface temperature
C6 is ε6 τ6
and
D6 is (1 - τ6)[1 + (1 - ε6) τ6]
where
ε6 is Emissivity of band 6
τ6 is Atmospheric transmittance of band 6
a6 is -67.355351 (coefficient of temperature range 0 - 70˚C) (Qin et al., p. 3726)
b6 is 0.458606 (coefficient of temperature range 0 - 70˚C) (Qin et al., p.3726)
T6 is brightness temperature at sensor)
Ta is effective mean atmospheric temperature (calculated using LOWTRAN 7 model)

21. T6 ε6 Atmospheric Transmittance
At-Sensor Radiance Emissivity based calculated by MODTRAN 4
on NDVI using atmospheric data from
NWS Ruskin office
Radiosonde image from NOAA website for Ruskin:
http://www.srh.noaa.gov/tbw/?n=tampabayofficetour

22. 1. RS image acquisition
2. Atmospheric data collection
3. Construct emissivity image
4. Landsat thermal band (6)
5. Run MWA program
6. Text file for display in GIS
7. Validation
8. Normalization of multi-
temporal images

23. Land surface temperatures
(excludes water)
Mean = 30.14˚C
Min = 16.83˚C
Max = 50.99˚C
SD = 4.2076
Validated within 0.423˚C of
the water sample sites

24. Land surface temperatures
(excludes water)
Mean = 27.46˚C
Min = 12.87˚C
Max = 50.57˚C
SD = 3.8163
Normalized to 27.76˚C using a
linear regression of the three
images

25. Land surface temperatures
(excludes water)
Mean = 32.40˚C
Min = 18.03˚C
Max = 61.399˚C
SD = 3.8345
Normalized to 28.72˚C using a
linear regression of the three
images

26. • Dependent Variable – LST as derived from remote sensing images
• Independent Variables -
1)ISA 2)NDVI 3)LULC
Impervious Surface Area Normalized Difference Land use land
2009 data 2002 USGS Vegetation Index 2009 cover 2008 data

27.  Stratified random sample
• Exclude water and land outside the study area
• 3000 pixels randomly chosen
• LST
• NDVI
• Impervious/not impervious 2009 image or actual
impervious percentage for the 2001 image
• LULC based on FLUCCS level 2 coding
• Divide LULC into rural/urban types

28. LST NDVI IMPERVIOUS
LST 1 -0.580** 0.468**
NDVI -0.580** 1 -0.678**
IMPERVIOUS 0.468** -0.678** 1
** significant at the α= .01 level
0 = not impervious M=29.23˚C
LST to NDVI R2 = 0.337 1 = impervious M=32.49˚C

29.  High-density residential Rural = 23.8% Urban = 76.1%
41.8%
 Commercial & Services
8.3%
 Med-density residential
6.7%
 Recreational
5.9%
 Institutional
4.0%
 Industrial
3.3%
 Low-density residential
2.7%
 Transportation
1.5%
 Wetland (all)
9.3%
 Intertidal
5.7%
 Upland Forest
4.6%

30. Mean Rural Temperature
25.03˚C
Mean Urban Temperature
31.62˚C
LST ΔT = 6.59˚C
at 11:48 EDT on 4/8/2009

31. LST NDVI Impervious
LST 1 -0.714** 0.628**
NDVI -0.714** 1 -0.748**
Impervious 0.628** -0.734** 1
** significant at the α= .01 level
Relationship of LST to NDVI, 2001 dataset Relationship of LST to Imperviousness, 2001 and 2002
(R2=.510) datasets (R2 =.395)

32.  In 2009 and 2001 image statistically
significant negative linear correlation of
LST and NDVI
 In 2009 and 2001 image statistically
significant positive linear correlation of
LST and Imperviousness
 Mean LST varies by LULC types, with
rural land cover having generally lower
temperature than urban land cover types
at the time of image capture.

34. LST North Pinellas Transect (South of Lake Tarpon)
50
Commercial
Temperature °C
40
Brooker Creek
30
LST
20
Water
10 Barrier--Gulf of Mexico----------LD--HD Resid------------------------------Wetland-Upland------->
Island Resid Forest Forest
0
LAND COVER
LST Gulf to Bay, Clearwater Transect
50
Transportation Recreational Transportation
Temperature ° C
40
30
LST
20
10 Gulf---------------HD<Water>--HD-Rec-------HD------<-Comm-HD--WetlandHDWetlandBay
Resid Resid Resid Resid Forest Resid Forest
0
LAND COVER
LST Central Ave., St. Petersburg Transect
50 Gulf Beaches Downtown
Waterfront
Temperature ° C
40
30 LST
20
Gulf------<-HD & Water--------------HD Residential-----------------------------------Recreational
10
0
LAND COVER

35. Descriptive mapping: Local
Scale 1km up to 50 km
•Generally cooler water and
coastal temperatures.
•Temperature increases with
distance from the coast
•Southern portion of the
peninsula shows evidence of
a pronounced SUHI

36. Descriptive mapping –
Local scale
•Central Plaza in the
center of the lower portion
of the peninsula.
•Temperatures 28 – 40˚C
•Area 4 x 5km
Highly urbanized with
Commercial and high-
Density residential

37. Descriptive mapping – Micro-
Scale.
•A series of “hot” islands and cooler
park areas which create an “oasis
effect” appear across the landscape
•“Hot” islands are MUHIs as
described by Aniello et al. (1995)
Temperature gradient
Land Use Temperature
Water/Parks 22-28˚C
Residential 28-32˚C
Commercial 32-36˚C
Institutional
High-density
Residential
MUHIs 36-50˚C
(structures)

38. The park is 4˚C cooler than the
surrounding land cover types.
This creates an “oasis effect”
Cannot tell how far this may
extend to the surrounding
area. Rosenzweig et al. (2007)
found that cooling of Central
Park extended no more than 60
meters. Cannot extrapolate LST
to near-surface air temp.
Temperature gradient
Land Use Temperature
Water/Parks 22-28˚C
Residential 28-32˚C
Commercial 32-36˚C
Institutional
High-density
Residential
MUHIs 36-50˚C
(structures)

41. 14 13
12
10
10
8
8
6
4
2 2
2 1
0
industrial Institutional Power Plant Services Shopping Shopping
Mall Plaza

42.  While urbanization is at too small a scale to
directly impact global climate change, the UHI
acts to compound broader regional heating
patterns intensifying them at the local level
(Grimmond, 2007)
 Public health – intense heat and higher mortality
rates for vulnerable segments of the population:
the elderly, children under 5, people with
medical conditions
 Vector-borne diseases – malaria, encephalitis,
dengue fever

43.  Personal discomfort causing increased use of air
conditioning. This is a counterproductive adaptation
strategy. (Richardson, Otero, Lebedeva, Chan, 2009)
 Increases use of electricity 1˚C increase above 15-20˚C
threshold results in 2-4% increase in electricity demand
(Akbari et al., 2001)
 Increased electrical consumption results in burning
of more fossil-fuels
 More fossil-fuel use results in increased Carbon
emissions, intensifying the problem of global climate
change

44.  IncreasedA/C use is maladaptive, though it
may be necessary for vulnerable
individuals (Richardson, Otero, Lebedeva,
Chan, 2009)
 Mitigation should be carbon neutral
 Sincechange in land cover is a primary
factor of the UHI, modifying land cover to
increase albedo and emissivity, and
increase vegetation can mitigate the UHI

45.  Cool and green roofs
Increase albedo (reflectivity) and emissivity (ability to
reradiate thermal energy) Increase vegetation and
insulation
 Increased vegetation – urban forestry
Increase shade
Increase evapotranspiration
Decrease thermal energy storage
 Increase permeable surfaces
Increase evapotranspiration
Decrease thermal energy storage

46. Cool roof Green roof
Structure Coating or roofing Structure to hold
material growing medium and
underlying membrane
Cost $ .50 to $6.00 ft2 $10.00 ft2 and up
Maintenance Cleaning and sealing Varies
Advantages Prevents absorption of Prevents absorption of
heat heat, adds benefits of
vegetation,
Provides winter
insulation
Promoters New York City (street Chicago and Toronto
trees)

47. Tropicana field –
Structure is at
background temperature
levels of 29˚C which is
12˚C cooler than
adjacent parking lot and
14˚C cooler than nearby
school.

50.  Urban forest already comprises 20-40% of the average
North American city (Oke,1989)
 Parks appear to have limited temperature moderating
impact (Rosenzweig et al., 2007)
 Street trees may have more impact since they shade the
pavement and structures and increase evapotranspiration
(Richardson et al., 2009)
 Quantification of energy savings. Strategic placement can
effect 25-50% reduction in cooling (Parker, 1983; Meier,
1991; Akbari, 2001)
 Studies emphasize in careful placement and a neighborhood
level approach (Richardson et al., 2009)

51.  Low-cost with extensive archive
 Efficient in surveying large areas
 Has
sufficient resolution to locate MUHIs for
remediation
 When used with aerial photography can be
effective in neighborhood level surveys of
urban forestry by evaluating NDVI levels.

52. Is there a discernable LST pattern in Pinellas? If
so, what are its spatio-temporal patterns?
Yes – There are patterns at both a local and
micro-scale level. A gradient of cool coastal
areas with temperature increases toward the
interior. A pattern of MUHIs (greater than 40˚C)
and cool park areas which create an “oasis
effect” exist across the landscape. This is well
resolved at the time of satellite over-flight
(˜15:30 UTC) and appears in all images.

53. How do the spatio-temporal characteristics
of the LST pattern in Pinellas correlate with
impervious surface area (ISA), vegetation
(NDVI), and land use/land cover?
Statistically significant correlation of LST
and both NDVI and Impervious surfaces.
LULC also appears to be associated with
significantly different mean temperature
levels between rural and urban land cover
types. Transects and mapping visually
confirm spatial relationship.

54. How effective are remote sensing techniques at
assessing the LST pattern within the study area,
and can they provide an efficient method of
analyzing spatial patterns indicative of the
surface urban heat island (SUHI)?
This thesis demonstrates the ability of LANDSAT
TM sensor imagery, when processed using the
MWA to provide accurate (within 0.432˚C) LST
images. They provide sufficient resolution to
identify MUHIs for possible remediation. It is
an efficient, low-cost surveying technique when
combined with aerial photography.

55.  Since human modification of land cover is
responsible for the UHI, it can be mitigated.
 Mitigation is worthwhile due to its effects on health,
comfort, and energy use.
 Direct benefits of mitigation are reduction in air
conditioning, and energy use. There are also indirect
benefits in reduced fossil-fuel use and carbon
emissions
 These changes can be made at the neighborhood
level and remote sensing provides an efficient, low-
cost method of identifying MUHIs for mitigation

56. Questions?