ma thesis presentation
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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
Introduction – Urbanization and UHI
Literature Review
Research Questions
Study Area - Pinellas County
Methods
Results
Mitigation Strategies - cool roofs/urban
forestry
Conclusions
• 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 Affairs2 http://nrs.fs.fed.us/units/urban/ Oct., 30, 2010, USDA, Forest Service
Think of a square meter of grass, and of
asphalt in the summer sun.
Which would you prefer to stand on?
Why?
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
What is the heat capacity?
grass – low
asphalt – high
What is the thermal conductivity?
grass – low
asphalt - high
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
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
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:
• Differences in materials of built urban areas which
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
Royal Meteorological Society
http://www.rmets.org/cloudb
ank/detail.php?ID=104
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
Automobile
transects, and point
measurement on a
large scale.
Comprehensive
statistical analysis.
Columbia, MD study
documented growth of
an urban heat island as
a rural landscape was
developed. Used point
data collection.
The Urban Climate, 1981
1968 population 1,000
1974 population 20,000
T.R. Oke, 1968 – present Boundary Layer Climates, 1978
• Population dynamics and the UHI
• Energy dynamics of the UHI
• 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
http://www.geog.ubc.ca/~toke/
• 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
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
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)?
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)?
• Subtropical climate (KoppenCfa) areas with this climate type have been understudied. (Roth, 2008)
• Densely populated -underwent a process of rapid urbanization in the last century
• With its flat local terrain and urbanized area, Pinellas County is an ideal subject for remote sensing techniques.
Madeira Beach
Downtown St. Petersburg
Used remote 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
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
imageTs = [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)
Radiosonde image from NOAA website for Ruskin:
http://www.srh.noaa.gov/tbw/?n=tampabayofficetour
T6
At-Sensor Radiance
ε6
Emissivity based
on NDVI
Atmospheric Transmittance
calculated by MODTRAN 4
using atmospheric data from
NWS Ruskin office
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
Land surface temperatures
(excludes water)
Mean = 30.14˚CMin = 16.83˚CMax = 50.99˚CSD = 4.2076
Validated within 0.423˚C of the water sample sites
Land surface temperatures
(excludes water)
Mean = 27.46˚CMin = 12.87˚CMax = 50.57˚CSD = 3.8163
Normalized to 27.76˚C using a linear regression of the three images
Land surface temperatures
(excludes water)
Mean = 32.40˚CMin = 18.03˚CMax = 61.399˚CSD = 3.8345
Normalized to 28.72˚C using a linear regression of the three images
• 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
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
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
LST to NDVI R2 = 0.337
0 = not impervious M=29.23˚C1 = impervious M=32.49˚C
High-density residential41.8%
Commercial & Services8.3%
Med-density residential6.7%
Recreational5.9%
Institutional4.0%
Industrial3.3%
Low-density residential2.7%
Transportation1.5%
Wetland (all)9.3%
Intertidal5.7%
Upland Forest4.6%
Rural = 23.8% Urban = 76.1%
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
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
(R2=.510)
Relationship of LST to Imperviousness, 2001 and 2002
datasets (R2 =.395)
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.
0
10
20
30
40
50
Tem
pera
ture
°C
LAND COVER
LST North Pinellas Transect (South of Lake Tarpon)
LST
Barrier--Gulf of Mexico----------LD--HD Resid------------------------------Wetland-Upland-------> Island Resid Forest Forest
Commercial
Water
Brooker Creek
0
10
20
30
40
50
Te
mp
era
ture
°C
LAND COVER
LST Central Ave., St. Petersburg Transect
LST
Gulf------<-HD & Water--------------HD Residential-----------------------------------Recreational
Gulf Beaches Downtown Waterfront
0
10
20
30
40
50
Te
mp
era
ture
°C
LAND COVER
LST Gulf to Bay, Clearwater Transect
LST
Gulf---------------HD<Water>--HD-Rec-------HD------<-Comm-HD--WetlandHDWetlandBayResid Resid Resid Resid Forest Resid Forest
Transportation TransportationRecreational
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
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
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
Institutional
High-density
Residential
32-36˚C
MUHIs
(structures)
36-50˚C
Temperature gradient
Land Use Temperature
Water/Parks 22-28˚C
Residential 28-32˚C
Commercial
Institutional
High-density
Residential
32-36˚C
MUHIs
(structures)
36-50˚C
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.
10
8
12 2
13
0
2
4
6
8
10
12
14
industrial InstitutionalPower Plant Services Shopping
Mall
Shopping
Plaza
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
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
Increased A/C use is maladaptive, though it may be necessary for vulnerable individuals (Richardson, Otero, Lebedeva, Chan, 2009)
Mitigation should be carbon neutral
Since change 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
Cool and green roofsIncrease albedo (reflectivity) and emissivity (ability to reradiate thermal energy) Increase vegetation and insulation
Increased vegetation – urban forestryIncrease shadeIncrease evapotranspiration
Decrease thermal energy storage
Increase permeable surfacesIncrease evapotranspirationDecrease thermal energy storage
Cool roof Green roof
Structure Coating or roofing
material
Structure to hold
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
heat
Prevents absorption of
heat, adds benefits of
vegetation,
Provides winter
insulation
Promoters New York City (street
trees)
Chicago and Toronto
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.
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)
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.
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.
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.
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.
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
Questions?
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