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?