Assessing the effects of land use and land cover patternson thermal conditions using landscape metrics in cityof Indianapolis, United States

Qihao Weng & Hua Liu & Dengsheng Lu

Published online: 20 March 2007# Springer Science + Business Media, LLC 2007

Abstract Direct applications of remote sensing thermal infrared (TIR) data in landscapeecological research are rare due to limitations in the sensors, calibration, and difficulty ininterpretation. Currently there is a general lack of methodology for examining therelationship between land surface temperatures (LST) derived from TIR data and landscapepatterns extracted from optical sensors. A separation of landscapes into values directlyrelated to their scale and signature is a key step. In this study, a Landsat ETM+ image ofIndianapolis, Unites States, acquired on June 22, 2000, was spectrally unmixed (usingspectral mixture analysis, SMA) into fraction endmembers of green vegetation, soil, highalbedo, and low albedo. Impervious surface was then computed from the high and lowalbedo images. A hybrid classification procedure was developed to classify the fractionimages into seven land use and land cover (LULC) classes. Using the fractional images, thelandscape composition and pattern were examined. Next, pixel-based LST measurementswere correlated with the landscape fractional components to investigate LULC basedrelationships between LST and impervious surface and green vegetation fractions. Anexamination of the relationship between the LULC and LST maps with landscape metricswas finally conducted to deepen understanding of their interactions. Results indicate thatSMA-derived fraction images were effective for quantifying the urban morphology and forproviding reliable measurements of biophysical variables. LST was found to be positivelycorrelated with impervious surface fraction but negatively correlated with green vegetationfraction. Each temperature zone was associated with a dominant LULC category. Furtherresearch should be directed to the theoretical and applied implications of describing suchrelationships between LULC patterns and urban thermal conditions.

Urban Ecosyst (2007) 10:203–219DOI 10.1007/s11252-007-0020-0

Q. Weng (*) : H. LiuDepartment of Geography, Geology, and Anthropology, Indiana State University,Terre Haute, IN 47809, USAe-mail: qweng@indstate.edu

D. LuSchool of Forestry and Wildlife Sciences, Auburn University,602 Duncan Drive, Auburn, AL 36849, USAe-mail: DZL0001@auburn.edu

Keywords Land surface temperature . Land use and land cover . Spectral mixture analysis .

Landscape metrics . Urban ecology

Introduction

The urban landscape is a complex mosaic of biological and physical patches within a matrixof infrastructure, human organizations, and social institutions (Machlis et al. 1997). Urbanlandscapes become increasingly fragmented and complex with human invasion over time(Alberti and Marzluff 2004). On the other hand, landscape pattern is one of theenvironmental variables that could be largely controlled by land use planning. Recentstudies have examined the spatial heterogeneity of urban landscapes, the dynamics of urbanlandscape patterns, and the effect of spatial heterogeneity on energy flow across thelandscapes (Pickett et al. 1997). Other researchers have studied the influences of urbanlandscape patterns on urban and suburban ecosystems (Lynch 1981; Jenks et al. 1996).Despite the increasing interest in urban ecology studies, several critical areas remain poorlyunderstood, such as the interactions between urban development and ecosystem dynamics,the effects of urban landscape patterns on the distribution of energy and organisms in theurban ecosystems, and the strategies for reducing urban ecological impacts (Alberti andMarzluff 2004). Landscape pattern changes associated with urbanization are importantdrivers of ecological and climatic changes at local, regional, and global levels.

In urban areas, land surface temperature (LST) is a function of several surface andsubsurface properties: albedo, emissivity, thermal properties of urban constructionmaterials, moisture, and the composition and structure of urban canopy (Goward 1981).In particular, the composition and structure of urban canopies is a crucial factor indetermining the reception and loss of radiation (Oke 1982). Because of its significance,literature has witnessed a growing interest in the relationship between LST and urbanmaterials and landscape compositions, especially between LST and vegetation abundance(e.g., Carlson et al. 1994; Gallo and Owen 1998; Gillies and Carlson 1995; Gillies et al.1997; Lo et al. 1997; Goward et al. 2002; Weng 2001; Weng et al. 2004), and between LSTand impervious surfaces (Lu and Weng 2006).

Current studies of landscape change and their linkage with environmental parametersdraw heavily upon landscape ecology (Turner 1989). In the meantime, while remotesensing has long been used in the studies of landscape dynamics, to date the use of thistechnology to characterize landscape patterns and to relate them to ecological processes hasnot been thoroughly addressed (Frohn 1998). Combining landscape ecology and remotesensing has the potential to provide a significant way to correlate urban surfacecharacteristics to biophysical parameters (such as LST) in assessing the thermal behaviorand dynamics of urban landscapes. However, to understand the dynamics of patterns andprocesses and their interactions in heterogeneous landscapes, one must be able to accuratelyquantify the spatial pattern of the landscape and its temporal changes (Wu et al. 2000). Aseries of landscape metrics have been developed and been successfully applied tocharacterize and quantify the spatial patterns of landscapes (O’Neill et al. 1988; Hunsakeret al. 1994; McGarigal and Marks 1995; Riitters et al. 1995; Gustafson 1998). Many of thestudies have examined land use and land cover (LULC) patterns derived from satelliteremote sensing data (Turner 1990). Clapham (2003), however, suggested the use ofcontinuum-based image classification, which aims to provide continuous data for the“functional classes”, provides a better approach than conventional LULC-based classi-

204 Urban Ecosyst (2007) 10:203–219

fications of satellite imagery. The latter was accused of losing both spatial resolution andstatistical information. The idea of a continuum-based classification is contained in Ridd’s(1995) vegetation-impervious surface-soil (V-I-S) model for urban landscape analysis. Thismodel has recently been successfully implemented by using the technique of spectralmixture analysis (SMA) (Ward et al. 2000; Madhavan et al. 2001; Rashed et al. 2001; Small2001; Phinn et al. 2002; Wu and Murray 2003, Lu and Weng 2004). Because reception andloss of radiation of urban surfaces correspond closely to the distribution of LULCcharacteristics, there has been a tendency to use thematic LULC data, not quantitativesurface descriptors, to describe urban thermal conditions (Voogt and Oke 2003). This trendof qualitative description of thermal patterns and simple correlations between LULC typesand their thermal signatures has slowed down the development of urban thermal remotesensing (Voogt and Oke 2003). Thus, a sub-pixel analysis based on the Ridd model mayprovide the potential for deriving urban biophysical components, and may be applicable toestablish parameters to describe urban fabrics for improving understanding of urban thermallandscapes.

The focus of this study is to examine the interplay between urban LULC and LSTpatterns by using landscape metrics. Marion County (where City of Indianapolis is located),Indiana, United States, has been chosen as the area of study. With over 0.8 millionpopulation, the city is the nation’s twelfth largest. A Landsat ETM+ image of 2000 thatcovers the City will be used in conjunction with other types of spatial data for the analysis.Specific objectives of this research are: (1) to employ spectral mixture modeling to derivelandscape fractional components, and to apply them to characterize the urban landscapes;(2) to derive LST from Landsat thermal infrared (TIR) data, and to examine the relationshipbetween LST and the landscape fractional components as a function of LULC type; and (3)to explore the relationship between the LULC and thermal patterns defined by the spatialvariations of LST. Landscape metrics will be utilized to quantify the characteristics ofLULC types falling in specific LST zones, and to relate the LST zones to the landscape.

Methods

Derivation of landscape fraction images from the satellite imagery

Landscape fraction images were derived by using linear spectral mixture analysis (LSMA).LSMA is a physically based image processing method, and assumes that the spectrummeasured by a sensor is a linear combination of the spectra of all components within thepixel (Adams et al. 1995; Roberts et al. 1998). Estimation of endmember fraction imageswith LSMA basically involves three steps: (1) image pre-processing, (2) endmemberselection, and (3) unmixing solution and evaluation of fraction images. Of these steps,selecting suitable endmembers is the most critical one in the development of high qualityfraction images. Two types of endmembers can be applied: image endmembers andreference endmembers. The former are derived directly from the image itself, while thelatter are derived from field measurements or laboratory spectra of known materials(Roberts et al. 1998). For most remote sensing applications, image endmembers are utilizedsince they are easily obtained and capable of representing the spectra measured at the samescale as the image data (Roberts et al. 1998).

In this research, a Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image (Row/Path: 32/21) dated June 22, 2000, was used. The data acquisition date has a highly clearatmospheric condition, and the image was acquired through the USGS Earth Resource

Urban Ecosyst (2007) 10:203–219 205

Observation Systems Data Center, which has corrected the radiometric and geometricaldistortions of the images to a quality level of 1 G before delivery. The Landsat image wasfurther rectified to a common Universal Transverse Mercator coordinate system based on1:24,000 scale topographic maps, and was resampled using the nearest neighbor algorithmwith a pixel size of 30 m by 30 m for all bands including the thermal band. The resultantRMSE was found to be less than 0.5 pixel.

The ETM+ bands 1–5 and 7 were used for LSMA. After the selection of endmembers, aconstrained least-squares solution was applied to decompose the ETM+ image into fourfraction images, i.e., green vegetation (GV), soil, high albedo, and low albedo (Fig. 1), andan error image. Impervious surfaces were then extracted by the addition of high- and low-albedo fraction images (Wu and Murray 2003). A detailed description of the aboveprocedures can be found in Lu and Weng (2006).

Fig. 1 Fraction images derived from spectral mixture analysis of the Landsat ETM+ image (a Greenvegetation; b Soil; c High albedo impervious surfaces; d Low albedo impervious surfaces)

206 Urban Ecosyst (2007) 10:203–219

Land use and land cover classification

The fraction images of green vegetation, soil, and impervious surface were used for LULCclassification via a hybrid procedure that combined maximum likelihood and decision treealgorithms. A total of 156 sample plots were identified from high-spatial resolution aerialphotographs, covering initially ten LULC types: commercial and industrial, high-densityresidential, low-density residential, bare soil, crop, grass, pasture, forest, wetland, andwater. The accuracy of the classified image was checked with a stratified random samplingmethod against the reference data of 150 samples collected from large-scale aerialphotographs. Seven LULC types were finally identified, including: (1) commercial andindustrial urban land, (2) residential land, (3) cropland, (4) grassland, (5) pasture, (6) forest,and (7) water (Fig. 2). An overall accuracy of 89% and a Kappa index of 0.8575 weredetermined. A comparison of SMA-based image classification performance with the moreconventional method, maximum likelihood classification of all multispectral bands of thesame image, found a significant improvement with the SMA-based method (overallaccuracy: 89% vs. 80%). Detailed procedures for LULC classification may be found in Luand Weng (2004).

Computation of LST from landsat TIR data and identification of LST zones

LSTs were derived from radiometrically and geometrically corrected ETM+ TIR band(10.44–12.42 μm) data. The ETM+ thermal band has a spatial resolution of 60 m, and thethermal imagery from Landsat 7 is generally well calibrated to ground truth data (Arvidson

Fig. 2 LULC map

Urban Ecosyst (2007) 10:203–219 207

2002, personal correspondence).1 The local time of satellite overpasses was in the morning(approximately 11:14 A.M. This was the best image available), so that the chance fordetecting a weak urban heat island (UHI) is maximized. However, satellite detection ofUHIs using TIR sensors has demonstrated that the heat island intensity is greatest in thedaytime and in warm season and least at the nighttime—the opposite to the UHI resultsbased on the measurement of air temperatures (Roth et al. 1989). The selection of the June22 image is therefore appropriate although not optimal. According to Professor Yang atSouth China Normal University, who has been studying the UHI of Guangzhou, China, fordecades using weather station and in situ data, air temperature difference between the urbanand the rural peaks at 21:00 P.M., and goes down gradually until it reaches a minimum at14:00 P.M. (Yang et al. 1984). Although the impact of the diurnal heating cycle on UHIswould be an interesting issue to address, it was not our attempt to address it here becauseLandsat ETM+ does not provide day and night infrared images on the same day. Moreover,because absolute temperatures were not used for the purpose of computation, the effects ofatmosphere and surface roughness on LST were not taken into account in this study. Lackof atmospheric correction may introduce a temperature error of 4–7°C for the mid-latitudesummer atmosphere (Voogt and Oke 1998). The magnitude of atmospheric correctiondepends upon image bands used as well as atmospheric conditions and the height ofobservation. However, we believe that the horizontal variation was minimized, because thisstudy used an image acquired on a highly clear day and covering a small area. Errors due tourban effective anisotropy depend upon surface structure and relative sensor position, andcan yield a temperature difference of up to 6 K or higher in downtown areas (Voogt andOke 1998). Satellite derived LSTs are believed to correspond closely with the canopy layerheat islands, although a precise transfer function between the LST and near ground airtemperature is not yet available (Nichol 1994). Byrne (1979) observed a difference of asmuch as 20°C between the air temperature and the warmer surface temperature of dryground.

The procedure to develop the surface temperature involves three steps: (1) converting thedigital number of Landsat ETM+ band 6 into spectral radiance; (2) converting the spectralradiance to at-satellite brightness temperature, which is also called blackbody temperature;and (3) converting the blackbody temperature to land surface temperature. A detaileddescription for developing the temperature image can be found in Weng et al. (2004).Figure 3 shows the distribution of LST values in Indianapolis. The radiant temperatureranged from 289.63 to 319.02 K with a mean of 302.14 K and standard deviation of 3.24.This choropleth map was produced based on the classification scheme of standarddeviation, in which the data values of LST Zone I are less than two standard deviationbelow the mean, Zone II values fall between two and one standard deviation below themean, Zone III are between the mean and one standard deviation below the mean, and soon. The standard deviation and quartile methods are most effective with normallydistributed data (Smith 1986). Spatial metrics were computed for this image to identifythe spatial characteristics of each LST zone.

Landscape metrics computation

Five class-based and two landscape-based spatial metrics were computed for the LULCimage and LST image. FRAGSTATS, a software program designed to compute a wide

1 Landsat 7 Senior Systems Engineer, Landsat Project Science Office, Goddard Space Flight Center,Washington, D.C.

208 Urban Ecosyst (2007) 10:203–219

variety of landscape metrics for categorical map patterns, was selected for use (McGarigaland Marks 1995). Table 1 lists the landscape metrics used in this study, their definitions,and equations, which include percentage of landscape (PLAND), mean patch size (MN),perimeter-area fractal dimension (PAFRAC), aggregation (AI), patch cohesion index(COHESION), contagion index (CONTAG), and Shannon’s diversity index (SHDI). Theselection of these metrics is based on a research result by Riitters et al. (1995).

Percentage of landscape measures the percentage of each LULC class in the totallandscape area. It quantifies the proportional abundance of each patch type in the landscapeand is a measure of landscape composition, which is important in many ecologicalapplications, and for comparing among landscapes of varying sizes (McGarigal et al. 2002).

Mean patch size measures the average area of all patches for an individual LULC class.Mean patch size is a major index of urban landscapes that affects biomass production,nutrition storage, species composition and diversity (Wang et al. 1999), which inevitablyaffect LST patterns.

Aggregation index is calculated from an adjacency matrix, which shows the frequencywith which different pairs of patch types (including like adjacencies between the samepatch types) appear side-by-side on the map. Aggregation index is one of the landscapemetrics revealing the texture of a landscape, an important aspect for many ecologicalprocesses. The subdivision of a patch type of course plays a crucial role in the process ofhabitat fragmentation (McGarigal et al. 2002). As habitat fragmentation proceeds, habitat

Fig. 3 Geographical distributionof land surface temperaturezones

Urban Ecosyst (2007) 10:203–219 209

Tab

le1

Selectedlandscapemetrics,descriptions,andequatio

ns(M

cGarigal

etal.20

02)

Metric

Abbreviation

Definition

Equation

Percentage

ofland

scape

PLAND

Propo

rtional

abun

dance

ofaclass

PLAND¼

Pi¼

100*Pn j¼

1a ij

!,

A

Pi:proportio

nof

thelandscapeoccupied

bypatchtype

i.a ij:area

(m2)of

patchij.

A:totalland

scapearea

(m2).

Meanpatch

size

MN

Average

size

ofon

eclass

MN¼Pn j¼

1Xij

,

n i

Xij:patchareasof

thesametype

n:thenu

mberof

patchesof

thesametype

Perim

eter-area

fractal

dimension

PAFRAC

Shape

indexbased

onperimeter

and

area

measurement

PAFRAC¼

2=ff

½ni*Pn j¼

1ðln

Pij*ln

a ij�

�½ðP

n j¼1ln

PijÞðP

n j¼1ln

a ij�g=fn

i*Pn j¼

1ln

ðPijÞ2 �

�ðPn j¼

1ln

PijÞ2 g

g

a ij:area

(m2)of

patchij.

Pij:perimeter

(m)of

patchij.

n i:nu

mberof

patchesin

thelandscapeof

patchtype

i.

Agg

regatio

nAI

Con

tagion

index.

The

focalclass,no

tadjacencieswith

other

patchtypes.

AI¼

g ij�max

�gii

�� *

100

g ij:nu

mberof

likeadjacencies(joins)betweenpixelsof

patchtype

(class)ibasedon

thesing

le-cou

ntmetho

dmax-g

ii:maxim

umnu

mberof

likeadjacencies(joins)betweenpixelsof

patchtype

(class)i(see

below)based

onthesing

le-cou

ntmethod.

Patch

Cohesion

Index

COHESIO

NConnectivity

index.

The

physical

connectedness

ofthecorresponding

patchtype

COHESIO

N¼

½1�ðPn j¼

1PijÞ=Pn j¼

1ðPij*ffiffiffiffiffi

a ij

pÞ�ð

1�1=

ffiffiffi Ap�

1*1

00

Pij:perimeter

ofpatchijin

term

sof

numberof

cellsurfaces.a ij:area

ofpatchijin

term

sof

numberof

cells.

A:totalnu

mberof

cells

intheland

scape.

Con

tagion

Index

CONTA

GAgg

regatio

nof

allpatch

types

CONTAG¼

f1þPm i¼

1

Pm k¼1

½Pi*ðg

ik=Pm k¼

1

g ik�*

ðlnPiðg

ik=Pm k¼

1

g ikÞÞ�

2lnm

g*10

0

Pi:proportio

nof

thelandscapeoccupied

bypatchtype

i.g ik:numberof

adjacenciesbetweenpixelsof

different

patchtypes.k:

basedon

thedouble-count

method.

m:nu

mberof

patchtypespresentin

the

landscape,

includingtheland

scapebo

rder

ifpresent.

Shann

on’s

Diversity

Index

SHDI

Adiversity

inthewho

lecommunity

ecolog

y

SHDI¼

�Pm i

Pi*lnPi

ðÞ

Pi=proportio

nof

theland

scapeoccupied

bypatchtype

i

210 Urban Ecosyst (2007) 10:203–219

contagion decreases, habitat subdivision increases, and eventually ecological function isimpaired (Saunders et al. 1991).

The patch cohesion index measures the physical connectedness of the correspondingpatch, and has a close relationship with habitat fragmentation. Patch cohesion increases asthe patch type becomes more clumped or aggregated in its distribution; hence, morephysically connected (McGarigal et al. 2002). Physical connectivity of patch types (orhabitat) may facilitate or impede ecological flows, and has a direct relationship with thefunctional connectedness of the landscape as perceived by an organism (McGarigal et al.2002). This metric was calculated to examine how the connectivity of LULC and that ofthermal landscape were related to each other.

Contagion refers to the probability that two randomly chosen adjacent cells belong to thesame LULC class. Landscapes with big and contiguous patches generally have largercontagion values, while those with smaller and more dispersed patches have lowercontagion values (McGarigal et al. 2002). This metric was applied to test the possibility ofaggregation of LULC patterns falling in a single LST zone.

Shannon’s diversity index is a popular measure of diversity in community ecology,applied here to both LULC and urban thermal landscapes. This metric is more sensitive torichness than evenness. The former refers to the number of patch types present, while thelatter to the distribution of area among different types. Richness and evenness are generallyreferred to as the compositional and structural components of diversity, respectively(McGarigal et al. 2002). The Shannon’s index has been widely applied to measurelandscape composition (O’Neill et al. 1988; Turner 1990).

Results

Landscape composition and patterns

Landscape compositions and patterns can be examined based on the V-I-S model usingimage fractions extracted from SMA. Pixel values of a fraction image represent the arealproportions of each biophysical descriptor within a pixel. GV fraction image shows a largedark area (low values) at the center of the study area that corresponds to the central businessdistrict of Indianapolis City. Bright areas of high GV values were found in the surroundingareas. Various types of crops were still at the early stage of growth or were beforeemergence, as indicated by medium gray to dark tone of the GV fraction image in thesoutheastern and southwestern parts of the city. Table 2 displays GV fraction values byLULC type. Forest had the highest GV fraction values (0.715), followed by grassland(0.370). In contrast, commercial and industrial land displayed the lowest GV values (0.119).Little vegetative amount was found in water bodies, as indicated by the GV fraction value(0.161). Residential land, pasture, and cropland yielded an intermediate level of GVfraction value around 0.25. Residential land had a bit higher value than pasture andcropland. However, the latter exhibited the largest standard deviation value, suggesting thatcropland may hold various amount of vegetation coverage.

The percentage of land covered by impervious surfaces varies significantly with LULCcategories and sub-categories (Soil Conservation Service 1975). This study shows asubstantially different estimate for each LULC type, as this study applied a spectralunmixing model to the remote sensing images, and the modeling introduced some errors.For example, a negative impervious fraction estimate was found in water. Generallyspeaking, a LULC type with a higher GV fraction appeared to have a lower impervious

Urban Ecosyst (2007) 10:203–219 211

fraction. The highest impervious coverage was discovered in commercial and industrialland with a value of 0.491 (Table 2). Residential land came in second, with a fraction valueof 0.254. Grassland, pasture, and cropland detected a lower value of impervious surfaceranging from 0.11 to 0.14. Forestland received a minimal impervious fraction value below 0.1.

Soil fraction estimates were generally low in the majority of the urban area, but high inthe surrounding areas. Especially, in agricultural fields in the southeastern and southwesternparts of the city, soil fraction image was very bright since various types of crops were still atthe early stage of growth. Table 2 shows that both pasture and cropland have a high fractionvalue (pasture: 0.373; cropland: 0.3). Grassland possessed a medium fraction value of0.211. Built-up lands, including residential, commercial and industrial land, displayedsubstantially lower soil fractions with 0.147 and 0.101, respectively. A minimal amount ofsoil was detected in forestland (fraction: 0.01). Water had a negative fraction value. LikeGV fraction, soil fraction displayed the highest standard deviation value in cropland due tovarious amount of emerged vegetation.

Relationship between LST and landscape fractional components

Previous studies have demonstrated that LULC changes, especially urban development, canalter LST patterns (Lo et al. 1997; Weng 2001). Since changes in LULC would lead tochanges in the composition of image fractions, the magnitude and spatial distribution ofeach fraction image should be related to LST patterns. Correlation analysis was conductedbetween the LST map with GV and impervious surface images using all pixels in theimages as observation units. The significance of each correlation coefficient wasdetermined using a one-tail Student’s t-test. Results indicate that LST was positivelycorrelated with impervious surface fraction (coefficient=0.57) but negatively correlate withGV fraction (coefficient=−0.46), both significant at 0.05 level. If negative values for eachGV and impervious surface fraction were set to zero, the associations between LST and thetwo fractions would become closer. Correlation coefficients between LST and impervioussurface would reach 0.58, whereas the relationship between LST and GV fraction wouldimprove to a higher level of negative correlation with a coefficient of −0.52. Theserelationships of LST with GV and impervious surface fractions follow the well-knownprinciples of how vegetation cover and impervious surface contribute to the surface energy

Table 2 Statistics of the biophysical descriptors by LULC type, and their correlations with LST (significantat 0.05 level)

Land use/cover type

Mean LST(standarddeviation)

Mean greenvegetation(standarddeviation)

Mean impervioussurface (standarddeviation)

Mean soilfraction(standarddeviation)

LST/greenvegetation

LST/impervioussurface

Commercialand industrial

305.3 (3.10) 0.119 (0.14) 0.491 (0.226) 0.101 (0.234) −0.6559 0.5254

Residential 303.8 (1.94) 0.276 (0.155) 0.254 (0.098) 0.147 (0.144) −0.6763 0.5373Cropland 299.5 (1.28) 0.248 (0.333) 0.129 (0.045) 0.3 (0.252) −0.7538 0.5558Grassland 300.5 (1.56) 0.37 (0.198) 0.112 (0.045) 0.211 (0.145) −0.3760 0.4742Pasture 299.5 (1.04) 0.258 (0.166) 0.138 (0.097) 0.373 (0.146) −0.4105 0.5890Forest 298.2 (1.37) 0.715 (0.104) 0.057 (0.030) 0.01 (0.077) −0.7343 0.3267Water 298.2 (4.43) 0.161 (0.178) −0.029 (0.090) −0.109 (0.156) −0.2416 0.3538

212 Urban Ecosyst (2007) 10:203–219

balance. As impervious surface is usually inversely related to vegetation cover in urbanareas, LST tends to increase as vegetation cover decreases and impervious cover increasesin a pixel.

To better understand the relationship between LST and urban surface biophysicaldescriptors, the statistics of LST, impervious fraction, and vegetation fraction by LULCtype were obtained by superimposing LULC image with the images of LST, impervious,and GV fractions. The result of the GIS overlays is shown in Table 2. It is clear thatcommercial and industrial land exhibited the highest temperature, followed by residentialland. The lowest temperature was observed in forest, followed by water bodies. Thisimplies that urban development brought up LST by approximately 6 K by replacing naturalenvironment (forest and water) with commercial, industrial, or residential uses. Thestandard deviation value of LST was large for commercial and industrial land, indicatingthat these surfaces experienced a wide variation in LST because of different constructionmaterials and the possibility to contain much larger buildings and a wide range of buildingsizes within the IFOV (instantaneous field of view). In contrast, the standard deviationvalue of LST was relatively small for residential land owing to their homogeneity.Residential land also possessed a smaller mean value than commercial and industrial land,where buildings were frequently mixed with forest and grassland. Grassland had anintermediate level of LST, as it had sparse vegetation and exposed bare soil. Similarly,pasture and agricultural land had an intermediate level of LST. Forests showed aconsiderably lower LST, because dense vegetation can reduce amount of heat stored inthe soil and surface structures through transpiration, evaporation, and shading. Allvegetative cover, regardless of natural or man-made, exhibited an extremely smalltemperature variation. Water tends to get warm slowly during the summer owing to itsrather high thermal inertia, and to convection and turbulence (e.g., wave action). Because ofdistinctive characteristics of rivers, lakes, reservoirs, and ponds, the LST values of waterbodies vary, leading to a large standard deviation value.

The demonstrated relationship between LULC and the three biophysical parameters,LST, GV, and impervious surface fraction, encourages us to investigate the interplay ofthese environmental variables within each LULC type. A pixel-by-pixel correlation analysiswas conducted by computing Pearson’s correlation coefficients between LST and GVfraction, and between LST and impervious surface fraction. Results are displayed in the lasttwo columns of Table 2. For all LULC types, LST values were negatively correlated withGV fraction values, but were positively correlated with impervious fraction values. Thestrongest negative correlation existed between LST and GV fraction values in agriculturalland and forest. The correlation coefficient values dropped slightly for residential, andcommercial and industrial land, with a sharp decrease for pasture and grassland. The leastcorrelation was found in water. On the other hand, the highest positive correlation betweenLST and impervious fraction values was found in pasture and agricultural land, followed byresidential land and commercial and industrial land. Grassland exhibited a moderatelysignificant correlation. The lowest correlations were observed in forestland and water.

The LULC and LST relationship by analysis of landscape metrics

Figure 4 shows the area percentages of pixels for each LULC category falling in eachtemperature zone. Each temperature zone was associated with a dominant LULC category.LST Zone I mainly consisted of water (92.09%), while Zone II was largely related tovegetation, including forest (33.09%), agriculture (24.23%), and grass (23.53%). Grass wasthe dominant type in LST Zone III, since it possessed 45% of the area, a value that was

Urban Ecosyst (2007) 10:203–219 213

larger than the sum of residential land and agriculture. Similarly, it is evident that residentialland was the dominant type in LST Zone IV (72.38%), and commercial and industrial landin Zone VI (86.30%). Zone V related to both residential land and commercial and industrialland. In other words, residential land had the highest area percentages in Zones IV and V,while commercial and industrial land had very high percentages in both Zones V and VI.

Table 3 gives a summary of metrics for each LULC type. It indicates that water possessedthe lowest value in percentage of landscape (PLAND) and a median mean patch size. Whilewater had the lowest value in the shape index, PAFRAC, it had the highest value in theaggregation index (AI), suggesting a least shape complexity and maximum aggregation.Moreover, water had a relatively higher value in connectivity index, COHESION. Comparingwith the metrics of LST Zone 1, there were many similarities. LST Zone 1 also yielded thelowest percentage of landscape, median mean patch size, relatively low shape complexity,relatively high physical connectivity, and the highest contagion.

Forest, agriculture, grass, and pasture all had median patch percentages and mean patchsizes, but relatively high shape complexities, especially for agriculture. Agriculture alsoshowed the lowest aggregation, but a relatively high physical connectivity in the study area.Table 3 indicates that LST Zone II held a relatively low percentage of landscape, butrelatively high mean patch size. It further shows that Zone 2 had a relatively low shapecomplexity while a high value in aggregation and physical connectivity. Since Zone II wasmainly associated with forest, agriculture, and grass, an explanation of its landscapecharacteristics may be sought based on the combination of these three dominant LULC types.

Grass and pasture may both be considered as significant LULC classes in Zone III.Although pasture only possessed 5.36% area in Zone III, its influence on LST configurationcan still be seen. Table 3 shows that both grass and pasture possessed a median value inpercentage of area, mean patch size, and physical complexity, and in contrast, a lowcontagion and high connectivity. When relating them to the metrics of LST Zone III, whichdisplayed a relatively high area percentage, complexity, and physical connectivity, a medianmean patch size and low contagion, it is evident that both grass and pasture were closelycorrelated with Zone III.

Fig. 4 Area percentage of each LULC category falling within each LST zone

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Residential land had the highest values in percentage of landscape, mean patch size,shape index, and connectivity index, suggesting a relatively complicated shape and highspatial connectivity. It also possessed a high aggregation. Accordingly, LST Zone IV, whichwas largely in residential land (72.38%), showed many similarities in landscape metrics asthose of residential land.

Commercial and industrial land may be regarded as the single dominant LULC type inLST Zone VI. However, the obvious decreases in percentage of landscape and shapecomplexity from commercial and industrial land to Zone VI indicate that other LULCtypes, especially residential land, also played an important role in determining thelandscape characteristics of Zone VI, although it covered only a limited area. Zone VI wasless complicated and connected, but more aggregated than commercial and industrial land.This implies that little connectivity occurred between commercial and industrial land andother LULC types of high LST.

Discussion

A possible explanation for the similarities in landscape metrics between water and LSTZone I is that LST Zone I was primarily associated with water. Although Zone I alsoincluded 4.66% of commercial and industrial land and 1.57% of forest, they did notsignificantly influence the landscape characteristics of Zone I. However, slight differencescan still be detected when comparing the landscape metrics of water and those of LST ZoneI side-by-side. LST Zone I had a bigger mean patch size, because of the incorporation ofcommercial and industrial land and forest. The higher values in physical aggregation andconnectivity for LST Zone I further demonstrated the effect of this incorporation, whereasthe larger shape complexity (as manifested by perimeter-area fractal dimension) resultedfrom more complicated spatial configurations in commercial and industrial land and forest.

All four vegetated LULC classes had a relatively high shape complexity. The fact thatagriculture possessed the highest value of perimeter-area fractal dimension suggests that theimpact of human activities was strong on the pattern of agricultural land. This is anindication that the fields were small and adjoining fields had different characteristics. The

Table 3 Metrics of LULC types and LST zones

Landscape category PLAND MN PAFRAC AI COHESION

Commercial and industrial land 13.97 1.66 1.40 74.40 96.90Residential land 36.77 3.64 1.55 73.45 99.64Cropland 10.06 1.24 1.55 63.16 92.44Grassland 21.62 1.07 1.48 63.71 95.05Pasture 2.81 0.94 1.47 63.06 87.91Forest 6.40 0.91 1.43 65.73 91.54Water 2.62 1.21 1.33 76.50 90.27LST Zone I 1.03 3.96 1.35 88.02 94.55LST Zone II 16.29 5.17 1.37 84.66 97.48LST Zone III 30.75 4.48 1.44 79.47 98.98LST Zone IV 30.99 7.93 1.46 80.61 99.49LST Zone V 12.60 4.14 1.46 79.42 96.69LST Zone VI 2.58 2.02 1.31 77.79 89.40

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higher values in physical aggregation and connectivity in LST Zone II may be attributed tothe integration of these vegetated LULC classes in the temperature zone.

Since residential land, agriculture, and commercial and industrial land accounted for44.54% of area in LST Zone III, they influenced the metrics of Zone III. Residential landshared 36.77% in the area of Zone III. The high mean patch size of residential landcertainly contributes to the metrics of Zone III such as the mean patch size. The lower valueof shape complexity for Zone III suggests that the integration of grass, residential land, andagriculture in the temperature zone decreased the physical complexity while it increased thephysical aggregation. The higher values in landscape contagion and physical connectivityin LST Zone III further confirmed the above observation.

Slight differences in landscape metrics between residential land and LST Zone IV may bedetected. First, residential land displayed a higher percentage of landscape than that of ZoneIV, which comprised of residential, grass land (11.94%), commercial and industrial land(12.93%), and other LULC classes (2.75% in total). The larger mean patch size of LST ZoneIV was a result of the combination between residential land and other LULC types. The lesscomplex but more spatially aggregated LST zone substantiated this observation. Finally, thelow value of physical connectivity in Zone IV indicated that residential land was lessconnected with commercial and industrial land/grassland in the temperature zone.

The vast majority of LST Zone V was composed of residential land and commercial andindustrial land (98.04%). Both classes displayed high values in percentage of landscape, meanpatch size, shape and connectivity indices, and landscape contagion. However, LST Zone Vshowed lower values in percentage of landscape, and mean patch size. A possible explanationfor these low values may be that the commercial and industrial land and residential land fallingin Zone V became less physically connected, but much more spatially aggregated.

The above discussions are based on some landscape metrics that can be derived byanalysis of LULC categories, and how these metrics can relate to LST patterns from onesatellite image. It is not clear from this one brief example what the correlations explainabout urban land surface temperature patterns in general. However, these metrics may be ofuse in comparing LULCs and LSTs for different urban areas at different times. Byconducting more empirical studies at various urban locations, we may be able to generalizeabout the LULC-LST relationship from the perspective of landscape ecology. It is alsoworthy to note that landscape metrics may vary with the spatial resolution of satelliteimages (Frohn 1998). For example, fractal dimension has inherent problems with itsregression estimation and does not always give consistent results for landscapes withpredictable geometric shapes, due to its ignorance of the perimeter/area relationship forraster data structures (Frohn 1998, p. 18). Similarly, the contagion index is based on pixeladjacency proportions, and is thus dependent upon the image resolution. The contagionequation measures not only clumping but also diversity, which could lead to biasedestimation (Frohn 1998). Our current research includes examining the sensitivity oflandscape patterns (LULC categories and LST zones) to pixel aggregation (ranging from 15to 1,100 m), and the degree of agreement between the two landscape structures at everylevel of spatial resolution.

Conclusions

This study has successfully developed a methodology to examine the relationship betweenLST and urban LULC patterns using remote sensing and landscape ecological methods.SMA-derived fraction estimates can be used as urban landscape fractional components to

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relate to pixel-based LST measurements, because fractional components represent thespectra measured at the same scale as the image data. An examination of the linkagebetween the two types of remote sensing data using landscape metrics made it possible for abetter understanding of the patterns and processes and their interactions in a heterogeneouslandscape. This knowledge is important for planning and management practices. Plannersmay use the results of such a study to indicate the need for new or revised urban design andlandscaping policies for mitigating the adverse thermal effects (Nichol 1996). Land usezoning, as a tool of urban planners, has a profound impact on the thermal conditions andbehavior of urban landscapes by imposing such restrictions as maximum building heightand density, the extent of impervious surface, open space, land use types, and activities. Apotential application of the relationship between LST and vegetation abundance has beendemonstrated recently for examining low-impact (in which development leads to a highlevel of greenness and a low level of LST) and high-impact (in which development resultsin a low level of greenness and a high level of LST) development within a particular zoningcategory (Wilson et al. 2003). Further research is warranted to interpret the theoretical andapplied implications of the relationship between urban LULC patterns and thermalconditions from a landscape ecological perspective.

Remote sensing TIR data have been applied to examine LST-vegetation abundancerelationship (Weng et al. 2004), and the response of LST to surface soil moisture andvegetation cover (Owen et al. 1998). However, it remains rare for direct applications of TIRdata in landscape ecological research, as TIR data seem to be too coarse, and calibration ofthese data for deriving measurements of landscape thermal energy fluxes may beproblematic (Quattrochi and Luvall 1999). Technically, it remains difficult to interpretTIR data, because inadequate methods are currently available for determining surfaceemissivity (Conway 1997), a key piece of information for accurate measurement of LST.Another important issue in interpreting TIR data of urban areas lies in the mismatchbetween the observational scale of satellite imaging (i.e., IFOV) and the operational scale ofa landscape that governs the landscape patterns and environmental processes. Our previousstudy indicated that the operational scale of the thermal landscape in Indianapolis wasaround 120 m, roughly the length of a city block. The operational scale was found to agreewith the scale suggested by Schmid (1988), who suggested that the directional variations ofthermal radiance (i.e., effective anisotropy) could remain constant over a range of scales,which correspond to areas of similar urban structures that generate the regionalhomogeneity of LST, but the lower bound of the range may be 25 m (street/alley to houserow spacing), 50 m (street to alley spacing), and up to 200 m in diameter. Urban surfaceanisotropy is expected to stay relatively constant as scales increase up to the limit whereground resolution begins to cover different LULCs or surface structures, and the scaleranges from approximately 12–1,000 m (Voogt and Oke 1998).

Acknowledgments This research is supported by National Science Foundation (BCS-0521734) for aproject entitled “Role of Urban Canopy Composition and Structure in Determining Heat Islands: A Synthesisof Remote Sensing and Landscape Ecology Approach”. We would also like to thank Associate Editor,Dr. Gordon M. Heisler, and three anonymous reviewers for their constructive comments and suggestions.

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