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URBAN MORPHOLOGICAL ANALYSIS FOR MESOSCALE METEOROLOGICALAND DISPERSION MODELING APPLICATIONS: CURRENT ISSUES

Steven J. Burian1*, Michael J. Brown2, Jason Ching3, Mang Lung Cheuk4,May Yuan4,WooSuk Han1, and Andrew T. McKinnon1

1Department of Civil & Environmental Engineering, University of Utah, Salt Lake City, Utah2

Energy and Environmental Analysis Group, LANL, Los Alamos, NM3ASMD, ARL, NOAA, on assignment to U.S. Environmental Protection Agency4Department of Geography, The University of Oklahoma, Norman, OK

1. INTRODUCTION

Accurate predictions of air quality and atmosphericdispersion at high spatial resolution rely on high fidelitypredictions of mesoscale meteorological fields thatgovern transport and turbulence in urban areas.However, mesoscale meteorological models do nothave the spatial resolution to directly simulate the fluiddynamics and thermodynamics in and around buildingsand other urban structures that have been shown to

modify micro- and mesoscale flow fields (e.g., seereview by Bornstein 1987). Mesoscale models thereforehave been adapted using numerous approaches toincorporate urban effects into the simulations (e.g., seereviews by Brown 2000 and Bornstein and Craig 2002).One approach is to introduce urban canopyparameterizations to approximate the drag, turbulenceproduction, heating, and radiation attenuation inducedby sub-grid scale buildings and urban surface covers(Brown 2000). Preliminary results of mesoscalemeteorological and air quality simulations for Houston(Dupont et al. 2004) demonstrated the importance ofintroducing urban canopy parameterizations to produceresults with high spatial resolution that accentuatesvariability, highlights important differences, andidentifies critical areas. Although urban canopyparameterizations may not be applicable to allmeteorological and dispersion models, they have beensuccessfully introduced and demonstrated in many ofthe current operational and research mode mesoscalemodels, e.g., COAMPS (Holt et al. 2002), HOTMAC(Brown and Williams 1998), MM5 (e.g., Otte and Lacser2001; Lacser and Otte 2002; Dupont et al. 2004), andRAMS (Rozoff et al. 2003).

The primary consequence of implementing anurban parameterization in a mesoscale meteorologicalmodel is the need to characterize the urban terrain ingreater detail. In general, urban terrain characterizationfor mesoscale modeling may be described as the

process of collecting datasets of urban surface coverphysical properties (e.g., albedo, emissivity) andmorphology (i.e., ground elevation, building and treeheight and geometry characteristics) and thenprocessing the data to compute physical cover andmorphological parameters. Many of the surface coverand morphological parameters required for mesoscale

____________________________________________*Corresponding author address: Steve Burian, 122 S.Central Campus Dr., Suite 104, Salt Lake City, UT84112; E-mail: burian@eng.utah.edu

meteorological models are also needed by atmosphericdispersion models. Thus, most of the discussion belowis relevant to both types of modeling.

In this paper, the term urban morphological analysiswill be used to define the component of urban terraincharacterization concerned with the morphologicalparameters. Furthermore, the focus will be buildingmorphological parameters; therefore, the term urbanmorphological analysis will refer exclusively to the task

of inventorying, computing or estimating buildingmorphological parameters. Several approaches toperform urban morphological analysis exist; however, allhave in common three types of practice issues relatedto the uncertainty of (1) data, (2) parameter definitionsand calculation methods, and (3) extrapolationtechniques. The objective of this paper is to describethe state-of-the-practice of urban morphological analysisby reviewing the primary approaches presented in theliterature and outlining and commenting on key aspectsof the three types of practice issues listed above.

2. BACKGROUND

As described above, the urbanization of numericalmodels has introduced the problem of defining theurban canopy with a set of representative geometric,radiation, thermodynamic, and surface coverparameters. These urban canopy parameters (UCPs)defined broadly include aerodynamic roughnessproperties (e.g., roughness length), building heightcharacteristics (e.g., mean height, standard deviation,histograms), building geometry characteristics (e.g.,height-to-width ratio, wall-to-plan area ratio, completeaspect ratio), building volume characteristics (e.g.building plan and frontal area densities), radiationtrapping parameters (e.g., sky view factor), surfacecover properties (e.g., impervious surfaces, albedo),surface material properties (e.g., heat storage capacity,

emissivity), vegetation type, height and geometry, andmore. The types and attributes of datasets required forurban terrain characterization depends on theprocesses being simulated and the spatial and temporalscales of interest (Grimmond and Souch 1994). But ingeneral characteristics at the micro-scale (10-2 to 103 m)must be determined and aggregated or averaged to thegrid cell size of the model. Representing this level ofdetail in mesoscale simulations is needed to accentuatethe spatial heterogeneities of simulated surfacetemperatures, increase the value of turbulent kinetic

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energy production, and increase the planetary boundarylayer height to better represent urban areas (e.g.,Dupont et al. 2004; Holt et al. 2002).

A handful of researchers over the years havepioneered the work on obtaining surface cover andmorphological parameters for cities at the micro toneighborhood scale (102 to 104 m) (e.g., Ellefsen

1990/1991; Theurer 1993,1999). Grimmond and Souch(1994) were among the first researchers to present ageographic information system (GIS)-based techniquefor representing surface cover and morphologicalcharacteristics of the urban terrain for urban climatestudies. Petersen and Parce (1994) and Petersen andCochran (1998) presented software (ROUGH) forestimating the geometric parameters of buildings andstructures in urban and industrial sites. Cionco andEllefsen (1998) and Ellefsen and Cionco (2002) updatedEllefsens (1990/1991) morphological inventoryingprocedure using a 100 m X 100 m grid cell size (andthen a 50 m X 50 m cell size) for use in a high resolutionwind flow model and included more characteristics ofurban canopy elements in the database.

Grimmond and Oke (1999) reviewed severalmethods to define aerodynamic characteristics of urbanareas using morphometric approaches. The workcompared several methods to determine the roughnesslength, displacement height, depth of roughnesssublayer and aerodynamic conductance based onmeasures of building and tree morphology. GIS wasdeveloped for 11 sites in seven North American citiesand were used to characterize the morphologicalcharacteristics of the terrain and, using themorphometric equations, the aerodynamic parameters.

With recent advancements in data collection andmanagement, digital 3D building and tree datasets havebeen developed for many locations in the U.S. providingan available data pool for automated and semi-

automated analyses to compute UCPs. Computersoftware products including GIS and image processingtools have also been enhanced and now large areascovered by 3D digital building and tree datasets can beanalyzed automatically to extract morphologicalinformation (e.g., building height and geometrycharacteristics, roughness length). Several researchershave developed automated and semi-automatedcomputational procedures to process the 3D buildingand tree data to obtain UCPs. Ratti and Richens(1999), for example, built upon the initial effort ofRichens (1997) to implement efficient urban terrainanalysis algorithms in an image processing frameworkbuilt within the MATLAB software package. Ratti et al.(2002) used the image processing approach to computebuilding plan and frontal area densities, distribution ofheights, standard deviation, aerodynamic roughnesslength, and sky view factor for three European cities(London, Toulouse, and Berlin) and two U.S. cities (SaltLake City and Los Angeles). The results illustrated theroughness length differences between European andU.S. cities.

Burian et al. (2002) presented an approach usingGIS to process 3D building datasets to compute buildingheight characteristics (mean, standard deviation, plan-

area-weighted mean, histograms), plan area density,frontal area density, wall-to-plan area ratio, completeaspect ratio, height-to-width ratio, roughness length, anddisplacement height. The UCPs were calculated foreach grid cell in predefined grid meshes and theaverage values for each land use type were alsodetermined. This automated GIS approach was used to

compute UCPs for Los Angeles, Phoenix, Salt LakeCity, Portland, Albuquerque, Oklahoma City, Seattle,and Houston. Comprehensive reports are available foreach city (e.g., Burian et al. 2002; visitwww.civil.utah.edu/~burian for copies of the reports).The GIS approach has recently been expanded toinclude analysis of 3D vegetation, other 2D GISdatasets (e.g., roads) and multi-spectral imagery tocompute an expanded set of parameters includingsurface cover fractions, impervious surfaces, sky viewfactor, predominant street orientation, and more (Burianet al. 2003). The processing capability continues to beenhanced and is currently available as a graphical userinterface tool using a VBA macro for the ESRI ArcGISsoftware package.

Long et al. (2002) developed and tested the DFMapsoftware to process vector building and vegetation data(BDTopo) available from the French NationalGeographic Institute (IGN). With DFMap, a user canselect a cell size and wind direction to compute a seriesof morphometric and aerodynamic roughnessparameters. Long (2003) used the DFMap software tocompute morphological statistics and define urban landuse/cover types using an unsupervised k-meansanalysis. The analysis tools and approach were testedusing data for the city of Marseille. Long et al. (2003)extended the DFMap application by incorporating theanalysis of multi-spectral and panchromatic imagery inan attempt to improve the definition of urban surfacecover.

Urban morphological approaches have evolved inless than two decades from detailed inventorying usingaerial photographs and extensive field surveys tocomputationally intensive processing of integrated 2Dand 3D GIS and multi-spectral imagery datasets. Theusers of such data have also expanded to includefederal agencies (e.g., LANL, LLNL (e.g., Chin et al.2000), DTRA (Pace 2002), U.S. EnvironmentalProtection Agency (e.g., Ching et al. 2002; Dupont et al.2004), U.S. Army and Naval Research Laboratories(e.g., Cionco and Luces 2002; Holt et al. 2002)),university research centers (e.g., University of HoustonInstitute of Multidimensional Air Quality Studies(IMAQS) (Daewon Byun, personal communication)),university researchers (e.g., Rozoff et al. 2003), andprivate consultants (Haider Taha and Robert Bornstein,personal communication). During this time, the basicconcept of defining UCPs for given homogeneous areas(e.g., land use, land cover, terrain zone) or model gridcells has remained the same, but new developmentshave improved the methods used to calcualte the UCPs.Even with the advancements, the need for datasetscovering large areas and extensive data managementand processing requirements limit the ability to derivegridded parameter datasets for entire mesoscale model

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domains. Further developments in data collection andprocessing are needed. In addition, there is a need tofurther refine urban morphological analysis methods bystandardizing processes and reducing the uncertainty ofmethods. The following section outlines several issuesthat are currently of interest to those conducting urbanmorphological analyses for mesoscale meteorological or

atmospheric dispersion modeling applications.Resolving these issues will contribute to improvementsof analysis methods and likely increase the level ofaccuracy of calculated UCPs.

3. MORPHOLOGICAL ANALYSIS ISSUES

Urban morphological analysis methods havematured during the past two decades, but there stillremain several operational issues to be resolved. Threeprimary types of issues facing those deriving UCPs are:

(1) Data Issues. What data are available, whatare the available levels of resolution, accuracyand detail, and what levels of resolution,

accuracy and detail are necessary for UCPcalculations?

(2) Definition and Calculation Issues. What arethe current UCP calculation methods and whatare the effects of ambiguity and uncertainties inparameter definitions and calculation methodson UCP values and mesoscale meteorologicaland dispersion simulation results?

(3) Extrapolation Issues. What are the mostaccurate means to extrapolate UCPs tomesoscale meteorological and dispersionmodel domains?

The salient points associated with the three types ofissues listed above are described in the following threesubsections.

3.1 Data Issues

3.1.1 What Data are Available?

The building elevation data used to define theurban morphology is usually obtained by:

Analyzing aerial photographs to estimatebuilding data (time consuming, feasible onlyfor very small areas)

Performing ground surveys (time consuming,feasible for very small areas; elevation can bemore accurately obtained than by analysis ofaerial photographs)

Analyzing stereographic images (can be timeconsuming to perform manual digitization;elevation can be accurately estimated)

Analyzing airborne LIDAR data (requiresmanaging and processing massive datasets;newness of LIDAR technology presentsseveral problems; elevation measurements can

be obtained with vertical and horizontalaccuracies of 15 cm RMSE)

Obtaining/purchasing datasets fromgovernment or municipal agencies (may beoutdated or lack information necessary tocompute all UCPs)

Purchasing datasets from commercial vendors

(potentially high cost; enhanced data productscan be delivered)

The data are obtained in two basic forms: rasterand vector. Raster data are usually square-grid cellswith one elevation attribute per cell. Vector data useone or more polygons to represent the building footprintand rooftop. Vector data can represent the geometryand details with higher precision than raster data. Inaddition, vector data can represent multiple buildinglayers and can contain multiple attributes per polygon(e.g., height, roof pitch, color, material). Buildingpolygon data in vector form can be obtained frommunicipal governments and commercial vendors. Dataproducts range from the raw stereographic pairs or

airborne LIDAR data to the finished end products ofbuilding polygon vector or full-feature raster digitalelevation model (DEM) datasets. Other options forobtaining building data are federal governmentagencies. For example, a potential major source ofbuilding morphology data in the U.S. is the so-called120 cities project. This project involves a consortiumof federal agencies to collect and prepare airborneLIDAR databases for cities for domestic preparednessapplications. Unfortunately, the current availability ofthe data and the ultimate distribution policy is uncertain.

3.1.2 How Accurate are the Data?

A critical question to be asked of all availablebuilding datasets is the accuracy. The primary errorspossible include:

1. Buildings may currently exist, but are notincluded in the dataset

2. Buildings may not currently exist, but areincluded in the dataset

3. Several individual buildings may berepresented as a single building

4. A single building may be represented asseveral individual buildings

5. Building outline may not accurately representthe actual building shape

6. Location of building polygon or parts of the

polygon may be inaccurate7. Building heights may be inaccurate

The causes of these data errors could be operator errorduring extraction, poor quality assurance/quality control(QA/QC) procedures, limitations of building featureextraction methods, changes of morphology during datacollection, and the age of the building dataset (whichmay cause the data to not represent the morphology forthe time period of interest). To illustrate several of thecommon building morphological dataset errors, Figure 1

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was created showing red outlines representing buildingfootprints extracted using an automated LIDAR DEMprocessing technique overlaid onto an aerial photo. Ofthe potential errors listed above, numbers 3, 4, 5, and 6can be directly observed in the figure. Although notobservable the other three errors were also contained inthis sample dataset.

Figure 1. Sample residential area in Salt Lake City.Red outlines represent a vector dataset derived throughan automated analysis of a full-feature DEM.

Airborne LIDAR has emerged as one of the primarysources for the development of 3D urban models(Priestnall et al. 2000). In most cases, both horizontaland vertical resolutions of LIDAR data can be down toone meter. Consequently, the high resolution may be

misunderstood as high accuracy. It should beunderstood that LIDAR data processing is a nontrivialtask. Three general approaches have been developedfor urban 3D data analysis using LIDAR data,categorized as using an edge operator, mathematicalmorphology, and height bins, respectively (Zhou et al.2004). In practice, LIDAR data processing may involveall three approaches. To assess possible LIDAR datainaccuracies a comparison was made between buildingheights derived from LIDAR data for Oklahoma City andfield observations. The LIDAR data was acquired in lateOctober 2001 by an Optech Airborne Laser TerrainMapper (ALTM) 2033 sensor with a differential GlobalPositioning System. The Joint Precision StrikeDemonstration (JPSD) Program Office of the U.S. Army

processed the raw LIDAR data and provided data ofLIDAR estimated building heights in downtownOklahoma City. Preliminary findings identified fourcategories of significant differences between buildingheights estimated from LIDAR data and those from fieldobservations. Significant differences were defined asthose greater than 5 meters, which represents at leastone floor of the structure. The four types of differencesare briefly described below; more details and exampleswill be included in the presentation.

The first LIDAR inaccuracy found from theOklahoma City analysis is common to all data collectionand building extraction approaches. It involves the sateof the building morphology changing from the time ofdata collection. Buildings that were recorded in thedataset may have been demolished or new buildingsmay have been built in areas where buildings previously

did not exist. The second type of LIDAR inaccuracywas observed at locations where buildings wereconstructed of highly reflective materials. Figure 2shows two buildings that had heights under-estimatedby the LIDAR data by more than 5 m. The buildingshown on the left side of the figure is a greenhouse witha curved roof-top. A significant portion of the south endof the building has an under-estimated height. Thesecond example is a building nearly completely glass-covered on the south side. The end of the buildingcovered by glass is noted to have a height difference ofgreater than 5 m. The third type of difference noted wasassociated with buildings having complex or narrowstructures, e.g., passages, gaps, platforms, extensions.The LIDAR-derived building heights contained both

under- and over-estimations in the vicinity of suchcomplex building elements. And the fourth differencebetween the LIDAR-derived heights and field measuredheights was noted in the vicinity of vegetation adjacentto buildings. Trees adjacent to buildings that overhangthe building rooftop will cause the LIDAR-derivedbuilding height to be over-estimated because the LIDARdoes not differentiate between objects.

Figure 2. Examples of association between buildingmaterials and under-estimated building heights basedon LIDAR data.

While four categories of differences between LIDARestimates and field observations have been presentedhere, these differences represent preliminary findingsand are by no means exhaustive. Current work focuseson trying to understand the physical causes of theobserved uncertainties associated with the LIDAR-derived building heights. It is also expected that otheruncertainties tied to building morphologicalcharacteristics will be noted. Inaccurate data isprevalent but the effect of data inaccuracies on UCP

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values and numerical model results is being studied.Results will be included in the presentation.

3.1.3 What are the Available Levels of Data Detail?

The detail of the data used to compute the UCPsmay also influence the resulting UCP value. In terms of

detail, the necessity to represent precise buildinggeometry and ancillary attached structures is theprimary issue. For example, a building dataset mightrepresent the change in building geometry with height orinclude rooftop structures. The alternative to this levelof detail is to simply represent each building with asingle polygon that has a uniform shape with height. Anexample of the difference in detail for a small section ofdowntown Salt Lake City is shown in Figure 3. The toppart of the figure shows a CAD dataset with a very highlevel of detail included, while the bottom part of thefigure shows a GIS shapefile with a relatively low levelof detail. Visual comparison of the top and bottom partsof the figure suggests that the low-level of detailcaptures the general form of the location. Therefore,

average UCPs for a 1-km model grid cell size are likelyto not be affected by approximating each building as auniform polygon with a single height. This needs to bequantitatively confirmed.

One question to consider if a uniform polygon withsingle height attribute is to be used to calculate the UCPis the choice of average or maximum height as theattribute used in the calculation. For some UCPs (e.g.,sky view factor), rooftop structures are probably notimportant and in areas with tall buildings, the rooftopstructures are also probably negligible. In these cases,the choice of average or maximum building height willprobably not impact the UCP value significantly. Inresidential land uses however the use of average ormaximum height may alter the UCP values. For

example, consider Figure 1, which contained an aerialphotograph of a residential block in downtown Salt LakeCity consisting of predominantly single-story, single-family homes with basements and pitched roofs. Thebuildings were extracted from LIDAR data using anautomated approach and the heights were determinedin two ways: (1) finding the average height of raster cellsinside each building polygon boundary and (2) selectingthe maximum raster height inside each building polygonboundary. Using the mean height for each building, themean and standard deviation of building height for theentire block were calculated to be 3.8 m and 0.7 m,respectively. However, using the maximum height ofthe rooftop, the mean and standard deviation werefound to be 5.5 m and 1.5 m. These differences aresignificant for the UCP value (~45% and 100%differences), but the significance of the magnitude ofdifferences is uncertain for mesoscale meteorological ordispersion model results considering that the UCP valuewill be aggregated with other data for a grid cell size onthe order of ~1 km2. Further analysis is needed.

Figure 3. Comparison between a high-detail buildingdataset (top) and a coarse dataset (bottom) for smallsection of downtown Salt Lake City. The Heber-WellsBuilding is identified in both for frame of reference.

3.2 UCP Definition and Calculation Issues

A second concern facing those deriving UCPsinvolves the decisions of what are the precise UCPdefinitions, what calculation methods and tools tochoose, what formats of input data to expect, and whatoutputs need to be produced. The following questionsare pertinent to these concerns.

3.2.1 What is the Effect of Ambiguous UCP Definitionson Calculated Values?

The definition of the UCP may be ambiguous andthus not account for all potential real-world building

arrangements. For these circumstances calculation ofthe UCP will be subjective because the individual willneed to decide whether to ignore the unforeseensituation, implement a simplified calculation approach,or develop a modified calculation approach accountingfor the encountered circumstance. The potential impactof UCP definition ambiguity on UCP values will beconsidered herein using mean building height. Thepresentation will contain the assessment of other UCPsincluding height-to-width ratio and frontal area index.

Mean building height is seemingly a well-defined

Heber-Wells

Building

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UCP. However, there are several ways in which themean can be computed. The most common are: simpleaverage, weighting by plan area (top or bottom oraverage), or weighting by frontal area. Based onanalysis of real building data the values can besignificantly different depending on the definitionchosen. For example, consider Table 1 which lists the

computed mean building heights using a simple averageand weighted by plan area for the downtown core areasof eight cities in the U.S. The plan area weighted meanbuilding height ranges from being approximately equalto the simple mean building height to being more than40% greater. What these differences mean in terms ofmesoscale simulation results must be determined.

Table 1. Comparison of mean building heights fordowntown core areas of U.S. cities.

City

MeanBuilding

Height (m) Simple

Average

MeanBuilding

Height (m) Plan Area

WeightedAlbuquerque, NM 15.2 24.6Houston, TX 22.8 36.0Los Angeles, CA 45.0 44.1Oklahoma City, OK 19.4 25.4Phoenix, AZ 17.2 21.2Portland, OR 18.1 18.5Salt Lake City, UT 23.6 41.5Seattle, WA 21.1 31.9

3.2.2 What is the Effect of Ambiguous UCP CalculationMethods?

In addition to the ambiguities associated with UCPdefinitions, there are ambiguities associated with UCPcalculation methods. Most notable is the manyequations available to compute the aerodynamicroughness parameters (roughness length anddisplacement height), with confusing guidance onrelative applicability and accuracy. Grimmond and Oke(1999) compared many of the methods to compute theaerodynamic roughness parameters and could notconclusively identify an order of preference, althoughseveral of the more intuitively accurate methods werehighlighted as recommended. Additional studies mustbe conducted to investigate the differences between theresults produced by the various approaches for realcities and how the differences affect mesoscale

meteorological and dispersion model results.

3.2.3 What are the Advantages and Disadvantages ofUCP Derivation Approaches?

Several approaches have been presented in theliterature to derive UCPs (see Background sectionabove):

1. Conduct a field survey to measure buildinggeometry and height information and recordconstruction materials.

2. Analyze high-resolution aerial photographs toestimate building geometry and heightinformation and record construction materials.

3. Perform automated or semi-automated image

analysis of remotely sensed data to computethe building information.4. Perform automated or semi-automated

analysis of full-feature digital elevation models(DEM) or vector representations of buildingsusing GIS or image processing software.

Conducting a field survey can provide a high level ofdetail, yet it is time consuming/labor intensive andrequires being in the location of the buildings. It isfeasible for analysis of very small areas of cities.

Analysis of high-resolution aerial photographs is alsotime consuming/labor intensive, but it does not requirethe analyst to be present at the building location. It ishowever similar to field surveys in that it is only

applicable for small areas of cities. Automated or semi-automated analysis approaches are the most timeefficient and once the codes are written to perform theautomated calculations, labor requirements are limited(computer time is the only requirement). However,removing the trained analyst from the interpretation andmeasurement of building characteristics eliminatessubjectivity at the cost of reducing quality assuranceand quality control and oversight. In addition, simplifiedapproximations to the calculations may be necessary toexecute an automated approach and this may furtherreduce the accuracy of the results.

Despite the reduction in accuracy potentiallycaused by using an automated analysis approach, it isthe only way to derive UCP coverage for large areas in

a reasonable amount of time. Besides being able toprocess large areas, the image and GIS processingapproaches have other advantages including:

Spatial data in GIS compatible formats (e.g.,roads) is readily available for incorporation intothe analysis and for map making.

The quality and availability of buildingmorphological data is continuously improvingand the automated approaches are specificallydesigned to work with these datasets.

GIS and image processing software continuesto improve and computer processing speedcontinues to be increased, all of which

enhances the ability of automated approachesto provide more accurate UCP values moreefficiently.

Some questions may arise regarding the use of imageprocessing versus GIS software. The image processingsoftware may provide more efficient computation thanthe GIS, but with the speed of todays computers thedifference in time is negligible compared to the time tobe invested in the modeling and analysis activities.

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3.2.4 How Important Are Trees for Accurate UCPValues?

Because of their relative importance in downtownareas, building morphological data have been collectedin cities more often than tree morphological data. Yettree morphology can play an important role in the overall

morphological characteristics of an urban area,especially residential zones and other areas withsignificant vegetation elements. Including trees isrecommended by Grimmond and Oke (1999) in order tobetter represent the overall roughness of the urbansurface. However, due to the difficulty to obtainsufficient tree morphological information for large areas,the importance of sparse coverage of urban treesremains relatively uncertain. Further study is needed.

3.2.5 How Sensitive are UCP Values to DataResolution?

Building datasets typically are vector products(perhaps derived from raster data, e.g., LIDAR), but the

computation of many UCPs is most easily performed onraster data. For such circumstances, the vector datamust be converted into a raster dataset with a selectedhorizontal resolution. Higher resolution data shouldproduce a more accurate UCP value, but at the cost ofhigher computational requirements. A study is neededto determine the methods of rasterization and how usingcoarser cell sizes will affect the building data and thenhow raster cell size affects processing time, accuracy ofthe UCP value, and the ultimate mesoscalemeteorological or dispersion model results.

3.2.6 Is a Consistent Data Model/Format Necessary?

Currently, a standard data model for urban

morphological input data or output format does not exist.Although some agencies have identified the need forstandardization of the collection of building andmorphological data (e.g., DTRA, John Pace, personalcommunication), consensus has not been establishedby those performing urban morphological analysis insupport of mesoscale modeling. In terms of the inputdata used to compute UCPs, having a standard datamodel would promote data sharing and the reuse ofdeveloped software products and methodologies forurban morphological analysis. Further, reachingconsensus on base data requirements would encouragedata collectors to obtain information needed by all usersthus making the collected data more valuable. In termsof output data, a consistent format may be unnecessarybecause the advancements in software products hasenabled numerous forms of building morphological datato be accessed and processed with a variety ofcomputational tools. For example, data in CAD formcan be accessed in GIS and processed and vice-versa.The use of proprietary data forms (e.g., ESRI shapefile)may limit the applicability of some software products,but conversion tools are generally available. Moreconsideration is needed to develop data standards thatmeet the needs of the widest section of the mesoscale

meteorological and atmospheric dispersion model usercommunity.

3.3 Extrapolation Issues

3.3.1 What is the Appropriate Size Area for UCPAnalysis to Obtain Meaningful Building Statistics?

Time or budget constraints often limit the size of thearea that can be morphometrically analyzed. Use of asmall analysis area to compute mean parameters as afunction of land use/cover types (or other homogeneousunits) can lead to errors when extrapolating to largerareas of the modeling domain outside of building datacoverage (Burian et al. 2003). The heterogeneity ofurban terrain may cause the resulting land use-specificmean UCPs to vary depending on the size of the areaincluded in the calculation.

To investigate this question an analysis of a large650,000 building dataset for Houston was performed.The downtown core area of the city was delineated anda set of UCPs were calculated for the delineated area

and the mean value was determined for each land usetype. Then the boundary of analysis was increasedincrementally and the UCPs re-calculated (see Table 2for a summary of the size characteristics of theincremental analysis zones). The trends of the UCPvalues as the boundary of the analysis zone increasedwere quantified. The choice of the initial analysis zoneto be the downtown core area may influence the resultsof this analysis. But, the selection of the downtown corearea is appropriate for this preliminary analysis becausein most cases the downtown will be of interest from amodeling perspective (and will have uniquemorphological characteristics). To eliminate thissubjectivity, the analysis will be repeated using a seriesof randomly selected initial analysis zones and the

results will be reported in the presentation.Based on results using the downtown core area as

the center of the analysis zones, Figure 4 shows thetrend of the mean building height for several urban landuse types as the size of analysis area increases. Themean building height in Residential and Industrial landuses was found to not be significantly sensitive to thesize of the analysis zone, but the Commercial &Services values do change significantly as the size ofthe analysis area increases. In the initial analysis areaextent encompassing the downtown core area, thebuildings are predominantly high-rise and the land useis predominantly Commercial & Services. As theanalysis extent increases the character of theCommercial & Services land use changes from high-riseto shopping malls, strip malls, and other forms withmuch shorter building heights (and less variableheights) than the downtown core area. Thus, theobserved change to mean building height of theCommercial & Services land use is understandable.However, Residential and Industrial land uses areusually not prevalent in the downtown core area and asthe analysis extent increases the building heightstypically do not become significantly smaller. Thisobservation is also noted in Figure 4.

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Table 2. Characteristics of analysis zones.

ZoneArea(km

2)

Number ofBuildings

Building Density(# /km

2)

1 3.4 489 1442 7.5 1539 2053 13.1 5126 3914 27.0 15888 588

5 53.3 46058 8646 105.9 99868 9437 207.0 180746 8738 371.4 278266 7499 722.1 434285 601

Figure 5 suggests that building plan area fraction issensitive to analysis area for all three land use types.The results indicate that the plan area fractiondecreases as the analysis zone increases, reflecting adecreasing building density with distance from thedowntown core area. This is most likely due to includingareas of lower density housing and industrial parksnormally found on the outskirts of urban areas. Analysisof the behavior of other UCPs as the analysis zone sizeincreases will be reported in the presentation.

3.3.2 Is a Standard Urban LULC Classification Needed?

Land use and land cover have served for manyyears as surrogate data layers to define surfaceparameters in mesoscale meteorological and dispersionmodels. The approach involves defining modelparameters for LULC classes based on analyzedsamples of the use/cover or literature valuesrepresentative of the use/cover (e.g., a roughnesslength for urban land use). The LULC dataset thenserves as the extrapolation medium to parameterize the

entire model domain using the established parametervalues. This approach has been necessary becausedatasets describing these model parameters or datasetsthat they are derived from (e.g., 3D building databases)were not available or could not be efficiently analyzedfor areas covering the extent of mesoscale modeldomains.

5

10

15

20

0 100 200 300 400 500 600 700 800

Analysis Area (km2)

MeanBuild

ingHeight(m) Commercial & Services

Residential

Industrial

Overall

Figure 4. Mean building height as a function of analysisarea size.

0.12

0.16

0.20

0.24

0.28

0 100 200 300 400 500 600 700 800

Analysis Area (km2)

PlanAreaFraction

Commercial & ServicesResidentialIndustrialOverall

Figure 5. Building plan area fraction as a function ofanalysis area size.

The available LULC datasets themselves, however,

may potentially have limitations. For example, the U.S.Geological Survey (USGS) LULC dataset has beencommonly used and is freely available in nationallyconsistent form for the U.S. But, the primary source ofdata for the USGS LULC dataset were NASA high-altitude aerial photographs and National High-AltitudePhotography (NHAP) program photographs collectedmostly during the 1970s. These data are outdated forrepresenting current conditions in areas at theurbanizing fringe of cities, especially those that haveexperienced massive growth during the past 30 years.Moreover, the urban land use categories in the

Anderson classification scheme used by the USGS(Anderson et al. 1976) are not based on morphologicalcharacteristics. A potential source of more updated

LULC data is the National Land Cover Dataset (NLCD)(Vogelmann et al. 1998). However, the NLCD is basedon semi-automated classification of remote sensing datawith some incorporation of ancillary data; consequently,the definition of urban land use types is limited to asmall number because of the heterogeneous surfaceproperties (Vogelmann et al. 1998). The four urban landuse types represented in the NLCD (Low IntensityResidential, High Intensity Residential, Commercial/Industrial/Transportation, and Urban Vegetation) in mostcases will include a wide range of urban surfacefractions (e.g., paved areas, rooftops, landscapedareas, bare soil, etc.) with different reflective propertieswithin each class, which will confuse classificationalgorithms and potentially cause misclassification.

Moreover, the aggregation of Commercial, Industrial,and Transportation land uses into a single land usecategory will mask the heterogeneity of the urbansurface because the building morphologicalcharacteristics of these three individual land uses arenot similar (e.g., see building statistics in Burian et al.2002 or Grimmond and Oke 1999).

As briefly noted, the USGS and NLCD datasetshave potential drawbacks for UCP definition andextrapolation to mesoscale meteorological anddispersion model domains. From a meteorological or

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dispersion modeling perspective, numerous researchershave devised more detailed LULC classificationschemes to overcome the potential deficiencies of theaforementioned national datasets and provide moreaccuracy for their analyses. Ellefsen (1990/1991) forexample, derived an urban LULC dataset for urbanclimate and meteorological modeling applications. His

classification system included 17 urban terrain zones(UTZ) that were homogeneous units from a buildingmorphological perspective. Each zone was defined tohave a distinctive mix of function, development age,street pattern, lot configuration, and type of constructionand density and height of buildings. A primarydifference between UTZ categories is whether thebuildings are attached or detached. UTZs withdetached structures were further subdivided by thecloseness of the structures.

Using a morphological-based classificationapproach similar to Ellefsen, Theurer (1993, 1999)identified a set of nine typical building arrangements forcities in Germany. Another classification approachincorporating morphological characteristics was

introduced by Grimmond and Souch (1994). They useda 36-category urban land use classification scheme torepresent urban terrain in a GIS-based surfacecharacterization methodology. The first level of landuse categories included traditional single and apartmentresidential, commercial/industrial, institutional,transportation, vacant, vegetated, impervious, andwater. Sub-categorization was based on building heightand density and cover fractions.

Burian et al. (2002) also used a two-tierclassification approach, but their scheme was based onthe Andersen Level 2 urban land use categories as thefirst tier. Morphology was then incorporated tosubdivide traditional USGS urban land use classes intomorphologically-based sub-categories. For example,

the Residential land use category was subdivided intolow-density or high-density based on a chosen thresholdbuilding density level. Long et al. (2003) used twoautomated approaches to classify land use, one basedon morphological parameters and the other based onanalysis of multi-spectral imagery (20-m resolutionSPOT). Originally, a 7-class scheme based onmorphological parameters was tested, but wasexpanded to 10-classes after high parameter variabilitywas noted in each class. The morphologicalclassification identified urban land use types well, but ingeneral did not define land cover well. The imageryapproach did give accurate information about the covertype, but was unable to accurately identify urbanelements and their combinations to form the urbanterrain classes.

This sampling of urban LULC classificationapproaches indicates that morphologically-basedapproaches are available and can be used in urbanmorphological analysis and may have application inmesoscale modeling. The question then becomes theneed for standardization of classification and thecreation of repeatable, objective, automated approachesto derive a spatially-consistent LULC dataset.Standardization would benefit morphological analysis

and may benefit the mesoscale meteorological modelingcommunity, but perhaps would decrease the value ofpreviously collected data and derived relationshipsunless the new land use/cover categories could bechosen to be consistent with categories from otherclassification approaches (or categories from otherclassification approaches can be generalized into the

categories of the new classification approach). Thepopularity of the USGS LULC dataset with themesoscale modeling community might also influencethe selection of classification categories. One possiblesolution is to use morphological characteristics (e.g.,building density, mean building height, vegetative cover,impervious surface fraction) to subdivide a generalizedfirst level of land use/cover, but this would require anationally consistent morphological database.

4. SUMMARY

This paper reviewed the historical advancements tourban morphological analysis approaches anddiscussed the details of several practice issues. The

issues discussed are not considered exhaustive, but doprovide a sampling of current practice inconsistenciesand uncertainties that could potentially reduce theaccuracy of urban canopy parameters and mesoscalemodel results. Further analysis of these issues andothers will be included in the presentation.

Acknowledgements

This work was partially supported by theDepartment ofHomeland Security Chemical and BiologicalCountermeasures Program. The United StatesEnvironmental Protection Agency through its Office ofResearch and Development also partially funded andcollaborated in the work described here. It has been

subjected to Agency review and approved forpublication. The authors wish to thank Tanya Otte forinsightful comments that helped to improve themanuscript.

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