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ESTIMATING THE EXTENT OF IMPERVIOUS SURFACES

AND TURF GRASS ACROSS LARGE REGIONS1

Peter R. Claggett, Frederick M. Irani, and Renee L. Thompson2

ABSTRACT: The ability of researchers to accurately assess the extent of impervious and pervious developed sur-faces, e.g., turf grass, using land-cover data derived from Landsat satellite imagery in the Chesapeake Baywatershed is limited due to the resolution of the data and systematic discrepancies between developed land-cover classes, surface mines, forests, and farmlands. Estimates of impervious surface and turf grass area in theMid-Atlantic, United States that were based on 2006 Landsat-derived land-cover data were substantially lowerthan estimates based on more authoritative and independent sources. New estimates of impervious surfaces andturf grass area derived using land-cover data combined with ancillary information on roads, housing units, sur-face mines, and sampled estimates of road width and residential impervious area were up to 57 and 45% higherthan estimates based strictly on land-cover data. These new estimates closely approximate estimates derivedfrom authoritative and independent sources in developed counties.

(KEY TERMS: impervious surface; turf grass; land cover; Chesapeake Bay.)

Claggett, Peter R., Frederick M. Irani, and Renee L. Thompson, 2013. Estimating the Extent of ImperviousSurfaces and Turf Grass Across Large Regions. Journal of the American Water Resources Association (JAWRA)1-21. DOI: 10.1111/jawr.12110

INTRODUCTION

In this article, we introduce a new method to con-sistently estimate the area of impervious surface andturf grass area across the Chesapeake Bay watershedover a 22-year timeframe. While the methods weredeveloped to improve the accuracy of the ChesapeakeBay Watershed Model (WSM) (Shenk and Linker,this issue) and to inform the development of stateWatershed Implementation Plans required in theChesapeake Bay Total Maximum Daily Load (TMDL)(USEPA, 2010b), they are largely based on nationallyavailable datasets and are therefore transferrable toother parts of the United States (U.S.).

Land cover, use, and management are essentialdata inputs to WSMs because they reflect sources ofnutrients and sediment and directly impactwatershed hydrology (Booth et al., 2002; Moglen andKim, 2007; Law et al., 2009). Changes in the esti-mated area of land cover, use, and management prac-tices directly impact modeled estimates of nutrientand sediment loads to the Chesapeake Bay from allsources. For watershed and stormwater management,accurately assessing the extent of impervious sur-faces (e.g., roads, rooftops, and parking lots) andpervious developed surfaces (e.g., lawns and landscapedareas) is critical (Law et al., 2009). Impervious sur-faces route precipitation to streams directly throughstorm drains or indirectly through overland flow. The

1Paper No. JAWRA-12-0069-P of the Journal of the American Water Resources Association (JAWRA). Received March 16, 2012; acceptedMarch 12, 2013. © 2013 American Water Resources Association. This article is a U.S. Government work and is in the public domain in theUSA. Discussions are open until six months from print publication.

2Geographers, U.S. Geological Survey, 410 Severn Avenue, Suite 112, Annapolis, Maryland 21403 (E-Mail/Claggett: [email protected]).

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION JAWRA1

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

AMERICAN WATER RESOURCES ASSOCIATION

percent area of impervious surfaces in a watershed iscorrelated with increases in stream flow flashinessand declines in stream benthic conditions (Snyderet al., 2005; Jacobson, 2011). As streamflow volumeand velocity increase, so do the hydraulic forces caus-ing streambank and bed erosion and the transport ofsediment downstream (Booth et al., 2002). Pervioussurfaces in developed areas are by definition notimpervious, but they may function similarly to imper-vious surfaces when compacted during the develop-ment process or when saturated during large stormevents (Gregory et al., 2006; Law et al., 2009). Nutri-ents from fertilizers applied to pervious developedsurfaces such as turf grass may migrate to streamsand groundwater depending on fertilizer nutrientconcentrations, application and plant uptake rates,soil characteristics, and weather (Law et al., 2004).Given the dominant use of turf grass as ground coverin developed areas and its likely presence under treesin small-lot residential subdivisions, we consider per-vious developed surfaces to be mostly composed ofturf grass and refer to them as “turf grass” in thisarticle.

While both impervious surfaces and turf grass canbe accurately measured using field mapping or high-resolution imagery interpretation techniques (Slo-necker and Tilley, 2004), applying such approachesacross the 165,760-km2 Chesapeake Bay watershedcan be labor and cost intensive. Moreover, the devel-opment of accurate retrospective estimates suitablefor comparing over time and space is hindered by thelack of historical high-quality high-resolution imag-ery. The use of Landsat-derived land-cover data tocharacterize the developed area of large watershedsis supported by other studies (Goetz et al., 2004;Jennings et al., 2004; Jacobson, 2011) and is repro-ducible. For these reasons, the U.S. Geological Surveypublished the Chesapeake Bay Land Cover Data(CBLCD) series for the years 1984, 1992, 2001, and2006 (Irani and Claggett, 2010). The CBLCD serieswere developed to provide a spatially consistent andtemporally comparable set of land-cover informationto support Chesapeake Bay restoration objectives andto provide data on impervious surfaces and turf grass

as inputs to the calibration of the Chesapeake BayProgram’s Phase 5.3.0 (P530) WSM used to establishthe Chesapeake Bay TMDL (USEPA, 2010a, b).

Landsat-derived land-cover data, however, haveknown limitations representing impervious surfacesassociated with low-density residential development(McCauley and Goetz, 2003; Jantz et al., 2005; Irwinet al., 2007; Moglen and Kim, 2007; Jones and Jarna-gin, 2009) and narrow linear features such as 2-laneroads (Lu and Weng, 2004). In recognition of theselimitations and the fact that the estimates of impervi-ous surface used in the P530 were 18% lower thanprevious estimates, which relied on the 2001 NationalLand Cover Dataset (NLCD) impervious surface datain combination with the CBLCD and ancillary dataon road density and residential housing (Peter R.Claggett, 2009, unpublished data), the ChesapeakeBay Program Partners requested a more completeaccounting of impervious surface and turf grass areato inform the current Phase 5.3.2 (P532) WSM(USEPA, 2010c).

METHODS

Estimating Impervious Surface and Turf Grass Areafor the P530 WSM

For the P530 WSM, only the CBLCD and NLCDimpervious data were used to assess impervious sur-face and turf grass area. The CBLCD series werederived from Landsat satellite imagery and consist of30 meter (m)-resolution raster datasets classified into16 land-cover classes (Table 1) using the level-2Anderson classification system (Anderson et al.,1976). To estimate impervious area, the areas of thefour CBLCD developed classes, High-Intensity Devel-oped (HID), Medium-Intensity Developed (MID),Low-Intensity Developed (LID), and Developed OpenSpace (DOS) were calculated using ESRI� ArcGISTM

(Redlands, California) software and multiplied bywatershed-wide estimates of the mean percent

TABLE 1. Chesapeake Bay Land Cover Data Series Classification.

Level Class Name Code Level Class Name Code

11 Open Water OW 42 Evergreen Forest EF21 Developed Open Space DOS 43 Mixed Forest MF22 Low-Intensity Developed LID 52 Scrub/Shrub SS23 Medium-Intensity Developed MID 71 Grassland/Herbaceous GH24 High-Intensity Developed HID 81 Pasture/Hay PH31 Barren BN 82 Cultivated Crops CC32 Unconsolidated Shore US 90 Woody Wetlands WW41 Deciduous Forest DF 95 Emergent Wetlands EW

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CLAGGETT, IRANI, AND THOMPSON

impervious cover for each developed class derived fromthe NLCD (Table 2). The remaining non-imperviousportion of each developed pixel was considered to bepervious and consist mostly of turf grass. Milesi et al.(2005) found a strong inverse relationship betweenthe fractional impervious and fractional turf grassarea within a 1-km grid. While urban trees may bepresent within a partially developed 30-m cell, thefull extent of urban tree canopy is not accurately rep-resented in 30-m resolution land-cover products (Mos-kal et al., 2011) and the potential presence of urbantrees does not preclude the presence of lawns under-neath the canopy.

Estimating Impervious Surfaces and Turf Grass Areafor the P532

The process for developing new estimates of imper-vious surface and turf grass as inputs to the P532WSM involved refining the characterization of devel-oped lands in the CBLCD, dividing the watershedinto three distinct analysis zones, estimating impervi-ous surface and turf grass areas separately withineach zone, and summarizing the results within eachof the 2,692 WSM modeling segments that have amedian size of 3,200 hectares (ha) and encompass theentire Chesapeake Bay watershed and adjoiningcounties (Figure 1). Distinguishing separate zoneswas necessary because the best available data forestimating impervious and turf grass area variedamong zones (Table 3). Therefore, different methodsand data were used in the three zones that wereloosely characterized as “urban,” “suburban,” and“rural” for the purposes of this study (Figure 2). Theterms “urban,” “suburban,” and “rural” were notmeant to conform to or reflect existing definitions ofsuch areas developed by the U.S. Census Bureau orother entities (U.S. Census Bureau, 2002). Ratherthey merely represented zones where different ana-lytical techniques and data were employed to esti-mate impervious and turf grass areas. Large patchesof contiguous developed lands in the 2006 CBLCDwere used to define and map the urban zone andinclude the majority of places that are conventionallyviewed as “suburban.” Outside of the urban zone,where the potential was high for under-detection of

impervious surfaces and turf grass in the CBLCD,secondary road density was used to map thesuburban zone. The suburban zone is small becauseit represents only residential developments under-represented in the land-cover data. By default, allremaining lands were considered to be in the ruralzone. ESRI� ArcGISTM software was used to conductall of the spatial analyses discussed in this article.

Refining the Representation of Developed Lands inthe CBLCD

The CBLCD was refined to improve the spatialrepresentation of developed “land use” in contrast todeveloped “land cover.” The refinements wereconducted to address some of the common limitationsof Landsat-derived land-cover datasets in represent-ing institutional properties and residential subdivi-sions. They were also performed to address potentialmisclassifications of developed lands as barren orsurface mines.

Mapping Institutions. Identifying institutionalproperties with Landsat imagery results in similarissues and concerns as those associated with mappingresidential areas. Institutional properties used in thisstudy include airports, airport roads, cemeteries, golfcourses, hospitals, industrial complexes, shoppingcenters, sports complexes, and universities/colleges.While some of these features, such as airports andshopping centers are identifiable with Landsat imag-ery as LID, MID, or HID, associated small buildings,narrow roads, and sidewalks are often missed andadjacent lawn areas are commonly confused with pas-ture or cropland. Recreational fields on school proper-ties in addition to cemeteries and landfills areparticularly prone to misclassification as pasture orcropland. For these reasons, the above-mentionedinstitutional features in the 2006 NAVTEQ Landusedatabase (NAVTEQ, 2006) were overlaid on theCBLCD and used to reclassify underlying herbaceousand barren land-cover classes (e.g., barren, scrub/shrub, grassland, pasture, and cropland) to DOSbased on the assumption that sidewalks, narrowroads, and other features typically associated withthe DOS class are also typical characteristics of

TABLE 2. National Land Cover Dataset Impervious Surface and Turf Grass Coefficientsfor Developed Land Cover Classes in the Chesapeake Bay Watershed.

Land Cover Class Description Impervious Surface (%) Turf Grass (%)

Developed Open Space Ball fields and parks 5.82 94.18Low-Intensity Developed ½ acre lot residential areas 20.18 79.82Medium-Intensity Developed Multifamily residential areas, townhomes 44.60 55.40High-Intensity Developed Commercial areas 71.04 28.96


ESTIMATING THE EXTENT OF IMPERVIOUS SURFACES AND TURF GRASS ACROSS LARGE REGIONS

0 40 80 120 16020Kilometers

Counties

Modeling Segments

Albers Equal Area Projection

FIGURE 1. Chesapeake Bay Program Phase 5.3.2 Watershed Model Segmentation.

TABLE 3. General Equations Used to Estimate Impervious Surface and Turf Grass Area for P532.

Urban zone Impervious surface area = Area of developed land per CBLCD land-cover class * NLCD coefficients of impervious surfaceper class

Turf grass area = Area of developed land per CBLCD land-cover class * (1 � NLCD coefficients of impervioussurface per class)

Suburban zone Impervious surface area = Suburban road area + (# of single-detached houses * coefficients of impervious surface forsuburban residential lots)

Turf grass area = Total suburban area � (suburban impervious surface area + suburban forests)Rural Zone Impervious surface area = Rural road area + (# single-detached houses * coefficients of impervious surface for rural

residential lots)Turf grass area = ([# of single-detached houses * mean rural residential lot size] � [# of single-detached

houses * coefficients of impervious surface for rural residential lots]) * proportion ofherbaceous cover to forest cover along residential roads

Note: CBLCD, Chesapeake Bay Land Cover Data; NLCD, National Land Cover Dataset.



institutional properties. However, this assumptionwas not tested as part of this study.

Mapping Low to Medium-Density ResidentialAreas. Generally, the land within large residentiallots (>0.2 ha) is a combination of lawns and land-scaped areas planted with herbs, shrubs, and trees.These land-cover types are typically classed as pasture,cropland, or forests in Landsat-derived land-coverdatasets because of their similar spectral characteris-tics. To identify and map low to medium-density resi-dential subdivisions, all potential residential roads wereidentified from the 2006 NAVTEQ Streets database.Residential roads included roads that have low to

moderate traffic volumes and permit automobileaccess. Service roads, highway access ramps, bridges,and tunnels were excluded. The line density of resi-dential roads was calculated for circular radii of 100,200, 500, and 1,000 m, each resulting in a 30-m reso-lution raster dataset of residential road density. Eachof these datasets was classified using Jenks’ naturalbreaks algorithm (Jenks, 1967) and overlaid on aerialimages to visually determine the optimum radius andclassification threshold for mapping residential sub-divisions.

As a result of this exercise, the length of roadswithin a 200-m radii circle was determined to bestapproximate the extent of residential subdivisions

BALTIMORE

HARFORD

HOWARD

PENNSYLVANIA

BALTIMORE CITY

Urban ZoneSuburban ZoneRural Zone

0 4 8 12 162Kilometers

FIGURE 2. Example of Urban, Suburban, and Rural Zones in Baltimore County and Surrounding Areas.



while minimizing commission errors (classifyingundeveloped areas as residential) and omission errors(excluding residential lands from the residentialclass). The 200-m road-density raster was dividedinto nine natural break classes. Classes six throughnine represented areas of highest road density andwere expanded into adjacent lower density classesfour and five to establish a potential residentialextent. Residential roads intersecting these potentialresidential areas were buffered by 60 m, clipped tothe potential residential extent, and then expanded100 m into adjacent buffered residential road areas.A 60-m buffer width was chosen based on theassumed average lot size of 0.2 ha, which roughlycorresponds to a 35-m 9 60-m rectangular lot with35 m of road frontage. The resulting raster dataset ofpotential residential land use was overlaid on the2006 CBLCD and incorporated into it by reclassifyingall developed, cropland, pasture, grassland, scrub/shrub, and forest CBLCD classes overlapping residen-tial areas and outside of urban areas to a new classcalled “residential.” The remaining CBLCD land-cover classes (e.g., open water, unconsolidated shore,and emergent wetlands) were unchanged. Barren andundeveloped residential pixels within 30 m of urbanareas were reclassified to match the nearest urbandeveloped pixel value (21-24) based on the assump-tion that these areas were more likely to exhibithydrologic functions typical of developed areas com-pared to functions typical of barren, forest, or agricul-tural land uses. The resulting raster dataset wasrenamed the Phase 5.3.2 Land Cover Dataset (P532).

Mapping Forests in Low-Density ResidentialAreas. Landsat-derived land-cover datasets like theCBLCD and NLCD include four “forest” classes thatrepresent areas with mature tree canopy and a scrub/shrub class that, in the Mid-Atlantic region, typicallyrepresents the early to mid-successional stages of for-est growth (Homer et al., 2004). The characteristics ofthe forest understory are not evident in the CBLCDbecause dense tree canopy obscures the underlyingland cover. In residential areas, the understory is

assumed to be composed of impervious surfaces and/orturf grass. In large unfragmented wooded areas, how-ever, the understory is assumed to be unmanaged withhydrologic functions more similar to forests thanlawns. Based on these assumptions, interior forest/scrub areas within patches greater than or equal to1 ha in the original 2006 CBLCD that were classifiedas residential in the P532 were reclassed back to theiroriginal forest or scrub/shrub values. To identify inte-rior forest/scrub areas, contiguous CBLCD forest andscrub/shrub pixels were grouped together and bufferedinward by 30 m. The interior patches were then frag-mented using a 30-m raster dataset representing allroads in the 2006 NAVTEQ Streets database. Theremaining unfragmented interior forest and scrub/shrub areas greater than or equal to 1 ha were thenreincorporated back into the P532.

Correcting Misclassifications of DevelopedLands as Barren or Surface Mines. The CBLCDbarren class generally represents areas denuded ofvegetation. Such areas might be quarries, rock out-crops, surface mines, clear-cut forests, beaches, mudfl-ats, areas cleared for agriculture, or represent theinitial stages of residential or commercial develop-ments. Likewise, areas classed as developed couldactually be quarries or surface mines. To accuratelyestimate the extent of developed lands in the Chesa-peake Bay watershed in 2006, misclassification amongthe CBLCD developed, extractive, unconsolidatedshore, and barren land-cover classes had to be mini-mized.

First, discrepancies between barren and extractivewere addressed using state mining permit data thatwere assumed to be spatially and categorically accu-rate. The CBLCD does not contain an “extractive”(quarries and surface mines) land-cover class. To fillthis data gap, spatial information on the type andextent of permitted, restored, and active surfacemines was obtained from each state (Table 4). OnlyWest Virginia and Delaware provided spatial polygoninformation delineating the size and shape oftheir mining operations. New York, Pennsylvania,

TABLE 4. Surface Mine Data Sources.

State Agency Dataset

Delaware Office of Management and Budget Geographic Data Committee 2007 ExtractiveMaryland Department of Environment Minerals, Oil and Gas Division 2009 Surface MinesNew York Department of Environmental Conservation Division of Mineral Resources Geographic Information System (GIS)

Mines through 2009Pennsylvania Department of Environmental Protection Bureau of Mining and Reclamation Reclaimed or Forfeited Mines

in PA, 2009-09-29Virginia Department of Mines, Minerals and Energy Active PermitsWest Virginia Office of Abandoned Mine Lands and Reclamation (AMLR) of the

West Virginia (WV) Department of Environmental ProtectionWV Permit Boundaries



Maryland, and Virginia provided attributed pointlocations.

All six states reported permitted acres and, exceptfor Maryland, all reported some measure of active ordisturbed acres (USEPA, 2010a, b). West Virginia pro-vided a polygon dataset delineating the extent of dis-turbed areas. Delaware provided a detailed land-covermap with an extractive class. Pennsylvania’s datasetcontained an “authorized” category corresponding toportions of permitted acres actively mined. The NewYork and Virginia datasets had “permitted” and “dis-turbed” field entries. Maryland’s dataset included onlyinformation on permitted acres, not active or disturbedacres. Because permitted acres may vastly exceed thearea of active mining, active acres for Maryland wereapproximated using a nonlinear regression equationderived from relating permitted and disturbed miningareas reported in Virginia. There was a positive corre-lation (R2 = 0.79) between the extent of permitted anddisturbed mining areas in Virginia.

Surface mine data that were spatially representedas points were converted into circular polygons equalin size to the reported or modeled active acreage ateach site. The circles were merged together with theextractive data provided originally as polygons to forman extractive land-use mask. Overlaying the extractivemask on the P532 revealed areas of potential misclas-sification where patches of contiguous clusters ofbarren-classed pixels were recognized as quarriesor surface mines in the ancillary state datasets. Allpatches of barren land in the P532 that intersected theextractive land-use mask were reclassed as “extrac-tive.” All patches of barren land in the P532 that inter-sected patches of developed land-cover pixels, but didnot intersect the extractive land-use mask or patchesof unconsolidated shore pixels were reclassified as “res-idential.”

Overlaying the extractive mask on the P532 alsorevealed quarries or surface mines that were misclassi-fied as development. Unfortunately, the methods usedto identify extractive areas misclassified as barren donot work effectively for identifying extractive areasmisclassified as development. Quarries and small sur-face mines can be nested within large spatially contig-uous developed areas and therefore it is inaccurate toassume that a large patch of developed land partiallyintersecting an area presumed to be extractive shouldbe reclassified in its entirety as extractive. Therefore,only the portions of developed patches that underlaythe extractive mask were reclassed as extractive.

Mapping Urban, Suburban, and Rural Zones

In densely developed areas, residential lot sizestend to be small and man-made structures typically

dominate the spectral reflectance within 30-m resolu-tion Landsat pixels. In such regions, we assumed thatdeveloped lands were accurately represented in theCBLCD. Therefore, only Landsat-derived land-coverdatasets were used to characterize densely developedareas defining the urban zone.

Densely developed areas include all interiorpatches of developed lands (i.e., patches of contiguousdeveloped pixels in the 2006 CBLCD within 60 mfrom the patch edge) with a minimum area of 1 haand intersecting major roads. These criteria eliminatesmall patches of developed lands and narrow linearfeatures from inclusion in the urban category. Majorroads are roads that provide for high volumes of traf-fic as defined using the functional class attributes inthe NAVTEQ streets database (NAVTEQ, 2006). Toeliminate small gaps between patches of developmentand densely developed areas resulting from the aboveprocess, all developed lands within 500 m and contig-uous to densely developed areas were incorporatedinto the urban zone.

The suburban zone was defined as all areas classedas “residential” in the P532. All areas outside of theurban and suburban zones were considered to be partof the rural zone.

Estimating Impervious Surfaces and Turf Grass Areain the Urban Zone

In each WSM modeling segment, the extent ofimpervious surfaces within the urban zone was esti-mated by multiplying the area of each developedland-cover class in the P532 by county-level estimatesof the mean percent imperviousness for each devel-oped class derived from the NLCD. Average county-level impervious surface percentages per developedland-cover class, i.e., impervious coefficients, wereused in place of calculations derived from overlayingthe 2001 NLCD on the 2001 CBLCD due to spatialdisagreement between the developed pixels in theCBLCD and impervious pixels in the NLCD. The spa-tial disagreement was particularly influential in sparse-ly developed counties. Due to the high degree ofcoefficient variation at the county level, county-levelcoefficients were preferred for use at the modelingsegment scale compared to more generalized state-level coefficients (Tables 5 and 6). Note that in somecounties, the impervious coefficient values were belowthe minimum value of the range used in the NLCDto define the developed classes. For example, by defi-nition LID impervious values range from 21 to 50%(Homer et al., 2004, 2007) yet in some counties, LIDareas in the CBLCD averaged less than 1% impervi-ous. To minimize the impact of spatial disagreementon impervious coefficient values, the first quartile of



county-level coefficients was calculated for all coun-ties in each state and set as the minimum coefficientvalues for each state. Pervious portions of each devel-oped land-cover pixel were used to represent theextent of turf grass in the urban zone.

Estimating Impervious Surfaces on Suburban andRural Residential Lots

The county-level impervious surface coefficientscoupled with the CBLCD developed land-cover classeswere not used in suburban and rural areas becausethe developed classes in the CBLCD do not suffi-ciently capture all suburban and rural developedlands. Instead, information on roads, single-detachedhousing units, and sampled estimates of imperviousarea per residential lot were used in suburban andrural areas. As an initial step, impervious surfacesassociated with suburban and rural residential lotswere sampled. Fifty sample points were randomlyallocated at least 1.6 km apart (to minimize spatialautocorrelation effects) along residential roads within

the suburban zone in each state and 50 additionalpoints were similarly allocated per state along resi-dential roads within the rural zone. Sampling wasnot conducted within the rural zone in the District ofColumbia due to its minimal extent in the District.For each sample point, impervious surfaces associ-ated with the nearest residence including the house,driveway, and associated outbuildings were heads-updigitized at a 1:200 scale from 1-m aerial imageryusing 2005-2009 leaf-on imagery collected by theFarm Service Agency’s National Agriculture ImageryProgram (NAIP) or leaf-off imagery collected by stateresource agencies. Of the 650 original sample points,impervious surfaces were digitized for 397 suburbanand rural residences. Sample points were eliminatedfrom consideration if they were further than 0.16 kmfrom the nearest residence or if they landed withinnonresidential areas (e.g., parks, cemeteries, airports,surface mines, farms, or apartment complexes). Thefinal sample size in each state in the suburban zoneranged from 17 to 34 and from 21 to 44 in the ruralzone (Table 7). The median values of impervious sur-face area within suburban and rural zones in each

TABLE 5. State-Level Mean Percent Impervious Coefficients per Developed Land-Cover Class.

Development Class District of Columbia Delaware Maryland New York Pennsylvania Virginia West Virginia

DOS 8.7 5.2 4.8 7.5 7.3 6.0 4.9LID 27.7 20.0 20.4 21.0 20.1 19.9 17.3MID 53.7 43.4 44.6 50.6 41.7 45.3 40.7HID 78.9 74.6 75.8 76.2 64.9 71.0 64.9

Note: DOS, Developed Open Space; LID, Low-Intensity Developed; MID, Medium-Intensity Developed; HID, High-Intensity Developed.

TABLE 6. County-Level Mean Percent Impervious Coefficients per Developed Land-Cover Class.

Development Class Minimum (%) First Quartile (%) Mean (%) Maximum (%)

DOS 0.27 2.11 6.30 19.15LID 0.73 11.38 18.58 37.15MID 0 32.45 41.98 68.72HID 19.0 53.01 64.72 90.78

Note: DOS, Developed Open Space; LID, Low-Intensity Developed; MID, Medium-Intensity Developed; HID, High-Intensity Developed.

TABLE 7. Median Impervious Surface Area of Residential Lots from Sample Data.

Jurisdiction

Suburban Rural

Count Median (ha) Mean (ha) Count Median (ha) Mean (ha)

District of Columbia 25 0.029 0.041 n/a1 n/a n/aDelaware 34 0.046 0.050 44 0.060 0.099Maryland 17 0.054 0.060 43 0.072 0.154New York 21 0.039 0.041 41 0.046 0.054Pennsylvania 31 0.031 0.039 39 0.060 0.084Virginia 24 0.035 0.036 35 0.061 0.098West Virginia 22 0.031 0.045 21 0.044 0.054All states 174 0.037 0.044 223 0.057 0.095

1There were no rural zone areas within the District of Columbia.



state were used as the best measure of central ten-dency because of the relatively small sample sizesand the presence of a few high-value outliers in eachstate’s sample set.

Single-detached housing units as reported by theU.S. Census Bureau were used to represent the num-ber of residential lots within the suburban and ruralzones. Within each WSM modeling segment, thenumbers of single-detached houses within both zoneswere estimated using dasymetric aerial interpolationtechniques (Wright, 1936). Dasymetric mappinginvolves disaggregating spatial data (e.g., U.S. Cen-sus polygons attributed with housing unit data) to afiner unit of analysis (e.g., 30-m cells) using ancillarydata to weight the disaggregation of the spatial data(Eicher and Brewer, 2001; Mennis, 2003). The norma-tive approach for disaggregating population andhousing data is to use land-cover data to weight thedisaggregation (Reibel and Agrawal, 2007). Thedeveloped land-cover classes represented in regionallyconsistent land-cover datasets such as the CBLCDand NLCD, however, do not differentiate betweenresidential, commercial, and industrial land uses andas previously discussed under-represent the presenceof low-density development. Therefore, for this study,residential road density was used to weight the dis-aggregation of data on total housing units and single-detached housing units to 30-m raster cells. Roaddensity has been used previously for fine-scale map-ping of U.S. Census attributes (Claggett and Bisland,2004; Reibel and Bufalino, 2005) and is further sup-ported by the strong log-linear relationship betweenresidential road density and total housing unitswithin Census Block Groups in the Chesapeake Baywatershed (R2 = 0.80). Data from the 2009 AmericanCommunity Survey (ACS) 5-Year Estimates (U.S.Census Bureau, ACS, accessed January 5, 2011,http://www2.census.gov/) were used to approximatethe total number of housing units in the year 2007within 2000 Census Block Groups. Data from the2000 Decennial Census (U.S. Census Bureau, Decen-nial Population and Housing Censuses, accessed July1, 2010, http://www2.census.gov/) were used to repre-sent the total number of housing units and the totalnumber of single-detached housing units within 2000Block Groups. While Census Blocks represent the fin-est geographic level for reporting Census information(U.S. Census Bureau, 1994), Block Groups were usedfor this analysis to ensure consistent mapping proce-dures for allocating both total and single-detachedhousing unit values and to ensure consistencybetween allocating data from the ACS and DecennialCensus.

To create 30-m resolution raster datasets of totalhousing units in 2000 and 2007 and single-detachedhousing units in 2000, potential residential areas in

the watershed were identified by excluding all publicand protected lands, open water, emergent wetlands,beach areas, and slopes greater than 21% from the200-m radii residential road density dataset used tomap the suburban zone. The remaining road densityvalues were summed within every 2000 Census BlockGroup. Block Groups were then converted to a 30-mresolution raster dataset using the total road densityattributes as the raster value. The residential roaddensity raster was divided by the Block Group totalroad density raster to produce a relative road densitydataset with the value of each pixel representing thepercentage of a Block Group’s total residential roaddensity falling within it. Finally, the Census BlockGroups were converted to three 30-m rasters usingthe total number of housing units in 2000, 2007, andsingle-detached housings in 2000, as the raster val-ues. These rasters were multiplied by the relativeroad density raster to produce the final dasymetricmaps of single-detached housing units and total hous-ing units. The values of these three raster datasetswere totaled within each WSM modeling segment.The total numbers of single-detached housing unitsin 2007 within suburban and rural areas were esti-mated by multiplying the total number of housingunits in 2007 by the ratio of single-detached to totalhousing units in 2000. The estimated number of sin-gle-detached housing units in 2007 was then multi-plied by the sampled state-level suburban and ruralresidential impervious surface coefficients to estimatesuburban and rural impervious area associated withresidential development. Note that the CBLCD devel-oped pixels outside of urban areas were not consid-ered in the estimates of suburban and ruralimpervious surface area due to the inability to avoiddouble counting impervious surfaces associated withroads and houses that are incompletely detected inthe CBLCD.

Estimating Impervious Surfaces for Suburbanand Rural Roads. In addition to estimating theextent of impervious surfaces associated with residen-tial lots, the area of impervious surface associatedwith suburban and rural roads was assessed. To mea-sure road area, information on road length and thecombined widths of paved lanes and compacted shoul-ders were needed. Road length was obtained from the2006 NAVTEQ Streets database. Information on roadand shoulder width was inferred and varied substan-tially by road type. The NAVTEQ Streets databaseincluded attributes on the number of lanes, directionof travel, controlled access, functional class (e.g., aclassification based on levels of access, traffic volume,and speed changes), and travel speed for all roads.These data were used to develop 17 unique possibleroad “types.” Ninety percent of the total road and



shoulder area in the watershed was composed of 2-lane/2-way noncontrolled access roads followed by8+ lane roads (~6% of road area) and 4- to 6-laneroads (3% of road area) (Table 8). The remaining roadarea, less than 1%, was composed of 1-lane/1-wayroads. All roads not represented in the NAVTEQStreets database, including most dirt and gravelroads, were not counted in this analysis.

Published recommendations for lane and roadwidth varied. For example, recommended lane widthsvaried between 3.0 and 3.6 m (9.8-11.8 ft) and shoul-der widths ranged from 1.2 to 4.9 m (4-16 ft)(AASHTO, 2004; VDOT, 2005; FHWA, 1997). Due tothe variation in published recommended lane andshoulder widths, the combined lane and shoulderwidths of the three major road types were randomlysampled in the three largest Chesapeake Baywatershed jurisdictions: Pennsylvania, Maryland,and Virginia (Table 9). The largest road class, 2-lane/2-way roads, was further divided into suburban andrural 2-lane/2-way roads based on the assumptionthat parking, turning, and bike lanes, hardenedmedians, and extra-wide residential streets may beprevalent in the suburban zone, but not in the ruralzone.

In each of the three states, 50 sample points wererandomly placed along the four classes of roads sepa-rated by a minimum distance of 1 km to reduce biastoward oversampling in areas with dense road net-works. Of the 600 possible samples, 510 were classi-fied using heads-up digital measurement of hardenedroad and shoulder surfaces from 2005 to 2009 leaf-onNAIP imagery or leaf-off state agency imagery. Gen-erally, these measurements were conducted at a1:200 scale. If trees or shadows obscured the roadswithin 0.16 km of the sample point locations, thosesamples were eliminated. The statistics developedfrom this sampling effort were used to inform selec-tion of road and shoulder widths by road type fromthe range of published recommendations (Table 8).Due to the variability in the sampled widths of4- to 6-lane/2-way roads, NAVTEQ database attribute

TABLE 8. Selected Combined Road andShoulder Width Estimates by Road Type.

Road TypeRoad

Area (%)

RecommendedRoad

Width (m)

ModeledRoadWidth(m)

1-lane/1-way 0.37 3.7 3.7Suburban2-lanes/2-way

10.53 6.7-11.0 7.9

Rural 2-lanes/2-way 79.66 6.7-11.0 6.74-6 lanes/2-way 3.00 12.8-25.6 7.9, 11.0,

or 17.78+ lanes/2-way 6.44 17.7-18.3 17.7

TABLE

9.Estim

atedCom

bined

Width

ofRoa

dLanes

andShou

ldersbyRoa

dTypefrom

Sample

Data.

Pennsy

lvania

Maryland

Virgin

ia

Suburban

2-lanes/

2-w

ay

Rural

2-lanes/

2-w

ay

4-6

lanes

8+lanes

Suburban

2-lanes/

2-w

ay

Rural

2-lanes/

2-w

ay

4-6

lanes

8+lanes

Suburban

2-lanes/

2-w

ay

Rural

2-lanes/

2-w

ay

4-6

lanes

8+lanes

Cou

nt

39

41

39

50

38

41

45

49

36

35

47

50

Med

ian(m

)8.15

6.70

9.97

14.42

7.80

6.72

12.10

18.64

8.59

6.02

10.14

13.84

Mea

n(m

)8.75

6.48

10.62

15.48

8.66

7.14

13.04

18.82

8.72

6.20

11.43

14.58

1st

Quartile(m

)6.73

5.00

8.64

12.62

7.09

6.00

7.83

14.81

6.59

5.06

7.73

10.89

3rd

Quartile(m

)10.39

7.63

11.71

16.91

9.74

7.56

16.28

21.97

10.86

7.01

13.92

17.25



information on functional class and speed categorywere further used to refine the estimates of road laneplus shoulder widths. The area of impervious surfacewas estimated for each road type by multiplying roadlength by the estimated combined width of all lanes andshoulders based on the assumption that evengravel shoulders are compacted and therefore effectivelyfunction as impervious surfaces.

Estimating Turf Grass Area in Suburban andRural Zones. To estimate turf grass extent in thesuburban zone, the area of impervious surfaces asso-ciated with residences and roads was subtracted fromthe total suburban zone area. In the rural zone, repli-cating this process required a means to estimate thetotal area associated with rural residential develop-ment starting with an estimate of average rural resi-dential lot size. The 2007 MdProperty View database(MDP, 2007) was used to estimate average rural resi-dential lot size. MdProperty View contains data onlot size, land use, and structural attributes and repre-sents all tax parcels in Maryland with pseudo-cen-troid points. All residential parcel points within areasdefined as rural in this study were selected for analy-sis. From those data, all records that were describedas “split foyer 2 levels of living area,” “split level 3 ormore levels of living area,” or “standard single familyunit 1, 2 or 3 story” were selected to approximate theU.S. Census Bureau’s definition of “single-detached”housing units. The distribution of residential lot sizeswas analyzed to identify a representative averagemeasure (Figure 3). Low-value outliers in the lot size

distribution were eliminated based on the assumptionthat rural lots less than the sampled median area ofimpervious surface on rural residential lots, 0.057 ha,are unrepresentative. Likewise, large lots, over 8 ha,were rare but exerted disproportionate influence onthe mean of the distribution. Rather than excludethese data, the sizes of all large lots were reduced to8 ha to partially reflect the presence and influence oflarge residential lots in Maryland.

After addressing the outliers, the mean of the dis-tribution between 0.057 and 8 ha was calculated(0.91 ha) and used to represent the size of all ruralresidential lots in the Chesapeake Bay watershed.The mean lot size multiplied by the number of ruralsingle-detached housing units in each modeling seg-ment was used to estimate total rural residentialarea. Subtracting the area of impervious surface asso-ciated with rural residences yielded an estimate oftotal residential pervious area. Unlike denser subur-ban residential developments, however, the perviousportions of large rural residential lots may be woodedand unmanaged, performing nutrient and sedimentprocessing functions more similar to forests than tolawns. To differentiate potential rural turf grass areafrom woodland, the relative proportions of forest toherbaceous land-cover types along rural roads wereestimated within each modeling segment. For thisanalysis, it was assumed that the majority of ruralhouses were located close to roads and that if lawnswere present on these lots, they would be locatednear the houses. Therefore, the 30-m raster datasetof residential roads in rural areas was buffered by

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0.1

0.3

0.5

0.7

0.9

1.1

1.3

1.5

1.7

1.9

2.1

2.3

2.5

2.7

2.9

3.1

3.3

3.5

3.7

3.9

4.1

4.3

4.5

4.7

4.9 10 20 30 40 50 60 70 80 90 100

1000

0

Resi

den

al P

arce

ls (c

ount

)

Lot Size (hectares)

FIGURE 3. Distribution of Residential Lot Sizes in the State of Maryland.



60 m and the ratio of all barren, grassland, cropland,and pasture areas to wooded areas (deciduous, mixed,and evergreen forest, scrub/shrub and woody wet-lands) was calculated within the buffer. This ratiowas multiplied by the total pervious area associatedwith rural residential development in each modelingsegment to estimate the acreage of rural turf grass.Turf grass associated with rural road medians andshoulders was not accounted for in this study.

Back-Casting Developed Land Area from 2006 to1984

Due to the substantial underestimation of devel-oped land and impervious cover in the CBLCD andthe lack of historical road data comparable to the2006 NAVTEQ Streets database, the extent of imper-vious surfaces and turf grass over the 20-year hydro-logic calibration period (1985-2005) of the WSM wasmodeled rather than directly measured with satellitedata. The CBLCD was used to establish the mini-mum extent of developed land in each WSM modelingsegment for 1984, 1992, 2001, and 2006. Estimatedchanges in total housing units at the modeling seg-ment level were used to assess decreases in developedland from 2006 to 1984 based on the strong correla-tion between total housing units to developed landcover at the modeling segment scale in theChesapeake Bay watershed (R2 = 0.78). Because themodeling segmentation is nested within counties,county-level estimates of total housing units wereused as control totals for the segment level estimates.The estimated numbers of housing units in eachcounty in 2006 and 2001 were obtained from the U.S.Census Bureau (U.S. Bureau of Census, PopulationEstimates, accessed October 1, 2010, http://www.census.gov/popest/estimates.html). The numbers of housingunits in 1984 and 1992 were derived by multiplying thenumber of housing units reported in the 1980 and1990 Decennial Censuses (Geolytics, CensusCD 1980,Release 2.15; U.S. Census Bureau, 1990 Census ofPopulation and Housing, Summary Tape File 1,accessed October 1, 2010, http://www2.census.gov/), bythe proportional change in county-level populationestimates between 1980 and 1984 and between 1990and 1992, respectively.

Methods used in the New Jersey Growth AllocationModel, GAMe (Reilly, 2003) were applied to estimatethe total number of housing units in 1984 and 2006to complement more direct estimates of the numberof total housing units produced for 1992 and 2001.GAMe was originally developed in the mid-1990s forthe New Jersey Office of State Planning. The modelhas been used effectively to generate municipal-scaleforecasts of housing and office space demand that

account for the availability of vacant housing, officespace, and open land for development while main-taining consistency with county-scale projections ofpopulation and employment (Reilly, 1997, 2003).GAMe uses the Gompertz family of growth curves torepresent development growth rates. Gompertzcurves have an exponential “S” shape that makesthem especially suitable for modeling situations inwhich: (1) growth is at first slow (such as when anagricultural or forested area is beginning to be devel-oped); (2) growth takes place rapidly (such as whenfairly large tracts are being developed into suburbanhousing subdivisions); and (3) growth trails off, butdoes not stop (such as when marginal areas withinsuburban areas or cities are developed or urban re-development entails marginal increases in housingdensity). In most modeling segments, extrapolationsof development produced with a linear equation aresimilar to those produced using the Gompertz equa-tion. However, the Gompertz family of curves is moresuitable for use than linear growth functions forextrapolating future development in areas that expe-rienced high historic growth rates and land availabil-ity is a constraining factor. Therefore, to ensure thatthe backcast of land use is methodologically consis-tent with planned forecast methods used in the Ches-apeake Bay watershed, the Gompertz equation wasused in place of linear extrapolation.

To estimate the number of total housing units ineach WSM modeling segment for 1992, a 30-m resolu-tion raster dataset of total housing units in 1990 wascreated using the dasymetric mapping methodsdescribed above and 1990 Census Block Groups. Thetotal numbers of housing units in 1990 were thensummarized by WSM modeling segment to comple-ment the previously summarized housing unit totalsfor 2000 and 2007. The total number of housing unitsin 1992 and 2001 were estimated by linearly inter-polating at the modeling segment scale between theyears 1990, 2000, and 2007. This approach benefitedfrom the fact that the geography of modeling seg-ments is nested within county boundaries.

Total housing unit trends over the 1990s at themodeling segment scale were fit to a Gompertz curve,which was then used to extrapolate housing trendsbackward to 1984 and forward to 2006. The county-level population estimates for 1984 and 2006 weretranslated into estimates of total housing units toconstrain the modeling segment scale forecasts gener-ated using the Gompertz curve equation. To convertthe 1984 Census population estimates to housingunits, total housing units from the 1980 and 1990Decennial Census were interpolated to 1984 based onthe proportional annual change in county-level popu-lation estimates reported by the U.S. Census.County-level housing unit estimates for 2006 were



obtained directly from the U.S. Census Bureau’s Pop-ulation Estimates Program. The Gompertz curveequation was used to estimate total housing units in2006 and 2007. We then adjusted our 2006 estimatesat the modeling segment scale using the ratio of the2007 estimates to the 2009 ACS 5-Year Estimates.

To estimate the extent of development in 2001,1992, and 1984 at the modeling segment scale, thearea of developed land in 2006 was multiplied by thepercentage change in total housing units from 2006to previous years. The estimates of developed landarea for 1984, 1992, 2001, and 2006 were thendivided into subcategories of impervious and turfgrass area using fixed proportions of impervious topervious area by modeling segment developed for theyear 2006 in this study.

This approach assumes that changes in rural, sub-urban, and urban areas are equally proportionalto changes in total housing units. Despite the stronglinear relationship between residential housing densityand impervious surface land cover (Bierwagen et al.,2010), local underestimates occur in urban areas dom-inated by commercial and industrial development.Overestimates occur in rural areas where tree canopymay obscure underlying impervious surfaces (Jantzet al., 2005; Theobald et al., 2009) or where the imper-vious features are smaller than the 30-m pixel resolu-tion of Landsat imagery (O’Neill et al., 1996). In thisstudy, housing and impervious surfaces are correlatedby definition because housing data were used in partto estimate impervious surfaces in rural areas. Inurban areas, where localized areas may be dominatedby commercial and industrial development, relianceon the CBLCD to represent the minimum historicalextent of development prevented modeling reductionsin historic developed area below that which can bediscerned from Landsat satellite imagery.

Verification

These methods include numerous assumptions thatimpact the estimated extent of developed lands in theChesapeake Bay watershed. To verify the estimatesof impervious cover, high-resolution impervious sur-face data representing all roads, driveways, struc-tures, and other impervious features in eight counties(i.e., Baltimore, Anne Arundel, Frederick, and Mont-gomery in Maryland; Lancaster in Pennsylvania; andall three counties in Delaware: Kent, New Castle,and Sussex) in the watershed circa 2004-2007 wereobtained from county agencies. These counties repre-sent a range of development intensities and patternsfrom mostly rural (Kent County, Delaware) to mostlydeveloped (Montgomery County, Maryland). The datawere converted to binary 1-m resolution raster data-

sets and summarized to a 30-m grid aligned to theP532. The percent impervious cover within each 30-mcell was calculated so that both the spatial footprintand level of impervious surface within developedareas could be compared. In addition, impervious sur-face area within each county was assessed separatelyin the urban, suburban, and rural zones to help iden-tify the source of errors.

The proportions of rural residential lot area in turfgrass in Baltimore County, Maryland were comparedwith similar proportions derived using high-resolutionland-cover data developed by the University ofVermont from 2007 NAIP imagery and rural residen-tial parcel boundaries obtained from the BaltimoreCounty Office of Information Technology. Residentialparcel boundaries within the rural zone were overlaidon the high-resolution land-cover data to assess theproportions of rural residential land in turf grass andto interpret the accuracy of the rural residentialimpervious surface coefficients.

To validate the overall estimates of turf grass inthe watershed, state-level statistics on the area ofagricultural land and turf grass produced by theUSDA Natural Resources Conservation Service andNational Agricultural Statistics Service were exam-ined. These data were compared with estimates fromthis study to provide a state-level verification of turfgrass estimates for Maryland.

RESULTS AND DISCUSSION

Including ancillary data on roads, institutions,housing units, and impervious surface and roadwidth coefficients substantially increased the esti-mated area of developed land in the Chesapeake Baywatershed (Table 10). The methods outlined hereproduced estimates of impervious surfaces and turfgrass that were 57 and 45% higher, respectively, forthe Chesapeake Bay watershed than methods basedsolely on the CBLCD. For reference purposes,

TABLE 10. Comparison of Developed Land UseExtents for 2006 in the Watershed.

Land Cover/Use DatasetImperviousSurface (ha) Turf Grass (ha)

CBLCD 327,520 947,604NLCD 397,707 1,456,540P532 Land Use Dataset 513,559 1,375,420P532-CBLCD difference 56.80% 45.15%P532-NLCD difference 29.13% �5.57%

Note: CBLCD, Chesapeake Bay Land Cover Data; NLCD, NationalLand Cover Dataset.



impervious and turf grass areas were also estimatedfrom the 2006 NLCD impervious surface dataset forthe Chesapeake Bay watershed. Impervious surfaceand turf grass areas estimated from the 2006 NLCDimpervious surface dataset were 29% lower and 6%higher, respectively, compared with the P532 esti-mates. These findings appear to contrast with twoprevious studies that documented low actual errorsin impervious surface estimates derived from theNLCD over large areas (Greenfield et al., 2009;Nowak and Greenfield, 2010). However, they are con-sistent with the low actual, but high relative errorrates outside dense urban areas documented throughan analysis of error that was stratified by develop-ment intensity over sampled areas within the Chesa-peake Bay watershed (Jones and Jarnagin, 2009).Our methods and results explicitly demonstrate theaccumulation of large relative differences in smallactual numbers across the majority of the Chesa-peake Bay watershed that is not densely developed.

The NLCD provided a more accurate representa-tion of total impervious surface and turf grass areacompared with the CBLCD. The difference betweenhow developed lands were represented in the CBLCDand NLCD was largely due to the use of bufferedroads as part of an urban mask used to minimizecommission errors in the NLCD impervious surfacedataset (Homer et al., 2007). An urban mask was notused in the development of the CBLCD. Rural ter-tiary roads are generally represented in the NLCDimpervious surface dataset as pixels ranging from 1to 10% impervious (Homer et al., 2007). These pixelsmake up 45% of the total NLCD developed area inthe Chesapeake Bay watershed and 56% of the NLCDturf grass area assuming that the non-imperviousportion all developed pixels is turf grass. In contrast,the P532 land-use methodology allows for the inde-pendent assessment of impervious surfaces associatedwith residential lots and roads, assumes realistic roadwidths for different types of roads, and assumes thatall of the turf grass in rural areas is associated withresidences rather than mostly with roads.

We did not assess the impact of these increasedimpervious surface and turf grass area estimates onnutrient and sediment loads to the Chesapeake Bayas part of this study, however, given past perfor-mance of the WSM, initial estimates of nutrient andsediment loads to the Chesapeake Bay from thesesources are expected to increase in proportion to theirestimated areas. Increased loads from developedlands will result in a decrease in loads from land usessuch as agriculture and/or forests that were con-verted to development. In the Chesapeake Baywatershed, 55% percent of development observedfrom 1984 to 2006 in the CBLCD series occurred onagricultural land and 45% on forested lands.

Because the WSM is calibrated to monitored loadsin the rivers, the loads from all sources must beincreased or decreased in proportion to the ratio oftotal source loads to total monitored loads whilemaintaining per-acre loading rates consistent withthose published in the peer-reviewed literature. Sowhile increasing the estimated area of impervioussurfaces and turf grass will increase loads from devel-opment, the absolute increase may be magnified orreduced through the WSM calibration process. Fur-thermore, because development represents only11.4% of the Chesapeake Bay watershed land areaand previously was estimated to represent only 7% ofthe land area, compensatory changes in agriculturaland forest loads are likely to be minor in most areas(Gary Shenk, U.S. Environmental Protection Agency,August 14, 2012, personal communication).

Approximately 51% of the impervious surfaces and48% of the turf grass in the Chesapeake Baywatershed were identified within the urban zoneusing Landsat-derived land-cover data even thoughthe urban zone covered less than 6% of the land areain the watershed (Table 11). The suburban zone cov-ered only 1% of the watershed and accounted for1.6% of the impervious surfaces and 13.5% of the turfgrass in the watershed. The rural zone accounted for93% of the land area, 17.7% of the impervious sur-faces and 38.1% of the turf grass in the watershed.While the suburban zone did not account for a largepercentage of the developed lands, it can be mappedat 30-m resolution and incorporated directly intosatellite-derived land-cover datasets to improve theaccuracy of those products. The same is true forancillary spatial data on institutional properties.

Roads in both suburban and rural areas accountedfor the remaining 29.3% of impervious surfaces. Themajority of the difference between the P532 and P530impervious surface estimates resulted from the

TABLE 11. Contribution of Residences and Roads to RegionalImpervious Surface and Turf Grass Area Estimates

for 2006 in the Chesapeake Bay Watershed.

Source

Zone

Area

Impervious

Surfaces Turf Grass

(ha) (ha) (%) (ha) (%)

Urban zone 950,229 263,887 51.38 664,968 48.35

Suburban zone,

residential lots

221,310 8,162 1.59 186,099 13.53

Suburban

zone, roads

16,431 3.20 0 0.00

Rural zone,

residential

lots

15,295,590 90,779 17.68 524,353 38.12

Rural zone,

roads

134,300 26.15 0 0.00

Total 16,467,129 513,559 100.00 1,375,420 100.00



inclusion of rural roads and residences in the esti-mates. This is because of the large size of the ruralzone coupled with the presence of an extensive net-work of 2-lane roads and large numbers of dispersedresidences, both of which are difficult to consistentlydetect in Landsat-derived land-cover datasets (Moglenand Kim, 2007; Jones and Jarnagin, 2009). Ruralresidential impervious surface coefficients werealso consistently higher than suburban residentialcoefficients due to longer driveways and the presenceof outbuildings and other structures on rural proper-ties.

While the area of impervious surface estimated forthe P532 WSM is substantially higher than that esti-mated using the CBLCD, the P532 estimates werelower than estimates derived from high-resolutionimpervious surface data for all eight counties exam-ined to verify the P532 estimates (Figure 4). TheP532 estimates were most accurate in the more devel-oped counties where a large proportion of the imper-vious cover was located in the urban zone andestimated using the CBLCD. The majority of theunderestimates occurred in the more rural countiesand particularly in Delaware. The large underesti-mates in Delaware were partially due to a differencein imagery dates. Landsat satellite imagery used inthe coastal plain to create the 2006 CLBCD was col-lected in 2005 and the imagery used to create thehigh-resolution impervious surface data was collectedin 2007. While a two-year difference may seem minorand residential construction permits issued in thewatershed declined in 2006 and 2007 from their peak

in 2005 (Figure 5), there was still substantial con-struction activity during those years that couldinfluence the underestimate of rural development.Another reason for the rural underestimate was thatrural commercial and farm buildings were not repre-sented in the road and residential housing data usedto assess the extent of rural impervious area. Under-estimates of rural impervious cover in Delaware werehighest in Sussex County, the county with the mostchickens and associated structures in Delaware.According to our analysis of the 2007 USDA Censusof Agriculture, 85% (43.6 million) of broiler chickensin Delaware were located in Sussex County and theCounty contained 21% of all broiler chickens in theChesapeake Bay watershed. Underestimates ofimpervious surfaces in the suburban zone contributedto only a minor proportion of the county-level under-estimates.

The P532 estimates of impervious cover were alsocompared to the high-resolution county data sepa-rately within the urban, suburban, and rural zones todetermine whether the increased accuracy achievedat the county level is supported by increased accura-cies at the sub-county urban, suburban, and rurallevels (Table 12). For example, in Baltimore County,the underestimate of impervious cover in the P532dataset mostly occurred in urban areas. This could bedue to an underestimate of the urban footprint orto an underestimate in the impervious surfacecoefficients associated with each of the four developedclasses in the CBLCD. As shown in Table 13, theNLCD-derived county-level impervious surface

8,000

10,000

12,000

14,000

16,000

18,000

20,000

22,000

Hectares

0

2,000

4,000

6,000

,

Baltimore, MD Anne Arundel,MD

Frederick, MD Montgomery, MD Lancaster, PA Kent, DE New Castle, DE Sussex, DE

s

CBLCD (NLCD Coefficients) P532 Land Use Local Impervious Data

FIGURE 4. Comparison of Chesapeake Bay Land Cover Data (CBLCD), P532, and Local Impervious Data.



coefficient for DOS was much lower than the local coeffi-cient for DOS derived from high-resolution countydata and DOS composed 33% of the urban zone in Balti-more County. When the local coefficients were appliedto the CBLCD developed classes the extent of imper-vious surface in the urban zone increased by over1,000 ha and exceeded the P532 estimate, however, itwas still more than 1,600 ha lower than the estimatebased on local high-resolution impervious data. Thisindicates that in Baltimore County, the actual inten-sity of development in the urban zone was higherthan could be estimated using the mean percentimpervious surface per developed class. Median esti-mates of impervious surface percentages might pro-vide more accurate representations of centraltendency.

The suburban and rural residential impervioussurface coefficients are generally lower than previ-ously published impervious coefficients for residentialland uses in the Chesapeake Bay watershed (Cappiel-la and Brown, 2001). Cappiella and Brown (2001)estimated impervious cover associated with differentland uses and found values ranging from 0.043 ha ofimpervious surface for 0.2-ha residential lots up to0.086 ha for 0.81-ha lots. The coefficients publishedby Cappiella and Brown (2001) included impervioussurfaces associated with roads bordering residentialparcels that were not included in our sampling of res-idential impervious cover. This discrepancy does notexplain, however, all of the coefficient differences. Inrural areas the mean residential impervious surfacecoefficients are substantially higher than the mediancoefficient values. Moreover, based on our analysis of

high-resolution land cover and parcel boundaries inrural Baltimore County, the median amount ofimpervious surface on residential lots in the ruralzone was 0.035 ha compared to 0.072 ha derived fromour sampling approach. Surprisingly, however, ourestimate of rural impervious surface area in Balti-more County was just 8% higher than the area ofimpervious surface as measured with high-resolutionland-cover data (Table 12). This evidence suggeststhat our overestimate of impervious surfaces on ruralresidential lots may have been balanced by our notaccounting for impervious surfaces associated withcommercial, industrial, and agricultural operationsand/or that our analysis underestimated impervioussurfaces associated with rural roads.

The approach outlined in this article producedplausible estimates of turf grass area at the modelingsegment, local, and regional scales. At the modelingsegment scale, we found a strong linear relationshipbetween our modeled estimates of turf grass and esti-mates derived from high-resolution land-cover data(R2 = 0.77) excluding all small segments less than40 ha. In Baltimore County, our analysis of high-resolution land cover and residential parcel datashowed that 27% of rural residential land was grassand shrubs. Using an estimated average lot size of0.91 ha and land-cover proportions adjacent to ruralresidential roads as a proxy for parcel data producedan estimate of 28% turf grass associated with ruralresidential lots in Baltimore County. Our analysisassumed that residences are evenly distributed alongrural residential roads yet this is seldom the casegiven zoning, accessibility, and environmental

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Res

iden

tial B

uild

ing

Perm

its

Year

Bay Watershed

Virginia

Maryland

Pennsylvania

West Virginia

Delaware

New York

District of Columbia

FIGURE 5. Residential Building Permit Trends, 1990-2010, in Chesapeake Bay Watershed Counties.



restrictions impacting residential development deci-sions. The suitability of forests and farms for develop-ment and other factors influencing the location of

residential development also vary spatially and there-fore their proportions along rural residential roadsmay not directly reflect the proportions of forests and

TABLE 12. Modeled and Measured Estimates of Impervious Surface Area within Urban, Suburban, and Rural Zones.

Urban (ha) Suburban (ha) Rural (ha) Total (ha)

Baltimore County, MarylandA. CBLCD (NLCD coefficients) 14,386 77 672 15,135B. CBLCD (Local coefficients) 15,610 96 864 16,570C. P532 Land Use 14,495 279 3,421 18,195D. Local Impervious Data (2007) 17,259 277 3,168 20,704CBLCD Absolute Error (A-D)/D �16.65% �72.06% �78.79% �26.90%P532 Absolute Error (C-D)/D �16.02% 0.72% 7.99% �12.12%

Anne Arundel County, MarylandA. CBLCD (NLCD coefficients) 9,381 309 832 10,522B. CBLCD (Local coefficients) 11,425 448 1,207 13,080C. P532 Land Use 10,125 944 2,957 14,026D. Local Impervious Data (2007) 10,759 967 2,793 14,518CBLCD Absolute Error (A-D)/D �12.81% �68.00% �70.21% �27.52%P532 Absolute Error (C-D)/D �5.90% �2.33% 5.89% �3.39%

Frederick County, MarylandA. CBLCD (NLCD coefficients) 3,056 77 1,162 4,294B. CBLCD (Local coefficients) 4,121 102 1,527 5,750C. P532 Land Use 3,667 264 3,415 7,346D. Local Impervious Data (2005) 3,959 301 4,306 8,566CBLCD Absolute Error (A-D)/D �22.83% �74.51% �73.01% �49.87%P532 Absolute Error (C-D)/D �7.39% �12.16% �20.70% �14.24%

Montgomery County, MarylandA. CBLCD (NLCD coefficients) 10,538 102 505 11,145B. CBLCD (Local coefficients) 11,762 123 603 12,488C. P532 Land Use 11,477 663 2,379 14,519D. Local Impervious Data (2006) 12,616 554 2,058 15,229CBLCD Absolute Error (A-D)/D �16.48% �81.55% �75.44% �26.81%P532 Absolute Error (C-D)/D �9.03% 19.61% 15.59% �4.66%

Lancaster County, PennsylvaniaA. CBLCD (NLCD coefficients) 7,541 122 1,368 9,030B. CBLCD (Local coefficients) 7,017 111 1,262 8,390C. P532 Land Use 8,110 518 5,779 14,407D. Local Impervious Data (2008) 7,744 685 6,634 15,064CBLCD Absolute Error (A-D)/D �2.63% �82.20% �79.39% �40.05%P532 Absolute Error (C-D)/D 4.72% �24.40% �12.89% �4.36%

Kent County, DelawareA. CBLCD (NLCD coefficients) 2,788 74 676 3,538B. CBLCD (Local coefficients) 3,080 85 792 3,957C. P532 Land Use 3,001 315 2,445 5,761D. Local Impervious Data (2007) 3,428 618 4,101 8,147CBLCD Absolute Error (A-D)/D �18.65% �88.09% �83.51% �56.57%P532 Absolute Error (C-D)/D �12.46% �49.00% �40.38% �29.29%

New Castle County, DelawareA. CBLCD (NLCD coefficients) 10,821 84 519 11,425B. CBLCD (Local coefficients) 14,055 127 710 14,892C. P532 Land Use 11,450 331 1,730 13,511D. Local Impervious Data (2007) 14,821 513 2,430 17,764CBLCD Absolute Error (A-D)/D �26.99% �83.58% �78.63% �35.69%P532 Absolute Error (C-D)/D �22.74% �35.49% �28.81% �23.94%

Sussex County, DelawareA. CBLCD (NLCD coefficients) 3,859 207 1,193 5,259B. CBLCD (Local coefficients) 4,157 237 1,331 5,725C. P532 Land Use 4,282 736 4,243 9,261D. Local Impervious Data (2007) 4,738 1,274 7,106 13,117CBLCD Absolute Error (A-D)/D �18.55% �83.76% �83.20% �59.91%P532 Absolute Error (C-D)/D �9.62% �42.22% �40.29% �29.40%

Note: CBLCD, Chesapeake Bay Land Cover Data; NLCD, National Land Cover Dataset.



turf grass on residential lots in all counties. To fullyverify the accuracy of this approach, the verificationanalysis should be expanded to include additionalcounties representing a range of development pat-terns. Using the dasymetric housing datasets toweight land-cover proportions along rural roadsmight yield further improvements in accuracy.

State agricultural statistics offices have indepen-dently assessed the extent of turf grass in Maryland,Virginia, Pennsylvania, and New York (Schueler,2010). Only data for Maryland were used to verifythe estimates of turf grass in this study, however,because the assessment date, 2005, was only one yearprevious to the average date of the imagery used todevelop the 2006 CBLCD and because the P532 turfgrass extent was assessed in all Maryland countiesenabling a direct state-level comparison with industrystatistics. For 2005, there was a reported 458,900 haof turf grass in Maryland (NASS, 2006) compared toan estimated 394,000 ha of turf grass in 2006 basedon P532. This comparison shows that while the P532methods resulted in a 45% increase in turf grass esti-mates for 2006 compared to previous estimates forthe same year based solely on the CBLCD, the newestimates do not exceed an independent statewideassessment of turf grass area.

Because turf grass associated with large-lot resi-dential developments was commonly misclassified asagriculture in the CBLCD, improved accuracy andaccounting of turf grass area should result in more

accurate estimates of agricultural land. For 2007, theUSDA Census of Agriculture reported 630,700 ha ofagricultural land in Maryland. According to the 2006CBLCD, there was 856,500 ha of cropland and pas-tureland in Maryland. After accounting for turf grass,we estimated that there was approximately663,400 ha of cropland and pasture in Maryland in2006. While we still overestimated the area of agri-cultural land by 5%, we believe that it is a reasonableapproximation given that the error in the 2007 Cen-sus of Agriculture “Land in Farms” estimate is 1.7%root mean squared error (NASS, 2009). Moreover, inaddition to improving the state-level estimates ofagricultural land and turf grass, our methods spa-tially represent these data at the modeling segmentscale.

Estimates of the area of rural turf grass varied inproportion to the assumed average rural residentiallot size. For example, increasing the average rural lotsize by 25% increased rural turf grass area propor-tionally and resulted in an 11% increase in the totalturf grass area in the watershed. Changing the esti-mated median rural residential impervious coefficientby 25% (0.014 ha) resulted in a 23% change in ruralresidential impervious cover and a 4% change in thetotal impervious cover in the watershed. Assumptionsabout rural road and shoulder width also impactedour results. Varying the estimated combined roadlane and shoulder width for 2-lane/2-way roads byless than 1 m (~10% change) resulted in a 2.6%

TABLE 13. Mean Impervious Surface Coefficients (% of a 30-m cell) Derived Using the National Land Cover Dataset (NLCD)and Local High-Resolution Impervious Data within Chesapeake Bay Land Cover Data (CBLCD) Developed Classes.

Developed Open Space Low Intensity Medium Intensity High Intensity

Baltimore County, MarylandNLCD (county-level) 6.9 27.1 54.5 78.8Local Impervious Data 17.1 28.4 47.9 77.7

Anne Arundel County, MarylandNLCD (county-level) 8.7 23.6 51.4 75.0Local Impervious Data 20.6 31.7 50.7 80.7

Montgomery County, MarylandNLCD (county-level) 6.4 23.5 50.3 79.1Local Impervious Data 9.5 27.7 51.5 78.3

Frederick County, MarylandNLCD (county-level) 8.5 32.1 52.4 66.9Local Impervious Data 19.6 38.7 62.2 79.9

Lancaster County, PennsylvaniaNLCD (county-level) 9.7 19.8 46.9 70.5Local Impervious Data 7.8 19.3 41.3 69.1

Kent County, DelawareNLCD (county-level) 11.0 27.0 60.9 85.3Local Impervious Data 16.1 33.8 58.8 86.1

New Castle County, DelawareNLCD (county-level) 10.0 23.4 52.1 75.6Local Impervious Data 17.1 37.0 58.8 80.7

Sussex County, DelawareNLCD (county-level) 11.1 25.5 56.0 72.8Local Impervious Data 16.1 30.1 51.6 75.5



change in the total extent of impervious surfaces inthe Chesapeake Bay watershed.

Users of these data should exercise caution withderiving or interpreting rates of change. The historicextents of impervious surfaces and turf grass in 1984,1992, and 2001 were generally assumed to be propor-tional to changes in total housing units. This assump-tion is likely true in areas where commercialdevelopment (e.g., gas stations, restaurants, grocerystores) is dominated by the service sector. It may alsobe true in areas with mixed-use regional activity cen-ters where commercial buildings are mixed with high-density residential condominiums and townhomes.The assumption may not be true, however, in areaswith older commercial infrastructure and those domi-nated by industrial development such as airports,warehouses, and business parks. Setting a minimumvalue for historic developed area based on the CBLCDprovided a control on estimates of historical develop-ment and served to increase the estimated total area ofhistorical development by 0.6% compared to estimatesbased solely on changes in housing units.

Historical road development, lane expansions,increases in average home size, and direct measure-ment of changes in commercial, industrial, and agri-cultural structures were not accounted for in thisanalysis. Within the visible footprint of developmentidentifiable in Landsat satellite imagery, some ofthese phenomena such as new roads, commercialbuildings, and chicken houses may be represented.However, these features are incompletely representedand difficult to distinguish from the information onroads and residential development derived from ancil-lary data.

CONCLUSIONS

The methods outlined in this article produced moreaccurate impervious surface and turf grass area esti-mates at the modeling segment and county scalescompared with estimates based solely on the CBLCD,a Landsat-derived land-cover dataset. Combiningancillary data on housing, roads, and regionallyderived coefficients of road width, lot size, and resi-dential impervious surfaces with Landsat-derivedland-cover products greatly enhanced the accuracy ofregional impervious surface and turf grass estimatesand by default also improved the accuracy of agricul-tural land area estimates. Users of watershed imper-vious surface and turf grass area estimates derivedsolely from automated and semi-automated classifica-tions of Landsat satellite imagery should be awarethat those data may underestimate the extent of

impervious surfaces and turf grass outside of denselydeveloped areas. This caution is especially relevant tothe use of impervious surface thresholds as indicatorsof watershed impairment as noted by Moglen andKim (2007).

The methods used in this study are transferrableto other regions because the 2001 and 2006 NLCDand ancillary data used in this study are nationallyavailable in the conterminous U.S. The methods andassumptions, however, may have to be uniquelyadjusted in different regions of the country to accu-rately represent local variability and to ensure theuse of the best available data. Variability in impervi-ous surface coefficients for developed land-cover clas-ses and residential lots, rural residential lot size,suburban road densities, and road median and shoul-der management assumptions all impact regionalestimates of impervious surfaces and turf grass basedon our methods. If our methods are applied to otherregions, consideration should be given to stratifyingthe region, increasing sample sizes, and examininglocal land-use and land-cover data across a rural-urban gradient to better understand and representlocal variability. The frequency distributions of sam-pled parameters such as residential lot size andimpervious surface area per lot could also be used inplace of mean and median values. This would enablethe production of a range of estimates based on theobserved variability in sampled parameters.

While we used Census Block Group level data todevelop our dasymetric maps of housing, we recom-mend the use of Census Block level data for mappingtotal housing units in future studies. These data arenow available for the year 2010 from the U.S. CensusBureau and they could be used as a weighting factorfor developing 30-m resolution dasymetric maps ofsingle-detached housing from Census Block Groups.

Accurate estimates of the area of impervious sur-faces and turf grass at a modeling segment scale arecritical inputs to the Chesapeake Bay WSM. Whilethe methods discussed in this article produce moreaccurate estimates of impervious surface and turfgrass area, they do so by capturing impervious sur-faces and turf grass in suburban and rural areas thatare unobserved and/or misclassified in the CBLCD.Suburban and rural impervious surfaces are typicallydispersed and disconnected from streams and maynot influence stream hydrology or nutrient and sedi-ment loads to the same degree as urban impervioussurfaces that are well represented in Landsat-derivedland-cover products (Mejia and Moglen, 2009; Yanget al., 2011). Additional research on measuring therelative hydrologic connectivity of impervious sur-faces and on understanding the effects of connectivityon hydrology and water quality is needed to improvethe parameterization of regional WSMs.



ACKNOWLEDGMENTS

The authors thank Dr. Claire Jantz and Mr. David Donato fortheir reviews of early versions of this paper. In addition, theauthors thank Mr. Richard Batiuk for his comments and sugges-tions on early drafts. They are also grateful for the comments pro-vided by Dr. John Jones and the extensive comments provided bythe Associate Editor and two anonymous reviewers. Finally, theauthors thank members of the Chesapeake Bay Program’s UrbanStormwater Workgroup for their review and feedback on the dataand analyses.

DISCLAIMER

Any use of trade, firm, or product names is fordescriptive purposes only and does not imply endorse-ment by the U.S. Government.

LITERATURE CITED

AASHTO (American Association of State Highway and Transporta-tion Officials), 2004. A Policy on Geometric Design of Highwaysand Streets (Fifth Edition). Washington, D.C.

Anderson, J., E.E. Hardy, J.T. Roach, and R.E. Witmer, 1976. ALand Use and Land Cover Classification System for Use withRemote Sensor Data. U.S. Geological Survey Circular 671, Pro-fessional Paper 964, 41 pp.

Bierwagen, B.G., D.M. Theobald, C.R. Pyke, A. Choate, P. Groth,J.V. Thomas, and P. Morefield, 2010. National Housing andImpervious Surface Scenarios for Integrated Climate ImpactAssessments. Proceedings of the National Academy of Science107(49):20887-20892.

Booth, D.B., D. Hartley, and R. Jackson, 2002. Forest Cover,Impervious Surface Area, and the Mitigation of StormwaterImpacts. Journal of the American Water Resources Association38:835-845.

Cappiella, K. and K. Brown, 2001. Impervious Cover and Land Usein the Chesapeake Bay Watershed. Center for Watershed Pro-tection, Ellicot City, Maryland.

Claggett, P.R. and C. Bisland, 2004. Assessing the Vulnerability ofForests and Farmlands to Development in the Chesapeake BayWatershed. Proceedings of the IASTED International Confer-ence Environmental Modeling and Simulation, November 22-24,2004, St. Thomas, US Virgin Islands, pp. 90-95.

Eicher, C.L. and C.A. Brewer, 2001. Dasymetric Mapping andAreal Interpolation: Implementation and Evaluation. Cartogra-phy and Geographic Information Science 28(2):125-138.

FHWA (Federal Highway Administration), 1997. Flexibility inHighway Design. U.S. Department of Transportation, FederalHighway Administration, Washington, D.C. http://www.fhwa.dot.gov/environment/flex/index.htm, accessed May 2010.

Goetz, S.J., C.A. Jantz, S.D. Prince, A.J. Smith, D. Varlyguin, andR.K. Wright, 2004. Integrated Analysis of Ecosystem Interac-tions with Land Use Change: The Chesapeake Bay Watershed.In: Ecosystems and Land Use Change, R.S. DeFries, G.P. Asner,and R.A. Houghton (Editors). Geophysical Monograph Series,153, pp. 263-275.

Greenfield, E.J., D.J. Nowack, and J.T. Walton, 2009. Assessmentof 2001 NLCD Percent Tree and Impervious Cover Estimates.

Photogrammetric Engineering & Remote Sensing 75(11):1279-1286.

Gregory, J.H., M.D. Dukes, P.H. Jones, and G.L. Miller, 2006.Effect of Urban Soil Compaction on Infiltration Rate. Journal ofSoil and Water Conservation 61(3):117-124.

Homer, C., J. Dewitz, J. Fry, M. Coan, N. Hossain, C. Larson, N.Herold, A. McKerrow, J.N. VanDriel, and J. Wickham, 2007.Completion of the 2001 National Land Cover Database for theConterminous United States. Photogrammetric Engineering andRemote Sensing 73(4):337-341.

Homer, C., C. Huang, L. Yang, B. Wylie, and M. Coan, 2004. Develop-ment of a 2001 National Land-Cover Database for the UnitedStates. Photogrammetric Engineering and Remote Sensing 70(7):829-840.

Irani, F.M. and P.R. Claggett, 2010. Chesapeake Bay WatershedLand Cover Change Data Series. U.S. Geological Data Series505.

Irwin, E., J.C. Hyun, and N.E. Bockstael, 2007. Measuring theAmount and Pattern of Land Development in Nonurban Areas.Review of Agricultural Economics 29(3):494-501.

Jacobson, C.R., 2011. Identification and Quantification of theHydrological Impacts of Imperviousness in Urban Catchments:A Review. Journal of Environmental Management 92:1438-1448.

Jantz, P., S.J. Goetz, and C.A. Jantz, 2005. Urbanization and theLoss of Resource Lands in the Chesapeake Bay Watershed.Environmental Management 36(6):808-825.

Jenks, G.F., 1967. The Data Model Concept in Statistical Mapping.International Yearbook of Cartography 7:186-190.

Jennings, D.B., S.T. Jarnagin, and D.W. Ebert, 2004. A ModelingApproach for Estimating Watershed Impervious Surface Areafrom National Land Cover Data 92. Photogrammetric Engineer-ing & Remote Sensing 70(11):1295–1307.

Jones, J.W. and T. Jarnagin, 2009. Evaluation of a Moderate Reso-lution, Satellite-Based Impervious Surface Map Using an Inde-pendent, High-Resolution Validation Dataset. Journal ofHydrologic Engineering 14(4):369-377.

Law, N.L., L.E. Band, and J.M. Grove, 2004. Nitrogen Input fromResidential Lawn Care Practices in Suburban Watersheds inBaltimore County, MD. Journal of Environmental Planning andManagement 47(5):737-755.

Law, N.L., K. Cappiella, and M.E. Novotney, 2009. The Need forImproved Pervious Land Cover Characterization in UrbanWatersheds. Journal of Hydrologic Engineering 14(4):305-308.

Lu, D. and Q. Weng, 2004. Spectral Mixture Analysis of the UrbanLandscape in Indianapolis with Landsat ETM+ Imagery. Photo-grammetric Engineering & Remote Sensing 70(9):1053-1062.

McCauley, S. and S.J. Goetz, 2003. Mapping Residential DensityPatterns Using Multi-temporal Landsat Data and a Decision-Tree Classifier. International Journal of Remote Sensing 24(00):1-19.

MDP (Maryland Department of Planning), 2007. MdPropertyView, 2007 Edition.

Mejia, A.I. and G.E. Moglen, 2009. Spatial Patterns of UrbanDevelopment from Optimization of Flood Peaks and Impervious-ness-Based Measures. Journal of Hydrologic Engineering 14(4):416-424.

Mennis, J., 2003. Generating Surface Models of Population UsingDasymetric Mapping. The Professional Geographer 55(1):31-42.

Milesi, C., C.D. Elvidge, J.B. Dietz, B.T. Tuttle, and R.R. Nemani,2005. Mapping and Modeling the Biogeochemical Cycling ofTurf Grasses in the United States. Environmental Management36(3):426-438.

Moglen, G.E. and S. Kim, 2007. Limiting Imperviousness: AreThreshold-Based Policies a Good Idea? Journal of the AmericanPlanning Association 73(2):161-171.



Moskal, L.M., D.M. Styers, and M. Halabisky, 2011. MonitoringUrban Tree Cover Using Object-Based Image Analysis and Pub-lic Domain Remotely Sensed Data. Remote Sensing 3:2243-2262.

NASS (National Agricultural Statistics Service), 2006. MarylandTurfgrass Survey, 2005. USDA National Agricultural StatisticsService, Maryland Field Office, Annapolis, Maryland, 24 pp.

NASS (National Agricultural Statistics Service), 2009. 2007 Censusof Agriculture: Maryland State and County Data. Volume 1,Geographic Area Series, Part 20. AC-07-A-20.

NAVTEQ, 2006. NAVTEQ SDC Data Dictionary, V1.0. 85 pp.Nowak, D.J. and E.J. Greenfield, 2010. Evaluating the National

Land Cover Database Tree Canopy and Impervious Cover Esti-mates Across the Conterminous United States: A Comparisonwith Photo-Interpreted Estimates. Environmental Management46:378-390.

O’Neill, R.V., C.T. Hunsaker, S.P. Timmins, B.L. Jackson, K.B.Jones, K.H. Riitters, and J.D. Wickham, 1996. Scale Problemsin Reporting Landscape Pattern at the Regional Scale. Land-scape Ecology 11:169-180.

Reibel, M. and A. Agrawal, 2007. Aerial Interpolation of PopulationCounts Using Pre-classified Land Cover Data. PopulationResource Policy Review 26:619-633.

Reibel, M. and M.E. Bufalino, 2005. A Test of Street WeightedAreal Interpolation Using Geographic Information Systems.Environment and Planning A 37:127-139.

Reilly, J., 1997. A Method to Assign Population and a ProgressReport on the Use of a Spatial Simulation Model. Environmentand Planning B: Planning and Design 24(5):725-739.

Reilly, J., 2003. The New Jersey (USA) Growth Allocation Model:Development, Evaluation and Ex

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