Impacts of residential development on vegetation coverfor a remote coastal barrier in the Outer Banks of NorthCarolina, USA

Thomas W. Crawford & Daniel J. Marcucci &Andrew Bennett

Received: 13 August 2012 /Revised: 25 February 2013 /Accepted: 6 March 2013# Springer Science+Business Media Dordrecht 2013

Abstract Coastal barrier environments are heavily influencedby human activities yet there are few examples of landscapeecological work investigating human dimensions of settlementdisturbance patterns and processes.We investigated the impactsof residential development on vegetation cover for a remoteroadless coastal barrier in Carova, North Carolina that is subjectto policies from the federal to local levels and addressed threeresearch questions: How has the region’s the policy historyinfluenced patterns of residential parcel development? Whatare the spatial and temporal patterns of parcel development?How has development impacted patterns of barrier vegetationcover? We traced the influences of the federal 1982 CoastalBarrier Resources Act (CBRA) designed to discourage devel-opment in risky coastal areas as well as state/local coastalpolicies and employed remote sensing change detection,NDVI analysis and spatial analysis and regression techniques.Results showed an acceleration of new housing structures since1990, contrary to the intended effects of CBRA. An estimatedvegetation cover loss of 437m2was associated with each newlydeveloped parcel. NDVI varied along spatial and temporalgradients with more recent development having lower NDVI

than older development. Recently developed parcels were larg-er in area, closer to the beach, and contained houses with largerfootprints compared to older developed parcels. Our approachrepresents a place-based analytical framework for coastal bar-rier landscapes. Beyond the Carova case study, adopting suchan approach coupling natural and human systems for the entireeastern US barrier system requires defining a comprehensiveset of coastal barrier spatial units to enable typological classifi-cation and subsequent systematic investigation to inform de-bates regarding coastal ecosystem services and sustainability.

Keywords Coastal barrier . Land use policy .

Parcel development . Vegetation analysis

Introduction

Understanding the causes and consequences of human landuse processes is a critical goal for effective coastal landscapemanagement and conservation. Landscape change in manyareas of the United States has experienced a shift fromagricultural and extractive uses to residential development.Prior work on rural landscape dynamics has examined therole of non-metropolitan population growth and settlementexpansion causing rural or exurban sprawl often associatedwith development in amenity-rich areas (Theobald 2001,2005; Brown et al. 2005; Bierwagen et al. 2010). Newhousing alters landscapes in multiple ways including: de-struction of natural vegetation; soil disturbance and erosion;introduction of non-native species; and landscape fragmen-tation due to roads and fences that limit wildlife movement.

US coastal regions are attractive sites for both per-manent and seasonal homes due to environmental ame-nities associated with beach and water access. Morethan half of the US population lives in coastal countiesas defined by the National Oceanic and Atmospheric

T. W. Crawford (*)Department of Geography, East Carolina University,Brewster Building, A-227,Greenville, NC 27858, USAe-mail: crawfordt@ecu.edu

D. J. MarcucciDepartment of Geography, Planning Program,East Carolina University,Brewster Building, A-227,Greenville, NC 27858, USA

A. BennettCoastal Resources Management PhD Program,East Carolina University,Brewster Building, A-227,Greenville, NC 27858, USA

J Coast ConservDOI 10.1007/s11852-013-0241-8

Administration, and it is estimated that in 2010 morethe 75 % of the US population lived within 50 mi ofthe coast (Culliton et al. 1990). To consume coastalamenities, residents and seasonal visitors must locatein coastal settings thereby necessitating land consump-tion via permanent or seasonal housing. Sites located inclose proximity to the ocean shoreline on coastal bar-riers are often particularly attractive sites for develop-ment. Demographic processes such as coastal in-migrationand the aging US baby-boom generation and their retirementlocation decisions are expected to continue and perhaps exac-erbate this trend (Crawford et al. 2011). These linked human–environmental land use processes have important implicationsdue to their potential to affect areas of high ecological value.

In this study, we evaluate the effects of residential devel-opment on vegetation cover for the coastal barrier commu-nity of Carova located in the northern Outer Banks of NorthCarolina, USA (Fig. 1). Carova is a residential communityunlike most others on the US east coast due to the fact that ithas no paved road access and no paved roads on site whilesimultaneously attracting development due to recreationaland environmental amenities. While we describe other fea-tures of Carova below, it differs from sites more commonlyinvestigated in the anthropogenic landscape change litera-ture due to its relatively extreme remoteness, yet it hassimilarities to sites investigated by exurban research related

to the role of amenity-driven development. We address thefollowing three questions for the Carova study site:

1. How has the region’s the policy history influenced thegrowth of residential parcel development?

2. What are the spatial and temporal patterns of parceldevelopment?

3. How has parcel development impacted patterns of bar-rier island vegetation cover?

Following an account of Carova’s policy history influenc-ing land use change, we perform a vegetation change detec-tion analysis to compare vegetation cover for a sample ofdeveloped and non-developed parcels and then integrate par-cel data with the normalized difference vegetation index(NDVI) for a single date image as an indicator of vegetationcover to quantify spatio-temporal patterns of development’simpact on vegetation cover.

Human dimensions and coastal landscape ecology

A growing literature examines human dimensions of land-scape dynamics in amenity-rich areas, but there are few ex-amples that focus explicitly on barrier landscapes (Ray andGregg 1991). Most vegetation studies of coastal barriers in-vestigate natural or protected sites in which contemporary

Fig. 1 Carova study arealocated within North Carolina’sCurrituck Banks section of thenorthern Outer Banks atapproximately −75.856, 36.467(WGS84)

T.W. Crawford et al.

residential or commercially developed land use is largelyabsent (see for example Hayden et al. 1991). Examples ofhuman dimensions coastal landscape research includeEberhardt et al. (2003), Von Holle and Motzkin (2007), andFoster et al. (2002) who used historical and ecological ap-proaches to describe historical land use and human and naturalvegetation disturbances in southern New England includingCape Cod National Sea Shore and Martha’s Vineyard.

In addition to this coastal New England researchnexus, researchers have examined human influences inthe Oregon Coast Range of the northwest US includingthe littoral and extending to interior coastal watersheds.Kline et al. (2003) developed a spatially explicit modelto project future building densities. Wimberly andOhmann (2004) assessed the relative importance of pub-lic versus private land ownership in predicting of forestland cover change. Stanfield et al. (2002) analyzed theeffects of land ownership diversity on forest cover di-versity. Staus et al. (2002) quantified rates and patternsof forest disturbance in the Klamath-Siskiyou ecoregionof the Oregon Coast Range for public and private lands.We cite these Oregon examples due the fact that theyrepresent efforts to assess human impacts on landscapeecological patterns in explicitly coastal settings.

Similarly, our study integrates human dimensionswith coastal landscape change but differs from priorwork in important ways. Scale is one important differ-ence. The Carova study area is relatively small anddoes not extend to interior watersheds by focusingexplicitly on the peri-ocean barrier landscape. Coastalbarriers are prominent sedimentary features along theUS east coast consisting of islands, spits, bay barriers,and tombolos (Ray and Gregg 1991). Carova’s geomor-phology is similar to barrier islands in North Carolina’sOuter Banks that have been dissected by inlet forma-tion, but technically Carova is a bay barrier due to itsland connection to the mainland in southeastern Virginia.Additionally, recreational housing development is the dom-inant human process with no involvement of non-residential commercial development or resource extractionother than recreational fishing. Natural environmental dif-ferences such as climate, topography and natural vegetationalso are evident.

We identify a third research nexus that examinesrelationships specifically between residential develop-ment and landscape change (e.g. residential growth,forest loss, forest fragmentation) in the north centralUS (Christensen et al. 1996; Brown 2003; Hammer etal. 2004). While this work is not ocean coastal andencompasses a variety of scales, it addresses questionssimilar to ours. Other selected examples explore patternsof development and landscape change in northwoodsregion of Wisconsin that, like our study area, contains

numerous recreational districts but differs from thecoastal barrier context by containing many lakes thatattract lakeshore development (LaGro 1998; Schnaiberget al. 2002; Hawbaker et al. 2004; Gonzalez-Abraham etal. 2007a, b). This literature operates in two differentdirections by investigating what factors predict lake-shore development and conversely how lakeshore devel-opment has affected landscape patterns.

Coastal barrier vegetation

Carova is located within Currituck Banks in the far northernpart of the North Carolina’s Outer Banks. Vegetation studiesof the Outer Banks have identified over 200 plant species(Levy 1976; Hosier and Cleary 1979). Prior work charac-terizes barrier vegetation patterns at eastern US sites similarto Carova finding spatial gradients from shoreline to theinterior that relate the important roles of geomorphology,soils, and salt spray to vegetation zonation (Oosting andBillings 1942; Schroeder et al. 1976; Hayden et al. 1995;Stallins 2001; Stallins and Parker 2003; Young et al. 2011)(Fig. 2). In the grassy dunes American BeachgrassAmmophila breviligulata and salt meadow cordgrassSpartina patens are dominant species. Red Bay Perseaborbonia waxmyrtle Myrica cerifera and Northern BayberryMyrica pensylvanica make up the most abundant species inthe maritime shrub. Loblolly Pine Pinus taeda and Live OakQuercus virginiana dominate the maritime forest. An array ofCattails Typha latifolia, Typha angustifolia, and Typhadomingensis; sedges Andropogon virginicus, Cyperusfillicinus, Carex kobomugi; and rushes Juncus roemerianus,Juncus coriaceus, Juncus effuses, Eleocharis obtuse,Eleocharis parvula, Elocharis microcarpa, and Eleocharisflavescens are present in the brackish marsh on the sound sideof the barrier island. The seabeach amaranth Amaranthuspumilus is the only plant species identified as a federal endan-gered species present in Carova.

Policy history

Tourism and resort development on the Outer Banks tookflight in the 20th century after a sound-crossing bridge andhard-surface road enabled motor vehicles to drive to thebeach. In 1928 the Wright Memorial Bridge was openedconnecting Currituck County mainland to Kitty Hawk inDare County. The bridge was made possible by a bondissued by Dare County to encourage development of itsbeaches (Stick 1973). In 1931, Highway NC 12 was pavedrunning to the south, and with it development began inearnest on the Outer Banks beach communities of KittyHawk, Kill Devil Hills, and Nags Head.

Impacts of residential development on vegetation cover

Over the ensuing decades there were numerous calls topave and extend NC 12 north to Currituck Banks andbeyond to provide paved access connecting to the resortareas of Virginia Beach, Virginia. The drive for real estatespeculators and developers to purchase and subdivide landalong the Currituck beaches gained traction in the late1960s; however, only limited development occurred as therewere no hard-surface roads and developable coastal landwas available elsewhere. In 1971, the United States Fishand Wildlife Service killed the prospects for paved accessfrom Virginia to Currituck Banks and Kitty Hawk by shut-ting down vehicular access through the Back Bay NationalWildlife Refuge located immediately north of the NorthCarolina border. In 1984, the State of North Carolina ac-quired a private road and extended paved NC 12 north intoCurrituck County to its current terminus in Corolla. Tallfences and cattle grates transect the barrier at the end ofNC 12 and 16 km to the north at the Virginia state line inorder to contain the wild Currituck pony herd. The onlyvehicular access to the homes and beaches in this area is via

4-wheel drive transport from Corolla along the beachfront.There is no public access from Virginia.

Historically “Carova” referred to the largest and north-ernmost subdivision in the roadless area, although it is oftenused now to refer to the entire roadless area, containingnumerous subdivisions and protected conservation lands.We use “Carova” in the latter sense. Roadway access, per-manent and second home development, and tourism are notthe only influences on land use in Carova. Governmentalactions and policy, both direct and indirect, influence landuse decisions and subsequent land cover change that mayimpact the landscape’s natural vegetation. Throughout theOuter Banks, direct action has largely consisted of road andbridge construction, building municipal utilities (especiallywater supply), bulldozing and managing the sand dunes, andpurchasing and managing conservation land. Carova is note-worthy for the absence of the first two. Indirect actions haveincluded local land use planning and zoning, state regula-tions, and federal laws aimed at protecting coastal andnatural resources.

Fig. 2 a vegetation zonation, bAmerican Beachgrass, c RedBay, Waxmyrtle, NorthernBayberry, d Loblolly Pine, LiveOak, e Sedge, Cattail, and Rushin foreground

T.W. Crawford et al.

In North Carolina, land use planning and zoning author-ity rest at the county level, or in the case of incorporatedmunicipalities, at the city level. There are no incorporatedmunicipalities in Currituck County. However, being a coast-al county there are various federal and state laws whichinfluence land use. In 1972, President Nixon signed theCoastal Zone Management Act (CZMA) requiring states todelineate the coastal zone and develop state laws and plans.North Carolina responded by adopting the NC Coastal AreaManagement Act (CAMA) in 1974. CAMA defined 20marine and estuarine counties, including Currituck, as coast-al and required them to develop and periodically updateCAMA Land Use Plans. County plans receive some statefinancial support and must be approved by the NorthCarolina Coastal Resources Commission. CAMA enabledCurrituck County to develop countywide comprehensiveplanning. The county’s land use plan specifically encour-aged development in the Corolla area served by the NC 12highway while prohibiting county investments that wouldencourage further development in the “roadless” Carovaarea north of Corolla. County zoning only allows residentialand conservation land uses. It does not allow for any com-mercial development in Carova, although the residencesthere are primarily vacation rentals. The capital improve-ments plan makes no provisions for public water or sewer.There are no paved roads in Carova.

Historically the federal government enabled, if not en-couraged, development on coastal barriers through financialassistance for the construction of infrastructure such ashighways and bridges, water supply and wastewater treat-ment facilities, and beach stabilization structures. Federaldisaster and flood insurance has also promoted developmentby transferring risks and costs associated with developmentfrom the private to public sector. Critics of these forms offederal assistance argue that federal subsidies lead to a cycleof subsidized development, destruction related to coastalhazards, and subsidized redevelopment or maintenance(Salvesen 2005).

In response to this critique, Congress enacted the CoastalBarrier Resources Act (CBRA) of 1982 to reduce federalinvestments that promote and perpetuate cycles of coastalbarrier development. CRBA’s stated purpose is to: (1) min-imize loss of human life by discouraging development inhigh risk areas, (2) reduce wasteful expenditures of federalresources, and (3) protect the natural resources associatedwith coastal barriers. The act and its later reauthorizationsdefine a national Coastal Barrier Resources System (CBRS)comprised of 585 system units and 272 otherwise protectedareas for which selected federal spending is prohibited.System units are primarily private lands for which accessto the National Flood Insurance Program is prohibited alongwith spending for infrastructure (e.g. roads, dredging, beachnourishment). Otherwise protected areas are primarily

conservation and recreation areas (e.g. national or stateparks or other institutionally owned lands) for which theonly federal spending prohibition is flood insurance. WhileCBRA was designed to discourage coastal development,development is still allowed to occur as governed by freemarket processes and subject to other existing coastal poli-cies described above.

Research has investigated the effectiveness of CBRAexamining the extent to which it has discouraged develop-ment (US GAO 1992; Salvesen 2005). The general ap-proach of this research has been to quantify rates andmagnitudes of private development for sample CBRS unitssince the 1982 enactment of CBRA. Findings have revealedthat high levels of private development have occurred inmany units despite the intended effects of CBRA. Reductionof wasteful federal expenditures is typically cited as thehallmark feature of CBRA (Salvesen 2005) that often over-shadows the protection of natural resources as another im-portant goal of the act. Few studies have performedintegrated analyses that link development policies such asCBRA with both patterns of development and their impacton coastal barrier natural resources.

Parcel development and vegetation change

Parcel development patterns

Local, state, and federal policies described above have,during the last three decades, likely acted to slow parceldevelopment in Carova. At the same time, economic andmarket forces encourage development, even in this area thatis inaccessible by paved roads. In this context, both parcelization and development have continued.

To quantitatively assess parcel development pattern, GISparcel data were acquired from Currituck County govern-ment in August 2007. The data included attribute informa-tion describing land use and dates of building improvements.No commercial development is permitted so that land use forall developed parcels is residential including permanent, sea-sonal, and rental homes. To enable comparability across de-fined temporal periods, we report patterns for the period 1960to 2004 in three 15-year intervals (Fig. 3). Eight parcels weredeveloped prior to 1960 (not shown) with the earliest occur-ring in 1917. Since 2004 and up to the date of data acquisitionin August 2007, 76 additional parcels (not shown) weredeveloped.

A total of 540 parcels were developed from 1960 to2004. Results show, that amid fluctuations, there was a clearacceleration of development. The initial 1960–1974 periodwitnessed low levels of development (67 parcels) that in-creased substantially during the second 1975–1989 period(196 parcels) with a notable spike occurring in 1987.

Impacts of residential development on vegetation cover

Development dwindled during the early 1990s yet acceler-ated dramatically since 1992. Development during the mostactive 1990–2004 period (267 parcels) was 36 % greaterthan during the prior 1975–1989 period.

Vegetation change analysis

Vegetation change detection analysis was performedthrough air photo classification and statistical analysis toestimate the effects of residential development on vegetationcover. Due to the limited availability of high resolutionimagery, we focus our analysis on vegetation change forparcels that were developed during 2004 to 2006. We ac-quired digital color orthophotos for the years 2003 and 2008which were the closest corresponding dates of availableimagery. The image dates ensured that we would captureactual change due to residential development for 2004 to2006. Photos were acquired from the NC OneMap (no date)website which serves as North Carolina’s clearinghouse forgeospatial information. Pixel resolution was 0.61 m (2 ft) forthe 2003 photos and 0.15 m (0.5 ft) for the 2008 photos. Asample frame of all 119 parcels that were developed during2004 to 2006 was used to select a stratified random sampleof 60 parcels containing 15 oceanfront and 45 non-oceanfront parcels. A corresponding random sample of 60undeveloped parcels was selected from a sample framecontaining all 2,399 undeveloped parcels. The combinedsample contained 120 parcels (30 oceanfront, 90 non-oceanfront) with which to assess the impact of developmenton vegetation change by comparing patterns for developed(n=60) versus undeveloped (n=60) parcels.

Vegetation cover was captured via heads-up digitizing ofvegetation polygons for 2003 and 2008 for each of the 120sample parcels (Fig. 4). Vegetation change was calculatedby subtracting the 2003 amounts from the 2008 amounts sothat negative values are interpreted as vegetation loss andvice versa. Percent vegetation cover and percentage point

change in vegetation cover were also calculated. Percentagepoint change is defined as the percent cover in 2008 minusthe percent cover in 2003. To assess statistical correlationswith vegetation change, in addition to development status, aset of independent variables was created (Tables 1 and 2).Parcel distance to ocean shoreline was calculated as theshortest Euclidean distance from the parcel centroid to theshoreline as represented by a GIS vector polyline. Parceldistance to the study area’s southern border was calculatedas the shortest Euclidean distance from the parcel centroid toa vector line segment positioned at the Carova’s southernextreme and spanning the barrier island.

Differences between developed and undeveloped parcelsfor all variables were analyzed using the Mann–Whitney Utest because many of the variables were not normally dis-tributed (Table 3). Results show that developed parcels hadgreater vegetation loss than undeveloped parcels measuredeither as absolute or percentage point change. Developedparcels also had less vegetation cover to begin with in 2003.There were no statistically significant differences for parcelarea, distance to ocean, and distance to Carova’s southernborder.

Multiple regression was used to evaluate relationshipswith absolute vegetation change (Table 4) and percentagepoint change (Table 5). Ordinary least squares (OLS)models were initially estimated followed by an assessmentof spatial autocorrelation of residuals using Moran’s I.Spatially autocorrelation of residuals violates OLS assump-tions and requires selecting an alternative model functionalform. Moran’s I results indicated significant spatial autocor-relation for the initial Model 1 using OLS. Model diagnos-tics revealed a spatial error model to be the appropriatefunctional form. Spatial error model estimation using amaximum likelihood technique was implemented usingGeoDa software (Anselin et al. 2005). Models 2–4 wereestimated using OLS and did not exhibit spatially autocorrelated residuals.

Model 1 (Table 4) for absolute change reveals multiplesignificant variables and a pseudo R-squared of 0.624. Thelargest effect according the standardized beta coefficientwas for vegetation cover in 2003 followed closely by de-velopment status. The independent effect of being devel-oped is a reduction of 437 m2 of vegetation cover. Model 2inserts an interaction term combining developed status withthe 2003 amount of vegetation. This insertion yields animproved R-square of 0.747, and the interaction term re-veals a large effect having a beta coefficient of −0.701.Notably, the independent effects of developed status and2003 vegetation cover, while still significant, declined con-siderably compared to Model 1. This means that parcels thatbecame developed during 2004–2006 and having higheramounts of vegetation to begin with in 2003 were associatedwith greater amounts of vegetation loss. Ocean proximity

Fig. 3 Annual amount of new house development on parcels, 1960–2004

T.W. Crawford et al.

was also significant in Model 2 with both oceanfront statusand distance from the ocean having a positive effect. Beingon the front row of oceanfront was associated with a 127 m2

vegetation increase. Every 1 km distance from the shorelinewas associated with a 224 m2 vegetation increase. Parcellocation with respect to the southern border did not have asignificant effect.

Models 3 and 4 (Table 5) for percentage point change invegetation cover reveal similar though slightly different re-sults compared to the prior models. Insertion of an interactionterm combining developed status and percent cover in 2003improves the R-squared from 0.516 (Model 3) to 0.762(Model 4). The interaction term again has a large effect;however, the independent effect of developed status is nolonger significant. Oceanfront status is not significant in eithermodel although distance to ocean is significant with every1 km distance from the shoreline being associated with a 16.79 percentage point increase in vegetation cover.

2007 NDVI analysis

Cross-sectional, single date analysis of variation in thenormalized difference vegetation index (NDVI) calculat-ed from a 2007 infra-red digital orthophoto was alsoperformed to assess the effect of development on vege-tation patterns. Unlike the prior analysis that employedtwo image dates to analyze change over time for asample of 120 parcels, here a single image was used,and NDVI was estimated for all 3,009 parcels in Carovato analyze variation across spatial gradients and temporalperiods of development. The 2007 infra-red photo mosa-ic was acquired via download from the NOAA CoastalServices Center website (NOAA CSC no date) andconsisted of 4-band imagery, including three color andone infra-red band, captured using a Z/I DMC (DigitalModular Camera) system on October 13 and 14, 2007.Pixel resolution was 1-m. Several studies have used

Fig. 4 2003–2008 vegetationchange detection: a–bdeveloped parcel ortho imagery,c–d area of developed parcelvegetation cover (black), e–fundeveloped parcel orthoimagery, g–h area ofundeveloped parcel vegetationcover (black)

Impacts of residential development on vegetation cover

DMC imagery for vegetation analysis (Ehlrers et al.2006; Holmgren et al. 2008; Erdody and Moskal 2010;Laliberte et al. 2010)

NDVI serves as a dimensionless indicator of vegetationbiomass (Jensen 2004). A large literature exists for whichNDVI is used to examine patterns of vegetation coverageand intensity. Selected coastal applications of NDVI includeTuxen et al. (2008, 2011) Naumann et al. (2009), Zinnert etal. (2011), and Aguilar et al. (2012). NDVI takes advantageof differing spectral properties of the near infra-red (NIR)and red bands in remotely sensed imagery for green vege-tated surfaces compared to non-vegetated surfaces.Vegetation reflects greater amounts of near infrared energycompared to non-vegetated surfaces. NDVI is defined by thefollowing equation:

NDVI ¼ NIR� REDð Þ NIRþ REDð Þ= ð1ÞNDVI values can range from −1 to +1. Values increas-

ingly greater the 0.10 typically indicate greater vegetationcoverage. Values near zero or less than zero are associatedwith bare soil or water. NDVI was calculated for each pixel.To obtain NDVI for each parcel, vector parcel polygonswere overlaid with the NDVI raster layer and the meanNDVI for the collection of pixels whose center points werelocated inside respective parcels was calculated as the par-cels’ NDVI values (Fig. 5).

Development was categorized using a set of binarydummy variables (Tables 1 and 2). Devperiod0 indicatesundeveloped parcels. Devperiod1 indicates developmentoccuring prior to 1990. Devperiod2 indicates developmentoccurring from 1990 to 2007. Of the total 3,009 parcels,2,399 were undeveloped, 270 were developed prior to1990, and 340 were developed from 1990 to 2007.

Numyears captures the length of time a parcel had beenin developed status as of 2007 and is a numeric scalealternative to the period dummy variables. To assess theeffect of structure footprint size relative to parcel size,structper quantifies structure footprint area as a percentageof parcel area. Structure footprints were heads up digitizedusing the air photo mosaic. The locational variables

Table 1 Variable definitionsVariable Description

vegchange Change in square meters of vegetation cover for 2003 to 2008.

pervegchange Percentage point change in parcel vegetation cover for 2003 to 2008.

ndvi2007 2007 Normalized Difference Vegetation Index.

veg2003 Square meters of vegetation coverage in 2003.

perveg2003 Percentage of parcel area covered by vegetation in 2003.

developed Parcel developed during 2004–2006; 1 = yes, 0 = not developed.

devperiod0 Dummy variable: 1 = undeveloped, 0 = otherwise.

devperiod1 Dummy variable: 1 = built 1917–1989, 0 = otherwise.

devperiod2 Dummy variable: 1 = built 1990–2007, 0 = otherwise.

numyears Number of years in developed status; 0 for undeveloped parcels,2008 minus year house built for developed parcels.

area Parcel area in hectares

structper Percent of parcel area covered by structure footprint.

oceanfront Indicates oceanfront location; 1 = yes, 0 = no.

distocean Distance in kilometers to ocean shoreline.

distsouth Distance in kilometers to Carova’s southern boundary.

Table 2 Descriptive statistics

Variable Mean Std. dev. Minimum Maximum

Variables for 2003–2008 vegetation change analysis, n=120 parcels

vegchange −160.41 375.26 −2524.26 485.05

pervegchange −9.11 20.48 −79.17 35.14

developed 0.50 0.50 0.00 1.00

veg2003 739.41 794.72 0.00 4663.05

perveg2003 42.08 34.71 0.00 100.00

area 0.21 0.21 0.03 1.77

oceanfront 0.25 0.43 0.00 1.00

distocean 0.39 0.30 0.00 1.45

dsouth 9.26 3.97 1.19 14.99

Variables for 2007 NDVI analysis, n=3,009 parcels

ndvi2007 0.13 0.06 −0.07 0.24

devperiod0 0.80 0.40 0.00 1.00

devperiod1 0.11 0.32 0.00 1.00

devperiod2 0.09 0.28 0.00 1.00

numyears 3.44 8.99 0.00 91.00

area 0.17 0.15 0.01 3.86

structper 2.08 4.55 0.00 25.79

oceanfront 0.12 0.32 0.00 1.00

distocean 0.46 0.30 0.00 1.45

dsouth 10.25 3.88 0.17 15.05

T.W. Crawford et al.

oceanfront, distocean, and distsouth are identical to thosedefined previously.

Mean NDVI was 0.140, 0.108, and 0.070 for the periodgroups undeveloped, developed 1917–1989, and developed1990–2007 (Fig. 6). The ANOVA F-statistic was 297.602(df=2) and with a p-value of 0.000. All pair-wise differ-ences were statistically significant. Thus, there were cleardifferences in NDVI related to period of development withmore recently developed parcels having the lowest NDVIand undeveloped parcels having the highest NDVI.

Regression models for 2007 NDVI required spatialerror regression (Table 6). Model 5 includes all parcelsand uses the period dummy variables to assess the impactof development status and period. Model 6 includes onlydeveloped parcels and uses numyears to assess in a non-categorical manner the impact of number of years that aparcels has been developed. Model 6 also uses structper.Structper was not used in Model 1 because the vastmajority of parcels, those that were undeveloped, neces-sarily had a value of zero. All variables in both modelswere highly significant and R-square magnitudes were 0.769 and 0.649 respectively.

Spatial gradient variables distocean and distsouth had thestrongest effects in both models. Parcels located furtherfrom the ocean shoreline and further north from the southernCarova border were associated with greater NDVI.Oceanfront had a negative effect that was relatively weaker.In Model 5 and controlling for all other variables, the effectof development on NDVI is negative. Parcels developedsince 1990 (devperiod2) were associated with a 0.048 lowerNDVI relative to undeveloped parcels. Parcels developedprior to 1990 (devperiod1) were associated with a 0.038lower NDVI relative to undeveloped parcels. Thus, not onlydevelopment status but the period of development was as-sociated with NDVI in 2007 with more recent developmenthaving lower NDVI relative to earlier development. Thisinterpretation is also evident in Model 6 that included onlythe developed parcels. Model 6’s positive coefficient fornumyears means that each year of being in developed statusis associated with a 0.0007 increase in NDVI (or beingdeveloped for 20 years is associated with a 0.014 increasein NDVI). older developed parcels had higher NDVI rela-tive to younger developed parcels. The effect of structperwas notably strong (beta=−0.275) and stronger than the

Table 4 Regression results for vegchange, vegetation change 2003–2008

Model 1, spatial error Model 2, OLS

Coef. SE Beta Sig. Coef. SE Beta Sig.

developed −437.078 41.874 −0.582 0.000 −128.554 47.877 −0.171 0.008

veg2003 −0.346 0.033 −0.682 0.000 −0.061 0.031 −0.130 0.049

developed x veg2003 −0.433 0.045 −0.701 0.000

oceanfront 75.570 65.948 0.087 0.252 127.405 52.092 0.147 0.016

distocean 465.386 134.533 0.374 0.000 223.683 77.464 0.180 0.005

distsouth −7.315 11.382 −0.077 0.520 −5.960 4.452 −0.063 0.183

lambda 0.529 0.093 0.221 0.000

R-squared 0.624a 0.747b

N 120 120

a Pseudo R-squaredb Adjusted R-squared

Table 3 Mann–Whitney UTests comparing developed toundeveloped parcels

Median Mean Rank Mann–Whitney Ustatistic

Asymptotic2-tailedsignificanceDeveloped

n=60Undevelopedn=60

Developedn=60

Undevelopedn=60

vegchange −234.72 13.22 36.87 84.13 382.00 0.00

pervegchange −9.44 0.95 37.07 83.93 394.00 0.00

veg2003 485.14 734.11 52.82 68.18 1339.00 0.01

perveg2003 29.39 49.18 51.57 69.43 1264.00 0.00

area 0.14 0.14 61.12 59.88 1837.50 0.84

distocean 0.25 0.41 56.23 64.77 1544.00 0.18

distsouth 10.42 10.64 59.57 61.43 1744.0 0.77

Impacts of residential development on vegetation cover

effect of numyears. A one percentage point increase instructure footprint area relative to parcel area is associ-ated with 0.003 lower NDVI (or a ten percentage pointincrease is associated with a 0.03 lower NDVI). Themean area (parcel size) for the more recently developeddevperiod2 group was 0.22 ha which a t-test showed tobe significantly greater than the mean of 0.16 for theolder devperiod1 group (p<0.001). Recently developedparcels tend to be larger. Mean structper was alsosignificantly higher for recent development at 10.6 %compared to 9.59 % for older development (p<0.005).Together this means that more recent development oc-curred on larger parcels and the structure footprints onthese parcels covered a greater percentage of the parcel

area. These results coupled with regression results helpexplain why recent development is associated with low-er NDVI.

Discussion

Policies at multiple levels apply to residential develop-ment in coastal barriers. Our results for Carova revealan acceleration of residential development after theCoastal Barrier Resources Act of 1982 which is consis-tent with findings of previous research. No research hasmodeled how levels of development would differ with-out CBRA. At the county level, the prohibition ofcommercial development, coupled with CBRA, acts tocreate a unique sense of place for Carova influenced byits remoteness, absence of paved roads, and sole resi-dential land use. These policies also create a quasi-gatedcommunity but one without the formal infrastructure of

Table 5 Regression results forpervegchange, percentage pointvegetation change 2003–2008

aAdjusted R-squared

Model 3, OLS Model 4, OLS

Coef. SE Beta Sig. Coef. SE Beta Sig.

developed −26.641 2.784 −0.650 0.000 −1.916 2.992 −0.047 0.523

perveg2003 −0.329 0.050 −0.558 0.000 −0.084 0.042 −0.142 0.048

developed x perveg2003 −0.638 0.058 −0.792 0.000

oceanfront 6.084 4.057 0.129 0.136 4.030 2.851 0.085 0.160

distocean 24.026 5.972 0.355 0.000 16.788 4.240 0.248 0.000

distsouth 0.016 0.343 0.003 0.963 −0.047 0.241 −0.009 0.846

R-squared 0.516a 0.762a

N 120 120

Fig. 5 September 2007 color infrared orthophoto (top) and derivedNDVI (bottom) with residential parcel boundaries; location at approx-imate image center −75.870, 36.537 (WGS84)

Fig. 6 Mean 2007 NDVI and 95 % confidence intervals by develop-ment status and period

T.W. Crawford et al.

elite gated communities (e.g. gate, fences, guardhouse)due to its policy-prescribed remoteness, higher costs ofdevelopment associated with more difficult beach ac-cess, and the absence of access to the National FloodInsurance Program. Private owners must be willing andable to absorb higher development costs, pay higherinsurance premiums within the free market, or havethe financial resources to forgo insurance altogether.

Spatially, developed parcels had a greater likelihood ofbeing oceanfront and were located at shorter distances to theocean shoreline than undeveloped parcels. More recentlydeveloped parcels were larger in area, closer to the beach,and had larger structure footprints. The finding of largerfootprints is consistent with patterns for the US as a whole(Wilson and Boehland 2005) and has obvious implicationsfor greater reduction in vegetation cover. The lack of publicinfrastructure related to local land use policy requires foreach house a well and septic tank which also reduces veg-etation cover.

Change analysis regression results revealed a vegetationcover loss of 437 square m2 per house developed. NDVIregression results showed developed parcels to have lowerNDVI with more recent development having the lowestNDVI. This temporal difference is similar to that found byLuck et al. (2009) for urban ecosystems and is in partexplained by the larger structure footprint size but may alsobe due to time lags where vegetation succession naturallyoccurs or homeowners intentionally establish plants (e.g.grass, trees) for aesthetic value which also has implicationsfor the introduction for non-native plant species (Rodgersand Parker 2003). A limitation of our analysis is our singulardefinition of vegetation cover where “cover” is defined asany type of cover. Grasses near the ocean dunes and densermaritime forest at greater distances from shoreline weretreated identically as vegetation cover. Clearly more vegeta-tion biomass is present with larger plant species. Regression

results indicate higher NDVI levels at parcels more distantfrom the ocean due to differences in both natural vegetationcover and type along the ocean-to-sound spatial gradient.

Conclusion

Landscape ecological research has traditionally been terres-trially focused despite the science’s relevance to marine andcoastal applications (Hinchey et al. 2008). Our work helpsto fill this gap by focusing on a particular niche, coastalbarrier landscapes. Ray and Gregg (1991) note that “fewenvironments have been so heavily influenced by human ac-tivities as coastal barriers” (p. 301). Coastal geomorphologistsand engineers have a long record of barrier research in settingswith strong human influences to which coastal landscape ecol-ogists can contribute. Our Carova work is for a single and ratherunique place which prohibits generalization to the broadereastern US barrier coast. Our results do however provide abaseline estimate for the impacts of new housing on vegetationcover that, with proper care and caveats, could be extrapolatedto project impacts for future residential development in Carovaand elsewhere.

The Carova work represents a shift to a form of place-based analysis as articulated by Potschin and Haines-Young(2012). We envision the possibility of a place-based frame-work to assess and analyze coupled natural and humansystems for the entire eastern US barrier system that couldbe applicable elsewhere. Such an approach would initiallyrequire defining a comprehensive set of coastal barrier unitsfor the entire coast to include both protected and human-developed barriers. This framework allows for comparativeanalytical research to classify a robust typology of barrierunits encompassing both human and natural dimensionssuch as policy, human demographics, economic factors,land use/cover, ecosystem services and functions, and

Table 6 Regression results forndvi2007

aPseudo R-squared

Model 5, spatial error, all parcels Model 5, spatial error, developed parcels

Coef. SE Beta Sig. Coef. SE Beta Sig.

devperiod1 −0.048 0.002 −0.267 0.000

devperiod2 −0.038 0.002 −0.192 0.000

numyears 0.0007 0.0001 0.190 0.000

area 0.017 0.006 0.045 0.006 0.020 0.008 0.093 0.009

structper −0.003 0.000 −0.275 0.000

oceanfront −0.024 0.009 −0.137 0.000 −0.022 0.005 −0.181 0.000

distocean 0.073 0.001 0.382 0.000 0.075 0.009 0.478 0.006

distsouth 0.006 0.000 0.410 0.000 0.003 0.001 0.263 0.000

lambda 0.811 0.016 0.659 0.000 0.757 0.058 0.466 0.000

R-squared 0.769a 0.649a

N 3.009 620

Impacts of residential development on vegetation cover

coastal environmental hazards. Units representing differingtypes can be strategically selected for more intensive land-scape ecological research involving field-based, GIS and re-mote sensing, quantitative and qualitative methods with whichto more systematically investigate coastal barrier systems andinform debates regarding ecosystem services and sustainability.

Acknowledgments This research was supported by the North Caro-lina Turnpike Authority and the North Carolina Department of Trans-portation, STIP R-2576. The contents of this paper reflect the views ofthe authors and not necessarily the views of East Carolina University.The authors are responsible for the facts and the accuracy of the datapresented herein. The contents do not necessarily reflect the officialviews or policies of either the North Carolina Department of Trans-portation or the USDOT-Federal Highway Administration at the timeof publication. This report does not constitute a standard, specification,or regulation. Resources from East Carolina University’s Center forGeographic Information Science supported this work.

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