Office of Population Research Princeton University WORKING PAPER SERIES

A New Source for Land Cover Change Validation: Wal-Mart from Space

Working Paper No. 2006-04

David Potere Princeton University

Neal Feierabend

Oak Ridge National Laboratory

Eddie Bright Oak Ridge National Laboratory

Alan Strahler

Boston University

Correspondence: David Potere, Office of Population Research, Princeton University, 207 Wallace Hall, Princeton, New Jersey, 08544-2091, USA. Phone: 609-258-4943. E-mail: dpotere@princeton.edu Key words: land cover change, remote sensing. Papers published in the OPR Working Paper Series reflect the view of individual authors. They may not be cited in other publications, but are intended to be work-in-progress. Comments are welcome.

Abstract

We introduce an event data set of the location and opening dates for 3,043 Wal-Mart stores

as a means for validating land cover change-related products at medium (30 m) to coarse (1 km)

resolutions throughout the conterminous United States (US). As validation data, these Wal-Mart

stores and distribution centers share several favorable attributes, including construction atop a

diverse array of vegetated environments, wide dispersion across the entire country, building and

parking lot footprints that measure between 100 m and 500 m on a side, and construction dates that

span much of the remote sensing record (1964-2005). To generate the data set, we geo-coded the

full Wal-Mart store address list, combined these locations with a listing of Wal-Mart store opening

dates, and geo-located the building footprints of 30 Wal-Mart stores using cost-free high-resolution

(4 m) imagery available from internet search engines.

Twenty-five stores constructed in North Carolina and Virginia between 1987 and 2002

served to validate a single scene (WRS2 p16 r035, 180 km per side) of the new Landsat Ecosystem

Disturbance Adaptive Processing System (LEDAPS) product – a 28.5 m resolution forest

disturbance map which is in production for the conterminous US. Disturbance events were clearly

discernable in the LEDAPS beta product at all 25 of the validation sites. In addition, we selected

five Wal-Mart sites constructed between 2000-2005 in Maine, North Carolina, Oklahoma, and

California to validate the University of Maryland’s 250 m Moderate Resolution Imaging

Spectroradiometer (MODIS) normalized difference vegetation index 16-day time series (MOD44C).

These five construction events are evident in the time series. At a Wal-Mart distribution center in

Gordonsville, Virginia, a similar construction signature is present at 1 km resolution for the

MOD13A2 enhanced vegetation index 16-day time series. These results demonstrate a new

approach for validating land cover change related products by combining an unusual disturbance

event data set with free high-resolution internet-based images.

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Introduction and the Wal-Mart Data Set

A host of new regional and global products identifying land cover change are becoming

available from both coarse and medium resolution satellites, including: the Landsat Ecosystem

Disturbance Adaptive Processing System (LEDAPS) Forest Disturbance Index product and

associated atmospherically corrected GeoCover scenes (in beta, http://ledaps.nacom.nasa.gov/);

the National Land Cover Database 1992/2001 products (Horner et al., 2004; Vogelman et al.,

2001; http://seamless.usgs.gov/); and any of the several global Moderate Resolution Imaging

Spectroradiometer (MODIS) vegetation index time series (MOD13 Vegetation Index product,

Wang et al., 2003, http://modis-land.gsfc.nasa.gov/; University of Maryland,

http://glcf.umiacs.umd.edu/). A major hurdle for validating these land cover change products is

obtaining a data set of land cover change events that are of adequate areal extent, geographic

coverage, and precision (both in time and space). The phenomenal growth of Wal-Mart stores

over the past 40 years presents the remote sensing community with a data set that meets these

basic requirements—Wal-Mart stores are numerous (3,219 Wal-Marts in the US as of June 2006),

frequently developed on vegetated land, large (roughly 300 m by 300 m with parking lots),

globally distributed, and contemporaneous with the satellite remote sensing record.

Since building its first store in 1962, Wal-Mart has opened more than 3,000 facilities

within the United States and more than 1,500 in Canada, Mexico, Puerto Rico, Argentina, Brazil,

China, Korea, Germany, and the United Kingdom (Wal-Mart press release, 2005); Figures 1a-1c

illustrate the geographic and temporal scope of the US portion of this potential validation data

set. There is no sign of a slow-down in new construction; in 2006 the company plans to open

roughly 400 new stores in the United States and 200 overseas stores in the Americas, Asia, and

Europe (2006 Wal-Mart Annual Report, http://walmartstores.com). US Wal-Marts are divided

roughly evenly between two classes of store: traditional Wal-Mart Discount Stores (~9,300 m2),

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and Wal-Mart Supercenters (~17,000 m2). When combined with their parking lots, both stores

have footprints averaging 300 meters on a side (Figure 2c). The 85 Wal-Mart Distribution

Centers are often an order of magnitude larger (roughly 11 hectares of enclosed space without

parking lots). These stores, supercenters, and distribution centers are commonly constructed on

the edge of existing urban areas, adjacent to major highway intersections, and on forested or

agricultural land. Figures 2a-2c show a typical supercenter, before, during, and after construction.

To create the validation set, we acquired a listing of the addresses and opening dates for

all US Wal-Marts from Wal-Mart public relations. TeleAtlas provided online geo-coding services

for translating the street addresses into geographic coordinates (http://www.teleatlas.com).

Similar geo-coding services can now be employed cost-free from GoogleEarth

(http://earth.google.com). Although the TeleAtlas road database is continually updated, Wal-

Mart stores are sometimes constructed on new streets not yet cataloged in the street database.

Roughly 82 percent of the addresses were geo-located at the highest two levels of precision. The

remaining stores were assigned to the geographic centroid of their postal zone.

Even the highest accuracy retrievals only represent the street address of the Wal-Mart

store, and often do not lie atop the store site (Figure 2c demonstrates this offset; the yellow circle

is the original geo-coded Wal-Mart address, and the red cross is the updated location based on

imagery). To correct this offset, we utilized the freely available high-resolution imagery archives

of GoogleEarth, Microsoft TerraServer (http://terraserver.microsoft.com), and TerraServer

(http://www.terraserver.com). When used in concert, these imagery databases are often able to

depict Wal-Mart sites before, during, and after construction in either color or panchromatic at

resolutions of between 1 and 5 meters (Figure 2a-2c). When the geo-coded location was too far

from the actual Wal-Mart site to make identification possible, GoogleEarth’s spatial text search

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capability helped make the match. Once the Wal-Mart location was established from the imagery

and matched to a store opening date, the site became available for use in validation.

LEDAPS Disturbance Validation

Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) is a NASA

initiative to map forest disturbance in North America from 1975 to the present. Pursuant to that

goal, LEDAPS is creating a North American surface reflectance product circa-1975, 1990, and

2000 using the EarthSat GeoCover Landsat data set and the MODIS atmospheric correction

algorithms (Tucker et al., 2004; Vermote et al., 1997). A forest Disturbance Index (DI) is

calculated for each time period using a linear combination of Kauth-Thomas brightness,

greenness, and wetness transforms that have been normalized using the signatures of mature

forest stands (Healey et al., 2005; Kauth and Thomas, 1976; Crist, 1985; Huang et al., 2001). The

difference between two successive DI images, ΔDI, is related to disturbance and re-growth

events within each scene. The LEDAPS group then establishes thresholds for ΔDI, normalized

difference vegetation index (NDVI), and several other parameters to construct maps of forest

disturbance between the two GeoCover epochs, 1975-1990, and 1990-2000.

Although the LEDAPS product is still in development and has not yet undergone

validation, a beta version of WRS2 scene p16r035 (central North Carolina and a central strip of

sourthern Virginia) is available (http://ledaps.nascom.nasa.gov/). The GeoCover Landsat

endpoint images for this scene were acquired in October 1987 and May 2002. There are 27 Wal-

Marts in the scene whose opening dates place their probable date of groundbreaking within the

1987-2002 GeoCover epoch (average construction time for a store is one year). For 25 of these

validation sites, we successfully geo-located the store footprint and searched for a corresponding

disturbance signal in the LEDAPS ΔDI image.

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The Thomasville, NC, Wal-Mart is a typical example (Figure 2a-2d). The store opened

on July 17, 2002. It is reasonable to assume that construction began approximately one year

earlier, in July 2001, placing the clearing of the forested site within the epoch spanned by the two

Landsat scenes. Figure 2d shows the store building clearly discernable in the LEDAPS ΔDI

product (store footprint, including parking lot, outlined in blue). Similar searches were repeated

for the 24 remaining Wal-Marts located within the North Carolina / Virginia scene and

constructed between the two Landsat Geocover acquisition dates.

Of the 25 store locations examined, all showed elevated disturbance signals

corresponding to a Wal-Mart store. Figure 2e illustrates the geographic distribution of these sites.

Two of the stores initially listed by Wal-Mart were not successfully located because of a lack of

high resolution imagery or poor geo-coding. In some cases, it was difficult to correctly identify

the exact Wal-Mart location because Wal-Mart stores are often accompanied by other “big-box”

stores (one example is visible in the upper right corner of Figures 2a-2d). We also noted

significant variance between stores in the clarity of the store footprint in the LEDAPS ΔDI

image. We could not discern any relationship between the strength of the ΔDI signal and the

size of the Wal-Mart (store versus supercenter). Initial results suggest that pre-construction land

cover is the most important factor in determining the strength of the signal. In those sites where

a Wal-Mart facility has replaced dense and healthy forest, the disturbance index is much higher

than in sites where the pre-construction land cover was grass, shrubs, or sparse forest.

MODIS Vegetation Index 16-day Time Series Validation

We next evaluated the University of Maryland’s (UMD) 250-meter MODIS NDVI North

American Mosaic (http://glcfapp.umiacs.umd.edu/). UMD creates 16-day composites of L2G

MODIS 250 m data. The UMD compositing procedure considers cloud flags, NDVI values, and

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view zenith angles to produce their MOD44C product

(http://glcf.umiacs.umd.edu/pdf/modis_compositing.pdf). Mosaics of band 1, band 2, and

NDVI are available for North and South America in an Albers projection, along with a cloud

mask. UMD’s file format allowed us to efficiently download the entire 2000-2005 time series for

all of North America (~40 gigabytes).

We selected five sites across the continental US based on image availability, diversity of

background vegetation, and Wal-Mart facility size. In all sites examined, we found a strong

response in the NDVI signal approximately one year prior to the opening of Wal-Mart facilities.

Figures 3a-3e illustrate this signature for three sites of decreasing size. Cloud contamination was

not a major concern at any of the sites, however, a small number (less than 1 percent) of the

observations exhibited single-observation shifts in NDVI of greater than 100 percent. We

classed these observations as cloud contaminated, and replaced them with the mean value of the

preceding and subsequent observations. An alterative method would have been to download the

entire UMD cloud-mask and screen for cloud contaminated pixels directly.

The 129-hectare Wal-Mart Distribution Center in Apple Valley, CA, opened in March of

2004 about 50 km north of San Bernardino, in Southern California (Figures 3a-3c). Figure 3a is a

TerraServer image of the site prior to construction—a largely barren desert shrubland, scored by

a network of small roads. Figure 3b shows the completed store. The 250-meter radius circles

atop both images mark the locations of NDVI time series samples. Red circles are UMD NDVI

pixels that overlap the distribution center, green circles are neighboring pixels that intersect the

parking lot and some of the background vegetation, and blue circles are atop background

vegetation of the same type that existed within the facility pre-construction.

Figure 3c traces each of these sample points (using the same color scheme) from 2000-

2005. All three sample classes—overlap, neighboring, and background—are tightly coupled from

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February 2000 through February 2003. In the moisture-limited environment of Southern

California, the rapid, short-duration increases in NDVI are likely responses to rain events. In

March of 2003, one year prior to the opening of the distribution center, the NDVI values for the

overlap and neighboring pixels break away from the background vegetation signal. For the

remainder of the time series, the neighboring pixels (green) are generally synchronized with the

background vegetation (blue), but exhibit consistently lower NDVI values. The behavior of the

pixels that overlap the distribution center (red) is more distinct from adjacent pixels; their NDVI

values are at least twice as depressed as neighboring pixels relative to the background vegetation

and their changes are often entirely decoupled from both the background and neighboring pixels.

The Fayetteville, North Carolina, Wal-Mart Supercenter opened in July 2002 in a forested

region (Figure 3d). Here, the phenology of the region is more regular than in Apple Valley, CA.

As in Apple Valley, overlap (red), neighborhood (green), and background (blue) pixels were

selected from the full NDVI time series. Because the store is much smaller than a distribution

center, no single 250-meter pixel entirely overlaps the store footprint. Until July 2001, the three

groups of pixels are tightly coupled. One year prior to the store opening date, both the overlap

and neighboring pixels separate from the background pixels, with depressed NDVI values that

persist through the remainder of the time series. The offset between background pixels and the

neighboring/overlap pixels remains relatively constant throughout the 2002-2005 seasons.

The Brewer, Maine, Wal-Mart Supercenter is the smallest store of the thirty-store sample.

Here only overlapping and background pixels were isolated. The store opened on May 18, 2003,

and the overlap pixels (red) began deviating from the background in spring 2002. There is no

discernable difference between background and overlap NDVI values during the middle of the

winter months, but by the early spring green-up and throughout the summer the difference is

evident. Results from our five validation sites (including two sites not discussed here) indicate

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that the Wal-Mart disturbance signal can be clearly identified in the 250 m NDVI time series

across a wide range of store sizes, climates, and background vegetation types.

Similar construction event signatures are also present in the 1 km MODIS record. Of

three sites examined, a Wal-Mart Distribution Center in Gordonsville, VA, had the clearest signal

(Figures 4a-4c). The Gordonsville facility is of a similar size to the Apple Valley, CA, site (Figure

3b). Figure 4c traces five pixels from the MOD13A2 Enhanced Vegetation Index (EVI) 16-day

time series from February 2000 to February 2005. As in the MOD44C time series (Figures 3c-

3e), the two pixels which most closely overlap the Wal-Mart facility (pixels 12 and 13) have EVI

values which decouple from the undisturbed surrounding vegetation (pixels 7, 17, and 18)

following the 2002 construction event.

Conclusions

The availability of internet-based cost-free, multi-date, high resolution imagery and geo-

coding services facilitates the efficient construction of land cover change event data sets. Our

results demonstrate the utility of these disturbance event data sets for validating existing land

cover change-related products at 28.5 m, 250 m, and 1 km resolutions. In terms of building a

medium resolution map of disturbance events, the LEDAPS forest Disturbance Index (DI)

appears to perform well, with all of the validation sites clearly indicated in the DI image. The full

US Wal-Mart disturbance event data set could contribute to the validation of the complete US

LEDAPS data set, with the caveat that such a validation set would only describe one form of

disturbance – undisturbed forest and natural vegetation to impervious surface.

Although MODIS vegetation index time series do not depict the spatial extent of Wal-

Mart disturbance events with the same precision of the LEDAPS products, these time series

characterize the temporal signature much more clearly —allowing one to infer the 16-day period

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in which construction began. Validation work at five sites of varying climate, size, and

background vegetation yielded encouraging results. In all cases the signature of Wal-Mart

construction was clear. The clarity of the NDVI and EVI signals holds promise for future

supervised classifications of land cover change at continental or global scales, with disturbance

event data like the Wal-Mart data set serving as training data (Zhan et al., 2002). Wal-Mart stores

are just one example of a broad new class of large land cover events that can be tracked using a

combination of geo-spatial records from industry and high resolution imagery.

A natural combination of the spatial information in the LEDAPS and the temporal

information in MODIS could yield algorithms that map the position and initial date of

disturbance for urban growth within the United States at 28.5 m spatial resolution and 16-day

temporal resolution. Such a product would be of value to those who seek to map the spatial

distribution of urban areas both frequently and at medium resolution (Dobson et al., 2000). An

open library of land cover change events would facilitate collaboration in the design and

evaluation of these land cover change detection algorithms. To be useful, such a geo-database

would include the spatial footprint of the change, the land cover classification before and after

the change event, and the date of the change.

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Figures

Figure 1a-1c. Distribution of US Wal-Marts. Figure 1a depicts the location and decade of construction for all operational US Wal-Marts (as of June 2004). Figure 1b highlights those Wal-Marts which may appear in the MODIS record. Figure 1c is a histogram of Wal-Mart store opening dates through 2005. Figure 2a-2e. Three stages in the development of a Wal-Mart in Thomasville, NC, are depicted in Figures 2a-2c (pre-development, construction, and post-development). The geo-coded Wal-Mart address is represented by the yellow ‘x’ in Figure 2c, and the adjusted location as the red ‘+’. The store footprint is outlined in red. Figure 2d shows the store outline in blue, atop the LEDAPS forest Disturbance Index (DI) image. Here, forest loss is red to orange, stable land is yellow, and forest re-growth is green. The Wal-Mart store and a similar development (upper right) are clearly visible as red patches of disturbance against a background of stable vegetation. Figure 2e depicts the location of all 25 North Carolina / Virginia validation sites. A disturbance signal was noted at each location. Figures 3a-3e. Figures 3a-3b are pre- and post-construction images of an 11 hectare Wal-Mart Distribution Center in Apple Valley, CA. The circles are 250 m diameter sample points of background vegetation (blue), the immediate neighborhood of the distribution center (green), and the actual building footprint of the distribution center (red). Figure 3c shows the neighborhood and building footprint pixels deviating from the background vegetation pixels approximately one year prior to the opening of the Apple Valley facility, in March 2003. Figure 3d depicts a similar three-class time series (with the same color scheme) for a much smaller Wal-Mart Supercenter in Fayetteville, North Carolina. As in Apple Valley, here the neighboring (green) and overlapping (red) pixels begin exhibiting lower NDVI values than the background vegetation (blue) approximately one year prior to store opening, in July 2001. Finally, Figure 3e follows the NDVI time series for a Wal-Mart Supercenter in Brewer, Maine. Only pixels which overlap the store footprint (red) and background vegetation pixels (blue) are traced. Here too, the overlapping pixels show reduced NDVI one year prior to the opening of the store, in the spring of 2002. Figures 4a-4c. Figures 4a-4c are pre- and post-construction images of a Wal-Mart Distribution Center in Gordonsville, VA. The circles approximate the IFOV of a single MODIS pixel at 1 km resolution. Pixel 12 (red) and pixel 13 (purple) are closest to the footprint of the facility, while pixels 7, 17, and 18 are undisturbed background vegetation (blue). Figure 4c traces the 16-day EVI time series (MOD13A2) for these five pixels from February 2000 to February 2005. Pixels 13 and 12 show reduced EVI after the 2002 construction event.

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Works Cited Crist, E.P., 1985. A TM tasseled cap equivalent transformation for reflectance factor data, Remote Sensing of the Environment, 17: 301-306. Dobson, J.E., E.A. Bright, P.R. Coleman, R.C. Durfee, B.A. Worley, 2000. Landscan: A Global Population Database for Estimating Populations at Risk, Photogrammetric Engineering and Remote Sensing 66:7 849-857. Healey, S., W.B. Cohen, Y. Zhiqiang, O. Krankina, 2005. Comparison of tasseled cap-based Landsat data structures for use in forest disturbance detection, Remote Sensing of the Environment, 97:3 301-310. Horner, C., C. Huang, L. Yang, B. Wylie, M. Coan, 2004. Development of a 2001 National Landcover Database for the United States. Photogrammetric Engineering and Remote Sensing, 70:7 829-840. Huang, C., B. Wylie, C. Homer, L. Yang, G. Zylstra, 2002. Derivation of a Tasseled-Cap transformation based on Landsat 7 at-satellite reflectance, International Journal of Remote Sensing, 23:8, 1741-1748. Kauth, R.J., G.S. Thomas, 1976. The tasseled cap – a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat, Proceedings, Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, IN: LARS, 41-51. Milesi, C., C.D. Elvidge, R.R. Nemani, S.W. Running, 2003. Assessing the impact of urban land development on net primary productivity in the southeastern United States, Remote Sensing of the Environment, 86:3 401-410. Molly E. Brown, J. E. Pinzon, J.T. Moriserre, K. Didan, C.J. Tucker, 2005, submitted. Evaluation of the Consistency of Long-Term NDVI Time Series Derived from AVHRR, SPOT-Vegetation, SeaWiFS, MODIS, and Landsat ETM+ Sensors. Transactions in Geophysics and Remote Sensing. Tucker, C.J., D.M. Grant, J.D. Dykstra, 2004. NASA’s global orthorectified Landsat data set. Photogrammetric Engineering and Remote Sensing, 70, 313-322. Vermote, E.F., et al., 1997. Atmospheric correction of visible to middle infrared EOS-MODIS data over land surfaces: Background, operational algorithm, and validation., Journal of Geophysical Research, 102, 17131-17141. Vogelmann, J.E., S.M. Howard, L. Yang, C.R. Larson, B.K. Wylie, J.N. Driel, 2001. Completion of the 1990s National Land Cover Data Set for the conterminous United States, Photogrammetric Engineering and Remote Sensing 67: 650-662. Wang, Zheng-Xing, C. Liu, A. Huente, 2003 submitted. From AVHRR-NDVI to MODIS-EVI: advances in vegetation index research. Acta Ecologica Sinica.

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Zhan, X., R. Sohlberg, J.R. Townsend, C. DiMiceli, M. Carroll, J.C. Eastman, M. Hansen, R.S. Defries, 2002. Detection of land cover changes using MODIS 250m data, Remote Sensing of the Environment, 83: 336-350.

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Figure 1c. Store Openings, 1962-2005.

1962 1972 1982 1992 2002

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Figure 3. UMD 250m MODIS time series at three validation sites.

Figure 3a. Apple Valley, CA May 1994 (TerraServer)

Figure 3b. Apple Valley, CA May 2005 (DigiGlobe / GoogleEarth)

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3c. Apple Valley, California

3d. Fayetteville, North Carolina

3e. Brewer, Maine

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Figure 4a. Gordonsville, 4 April 1994 (Pixxures image).

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Figure 4. Wal-Mart Dist. Center in Gordonsville, VA, and MOD13A2.

Figure 4b. Gordonsville, 1 June 2003 (GlobeExplorer image).

Figure 4c. EVI 16-day time series, MOD13A2.

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