int. j. remote sensing, 2000, vol. 21, no. 18, 3473–3486
Dynamics of urban growth in the Washington DC metropolitan area,
1973–1996, from Landsat observations
J. G. MASEK, F. E. LINDSAY and S. N. GOWARD
Department of Geography, University of Maryland, College Park,
MD 20742, USA
(Received 10 May 1998; in Ž nal form 11 November 1999 )
Abstract. Like other human-induced landcover changes, urbanization represents
a response to speciŽ c economic, demographic, or environmental conditions. We
use the Washington D.C. area as a case study to relate satellite-derived estimates
of urban growth to these economic and demographic drivers. Using the Landsat
data archive we have created a three epoch timeseries for urban growth for the
period 1973–1996. This map is based on a NDVI-diŒerencing approach for
establishing urban change Ž ltered with a landcover classiŽ cation to minimize
confusion with agriculture. Results show that the built-up area surrounding
Washington DC has expanded at a rate of ~ 22 km2 per year during this period,
with notably higher growth during the late-1980s. Comparisons with census data
indicate that the physical growth of the urban plan, observable from space, can
be reasonably correlated with regional and national economic patterns.
1.
Introduction: Urban growth and landcover transformation
While the global population has grown dramatically during the last century, we
also have witnessed a ‘population implosion’: the unprecedented concentration of
humans into urban areas around the globe. Since 1800 the number of urban dwellers
has jumped by a factor of 100 to 2.5 billion individuals, or nearly one-half of the
world’s population (Ž gure 1) (Cohen 1995 ). Once conŽ ned to the industrialized
Figure 1. Global trends in urbanization since 1800, showing both total global urban population ( black diamonds) and the fraction of the global population living within urban
areas (white squares). The shaded region corresponds to that portion of the growth
curve documented by civilian remote sensing data.
International Journal of Remote Sensing
ISSN 0143-1161 print/ISSN 1366-5901 online © 2000 Taylor & Francis Ltd
http://www.tandf.co.uk/journals
3474
J. G. Masek et al.
regions of Europe and North America, the trend is now global, with cities in
developing nations growing by up to 7% per year (Chadwick 1987 ). For this reason
the global change that most of Earth’s inhabitants will experience during the next
decades may well be dominated by the changing demographic, economic, and environmental conditions of the world’s cities, rather than by comparatively subtle shifts
in climate.
From a broader perspective urbanization is just one of many ways in which
humans are altering the landcover of the globe. Most of these landscape transformations occur within a regional context, but the speciŽ c, year-to-year changes occur at
local scales, often distributed in a seemingly random pattern. Although these humaninduced land transformations may seem of relatively minor impact considered against
the vast reaches of the planet, these changes are estimated to have signiŽ cantly
altered more than 80% of the Earth’s land area over the last several centuries
(Thomas 1956, Vitosek et al. 1997 ).
Of critical importance is linking these observed changes in landcover to the
driving socio-economic or environmental origins. In particular, the geography of
urban growth oŒers a graphic depiction of the interplay between economics, political
systems, and the environment. While the growth of cities may appear inexorable and
monolithic, it re ects a multitude of conscious choices made by individuals and
institutions reconciling these competing factors for their own ‘best interests’. The
sum of these choices appear as the suburbs, shopping complexes, and industrial
centres that now populate the globe. It is also true that urban development usually
does not follow any simple plan. Early urban growth models based on the steady
outward expansion from an intact urban core have yielded to a reality that is far
more varied and complex. The spatial organization of cities varies widely, and in
part re ects the culture and economic standing of the host region. Viewed through
time, urban areas rarely remain static and changes in urban infrastructure, the
reallocation of capital, and land conversion can all alter the fabric of the urban plan.
Taken in sum the modern urban plan looks less like clockwork, and more the result
of a complex, dynamic system, responding to competing forces.
Assessment and monitoring of urbanization and other localized land transformation is exceptionally di cult at regional and global scales. For some regions of the
world, where sophisticated government agencies maintain accurate records for taxation and development purposes, it is often possible to extract at least region-level
statistics. However, even in these cases there is rarely speciŽ c geographical information to support such Ž gures. In many regions of the world there are no regionally
accurate Ž gures on land transformations. This information simply is not gathered,
or even when gathered, made publicly available.
One technology which oŒers considerable promise for monitoring landcover
change is satellite remote sensing. This observation technology provides globally
consistent, repetitive measurements of earth surface conditions relevant to climatology, hydrology, oceanography and land cover monitoring. One mission in particular,
the Landsat series begun in 1972, was designed and continues to operate with the
objective of tracking changes in landcover conditions. The high spatial resolution
and regular revisit times of the Landsat mission are well suited to studies of regional,
national, and global urbanization. While census data provide a statistical view of
demographics and economics, the actual spatial patterns of urban infrastructure only
emerge from remotely sensed imagery. Furthermore, the frequent revisit times of
satellite sensors constantly update our view of the urban landscape, creating a
Dynamics of urban growth in the Washington DC area
3475
detailed time-series of urban growth. Rather than simply showing the gross change
over a long period, these satellite time-series can record the variability of urban
development in space and time, thus permitting a rigorous comparison with economic
and demographic data.
2.
Objective
In this paper we use the 25-year Landsat archive to estimate rates of urban
growth for the Washington, DC region, and relate these growth estimates to the
historical record of regional economic and demographic changes during that period.
We wish to explore, for a case study in our own neighbourhood, the extent to which
landcover changes observed by satellite re ect the underlying socio-economic context.
This study is also part of a larger, ongoing eŒort to capture the considerable land
cover change information value inherent in the 25 year Landsat observation record.
The Landsat-7 platform launched in April 1999 and a substantial research eŒort is
now underway to bring these observations to the study of Earth System Science and
Global Change (Goward and Williams 1997 ). With Landsat-7 a highly aggressive
observation is scheduled, with over 250 scenes per day or over 90 000 scenes per
year planned. Combined with the over one-million scenes already in the US and
international Landsat archive, these data provide a rich source of knowledge
concerning land transformations which have occurred over the last several decades.
Already the Landsat Humid Tropical Forest Inventory Project has made great
progress in evaluating the human impacts of tropical deforestation in the Americas,
Africa and south-eastern Asia (Skole and Tucker 1993 ). Similar eŒorts are being
considered to examine forest dynamics in the mid-latitude and boreal regions of the
planet. There are, however, many other aspects of land cover change, including
urbanization, which are poorly measured but of critical importance to the human
occupants of Earth. Similar uncertainties surround agricultural production, fresh
water resources and other land uses.
3.
Study site and data
Our study comprises the urbanized portion of the Washington DC Metropolitan
Statistical Area, including the District of Columbia, and all or part of Montgomery,
Prince George’s, and Charles Counties in Maryland, and Fairfax, Arlington, Prince
William, and Louden Counties in Virginia (Ž gure 2). The Washington region presents
a particularly interesting laboratory for studying urban growth. The region has
developed rapidly since the Second World War, with the metropolitan area increasing
in population from 3 million persons in 1970 to 4.5 million persons in 1994 (US
Bureau of the Census 1996, 1986 ). The vast majority of this development occurred
in outlying suburban regions. Economically, Washington’s commercial base depends
on the Federal government, although recent economic restructuring has shifted
activity toward the private sector. Politically the region is governed by a patchwork
of Federal, state, metropolitan, and county entities, with varying attitudes toward
economics and regional planning.
To generate our time-series of growth in the Washington DC area, we obtained
Landsat scenes from 1973, 1985, 1990, and 1996. The earliest image (1973 ) was
acquired with the 79-m spatial resolution Landsat Multispectral Scanner (MSS),
while the other three scenes were acquired with the 28.5-m spatial resolution Landsat
Thematic Mapper (TM). All scenes were imaged during the April–October regional
growing season to minimize seasonal variability. A master scene (1996 ) was
3476
Figure 2.
J. G. Masek et al.
Washington DC metropolitan area, showing state and county boundaries, and
county names.
coregistered to US Bureau of the Census TIGER Ž les in a UTM projection, and all
other scenes were co-registered to this master scene using manual selection of ground
control points (GCPs) followed by a linear (a ne) transformation using nearestneighbour resampling. RMS errors for the GCP sets were less than 0.60 pixels in all
cases. The coarse resolution MSS image was resampled to 28.5-m resolution as part
of the georegistration procedure. Image processing was performed with PCI software,
and integration with Census data was performed with ArcInfo and ArcView GIS
by ESRI.
4.
Strategies for urban change detection
A variety of landcover change detection techniques exist for satellite imagery.
Broadly, these may be separated into two approaches: (1) detection of changes in
independently-produced classiŽ cations and (2) determining change directly from
radiometry (Malila 1980, Lambin and Strahler 1994 ). The advantage of the Ž rst
approach is that the semantic meaning of the landcover change is immediately
obvious (e.g. ‘from grassland to residential’ ), thus avoiding confusion between diŒerent
kinds of landcover change. While this is probably the most common change detection
technique, major errors can occur when the amount of change is very small compared
to the total area under consideration. In these cases the spatial pattern of change
may be masked by random, independent errors from the image classiŽ cations,
Dynamics of urban growth in the Washington DC area
3477
necessitating unreasonably high classiŽ cation accuracy (Gordon 1980, Howarth and
Boasson 1983 ). This represents a severe problem for automated detection of urban
growth, where landcover classiŽ cations often contain classes with accuracy as low
as 70–80% (Toll 1985, Haack et al. 1987, Martin et al. 1988, Harris and Ventura
1995 ) and real growth is often only ~ 1–2% per year when considered over the
entire metropolitan area. While some authors have reported success in using independently produced classiŽ cations (Royer and Charbonneau 1988 ), our attempts
resulted in unacceptably high errors.
Improved estimates of urban growth have been attained by using the second
methodology, directly comparing the radiometry between subsequent images. SpeciŽ c
approaches include band-by-band image diŒerencing, image ratioing, change vector
analysis, and vegetation index diŒerencing. Working with Landsat MSS data, Jensen
(1981 ) investigated a suite of image processing techniques for change detection,
including change-vector analysis, image diŒerencing, and comparison of derived
classes for multiple dates. Jensen and Toll (1982 ) applied the image diŒerencing
technique to MSS data for the Denver, Colorado area. They found that diŒerences
in band 5 (0.6–0.7 mm) correlated with observed sites of urban growth, particularly
when texture information was included. They achieved an overall accuracy (the sum
of the diagonal elements of the error matrix divided by the total number of samples)
of 81% using this technique, and an urban growth accuracy (detected growth divided
by the total number of actual growth samples) of 77%. Ridd and Liu (1998 ) have
extended this work, evaluating a suite of radiometry-based change detection techniques for various urban land-cover conversions using Landsat TM. The study
concluded that image diŒerencing of TM band 2 (0.52–0.60 mm) was superior for
most urban change applications, although changes in the tasselled cap greenness
index correlated with vegetation changes within urban regions.
Other studies have proposed change detection techniques for monitoring urban
growth based on changes in the Normalized DiŒerence Vegetation Index (NDVI).
DeŽ ned as the normalized diŒerence between near-infrared and visible re ectance,
NDVI can be directly related to the amount of photosynthetic (green) biomass within
a pixel (Rouse et al. 1973, Tucker et al. 1981 ). Since urbanization in non-arid regions
replaces vegetation (high NDVI) with building materials ( low NDVI), sudden
decreases in NDVI should indicate urban development. Howarth and Boasson (1983 )
found that this was true, with changes in vegetation indices strongly correlated with
urban growth. More recently, Nicoloyanni (1990 ) used a change-vector technique,
with axes for MSS NDVI and brightness to produce an urban growth map for
Athens, Greece for the period 1975–1981. NDVI variability was also used as the
basis for a preliminary urban growth study of Washington DC by Johnston and
Watters (1996 ), who, using an 11-year record Landsat TM, observed that the
Washington metropolitan area grew at a rate of slightly less than 1% per year during
that period.
Applying the NDVI change technique to south-east England, Gri ths (1988 )
found that NDVI diŒerencing alone tended to include areas of agricultural change
(e.g. crop rotation) in addition to real urban growth, causing extremely high errors
of commission. Gri ths (1988 ) suggested that NDVI-change maps be ‘Ž ltered’ to
remove unwanted agricultural noise. Using a proximity measure (in which urban
growth was preferentially identiŽ ed if it occurred near other urban areas) as a Ž lter,
he dramatically improved the results, although urban development was still
overestimated by a factor of two.
J. G. Masek et al.
3478
It is worth noting that most studies have relied on only a pair of images separated
in time to map urban growth. This limitation re ects the substantial cost of highresolution satellite imagery, as well as the technical hurdles associated with processing
multiple scenes. In fact, the application of remote sensing to real problems in urban
growth requires a time-series approach, with su cient measurements for comparison
with demographic and economic data. This study presents a preliminary attempt at
charting urban development for a major metropolitan area through time. By creating
such an ‘image time-series’, we hope that the 25-year Landsat archive may be applied
to enhancing current knowledge of urban areas.
5.
Study methodology
To measure rates of urban growth, we use diŒerences in NDVI between
subsequent images, Ž ltered through a landcover classiŽ cation derived from the
later image to remove eŒects of agricultural variability. We Ž rst calculate NDVI
for each image (Ž gure 3). For Landsat TM, NDVI is deŽ ned as (band4 Õ
band3 )/(band4 1 band3 ), while for MSS it is deŽ ned as band4 Õ band2/band4 1
band2 (Jensen 1995 ). NDVI images from subsequent dates are then subtracted,
producing a map of DNDVI in which positive values represent ‘greening’ (increased
vegetation) and negative values represent ‘browning’ (decreased vegetation). We then
pick a threshold DNDVI value by visual inspection to distinguish true urban growth
( large negative DNDVI) from noise (small negative DNDVI). Typically, threshold
values are found within recently-developed residential areas where the spatial pattern
of roads clearly indicates growth but the introduction of landscaping typically
modulates DNDVI values.
The resulting DNDVI map shows both urban growth and agricultural change
Figure 3.
Processing  ow for urban change detection. See text for discussion.
Dynamics of urban growth in the Washington DC area
3479
(Gri ths 1988 ) (Ž gure 4). The latter frequently occurs as Ž elds, green during one
year, are left fallow or tilled in a subsequent year, mimicking the change expected
from urban development. To remove this unwanted agricultural signal from the map,
we use a classiŽ ed version of the later image as a Ž lter (Ž gure 3 ). Using the ISOCLUS
unsupervised classiŽ cation algorithm implemented in PCI software, 22 spectral
classes were extracted from each Landsat TM scene, and then aggregated into four
landcover types: residential, commercial (including high-density urban), agricultural
(including Ž elds, grassland, and bare earth), and forest. Pixels were labeled as urban
growth if (1) they exhibited DNDVI values below the required threshold, and (2 )
more than 20% of pixels within a 6 Ö 6 contextual window were classiŽ ed as either
commercial or residential. In this manner, the vast majority of the agricultural signal
was Ž ltered out. As a Ž nal step, the growth map was passed through a 3 Ö 3 majority
Ž lter to remove isolated pixels (speckle), mostly associated with registration errors.
Repeating this procedure for all image pairs resulted in our Ž nal urban growth map,
with growth displayed by change epoch (Ž gure 5).
6.
Map validation and accuracy assessments
To assess the accuracy of the urban growth map (Ž gure 5) we have conducted
two validation exercises. First, to estimate the remaining amount of background
speckle (errors of commission or false-positives), we have identiŽ ed by visual inspection several regions which have remained agricultural or forested during the entire
period 1973–1996. Within these regions we consider all growth pixels to represent
errors of commission; summing these pixels gives an estimate of the frequency of
speckle within the growth map for each change epoch. The average amount of
speckle as a percentage of total image area is consistently low, amounting to 0.9%,
0.2%, and 0.1% for the 1973–1985, 1985–1990, and 1990–1996 epochs, respectively.
We note, however, that since actual amounts of urban landcover conversion as a
percentage of the total image area is relatively low, commission errors of ~ 1% are
su cient to cause signiŽ cant overestimation of growth when integrated over the
entire metropolitan area. We have adjusted our Ž nal growth Ž gures downward
accordingly to reduce this potential overestimation. The higher error associated with
the 1973– 1985 epoch appears to result from merging coarse resolution (MSS) and
Ž ne resolution (TM) data. To test this hypothesis we subsampled the 1985 image to
MSS resolution (rather than supersampling the MSS), and counted pixels below the
NDVI threshold. Results diŒered by 1.5%, a value comparable to the 0.9% error
given above.
As a second approach to validation, a trained operator analysed three speciŽ c
regions in Prince George’s County, Maryland (30–40 km2 each) on the 1985 and
1990 TM images, labelling urban growth by inspection. The assessment was aided
by using 10 m SPOT panchromatic data from 1993 to identify particular structures.
Treating these validation regions as ground-truth, we compared a random set of
validated pixels with the corresponding automated classiŽ cation (table 1). The overall
accuracy of the algorithm is 85%, and the estimate of total growth area is within
10% of the actual Ž gure. For comparison, Gri ths’ (1988 ) best estimate (using
NDVI change in conjunction with a proximity constraint) yielded an estimate of
urban growth four times in excess of that measured by visual inspection. In fact, the
spatial pattern of the automated classiŽ cation matches closely the spatial pattern of
validation sites (Ž gure 6 (a, b)), indicating that the algorithm is more adept at detecting
3480
J. G. Masek et al.
Figure 4. Example DNDVI map for the Washington DC region, for the epoch 1985–1990.
Green areas correspond to increased vegetation density (primarily re ecting crop
rotation), and red areas correspond to decreased vegetation density (primarily
re ecting urban growth as well as crop rotation).
Figure 5.
Urban growth from Landsat MSS and TM imagery, colour coded according to
change epoch: 1973–1985 (green), 1985–1990 ( blue), and 1990–1996 (red).
Dynamics of urban growth in the Washington DC area
Table 1.
Actual growth
Actual non-growth
3481
Validation results.
Growth
Non-growth
Total
% correct
3640
199
1312
5146
4952
5345
74
96
Overall accuracy 5 (3640 1 5146 )/10 297 5 85%.
Figure 6. Graphic comparison of (a) validated urban growth map produced by visual interpretation of Landsat TM and SPOT with (b) results of the urban growth algorithm
shown in Ž gure 3. Ancillary roads data from US Census TIGER Ž les.
the geography of urban development than absolute area totals. Other data sources
could also be used for this eŒort, including Digital Orthoquads and high-resolution
space imagery (e.g. CORONA, IKONOS).
J. G. Masek et al.
3482
7.
Dynamics of urban growth from satellite imagery
Our analysis indicates that the Washington DC metropolitan area has grown by
~ 500 km2 during the past 23 years, or ~ 22 km2 yearÕ 1 on average. For comparison,
it took 150 years to populate the extent of the District of Columbia; since 1973 the
metropolitan area has added nearly twice this original area. The estimates given
here agree with those of Johnston and Watters (1996 ), who also examined urban
growth in the Washington DC area using a shorter record of Landsat TM data.
Overlaying classiŽ ed images from 1985 and 1996 on the growth map gives an
indication of the landcover conversion associated with this rapid development
(table 2). While residential development has strongly favoured the clearing of forest,
commercial development has converted both agricultural and forested land. This
may re ect consumer preference for forested homesites, or it may re ect a lack of
agricultural land adjacent to existing communities where new residential growth is
likely to occur. It is also worth noting that visual inspection suggests that substantial
parts of the metropolitan area have reforested, most likely as cleared agricultural
land falls out of use near developed areas.
The spatial patterns of development highlight regional economic and political
variations. It is evident that the majority of development since 1973 has taken
place in Northern Virginia, rather than in Maryland. This observation re ects
vastly diŒerent approaches toward development between local jurisdictions. In
Montgomery County, Maryland, restrictive zoning policies intentionally concentrate
new development along existing transportation corridors to limit sprawl (Ž gure 5).
In comparison, development in Northern Virginia spreads across Fairfax and Louden
Counties (Ž gure 5), where lower taxes and liberal zoning have allowed diŒuse growth
throughout the region. Indeed, road building appears much more pronounced in
Northern Virginia, with transportation routes following development rather than
vice-versa. Beyond public policy, regional diŒerences in growth may be, to some
extent, self-perpetuating. As Northern Virginia captures an increasing share of
Ž nancial and high-technology industries, skilled workers may relocate to the area
simply to be near the locus of employment. This will, in turn, drive further
development.
As expected, the spatial patterns of urban growth do not follow any simple
model. There does appear to be a rough expansion of development toward outlying
areas through time, recalling traditional theories of land-use evolution (Latham and
Yeates 1970 ). Equally prominent, however, are growth centres emerging from (formerly) small towns at some distance from Washington (e.g. Charles County), and
growth centres emerging near transportation hubs (e.g. Louden, Fairfax Counties,
near Dulles airport).
Viewed as a time-series urban growth has varied substantially over the last 25
years. During the 1985–1990 period growth accelerated to 40 km2 yearÕ 1, compared to the 1973–1985 average of 14 kmÕ 2 yrÕ 1 and the 1990–1996 average of
Table 2.
From forest
From agriculture
Landcover conversion matrix*.
To residential (%)
To commercial (%)
44
15
21
20
*Excludes pixels that exhibited other landcover change trajectories.
Dynamics of urban growth in the Washington DC area
3483
24 kmÕ 2 yrÕ 1 (Ž gure 7). Urbanization appears to exhibit substantial variability on
half-decadal timescales. These varying growth rates correlate with regional population and personal income data, suggesting a link between high economic growth
rates of the late-1980s and rapid development within the metropolitan area (Ž gure 7 ).
This development may also re ect the dramatic expansion of credit during this
period, allowing speculative development of residential and commercial areas.
Conversely, slow economic growth in the 1970s and the recessions of 1982– 1983 and
1991–1992 may have hindered growth during these epochs. While cause and eŒect
are di cult to deŽ nitively establish in a system this complex, it seems plausible that
the physical development of the region should respond to major economic and
demographic trends. It is certainly interesting to consider that these underlying
economic variations may be re ected in landcover changes observed from space.
The correlation between population change and urban growth appears particularly strong for the Washington area. This linkage extends to the level of individual
counties. Plotting our growth estimates for individual counties against their respective
population changes indicates a systematic relationship between population increase
and urban expansion at the county scale (Ž gure 8). This scatterplot also serves as a
crude indicator of the e ciency of land use—points above the regression line indicate
high amounts of landcover change per added person, a component of urban sprawl,
while points below the regression indicate relatively denser population distribution.
The 23-year temporal trajectory of Montgomery County, MD, with its restrictive
growth policies, consistently falls below the line, while Prince George’s County, MD
and Prince William County, VA, consistently fall above the line (Ž gure 8). Trajectories
for all counties indicate relatively rapid population and urban growth during the
late-1980s. For the region as a whole, the mean landcover change per person has
been relatively constant since 1973, with each person ‘adding’ ~ 600 mÕ 2 to the
Figure 7. Time series of population growth rate (white), urban growth rate (dark grey), and
rate of change of per capita personal income ( light grey). Rates have been averaged
over the change epochs considered in this study. Population and personal income
data from US Department of Commerce Regional Economic Information Series
(REIS) data. Note that person income data for 1994–1996 have been extrapolated
from the REIS data, using personal income data for Virginia and Maryland from the
1996 and 1997 Statistical Abstracts (US Census).
3484
J. G. Masek et al.
Figure 8. Scatterplot of population growth rate and urban growth rate. Each point represents
a speciŽ c county in the study area, during a speciŽ c growth epoch. Arrows indicate
temporal trajectories for Prince Georges County and Montgomery County during the
23-year study period.
metropolitan area, roughly two-thirds the size of a single TM pixel, via residential,
commercial, and transportation development.
8.
Conclusions and future directions
We have shown that, for the Washington DC region, satellite observations of
urban growth can be related to underlying socio-economic trends and the outcome
of local policies. Urban systems, like ecosystems, span a wide range of scales and
are primarily organized from within. In a complex way regional and national trends
aŒect individuals, whose subsequent decisions determine the character and evolution
of the urban area. Exactly how this linkage occurs is not clear; cause and eŒect, as
noted above, are di cult to untangle in urban studies. A logical next step will be
the integration of business and transportation data with our urban growth images
to deŽ ne precursors of residential development. The true value of this approach will
emerge as it is extended nationally and internationally, to create national and global
portraits of urbanization.
More broadly, we also note that Landsat TM and MSS, while inappropriate for
many practical planning applications, appears extremely well suited for synoptic
views of urban development. The ~ 30-m spatial resolution of Landsat TM is
su cient to capture the characteristic scales of human development, and the spectral
range of the instrument is able to distinguish urbanization from other types of
landcover change. An interesting fact is that, as the Landsat archive ages, many
long-anticipated applications are Ž nally becoming feasible. For example, this study
capitalizes on the fact that the duration of the Landsat archive is signiŽ cantly longer
than many national business cycles, thereby oŒering correlation between economic
and landcover variability. As our legacy of remote sensing data grows during the
coming decades we should anticipate other applications which tie historical records
of human activity to landcover change.
Moving toward national and global comparative portraits of urbanization (and
Dynamics of urban growth in the Washington DC area
3485
other types of landcover change) will require a signiŽ cant change in analysis methodology. Landsat observations have variously been subjected to visual inspection and
numerical analysis. However, in most cases, each new Landsat scene has been treated
as a unique analytical problem. The approach used for one scene is rarely extended
to more than a few adjacent scenes, acquired near the same ecological time.
Assessment of global-scale land cover dynamics from the entire Landsat record, as
well as the Landsat-7 schedule requires a much more highly automated methodology
which employs, as much as possible, the knowledge of expert analysts without their
constant involvement in the analysis process. Thus, an additional outcome of this
study is the development of a knowledge base which may be used for automated
Landsat land cover change studies. This will also provide the quality control needed
to assess whether simple automated methods are capable of extracting the diagnostic patterns of land cover changes which we have observed in the Washington
metropolitian region.
Acknowledgments
This work was supported by the National Aeronautics and Space Administration
under Grant NAG53454 to the EOS Landsat Science Team. Roger Edwards and
Jake Johnson provided technical assistance for this project.
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