Mapping Impervious Surfaces Using Object-oriented
Classification in a Semiarid Urban Region
Zachary P. Sugg, Tobias Finke, David C. Goodrich, M. Susan Moran, and Stephen R. Yool
Abstract
Mapping the expansion of impervious surfaces in urbaniz-
ing areas is important for monitoring and understanding the
hydrologic impacts of land development. The most common
approach using spectral vegetation indices, however, is
difficult in arid and semiarid environments where vegetation
is sparse and often senescent. In this study object-oriented
classification of high-resolution imagery was used to devel-
op a cost-effective, semi-automated approach for mapping
impervious surfaces in Sierra Vista, Arizona for an individual
neighborhood and the larger sub-watershed. Results from
the neighborhood-scale analysis show that object-oriented
classification of QuickBird imagery produced repeatable
results with good accuracy. Applying the approach to a 1,179
km
2
region produced maps of impervious surfaces with a
mean overall accuracy of 88.1 percent. This study demon-
strates the value of employing object-oriented classification
of high-resolution imagery to operationally monitor urban
growth in arid lands at different spatial scales in order to fill
knowledge gaps critical to effective watershed management.
Introduction and Background
Recent trends of population in-migration related to environ-
mental amenities in Arizona and many other parts of the
Rocky Mountain region of the US have been associated with
high rates of urbanization and land development (Vias and
Carruthers, 2005). Impervious surfaces (materials that pre-
vent the infiltration of water into soil (Slonecker
et al.
, 2001))
are created by construction activities, affecting land surface
temperature, water quality, and watershed properties direct-
ly. Increases in the amount and distribution of impervious
surfaces in rapidly urbanizing areas can produce potentially
significant changes in hydrological processes in watersheds
by altering runoff regimes, increasing peak flows, and degrad-
ing water resources (Arnold, Jr. and Gibbons, 1996; Kennedy
et al.
, 2013; Shuster
et al
., 2005). Additionally, the spatial
distribution of impervious areas is an important descriptor of
the physical content of urban environments (Chormanski
et
al
., 2008; Shuster
et al
., 2005). Mapping impervious surfaces
with remote sensing techniques is an effective way to quantify
impervious cover (Slonecker
et al.
, 2001; Weng, 2007) and
thereby improve understanding of the impacts of urbanization
on runoff processes. The most common approach using spec-
tral vegetation indices, however, is problematic in arid and
semiarid environments where vegetation is patchy and often
senescent.
This paper describes a method for mapping impervious
surfaces using supervised object-oriented classification of
high-resolution imagery for an urbanizing semi-arid area.
Insights are provided at the scale of an individual neighbor-
hood as well as the larger sub-watershed to show that despite
utilizing high-resolution imagery, the method is not limited to
only small geographical areas. The first section provides back-
ground on the use of object-oriented classification approaches
for detecting impervious surfaces and identifies the need for
applications to arid and semi-arid locations, followed by a
description of the study areas and imagery used. The next
section describes the methods and results from the neighbor-
hood scale classification (phase 1); then, the methods, results,
and errors and limitations of the regional scale classification
(phase 2) follow. The final section offers conclusions and rec-
ommendations for refining the classification method.
Object-oriented Approaches to Mapping Impervious Surfaces
Earlier strategies for mapping impervious surfaces are based
largely on user-guided, manual delineation (Lee and Heaney,
2003; Shuster
et al.
, 2005). The advantage of this method is its
ability to distinguish between directly and indirectly con-
nected impervious areas, which is important information for
hydrologic modeling. The major disadvantage, however, is the
time and effort required to produce delineations, thus limit-
ing application to small areas (McMahon, 2007). A secondary
drawback is that the digitization of impervious areas by hand
can affect data consistency and accuracy.
Recent remote sensing approaches for automated mapping
of urban impervious areas frequently use spectral vegetation
indices as proxies for imperviousness, assuming for example
that vegetated areas represent pervious surfaces (Bauer
et al.
,
2002; Sawaya
et al.
, 2003; Thanapura
et al
., 2007). Proxies are
thus based on indices such as Normalized Difference Vegeta-
tion Index (
NDVI
), where:
NDVI = (DN
NIR
– DN
RED
) / (DN
NIR
+ DN
RED
)
(1)
Zachary P. Sugg is with the School of Geography and Devel-
opment, University of Arizona, 443 Harvill Building, Univer-
sity of Arizona, Tucson, AZ 85721 (
).
Tobias Finke is a Consultant; 756 E. Winchester Street, Ste.
400, Salt Lake City, UT, and formerly with School of Geog-
raphy and Development, University of Arizona, Tucson, AZ
85721.
David C. Goodrich and M. Susan Moran are with the USDA
ARS Southwest Watershed Research Center, 2000 E. Allen
Road, Tucson, AZ, 85719.
Stephen R. Yool is with the School of Geography and De-
velopment, 435C Harvill Building, University of Arizona,
Tucson, AZ 85721.
Photogrammetric Engineering & Remote Sensing
Vol. 80, No. 4, April 2014, pp. 343–352.
0099-1112/14/8004–343
© 2014 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.80.4.343
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
April 2014
343
