Impervious
Surface Classification
A Concrete Problem
Before
satellite technologies
were available,
impervious surface
was often calculated
by assigning average
percent impervious
values to land
use classes that
had been developed
from parcel maps
or interpreted
from aerial photography.
For example,
single family residential
would be classified
as 35% impervious,
commercial as 85%
impervious,
industrial as 75%
impervious,
and parks as
15% impervious.
However, the average
values may not
accurately represent
any particular
location due to
building and design
variations, and
it was often expensive
to obtain the land
use data. Further,
when results are
needed over larger
geographic regions,
(e.g., multiple
cities) there are
likely
differences
in methods and
dates of data
collection
that lead to inconsistencies
in comparisons
over time and
location.
Zero
to 100 -- Digitally
Recently
researchers at
several universities,
including the University
of Minnesota, have
investigated the
potential of satellite
remote sensing
as a means to accurately
and economically
map impervious
surface area. The
results demonstrate
that digital multispectral
satellite imagery
can be used to
accurately determine
and map the amount
of impervious surface
area. The methods
depict the degree
of imperviousness
from 0 to 100%
at the pixel level
- 30 meters on
a side or about
1/4-acre for Landsat
Thematic Mapper
(TM) data. Finer
resolution maps
can also be generated
using higher resolution
satellite data.
How "green" is
your pixel?
The
method is based
on a strong relationship
of the "greenness" component
of the "tasseled
cap" transformation
of Landsat TM
data to the
amount of green
vegetation,
and therefore
to the lack
of green vegetation,
or impervious
surfaces, in
a pixel (1/4-acre
cells). Although
the majority
of Landsat pixels
in an urban
area are mixtures
of two or more
cover types,
this method
provides a means
to estimate
the fraction
of each pixel
that is impervious.
Following conversion
of the six reflective
spectral bands
of the Landsat
data to greenness,
a polynomial
regression model
of the relationship
of greenness
to percent
impervious
area is
developed
based on measurements
from high
resolution
digital orthophotos
for a random
sample of
approximately
50 sites with varying
amounts and
kinds of
imperviousness
and vegetation.
Once the
regression
model is
developed
representing
different
kinds
and amounts
of impervious,
it
is applied
to all of
the pixels
in the
landsat
image that
have
been classified
as urban
or developed.
Consistent
Results
A
comparison of measurements
from an independent
set of samples
to the Landsat estimates
of the amount of
impervious area
is shown for the
entire state for
the 2000
impervious
surface classification.
The accuracy
or
agreement between
the Landsat estimates
and measurements
from the aerial
photography has
been high - with
average accuracy
of ~90% (as measured
by R2)
and standard
errors of 8
- 11%.)
The
strong relationship
of greenness to
percent impervious
surface area enables
accurately classifying
or mapping the
degree of imperviousness
for large areas.
Impervious maps
of the Twin
Cities
Metropolitan Area have
been generated
for 1986, 1992,
1998 and 2002
and
the entire
state
of Minnesota has been classified
for ~1990 and
~2000.
Once
the maps
are
created, we can
look more closely
at areas that are
experiencing
a significant
amount of growth,
derive
watershed,
county and city-level
statistics,
and determine areas
of emerging increasing
imperviousness that
can still be mitigated
by local and regional
policy makers to minimize
impacts on the surrounding
watershed systems.
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