Geoscience Reference
In-Depth Information
serving as baseline on which to compare changes. The second section continues
with an analysis of the effects of spatial resampling methods on the distribution
of imperviousness values and the geotechnogical implications in the context of
environmental management and policy. Through a proposed method to estimate
imperviousness based on developed land cover class data, an analysis of the rates of
temporal change on imperviousness during the 1992-2005 time period follows as
the third section. To conclude the chapter, some estimates of the spatial distribution
of these temporal variations as aggregate areas and density analyses are presented.
A discussion concerning the importance of these findings on stormwater policies
and watershed management implications concludes this chapter.
8.2 Imperviousness: A Key Environmental Indicator
for Watershed Management
Imperviousness is an important variable influencing hydrologic behavior and bio-
logical integrity (Arnold and Gibbons
1996
; Miltner et al.
2004
). This is reflected
in multiple land cover and land use modifying processes and is utilized in several
hydrologic models, and has often been implicated as a cause of water quality degra-
dation (U.S. EPA
2008
). Over time, changes in land cover are likely to alter the
distribution of imperviousness in landscapes, bringing possible negative effects on
watersheds due to degradation of water quantity and quality parameters (Schueler
1987
,
1994
; Booth and Reinelt
1993
). The likely negative impact of imperviousness
can drive the development, installation, and monitoring of stormwater management
facilities and practices. Imperviousness can be characterized by GIS methods such
as overlay and buffering operations of data layers extracted using either planimet-
ric (e.g. subwatersheds, parcels, transportation network, and structures), or from
remotely sensed information with methods such as sub-pixel classification, feature
extraction or object-based analysis (Mid-Atlantic RESAC
2005
; Obusek and Tribble
2006
).
In Kentucky, the imperviousness data derived from 2001 epoch Landsat data
is the only statewide, moderate spatial resolution source of information available
(Kentucky Division of Geographic Information
2009c
). Methods exist for esti-
mating the total impervious area (TIA) based on GIS data which are proxies for
imperviousness (Barber et al.
2002
). Estimating TIA at the river basin scale is rel-
atively simple when the classification of imperviousness for each pixel is binary:
either the area represented by the pixel is 0 or 100% impervious. Another approach
is to generate imperviousness or impervious cover data layers through subpixel clas-
sification approaches (Yang et al.
2002
; Woods Hole Research Center
2007
). A
propagation method must be then used to estimate the TIA by utilizing this type of
dataset. Calculation of “pooled” TIA by extracting subpixel contributions to imper-
viousness based on discrete percentage imperviousness classes (i.e. 101 classes: 0,
1%, etc. through 100%) can be determined by:
