January 13, 2004
14:38
WSPC/101-CEJ
00092
D. M. Fitzgerald, G. A. Zarillo & S. Johnston
590
from government agencies or private aerial surveying firms that maintain archives of
their photography. Very often more than a century of coastal change can be repre-
sented using a combination of historical maps and aerial photographs. Using GIS as
a common platform, it is possible to reduce maps in digital form and digital aerial
photos to a common geographic datum.
Since digital imagery can be easily imported to most GIS platforms and georef-
erenced to base maps or other images (i.e. USGS DOQQ's) the next logical step is to
apply image analysis methods for enhancing the utility of coastal aerial photography.
Recent developments in this area include extensions to GIS software that combine
the power of image analysis with the convenient manipulation of geographic data.
Image analysis methods that have been applied to accomplish this include relatively
simple image enhancement and image classification techniques. Hoeke and Zarillo
(2001) described an extension to ArcViewTM 3. that allows automated mapping
of the shoreline and vegetation lines along the coast. Similarly, Connell and Zarillo
(2003) extended the method to include automated mapping of inlet channels and
shoals. The most important feature of these tools is the ability to reduce all data to
a common horizontal datum, compare data sets over time, and automate extraction
digital data for time series analysis. Thus, the image analysis method allows data
acquisition using a spectral signature, whereas the GIS aspect of the methodology
allows comparison of temporal change in a common geographic reference system.
Hoeke and Zarillo (2001) demonstrated how image analysis methods can be ap-
plied to automate mapping of important coastal features using simple image analysis
within a GIS platform. The Beachtools GIS extension (Hoeke and Zarillo, 2001) de-
veloped as a tool for studying historical shoreline change from aerial photography;
specifically, to automatically map the wet/dry line and the vegetation line of the
beach (Fig. 19), and generate transects from a standardized baseline to these fea-
tures. The Beachtools extension was coupled with the ArcViewTM 3., since this
widely used GIS platform already included a powerful image analysis extension
termed ImageAnalyst. The inclusion of vegetation line measurements is considered
an important indicator of shoreline position, as well as the wet/dry line, which
approximates the position of mean high water (Smith and Zarillo, 1990). The vege-
tation line provides an estimate of the position of the toe of the dune and is a less
variable indicator of long-term shoreline change since the response of the vegetation
line to erosion or accretion is on the order of months to years, rather than the higher
frequency changes of the wet/dry lines.
The Beachtools GIS extension is based on a supervised spectral classification
of the highly reflective beach sand with respect to the surrounding wet or satu-
rated areas below the high water line and vegetated zone. Similar to a supervised
classification for extracting land cover thematic information, a group of image pic-
ture elements (pixels) representing the reflective beach are selected as a training
site (Jensen, 1996). The statistics of the spectral reflectance within the training site
are used to identify all other pixels in the digital image having similar reflectance
Previous Page
