GIS Modelling of Intertidal Wetland Exposure Characteristics
ARTICLE SUMMARY
Melissa Thrush
A study by Nathan Crowell,
Timothy Webster, and Nelson J. O’Driscoll (2011) examined the use of GIS to
create a model to capture exposure to solar radiation to use in conjunction
with existing wetland mapping to provide information on ecosystem
characteristics.
Calculating an ecosystem’s
exposure to solar radiation and patterns of tidal inundation could enable
scientists to gain insight into the components and processes that create a
sustainable biodiverse ecosystem.
Accurate modelling and
computation of solar radiation exposure is difficult because data is often captured
at specific sampling sites which cannot consider dynamic tidal inundation
patterns.
Crowell et. al. believe that
GIS can help solve the difficulty in accurate modelling and capture of these
data. They hypothesize that the use of LIDAR
derived high-resolution DEMs (digital elevation models) along with “existing wetland
mapping information” can create a successful model for solar radiation and
atmospheric exposure (Crowell, 2011).
Their model would then be able to provide information on “exposure
characteristics for each 4-m2 cell within the study area” (Crowell,
2011).
The Southern Bight portion of
the Minas Basin located between Kings and Hants Counties in Nova Scotia, Canada
served as the study area. This
particular study region experiences a semidiurnal tidal cycle, which is two
high and two low tides of approximately equal size every lunar day. The area was also listed as a Wetland of International
Importance in 1987. Marine flats located
within the study area “sustain the largest number of mixed-species shoreline
birds in all of North America during fall migrations” (Ginn and Hamilton, 2009).
The LIDAR digital elevation
data was acquired by the Applied Geomatics Research Group in Middleton, Nova
Scotia. Data for the western extent of
the study area was obtained in 2003 and for the eastern extent in 2007. Airborne systems were used to survey the area
at low tide to establish a baseline surface.
A tidal inundation model was
created using a Visual Basic script within ArcGIS the researchers created a
model using cell elevations from the LIDAR raster data and tidal prediction
data obtained in 2009 “to identify high-risk areas for flood damage from storm
surge events and sea level rise” (Webster and Stiff), 2008.
A solar exposure model was
created using sunrise and sunset times from 2009 to create two classifications
of solar exposure, exposed or shaded.
Three distinct vegetation
classes, low salt marshes, high salt marshes and marine flats, were created
whose spatial extents were acquired from the Nova Scotia Wetlands and Coastal
Habitats Inventory in 2000. Using GIS
these delineated vegetation classes were located in the study area.
Using a Python script, within
ArcGIS, an intertidal analysis of each layer was performed that displayed solar
and atmospheric exposure characteristics for areas of seashore that are covered
at high tide and exposed at low tide.
The study results showed,
pretty convincingly, that oxygen availability and solar exposure were strongly
correlated to the intertidal elevation (Figure 2). The
greater the intertidal elevation the more atmospheric and solar exposure.
The use of GIS enabled the
researchers to create solar and tidal models that created realistic layers
based on tidal elevations. Using GIS tools,
they extracted submerged tidal areas and were able to add a layer depicting the
three different wetland classes. Then
correlations between wetland classes and their exposure to oxygen and solar
radiation could be made. Vegetation
classes existed in areas with the highest annual atmospheric and solar exposure
(Figure 4). Understanding the various
factors that contribute to the survival of various vegetation species can help
scientists make more accurate predictions and create more robust restoration
initiatives. The researchers also noted
that their models and analysis methods could be “used to analyze any spatially
relevant data without change to the underlying methodology” (Crowell,
2011).
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| Figure 2 - GIS Modelling of Intertidal Wetland Exposure, Crowell (2011) |
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