Wetlands Investigation Utilizing GIS and Remote Sensing Technology for Lucas County, Ohio: a hybrid analysis.

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Wetlands Investigation Utilizing GIS and Remote
Sensing Technology for Lucas County, Ohio a
hybrid analysis.
Update on current wetlands research in GISAG
Nathan Torbick Spring 2003
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Component One Remote Sensing The ERDAS Imagine
Expert Classifier has two main elements the
Knowledge Engineer and the Knowledge Classifier.
The Knowledge Engineer provides methodology for
users with advanced information and experience to
define variables, rules, and classifying
interests to design a hierarchical decision tree
and knowledge database. The Knowledge Classifier
provides methodology to utilize the knowledge
database created by the user and Engineer.
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ERDAS Imagine Knowledge Engineer Knowledge
Classifier
Previous attempts at classifying wetland types
provides confirmed accurate training sites that
can be utilized. Using the inquirer cursor
function and signatures editor precise pixel
values and signatures can be extracted for an
AOI. With the hierarchical decision tree a
hypothesis can be created with rules defining
variables. The Knowledge Engineer feature allows
the user to define nearly every aspect of the
image.
Hypothesis
Rules
Variables
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2km coastal zone buffer
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Radiometirc Enhancement Atmospheric Haze
Reduction For multi-spectral images, this method
is based on the Tasseled Cap transformation which
yields a component that correlates with haze.
This component is removed and the image is
transformed back into RGB space. For panchromatic
images, an inverse point spread convolution is
used.
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Histograms of Landsat 7 ETM reflectance
responses. Left graph displays single image.
Right graph displays spectral response of a
multitemporal stacked image with an applied
tasselcapped based algorithm for atmospheric haze
enhancement.
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Pixel value/Histogram examination
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Knowledge database output over Landsat image
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Knowledge database output over Lucas aerial photo.
Coastal
Green Wet Forest
Yellow Wet Prairie
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Example of classified Landsat 7 ETM output from
ERDAS. Ottawa Park off Brancroft St. across from
UT.
Blue Wet Forest Red Wet Prairie
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Component Two. GIS
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DEM
Soils
Slope
Landsat ETM classification
Drift thickness
Layered Map Matrix
LayeredMap Matrix
Bedrock/Geology
Floodplains
Wetlands
Water Table
Hydrology
Ground Water
Fig. 1. Conceptual framework for wetlands
cartographic model.
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Data Collection Flood data was complied from Ohio
Department of Natural Resources. The flood data
layer is a combination of the 100 500 year
floodplains and flood hazard areas in Lucas
County. The ODNR GIS also provided soils data.
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Some wetland characteristics and parameters
required large amounts of time and analysis for
collection and processing. Water table depth,
drift depth, and bedrock geology data was
acquired from published hydrogeology reports.
Maps required scanning into digital format,
georeferencing, and digitizing.
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Watershed Drainage Network. Channel networks
with arbitrary drainage or resolution can be
extracted from digital elevation data (Tarboton
1991). This method is based on elevation
gradients, flow accumulation, and drainage
networks. Using Arc/INFO a flow-order-accumulation
network can be designed. This takes substantial
experimenting to capture the desired results
(1.53km2).
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GPS integration. Wet Prairie near Kitty Todd
reserve.
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GPS Integration.
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DEM
Soils
Slope
Landsat ETM classification
Drift thickness
Layered Map Matrix
LayeredMap Matrix
Bedrock/Geology
Floodplains
Wetlands
Water Table
Hydrology
Ground Water
Fig. 1. Conceptual framework for wetlands
cartographic model.
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Model Simulation and Analysis.
A rating system has been/is being developed and
tested for each model parameter. Within each
coverage, value fields have been added to
attribute tables and reclassified for model
input. The amount of weight, percentage of input,
and strength of variable for input to the model
can be manipulated for desired results. This is
the main human interaction/element in the
project. Different scenarios with changes in
parameter strength and methods can be run
outputting different end results ranging from
regression analysis to constraint mapping to
weighted variables.
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Scenario One.
Percentage of Input - Strength
Scenario One Output Table
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• Theory Higher values reflect wetland
  characteristics

Matrix output for wetland model.
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• Future Directions
• CAUV Additional season scenes
• Ground truthing/Accuracy Assessment
• Summer REU
• Accessibility of Data
• Visual Basic Code of model manipulation and
  parameter adjustments