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

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

Nathan Torbick

Spring 2003

Update on current wetlands research in GISAG

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.

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.

Hypothesis Rules Variables

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.

2km coastal zone buffer2km coastal zone buffer

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.

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.

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.

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.

Pixel value/Histogram examinationPixel value/Histogram examination

Knowledge database output over Landsat image

Knowledge database output over Lucas aerial photo.

Coastal

Green = Wet Forest

Yellow = Wet Prairie

Blue = Wet Forest

Red = Wet Prairie

Example of classified Landsat 7 ETM+ output from ERDAS. Ottawa Park off Brancroft St. across from UT.

Component Two. GIS

Wetlands

LayeredMap Matrix

Water Table

Ground Water

Soils

Bedrock/Geology

DEM

Slope Landsat ETM classification

Floodplains

Drift thickness

Hydrology

Layered Map Matrix

Fig. 1. Conceptual framework for wetlands cartographic model.

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.

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.

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).

GPS integration. Wet Prairie near Kitty Todd reserve.

GPS Integration.

Wetlands

LayeredMap Matrix

Water Table

Ground Water

Soils

Bedrock/Geology

DEM

Slope Landsat ETM classification

Floodplains

Drift thickness

Hydrology

Layered Map Matrix

Fig. 1. Conceptual framework for wetlands cartographic model.

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.

Layer Class Value Count - 30m Layer Class Value Count 30mSoil Nonhydric 0 546761 Water Table Low End 0 390Soil Hydric 36 458394 Water Table … 1 18370

Water Table … 2 50080Ground Water Ability Low 0 1004942 Water Table … 3 47163Ground Water Ability … 1 1107345 Water Table … 4 126689Ground Water Ability … 2 1088580 Water Table … 5 113951Ground Water Ability … 4 698424 Water Table Mid Range 6 52256Ground Water Ability High 5 6634767 Water Table … 7 178577Ground Water Ability surface water 6 197938 Water Table … 8 146845

Water Table … 9 20465Drift Depth Deep 0 21430 Water Table … 10 10122Drift Depth … 1 52158 Water Table … 11 137026Drift Depth … 3 130518 Water Table High End 12 93470Drift Depth … 5 293099Drift Depth … 7 376286 Classification Coastal 35.25 553Drift Depth … 9 113922 Classification Forest 35.75 8083Drift Depth Shallow 11 9426 Classification Prairie 35.5 4770

Classification Water 35.35 273Flood Data Non-Flood 0 844259Flood Data Flood Zone 8 153237 Slope Data High Slope -2 1252634

Slope Data Low Slope -1 808130Drainage Network 20 83261 Slope Data No Slope 0 190524

Soils 27Ground Water Ability 5Drift depth 8Flood Data 6Drainage Network 15Water Table 10Landsat Classification 27Slope 1

Scenario One.

Percentage of Input - Strength

Scenario One Output Table

Matrix output for wetland model.

Theory: Higher values reflect wetland characteristics

Future Directions

CAUV & Additional season scenes

Ground truthing/Accuracy Assessment

Summer REU

Accessibility of Data

Visual Basic Code of model manipulation and parameter adjustments