LOCATING SUITABLE REPLACEMENT PROPERTY

FOR THE CACHE RIVER NATIONAL WILDLIFE REFUGE

BY USE OF A GIS MITIGATION SITE LOCATOR

by

Robert Reed

A thesis presented to the Department of Geography

and the Graduate School of University of Central Arkansas in partial

fulfillment of the requirements for the degree of

Master of GIS

Conway, Arkansas

August 2012

All rights reserved

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TO THE OFFICE OF GRADUATE STUDIES:

The members of the Committee approve the thesis of

Robert Reed presented on July 3,2012.

ooks Pearson, ommittee ChaIrperson

Dr. Brooks Green

-

PERMISSION

Title Locating Suitable Replacement Property for the Cache River National Wildlife Refuge by Use of a GIS Mitigation Site Locator

Department Geography

Degree Master of Geographic Information Systems

In presenting this thesis/dissertation in partial fulfillment of the req uirements for

graduate degree from the University of Central Arkansas, I agree that the Library

of this University shall make it freely available for inspections. I further agree that

permission for extensive copying for scholarly purposes may be granted by the

professor who supervised my thesis work, or, in the professor's absence, by the

Chair of the Department or the Dean of the Graduate School. It is understood that

due recognition shall be given to me and to the University of Central Arkansas in

any scholarly use which may be made of any material in my thesis.

July 3, 2012

iv

ACKNOWLEDGEMENTS

I would like to thank Dr. Brooks Pearson for serving as the chair of my committee and

for helping me along my journey through graduate school. I would also like to thank Dr.

Brooks Green for helping me not only get through my thesis but my undergraduate

studies as well, which helped get me to this point. I would like to give a special thanks to

Dr. Sally Entrekin for taking the time to work with me on my thesis and provide insight

into the environmental aspects.

I would also like to thank the members of the Environmental Division of the Arkansas

Highway and Transportation Department. They have made my job something truly great,

and have had patience with me as I have made this endeavor to complete my thesis.

v

Abstract

Locating potential areas to be used as a mitigation bank site for impacts to existing

wetlands due to highway projects is the responsibility of the Environmental Division of

the Arkansas Highway and Transportation Department. The focus of this study is to use

a Geographic Information System (GIS) to determine suitability for proposed areas to be

used as a mitigation bank site. By combining data layers with characteristics specific to

wetlands, such as soils, land use, and topographical conditions, with key spatial analyses,

a GIS can successfully locate viable candidates to be used as a mitigation bank. From

there, a range of suitability can be devised to classify the results as a guide for

determining which area is most likely to be successful as a mitigation bank site.

vi

TABLE OF CONTENTS

Acknowledgements……………………………………………………………………….iv

Abstract……………………………………………………………………………………v

Table of Contents…………………………………………………………………………vi

List of Tables……………………………………………………………………………viii

List of Figures………………………………………………………………………….…ix

List of Abbreviations……………………………………………………………………...x

Chapter 1 Introduction…………………………………………………………………….1

Background………………………………………………………………………………..1

Theoretical/Conceptual Framework……………………………………………….………3

Objectives and Hypothesis……………………………………………………………..….9

Chapter 2 Literature Review……………………………………………………………..12

Purpose…………………………………………………………………………………...12

Mitigation Banks…………………………………………………………………………12

Data Sources……………………………………………………………………………..13

Analyzing Wetlands Using GIS………………………………………………………….18

Suitability Modeling……………………………………………………………………..20

Wetlands and Highway Management……………………………………………………21

Summary…………………………………………………………………………………22

Chapter 3 Methodology………………………………………………………………….23

vii

Software Used……………………………………………………………....……………23

Current Methodology………….…………………………………………………………23

Data…………………………...………………………………………………………….24

Analysis of Data………………………………………………………………………….29

Classification……………………………………………………………………………..33

Method of Application…………………………………………………………………...33

Summary…………………………………………………………………………………35

Chapter 4 Results……………………………………..………………………………….36

Land Classification………………………………………………………………………36

Accuracy Evaluation……………………………………………………………………..39

Examination of Results…………………………………………………………………..39

Summary of Results……………………………………………………………………...41

Chapter 5 Discussion…………………………………………………………………….43

Application within the Environmental Division…………………………………………43

Benefits…………………………………………………………………………………..44

Limitations……………………………………………………………………………….44

Chapter 6 Conclusion……………………………….……………………………………46

Works Cited…………………………………...…………………………………………48

Appendix…………………………………………………………………………………51

viii

LIST OF TABLES

Table 1 Data layers used for the MiSiL analysis………………………………………...16

Table 2 A detail of the six level MiSiL classification system…………………………...34

Table 3 Acres and percentages of data layers calculated by MiSiL……………………..37

ix

LIST OF FIGURES

Figure 1 The Cache River National Wildlife Refuge acquisition boundary managed

by the U.S. Fish and Wildlife Service……………………………………………………..4

Figure 2 The Western Lowlands 2-year and 5-year floodplains…………………………..8

Figure 3 Natural Resource Conservation Services digital soil database for hydric and

non-hydric soils…………………………………………………………………………..15

Figure 4 The National Wetland Inventory database which outlines existing wetlands….17

Figure 5 Agricultural and forested land use/land cover………….………………………27

Figure 6 A comparison between a hillshaded topographic map and the same area as a

slope generated raster file………………………………………………………………..28

Figure 7 A layout of the input data layers and the additional criteria that leads to the

MiSiL classification system……………………………………………………………...31

Figure 8 Level 1 through Level VI classification coverages…………………………….38

Figure 9 Level I and Level IV suitability results of the MiSiL classification system…...40

x

LIST OF ABBREVIATIONS

AHTD – Arkansas Highway and Transportation Department

ANL-E – Argonne National Laboratory-East

CAST – Center for Advanced Spatial Technologies

CWA – Clean Water Act

DEM – Digital Elevation Model

EPA – Environmental Protection Agency

GIS – Geographic Information Systems

LULC – Land Use Land Cover

MAWPT – Multi-Agency Wetland Planning Team

MiSiL – Mitigation Site Locator

NRCS – Natural Resources and Conservation Services

NWI – National Wetland Inventory

SSURGO – Soil Survey Geographic Database

SWAMP – Spatial Wetland Assessment for Management & Planning

USACE – United States Army Corps of Engineers

USFWS – United States Fish and Wildlife Service

1

Chapter 1

Introduction

Background

The Environmental Division of the Arkansas Highway and Transportation

Department (AHTD) establishes and maintains areas throughout the state known as

mitigation banks. The Environmental Protection Agency (EPA) defines mitigation

banking as “the restoration, creation, enhancement and, in exceptional circumstances,

preservation of wetlands and/or other aquatic resources expressly for the purpose of

providing compensatory mitigation in advance of authorized impacts to similar

resources.” This definition provides the key criteria that the Environmental Division has

to address in order to classify a new area as a mitigation bank.

The purpose of the AHTD mitigation banks is compensatory mitigation. In a

federal register that was coordinated between the U.S. Army Corps of Engineers

(USACE) and the EPA, the term compensatory mitigation is described as involving

“actions taken to offset unavoidable adverse impacts to wetlands, streams and other

aquatic resources authorized by Clean Water Act section 404 permits and other

Department of the Army (DA) permits” (U.S. Army Corps. of Engineers & EPA, 2008).

The register goes on to describe the term “no net loss” of a wetland area, even if due to

unavoidable means. This simply states that for any wetland or aquatic area that is

removed, compensation for its replacement must be met.

The U.S. Army Corps of Engineers and the EPA describe four strategies for

compensatory mitigation: restoration of a previously existing wetland, enhancement of

2

an existing aquatic site, establishment of a new aquatic site, and preservation of an

existing aquatic site (U.S. Army Corps. of Engineers & EPA, 2008).

Whenever a highway project is to impact a wetland area, the amount of

potentially disturbed acreage is determined by wetland biologists. These acreage values

are included in the overall determination process for selecting the best highway alignment

alternative. When a wetland or aquatic area is disturbed and no longer suitable as a

functioning ecological system from highway construction, the Environmental Division of

AHTD is required to follow protocols set forth by Section 404 of the Clean Water Act

(CWA) (U.S. Fish and Wildlife Service, 2011). The CWA was established in 1972 and

provides a basic structure for the regulation of discharge of pollutants into the waters of

the United States as well as quality standards for surface waters (Environmental

Protection Agency, 2012). The CWA also includes steps for permit review and issuance

of permits that supports the avoidance of impacts to wetlands or aquatic areas. However,

if unavoidable impacts occur then mitigation is required.

Currently the process of identifying land suitable as a mitigation bank generally

involves one of the staffed wetland biologists of the Environmental Division looking over

aerial imagery and cross-referencing with soil types, to attempt to locate viable areas.

Topographic maps are also consulted to aid the determination for suitable hydrography

for a wetland. The purpose of a Geographic Information Systems (GIS) based mitigation

site locator (Mitigation Site Locator or MiSiL) will be a tool that will streamline the

determination process used for compensatory mitigation. MiSiL will be able to access

multiple datasets and provide levels of suitability for all tracts of land within a study area.

This paper will provide the details of how the MiSiL procedure was created within the

3

GIS Section of the Environmental Division of the AHTD. Presently any analyses

performed, even by the GIS Section, to determine the suitability of a tract of land to be

used as a mitigation bank are conducted separately from each other, with no unifying

examination. A digital soil layer can be viewed and even overlaid onto a topographic

map or aerial image, but this is as far as the analysis currently goes. MiSiL will greatly

change how these analyses are managed by incorporating every available dataset that

pertains to wetlands and mitigation banks into one analysis, and will then provide sets of

polygons ranked by suitability.

The MiSiL procedure has access to multiple datasets, imagery, and search criteria

which will greatly reduce the time and resources currently used. The search criteria will

attempt to locate areas that are not currently aquatic or wetland areas. The purpose of

this is to determine areas that are presently not functioning as a wetland, have the

characteristics of a wetland and can be converted into one. This is to provide an

exchange of wetlands impacted by highway projects with the establishment of wetlands

where they do not currently exist.

Theoretical/Conceptual Framework

The AHTD Environmental Division has recently gone through the process of

determining a viable location for mitigation bank-type property in the Cache River

National Wildlife Refuge acquisition area (Figure 1), which is an 184,686 acre tract of

land that stretches across Jackson, Monroe, Prairie, and Woodruff Counties in eastern

Arkansas.

4

Figure 1. The Cache River National Wildlife Refuge acquisition boundary

managed by the U.S. Fish and Wildlife Service.

5

A suitable tract of land within the refuge has been found by the AHTD

Environmental Division which is thought to be favorable for a mitigation bank, and will

be used to test the MiSiL procedure. This area will be used as a control because wetland

characteristics, such as hydric soils with poor drainage, low-lying slope, and the inclusion

of floodplains, are found at this site.

Lands with hydric soils that have been cleared for agriculture are considered ideal

mitigation sites by the AHTD Environmental Division because it indicates a past wetland

that has potential to maintain natural hydrology. A hydric soil is defined as “a soil that is

saturated, flooded, or ponded long enough during the growing season to develop

anaerobic conditions that favor the growth and regeneration of hydrophytic vegetation”

(Mitsch & Gosselink, 2000, p.756). The U.S. Army Corps of Engineers (2008) describes

hydrophytic vegetation as a plant species that is able to live in areas of saturated soil

conditions.

A digital soil classification layer developed by the Natural Resources

Conservation Service (NRCS, 2012) will be used to locate hydric soils. According to the

NRCS’s website, the soil layer was digitized at scales ranging from 1:12,000 to 1:63,360

and is described as the most detailed level of soil mapping prepared by the NRCS. The

soil layer contains attributes describing the hydric condition of each soil type, such as if

the soil is all hydric, partially hydric, or not hydric. Soils that are described as at least

partially hydric will be used in the MiSiL analysis. Also, this soil layer provides

information about the drainage characteristics, such as poorly drained or well drained, of

each soil type, and will be used as a criterion for the study. Poorly drained soils will keep

6

water present at a site longer than well-drained soils, which is why MiSiL will target soils

that are poorly drained.

A statewide land use/land cover dataset (Gorham, 2007) is used as an aid to

determine agricultural or cleared areas. This dataset was developed by the Center for

Advanced Spatial Technologies (CAST) from a combination of satellite imagery, river

gage data, and field inspections. The land use/land cover data is stored as a raster file

with each cell associated with a particular value that correlates to a land use category,

which includes urban, agricultural, and forested. In addition to the agricultural coverages,

Henry Langston of the Environmental Division has specified tree species found within

the land use/land cover layer that are good indicators of conditions favorable for a

wetland. Those categories include:

American Sycamore

Bald Cypress

Cherrybark Oak

Cottonwood

Gum

Mixed Ash

Mixed Hardwoods

Mixed Hickory

Mixed Oak

Nutall Oak

Overcup Oak

River Birch

Sugar Maple

Sugarberry

Sweetgum

Water Hickory

Water Tupelo

White Ash

Willow Oak

The statewide land use/land cover dataset includes these particular species and their

location can be determined because the attribute value for each of these tree types is

associated with the cells in the land use/land cover raster file.

A 5-meter digital elevation model (DEM) identifies regions that have a low and

uniform transition in slope where water would accumulate rather than run off. The

study’s slope analysis identifies areas most favorable for the creation of a new wetland by

7

depicting the lowest slopes, indicating areas of inundation. This is done by calculating

the percent slope of the entire study area and then a threshold value will be used to query

the percent values. The DEM is maintained by the Arkansas Geographic Information

Systems Board and is a secondary product derived from a statewide aerial imagery

acquisition project.

The National Wetlands Inventory (NWI) (USFWS, 2011) provides areas

designated as wetlands and will be used to identify existing wetland areas, which will

also outline non-wetland areas. The NWI is a system of wetlands classification

administered by the USFWS and is based on color-infrared photography, manual

photointerpretation, and field reconnaissance (Mitsch & Gosselink, 2000). If an area is

currently designated as a wetland within the NWI dataset, then this area will be avoided.

Using 2-year and 5-year floodplain classifications will identify land with regular

periods of inundation. The MiSiL model uses the Western Lowlands dataset provided

by CAST and classifies lands that fall within a 2-year and 5-year floodplain (CAST,

Figure 2). The Western Lowlands dataset stretches from northeastern Arkansas south to

Desha County and is named such due to its location west of Crowley’s Ridge. Flooded

areas which demonstrate the potential to hold water and keep an area inundated will be

indicative of a potential mitigation bank site location.

New mitigation bank sites have a minimum area requirement that must be met by

the AHTD Environmental Division, which is based on a current or projected need that the

Environmental Division determines by assessing potential impacts due to on-going or

future highway projects. In the case of Cache River National Wildlife Refuge the new

8

Figure 2. The Western Lowlands 2-year and 5-year floodplains.

9

site must be at least ten acres. Areas that meet this minimum area will be selected from

the entire set of results.

Once the different data layers have been compiled, queried, and extracted, the

results will be classified based on a tiered scale of conditions ranging from most optimal

to least favorable. Ideally the new mitigation bank site location would fall within the 2-

year floodplain, have all hydric soils, and have been cleared for timber or agriculture,

which would indicate no existing wetland. However, since the MiSiL procedure is able

to provide ranges of suitability, areas that are less than optimal will still be able to be

considered.

Objectives and Hypothesis

The MiSiL procedure will successfully locate potential areas for a mitigation bank,

categorizing each based on its relative suitability for wetland conditions. Success will be

tested by comparing results produced by the MiSiL procedure with a known viable site.

If MiSiL does not provide any level of suitability when evaluated against an area that has

been field inspected by the Environmental Division, then the procedure will be

considered unsuccessful.

The MiSiL procedure introduces a new contribution not only to the AHTD

Environmental division, but also to the realm of geographic information science, by

providing results that are based on suitability levels. Currently the process of finding a

suitable tract of land for a new mitigation bank involves consulting soil survey manuals

and an online imagery viewer such as Google Earth, and then relying on a field visit to

provide the bulk of the remaining information to determine if the area is acceptable.

10

Each component of the study is separate, and there is no use of a GIS. The MiSiL

procedure will drastically change this by providing a significant amount of information,

such as soils, land cover, topography, and existing conditions, in the early stage of the

selection process. Furthermore, the results will be classified according to suitability.

This is a major contribution to the geographic information sciences because the MiSiL

procedure not only provides spatial results of how the various geographic input layers

relate to each other but also rank areas for potential of success for the establishment of an

ecologically sustainable wetland. Another major contribution of the MiSiL procedure is

the practically limitless size of a study area which can be examined, as MiSiL could just

as easily perform an analysis of a potential new mitigation bank on a statewide level as it

could a much smaller area such as a watershed.

The goal of this study is to determine potential mitigation bank areas at the

earliest possible stage in the selection process. Before any work can be done for

acquiring property and converting the land into a wetland area, a suitable location has to

be found. The processes outlined in this study should provide multiple alternatives to

potential locations that can be used for the establishment of a wetland area. For AHTD,

the functionality of MiSiL is a significant step towards streamlining the current procedure,

facilitating a much faster development time from inquiry to results.

It is predicted that the study will yield positive results when performed. The

combination of input variables, such as hydric soils, existing wetlands, tree types, etc.,

that will be used in the study have the capability to determine and locate a suitable area

for a mitigation site. Also, as the input data is updated, the results can be updated as well.

The only foreseeable reason why MiSiL would not generate positive results is if there

11

were inaccuracies in the input layers. Fortunately, the agencies providing these layers are

a trustworthy source.

Based on the potential success of the MiSiL procedure, there is one primary

research question that will be answered: Is the study capable of providing accurate

results for an area of the Cache River National Wildlife Refuge acquisition boundary in

finding suitable mitigation bank property?

The study develops an automated method of selecting areas of land based on a set

of input variables and extracting these tracts of land as potential mitigation bank sites.

The accuracy addressed in the research question can be tested. A new site has already

been selected for the creation of a new mitigation bank. Since this area was determined

suitable before this study was established it can be used as control location. Theoretically,

the processes included in this study should provide this selected mitigation bank as a

suitable candidate. If this occurs, then the results of the study will be positive.

12

Chapter 2

REVIEW OF LITERATURE

Purpose

It has been estimated that the world has lost as much as fifty percent of its original

wetlands (Mitsch & Gosselink, 2000). Wetlands are important factors involved in the

global cycles of natural resources for their ability to reduce floods, recharge groundwater,

and increase dry season flows (Bullock & Acreman, 2003). A cost estimate analysis of

wetlands was performed by members of the Illinois Institute of Natural Resources. The

study determined that the replacement of services, such as fish production and flood

control, of a 770 hectare wetland found in northeastern Illinois would need $939,000 a

year to perform activities that the wetland was providing (Mitsch & Gosselink, 2000). To

avoid impacts on the overall contribution towards the environment that wetlands provide,

replacement property is acquired and maintained by the Environmental Division

whenever an existing wetland area will be disturbed.

Mitigation Banks

Mitigation banks allow for a tract of land to be set aside as a developed wetland

with the amount of acreage thus maintained to be used as credit toward areas where

existing wetlands are impacted due to man-made projects such as highway development.

A mitigation bank is a site where a wetland is restored, created, enhanced, or preserved

for the purpose of being prepared in advance for authorized impacts to an existing

wetland (Mitch & Gosselink, 2000). A preserved wetland is ecologically effective in that

13

it is already functioning. However, a preserved wetland is not offering any compensation

for the loss of another wetland since it is already in use. Restoration of a wetland not

only provides an area previously unused as a wetland as compensation to impacts to

another existing wetland, it has high potential to succeed as a wetland.

The characteristics of a mitigation bank ideally include hydric soils, non-sloping

or low-lying topography, no existing wetland, and the presence of water. To emphasize

the importance of these conditions, literature from the Washington State Department of

Transportation (WSDOT) was reviewed. The WSDOT describes two different wetlands

that are located within a proposed mitigation site study area in one of their technical

memos. Both wetlands have hydric soil conditions found within a 16-inch deep soil pit.

Also, the two wetlands are located in low-lying areas and both are outside of an existing

wetland. The mitigation site is located within a 100-year floodplain. If such conditions

are not present, artificial processes can be utilized to aid in restoration, such as excavation

to use a local water supply and planting desirable vegetation.

Locating soils with hydric conditions is the “foundation” by which determining

potential areas for mitigation bank sites is achieved (Tiner, 2002). Existing wetlands are

removed as potential sites and existing land use classifications are considered (Tiner,

2002). Since the focus of this study is to create a mitigation bank site from an area that is

not currently a wetland, existing wetlands are excluded.

Data Sources

The GIS Bureau of the Montana Department of Administration developed an

application to determine potential sites for wetland mitigation using the Natural Resource

14

Conservation Service’s (NRCS) Soil Survey Geographic (SSURGO) database (Blount,

2011). MiSiL will incorporate SSURGO data (http://soils.usda.gov/) to also provide

hydric soil locations (Figure 3). But simply being designated as a hydric soil does not

indicate if there are preferable conditions for the establishment of a wetland for a

mitigation site. Hydric soils can either be drained or undrained, and drained hyrdic soils

are not necessarily able to support hydrophytic vegetation, which means that the presence

of hydric soils does not automatically qualify an area as a wetland (U.S. Army Corps of

Engineers, 1987).

As stated previously, tree species that are indicators of favorable conditions for a

wetland area were provided by Henry Langston, a biologist with the AHTD

Environmental Division. A land cover layer provided by CAST contains these tree

species that have been outlined in Chapter 1. Also, the land cover layer contains land

cover types that are not conducive to providing adequate conditions for the establishment

of a new wetland, such as urban and barren ground (Carpenedo, et al., 2007).

A five meter resolution DEM provides elevation data, so that contours can be set

at such an interval to show steeper elevation changes versus more gradual changes. Also,

areas of lower elevation can be located, which could be indicative of water collection and

thus inundation of the area. A United States Geological Survey (USGS) topographic map

series shows contour lines and provide information about the topography of the area. The

same task will be accomplished with the DEM, but provides a more recent coverage of

the terrain, as the last set of topographic maps are dated before the year 2000, and the

DEM to be used is from 2005.

15

Figure 3. Natural Resource Conservation Services digital soil database

for hydric and non-hydric soils.

16

The NWI provides a digital dataset of wetlands which have been mapped in the

United States, and covers this study’s areas of interest within the Cache River National

Wildlife Refuge (Figure 4). The NWI is based on the Cowardin, et al. (1979)

classification scheme (Mitsch & Gosselink, 2000) and is described as having established

the principles of how the USFWS manages the NWI (Mitsch & Gosselink, 2000). This

classification scheme divides the potential wetland area into a series of systems, then

subsystems, and finally a class based on the system and subsystem the area falls under

(Cowardin, et al., 1979). The NWI will eliminate areas as potential mitigation site

locations due to the fact that a wetland area is already established at that location.

NWI spatial data is available on the USFWS National Wetlands Inventory

website (www.fws.gov/wetlands/Data/index.html). The data includes both polygons

categorized as either wetlands or swamps, which are based on USGS 1:100,000

topographic maps. According to the USFWS’s website (2012), the wetlands are digitized

based on high altitude aerial imagery, and are determined from the vegetation, hydrology,

and geography seen in the images.

Table 1 outlines all of the input data layers used in the MiSiL procedure. It

provides their source and the date of the last update.

Table 1. Data layers used for the MiSiL analysis.

17

Figure 4. The National Wetland Inventory database which outlines existing

wetlands.

18

Analyzing Wetlands Using GIS

A GIS’s ability to analyze the spatial relationships between multiple layers makes

it ideal to locate areas for potential conversion into wetlands to be used as a mitigation

bank site. The University of Chicago developed a GIS-based spatial modeling process

for the Argonne National Laboratory-East (ANL-E) site, which is located in southeast

DuPage County, Illinois, and will be used as a mitigation site (Van Lonkhuyzen, et al.,

2004). Variables used in the GIS analysis of the ANL-E included soils, vegetation

coverage, and land use, and the study area was determined to have a relatively level slope,

ranging from 2% to 5% (Van Lonkhuyzen, et al., 2004). Input variables will have to be

prioritized in order to rank an area based on its suitability as a wetland. Land use and

vegetative cover were assigned the highest weights for use in the ANL-E study (Van

Lonkhuyzen, et al., 2004), and this is likely due to the fact that the presence of an open

area not being used for urban development which has vegetation that may be hydrophytic

would indicate that the conditions are favorable for the establishment of a wetland. Soil

type was given a lower priority, although locations in the ANL-E where “hydric soils

occur would be better suited to wetland establishment” (Van Lonkhuyzen, et al., 2004).

Even though hydric soils are important for the creation of a new wetland, the lower

weight given to the soils layer was due to the fact that assigning the value of one layer

over another is subjective in its nature (Van Lonkhuyzen, et al., 2004). In this particular

study, many areas of hydric soils were found in portions of the site that currently did not

support many wetlands. This is an example of site specific knowledge being used in

conjunction with the data, since the users knew that hydric soils were not as reliable an

19

indicator for this particular study. The users of the GIS process have the responsibility of

deciding which factors are more suited for their purpose.

Each of the input variables for the ANL-E site was determined to be either

optimally suitable or not suitable and was overlaid onto the ANL-E site separately (Van

Lonkhuyzen, et al., 2004). The suitability of each variable was based on “professional

judgment, data availability, and site-specific knowledge” (Van Lonkhuyzen, et al., 2004).

Viewing the results of each variable independently makes it difficult to determine a

location suitable for a new wetland because an area may be well suited based on one

variable, but that same area may be poorly suited for another variable. The solution is to

overlay the results of all of the variables and then examine where the best suited areas are

found.

Another application developed to analyze wetland areas was created by the

National Oceanic and Atmospheric Administration’s (NOAA) Coastal Services Center

and was named the “Spatial Wetland Assessment for Management & Planning” or

“SWAMP” (Tiner, 2002). The focus of SWAMP is to quantify the wetland’s functions

as opposed to locating a new wetland area. However, the structure of the analyses can be

incorporated into the process of locating new mitigation sites. SWAMP also uses the

NWI dataset to provide wetland attributes that are “critical and form the starting point for

the assessment” (Tiner, 2002, p.64). SWAMP provides a ranked set of results that are

divided as exceptional significance, substantial significance, and beneficial significance.

These classifications are used instead of a numerical ranking because of current

limitations in the understanding of wetland functions. However, this classification

20

scheme still provides users with an idea of how an impact to the wetland will affect the

functions found there.

Suitability Modeling

Suitability modeling analyzes key input factors and then provides results as to

which areas are best suited for the intended use. An example of suitability modeling has

been implemented for use in Finland to analyze the habitats of threatened forest species

in order to protect them from any activity which may cause adverse impacts (Store &

Jokimäki, 2003). Both the actual habitat and the surrounding landscape were converted

to a raster file and attributed for use in a GIS. In order to accommodate for the

landscape’s characteristics a certain distance is applied around every pixel of the raster

file that represents the habitat (Store & Jokimäki, 2003).

A habitat model is then constructed and outlines the areas not suitable for the

species and the areas that are well suited for the species, referred to as the feasible area

(Store & Jokimäki, 2003). Since all of the inputs are maintained as a raster file, the

results are stored as such with the cell values ranging between 0 (unsuitable) to 1 (most

suitable) (Store & Jokimäki, 2003).

The principle of the suitability modeling exhibited by the habitat study can be

applied to determine a location for a mitigation bank site. Hydric soils, low-lying

topography, and no existing wetland are inputs that would lead to a feasible area. Also,

distance to wetland indicators such as the proper vegetation would be similar to the

landscape analysis.

21

Wetlands and Highway Management

Highways have contributed to the loss of over half of the original 230 million

acres of wetlands which have been destroyed since the arrival of the earliest settlers (Neal,

1999). Potential impacts to a wetland due to a proposed highway project are evaluated by

a state’s department of transportation. This step is preferably performed in the planning

phase of a project, a time when there are multiple alternatives that may be selected from

to minimize the impact to the wetland.

There may be times when a wetland will be impacted regardless of the highway

alternative selected. In these cases minimization of impact is the objective. The

SWAMP method can determine the highway alternative that has the least impact on the

wetland function (Tiner, 2002). Unlike other methods which attempt to find the least

amount of wetland acreage that will be impacted, SWAMP instead focuses on ranking a

wetland’s functions and determines which ones are most important. It is then these

criteria that dictate which highway alternative would be better suited to reduce the overall

impact of the highway project to the wetland area.

A study by the Montana Department of Natural Resources and Conservation

(DNRC) examined the idea of using state owned land adjacent to highways for the

creation of wetlands (Blount, 2011). The Montana DNRC uses the high cost of the

creation of a wetland as the reason for such a proposal. A state’s Department of

Transportation would not only have adequate funding to cover the cost of the

establishment of a new wetland, but would also need to provide replacement wetland

property for any that was impacted due to a highway project.

22

Summary

The literature focuses on one key concept: the variables input for a wetland site

selection need to be ranked on their level of suitability. The ANL-E study assigns

weighted values for the categories such as soils, vegetation, and land use, and SWAMP

prioritizes the functionality of the wetlands. MiSiL will accomplish this same task, by

producing a set of ranked results based on the suitability of an area to support a wetland

to be used as a mitigation bank site.

23

Chapter 3

METHODOLOGY

The MiSiL procedure separates potential areas to be used for mitigation bank sites

into ranked categories of suitability. This is accomplished by using datasets such as for

soils, land cover, floodplains, and slope in a variety of analyses that are discussed

throughout this chapter. The resulting products can then be placed in a tiered

classification system that assigns a suitability level.

Software Used

The GIS platform used for the analysis was Esri’s ArcGIS 10 (www.esri.com). In

addition, the add-on ET GeoWizards was used alongside ArcGIS 10. ET GeoWizards is a

set of tools that can be used within the ArcGIS desktop environment and is available at

www.ian-ko.com. Esri’s Spatial Analyst extension was also used.

Current Methodology

Before any preliminary analysis is performed, the Environmental Division at the

AHTD may either receive a referral from a realtor or by seeing a listing of land for sale

on a website such as Mossy Oak’s (www.mossyoakproperties.com) to be used as a

mitigation bank site. A realtor will typically be aware of areas of interest from the

Environmental Division’s perspective since they would have worked with the division on

past properties. The realtor will place a phone call to a staff member who works with the

division’s mitigation sites and provide them with a location of property for sale. Then,

24

the Environmental Division staff member will look on a site such as Google Earth to

view aerial imagery of the proposed property.

Once a prospective property has been located, the Environmental Division staff

will cross reference the soil types to determine if there are conditions favorable for a new

mitigation site. This can be accomplished by either looking through a printed copy of a

soil survey or more recently by adding a soil layer developed by the Natural Resources

Conservation Service (NRCS) into Google Earth which outlines the different soil

classifications and can be overlaid onto aerial imagery.

After the office research has been conducted, field visits to the potential site are

made, and characteristics such as vegetation, land cover, hydrology, and other ground

conditions are ascertained.

The current methodology is successful, but with the use of the MiSiL procedure a

significant amount of the information collected from multiple analyses can be

streamlined into one workflow. The MiSiL procedure provides instant access to soil

types, statewide land cover, slope values, aerial imagery, topographic maps, etc. Also, by

using the MiSiL procedure, acreage determinations can be made for the amount of land

within the proposed property and the surrounding area.

Data

According to the Standard GIS Methodology for Wetland Analysis manual (CAST

& MAWPT, 2012), which is the result of collaboration between CAST and the Multi-

Agency Wetland Planning Team (MAWPT), any form of point, line, polygon, or pixel

spatial element data within a study area is able to have a value placed on it by the use of a

25

GIS. The spatial elements that meet more of the search criteria will be provided with a

higher ranking than those who meet fewer of the criteria. The manual goes on to explain

that an example would be if a location had three of the search criteria, such as wetland

hydrology, soils, and vegetation, it would receive a higher ranking than an area that only

contained two of these criteria. The Environmental Division at the AHTD places more

emphasis on the soil type (hydric) and the land cover (forested or agricultural) than the

other criteria because these two factors are indicative of conditions found at previous

areas that were converted to mitigation sites.

The soils data layer used to determine hydric soils was developed by the Natural

Resources Conservation Service (NRCS) and is available for download for both

individual states and for the entire United States. This data layer is the Soil Survey

Geographic (SSURGO) Database, which is a digital version of previously printed soil

survey manuals that contains polygons for each soil type and was digitized at scales

ranging from 1:12,000 to approximately 1:64,000. One of the attributes available for all

of the soils is the HydrcRatng field, which classifies the soils as “All Hydric”, “Partially

Hydric”, or “Not Hydric”. Soil polygons were queried and separated into two sets as

“All Hydric” and “Partially Hydric”.

In addition to the hydric rating, only soils that are described as being poorly

drained will be included in the final outputs. These soils that fell within the refuge

boundary were queried based on their classification as poorly drained.

Statewide land use/land cover raster files were provided by CAST for the years

1999, 2004, and 2006. An Excel worksheet was also provided with the raster files as a

legend for the classification of the pixel values. Certain land cover categories were only

26

available from certain years, which lead to all three time periods of data to be used for

querying for forested and agriculture coverages. The forested areas were based on the

species listed in Chapter 1. The tree species found at these areas are indicative of

wetland conditions, and will be considered as areas that are already developed wetlands.

The Extract by Attributes tool from the Spatial Analyst toolset allowed the land

use/land cover raster files to be queried for select pixel values for both forested and

agricultural. Once these pixels were selected, the Raster to Polygon tool was used to

convert the pixels into polygons which would allow for them to be used easier in

conjunction with the other vector based layers. The forested polygons and the

agricultural polygons were kept as separate layers (Figure 5).

The DEM used by this study is a 5-meter resolution raster file published in 2007

by the Arkansas Geographic Information Systems Board (http://www.gis.state.ar.us/

GISB/gisb.html). Each DEM is available as a county mosaic and the counties included in

the download were Jackson, Monroe, Prairie, and Woodruff. The percent slope was

generated for each DEM in ArcGIS and ranged from 0% to 206.5%. The typical slope for

each county was very low, due to the fact that the study area lies within the Arkansas

Mississippi Alluvial Plain region of Arkansas. The higher slope values can be explained

by Figure 6, which illustrates that the upper end values, which are shown in reds and

yellows, are due to gullies that have a stream passing through them. A hillshaded U.S.

Geological Survey (USGS) topographic map is used to best depict the relief of the

topography.

A slope value of 15% or less was queried for each of the DEMs. This threshold

was based on a study (Haggard, et al., 2005) where surface runoff was measured on

27

Figure 5. Agricultural and forested land use/land cover.

28

Figure 6. A comparison between a hillshaded topographic map and the same

area as a slope generated raster file.

29

different slope increments by using a variable slope box. The variable slope box used

was 1.5-m wide by 3-m long, and was lined with a silt loam soil, gravel, and sand

(Haggard, et al., 2005). Artificial rainfall was produced and the amount of runoff was

measured at varying slopes. According to a figure in this study, from 0% to 15% slope

the surface runoff appeared fairly uniform, but slope values higher than that lead to an

approximate 25% increase in the amount of surface runoff (Haggard, et al., 2005). Each

DEM raster used in the MiSiL study was reclassified and all pixel values with a percent

slope of 15% or less were assigned an attribute of 1, and all pixels with a slope value

higher than 15% were assigned 0. The raster files with a value of 1 were converted to a

vector polygon.

The Western Lowlands floodplains were produced as a joint effort between CAST

and the Multi-Agency Wetland Planning Team (MAWPT). The Western Lowlands are

described on CAST’s website as a part of the White River watershed. The website goes

on to describe that the floodplains were derived from historic river gage station data. As

with the land use/land cover and the DEM raster files, the raster file for the Western

Lowlands floodplain was converted into vector polygons separated based on 2-year and

5-year groups.

Analysis of Data

The study area for this particular project is the Cache River National Wildlife

Refuge acquisition area. All of the data layers are clipped to this boundary. Most of the

data being used is at a statewide level, but only areas within the refuge boundary are of

interest.

30

The first step was to eliminate any area in the refuge that was already classified as

a wetland or swamp based on the NWI layer. The Erase tool from the ET GeoWizards

toolset subtracted the area of the refuge boundary that was inside the polygons for either

wetlands or swamps. This created refuge property that is not already a wetland type area

and has the potential for being suitable for the development of a mitigation bank.

Once polygons were created for areas within the refuge that were not already

wetlands, they were ready to be combined with the polygons for poorly drained soils,

agricultural land use/land cover (LULC), and slope. This was done by using the Clip

geoprocessing command within ArcGIS. Each layer was clipped based on its intersection

with each other. This resulting layer, named Base Layer, was used to compare with areas

of hydric soils and also of 2-year and 5-year floodplains. The Base Layer feature was

created first because all of the queries to be performed with the soils and floodplains

would all use this same base information: no existing wetland from the NWI, poorly

drained soil, the 15% or lower slope from the DEM, and the land use/land cover

categories.

The next step was to find areas of either all hydric or partially hydric soils that

were within the 2-year floodplain, the 5-year floodplain, or neither. The soil was to be at

least partially hydric since that would be better suited for the creation of a wetland area.

The Intersect tool found areas of coincidence among the input layers, and a breakdown of

the various inputs and results can be seen in Figure 7. This figure illustrates the different

input layers that created the Base Layer feature class, and then how the Base Layer

feature class was paired with the two soil classifications and the floodplains, all of which

lead to the six classifications.

31

Figure 7. A layout of the input data layers and the additional criteria that

leads to the MiSiL classification system.

32

The resulting layers generated from the intersection of all of the input data lead to

six categories: all hydric soils in the 2-year floodplain, all hydric soils in the 5-year

floodplain, partially hydric soils in the 2-year floodplain, partially hydric soils in the 5-

year floodplain, all hydric soils not within the 2 or 5-year floodplain, and partially hydric

soils not within the 2 or 5-year floodplain. All six of these different result layers were

exported from ArcGIS as a separate data layer.

Once these six different categories were ready, area calculations were performed

to find a contiguous tract of land that met a minimum area requirement. For this specific

study, the AHTD Environmental Division was looking at areas that were at least ten acres.

Since the intersection analyses created multiple separate polygons within each of the six

categories, the Dissolve geoprocessing tool was used to merge all touching polygons

together. A new field was created in the attribute table in each of the categories and the

acreages were calculated and added to this field. From this polygons of ten or more acres

could be queried from each of the categories.

Using the principle of spatial autocorrelation, the idea that areas that are near each

other geographically share the same characteristics, the forested land use/land cover layer

was used to further identify potential mitigation bank sites. The tree species found within

the forested land use/land cover layer are typically found within a wetland and thus

support the idea that wetland conditions are present. The resulting polygons selected for

being ten or more acres were further filtered if they were located within 250 feet of an

area forested with the tree species listed in Chapter 1. The 250-foot distance is used to

account for any gaps that may exist between the various datasets used. Ideally, only

areas touching the forested land use/land cover feature would be included, but the

33

datasets used were digitized independently from each other, and may contain gaps in their

coverages.

The steps outlined produced six output layers based on the combination of the

Base Layer, the soils, and the floodplains, and that they meet the minimum area

requirement and proximity to the forested areas. Once potential areas were selected

based on the aforementioned process, the results were ready to be classified based on

their suitability.

Classification

A six stage classification system was developed to rank potential areas from most

favorable to least favorable for a new mitigation bank site. The classification system

ranges from Level I (more suitable) to Level VI (less suitable) (Table 2). The Level I

classification includes the optimal conditions, which are all hydric soils, within the 2-year

floodplain, no existing wetland, poorly drained, 15% or less slope, within the desired land

use/land cover categories, 10 or more acres, and within 250 feet of a forested area. As

the levels increase, the conditions become less preferred, allowing for either only

partially hydric soils to be found or for the area to be outside of the floodplain.

Method of Application

The MiSiL study was performed by the author. This has lead to standard sets of

data that will be used in any project in the state, such as the hydric soils, LULC, slope,

and NWI. These layers have already been queried for select categories, which will not

34

Table 2. A detail of the six level MiSiL classification system.

35

have to be repeated in the future, whereas any updates to the datasets will simply

automatically overwrite the existing queries.

With each new project there is the potential of data that is available only for select

parts of the state which may or may not coincide with the proposed study area. This

determines that the best method of application is a standardized workflow to be followed

by a GIS specialist. However, a manual can be written to direct such a specialist through

the steps of the MiSiL procedure. Not only could this be shared within one agency

among GIS users, but could also be applied by other agencies.

Summary

MiSiL created standardized Base Layer and hydric soils combinations that can be

used in conjunction with any additional information available. In this particular study

these combinations were used alongside the 2-year and 5-year floodplain data. Also,

MiSiL is able to adjust for any minimum area requirement that is needed. These all

indicate a high level of flexibility of the procedure.

36

Chapter 4

RESULTS

Land Classification

The Cache National Wildlife Refuge acquisition boundary covers 184,686 acres.

The area covered by the different input data layers, both used within and generated by

MiSiL, as well as the classification levels can be seen in Table 3. The table shows that

many of the input layers on their own comprised a significant portion of the acquisition

boundary, with the smallest amount coming from the 5-year floodplain and still covering

approximately one-fifth of the boundary.

The amount of land covered by the different categories substantiates that the

acquisition boundary was very well suited for finding property to be converted into a new

wetland area. Eighty-one percent of the area was covered by either all or partially hydric

soils, with a little over half of the area not currently a wetland, and almost the entire site

below a 15% slope. Also, over one-third of the acquisition boundary fell within the

agricultural land use/land cover categories. However, when all of the criteria needed to

be met as devised by the six level classification systems, the chances of locating a new

area for a mitigation bank site diminished. The absence of all the input layers

overlapping is what has lead to the reduced percent coverage of the classification levels

for the study area. The results of each level of classification can be seen in Figure 8.

37

Table 3. Acres and percentages of data layers calculated by MiSiL.

38

Figure 8. Level I through Level VI classification coverages.

39

Accuracy Evaluation

The AHTD Environmental Division has located a thirty-eight acre tract of land

that will be used as a new mitigation bank site. This site has been field checked and

evaluated to the degree that the division is proceeding with acquisition to create a new

mitigation bank. The thirty-eight acre tract contains hydric soils, has topography suitable

for a wetland, and is located where there is not currently a wetland. This site will be used

to test the results of MiSiL.

An evaluation of the accuracy of the MiSiL procedure is accomplished by

comparing the known tract of land, in this case the thirty-eight acres, with the results

produced. If MiSiL was successful, then at least some of the classification level results

should coincide with the thirty-eight acres. Both Level I and Level IV produced

polygons that intersected the new tract of land, and are shown as areas in green in Figure

9. The fact that the Level I classification, which is the category with the highest

suitability, is found within the thirty-eight acres yields a positive conclusion about the

accuracy of the MiSiL procedure.

Examination of Results

As with all of the levels, agricultural land use/land cover categories, no existing

wetlands, poorly drained soils, and a 15% or lower slope were all mandatory for inclusion

in the MiSiL procedure, and formed the Base Layer feature class. After this was

accomplished, the Base Layer feature class was able to be paired with the 2-year or 5-

year floodplain and whether the soils were all hydric or only partially hydric.

40

Figure 9. Level I and Level IV suitability results of the MiSiL classification

system.

41

Approximately one-third of the thirty-eight acres intersects the 2-year floodplain.

This same area also has hydric soils in the form of Kobel silty clay loam. According to

the SSURGO database, Kobel is described as having a 0% to 1% slope. This type of

topography is ideal since low slope promotes ponding. Also, with the composition

consisting of silty clay loam, the permeability of the soil is low, leading to water slowly

seeping downward through the soil. These are the factors that lead to this portion of the

new tract of land being categorized as Level I suitability.

Half of the thirty-eight acres is categorized as Level IV suitability. The inclusion

of the 5-year floodplain and partially hydric soils is what comprises Level IV. The soils

present within this half of the tract are classified as Yancopin soils, and according to

SSURGO they are partially hydric. Yancopin soils have a 1% to 3% slope, which is

somewhat steeper than the Kobel soils. This causes more runoff of water, diminishing

the amount that is pooled on the surface as opposed to the Kobel series. More than likely

this is what leads to the Yancopin soils being categorized as only partially hydric.

Summary of Results

The breakdown among the six levels of classification yielded positive results,

with two of the six levels identifying a significant amount of the new thirty-eight acre

tract. The entire thirty-eight acres is included by either the Level I or Level IV results.

Since the suitability classifications are kept as separate results, a breakdown can be

performed to see exactly where the coverage of each level classification occurs. For

example, the Level I results for the thirty-eight acre tract can be seen separately from the

Level IV results, allowing restoration efforts to be focused in certain areas.

42

Reasons should be provided as to why the remaining levels did not have results

that intersected with the new tract. Level II produced mostly small polygons, with few

being contiguous to amount to at least ten acres. Level III’s polygons were scattered

throughout the refuge boundary, with no results being near the thirty-eight acre tract.

Level V produced a few polygons of significant size, but like Level III was not found

near the new tract. Level VI results were found mainly along the perimeter of the refuge

boundary, away from the thirty-eight acres.

Overall, MiSiL produced a set of suitability results for the entire Cache River

National Wildlife Refuge acquisition boundary. Categorizing the results into the six

classifications provides a streamlined breakdown of a potential area’s suitability.

43

Chapter 5

DISCUSSION

Application within the Environmental Division

With the Environmental division’s continuous acquisition of property to be used

as mitigation banks, the MiSiL procedure would most definitely be a useful application,

and could be implemented in various ways within the division.

One significant application would be that a realtor could e-mail a Google

Earth .kmz file to the division. This would allow the property’s boundary to be brought

directly into our GIS, and could then be analyzed via the MiSiL procedure to determine

the viability of the proposed property.

Another application is that the results of the procedure can be easily shared with

multiple staff within the division. Every computer in the Environmental Division has

some GIS capability, whether it is a license of ESRI’s ArcView or their free viewer

ArcExplorer. The output classifications of the MiSiL procedure could be provided to any

staff member working on the acquisition of property for a new mitigation bank site.

The most significant aspect of the application within the Environmental Division

is that the current procedure will be replaced with an updated methodology. Analyzing

characteristics for potential properties will no longer be performed through separate

actions, such as comparing a soil survey map alongside a topographic map. All of these

data sets will be brought into one comprehensive analysis, which will provide multiple

potential areas for a site as well as the suitability of these areas.

44

Benefits

Using MiSiL provides an analysis tool to narrow potential areas that may be

suitable for use. The Montana Department of Administration’s GIS Bureau stated, about

a procedure similar to MiSiL, that “the cost-benefit of using GIS technology in the

determination of suitable lands for wetlands mitigation is high” (Blount, 2011). By

ranking the areas that meet the criteria put forth, the location process will be much more

streamlined relative to the current system.

Another benefit of MiSiL is that it can aid in the avoidance of fragmented

wetlands, which are simply areas developed as wetlands but are small and isolated from

any other wetland area. Isolated, or “patch”, wetlands are the result of on-site

compensatory mitigation which leads to a wetland being created on the actual project site

to offset any impacts to existing wetlands, but are placed in an area that has no

connection to surrounding wetland ecosystems (Neal, 1999). MiSiL is able to provide

not only enough of a minimum acreage of a potential wetland to be used for a mitigation

bank site that it is able to support itself, but also can determine if there are any wetland

supporting factors in the surrounding area.

Limitations

In order for the MiSiL procedure to be most effective, the data being input must

be as current as possible. Any changes to the landscape of an area would affect its

involvement in the procedure. If an area that was previously agricultural became

urbanized, it would no longer be able to be considered for a potential site.

45

Another limitation could be the lack of additional data layers for the study area.

For the Cache River National Wildlife refuge the Western Lowlands provided two and

five year flood data. However, this type of information is not available at a statewide

level. Even though the base layers used in the MiSiL procedure are capable of

determining a new mitigation site, additional information would of course refine the

analysis.

46

Chapter 6

CONCLUSION

The Environmental Division at the AHTD is looking for a GIS based method of

locating potential areas for mitigation bank sites. MiSiL addresses this by providing a

locator analysis that is accomplished completely within a GIS and provides a set of

results based on suitability.

Based on the results discussed previously, MiSiL is ready for implementation

within the Environmental Division. When compared to a field tested site selected for use

as a mitigation bank, MiSiL produced suitability results that corresponded to that site.

The data layers that MiSiL uses for its analyses all combine to determine an area

that is not currently functioning as a wetland, but has the characteristics to be restored to

such. The soils data layer provides both hydric soil classifications as well as the drainage

type for each soil. The DEM depicts areas of slope that are conducive for a wetland. The

land use/land cover not only outlines areas that are not currently being used as a wetland,

such as agricultural, but also tree species that are indicative of wetland conditions.

Of course, there is always the potential of additional data for a particular area of

the state. That was the case for this study being conducted within the confines of the

Cache River National Wildlife Refuge boundary. The Western Lowlands flood plain

data provided areas prone to both two and five year flooding.

Any additional data is easily incorporated into MiSiL because of the level of

flexibility that it has when performing its analyses. MiSiL will always use certain base

layers, but can accommodate for site specific data. Even data generated by the

47

Environmental Division for a specific area can be added to the MiSiL procedure. The

addition of new data layers can also be reflected in the suitability classification levels. A

more elaborate breakdown between the varying levels can be performed with the

introduction of additional data.

MiSiL has the potential for further study. Smaller scale study areas could have

the land use/land cover updated by digitizing features from current high-resolution aerial

photography. A survey team could collect a more accurate terrain model to aid with the

hydrography analysis. MiSiL is not simply a static procedure that is only useful for the

Cache River National Wildlife Refuge, but is a dynamic analysis that can be utilized

anywhere in this state or others.

MiSiL provides something to the AHTD Environmental Division that has never

before been utilized. A locator analysis performed strictly through a GIS that is able to

take data layers specific to wetlands and provide potential viable areas for the restoration

of a wetland to be used as a mitigation bank is a major step towards streamlining within

the division.

48

Works Cited

Arkansas Geographic Information Systems Board. 2005. Digital Elevation Model

(DEM). <http://www.gis.state.ar.us/GISB/gisb.html>.

Blount, Keith. 2011. Wetland Mitigation Restoring Montana’s Wetlands. Montana

Department of Administration, Information Technology Service Division, GIS

Bureau. Helena, Montana.

<http://www.esri.com/mapmuseum/mapbook_gallery/state1/ mt1.html>.

Bullock, Andy and Mike Acreman. “The role of wetlands in the hydrological cycle.”

Hydrology and Earth System Sciences. 7. 3 (2003): 358-389.

Carpenedo, Stephen M., Elizabeth Kramer, Jason Lee, and Kevin Samples. “Using GIS

to Model the Effects of Potential Wetland Mitigation Sites on Water Quality in

Georgia.” Proceedings of the 2007 Georgia Water Resources Conference.

University of Georgia, March 2007.

Center for Advanced Spatial Technologies (CAST) and Multi-Agency Wetland Planning

Team (MAWPT). 2012. The Standard GIS Methodology for Wetland Analysis.

<http://cast. uark.edu/home/research/environmental-studies/analysis-of-arkansas-

wetland-planning-areas.html>.

Cowardin, Lewis M., Virginia Carter, Francis C. Golet, and Edward T. LaRoe. 1979.

Classification of Wetlands and Deepwater Habitats of the United States. U.S.

Department of the Interior, Fish and Wildlife Service, Washington D.C.

Jamestown, ND: Northern Prairie Wildlife Research Center Online.

<http://www.npwrc.usgs.gov/ resource/wetlands/classwet/index.htm>.

49

Environmental Protection Agency. “Federal Guidance for the Establishment, Use and

Operation of Mitigation Banks.” Federal Register 60. 228 (1995).

Environmental Protection Agency. 2012. Summary of the Clean Water Act.

Washington D.C. <http://www.epa.gov/lawsregs/laws/cwa.html>.

Gorham, Bruce. “Mapping the ever-changing landscape: Lessons from the Arkansas

land-use/land-cover mapping project.” RGIS Innovator (November 2007).

Distributed by the National Consortium for Rural Geospatial Innovations.

Haggard, B.E., P.A. Moore, Jr., and K.R. Brye. “Effects of Slope on Runoff from a

Small Variable Slope Box-Plot.” Journal of Environmental Hydrology 13, 25.

(2005)

Mitsch, William J., and James G. Gosselink. Wetlands 3rd

ed. New York: John Wiley

& Sons Inc., 2000.

Natural Resources Conservation Services. 2012. Soil Survey Geographic (SSURGO).

<http://soils.usda.gov/>.

Neal, Jennifer. Paving the Road to Wetlands Mitigation Banking. 27 B.C. Envtl. Aff. L.

Rev. 161 (1999). <http://lawdigitalcommons.bc.edu/ealr/vol27/iss1/6>.

Store, Ron and Jukka Jokimäki. “A GIS-based multi-scale approach to habitat

suitability modeling.” Ecological Modeling 169 (2003): 1-15.

Tiner, R.W. (compiler). 2002. Watershed-based Wetland Planning and Evaluation. A

Collection of Papers from the Wetland Millennium Event. (August 6-12, 2000;

Quebec City, Quebec, Canada). Distributed by the Association of State Wetland

Managers, Inc., Berne, NY. 141 pp.

50

U.S. Army Corps of Engineers. 1987. Corps of Engineers Wetlands Delineation

Manual. U.S. Department of Defense, Washington D.C.

<http://www.wetlands.com/regs/tlpge02e. htm>.

U.S. Army Corps. of Engineers and EPA. “Compensatory Mitigation for Losses of

Aquatic Resources.” Federal Register 73. 70. (2008): 19594-19705.

U.S. Fish and Wildlife Service. 2011. Clean Water Act Section 404. Department of the

Interior, Washington D.C. <http://www.fws.gov/habitatconservation/cwa.htm>.

U.S. Fish and Wildlife Service. 2011. National Wetlands Inventory (NWI).

<www.fws.gov/wetlands/Data/index.html>.

Van Lonkhuyzen, Robert A., Kirk E. Lagory, and James A. Kuiper. “Modeling the

Suitability of Potential Wetland Mitigation Sites with a Geographic Information

System.” Environmental Management 33. 3 (2004): 368-375.

WSDOT. SRXX: Happy St. Vicinity to Golucky Way (XL1111) Phase II Mitigation Site

Wetland Delineation. Technical Memo Example to the Washington State

Department of Transportation, Olympia, WA.

APPENDIX:

LAND USE/LAND COVER VALUES

A land use/land cover values table was provided along with the raster files from Bruce

Gorham from CAST. Specific values were extracted from this table to be used in this

study, but an overview of all the available values are provided in the table.

52

53