development and application of a gis based …

DEVELOPMENT AND APPLICATION OF A GIS BASED EVALUATION FOR

PRIORITIZATION OF WETLAND RESTORATION OPPORTUNITIES

by

Jennifer L. Kauffman

A Thesis

Presented to

The Faculty of Humboldt State University

In Partial Fulfillment

Of the Requirements for the Degree

Masters of Science

In Natural Resources: Natural Resources Planning and Interpretation

October, 2007

ABSTRACT

Development and Application of an Automated GIS Based Evaluation for Prioritization of Wetland Restoration Opportunities

Jennifer L. Kauffman

Oregon's tidal wetlands were recently mapped with a geographic information

system (GIS). State-wide, over 2,000 restoration consideration areas were delineated.

These require on-the-ground assessment to determine actual restoration potential. Given

the large number of potential restoration opportunities, automated GIS tools were

developed to assist resource managers in prioritizing areas which have less hydrologic

alteration and more favorable landscape ecology criteria. Areas with less cumulative

alteration may be preferred as restoration opportunities and merit initial on-site feasibility

studies. The Coos estuary and watershed located in southern Oregon was used as an

example of a regional application. A prioritization of 530 potential restoration sites was

performed. Automated GIS tools were developed for nine parameters. Parameters were

limited to factors affecting wetland hydrology at local or landscape scales and landscape

ecology criteria. Analysis of each parameter was successfully implemented with

automation techniques available in ArcGIS 9.2. Tabular output of the automated models

was used to prioritize potential restoration sites. Prioritization ranks were calculated with

a three tiered weighted summation determined by restoration practitioners from the Coos

estuary region. Standardized prioritization ranks ranged from 0.479 –1.000 with a mean

of 0.724. Higher scores indicate less cumulative hydrologic alteration and more favorable

landscape ecology criteria. Calculated values, standardized parameter scores, and

iii

prioritized rankings of potential restoration sites were stored in a Microsoft Access

database coupled with a geodatabase containing spatial geometry. The use of automated

spatial evaluations of ecologically important criteria across large geographic regions

ensures objective and consistent methods across multiple locations and users. These

automated tools present repeatable and flexible methods of spatial analysis for evaluation

and prioritization of potential restoration sites in Oregon.

iv

ACKNOWLEDGEMENTS

I’d like to recognize the following individuals for making important contributions

to this thesis: my graduate advisor, Dr. Steven Steinberg, and committee members, Drs.

Steven Rumrill, and Andrew Stubblefield for guidance and advice. Their individual

expertise contributed greatly to the refinement and focus of this thesis. Dr. Steven

Carlson, Mr. Michael Gough, and Dr. Lawrence Fox III also provided insight and

excellent instruction in GIS methods and applications. My fellow graduate students and

Spatial Analysis Lab peers contributed to collaborative problem solving. Chris Moyer

and the Aquatic and Riparian Effectiveness Monitoring Program first introduced me to

the analytical potential of GIS. My family, friends, Paul, and Glynis were continually

patient and supportive. Thank you all very much.

v

TABLE OF CONTENTS

Page ABSTRACT ....................................................................................................................... iii ACKNOWLEDGEMENTS ................................................................................................v LIST OF TABLES ........................................................................................................... vii LIST OF FIGURES ........................................................................................................ viii LIST OF APPENDICES ................................................................................................... ix INTRODUCTION ..............................................................................................................1 STUDY SITE ....................................................................................................................10 MATERIALS AND METHODS ......................................................................................12

Data Organization and Preprocessing ...................................................................12

Model Development ..............................................................................................12 Prioritization methods ...........................................................................................17

RESULTS .........................................................................................................................19 DISCUSSION ...................................................................................................................24

Recommendations for Future Research ................................................................27 LITERATURE CITED .....................................................................................................30 APPENDICIES .................................................................................................................35

vi

LIST OF TABLES

Table Page

1 Evaluation parameters for which automated GIS tools were developed. Parameters parallel or are modifications of criteria used in other wetland prioritizations or evaluations. …………………………………………………....13

2 Spatial datasets used in the evaluation and source agency. .................................. 14 3 Standardized model output and prioritization rank for the ten highest

prioritized sites. In the score range of 0-1, a score of one represents the highest value for a parameter or the most favorable condition. Prioritization ranks were calculated by applying a three tiered weighted summation. Comparisons between sites reflect relative levels of alteration. ...........................18

4 Descriptive statistics by parameter (not weighted) for standardized output

of all sites and top and bottom ranking 10% of prioritized sites. .........................20

vii

LIST OF FIGURES

Figure Page 1 Workflow of a spatial analysis represented as an interactive flow chart in

Model Builder (ESRI 2006). Processes documented in the model are available as automated spatial analysis. ................................................................. 3

2 Conceptual model of relationship between wetland controlling factors, habitat structure, and ecosystem functions (adapted from Thom et al.2003). Parameters evaluated influence the hydrologic processes shown in italics. ..........5

3 Location of Coos County, Oregon and state-wide distribution of restoration consideration areas as mapped by Scranton (2004). Inset illustrates Coos estuary restoration consideration areas and their respective catchments. ............................................................................................9 4 Prioritized rankings of 530 potential restoration sites. Site ranks were

determined by applying a weighted summation of standardized parameter scores. Ranks range from 0.479 to 1.00 with a mean of 0.724. The top ranked 10% of prioritized sites are shown in green and bottom 10% are shown in orange. Rankings represent relative levels of hydrologic alteration and landscape ecology criteria. Higher scores indicate less cumulative alteration. ..............................................................................................................21

5 Map illustrating top ranked 10% of prioritized sites (green) and the catchments (dark gray) in which they reside. .......................................................22

6 Model viewed as a traditional GIS tool. Users are required to enter the location of input datasets and specify where the output will be stored. Informative user documentation describes the underlying calculations and the online source of required input datatset. ..................................................23

viii

LIST OF APPENDICES

Appendix Page A Model developed to calculate hydrologic connection. .........................................35 B Model developed to calculate area of adjacent water. ..........................................37 C Model developed to calculate percent of perimeter adjacent to filled land. .........38 D Model developed to calculate vegetation composition. ........................................39 E Model developed to calculate percent forested in catchment. ..............................40 F Models developed to calculate percent impervious surface area in

catchment. .............................................................................................................41 G Model developed to calculate road density in catchment. ....................................42 H Model developed to calculate number of road-stream intersections in

catchment. .............................................................................................................43 I Model developed to calculate number of tidegates in catchment. ........................44 J PYTHON Script written to calculate area of adjacent wetlands. ..........................45

ix

INTRODUCTION

Technological developments in geographic information systems (GIS) have

revolutionized land management and planning (Lyon and McCarthy 1995). The ability of

GIS to relate absolute and relative locations of features, and properties of those features

(Bolstad 2005), makes it an ideal tool for prioritization. The utilization of GIS in wetland

restoration planning is increasing (Lyon and McCarthy 1995, Brooks et al. 2004, Liu et

al. 2006). Wetland assessments traditionally focused on site specific factors with few

landscape scale measurements (Bartoldus 1999). When landscape level factors were

included, GIS was typically used to identify distribution and abundance of wetlands

(Dahl 2006). However, GIS can be effective in efficiently evaluating factors which

operate at broader scales and which are not easily measured or observed in the field

(Johnston et al. 1988, Houlahan and Findlay 2004, Tiner 2005, Liu et al. 2006).

Geospatial data has successfully been used in hydrogeomorphic (HGM) modeling of

wetlands and could potentially be used as a proxy for field-based assessments (Whigham

et al. 2003, Weller et al. 2007).

A GIS based approach can be used to effectively prioritize areas requiring site

specific inspection across large geographic regions (Russel et al.1997, Cedfeldt et al.

2000, Dean et al. 2000, Sutter 2001, Brooks et al. 2004, Van Lonkhuyzen et al. 2004,

Brophy 2005, Evans et al. 2006, Liu et al. 2006). One wetland prioritization effort, the

Spatial Wetland Assessment for Management and Planning (SWAMP), utilized the

capabilities of a GIS for automation of spatial analysis (Sutter 2001). The SWAMP tool,

1

2

developed by the National Oceanic and Atmospheric Administration (NOAA) Coastal

Services Center, is an extension to ArcView 3.x software (ESRI 2000). It provides an

interface which guides users through a spatial analysis to evaluate wetland significance

(Sutter 2001). The interface allows users to generate results of an analysis without direct

manipulation of the GIS software. Only a basic level of GIS knowledge is necessary to

use the extension and produce results. There are several drawbacks to SWAMP’s

automation methods. Current versions of ArcGIS software (ESRI 2006) do not support

the programming language used to develop SWAMP. Second, explicit GIS processes

used to evaluate parameters are executed by the program behind the scenes. Users are not

exposed to analysis methods, nor can they make adjustments to GIS process default

settings or modify the analysis algorithms. Third, SWAMP’s method for combining

parameters lumps sites into one of three categories. Categorical rankings do not allow for

site by site comparison of actual parameter values. Finally, SWAMP was developed for

the coastal ecology of South Carolina. Evaluation parameters may not be appropriate for

wetland ecosystems in the Pacific Northwest.

A better approach is to use the Model Builder environment available within

ArcGIS 9.2 (ESRI 2006). Model Builder is an interface which allows for automation and

documentation of a stepwise spatial model (ESRI 2005). Workflow of an analysis is

represented as an interactive flowchart (Figure 1). Processes documented in the flowchart

are available as an automated spatial analysis which can be executed repeatedly. Users

can visualize, alter, remove, and build upon modeled processes. As additional or updated

3

Figure 1. Workflow of a spatial analysis represented as an interactive flow chart in Model

Builder (ESRI 2006). Processes documented in the model are available as automated spatial analysis.

4 data become available, the model can be modified and re-run. Independent steps of an

evaluation can be developed as individual models. These models may act as standalone

applications or be combined into a complex analysis. Model Builder is also compatible

with standard programming languages such as PYTHON, VBScript, and Jscript (ESRI

2005). After a model is developed, it can be employed as a traditional tool accessed in the

GIS graphic interface. Automation capabilities of Model Builder provide a repeatable,

flexible, and dynamic method of spatial analysis (Hiers et al. 2003, Lin et al. 2006).

Probable success of a restoration or enhancement strategy is related to the level of

alteration at a site and the landscape it resides in (Shreffler and Thom 1993). Failure in

restoration of a specific wetland habitat most often results from lack of a proper

hydrologic regime (Mitsch and Gosselink 2000). The importance of hydrology as the

primary influence in development and maintenance of wetlands is well-accepted (Kusler

and Brooks 1987, National Research Council 1995, Bedford 1996, Mitsch and Gosselink

2000, Zedler 2000, Callaway 2001). The conceptual model (Figure 2) used in this

evaluation parallels the model described by Thom et al. (2003). They asserted that

physical controlling factors determined development of a habitat structure supporting

associated flora and fauna. Once a habitat structure is developed, ecological functions are

supported. Hydrology is the fundamental controlling factor in wetlands since a majority

of functions depend upon the hydrologic regime (Kusler and Brooks 1987, Callaway

2001, Adamus and Field 2001). Restoration of controlling factors is essential for long

term success of a restoration project (Thom et al. 2003, Williams et al. 2004, Evans et al.

2006). Removal of human alterations is a common restoration approach (Mitsch and

5

Physical Controlling

Factors

Climate Hydrology Geomorphology

Habitat Structure

Ecological Functions

HydroperiodPrecipitation

Surface inflow/outflowGroundwater inflow/outflow

EvapotranspirationTidal inflow/outflow

Physiochemical EnvironmentSoil & Water Chemistry

VegetationAnimalsMicrobes

Flood flow attenuationProduction export

Nutrient and pollutant cyclingAquatic and terrestrial wildlife habitat

Aquifer rechargeShoreline stabilization

Recreation and aesthetics____________________________________________________

____________________________________________________

Figure 2. Conceptual model of relationship between wetland controlling factors, habitat

structure, and ecosystem functions (adapted from Thom et al.2003). Parameters evaluated in this model (shown in italics) influence the hydrologic processes.

6

Gosselink 2000, Bruland et al. 2003). Disruption to wetland hydrology can be caused by

structures altering hydrologic surface flow such as dikes, tide gates, roads, and culverts

(Bedford 1999, Brophy 2005, Giannico and Souder 2005, Evans et al. 2006). Hydrologic

disruption occurs where dikes prevent tidal and (or) stream waters from entering historic

wetlands. Tide gates alter the physical, chemical, and biological composition of a wetland

(Giannico and Souder 2005). Tide gates prevent tidal water from entering diked lands and

permit only downstream flow where historically flow would be bidirectional. This alters

velocity, turbulence, and patterns of freshwater discharge that would otherwise exist

(Giannico and Souder 2005). The presence of roads is correlated with changes in

hydrologic processes at local and landscape scales (Jones et al. 2000). Most road-stream

intersections contain a culvert affecting the hydrologic regime by restricting flow and

changing water velocity (Giannico and Souder 2005). Because of these factors, wetland

restoration often entails re-connection to the surface water network (Cedfeldt et al. 2000).

Prior studies suggest that alteration at landscape scales can constrain wetland

restoration due to disturbance patterns throughout a watershed (Bedford 1999, Zedler

2000). Disturbance of watershed hydrology can influence functions for all wetlands

sharing a common watershed (Evans et al. 2006). Urban development, with its associated

impervious surfaces and roads, leads to an increase in velocity and volume of surface

runoff (Arnold and Gibbons 1996). With more water reaching streams faster, erosive

force and peak flows also increase (Arnold and Gibbons 1996). Reduced groundwater

recharge and base stream flows, due to decreased infiltration, are also associated with

7

development (Arnold and Gibbons 1996). Timber harvest can alter watershed hydrology

by increasing stream discharge and turbidity (National Research Council 1994). Land use

practices influence watershed hydrology and are useful in restoration prioritization and

assessing wetland condition (National Research Council 1995, Whigham et al. 2003,

Brooks 2004, Evans et al. 2006, Liu et al. 2006).

In structural landscape ecology, connectivity is defined as spatial continuity of a

habitat across a landscape (Turner et al. 2001). Wetlands with good connectivity can

better perform natural ecological functions (Adamus and Field 2001, Cook and Hauer

2007). Wetlands located near other wetlands provide sources of seed banks and

contribute to animal establishment and dispersal (Mitsch and Gosselink 2000, Amezaga

et al. 2002). Conversion of wetlands to agricultural use is a common reason for loss of

wetland habitats (Mitsch and Gosselink 2000). Relatively quick reestablishment of

wetland hydrology has been associated with conversion of agricultural lands back to

wetlands (Bruland et al. 2003).

Automated spatial evaluation of ecologically important criteria across large

geographic regions ensures that objective and consistent methods are applied across

multiple locations and users. Prioritization of locations based upon a spatial evaluation

identifies areas meeting ecologically important criteria and can help to better direct

restoration planning. The objective of this study was to develop automated GIS tools to

evaluate parameters used to prioritize a large number of potential wetland restoration

sites for further on-site feasibility studies. Factors which affect wetland hydrology at

8

multiple scales and landscape ecology criteria were selected as model parameters. For

this study, prioritization was applied using output of the models to identify potential

restoration sites with less cumulative hydrologic alteration and more favorable landscape

ecology criteria. Providing transparent and modifiable GIS methods using widely

available datasets was paramount in developing a flexible evaluation tool transferable to

estuaries throughout Oregon. The Coos estuary and watershed located in southern Oregon

was used as an example of a regional application (Figure 3).

9

Figure 3. Location of Coos County, Oregon and state-wide distribution of restoration

consideration areas as mapped by Scranton (2004). Inset illustrates Coos estuary restoration consideration areas and their respective catchments.

STUDY SITE

The Coos estuary is located in Coos County along the southern Oregon coast

(Figure 3). It is the sixth largest Pacific coast estuary in the continental United States and

is the largest estuary in Oregon encompassing approximately 5,383 hectares (ha) (Rumrill

2006). Average water surface area covers 5,010 ha with tidal prism volume estimated at

765 million cubic meters (Rumrill 2006). An estimated 88% of the 65,103 ha watershed

is managed for timber production (Rumrill 2006). The nation’s first National Estuarine

Research Reserve was established in the South Slough arm in 1974 (Jennings et al. 2003)

protecting it from future anthropogenic alteration (Rumrill 2006). Wetland restoration is

occurring throughout the estuary. However, 59% of current tidal wetland habitats are

classified as restoration consideration areas (Scranton 2004).

Scranton (2004) developed a state-wide tidal wetland GIS dataset for Oregon

classified using the hydrogeomorphic (HGM) classification system (Brinson 1993).

Interpretation of historic and recent data sources facilitated delineation and classification.

Sources included aerial photography, the National Wetlands Inventory (NWI), soils data,

digital elevation models, head-of-tide-locations, and other information (Scranton 2004).

A class labeled “Restoration Consideration Areas” is included in the dataset (Figure 3).

These areas are defined by Scranton as,

“…upland or non-tidal areas that might deserve closer scrutiny as possible candidates for restoration of tidal circulation, pending landowner involvement. These areas were identified based solely on coarse-scale geotechnical information from available data layers. No on-site feasibility investigations were conducted, and sociopolitical factors were not considered. These are generally lands that are diked or may have been partially filled or ditched for agricultural or commercial purposes. An unknown portion of the restoration consideration areas are

10

11 palustrine wetlands or riparian uplands that never experienced tidal flooding, due to naturally-formed barriers”. Throughout Oregon, there are a total of 2,264 restoration consideration areas

totaling approximately 18,108 ha (Figure 3). Additional field verification and further

evaluation is recommended to determine actual restoration potential (Scranton 2004). The

Coos estuary ranks third in state-wide restoration consideration area with 562 sites

covering about 2,783 ha (Scranton 2004). Given the large number of potential restoration

opportunities, automated GIS evaluation tools were developed to assist resource

managers in identifying relatively less hydrologically altered areas which have more

favorable landscape ecology criteria. These areas may merit initial on-site feasibility

studies. Coos estuary restoration consideration areas are evaluated and prioritized as

potential restoration opportunities.

MATERIALS AND METHODS

Automated GIS tools were developed for nine parameters (Table 1). Parameters

were limited to factors affecting wetland hydrology at local or landscape scales and

landscape ecology criteria (Jones et al. 2000, Cedfeldt et al. 2000, Dean et al. 2000,

Whigham et al. 2003, Brooks et al. 2004, Brophy 2005, Evans et al. 2006, Liu et al. 2006,

Weller et al. 2007). Both vector and raster GIS analyses were used to assess individual

potential restoration sites and catchments. Analysis of each parameter was implemented

with automation techniques available in ArcGIS 9.2 (ESRI 2006). To accurately calculate

the area of adjacent wetlands in a 1,609 meter (1 mile) radius a script was developed.

This was necessary because of the close proximity of sites to each other and to

accommodate processing the large number of sites. The PYTHON programming

language was used to encode spatial analysis processes into a script and generate tabular

output for this one parameter. Model Builder was employed for automation and

evaluation of the remaining eight parameters. Tabular outputs of the tools and script were

used to prioritize 530 potential restoration sites. Less cumulative alteration to wetland

hydrology and more favorable landscape ecology were used as the preferred conditions

for prioritization.

Data Organization and Preprocessing

State-wide geospatial datasets were acquired for use as model input (Table 2).

Data specific to the Coos estuary and watershed were extracted and a common coordinate

system and datum, Oregon state-wide Lambert NAD83, was applied. Data organization

and file structure are important when developing multiple models. Isolation of input,

12

13 Table 1. Evaluation parameters for which automated GIS tools were developed.

Parameters parallel or are adaptations of criteria used in other wetland prioritizations or evaluations.

Area of adjacent wetlands in a 1,609 meter (1 mile) radius. 6, 8

Number of wetlands or other potential restoration sites each site is connected to by streams. 2, 7

Percent of a site’s vegetation classified as wetland or grassland. 1, 8, 9

Percent of a sites perimeter adjacent to filled areas. 7

Number of road-stream intersections in a site's catchment. 1, 7

Percent Forested within a site's catchment. 4, 5, 7, 9

Percent Impervious Surfaces and Road Density within a site's catchment. 3, 7, 8, 9

Number of tidegates within a site's catchment. 6

Area of adjacent water in a 500 meter radius.2

1 Jones et al. 2000, 2 Cedfeldt et al. 2000, 3 Dean et al. 2000, 4 Whigham et al. 2003, 5 Brooks et al. 2004, 6 Brophy 2005, 7 Evans et al. 2006, 8 Liu et al. 2006, 9 Weller et al. 2007.

14 Table 2. Spatial datasets used in the evaluation and source agency.

Dataset Scale Source Currency HGM Tidal Wetlands, Potential

Restoration Sites, Filled

Lands

~1:2,000 Russel Scranton, Oregon State University

Based on 2002 Imagery

Watershed Catchments unknown USGS Earth Resources

Observation and Science 2001

Hydrography 1:24,000 Regional Ecosystem Office Frequent Updates

National Wetlands Inventory

1:24,000 US Fish and Wildlife Service unknown

Tidegates 1:24,000 Oregon Department of Fish and Wildlife 2004

Landcover 30m Resolution

NOAA Coastal Services Center 2000-era

Roads unknown Oregon Bureau of Land Management Updated as needed

Impervious Surfaces

30m Resolution USGS 2001

Oregon Estuary Plan 1:1,000 Division of State Lands Late 1970's

15

intermediate, and final output data was implemented by establishing three personal

geodatabases for data storage. Attribute data for each geodatabase were stored in a

Microsoft Access database to facilitate aggregation of tabular data.

When restored, the potential exists for groups of small restoration sites to

cumulatively contribute to an overall net gain in tidal wetlands. Sites smaller than 0.405

ha (1 acre) but within 50 meters of tidal wetlands, or another potential restoration site

larger than 0.405 ha, were included in the evaluation. Due to varied spatial scales of input

datasets (Table 2), not all common boundaries have precise overlap. Sixty two sites were

located within two or more catchments while seven were not associated with any. If a site

was located within more than one catchment, it was assigned to the catchment it shared

the most area with. The seven sites not within a catchment were removed from the

analysis. In total, 530 potential restoration sites and 49 catchments were evaluated

(Figure 3).

Models evaluating catchment scale parameters required the dataset representing

sites to contain a unique identifier, indicating that site’s catchment in their attribute table.

A merge process was performed to generate a spatial dataset with the combined

information.

Model Development

Individual models were developed for each parameter. Automation of parameter

evaluation consisted of adding input data to the Model Builder environment. Spatial

analysis processes were then constructed in an interactive flow chart diagram. Processes

16 to synthesize input data and calculate the parameter were executed by running the model.

Input and final output data were set as model parameters. Variable processes, such as

buffer distances, were also set as model parameters assuring distance measurements were

not fixed. Descriptive information for each parameter was added via the Documentation

Editor (ESRI 2006). This information is visible to end users when a model is accessed as

a tool in ArcGIS (ESRI 2006) and explicit model processes are not visible.

Specific information from input datasets was utilized within the models and

PYTHON script. To determine the percentage of a catchment which was forested, NOAA

landcover classes deciduous, evergreen, and mixed forest were used. Two datasets

provided information on impervious surfaces. NOAA landcover classes high, medium,

and low intensity developed and open space developed were considered impervious

surfaces. United States Geological Survey (USGS) national landcover pixels classified

with greater than one percent imperviousness were combined with the NOAA landcover

classes. The percentage of a site consisting of wetland or grassland vegetation utilized

NOAA landcover classes palustrine forested, scrub-shrub, and emergent wetland,

estuarine emergent, and grassland. Adjacent wetlands within a 1,609 meter (1 mile)

radius of a potential site were defined as Oregon Estuary Plan Book eelgrass and algae

beds and NWI classes of non-diked emergent, scrub-shrub, and forested wetlands

(adapted from Brophy 2005). Select classes from the NWI and HGM tidal wetlands

layers were merged with potential restoration sites to determine hydrologic connectivity

among a variety of wetland types. Tidal wetland classes included high and low marine

17

sourced, river sourced, and potentially forested tidal wetlands. NWI classes included

non-diked palustrine emergent, scrub-shrub, and forested wetlands. NWI and tidal

wetlands were required to be contiguous areas greater than 0.405 ha (1 acre) in size. The

area of water within 500 meters of a site was determined with the HGM tidal wetlands

water class.

Prioritization methods

Model and script output consisted of calculated parameter values for each site.

Tabular output from each model and the PYTHON script was combined in Microsoft

Access. In order to combine the various measurement scales, the range of values for each

parameter were standardized on a zero to one scale with one representing the most

favorable condition. Inverse relationships were accounted for. The relative range of the

data were preserved allowing for a site to site comparison of each parameter. All sites

within a common catchment received standardized parameter values for that catchment.

Road density and percentage of a site’s catchment covered by impervious surfaces were

averaged as one parameter. These datasets have areas of overlap and therefore were

combined. Prioritization ranks were calculated with a three tiered weighted summation

(Table 3). Parameters were weighted based on consultation with regional experts. These

were the preferred weightings considering local restoration goals and management

objectives (personal communication, S. Rumrill 2007. South Slough National Estuarine

Research Reserve, P.O. 5417, Charleston, OR 97420). Standardization was applied to the

weighted calculation to determine each sites prioritization rank (Table 3).

Table 3. Standardized model output and prioritization rank for the ten highest prioritized sites. In the score range of 0-1, a score of one represents the highest value for a parameter or the most favorable condition. Prioritization ranks were calculated by applying a three tiered weighted summation. Comparisons between sites reflect relative levels of alteration.

Site Number

Hydrologic Connection

Adjacent Wetlands

Adjacent Water

Percent Adjacent

Fill

Vegetation Composition

Percent Catchment Forested

Catchment Road

Density & Impervious

Surfaces

Tidegates Road-Stream

Intersections

Weighted Calculation

Prioritization Rank

Weight 3

Weight 3

Weight 3

Weight 3

Weight 2

Weight 2

Weight 2

Weight 1

Weight 1

Weighted Calculation Scaled 0-1

7347 0.000 0.738 1.000 0.930 0.920 0.000 0.845 1 1.000 13.533 1.000 6533 0.815 0.377 0.002 1.000 0.970 0.882 0.939 0.5 0.705 13.366 0.988 1698 0.759 0.376 0.006 0.865 0.950 0.957 0.900 1 0.715 13.349 0.986 1673 0.500 0.513 0.016 0.950 0.900 0.957 0.900 1 0.715 13.165 0.973 6535 0.870 0.326 0.002 1.000 0.840 0.882 0.939 0.5 0.705 13.121 0.970 790 0.611 0.384 0.005 0.880 1.000 0.957 0.900 1 0.715 13.068 0.966 782 0.852 0.523 0.036 0.970 0.600 0.602 0.764 1 0.980 13.055 0.965

7338 0.000 1.000 0.563 0.605 0.990 0.151 0.985 1 0.995 12.750 0.942 6667 0.981 0.463 0.011 0.745 0.620 0.882 0.939 0.5 0.705 12.687 0.937 6968 0.056 0.938 0.463 0.880 0.700 0.258 0.890 1 0.910 12.614 0.932

18

RESULTS

Evaluation of all parameters was successfully automated with ArcGIS 9.2 Model

Builder and PYTHON scripting. Nine individual models and one PYTHON script were

developed (Appendices A-J). Calculated values and standardized scores for each

parameter and prioritization rankings of 530 potential restoration sites were stored in a

Microsoft Access database (Table 3) supported by an ArcGIS geodatabase containing

spatial geometry of each site. Descriptive statistics were generated on a parameter by

parameter basis for the top and bottom 10% of prioritized sites (Table 4). Standardized

prioritization ranks ranged from 0.479 to 1.000 with a mean of 0.724 (Figure 4). The top

ranking 10% of prioritized sites ranged from 0.821 to 1.00 with a mean of 0.898 while

the bottom ranking 10% ranged from 0.479 to 0.598 (Figure 4). Ranks represent relative

levels of hydrologic alteration and landscape ecology criteria. Higher scores indicate less

cumulative alteration. Graphic illustration of the top 10% of prioritized sites identify

areas which had cumulatively less hydrologic alteration and more favorable landscape

ecology criteria (Figure 5). Combined, the top 10% of prioritized sites occupy

approximately 446 ha of land.

Once a model or script is developed it can be distributed as a tool and accessed in

the GIS graphic interface (Figure 6). When models are viewed as a traditional GIS tool, a

user wishing to evaluate one of the aforementioned parameters has only to download the

input datasets and apply the tool. The spatial analysis outlined in the flowchart (Figure 1)

is executed behind the scenes. Informative user documentation was included with each

tool to describe underlying calculations as well as the online source of any required input

datasets (Figure 6).

19

20

Table 4. Descriptive statistics by parameter (not weighted) of standardized output for all sites and top and bottom ranking 10% of prioritized sites.

Evaluation Parameter Minimum Maximum Mean Hydrologic Connection

Top Ranking 10% 0.000 1.000 0.479 Bottom Ranking 10% 0.000 0.241 0.029

All sites 0.000 1.000 0.128

Adjacent Wetlands Top Ranking 10% 0.150 1.000 0.424

Bottom Ranking 10% 0.043 0.395 0.199 All Sites 0.043 1.000 0.310

Percent Adjacent Filled Land


All Sites 0.315 1.000 0.832

Adjacent Water Top Ranking 10% 0.000 1.000 0.080


Percent Catchment Forested


All Sites 0.000 1.000 0.737

Catchment Road Density & Impervious Surfaces Top Ranking 10% 0.713 1.000 0.910


Vegetation Composition


All Sites 0.000 1.000 0.597

Tidegates Top Ranking 10% 0.500 1.000 0.783


Road-Stream Intersections


All Sites 0.090 1.000 0.740

21

Prioritization Rank

Site

Fre

quen

cy

1.000.950.900.850.800.750.700.650.600.550.500.45

25

20

15

10

5

0

Figure 4. Prioritized rankings of 530 potential restoration sites. Site ranks were

determined by applying a weighted summation of standardized parameter scores. Ranks range from 0.479 to 1.00 with a mean of 0.724. The top ranked 10% of prioritized sites are shown in green and bottom 10% are shown in orange. Rankings represent relative levels of hydrologic alteration and landscape ecology criteria. Higher scores indicate less cumulative alteration.

Prioritization Rank

Site

Fre

quen

cy

22

Figure 5. Map illustrating top ranked 10% of prioritized sites (green) and the catchments

(dark gray) in which they reside.

23

Figure 6. Model viewed as a traditional GIS tool. Users are required to enter the location

of input datasets and specify where the output will be stored. Informative user documentation describes the underlying calculations and the online source of required input datatsets.

DISCUSSION

Over 2,000 potential wetland restoration sites are situated in multiple coastal

watersheds along the Oregon coast (Scranton 2004). These areas require on the ground

assessment to determine if wetland restoration is feasible. Appropriate restoration or

enhancement strategies depend upon overall alteration levels at site and landscape scales

(Shreffler and Thom 1993). Sites with less cumulative alteration may be appropriate for

conservation or respond more favorably to restoration, while enhancement may be

appropriate for sites with higher levels of disturbance (Shreffler and Thom 1993). These

custom GIS tools were developed to aid resource managers throughout Oregon in

locating areas which have less cumulative hydrologic alteration and more favorable

landscape ecology criteria. The prioritization of potential restoration opportunities in the

Coos estuary demonstrates how these tools can be applied to focus site visits in

ecologically desirable locations. The top 10% of prioritized sites represent 53 of the 530

potential sites which should be visited first for restoration feasibility studies (Figures 4,

5).

The use of Model Builder is suggested by Lin et al. (2006) as a method for

creating a flexible decision support system for wetland restoration that is easily modified

and transferred for use in multiple regions. When models are accessed via the interactive

flowchart (Figure 1), input datasets and explicit geoprocessing tasks are available for user

review, modification, and editing. Setting model parameters allows for use of variable

input datasets and user control of intermediate and final output data storage. If local

datasets exist which provide more accurate or current information, they can be easily

24

25

substituted for state-wide information within the models or script. The models provide

flexibility needed for transfer to other Oregon estuaries. With appropriate data and

modification, these tools could also be applied in other Pacific Northwest estuaries.

Model builder and scripting provide transparent GIS analysis methods. Quite

often GIS methods are provided as verbal descriptions of the calculations performed and

explicit geoprocessing required to achieve a calculation are not provided. Automation

techniques detail the workflow of a spatial analysis and provide exact geoprocessing

tasks which were completed to perform an analysis. This gives end users information

needed to evaluate if an analysis will be sufficient to meet their needs in combination

with an automated execution of the calculation.

A site’s position in the landscape and accuracy of source data influences

calculated output. For example, sites situated lower in the watershed may have lower

hydrologic connectivity values than ones in upper reaches. This is due to the use of

stream route data which is represented as a centerline. Conversely, sites lower in the

watershed may have a greater amount of adjacent water due to their proximity to the

estuary. Some catchment boundaries are relatively small because they were generated

from digital elevation models. Delineation of catchment boundaries across areas with

minimal changes in elevation could lead to catchments with smaller areas in the lowlands

surrounding an estuary. Therefore the relative levels of forest cover and impervious

surfaces in the smaller catchments may be exaggerated.

26

Descriptive statistics provide a comparison between the top and bottom 10% of

sites and all sites for each parameter (Table 4). Parameters can be evaluated individually.

For example, it is evident that the average area of a site’s perimeter adjacent to filled

lands is similar for all sites (Table 4). This makes sense since metadata for the dataset

which delineates site boundaries states that most of these areas are generally diked

(Scranton 2004). As accuracy of geospatial data increases, using calculated parameter

values from the models will be more informative and precise. Model output is only as

accurate as input datasets used to calculate parameters and care should be taken when

interpreting individual parameter calculations for a potential restoration site.

Standardization was applied to the calculations not only to combine various measurement

scales but also to summarize the relative range of a parameter across all sites. Relative

comparisons are appropriate when considering only tabular data and further inquiry into

the actual calculated values for any given site should incorporate additional sources of

information.

For a better understanding of the calculated values users should consult metadata

for input datasets available from source agencies, use a GIS for visual representation of

all input datasets used in calculations, and refer to modeled geoprocesses used in

generating the calculations. This can be achieved by using the geodatabase created by

these models to house tabular and spatial datasets used in the evaluations and

prioritization. The combination of these information sources should provide sufficient

detail of how any particular site received its’ calculated and standardized values. Overall,

27 the final prioritization rank represents the combination of multiple factors and can

provide a relative comparison among sites to determine areas with less cumulative

alteration. The prioritization can be used to guide on-site visits for restoration feasibility

studies.

Recommendations for Future Research

Recent studies involving statistical calibration of landscape scale criteria with

HGM field assessment data support the use of spatial data to predict wetland condition

(Whigham et al. 2003, Hychka et al. 2007, Weller et al. 2007). Hychka et al. (2007)

identified multiple landscape metrics which were correlated with on-site conditions and

enhance existing landscape assessments. Weller et al. (2007) also determined correlations

between site conditions and landscape indicators. They suggest that cost-effective

watershed planning can be met with tools that objectively use landscape scale criteria to

predict site conditions (Weller et al. 2007). Insights gained from these studies should be

used to refine the models produced in this study and in selecting additional parameters for

development of future models. Field based studies which provide validation of specific

geospatial parameters will greatly enhance the use of GIS for restoration planning.

The GIS tools developed in this study were designed specifically for wetland

restoration planning based upon hydrologic alteration and landscape ecology but, the

automation methods could be applied to numerous other parameters. Additional criteria

should be modeled to enhance the prioritization process. Water quality, physical and

biological survey data, and socio-political information should be included in the suite of

parameters. The suitability of surrounding habitat, species abundance and diversity,

28

number of property owners, zoning information, and metrics on water quality would

provide useful information for restoration planners. Tools could also be developed for

change detection studies, t-o incorporate local knowledge on past or current restoration

efforts, and temporal monitoring of wetland conditions across large geographic regions.

Aerial photo interpretation to identify site specific information would also enhance these

models (Brophy 2005). Once a comprehensive suite of parameters are developed and as

accuracy of spatial data increases, calculated values could provide insight to appropriate

restoration strategies.

The willingness of private land owners to engage in restoration on their property

would be difficult to model in a GIS environment. However, information contained in the

geodatabase created during this project could be used to engage land owners and create a

dialogue about restoration potential on their property. These models could be used to

illustrate objective calculations which lead to identification of a particular location within

the region. Numerous maps can also be generated based upon the information contained

in the geodatabase. These maps could be utilized as visual aids for conveying geographic

information to landowners as well as for resource managers.

Currently documentation does not exist detailing the substitution of state-

wide data for local datasets on how to modify an analysis. Although these are relatively

easy tasks for someone familiar with ArcGIS software, documentation on how to modify,

edit, and update the models would be useful for a larger user base. To enhance

distribution capabilities and for ease of application to end users, the development of a

detailed user guide is recommended.

29

The development of GIS tools that can be easily applied to prioritization for

wetland restoration in Oregon presents an important step forward. Planners from a

variety of organizations now have rapid assessment tools which use widely available data

to obtain consistent results across hydrologic systems. As understanding of these systems

improves, or additional information becomes available, models are ready for modification

to incorporate these changes with a minimum effort. By providing a consistent means to

prioritize potential sites before going to the field, valuable time and resources will be

better allocated to achieve restoration objectives.

LITERATURE CITED Adamus, P. R. and D. Field. 2001. Guidebook for Hydrogeomorphic (HGM) Based Assessment of Oregon Wetland and Riparian Sites. Oregon Department of State Lands, Salem, OR, USA. Amezaga, J. M., L. Santamaria, and A. J. Green. 2002. Biotic wetland connectivity- supporting a new approach for wetland policy. Acta Oecologica 23:213-222. Arnold, C. L. Jr., and C. J. Gibbons. 1996. Impervious surface coverage: the emergence of a key environmental indicator. Journal of the American Planning Association 62(2):243-258. Bartoldus, C. C. 1999. A Comprehensive Review of Wetland Assessment Procedures. A Guide for Wetland Practitioners. Environmental Concern Inc., St. Michaels, MD, USA. Bedford, B. L. 1996. The need to define hydrologic equivalence at the landscape scale for freshwater wetland mitigation. Ecological Applications 6:57-68. Bedford, B. L. 1999. Cumulative effects on wetland landscapes: links to wetland restoration in the United States and southern Canada. Wetlands 19(4):775-788. Bolstad, P. 2005. GIS fundamentals: A First Text on Geographic Information Systems, second edition. Eider Press. White Bear Lake, MN, USA. Brinson, M. M. 1993. A hydrogeomorphic classification for wetlands. U.S. Army Corps of Engineers Waterways Experiment Station, Wetlands Research Program Technical Report WRP-DE-4, Vicksburg, MS, USA. Brooks, R. P., D. H. Wardrop, and J. A. Bishop. 2004. Assessing wetland condition on a watershed basis in the mid-Atlantic region using synoptic land-cover maps. Environmental Monitoring and Assessment 94:9-22. Brophy, L. S. 2005. Tidal wetland prioritization for the Siuslaw River estuary. Report of Greenpoint Consulting prepared for the Siuslaw Watershed Council. Mapleton, OR, USA. Bruland, G. L., M. F. Hanchey, and C. J. Richardson. 2003. Effects of agriculture and wetland restoration on hydrology, soils, and water quality of a Carolina bay complex. Wetland Ecology and Management 11:141-156. Callaway, J. C. 2001. Hydrology and substrate. p. 89-111. In J. B. Zedler (ed.) Handbook for Restoring Tidal Wetlands. CRC Press LLC, Boca Raton, FL, USA.

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Cedfeldt, P. T., M. C. Watzin, B. D. Richardson. 2000. Using GIS to identify functionally significant wetlands in the Northeastern United States. Environmental Management 26(1):13-24. Cook, B. J. and F. R. Hauer. 2007. Effects of hydrologic connectivity on water chemistry, soils, and vegetation structure and function in an intermontane depressional wetland landscape. 2007. Wetlands 27(3)719-738. Dahl, T. E. 2006. Status and trends of wetlands in the Conterminous United States 1998 to 2004. U.S Fish and Wildlife Service, Washington DC, USA. Dean, T., Z. Ferdaña, J. White, and C. Tanner. 2000. Skagit estuary restoration assessment, identifying and prioritizing areas for habitat restoration in Puget Sound’s largest rural estuary. Report by People for Puget Sound and U.S. Fish and Wildlife Service, <http://gis.esri.com/library/userconf/proc00/professional/papers/PAP230/ p230.htm>. Division of State Lands, “Habitat Types for the Coos Bay Esturay Plan”, 1970, <http://www.coastalatlas.net>. ESRI. 2000. ArcView spatial analyst. Environmental Systems Research Institute, Redlands, CA, USA. ESRI (ed.). 2005. ArcGIS 9 Writing geoprocessing scripts with ArcGIS. ESRI, Inc., Redlands, CA, USA. ESRI 2006. ArcGIS 9.2. ESRI, Inc., Redlands, CA, USA. Evans, N. R., R. M. Thom, G. D.Williams, J. Vavrinec, K. L. Sobocinski, L. M. Miller, A. B. Borde, V. I. Cullinan, J. A. Ward, C. W. May, and C. Allen. 2006. Lower Columbia River restoration prioritization framework. Report PNWD-3652 of Battelle Marine Sciences Laboratory prepared for the Lower Columbia River Estuary Partnership. Portland, OR, USA. Giannico, G. R. and J. A. Souder. 2005. Tide gates in the Pacific Northwest, operation, types, and environmental effects. Report ORESU-T-O5-001 of Oregon State University prepared for Oregon Sea Grant, Corvallis, Oregon, USA. Hiers, J. K., S. C. Laine, J. J Bachant, J. H. Furman, W. W. Greene, and V. Compton. 2003. Simple spatial modeling tool for prioritizing prescribed burning activities at the landscape scale. Conservation Biology 17(6):1571-1578.

32 Houlahan, J. E., and C. S. Findlay. 2004. Estimating the ‘critical’ distance at which adjacent land-use degrades wetland water and sediment quality. Landscape Ecology 19:677-690. Hychka, K. C., D. H. Wardrop, R. P. Brooks. 2007. Enhancing a landscape assessment with intensive data: A case study in the upper Juniata watershed. Wetlands 27(3): 446- 461. Jennings, A., T. Jennings, and R. Bailey. 2003. Estuary management in the pacific northwest: an overview of programs and activities in Washington, Oregon, and Northern California. Pacific Northwest Coastal Ecosystems Regional Study. Report ORESU-H- 03-001, Oregon Sea Grant, Corvallis, OR, USA. Johnston, C. A., N. E. Detenbeck, J. B. Bonde, and G. Niemi. 1988. Geographic information systems for cumulative impact assessment. Photogrammetric Engineering and Remote Sensing 54(11):1609-1615. Jones, J. A., F. J. Swanson, B. C. Wemple, and K. U. Snyder. 2000. Effects of roads on hydrology, geomorphology, and disturbance patches in stream networks. Conservation Biology 14(1):76-85. Kusler, J. A. and G. Brooks (eds.). 1987. Proceedings of the National Wetland Symposium: Wetland Hydrology. Association of State Wetland Managers. Chicago, IL, USA. Lin, J. P., S. G. Bourne, and B. A. Kleiss. 2006. Creating a wetland regulation decision support system using GIS tools. U.S Army Engineer Research and Development Center, Technical note ERDC-TN-EMRRP-EM-05, Vicksburg, MS, USA. Liu, C., P. Frasier, L. Kumar, and C. Macgregor. 2006. Catchment-wide wetland assessment and prioritization using the multi-criteria decision making method TOPIS. Environmental Management 38(2):316-326. Lyon, J. G. and J. McCarthy (eds.). 1995. Wetland and Environmental Applications of GIS. CRC Press, Inc., Boca Raton, FL, USA. Mitsch, W. J. and J. G. Gosselink. 2000. Wetlands, third edition. John Wiley & Sons, Inc., New York, NY, USA. National Research Council. 1994. Priorities for Coastal Ecosystem Science. National Academy Press, Washington, D.C., USA.

33 National Research Council. 1995. Wetlands, Characteristics and Boundaries. National Academy Press, Washington, D.C., USA. NOAA Coastal Services Center, “Pacific Coast Land Cover”, 2000, < http://www.csc.noaa.gov/crs/lca/pacificcoast.html>. Oregon Bureau of Land Management, “Ground Transportation”, 2006, < http://www.blm.gov/or/gis/>. Oregon Department of Fish and Wildlife, “Fish Passage Barriers”, 2004, < http://nrimp.dfw.state.or.us/nrimp/default.aspx?pn=fishbarrierdata >. Regional Ecosystem Office, “Hydrography”, 2007, <http://hydro.reo.gov/>. Rumrill, S. S. 2006. The ecology of the South Slough estuary: site profile of the South Slough National Estuarine Research Reserve. OregonDepartment of State Lands, NOAA Estuarine Reserves Division, Technical Report. Coos Bay, OR, USA. Russell, G. D., C. P. Hawkings, M. P. O’Neill. 1997. The role of GIS in selecting sites for riparian restoration based on hydrology and land use. Restoration Ecology 5(4S):56-68. Scranton, R. J. 2004. The application of geographic information systems for delineation and classification of tidal wetlands for resource management of Oregon's coastal watersheds. Marine Resource Management Program, Oregon State University, Corvallis, OR, USA. Scranton, R. J. “Tidal Wetlands of Oregon’s Coastal Watersheds”, Oregon State University, 2004, <http://www.coastalatlas.net/>. Shreffler, D. K. and R. M. Thom. 1993. Restoration of urban estuaries: new approaches for site location and design. Report of Battelle Pacific Northwest Laboratories prepared for Washington State Department of Natural Resources. Sequim, WA, USA. Sutter, L.A. 2001. Spatial wetland assessment for management and planning (SWAMP): technical discussion. NOAA Coastal Services Center. Publication No. 20129-CD. Charleston, South Carolina, USA. Thom, R. M., G. D. Williams, and A. D. Borde. 2003. Conceptual models as a tool for assessing, restoring, and managing Puget Sound habitats and resources. Proceedings of Puget Sound Research, Puget Sound Water Quality Action Team, Olympia, WA, USA.

34 Tiner, R. W. 2005. Assessing cumulative loss of wetland functions in the Nanticoke River watershed using enhanced national wetlands inventory data. Wetlands 25(2):405- 419. Turner, M. G., R. H. Gardner, and R. V. O’Neill. 2001. Landscape ecology in theory and practice: pattern and process. Springer Verlag, New York, NY, USA. Van Lonkhuyzen, R. A., K. E. LaGory, and J. A. Kuiper. 2004. Modeling the suitability of potential wetland mitigation sites with a geographic information system. Environmental Management 33:368-375. US Fish and Wildlife Service, “National Wetlands Inventory”, 2006, <http://www.fws.gov/nwi/>. USGS, “Impervious Surfaces”, 2001, <http://seamless.usgs.gov/>. USGS Earth Resources Observation and Science, “Watershed Cathcments”, 2001, <http://edna.usga.gov/>. Weller, D. E., M. N. Snyder, D. F. Whigham, A. D Jacobs, and T. E. Jordan. 2007. Landscape indicators of wetland condition in the Nanticoke River watershed, Maryland and Delaware, USA. Wetlands 27(3):498-514. Whigham, D., D. Weller, A. Jacobs, T. Jordan, and M. Kentula. 2003. Assessing the ecological condition of wetlands at the catchment scale. Landschap 20(2):99-111. Williams G. D., R. M. Thom, and N. R. Evans. 2004. Bainbridge Island nearshore habitat characterization and assessment. Management strategy prioritization and monitoring recommendations. Report PNWD-3391 of Battelle Marine Sciences Laboratory prepared for the City of Bainbridge Island. Bainbridge Island, WA, USA. Zedler, J. B. 2000. Progress in wetland restoration. Trends in Ecology and Evolution 15(10):402-40.

Appendix A. Model developed to calculate hydrologic connection.

APPEN

DIC

ES35

Appendix A. Model developed to calculate hydrologic connection (continued). 36

Appendix B. Model developed to calculate area of adjacent water. 37

Appendix C. Model developed to calculate percentage of perimeter adjacent to filled land. 38

Appendix D. Model developed to calculate vegetation composition. 39

Appendix E. Model developed to calculate percentage forested in catchment.

40

Appendix F. Model developed to calculate percentage impervious surface area in catchment. 41

Appendix G. Model developed to calculate road density in catchment.

42

Appendix H. Model developed to calculate number of road stream intersection in catchment. 43

Appendix I. Model developed to calculate number of tidegates in catchment.

44

Appendix J. PYTHON script written to calculate area of adjacent wetlands in a 1,609 meter (1 mile radius). # Import system modules import sys, string, os, arcgisscripting # Create the Geoprocessor object gp = arcgisscripting.create() # Set the necessary product code gp.SetProduct("ArcInfo") # Set local variables potential_restoration_sites = ".\\DATA\\BASE_DATA\\Tidal_Wetlands\\Tidal_Wetlands_Geodatabase.mdb\\potential_restoration_sites" site_buffer = ".\\DATA\\INTERMEDIATE_DATA\\site_buffer" site_select = ".\\DATA\\INTERMEDIATE_DATA\\site_select" site_buffer_erase = ".\\DATA\\INTERMEDIATE_DATA\\site_buffer_erase" or_estuary_plan_shp = ".\\DATA\\BASE_DATA\\Oregon_Estuary_Plan\\or_estuary_plan.shp" oep_proximity_function_selection_shp = ".\\DATA\\INTERMEDIATE_DATA\\oep_proximity_function_selection.shp" nwi_shp = ".\\DATA\\BASE_DATA\\NWI\\nwi.shp" nwi_type_selection_shp = ".\\DATA\\INTERMEDIATE_DATA\\nwi_type_selection.shp" nwi_adjacency_selection_shp = ".\\DATA\\INTERMEDIATE_DATA\\nwi_adjacency_selection.shp" oep_nwi_merge_shp = ".\\DATA\INTERMEDIATE_DATA\\oep_nwi_merge.shp" oep_nwi_merge_dissolve_shp = ".\\DATA\\INTERMEDIATE_DATA\\oep_nwi_merge_dissolve.shp" wetlands_in_1mi_buffer = ".\\DATA\\INTERMEDIATE_DATA\\wetlands_in_1mi_buffer" adjacent_wetlands_shp = ".\\DATA\\INTERMEDIATE_DATA\\adjacent_wetlands.shp" Analysis_output_mdb = ".\\DATA\\FINAL_DATA\\Analysis_output.mdb" Analysis_output_mdb__2_ = ".\\DATA\\FINAL_DATA\\Analysis_output.mdb" # Load required toolboxes... gp.AddToolbox("C:/Program Files/ArcGIS/ArcToolbox/Toolboxes/Analysis Tools.tbx") gp.AddToolbox("C:/Program Files/ArcGIS/ArcToolbox/Toolboxes/Data Management Tools.tbx") gp.AddToolbox("C:/Program Files/ArcGIS/ArcToolbox/Toolboxes/Conversion Tools.tbx") # Process: Select Oregon Estuary Plan polygons... gp.Select_analysis(or_estuary_plan_shp, oep_proximity_function_selection_shp, "\"LABEL\" = '1.3.9' OR \"LABEL\" = '1.3.10' OR \"LABEL\" = '2.3.9' OR \"LABEL\" = '2.3.10'") print "select oep" # Process: Select Emergent, Scrub/shrub, and Forested NWI polygons... gp.Select_analysis(nwi_shp, nwi_type_selection_shp , "\"ATTRIBUTE\" LIKE '%EM%' OR \"ATTRIBUTE\" LIKE '%SS%' OR \"ATTRIBUTE\" LIKE '%FO%'")

45

print "select nwi type" # Process: Select undiked NWI polygons... gp.Select_analysis (nwi_type_selection_shp, nwi_adjacency_selection_shp, "\"ATTRIBUTE\" NOT LIKE '%h'") print "select nwi undiked" # Process: Merge NWI and OEP polygons gp.Merge_management(".\\DATA\\INTERMEDIATE_DATA\\nwi_adjacency_selection.shp;.\\DATA\\INTERMEDIATE_DATA\\oep_proximity_function_selection.shp", oep_nwi_merge_shp, "") print "merge" # Process: Dissolve boundaries of NWI and OEP merge gp.Dissolve_management(oep_nwi_merge_shp, oep_nwi_merge_dissolve_shp, "", "", "MULTI_PART") print "dissolve" # Get the Object ID Field for the Potential Restoration Sites OIDField = gp.Describe(potential_restoration_sites).OIDFieldName print OIDField #Set an update cursor on the rows of the Potential Restoration Sites rows = gp.UpdateCursor(potential_restoration_sites) row = rows.Next() #Loop through each site and perform the following processes on each individual Potential Restoration Site while row: # Process: Select the site and append it's name with it's object id gp.Select_analysis(potential_restoration_sites, site_select + str(str(row.OBJECTID) + ".shp") , OIDField + " = " + str(row.GetValue(OIDField))) print "select site" #Process: Buffer the selected site by 1 mile (append output with object id) gp.Buffer_analysis(site_select + str(str(row.OBJECTID) + ".shp"), site_buffer + str(str(row.OBJECTID) + ".shp"), "1610 Meters", "FULL", "ROUND", "LIST", "SITE_NUM") print "buffer" # Process: Erase the area of the site from the buffer (append output with object id) gp.Erase_analysis(site_buffer + str(str(row.OBJECTID) + ".shp"), site_select + str(str(row.OBJECTID) + ".shp") , site_buffer_erase + str(str(row.OBJECTID) + ".shp"), "") print "erase" # Process: Intersect the erased buffer with the NWI and OEP polygons gp.Intersect_analysis(".\\DATA\\INTERMEDIATE_DATA\\oep_nwi_merge_dissolve.shp #;.\\DATA\\INTERMEDIATE_DATA\\site_buffer_erase" + str(str(row.OBJECTID) + ".shp"), wetlands_in_1mi_buffer + str(str(row.OBJECTID) + ".shp"), "ALL", "", "INPUT") 46

print "intersect" #####Clean up the files not needed # Process: Delete the individual selected sites gp.Delete_management(site_select + str(str(row.OBJECTID) + ".shp"), "ShapeFile") print "delete site" # Process: Delete the intermediate shapefile for the site buffer gp.Delete_management(site_buffer + str(str(row.OBJECTID) + ".shp"), "ShapeFile") print "delete buffer" # Process: Delete the intermediate shapefile for erased buffer... gp.Delete_management(site_buffer_erase + str(str(row.OBJECTID) + ".shp"), "ShapeFile") print "delete erased buffer" # Reset the cursor to the next row rows.UpdateRow(row) row = rows.Next() #####Merge all of the individual feature classes into one and send the output to a geodatabase # Set the workspace that the feature classes are in gp.Workspace = ".\\DATA\\INTERMEDIATE_DATA" # Get a list of feature classes in the workspace fcs = gp.ListFeatureClasses("wetlands_in_1mi_buffer" + "*") #Create a value table that will hold all of the input features to Merge. vtab = gp.createobject("ValueTable") fc = fcs.next() print "create a value table" #Add each feature class to the value table while fc: print fc vtab.AddRow(fc) fc = fcs.next() # Process: Merge all of the feature classes in the value table gp.Merge_management(vtab, adjacent_wetlands_shp, "") 47

print "merge all of the single files" #Process: Send the final feature class to a geodatabase gp.FeatureClassToGeodatabase_conversion(".\\DATA\\INTERMEDIATE_DATA\\adjacent_wetlands.shp", Analysis_output_mdb__2_) print "send final output to geodatabase" #########Clean up the remaining files # Set the workspace that the feature classes are in gp.Workspace = ".\\DATA\\INTERMEDIATE_DATA" # Get a list of feature classes in the workspace fcs = gp.ListFeatureClasses("wetlands_in_1mi_buffer" + "*") fc = fcs.next() while fc: print fc # Process: Delete the intermediate wetlands_in_1mi_buffer shapefiles gp.Delete_management(fc) print "delete intermediate files" fc = fcs.next

48

development and application of a gis based …

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