Date of Award
Open Access Thesis
Master of Science (MS)
Stephen C. Richter
David R. Brown
Wetlands are a vital part of our environment and serve important functions, including water quality improvement, nutrient management, pollution control, storm buffering, flood control, sediment stabilization, groundwater replenishment, fish and wildlife habitat, aesthetics, and recreation opportunities (Costanza et al., 2008; Mitsch & Gosselink, 2000; USEPA, 2006; Woodward & Yong-Suhk, 2001). A number of laws have been enacted to help to protect existing wetlands, promote rehabilitation of degraded wetlands, and encourage the creation of new wetlands. Also, since 1988 all United States presidents have stated a "No Net Loss" policy for wetlands in the United States. There is a need to better assess and monitor the amount and condition of existing wetlands to assess the No Net Loss policy, to determine the effectiveness of preservation and mitigation efforts, and to provide a better description of wetland assets.
In an effort to identify, describe, and assess wetlands, a 3-level framework has been broadly adopted by federal and state government agencies. The levels vary in methodology and intensity from the broadest spatial scale and lowest detail (Level I) to the narrowest spatial scale and highest intensity (Level III) (Fennessy et al., 2004, 2007). Rapid Assessment Methods (RAMs) are Level II assessments and considered instrumental in many state-level wetland monitoring and regulatory programs (Fennessy et al., 2007; Stein et al., 2009). A RAM for use with Kentucky wetlands (KY-WRAM) is in development based on recommendations by Fennessy et al. (2007) (KYDOW, 2013a, 2013b).
The KY-WRAM has six metrics, each with submetrics (KYDOW, 2013a, 2013b). Several submetrics in the KY-WRAM are well suited for scoring with GIS analysis techniques. The purpose of this study was to develop GIS methods to calculate scores for the three submetrics described below, and compare those resulting scores to scores for the same Wetland Assessment Area (WAA) sites that had already been manually rated by human analysts. It was expected that in utilizing GIS techniques that there would be benefits in improved consistency, speed, accuracy, and transparency. Python scripting language was used with ArcGIS 10.1 to programmatically apply GIS methods to score Wetland Assessment Areas (WAA) for KY-WRAM submetrics 1b, 2a, and 2b. Scores determined using a GIS were statistically compared to scores determined from manual methods for the same submetrics and WAA. For all three submetrics, there were no statistical differences between GIS-method scores and scores from manual methods. In addition to statistical analyses, I qualitatively illustrate some of the advantages and drawbacks of using a programmatic GIS solution.
Consistent with the literature it was found that developing a GIS solution has greater up-front costs than solutions using manual methods. Manual methods could be changed with less cost in effort and time compared to making changes to a GIS solution. In this study scripted GIS methods out-performed manual methods in terms of repeatability of results, consistency in reporting, and transparency of steps taken to score submetrics for each WAA. GIS scoring of wetlands was at least as fast as manual methods but was more efficient because it did not require a user to attend during processing. Although it could not be demonstrated quantitatively within the scope of this study, it follows logic that the GIS solution's precise results for calculated areas would be more accurate than those results from visual estimation based on manual methods using the same spatial data. The GIS solution relies on spatial data and therefore is limited by those data in terms of availability and quality. When used in conjunction with ground-truthing during the on-site visit to a WAA a GIS solution can be a useful tool to aid in the assessment of wetlands.
Copyright 2014 Douglas Robert Mott
Mott, Douglas Robert, "Using GIS To Evaluate The Kentucky Wetland Rapid Assessment Method" (2014). Online Theses and Dissertations. 217.