As urban sprawl swallows the areas around cities, planners are looking for alternative methods of development that help to protect and preserve the environment, enhance the lives of residents, and help reduce the skyrocketing costs of maintaining sprawling infrastructure. Green Infrastructure (GI) planning principles have gained in popularity due to their holistic nature and ability to balance preservation and development. A GI plan seeks to identify the critical “green” infrastructure in an area (the environmental resources that we rely on for clean air and water) and proposes complementary development strategies. One plan component of particular interest is the analysis of the hydrologic network, since it is water quality that drives many ecological and environmental planning issues. Over the last 30 years, riparian buffering has emerged as an accepted best practice for the protection and restoration of sensitive hydrologic features.When creating a GI plan, the power of geographic information systems (GIS) is leveraged to help organize, analyze, and display the large datasets needed to synthesize the plan components. The plan components can be quite complex, and the need for solid, well-defined methodologies is great. In response, this thesis proposes a data model that defines the database structure and attributes needed for hydrologic network conservation analysis, based on research conducted during the creation of the Beaver Creek Watershed Green Infrastructure Plan in Knox County, Tennessee. The analysis methodology and some common hydrologic feature buffer practices are described. The specific methods chosen for this project are detailed and a geoprocessing model that generates the datasets necessary to visualize the hydrologic network buffers is presented.
| Identifer | oai:union.ndltd.org:UTENN_/oai:trace.tennessee.edu:utk_gradthes-1534 |
| Date | 01 December 2008 |
| Creators | Wunderlich, Andrew Lorenz |
| Publisher | Trace: Tennessee Research and Creative Exchange |
| Source Sets | University of Tennessee Libraries |
| Detected Language | English |
| Type | text |
| Source | Masters Theses |
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