Type of Document Master's Thesis Author Sandy, Alexis Emily Author's Email Address firstname.lastname@example.org URN etd-06142006-172204 Title Environmental and Digital Data Analysis of the National Wetlands Inventory (NWI) Landscape Position Classification System Degree Master of Science Department Crop and Soil Environmental Sciences Advisory Committee
Advisor Name Title Galbraith, John M. Committee Chair Campbell, James B. Jr. Committee Member Daniels, Walter Lee Committee Member Prisley, Stephen P. Committee Member Keywords
- landscape position
- National Wetlands Inventory
- Lotic Stream
- Lotic River
- slope curvature
- hydrology primary and secondary indicators
- functional descriptors
- field indicators of hydric soil
- wetland plant indicator status
Date of Defense 2006-05-31 Availability unrestricted AbstractThe National Wetlands Inventory (NWI) is the definitive source for wetland resources in the United States. The NWI production unit in Hadley, MA has begun to upgrade their digital map database, integrating descriptors for assessment of wetland functions. Updating is conducted manually and some automation is needed to increase production and efficiency. This study assigned landscape position descriptor codes to NWI wetland polygons and correlated polygon environmental properties with public domain terrain, soils, hydrology, and vegetation data within the Coastal Plain of Virginia. Environmental properties were applied to a non-metric multidimensional scaling technique to identify similarities within individual landscape positions based on wetland plant indicators, primary and secondary hydrology indicators, and field indicators of hydric soils. Individual NWI landscape position classes were linked to field-validated environmental properties. Measures provided by this analysis indicated that wetland plant occurrence and wetland plant status obtained a stress value of 0.136 (Kruskal’s stress measure = poor), which is a poor indicator when determining correlation among wetland environmental properties. This is due principally to the highly-variable plant distribution and wetland plant status found among the field-validated sites. Primary and secondary hydrology indicators obtained a stress rating of 0.097 (Kruskal’s stress measure = good) for correlation. The hydrology indicators measured in this analysis had a high level of correlation with all NWI landscape position classes due the common occurrence of at least one primary hydrology indicator in all field validated wetlands. The secondary indicators had an increased accuracy in landscape position discrimination over the primary indicators because they were less ubiquitous. Hydric soil characteristics listed in the 1987 Manual and NTCHS field indicators of hydric soils proved to be a relatively poor indicator, based on Kruskal’s stress measure of 0.117, for contrasting landscape position classes because the same values occurred across all classes.
The six NWI field–validated landscape position classes used in this study were then further applied in a public domain digital data analysis. Mean pixel attribute values extracted from the 180 field-validated wetlands were analyzed using cluster analysis. The percent hydric soil component displayed the greatest variance when compared to elevation and slope curvature, streamflow and waterbody, Cowardin classification, and wetland vegetation type. Limitations of the soil survey data included: variable date of acquisition, small scale compared to wetland size, and variable quality. Flow had limitations related to its linear attributes, therefore is often found insignificant when evaluating pixel values that are mean of selected pixels across of wetland landscape position polygons. NLCD data limitations included poor quality resolution (large pixel size) and variable classification of cover types. The three sources of information that would improve wetland mapping and modeling the subtle changes in elevation and slope curvature that characterize wetland landscapes are: recent high resolution leaf-off aerial photography, high-quality soil survey data, and high-resolution elevation data.
Due to the data limitations and the choice of variables used in this study, development of models and rules that clearly separate the six different landscape positions was not possible, and thus automation of coding could not be attempted.
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