Monday, August 4, 2008: 4:00 PM
202 E, Midwest Airlines Center
Ernie F. Hain1, Stacy A.C. Nelson2, Damian Maddalena3 and Christopher R. Derolph2, (1)Forestry and Natural Resources, North Carolina State University, Raleigh, NC, (2)Forestry and Environmental Resources, NC State University, Raleigh, NC, (3)Forestry and Environmental Resources, North Carolina State Univeristy, Raleigh, NC
Background/Question/Methods We describe a process for developing a GIS-based approach to modeling landscape-scale watershed conditions at two national parks. George Washington Birthplace National Monument (GEWA) and Thomas Stone National Historic Site (THST) lie on opposite banks of the Potomac River in Virginia and Maryland, respectively. An extensive collection of biotic and abiotic natural resource data has been integrated into a relational database by the Center for Earth Observation at North Carolina State University. This database includes spatial datasets developed for the NPS Inventory and Monitoring Program, NPS Northeast Region geographic information system (GIS) files, data from the Conservation Fund’s GEWA Community Profile, current demographic information, and datasets developed by the Chesapeake Bay Program (CBP). While inventories of the flora and fauna within the park have provided extensive data, a complete ecosystem health assessment, invaluable for planning purposes, had not been completed before this study.
Results/Conclusions
Biological monitoring methods and protocols of various local, state, and federal agencies/organizations operating within eastern Virginia and Maryland have been assimilated into an integrated watershed assessment index. By incorporating a broad mix of ecological and human impact indicators into the index, a complete assessment of terrestrial, riparian, and aquatic resources is obtained. Additionally, an overall “score” of environmental conditions is provided and gaps in available data are identified. All watershed index metrics were evaluated by regression analyses to determine statistical correlations between reference and in-park sites. Stepwise linear regression techniques were used to select metrics and habitat variables that are statistically significant predictors of region-specific metrics. A custom user interface has been developed using Python scripting language and VBA/object-orienting programming, allowing park managers to run the model from a series of “drop-down” menus once the customized extension is activated in ArcGIS. This user interface allows park managers to update the model as additional data is collected. The results provided by the integrated watershed assessment index provide park planners and resource managers with detailed information for natural resource management and conservation.