Modeling Vernal Pool Basin Morphology above the Headwaters of the Appalachian Mountains
Open Access
- Author:
- Prabhu, Ritvik
- Area of Honors:
- Environmental Resource Management
- Degree:
- Bachelor of Science
- Document Type:
- Thesis
- Thesis Supervisors:
- Jonathan M Duncan, Thesis Supervisor
Robert David Shannon, Thesis Honors Advisor - Keywords:
- vernal pool
wetland delineation
morphology
Appalachians
National Wetlands Inventory
Wetland Hydrology Analyst Toolbox - Abstract:
- Vernal pools are seasonal, depressional wetlands that typically become inundated in the winter and spring and then dry up in the summer and fall. They offer a number of important ecosystem services including providing habitat for amphibian reproduction and other aquatic life, accumulating organic matter, and facilitating enhanced nutrient cycling. The US Environmental Protection Agency states that human activity has caused large percentages of recorded vernal pools to be impacted or destroyed by development and pollution. To properly regulate and protect vernal pools, reliable delineation methods are needed to identify vernal pool locations and characterize basin morphology. Although the term ‘vernal pool’ is used homogenously across the United States, vernal pools can differ by region in morphology and hydrologic regime. This variability makes it difficult to create generalized delineation methods. The Wetland Hydrology Analyst Toolbox (WHAT) is an approach developed for use in the Prairie Pothole Region that has proven effective in delineation. This thesis applied the WHAT to the Northern Ridges and Valleys region of the Appalachians to see whether eleven small forested vernal pools could be detected and accurately depicted. Optimal parameters of this toolbox were compared to other notable delineation methods—aerial imagery analysis represented by the National Wetlands Inventory (NWI) and object-based imagery analysis (OBIA) represented by a 2013 dataset of Pennsylvania wetlands—along with manual measurements and local knowledge to determine accuracy levels. The NWI method was found to be the most accurate, with the OBIA method less accurate, followed closely behind by the WHAT method. Although the least accurate, the WHAT has the potential to improve data quality by adjusting the parameter values or by revising the Python scripts to cater more to the region.