Date of Award



Deane Wang, PhD.

Scott Merrill, PhD.

Jennifer Pontius, PhD.

Document Type



The practice of ecological restoration requires identifying a reference, or desired condition. In the Eastern United States, reference conditions can be difficult to identify due to the widespread impacts of anthropogenic influences like logging, fire suppression, and forest fragmentation on ecosystem structure. The Southern Appalachian Mountains contain some of the largest patches of unlogged primary forests in the Eastern United States. Although not entirely unaltered by humans, these existing primary forests can provide important information about the composition and structure of the Southern Appalachian landscape prior to Euro-American settlement.

Cove forests occupy mid-elevation slopes throughout the Southern Appalachians. Due to their sheltered topography, mesic nature, and low fire return interval, existing primary cove forests can serve as an appropriate community-specific reference condition. This project uses Light Detection and Ranging (LiDAR)-derived canopy height models to predict old growth forest stand characteristics in Southern Appalachian cove forests. By using grey-level co-occurrence texture analyses in primary forests throughout Pisgah and Nantahala National Forests, and Great Smoky Mountains National Park, the project introduces a new LiDAR-based methodology for describing ecosystem structure in forest landscapes. This methodology provides insight into the canopy structure of old growth cove forests, which may be useful for both ecological restoration and forest management in this forest community.