Mapping and Monitoring Plant Communities in the Coastal Plain of North Carolina: A Basis for Conservation Planning

Abstract

The most effective tool for conservation of biodiversity is high quality information on the extent and status of species and their habitats. To guide that conservation, the National Gap Analysis Program (GAP) has been working to develop thematically rich land cover that can be used to assess the conservation status of native plant communities and as a basis for modeling the predicted distributions of species. In this research our goal was to integrate methods for developing a high quality land cover map using change detection, as the basis for monitoring plant communities and species habitats over time. We mapped the Ecological Systems of the Onslow Bight, NC using Landsat TM satellite imagery and ancillary datasets (e.g., soils). We tested the application of decision tree modeling for mapping 6 forested systems and used image objects map managed evergreen stands. A total of 42 land cover classes were mapped with an overall accuracy of 77% and a kappa statistic of .75. We then mapped the amount and type of land cover change between 1992 and 2001 using Change Vector Analysis. Change was mapped on 13% of the landscape, with an overall accuracy of 95% and a kappa statistic of .75. Using the 1992 and 2001 land cover maps we modeled the predicted distribution of 141 vertebrate species for both dates. The species had been identified by either the North Carolina Wildlife Resources Commission in their State Wildlife Action Plan (SWAP, 125 species) or by Partners in Flight (PIF, 40 species) as priority species in need of conservation action. We quantified change between the two dates and provide summaries by species and agency list. Finally we quantified the overlap in the hotspots for the predicted distributions and the existing conservation network. Sixty-eight percent of the existing managed lands in the Onslow Bight co-occur with hotspot areas for SWAP and PIF, while only 44% of the landscape met those criteria.

Description

Keywords

change detection, mapping, vegetation

Citation

Degree

PhD

Discipline

Botany

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