Use of Landsat ETM and Topographic Data to Characterize Evergreen Understory Communities in Appalachian Deciduous Forests

Abstract

Evergreen understory vegetation was classified using Landsat ETM imagery and ancillary data in two physiographic provinces in the central Appalachian highlands; the Ridge and Valley and the Allegheny Plateau. These evergreen understory communities are dominated by rosebay rhododendron (Rhododendron maximum L.) and mountain laurel (Kalmia latifolia L.), which are spatially extensive and ecologically important to the structure and functioning of Appalachian forests. DEM-derived topographic information was integrated with Landsat data to assess its potential to improve classification accuracy, maximum likelihood, and minimum distance, and decision tree classification approaches were tested with these data in a factorial manner. An overall accuracy of 87.1 percent (K hat =.806) was achieved in the Ridge and Valley province by employing a maximum likelihood approach using Landsat data alone, while an 82.9 percent overall accuracy (K hat =.755) was obtained for the Allegheny Plateau employing a hybrid decision tree classification approach with Landsat and topographic data.