Southern Appalachian spruce-fir forest is a restricted and imperiled habitat characterized by two evergreen species, Red Spruce, Picea rubens, and endemic Fraser Fir, Abies fraseri, found at high elevations in North Carolina, Virginia, and Tennessee. This habitat contains rare and imperiled species such as the Federally Endangered spruce-fir moss spider. Spruce-fir forests have been severely impacted by historical logging, acid rain, and invasive balsam wooly adelgid. The forests are likely to be severely impacted by warming climates, as they are restricted to a narrow climatic window. Remote sensing utilizing LandSat 8 surface reflectance data is an important and effective tool for identifying and mapping evergreen ecosystems such as spruce-fir forest. GIS layers and maps produced from this data can help researchers and conservation practitioners gain a greater and more nuanced knowledge of the ecosystem as a whole. Currently no published research is available outlining a comprehensive and statistically sound validation of spruce-fir habitat classifications or derived population level statistics. The purpose of this study is to develop an understanding of the effectiveness of classification algorithms for identifying spruce-fir Forests and utilize this classification to understand the coverage and environmental parameters of these forests. Careful consideration of choices made in the classification and validation process establishes a methodology for both producing and using remotely sensed presence/absence maps. Three machine learning habitat classification algorithms, Support Vector Machines, Random Forest, and MaxEnt, were compared, as was the addition of EVI and NDVI vegetation indices. A proportional validation scheme was developed to produce relevant and comparable measures of classification accuracy. Machine learning classifications of spruce-fir forests were found to be an effective and efficient method to produce presence-absence classifications and population level parameters for spruce-fir forests, with Support Vector Machines classification performing best.Bachelor of Scienc
