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Strong tornadoes have impacted the central Appalachian Mountains multiple times in recent years. The topography of this region leads to unique spatial patterns of tornado damage as the tornado vortices pass over ridges in forested areas, and this damage can be detected with vegetation indices derived from remotely sensed imagery. The objectives of this study were to 1) Classify forest damage from the April 19, 2019 EF-3 tornado in Franklin County, VA using remotely-sensed images, 2) Quantify the spatial patterns of forest damage intensity across the path using derived vegetation indices and terrain variables (primarily slope, aspect, elevation, and exposure), and 3) Use regression models to determine if relationships exist among terrain variables along the and forest damage patterns. I generated EVI and NDII vegetation indices from Sentinel-2 imagery and compared the derived damage to the underlying terrain variables. Results revealed that the two vegetation indices were effective for classifying tornado damage, and discrete damage classes aligned well with NWS EF-scale tornado intensity estimations. ANOVA testing suggested that EF-3 equivalent damage was more likely to occur on downslope topography, leeward of the tornado's direction of travel. OLS and geographically weighted regression (GWR) modeling performed poorly, suggesting that an alternative method may be more suitable for modeling, the scale of assessment was inadequate, or that important predictor variables were not captured. Overall, the intensity of the tornado was clearly modified by terrain interactions, and the remote sensing methodology used was effective for reliably identifying and rating damage in forested areas. Master of Science Strong tornadoes have impacted the central Appalachian Mountains multiple times in recent years. The topography of this region leads to unique spatial patterns of tornado damage as the tornado vortices pass over ridges in forested areas, and this damage can be detected with vegetation indices derived from remotely sensed imagery. The objectives of this study were to 1) Classify forest damage from the April 19, 2019 EF-3 tornado in Franklin County, VA using remotely-sensed images, 2) Quantify the spatial patterns of forest damage intensity across the path using derived vegetation indices and terrain variables (primarily slope, aspect, elevation, and exposure), and 3) Use regression models to determine if relationships exist among terrain variables along the and forest damage patterns. I generated EVI and NDII vegetation indices from Sentinel-2 imagery and compared the derived damage to the underlying terrain variables. Results revealed that the two vegetation indices were effective for classifying tornado damage, and discrete damage classes aligned well with NWS EF-scale tornado intensity estimations. ANOVA testing suggested that EF-3 equivalent damage was more likely to occur on downslope topography, leeward of the tornado's direction of travel. OLS and geographically weighted regression (GWR) modeling performed poorly, suggesting that an alternative method may be more suitable for modeling, the scale of assessment was inadequate, or that important predictor variables were not captured. Overall, the intensity of the tornado was clearly modified by terrain interactions, and the remote sensing methodology used was effective for reliably identifying and rating damage in forested areas.

Glacier retreat is considered a clear sign of global climate change. Although a rich body of work has documented glacial response to climate warming trends, comparatively little research has assessed vegetation change in recently deglaciated areas. In this study, we assess vegetation change at two glacier forefronts in Glacier National Park, Montana, through remote sensing analysis, fieldwork validation, and statistical modelling. The research objectives were to: 1) quantify the spatial and temporal patterns of landcover change of five classes"ice, rock, tree, shrub, and herbaceous at the two glacier forefronts in Glacier National Park, and 2) determine the role of selected biophysical terrain factors (elevation, slope, aspect, solar radiation, flow accumulation, TWI, and geology) on vegetation change at the deglaciated areas. Landsat imagery of the study locations in 1991, 2003, and 2015 were classified and validated using ground truth points and visual interpretation for accuracy. Overall accuracies were above 75% for all classified images. To identify biophysical correlates of change, we used generalized linear mixed models with non-vegetated surfaces to vegetation (code=1) or stable non-vegetation class (code=0) as a binary response variable. Results revealed elevation, slope, TWI, geology, and aspect to be associated with increased vegetation over time at Jackson Glacier forefront, whereas elevation, slope, solar radiation, and geology were significant at Grinnell Glacier forefront. New case studies on vegetation change in recently deglaciated regions can deepen our knowledge about how glacier retreat at local scales results in recharged ecosystem dynamics. Master of Science Glacier retreat is considered a clear sign of global climate change. Although glaciers are retreating globally, comparatively little research has assessed how vegetation changes in recently deglaciated areas. The research objectives were to: 1) quantify patterns of landcover change of five classes—ice, rock, tree, shrub, and herbaceous at two glacier forefronts in Glacier National Park, and 2) determine the environmental and terrain factors that affect vegetation change at the deglaciated areas. Landsat imagery of the study locations in 1991, 2003, and 2015 were classified and validated using ground truth points and visual interpretation for accuracy. To identify terrain and environmental factors that influence change, we modeled change from nonvegetated surfaces to vegetation (code=1) and the stable non-vegetation class (code=0). Results revealed elevation, slope, topographic moisture, geology, and aspect to be associated with increased vegetation over time at Jackson Glacier forefront. Elevation, slope, solar radiation, and geology were significant at Grinnell Glacier forefront, indicating some geographic differences in important factors. New case studies on vegetation change in recently deglaciated regions can deepen our knowledge about how glacier retreat at local scales results in recharged ecosystem dynamics. This study provides further insight on the future of alpine ecosystems as they respond to global climate change and a compelling new perspective on the future of the Park. Additionally, we demonstrate the benefits of using remote sensing applications to study land cover change as a proxy for vegetation colonization, especially in remote mountainous environments.