## 1 Introduction  
The far northern regions of the globe are warming at twice the global average, resulting in significant effects on the vegetation composition of arctic tundra and boreal ecosystems. Prominent among the changes to plant communities include the pan-arctic northward expansion of shrubs into tundra, and the spread of wetland vegetation as a result of permafrost thaw and ground subsidence [1; 2; 3; 4]. Boreal forest ecosystems are also shifting from dominance by conifers to deciduous species as a result of increased fire prevalence [5]. The relationship between climate and plant community composition has important implications for biogeochemical cycles. For example, research indicates that elevated temperatures in arctic environments increase the rate of nitrogen mineralization, promoting the encroachment of woody plants into the tundra [6]. Increased shrub cover leads to an increase in the rate of carbon uptake, but with uncertain consequences for the overall arctic carbon cycle [4; 7; 8]. Shifts in arctic and boreal plant community composition and biogeochemical function can have significant impacts on wildlife [9; 10] and the livelihoods of local communities [11; 12]. Despite these wide-ranging impacts, resource and logistical challenges present an obstacle to the study of remote ecosystems. These challenges make the examination of factors such as plant community compositional change difficult. Indeed, given the extent and remoteness of northern ecosystems, remote sensing approaches to assessing vegetation change are necessary for understanding the ecological effects of climate change over large spatial scales. Peatlands cover approximately 15% of land cover in the northern hemisphere and factor significantly in the global carbon cycle [13; 14; 15]. Peatlands are highly heterogeneous ecosystems characterized by thick organic soils and dominated by a variety of plant functional types (PFTs) depending on landscape position and hydrology [16; 17]. PFTs refer to the physiological, morphological, structural, and chemical attributes that determine how plants allocate resources, take up carbon, and reproduce themselves [18; 19]. In this study, we use the PFT framework as a trait-based method of vascular plant-species classification based on shared structure and function [18]. Changes in mean annual temperature and hydrologic conditions in peatlands can lead to shifts in the composition of PFTs, ultimately affecting ecosystem productivity [20; 21; 22; 23]. Prior research in peatlands has indicated that warming and drying affect PFT composition by promoting dominance by woody shrubs [24; 23; 25]. Conversely, raised water tables from increased precipitation or permafrost thaw lead to an increase in graminoid species such as sedges [20; 21; 22]. Lowered water tables and increased shrub dominance have been associated with decreased annual gross primary productivity (GPP) relative to elevated water tables [22; 26]. Studies have also demonstrated a link between the graminoid cover and methane emissions rates from wetland ecosystems [27; 28]. Given their importance in regulating ecosystem function, mapping the distribution of PFTs could be an important element of predicting ecosystem responses to environmental change [29; 30]. High spatial resolution hyperspectral imagery is emerging as a promising method for conducting remote assessments of PFT distribution [19; 31]. Hyperspectral data leverages a far greater number of spectral bands as compared to multi-spectral data, ultimately allowing for the differentiation of vegetation characteristics with a high degree of fidelity [32]. For example, prior research has demonstrated that variation in the structural, physiological, and chemical characteristics of particular species or PFTs interact uniquely with different regions of the electromagnetic spectrum, allowing them to be distinguished from other species [33; 34].