## 1 Introduction
Species-specific properties that affect reflectance include unique leaf pigments such as chlorophyll and anthocyanin [35; 36; 37; 38], leaf nitrogen content [39; 40], and leaf water content [41]. Recent research has shown that phylogenetic distance both within and among species is correlated with spectral dissimilarity, a relationship that scales from leaf-level to plot-level canopy observations [42] ([PERSON] et al. 2018). Leveraging this type of information could allow for species to be mapped over large areas through classification analysis of hyperspectral data, improving our ability to track changes in species' distributions and diversity across broad spatial extents. Although mapping PFTs rather than individual species may not allow for accurate estimates of species diversity, it still affords valuable information regarding the structure and function of ecosystemsas a whole [29]. The use of hyperspectral remote sensing as a tool for mapping PFTs could provide ecologists with the ability to track ecosystem responses to climate change [43]. Furthermore, these types of analyses could ultimately help inform management strategies and environmental policy approaches focused on ecosystem conservation and management in the face of climate change. In addition to tracking compositional changes using remote sensing, a number of approaches to characterizing species and functional diversity using hyperspectral methods have also been investigated. In low diversity environments such as arctic and boreal ecosystems, the reduction of species richness may have amplified effects on ecosystem productivity due to low functional redundancy in plant types [44; 45; 46; 47]. For example, peatland studies have found a loss of species in arctic systems in response to the warming effects of carbon cycling rates [45; 48]. Thus, tracking changes in the vascular functional diversity of boreal peatlands is important for understanding the potential impacts of climate change on the function of these ecosystems [19]. Recent research using hyperspectral methods has linked spectral variation among pixels with species diversity by treating a stack of pixels as a reflectance signature that captures the optical characteristics of vegetation across hundreds of spectral bands [49; 50]. Remote sensing estimation of diversity has previously been accomplished by linking spectral heterogeneity among pixels with species richness, a method based on the spectral variation hypothesis that unique species will present with strongly differentiated spectral properties [42; 51; 52; 53; 54]. Several different statistical approaches have been used in the calculation of spectral heterogeneity. A simple method of estimating spectral heterogeneity from a multi- or hyperspectral dataset is comparing the coefficient of variation of multiple measurements [55; 56; 57; 58]. Another approach is calculating Euclidian distances among different scans or pixels derived from multivariate eigenvector methods such as principal component analysis [52]. The further a pixel lands from the centroid of the data, the greater the spectral heterogeneity of that pixel [51; 52]. This approach has been correlated with alpha diversity metrics that account for both species richness and evenness, such as the Shannon index, as well as with metrics such as functional diversity or the number of different plant types present in a pixel [42; 59]. Together, these studies indicate the ability of multiple different remote sensing approaches to characterize the ecological diversity of a landscape. Ultimately, remotely sensed estimates of diversity are integral to mapping essential biodiversity variables (EBVs) - structural and functional characteristics of ecosystems that predict biodiversity levels at a global scale [60; 61; 62]. The objective of this research was to assess the ability of remote sensing approaches for characterizing and tracking changes in the plant communities of peatland ecosystems in response to global change drivers.