## 4 Discussion
### Hyperspectral Characterization and Mapping of Plant Functional Types  
Our results are in conversation with a growing body of research leveraging remotely sensed data to track vegetation change in peatland ecosystems [17; 86; 87]. The results of our analyses of both our plot-level and aerial datasets demonstrate the efficacy of using hyperspectral measurements to characterize the distribution of peatland PFTs. Through our close-range hyperspectral data collection we saw that the cover of PFTs was a driver of spectral variation. Despite relatively weak trends in our vegetation cover data at both sites, the reflectance spectra were highly responsive to subtle changes in the vegetation cover resulting from treatment. In particular, at SPRUCE, differences in the relative cover of shrubs versus forbs had a strong effect on spectral reflectance. This is likely due to the differences in stature, leaf structure, and foliar chemistry between high-growing woody shrubs compared with ground-layer herbaceous PFTs such as forbs [99; 36]. We also predict that collecting data in the early fall improved our ability to detect variation among treatments because carotenoids and anthocyanins present in leaf tissues around senescence may have increased optical diversity, allowing for greater distinction among PFTs. Future research could examine the role of seasonality in improving the efficacy of hyperspectral remote sensing in making species-level distinctions. Our plot-level findings prompted us to apply hyperspectral remote sensing to map peatland plant functional types over a larger spatial scale. The cover types represented within the aerial hyperspectral dataset included the PFTs that were present in our field measurements. We found the land-cover classification analysis was successful at distinguishing among different PFTs. This was particularly true for forest and shrub cover, which had the lowest error rate in the classification analysis. The percent cover of shrubs was also one of the strongest sources of variation in the field-based measurements at SPRUCE. We predict that the forested and shrub-covered regions of our study area were the most successfully mapped because their stature and leaf structure are clearly distinguishable from other PFTs, whereas graminiod fen and tussock grasses were less optically distinct from each other. Our results indicate that reflectance in the short-wave infrared regions of the electromagnetic spectrum are particularly important in mapping peatland PFTs. Variable importance indicators such as model accuracy indicated that several SWIR bands between 1800 and 2500 nm were instrumental in creating the final classification analysis. These results support prior research in peatlands thathas shown SWIR reflectance to be particularly sensitive to changes in peatland plant cover and moisture status [85]. Given the importance of changing shrub cover within the greater arctic and boreal landscape, these results support the application of hyperspectral measurements to track changes in the distribution of shrubs [4]. In arctic and boreal ecosystems where the environment is undergoing rapid change, the methods described here could prove invaluable to developing assessments of shifts in species composition over time. Due to the large spatial extent and low population density of the far north, examining changes to the landscape across broad spatial scales and over time must largely be achieved using remote sensing techniques. The use of hyperspectral imagery is integral to this approach because of the ability to select spectral bands that best capture variation across community types [90]. The high spatial resolution of aerial over satellite data is also key to this approach because it allows for fine-scale distinctions among cover classes. There are multiple research programs that collect hyperspectral data and make it publicly available to scientists.