## 4 Discussion  
### Hyperspectral Remote Sensing of Peatland Response to Climate Change  
By leveraging two large ecosystem manipulation experiments we have demonstrated that hyperspectral remote sensing can capture the response of peatland functional composition and species diversity to hydrologic change and increasing temperatures. Prior research has already demonstrated that hydrology, temperature, and CO\({}_{2}\) affect peatland ecosystem structure and function [22; 23; 26; 83; 84; 85]. Remote sensing studies in peatlands have also demonstrated the efficacy of hyperspectral methods in mapping peatland flora [17; 86; 87]. However, we have not encountered any study that has used hyperspectral remote sensing to track peatland ecological response to the diverse suite of global change drivers explored here. Our field results from the APEX site indicate that long-term hydrologic change in rich fen peatlands leads to shifts in species diversity and ecosystem productivity. The dominant source of variation within the principal component analysis of the APEX spectral data appeared largely driven by the greater diversity and overall cover associated with the control and raised plots, and a higher percent cover of litter associated with the lowered plots, which describes a gradient of ecosystem GPP [47]. At the SPRUCE experiment, we found that species composition varied among treatments. The results of our vegetation cover sampling indicated an increasing trend in shrub cover, and an associated decreasing trend in forb cover with warming. These results align with previous research linking warmer air temperatures and increases in soil nitrogen availability with an increased shrub growth in far northern ecosystems [4; 88; 89]. In our analysis of the SPRUCE spectral data, shrub and forb cover both emerged as statistically significant vectors within the multi-variate analysis of reflectance. Our results support the feasibility of tracking changes in PFT cover, in particular of shrubs, using hyperspectral methods.  
We did not track non-vascular moss cover at SPRUCE because they were ubiquitous across the site. However, prior research indicates that the moisture status of peat-forming mosses can strongly influence reflectance spectra [90; 85]. Further research at SPRUCE would characterize the role of moss moisture status on hyperspectral reflectance properties. However, focusing solely on vascular plant cover, our results highlight the potential to use hyperspectral data to identify and track a range of different climate change-induced impacts to northern boreal peatlands.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Forest** & **Graminoid** & **Shrub** & **Tussock** & **Class Error** \\ \hline Forest & 94 & 5 & 0 & 0 & 0.051 \\ Graminoid fen & 7 & 80 & 2 & 10 & 0.192 \\ Shrub & 1 & 3 & 95 & 0 & 0.040 \\ Tussock Grass & 1 & 9 & 1 & 88 & 0.111 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of supervised random forests classification analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Model Accuracy**} & \multicolumn{2}{c}{**Gini/Impurity**} \\ \hline _Band number_ & _Mean Decrease_ & _Band number_ & _Mean decrease_ \\ \hline
1210 & 7.53 & 1261 & 4.68 \\
1233 & 6.99 & 1210 & 4.32 \\
1261 & 6.77 & 1546 & 3.78 \\
1193 & 6.76 & 1255 & 3.71 \\
2441 & 6.71 & 1233 & 3.60 \\
1204 & 6.66 & 1552 & 3.49 \\
2447 & 6.66 & 1187 & 3.47 \\
2430 & 6.61 & 1244 & 3.40 \\
2412 & 6.56 & 1267 & 3.35 \\
474 & 6.52 & 1215 & 3.29