# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 2 Materials and Methods
### Phenomenam Imagery  
Data from a Stardot camera (CAM-SEC5 IR, Stardot Technologies, Buena Park, CA, USA) were employed as a near-continuous time-series of green-leaf phenology from its installation in July 2018 until the end of 2021. This phenocam was mounted next to the flux measurement tower (see Figure 1a) at a 3.29 m height, pointing northward (343\({}^{\circ}\) N) and inclined downward at a 10\({}^{\circ}\) angle. Camera settings adhere to the ICOS phenocam protocol [71], with automatic exposure and a fixed white balance, which is known to improve consistency in the brightness of images taken under variable illumination conditions [72]. Captured RGB images were 5 megapixels in size (2592 \(\times\) 1944 pixels) and stored as 24-bit JPEG files. We restricted our analysis to imagery collected at midday (12:00 UTC), as this coincided with the average time of the UAV image acquisition. Technical problems with the phenocam setup resulted in some periods of missing data, the largest being in July 2019 (15 days: 10 June-25 June 2019) and August-September 2019 (29 days: 13 August-11 September 2019). The phenocam imagery was screened for quality. Hereby, images were visually assessed to identify: (i) water condensation on the camera housing; (ii) grazing sheep in the ROI; (iii) snow-cover; or (iv) images with poor illumination conditions (foggy, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Index (VI)** & **Abbreviation** & **Equation** & **Description** \\ \hline Red-Edge Chlorophyll & REChl & \(\frac{p_{NIR}}{p_{CRLEdge}}-1\) &
\begin{tabular}{c} Indicator of photosynthetic activity of \\ vegetation canopy \\ \end{tabular} \\ Green Chlorophyll & gChl & \(\frac{p_{NIR}}{p_{oven}}-1\) & Used to estimate leaf chlorophyll content \\ Red Chlorophyll & rChl & \(\frac{p_{NIR}}{p_{col}}-1\) & Used to identify senescence and areas of \\  & & & yellowing vegetation \\ \hline Normalised Difference & NDVI \({}^{\star}\) & \(\frac{p_{NIR}-p_{col}}{p_{NIR}+p_{red}}\) & Sensitive to structural and physiological \\  & & & properties. Used to estimate leaf area index \\ \hline Normalised Difference & reNDVI1 \({}^{\star}\) & \(\frac{p_{NIR}-p_{NIR}p_{col}}{p_{NIR}+p_{RedEdge}}\) & Modification of the NDVI utilising the \\  & Red-Edge & & red-edge band \\ \hline Normalised Difference & reNDVI2 \({}^{\star}\) & \(\frac{p_{POLEdge}-p_{col}}{p_{RedEdge}+p_{red}}\) & Modification of the NDVI utilising the \\  & Red-Edge/red & & red-edge and red bands \\ \hline Green Normalised Difference & gNDVI \({}^{\star}\) & \(\frac{p_{NIR}-p_{green}}{p_{NIR}+p_{green}}\) & Sensitive to chlorophyll content and has a \\  & & & higher point of saturation than the NDVI \\ Normalised Green/Red & & & \\  & & & \\  & & & \\  & & & reflectance in the visible (green and red) bands \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of the vegetation indices (VIs) calculated from the multispectral Parrot Sequoia data. The notation, \(\rho\), denotes reflectance in each band and an asterisk (\({}^{\star}\)) indicates where a given VI ranges from \(-1\) to \(+1\). cloud shadows, sun glint; see Figure 2). All images captured by the above screening were removed from subsequent analysis. RGB images were then processed using the open-source R package, Phenopix [73]. We selected a single region of interest (ROI) outside of the flux measurement compound (see Figure 2) for which colour information in each band was extracted. In each image, the Green Chromatic Coordinate (\(G_{cc}\)) was calculated as the average 'greenness' normalised to the total brightness across all bands:  
\[G_{cc}=\frac{G_{DN}}{(R_{DN}+G_{DN}+B_{DN})} \tag{1}\]  
where \(R\), \(G\), and \(B\) refer to the red, green and blue bands, respectively, and DN denotes the average digital number (i.e., brightness) of pixels inside the ROI for each band. The \(G_{cc}\) was used as a proxy of phenological development and physiological status of the vegetation canopy.