# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 4 Discussion
### Scaling Above- and Below-Canopy Imagery  
We found there are optimal, but differing plot radii over which GCC\({}_{\text{UAV}}\) is averaged that best approximates below-canopy measured NDVI and structural attributes, LAI, and gap fraction. As the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Canopy Closure Date (DOY)** & **Standard Error (+/–)** \\ \hline GCC\({}_{\text{UAV}}\) (20 m buffer) & 124.51 & 1.17 \\ GCC\({}_{\text{UAV}}\) (Full Scene) & 123.91 & 1.05 \\ NDVI & 125.06 & 1.46+ \\ Gap Fraction & 123.77 & 2.34 \\ LAI & 125.87 & 1.47 \\ GCC\({}_{\text{PhenoCam}}\) & 131.55 & 0.58 \\ MODIS & 121.49 & 1.15 \\ Landsat 8 & 141.72 & 17.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Canopy Closure data (DOY) as estimated by UAV-, ground-, and tower-based imagery. averaging area for GCC\({}_{\text{UAV}}\) increases, the relationship between below-canopy measured NDVI and GCC\({}_{\text{UAV}}\) strengthens as evidenced by increasing values of R\({}^{2}\) that are accompanied by narrowing confidence intervals (Figure 4). The statistical relationship between NDVI and GCC\({}_{\text{UAV}}\) stabilizes around 35 m in buffer size where R\({}^{2}\) becomes asymptotic. We also observe a marked reduction in the size of the confidence intervals around 20 m. Composite imagery consistently outperformed center-only imagery, though the effect appears to be small. For LAI, however, the 14 to 16 m buffer size range resolved the most variance, followed by a decrease in R\({}^{2}\) values at greater buffer sizes. The small difference between center and composite imagery indicates no apparent advantage to composite imagery over center-only imagery. The strong agreement between GCC\({}_{\text{UAV}}\) and both LAI and gap fraction is sufficient to scale these findings via the UAV-based metric. Similar to [PERSON] et al. [37], we observed non-linear relationships between GCC\({}_{\text{UAV}}\) and gap fraction (S1) which indicates saturation of greenness at high levels of leaf area and low levels of gap fraction. Comparing our late April to early June measurements, we see consistent greening leading to consistent decreases in gap fraction. These relationships are strong for this forest type, but it is unknown how a more heterogeneous forest (e.g., patchy disturbance, more species diverse) would compare. Given our interpretation of the data, we suggest that LAI would be more variable than gap fraction, or even NDVI/GCC, in a more heterogeneous forest than our even-aged, predominately broadleaf temperate forest. We also show that each sensor approximates dormant to growing season transitions to full-canopy closure within +/- 2 days, which is in line with the MODIS estimate, though MODIS data estimates a slightly earlier transition. However, PhenoCam estimates are 5 days later, a finding concurrent with other work [37]. Landsat 8 provides the latest canopy closure date nearly 15 days later than other sensors, likely due to longer return intervals and higher likelihood of less quality data due to cloud cover. While it is out of the scope of this work, the 20 m radius buffer we show is suitable for comparison between above- and below-canopy measures of phenology (Figure 5), and potentially structural attributes, and within the sensor capabilities of some spaceborne remote sensing platforms such as, but not limited to, Harmonized Landsat Sentinal-2 (10-20 m) or Planet Labs (3-5 m) satellite constellations. NDVI and GCC have similar temporal trends, but appear to represent different aspects of canopy structure. The periods of greatest uncertainty between GCC and NDVI typically occur during the end of the growing season when GCC tends to decline sooner than NDVI does due to changes in pigment concentrations [37; 38]. This analysis may be particularly useful for applications where plot-based research can be augmented by UAV data.