# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 2 Materials and Methods
### Statistical Analysis  
To address question one, we compared UAV data to below-canopy imagery using two different approaches. For the first, only images taken at plot center for each period were used--these data are noted as _center_ in all text, tables, and figures. For the second approach, we used all available images per plot, per measurement period, averaged to make a plot level mean of NDVI, LAI, and gap fraction (Figure 2). These are denoted as _composite_ in all text, tables and figures. Note, this does mean that there is no June imagery used in _composite_ analyses. To test for the appropriate spatial extent with which to scale plot data with coincident UAV imagery, we averaged GCC\({}_{\text{UAV}}\) at increasing distances from plot center, starting at a 5 m buffer (i.e., distance from plot center) and increasing iteratively by 1 m, to a maximum of 50 m. The range of 5 to 50 m radius was chosen based on assumptions of how much canopy the hemispherical camera can see given its focal length. Standard forestry inventory plots rarely exceed 20 m in radius, thus making this range of comparison reasonable, even when considering that forest canopies associated with trees whose poles would be situated within a plot, will extend up to 5 m outside of the plot boundaries [20]. This was done for each of the 8 plots, for each of the 5 measurement periods, resulting in a total of 1147 clipped raster grid cells of mean GCC\({}_{\text{UAV}}\). Note, that for 7 May 2018 (DOY 127), only 5 plots were used due to the orthoimage error (see Section 2.2 above). Linear regression was used to evaluate relationships among buffer size of GCC\({}_{\text{UAV}}\) with below-canopy measures of NDVI, LAI, and gap fraction with goodness-of-fit determined based on coefficient of determination (\(R^{2}\)), directionality and internal calibration from the slope, and uncertainty quantified as residual mean standard error (RMSE). Moreover, 95% Confidence intervals were determined using bootstrapping analysis via the _boot_ package in R [31; 32].  
We used the buffer size analysis on center and composite imagery to inform upscaling of both LAI and gap fraction from the plot to the entire UAV scene. For LAI, a 15 m buffer size was chosen to scale GCC\({}_{\text{UAV}}\) data to LAI using a linear model:  
\[\text{LAI}=m\text{GCC}_{\text{UAV}}-b \tag{2}\]  
We chose a 20 m buffer size to scale GCC\({}_{\text{UAV}}\) to gap fraction. While we have included the iterative linear regressions statistics for gap fraction (Figure 4g), for upscaling purposes, we chose a non-linear model based on our analysis that better fit the data using non-linear least-squares via the nls function in R 3.6.2.:  
\[\text{Gap Fraction}=ae^{(b\ GCC_{\text{UAV}})} \tag{3}\]  
Rasters were resampled using bilinear sampling with the _rgdal_[33] and _raster_[34] packages in R 3.6.2 [35] to the appropriate resolution--15 m for LAI and 20 m for gap fraction. This was not done for NDVI, as GCC\({}_{\text{UAV}}\) and NDVI are already similar and the intent is to test the certainty with which LAI and gap fraction may be inferred from UAV imagery.  
To address question two, we used breakpoint analysis with the _segmented_ package [36] in R 3.6.2 to determine how each method estimates the phenological transition, specifically canopy closure. Breakpoint analysis, specifically segmented or piecewise regression, identifies thresholds that indicate critical changes in the directionality or slope of a dataset. On phenological data this method will indicatewhen the canopy is no longer increasing in \"greenness,\" indicating stability that we interpret as canopy closure. To evaluate how representative plot averages of GCCUAV were of the entire forest, means of whole scenes were compared against plot level means using root mean square error. For analysis scripts, code, and data, see Supplementary Materials.