## 4 Results  
### Assessment of Network Usability  
Out of the 900 cameras deemed suitable for use, 72% of images provided sets from which phenological metrics could potentially be extracted automatically. The main reason for images being unsuitable for use included road structures (such as bridges) appearing too close to the camera; frequent motion in camera meaning that images available from the most frequently occurring camera angle are very sparse in temporal coverage; and large changes in camera angle which lead to low correlations between images in the temporal sequence. Although for illustrative purposes only 18 cameras are discussed in this paper, the 72% return would result in approximately 650 cameras and associated image sets for potential use. This represents a high density spatial coverage from which phenological information could be discovered to support research. It is acknowledged that there may be some micro-location effects in measurement of vegetation phenology at roadsides in the same way that micro-topography had an impact in the study by [PERSON] _et al._[27]. Nevertheless, local processes are important to understand when scaling to regional representations such as those captured by satellite data [28]. As such this dataset represents an untapped potential particularly in a locality where high cloud coverage often precludes the construction of a high quality temporal time-series required for phenological measurement [41].  
In each of the camera's field of view the composition and distribution of the varied vegetation vary both in relation to the road and with each other; the method employed for the automatic extraction of areas of interest (_i.e._, vegetation) proved successful generally for the 18 cameras under scrutiny here as illustrated in Figure 4. In each case the road, street furniture, sky, areas frequently obscured by vehicles, and buildings are masked out leaving only the vegetation of interest. The proportion of masked areas differed from camera to camera but a visual inspection of each confirmed that the  
Figure 3: Graphical description of the determination of phenological dates. A, season maximum; B, start of senescence, 80% between D and A; C, end of senescence, 20% between D and A; D, inter-seasonal minimum; E, start of leaf-up, 20% between D and G; F, end of leaf up, 80% between D and G; G, season maximum. Vertical lines show intersections for date read-off.  
unmasked parts of the images were only vegetation. Further validation of this was provided by summing all of the images from a set which causes the cars to disappear gradually from the resulting image since their positions are randomly distributed along the roads within the image. This leaves only the constant objects such as the road, sky, trees, signs, buildings and street lamps. This procedure allowed the quality of the alignment of the images to be ascertained, with poorly aligned images looking blurred. This is the most crucial step with respect to camera usability. Other limiting factors in image quality via noise through environmental factors such as precipitation on the lens are ignored and accounted for via the smoothing of the high temporal frequency of image acquisition.  
Figure 4: Views of the cameras shown in Figure 1, after realignment and averaging over all images with corresponding calculated masks (white) of vegetation used for analysis.