# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 4 Discussion
By restricting analysis to clusters larger than the pixel size of satellite imagery (4-16 trees) and to data from 64-87 days after treatment the mean reduction across treated groups for imagery from the UAV, resampled UAV imagery, and satellite imagery averaged \(-11.33\), \(-12.67\), and \(-3.94\), respectively, for NDVI and \(-4.71\), \(-5.08\), \(-3.84\), respectively, for RENDVI. This result suggests that the lower sensitivity of the satellite imagery was not solely attributable to differences in spatial resolution. Substantial differences remained between the UAV and satellite data after the UAV data were resampled to the same spatial resolution. Resampling minimised the effect of spatial resolution on sensitivity but the effects of differences caused by atmospheric conditions and sensor spectral properties remained. It is logical to conclude that the greater sensitivity of the UAV data to minor changes is attributable to the narrower bandwidths of the UAV sensor compared to the RapidEye sensor and the proximity of the UAV platform to the subject trees. However, the increase in the sensitivity of resampled spectral indices may be a product of the resampling process and should not be interpreted as direct evidence that coarser spatial resolution imagery has greater utility for stress monitoring. Alternatively, this could be the result of a greater signal-to-noise ratio in coarser resolution imagery when the subject phenomenon is equal to or larger than the pixel size of the imagery used. Further research should be undertaken to more precisely partition the influences of spectral and spatial resolution on detection of stress so that the limits of multispectral imagery can be more robustly defined. Imagery acquired from a UAV was sensitive to stress in tree clusters of all sizes. It was interesting to note that changes in NDVI were of a similar magnitude for cluster sizes greater than 25 m\({}^{2}\) when the imagery was resampled to a 5 m resolution. This finding is consistent with previous research that demonstrated the utility of moderate resolution multispectral imagery for detection of stress. Studies that have used multispectral imagery to detect insect damage suggest the optimal resolution to be 2.4 m [48] for _Pinus contorta_ dominated stands, 1.25-2.25 m for _Eucalyptus_ spp. [98] and 1.75-2.3 m for _P. radiata_[101]. However, the magnitude of the change in spectral indices for smaller clusters and individual trees was lower for the resampled imagery. Our findings clearly show that spectral indices derived from satellite imagery were only useful for detecting stress in clusters of four or more trees. Satellite imagery has been successfully used to detect major outbreaks of devastating insect damage that result in tree mortality. For example, recent studies, that investigate mortality from outbreaks of mountain pine beetle _Dendroctous ponderosae_ ([PERSON]), showed classification accuracies ranging from 67-78% for medium resolution [91; 102; 103]to accuracies of 71-93% when high-resolution satellite imagery was used [20; 21; 90; 104]. However, no research has examined the precision of satellite imagery in detecting different sized clusters of stressed or diseased trees under controlled conditions. Our results demonstrate the sensitivity of satellite imagery for detecting stress symptoms and define the detection limits of stress in mature _P. radiata_ for this sensor type. The success of forest surveillance is largely governed by being able to match the temporal, spatial, and spectral characteristics of symptoms with an appropriate platform, sensor, and analytical method. Results from this study demonstrate the complementary role that UAV and satellite platforms could play in the detection of stress as symptoms develop. Solutions that can automate disease detection from fine to medium resolution satellite imagery could be used to identify the location of potential outbreaks.