# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 4 Discussion  
Spectral indices calculated from multispectral imagery were sufficiently robust to detect the onset of physiological stress within this experiment. Conifer foliage may be damaged and change colour due to a variety of agents, such as insects, root rot, fungi, and drought [83]. Independent of the causal agent, under physiological stress foliar moisture declines, chlorophyll and other pigment molecules break down, and this is followed by a break down of intra-cellular and cellular structures [63; 83]. Hyperspectral imagery has been most often used to detect natural variation in pigments [84; 85] and elemental concentrations [86] although recent research suggests that precise models can be developed for chlorophyll concentration within tree canopies from multispectral imagery [87]. Detection of the more pronounced changes in needle chemistry and appearance associated with needle mortality can be robustly predicted using multispectral imagery [19; 88; 89; 90; 91; 92] which is consistent with our findings. Results from this study, and previous analyses [12], showed NDVI and RENDVI to be the most sensitive indicators of the onset of physiological stress. High values of NDVI in healthy foliage occur due to the contrast of low leaf reflectance in the red band and higher reflectance within the near-infrared band. As the foliage senesces, chlorophyll declines to cause an increase in red band reflectance resulting in reductions in NDVI [46]. NDVI is one of the most widely-used spectral indices developed over the last 40 years [93] and has been widely used to discriminate healthy from stressed foliage within forests [94; 95; 96; 97; 98]. A large body of research has also demonstrated that increased reflectance in stressed vegetation relative to healthy vegetation is also strongly evident along the red edge (680-750 nm) region [61; 88] and many studies have shown the red edge to be a more sensitive indicator of the onset of stress than either the green or red bands [51]. Consequently, indices based on the red edge band have  
Figure 7: Conditional importance scores for all variables in the RF regression model. also been widely used to detect stress [51] and defoliation resulting from insect attack and drought in conifers [19; 89; 90; 92]. Differences in the relative importance of indices were observed between platforms in our study. NDVI derived from the UAV data was the most important predictor of field measured discolouration while RENDVI was the strongest predictor of discolouration for the satellite data. These variations may reflect the different spectral ranges covered by the two sensors examined. The greater importance of RENDVI for the satellite data may be attributable to broader coverage of the red edge from this platform (690-730 nm), compared to the red edge region covered by the UAV mounted sensor which is limited to a narrower spectral window (707-727 nm). Previous studies have indicated that across a range of higher plants the optimum wavelength for early detection of stress was 700 nm [99]. However, an investigation of the stress detection in the conifer Balsam fir (_Abies balsamea_ (L.) [PERSON]) indicated that reflectance at 711 nm was the most responsive [100]. These studies indicate that there may be some variance in the precise wavelength where stress is most evident. As the RapidEye sensor covers the entirety of the red edge region for the typical spectral response of plants it is likely to be highly sensitive to changes in this region. The considerably narrower bands of the multispectral sensor could result in a failure to detect very early spectral changes associated with physiological stress. Furthermore, it should be noted that the incomplete time series provided by both sensors means that the sensitivity of both platforms to the earliest signs of physiological stress could be confounded. Although sensors on both platforms were sensitive to the symptoms of physiological stress, changes in NDVI associated with stress were more pronounced using UAV data.