# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 2 Methods
### Image Processing  
Several spectral indices were chosen and calculated from each set of images following a review of the literature on the topic of stress detection using multispectral imagery (Table 2). Spectral indices were selected for inclusion based on the spectral bands available in both datasets and evidence of correlation with plant physiological stress from previous research. The normalised difference vegetation index (NDVI) is a very well known and widely used ratio that has been shown many times to be correlated with plant condition, physiological stress, and photosynthetic activity in a wide range of vegetated environments [55; 56; 57; 58; 59]. The green normalised difference vegetation index (GNDVI) and the red edge normalised difference vegetation index (RENDVI) were included specifically to examine the sensitivity of indices including the red edge and green bands from each sensor and to compare these directly to an index based on the red band (NDVI). As data collection spanned several months, methods were required to account for background changes in the target vegetation that were not caused by the experimental treatments. These changes include physiological response to the growing season, changes caused by climatic events, and atmospheric effects that could not be controlled for using other methods [12]. Without a suitable method for control, these influences would confound the physiological stress signal induced by the application of herbicide. Following the method of [PERSON] et al. [64], spectral indices were de-trended to remove the extraneous effects. The approach was based on the assumption that changes in adjacent but untreated parts of the study site could be assumed to be caused by factors unrelated to the herbicide-induced stress. By characterising these unrelated trends we could minimise the potential impact on any signal from the stressed study trees [51]. For the UAV imagery, spectral indices were de-trended by identifying 25 regions of the study area in the final orthomosaic to act as de-trending control areas. A review of the full set of time-series mosaics was carried out to carefully select areas that covered conditions representative of the study plots and the trends in the multi-temporal imagery. The control areas were 5 m\({}^{2}\) and were carefully placed to ensure these areas did not overlap the experimental tree clusters. For the RapidEye data, control areas were selected from a nearby _P. radiata_ stand with similar growing conditions, terrain, and tree age. Thirty control areas were carefully selected and used to de-trend the RapidEye imagery. For both sets of imagery, the spectral indices extracted from the control areas were deemed to represent the average conditions of the study forest within a specific time step. Spectral indices extracted from both the treated and untreated plots within the experiment were rescaled to the standard deviation above or below the mean forest value for the spectral indices as extracted from the de-trending control areas [64] as follows:  
\[S_{i}=\left(S-S_{i\mu}\right)/S_{\delta} \tag{1}\]  
where \(S_{i}\) is the rescaled spectral index, \(S\) is the original spectral index value, \(S_{\mu}\) is the mean value for the control areas within a time step and \(S_{\delta}\) is the standard deviation of values from the control \begin{table}
\begin{tabular}{c c c} \hline \hline
**Spectral Index** & **Equation** & **Source** \\ \hline NDVI & \(\frac{(NIR-RED)}{(NIR+RED)}\) & [60] \\ GNDVI & \(\frac{(NIR-Green)}{NIR+Green}\) & [61] \\ RENDVI & \(\frac{(NIR-REDEDGE)}{NIR+REDDE}\) & [62; 63] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spectral indices calculated from the multispectral imagery. This table is modified from [12]. areas. Using this method, the time-series spectral indices can be de-trended in a robust and easy to implement manner provided that the control areas selected are representative of the average forest conditions within a time step [64].