# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 1 Introduction
A study based in Sumatra [42] used a combination of Landsat and UAV imagery to monitor orangutan habitat. The UAV data were used to train classification algorithms applied to Landsat data to differentiate land use classes (oil palm plantation vs. reforestation vs. logged forest) relevant to orangutan conservation with considerable success (classification accuracy over 75%) [42]. In a similar manner, a study conducted in a Costa Rican ecological reserve used UAV data to identify land cover classes and then applied these to a historical archive of Landsat data [43]. Methods have been proposed for integrating imagery from the European Space Agency's Sentinel-2 satellites with UAV data for forest inventory purposes. In the aforementioned study, a hierarchical, model-based mode of inference was used to provide viable population estimates and corresponding uncertainties based on datasets with differing spatial coverages [44]. Initial methods have also been developed to extrapolate UAV data on tree measurements using imagery from the Pleiades satellite constellation [45]. Change detection techniques provide a valuable starting point for the identification of subtle changes in forest health based on the spectral information. The aggregation of multi-temporal images can greatly improve the signal-to-noise ratio [46] and the use of longer time periods can mitigate the confounding impacts of environmental factors such as cloud cover on detection. Using this technique, acceptable accuracies have been demonstrated for the detection of bark beetle attack on conifer species [47; 48; 49; 50] and needle discolouration resulting from _Dothistroma pini_[14]. Analysis of time-series satellite imagery was used by [PERSON] et al. (2011) who chainsaw girdled patches of piron-juniper woodland and then successfully monitored the induced tree stress before mortality using a dense sequence of multispectral images from the RapidEye (Planet, San Francisco, USA) satellite constellation [51]. Invoking physiological changes in this manner provides superior experimental control and is well suited to testing and calibrating methods for the early detection of symptoms such as pathogen-induced physiological changes in foliage. In a similar fashion, [PERSON] et al. [12] injected herbicide into the stems of mature plantation trees and tracked changes in canopy colour and density using time-series multispectral UAV imagery. This study [12] confirmed the utility of high-resolution, time-series UAV data for monitoring physiological stress in trees and identified suitable spectral indices for this task. However, these findings were limited to small areas due to the limited data collection capability of UAV platforms. Further research was required to investigate whether widely-used satellite imagery products could be used as a complementary data source to expand the detection of physiological stress to larger areas. In this study, we investigated the utility of UAV and satellite multispectral imagery for detecting physiological stress in forest trees and sought to define the detection capability. To accomplish this, we developed methods to link UAV and satellite data for detection of physiological stress and tested whether these could provide increased area coverage and models with greater predictive power than either dataset alone. We hypothesised that very high resolution UAV imagery collected with a narrow-band multispectral camera could provide greater predictive capability for detecting tree stress than broad-band satellite imagery. We also tested whether differences in sensitivity were associated with differences in the spatial or spectral resolution of the sensors.