Characterizing tree canopy temperature heterogeneity using an unmanned aircraft-borne thermal imager

Leaf temperature (T_leaf) is an important control on many physiological processes such as photosynthesis and respiration, is a key variable for characterizing canopy energy fluxes, and is a valuable metric for identifying plant water stress or disease. Traditional methods of T_leaf measurement involve either the use of thermocouples, a time and labor-intensive method that samples sparsely in space, or the use of air temperature (T_air) as a proxy measure, which can introduce inaccuracies due to near constant canopy-atmosphere energy flux. Thermal infrared (TIR) imagery provides an efficient means of collecting T_leaf for large areas. Existing satellite and aircraft-based TIR imagery is, however, limited by low spatial and/or temporal resolution, while crane-mounted camera systems have strictly limited spatial extents. Unmanned aerial systems (UAS) offer new opportunities to acquire high spatial and temporal resolution imagery on demand. Here, we demonstrate the feasibility of collecting tree canopy T_leaf data using a small multirotor UAS fitted with a high spatial resolution TIR imager. The goals of this pilot study were to a) characterize basic patterns of within crown T_leaf for 4 study species and b) identify trends in T_leaf between species with varying leaf morphologies and canopy structures. TIR imagery was acquired for individual tree crowns of 4 species common to the North Carolina Piedmont ecoregion (Quercus phellos, Pinus strobus, Liriodendron tulipifera, Magnolia grandiflora) in an urban park environment. Due to significantly above-average summer precipitation, we assumed that none of the sampled trees was limited by soil water availability. We flew the TIR imaging system over 3-4 individuals of each of the 4 target species on 3 separate days. Imagery of all individuals was collected within the same 2-hour period in the afternoon on all days. There was low wind and partly cloudy skies during imaging. T_air, relative humidity, and wind speed were recorded at each site. Emissivity was assumed to be 0.98 for all species. Acquired images had a pixel resolution of <3 cm and measurement accuracy of ×1° C. We found the UAS-borne TIR imaging system to be an effective tool for collection of high resolution canopy imagery. The system imaged all targeted crowns quickly and reliably, providing a viable alternative to current methods of canopy T_leaf measurement. Analysis of the imagery indicated significant variability in T_leaf both within and between crowns. We identified trends in T_leaf related to average leaf size, shape, and crown structural traits. These data on the heterogeneity of T_leaf can further our understanding of canopy-atmosphere energy exchange. This pilot study demonstrates the promise of UAS-borne TIR sensors for acquiring high spatial resolution imagery at the scale of individual tree crowns.