Near-surface Thermal Infrared Imaging of a Mixed Forest
Abstract:Measurement of an organism’s temperature is of basic physiological importance and therefore necessary for ecosystem modeling, yet most models derive leaf temperature from energy balance arguments or assume it is equal to air temperature. This is because continuous, direct measurement of leaf temperature outside of a controlled environment is difficult and rarely done. Of even greater challenge is measuring leaf temperature with the resolution required to understand the underlying energy balance and regulation of plant processes.
To measure leaf temperature through the year, we have mounted a high-resolution, thermal infrared camera overlooking the canopy of a temperate deciduous forest. The camera is co-located with an eddy covariance system and a suite of radiometric sensors. Our camera measures longwave thermal infrared (λ = 7.5–14 microns) using a microbolometer array. Suspended in the canopy within the camera FOV is a matte black copper plate instrumented with fine wire thermocouples that acts as a thermal reference for each image.
In this presentation, I will discuss the challenges of continuous, long-term field operation of the camera, as well as measurement sensitivity to physical and environmental parameters. Based on this analysis, I will show that the uncertainties in converting radiometric signal to leaf temperature are well constrained. The key parameter for minimizing uncertainty is the emissivity of the objects being imaged: measuring the emissivity to within 0.01 enables leaf temperature to be calculated to within 0.5°C.
Finally, I will present differences in leaf temperature observed amongst species. From our two-year record, we characterize high frequency, daily, and seasonal thermal signatures of leaves and crowns, in relation to environmental conditions. Our images are taken with sufficient spatial and temporal resolution to quantify the preferential heating of sunlit portions of the canopy and the cooling effect of wind gusts.
Future work will be focused on correlations between hyperspectral vegetation indices, fluxes, and thermal signatures to characterize vegetation stress. As water stress increases, causing photosynthesis and transpiration to shutdown, heat fluxes, leaf temperature, and narrow band vegetation indices should report signatures of the affected processes.