Investigating the capabilities of thermal technologies to detect pathogen stress in commercial forest compartments

Remote sensing has provided a means to cover large areas of forest compartments through monitoring health and improving, sustainability of forest stands. The continued threat of existing pathogens, as well as the introduction of new pests or diseases, will require monitoring to aid in these practices. Thus, there exists a need to acquire information from the physiological process of plants that can potentially be related to stress, to aid in management and planning. Foliar temperature is directly linked to plant functions such as transpiration and stomatal conductance. Furthermore, a constant increase in temperature may indicate a lack of plant cooling and thus indicate water or pathogen stress. Thus, there is potential for temperature to be used as a representative of forest health, by indicating the presence of stress. This study succeeded in classifying disease manifestation affecting tree performance at a compartment level, by investigating the applicability of the thermal capabilities of a handheld FLIR camera, Landsat 9 and drone images to detect foliar damage from a pathogen. All three sensors scored high in algorithm scoring. The highest correlation of 72.2%, was found between drone and Landsat 9 images. Accuracies of the algorithms used, namely Deep Learning and Generalized Linear Model, obtained 98.9% and 97.3% respectively. Both these algorithms are suited for future research. The findings in this study suggests that thermal applications can contribute to future research, as part of a framework of forest health monitoring.