Monoculture plantation woodlands are particularly vulnerable to disturbance events as species uniformity makes such stands highly susceptible to pests and diseases. Red band needle blight (caused by the fungus Dothistroma septosporum) is a disease which has a particularly significant economic impact on pine plantation forests worldwide, affecting diameter and height growth. However, monitoring its spread and intensity is complicated by the fact that the diseased trees are often only visible from aircraft in the advanced stages of the epidemic. Remote sensing could potentially aid in the detection of infected stands and in monitoring disease development and spread. Thermography is one of the techniques that can be used for monitoring changes in the physiological state of plants following infection. However, the use of thermography in forestry has so far been restricted by poor spatial resolution (satellite-based sensors) or high data acquirement costs (airborne sensors). This paper investigates the use of Unmanned Aerial Vehicle (UAV)-borne thermal systems for detecting disease-induced canopy temperature increase and explores the influence of the imaging time and weather conditions on the detected relationship. Furthermore, the potential of a number of airborne LiDAR-derived structural metrics for detection of changes in the canopy structure following the infection are investigated. The study was located in a diseased Scots pine (Pinus sylvestris) stand in Queen Elizabeth II Forest Park (central Scotland, UK), where 60 sample trees were surveyed. The thermal imagery was acquired at six different times of a day from an altitude of 60 m. Statistically significant correlation between canopy temperature depression (CTD) and disease levels was found for most of the flights (R2 between 0.27 and 0.41), which may be related to the needle damage symptoms caused by the disease, i.e. loss of cellular integrity, necrosis and eventual desiccation. Furthermore, the standard deviation of the crown temperature exhibited weak but statistically significant correlation (R2 between 0.11 and 0.13). The combination of CTD and standard deviation of crown temperature in a partial least squares regression (PLSR) further improved the observed relationship with the estimated disease level. Inclusion of LiDAR structural metrics was also investigated but only provided a slight improvement. A change in environmental conditions altered the magnitude of differences between canopy temperatures; no significant correlation with disease level was found in the morning flight, whilst the strongest relationship was obtained at the time of highest solar radiation, which coincides with the time of maximum photosynthetic activity.
|Journal||Forest Ecology and Management|
|Publication status||Published - 15 Feb 2019|
- Forest health
- Laser scanning