E4 PhD student Liz Poulsom is studying methods to remotely sense the early onset of forest disease using a variety of sensing techniques. Forestry is an important industry across the world, and particularly so in Scotland, playing a key role in the economy and providing ecosystem services such as carbon sequestration, habitat provision and areas for recreation. However, forest areas, particularly single species stands, are potentially vulnerable to numerous diseases and invasive pests, and once established in a forest these problems can be exceedingly difficult to control or eradicate. The prevalence of pests and diseases may also be compounded by climate change which can place additional stress on trees. The earlier that such problems can be detected, the better the chance that forest managers will be able to intervene to prevent further spread. In collaboration with Forest Research, E4 PhD student Liz Poulsom, herself an experienced forest manager and researcher, is studying how advanced sensor technologies deployed on drones and aircraft may be able to spot the early signs of forest health problems by picking up tiny signals in very specific parts of the electromagnetic spectrum. To this end Liz is using drone- and aircraft-based hyperspectral imaging - a sensor that measures reflected light in hundreds of very narrow wavelength bands. She is also using multispectral imaging (a sensor that uses a handful of broader wavelength bands), normal photographic imaging passed through photogrammetric software, thermal imaging, and LiDAR (laser-scanning) to create an extremely detailed picture of the state of her forest study sites. These sites, which are based in Scotland's central belt, are predominantly of Sitka Spruce and Scots Pine. Alongside her extensive aerial fieldwork, Liz is conducting closely controlled ground-based experiments to help disentangle the signals generated by disease from background signals caused by more normal stress factors such as drought. ARI is supporting Liz's work through training, equipment loan and directly supported data acquisition using our DJI Mavic 3 Multispectral and DJI Matrice 300 RTK drones, with DJI-Zenmuse L1 (LiDAR) and P1 (45Mp photogrammetry) sensors on the Matrice 300 RTK, and in 2024 the ECO-Dimona's hyperspectral and photogrammetry system. Liz has also been assisted by the loan of an XT2 thermal sensor from FSF, and the use of Forest Research's Headwall drone-hyperspectral sensor. Find out more about Liz's work on forest health monitoring This article was published on 2024-06-25
