Epsilon Archive for Student Projects

Abstract

Forests are at risk of pest outbreaks as climate change creates favourable conditions for them. These events have negative ecological, economic, and social impacts. The purpose of this study is to investigate the potential of early detection with remote sensing for the sharp-dentated bark beetle (Ips acuminatus) infestation of Scots pine (Pinus sylvestris). Remote sensing was conducted using a drone equipped with a multispectral sensor, collecting data on four study plots with healthy and infested pine trees from April to October 2025. Infestation of trees was provided by using pheromone lures. Reflectance of red, green, blue, Red Edge, and NIR was extracted, vegetation indices were calculated, and the results were analysed using statistical tests. Vegetation indices such as TCARI_OSAVI, SAVI, NDRE, and CI showed significant differences between infested and healthy trees. The earliest signs of infestation were detected in Week 8 (June 10), three weeks after the swarming of the sharp-dentated bark beetle was recorded. The first signs of infestation were reflected in vegetation indices sensitive to changes in chlorophyll content. Even though there was no visible discolouration in infested trees, vegetation indices revealed differences in health status, indicating that remote sensing can detect the early stages of infestation.