Using multitemporal hyper- and multispectral UAV imaging for detecting bark beetle infestation on Norway spruce - Honkavaara, E.; Näsi, R.; Oliveira, R.; Viljanen, N.; Suomalainen, J.; Khoramshahi, E.; Hakala, T.; Nevalainen, O.; Markelin, L.; Vuorinen, M.; Kankaanhuhta, V.; Lyytikäinen-Saarenmaa, P.; et al. (ISPRS Council., 2020)
Various biotic and abiotic stresses are threatening forests. Modern remote sensing technologies provide powerful means for monitoring forest health, and provide a sustainable basis for forest management and protection. The objective of this study was to develop unmanned aerial vehicle (UAV) based spectral remote sensing technologies for tree health assessment, particularly, for detecting the European spruce bark beetle (Ips typographus L.) attacks. Our focus was to study the early detection of bark beetle attack, i.e. the “green attack” phase. This is a difficult remote sensing task as there does not exist distinct symptoms that can be observed by the human eye. A test site in a Norway spruce (Picea abies (L.) Karst.) dominated forest was established in Southern-Finland in summer 2019. It had an emergent bark beetle outbreak and it was also suffering from other stress factors, especially the root and butt rot (Heterobasidion annosum (Fr.) Bref. s. lato). Altogether seven multitemporal hyper- and multispectral UAV remote sensing datasets were captured from the area in August to October 2019. Firstly, we explored deterioration of tree health and development of spectral symptoms using a time series of UAV hyperspectral imagery. Secondly, we trained assessed a machine learning model for classification of spruce health into classes of “bark beetle green attack”, “root-rot”, and “healthy”. Finally, we demonstrated the use of the model in tree health mapping in a test area. Our preliminary results were promising and indicated that the green attack phase could be detected using the accurately calibrated spectral image data.
Keywords: Hyperspectral; Remote Sensing; Radiometric calibration; Forest disturbance; Insect pest; Machine learning; Picea abies