Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network
Kanerva, H., Honkavaara, E., Näsi, R., Hakala, T., Junttila, S., Karila, K., Koivumäki, N., Alves Oliveira, R., Pelto-Arvo, M., Pölönen, I., Tuviala, J., Östersund, M., & Lyytikäinen-Saarenmaa, P. (2022). Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network. Remote Sensing, 14(24), Article 6257. https://doi.org/10.3390/rs14246257
Published inRemote Sensing
DisciplineTietotekniikkaLaskennallinen tiedeComputing, Information Technology and MathematicsMathematical Information TechnologyComputational ScienceComputing, Information Technology and Mathematics
© 2022 by the authors. Licensee MDPI, Basel, Switzerland
Various biotic and abiotic stresses are causing decline in forest health globally. Presently, one of the major biotic stress agents in Europe is the European spruce bark beetle (Ips typographus L.) which is increasingly causing widespread tree mortality in northern latitudes as a consequence of the warming climate. Remote sensing using unoccupied aerial systems (UAS) together with evolving machine learning techniques provide a powerful tool for fast-response monitoring of forest health. The aim of this study was to investigate the performance of a deep one-stage object detection neural network in the detection of damage by I. typographus in Norway spruce trees using UAS RGB images. A Scaled-YOLOv4 (You Only Look Once) network was implemented and trained for tree health analysis. Datasets for model training were collected during 2013–2020 from three different areas, using four different RGB cameras, and under varying weather conditions. Different model training options were evaluated, including two different symptom rules, different partitions of the dataset, fine-tuning, and hyperparameter optimization. Our study showed that the network was able to detect and classify spruce trees that had visually separable crown symptoms, but it failed to separate spruce trees with stem symptoms and a green crown from healthy spruce trees. For the best model, the overall F-score was 89%, and the F-scores for the healthy, infested, and dead trees were 90%, 79%, and 98%, respectively. The method adapted well to the diverse dataset, and the processing results with different options were consistent. The results indicated that the proposed method could enable implementation of low-cost tools for management of I. typographus outbreaks. ...
ISSN Search the Publication Forum2072-4292
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Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF
Additional information about fundingThis research was funded by the Academy of Finland under grants 327861, 327862 and 330422, by the Ministry of Agriculture and Forestry of Finland with the projects MONITUHO (no. 647/03.02.06.00/2018), SPRUCERISK (no. VN/5292/2021) and MMM_UNITE (no. VN/3482/2021) and by Maj and Tor Nessling Foundation with IPSCAR project (no. 2014462). This study has been performed with affiliation to the Academy of Finland Flagship Forest–Human–Machine Interplay—Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences (UNITE) (decision no. 337127). ...
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