A1 Journal article (refereed)
Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network (2022)

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

JYU authors or editors

Publication details

All authors or editorsKanerva, Heini; Honkavaara, Eija; Näsi, Roope; Hakala, Teemu; Junttila, Samuli; Karila, Kirsi; Koivumäki, Niko; Alves Oliveira, Raquel; Pelto-Arvo, Mikko; Pölönen, Ilkka; et al.

Journal or seriesRemote Sensing


Publication year2022

Publication date10/12/2022


Issue number24

Article number6257


Publication countrySwitzerland

Publication languageEnglish


Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/85315

Publication is parallel publishedhttps://helda.helsinki.fi/handle/10138/352172


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.

KeywordsforestsNorway spruceforest damageinsect damageIps typographusmonitoringestimating (statistical methods)remote sensingunmanned aerial vehiclesmachine learningdeep learningneural networks (information technology)

Free keywordsbark beetle; deep learning; drone; object detection; remote sensing; tree health

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Ministry reportingYes

Reporting Year2022

JUFO rating1

Last updated on 2024-30-04 at 18:16