Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2022-02-01
Department
Department of Built Environment
Major/Subject
Mcode
Degree programme
Language
en
Pages
26
Series
Remote Sensing, Volume 14, issue 4
Abstract
Climate change is increasing pest insects’ ability to reproduce as temperatures rise, resulting in vast tree mortality globally. Early information on pest infestation is urgently needed for timely decisions to mitigate the damage. We investigated the mapping of trees that were in decline due to European spruce bark beetle infestation using multispectral unmanned aerial vehicles (UAV)-based imagery collected in spring and fall in four study areas in Helsinki, Finland. We used the Random Forest machine learning to classify trees based on their symptoms during both occasions. Our approach achieved an overall classification accuracy of 78.2% and 84.5% for healthy, declined and dead trees for spring and fall datasets, respectively. The results suggest that fall or the end of summer provides the most accurate tree vitality classification results. We also investigated the transferability of Random Forest classifiers between different areas, resulting in overall classification accuracies ranging from 59.3% to 84.7%. The findings of this study indicate that multispectral UAV-based imagery is capable of classifying tree decline in Norway spruce trees during a bark beetle infestation.Description
Funding Information: The research was funded by the Academy of Finland under grants 330422, 327861, 315079, 345166, 348643, 346383, 337810, by the Ministry of Agriculture and Forestry of Finland (project Monituho, decision number VN/5514/2020) and by the Doctoral Program in Sustainable Use of Renewable Natural Resources (AGFOREE) at the University of Helsinki. 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). Funding Information: Funding: The research was funded by the Academy of Finland under grants 330422, 327861, 315079, 345166, 348643, 346383, 337810, by the Ministry of Agriculture and Forestry of Finland (project Monituho, decision number VN/5514/2020) and by the Doctoral Program in Sustainable Use of Renewable Natural Resources (AGFOREE) at the University of Helsinki. 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). Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords
Bark beetle infestation, European spruce bark beetle, Forest damages, Forest decline, Forest monitoring, Ips typographus, Pest insects, Random Forest, Remote sensing, Tree vitality, Unmanned aerial vehicle
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Citation
Junttila, S, Näsi, R, Koivumäki, N, Imangholiloo, M, Saarinen, N, Raisio, J, Holopainen, M, Hyyppä, H, Hyyppä, J, Lyytikäinen-Saarenmaa, P, Vastaranta, M & Honkavaara, E 2022, ' Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season ', Remote Sensing, vol. 14, no. 4, 909 . https://doi.org/10.3390/rs14040909