Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season

Samuli Junttila*, Roope Näsi, Niko Koivumäki, Mohammad Imangholiloo, Ninni Saarinen, Juha Raisio, Markus Holopainen, Hannu Hyyppä, Juha Hyyppä, Päivi Lyytikäinen-Saarenmaa, Mikko Vastaranta, Eija Honkavaara

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
21 Downloads (Pure)

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.

Original languageEnglish
Article number909
Number of pages26
JournalRemote Sensing
Volume14
Issue number4
DOIs
Publication statusPublished - 1 Feb 2022
MoE publication typeA1 Journal article-refereed

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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