Hyperspectral drone images indicate that green shoulder indices are robust in pre-emergence detection of spruce bark beetle infestation across spatial and temporal scales

Abstract

Bark beetle (Ips typographus L.) outbreaks are one of the main threats to forest health in northern Europe, with recent events causing extensive damage to spruce forests. While management for population control relies on detecting infested trees before the emergence of the filial generation, identifying robust spectral indicators remains a major challenge. In this study, we evaluate the performance of vegetation indices (VIs) derived from hyperspectral drone imagery for detecting bark beetle infestations in southern Sweden. We calculated detection rates based on the cases where VI values for infested trees deviated from the value range observed in healthy trees. We tested different scenarios for defining the range of healthy values to assess spatial and temporal consistency of VI performance. Green shoulder VIs, particularly GSCR1MS and GSCR2MS, consistently showed the highest detection rates. Their performance was stable across different weeks and forest stands, indicating stronger generalizability and higher potential for pre-emergence detection. In contrast, red edge VIs showed limited temporal consistency and strong dependence on normalization. SWIR-based VIs presented low detection rates in all scenarios, therefore showing limited potential.

Keywords

European spruce bark beetle; Ips typographus; forest damage; forest disturbance

Published in

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
2025, volume: XLVIII-2/W11, pages: 73-79
Publisher: International Society of Photogrammetry and Remote Sensing (ISPRS)

Conference

UAV-g 2025 Uncrewed Aerial Vehicles in Geomatics, 10–12 September 2025, Espoo, Finland

SLU Authors

• Elias Cosimo, Luiz Henrique

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Lindberg, Eva

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Persson, Henrik

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Bohlin, Jonas

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Huo, Langning

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

Associated SLU-program

Remningstorp
SLU Forest Damage Centre

UKÄ Subject classification

Forest Science
Earth Observation

Publication identifier

• DOI: https://doi.org/10.5194/isprs-archives-XLVIII-2-W11-2025-73-2025

Permanent link to this page (URI)

https://res.slu.se/id/publ/144859