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Conference paper2024Peer reviewed

Influence of Crown Pixel Selection on the Early Detection of Bark Beetle Infestations Using Multispectral Drone Images

Huo, Langning; Yu, Run; Lindberg, Eva; et al.

Abstract

In recent years, the European spruce bark beetle (Ips typographus, L.) has damaged large amounts of forests in Europe, and detecting infested trees is crucial for damage control and informative decision-making regarding management. This study explores efficient methods of detecting infestations using multispectral drone images, focusing on how using different pixels from the crown segments influences the detection rates. Tree crowns were first segmented using marker-controlled watershed segmentation, and then two pixel-selection strategies were tested, including selecting the pixels closer to the tree tops, and selecting the bright pixels with values higher than certain percentiles of the entire crown segments. Two datasets were used from the same area, including 2021 with an epidemic outbreak and 2023 with an endemic outbreak, to present the potential differences caused by attack intensity. The results showed that, in the early stages (1 – 9 weeks of infestation), using the centermost pixels or the brightest pixels in the tree crowns had higher detectability than using all pixels. Red-edge-based VIs were more sensitive than red-green-based VIs. In the middle stage (10 – 16 weeks of infestation), using pixels from the entire tree crown, including tree tops and the low branches, showed higher detectability than using fewer pixels. For the late stages (after 19 weeks of infestation), using only the center pixel was sufficient, and there were minor differences between different VIs. The results were supported by observations from two datasets from different years, although variations in the detectability between different years and stands were also observed.

Keywords

Image segmentation; Epidemics; Forests; Decision making; Europe; Geoscience and remote sensing; Focusing; Forest damages; bark beetles; early detection; multispectral imagery; drone imagery

Published in

Title: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium : proceedings
Publisher: Institute of Electrical and Electronics Engineers

Conference

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024-07-07 - 2024-07-12

SLU Authors

Associated SLU-program

Remningstorp
SLU Forest Damage Center

UKÄ Subject classification

Remote Sensing

Publication identifier

  • DOI: https://doi.org/10.1109/IGARSS53475.2024.10640385
  • ISBN: 979-8-3503-6032-5

Permanent link to this page (URI)

https://res.slu.se/id/publ/131970