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Early Detection of Forest Bark Beetle Attack Using Time Series Spatial Variability of Spectral Indexes from Sentinel-2

Jamali, Sadegh LU orcid ; Olsson, Per Ola LU ; Muller, Mitro LU orcid and Ghorbanian, Arsalan LU (2024) 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 p.10157-10160
Abstract

Bark beetle infestations pose a significant threat to forest ecosystems, necessitating timely intervention for effective management and conservation. This study focuses on an 8,100 km2 area in southern Sweden, heavily impacted by a bark beetle outbreak triggered by a severe drought in 2018. Using spectral indices derived from Sentinel-2 data, it explores the potential of a window-based approach for early detection of forest bark beetle attack, considering spatial variability between adjacent pixels. Four spectral indices (NDVI, NDWI, CCI, NDRS) are analyzed using a time series approach, and coefficient of variation (CV) between pixels in a window is employed to capture changes in vegetation health. The Chlorophyll Carotenoid... (More)

Bark beetle infestations pose a significant threat to forest ecosystems, necessitating timely intervention for effective management and conservation. This study focuses on an 8,100 km2 area in southern Sweden, heavily impacted by a bark beetle outbreak triggered by a severe drought in 2018. Using spectral indices derived from Sentinel-2 data, it explores the potential of a window-based approach for early detection of forest bark beetle attack, considering spatial variability between adjacent pixels. Four spectral indices (NDVI, NDWI, CCI, NDRS) are analyzed using a time series approach, and coefficient of variation (CV) between pixels in a window is employed to capture changes in vegetation health. The Chlorophyll Carotenoid Index (CCI) emerges as the most sensitive indicator for early detection. Detection algorithms, including DBEST, MLS, and CUSUM, pinpoint June 2018 as the month of bark beetle attack identification after a main swarming in May 2018, demonstrating superior performance compared to pixel-based frameworks. The results highlight the efficacy of integrating spatial properties with spectral indices in a time series analysis for enhanced early detection of bark beetle infestations.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
early detection, European spruce bark beetle, forest disturbance, Sentinel-2, time series analysis
host publication
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
pages
4 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
conference location
Athens, Greece
conference dates
2024-07-07 - 2024-07-12
external identifiers
  • scopus:85204886057
ISBN
9798350360325
DOI
10.1109/IGARSS53475.2024.10641274
language
English
LU publication?
yes
id
3842f950-11cf-42ee-ba9a-45dae278baf6
date added to LUP
2025-01-16 11:31:47
date last changed
2025-04-04 13:57:00
@inproceedings{3842f950-11cf-42ee-ba9a-45dae278baf6,
  abstract     = {{<p>Bark beetle infestations pose a significant threat to forest ecosystems, necessitating timely intervention for effective management and conservation. This study focuses on an 8,100 km<sup>2</sup> area in southern Sweden, heavily impacted by a bark beetle outbreak triggered by a severe drought in 2018. Using spectral indices derived from Sentinel-2 data, it explores the potential of a window-based approach for early detection of forest bark beetle attack, considering spatial variability between adjacent pixels. Four spectral indices (NDVI, NDWI, CCI, NDRS) are analyzed using a time series approach, and coefficient of variation (CV) between pixels in a window is employed to capture changes in vegetation health. The Chlorophyll Carotenoid Index (CCI) emerges as the most sensitive indicator for early detection. Detection algorithms, including DBEST, MLS, and CUSUM, pinpoint June 2018 as the month of bark beetle attack identification after a main swarming in May 2018, demonstrating superior performance compared to pixel-based frameworks. The results highlight the efficacy of integrating spatial properties with spectral indices in a time series analysis for enhanced early detection of bark beetle infestations.</p>}},
  author       = {{Jamali, Sadegh and Olsson, Per Ola and Muller, Mitro and Ghorbanian, Arsalan}},
  booktitle    = {{IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings}},
  isbn         = {{9798350360325}},
  keywords     = {{early detection; European spruce bark beetle; forest disturbance; Sentinel-2; time series analysis}},
  language     = {{eng}},
  pages        = {{10157--10160}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Early Detection of Forest Bark Beetle Attack Using Time Series Spatial Variability of Spectral Indexes from Sentinel-2}},
  url          = {{http://dx.doi.org/10.1109/IGARSS53475.2024.10641274}},
  doi          = {{10.1109/IGARSS53475.2024.10641274}},
  year         = {{2024}},
}