Classifying the Degree of Bark Beetle-Induced Damage on Fir (Abies mariesii) Forests, from UAV-Acquired RGB Images

Research output: Contribution to journal › Article › Research › peer review

Authors

  • Tobias Leidemer
  • Orou Berme Herve Gonroudobou
  • Ha Trang Nguyen
  • Chiara Ferracini
  • Benjamin Burkhard
  • Yago Diez
  • Maximo Larry Lopez Caceres

Research Organisations

External Research Organisations

  • Yamagata University
  • University of Turin
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Details

Original languageEnglish
Article number63
JournalComputation
Volume10
Issue number4
Publication statusPublished - 18 Apr 2022

Abstract

Bark beetle outbreaks are responsible for the loss of large areas of forests and in recent years they appear to be increasing in frequency and magnitude as a result of climate change. The aim of this study is to develop a new standardized methodology for the automatic detection of the degree of damage on single fir trees caused by bark beetle attacks using a simple GIS-based model. The classification approach is based on the degree of tree canopy defoliation observed (white pixels) in the UAV-acquired very high resolution RGB orthophotos. We defined six degrees (categories) of damage (healthy, four infested levels and dead) based on the ratio of white pixel to the total number of pixels of a given tree canopy. Category 1: <2.5% (no defoliation); Category 2: 2.5–10% (very low defoliation); Category 3: 10–25% (low defoliation); Category 4: 25–50% (medium defoliation); Category 5: 50–75% (high defoliation), and finally Category 6: >75% (dead). The definition of “white pixel” is crucial, since light conditions during image acquisition drastically affect pixel values. Thus, whiteness was defined as the ratio of red pixel value to the blue pixel value of every single pixel in relation to the ratio of the mean red and mean blue value of the whole orthomosaic. The results show that in an area of 4 ha, out of the 1376 trees, 277 were healthy, 948 were infested (Cat 2, 628; Cat 3, 244; Cat 4, 64; Cat 5, 12), and 151 were dead (Cat 6). The validation led to an average precision of 62%, with Cat 1 and Cat 6 reaching a precision of 73% and 94%, respectively.

Keywords

    UAV, bark beetles, defoliation, degree of damage, infestation, white pixel value

ASJC Scopus subject areas

Cite this

Classifying the Degree of Bark Beetle-Induced Damage on Fir (Abies mariesii) Forests, from UAV-Acquired RGB Images. / Leidemer, Tobias; Gonroudobou, Orou Berme Herve; Nguyen, Ha Trang et al.
In: Computation, Vol. 10, No. 4, 63, 18.04.2022.

Research output: Contribution to journal › Article › Research › peer review

Leidemer, T, Gonroudobou, OBH, Nguyen, HT, Ferracini, C, Burkhard, B, Diez, Y & Lopez Caceres, ML 2022, 'Classifying the Degree of Bark Beetle-Induced Damage on Fir (Abies mariesii) Forests, from UAV-Acquired RGB Images', Computation, vol. 10, no. 4, 63. https://doi.org/10.3390/computation10040063
Leidemer, T., Gonroudobou, O. B. H., Nguyen, H. T., Ferracini, C., Burkhard, B., Diez, Y., & Lopez Caceres, M. L. (2022). Classifying the Degree of Bark Beetle-Induced Damage on Fir (Abies mariesii) Forests, from UAV-Acquired RGB Images. Computation, 10(4), Article 63. https://doi.org/10.3390/computation10040063
Leidemer T, Gonroudobou OBH, Nguyen HT, Ferracini C, Burkhard B, Diez Y et al. Classifying the Degree of Bark Beetle-Induced Damage on Fir (Abies mariesii) Forests, from UAV-Acquired RGB Images. Computation. 2022 Apr 18;10(4):63. doi: 10.3390/computation10040063
Leidemer, Tobias ; Gonroudobou, Orou Berme Herve ; Nguyen, Ha Trang et al. / Classifying the Degree of Bark Beetle-Induced Damage on Fir (Abies mariesii) Forests, from UAV-Acquired RGB Images. In: Computation. 2022 ; Vol. 10, No. 4.
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title = "Classifying the Degree of Bark Beetle-Induced Damage on Fir (Abies mariesii) Forests, from UAV-Acquired RGB Images",
abstract = "Bark beetle outbreaks are responsible for the loss of large areas of forests and in recent years they appear to be increasing in frequency and magnitude as a result of climate change. The aim of this study is to develop a new standardized methodology for the automatic detection of the degree of damage on single fir trees caused by bark beetle attacks using a simple GIS-based model. The classification approach is based on the degree of tree canopy defoliation observed (white pixels) in the UAV-acquired very high resolution RGB orthophotos. We defined six degrees (categories) of damage (healthy, four infested levels and dead) based on the ratio of white pixel to the total number of pixels of a given tree canopy. Category 1: <2.5% (no defoliation); Category 2: 2.5–10% (very low defoliation); Category 3: 10–25% (low defoliation); Category 4: 25–50% (medium defoliation); Category 5: 50–75% (high defoliation), and finally Category 6: >75% (dead). The definition of “white pixel” is crucial, since light conditions during image acquisition drastically affect pixel values. Thus, whiteness was defined as the ratio of red pixel value to the blue pixel value of every single pixel in relation to the ratio of the mean red and mean blue value of the whole orthomosaic. The results show that in an area of 4 ha, out of the 1376 trees, 277 were healthy, 948 were infested (Cat 2, 628; Cat 3, 244; Cat 4, 64; Cat 5, 12), and 151 were dead (Cat 6). The validation led to an average precision of 62%, with Cat 1 and Cat 6 reaching a precision of 73% and 94%, respectively.",
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note = "Funding Information: Very special thanks go to all the members of Larry Lopez laboratory at Yamagata University for all their help during the fieldwork. Furthermore, I would like to express my gratitude to friends and family, who were there for me in Japan and Germany. ",
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AU - Leidemer, Tobias

AU - Gonroudobou, Orou Berme Herve

AU - Nguyen, Ha Trang

AU - Ferracini, Chiara

AU - Burkhard, Benjamin

AU - Diez, Yago

AU - Lopez Caceres, Maximo Larry

N1 - Funding Information: Very special thanks go to all the members of Larry Lopez laboratory at Yamagata University for all their help during the fieldwork. Furthermore, I would like to express my gratitude to friends and family, who were there for me in Japan and Germany.

PY - 2022/4/18

Y1 - 2022/4/18

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JF - Computation

SN - 2079-3197

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ER -

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