Details
Originalsprache | Englisch |
---|---|
Aufsatznummer | 63 |
Fachzeitschrift | Computation |
Jahrgang | 10 |
Ausgabenummer | 4 |
Publikationsstatus | Veröffentlicht - 18 Apr. 2022 |
Abstract
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
- Mathematik (insg.)
- Modellierung und Simulation
- Mathematik (insg.)
- Angewandte Mathematik
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in: Computation, Jahrgang 10, Nr. 4, 63, 18.04.2022.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Classifying the Degree of Bark Beetle-Induced Damage on Fir (Abies mariesii) Forests, from UAV-Acquired RGB Images
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
N2 - 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.
AB - 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.
KW - UAV
KW - bark beetles
KW - defoliation
KW - degree of damage
KW - infestation
KW - white pixel value
UR - http://www.scopus.com/inward/record.url?scp=85129158647&partnerID=8YFLogxK
U2 - 10.3390/computation10040063
DO - 10.3390/computation10040063
M3 - Article
VL - 10
JO - Computation
JF - Computation
SN - 2079-3197
IS - 4
M1 - 63
ER -