Detection of green attack and bark beetle susceptibility in Norway Spruce: Utilizing PlanetScope Multispectral Imagery for Tri-Stage spectral separability analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F24%3A100097" target="_blank" >RIV/60460709:41320/24:100097 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1016/j.foreco.2024.121838" target="_blank" >http://dx.doi.org/10.1016/j.foreco.2024.121838</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.foreco.2024.121838" target="_blank" >10.1016/j.foreco.2024.121838</a>

Result language

angličtina

Original language name

Detection of green attack and bark beetle susceptibility in Norway Spruce: Utilizing PlanetScope Multispectral Imagery for Tri-Stage spectral separability analysis

Original language description

The detection of susceptible and attacked trees is a key factor in the management of bark beetle infestations. The challenge of early detection of infestations, due to invisible changes in the canopy color, consequently hinders the control of outbreaks in a timely manner. While many studies have examined the spectral characteristics during the green-attack stage, with no discernible needle discoloration, few have focused on the temporal-scale spectral characteristics from pre-infestation to the infestation stages, accurately detected in the field. We investigated the spectral differences among three classes of forest areas: healthy, susceptible to bark beetle attacks, and green-attacked trees using individual wavebands and spectral vegetation indices (SVIs). We assumed that the spectral characteristics of these forests vary significantly due to different temporal-scale susceptibility to the bark beetle attacks. We used 16 imageries, between 2 April and 5 September 2020, acquired from PlanetScope satellite. The spatial position of attacked trees during the growing season was recorded in the field using the ArcGIS Collector App. Ancillary datasets of forest management units were used to randomly select nonattacked trees from areas with properties similar to the attacked areas. The statistical analyses were performed on the final cleaned datasets to assess the separability among the tree classes. Our findings indicate that the Green and Red wavelengths are promising bands to distinguish healthy, susceptible, and green-attacked trees. We also found the existing spectral differences between healthy and susceptible trees before bark beetle attacks, suggesting the presence of abiotic stress to facilitate the infestation. Among different SVIs, the Enhanced Vegetation Index (EVI) and Visible Atmospherically Resistant Index (VARI) were found to be capable of specifically detecting susceptible trees to the infestation. However, it is important to note that the scope of our study is limited to a one-year period and does not focus on individual trees but rather on groups of trees, indicating that future research could benefit from a multi-year analysis at the tree level to enhance the understanding and management of bark beetle dynamics.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

40102 - Forestry

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Forest Ecology and Management

ISSN

0378-1127

e-ISSN

0378-1127

Volume of the periodical

560

Issue of the periodical within the volume

121838

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

12

Pages from-to

1-12

UT code for WoS article

001219197700001

EID of the result in the Scopus database

2-s2.0-85187959383