Evaluation of the bark beetle green attack detectability in spruce forest from multitemporal multispectral UAV imagery

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F23%3A10476254" target="_blank" >RIV/00216208:11310/23:10476254 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1033-2023" target="_blank" >https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1033-2023</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5194/isprs-annals-X-1-W1-2023-1033-2023" target="_blank" >10.5194/isprs-annals-X-1-W1-2023-1033-2023</a>

Result language

angličtina

Original language name

Evaluation of the bark beetle green attack detectability in spruce forest from multitemporal multispectral UAV imagery

Original language description

Forests have always been a major concern for public authorities given their significant role as a resource for timber production, as well as for climate regulation. The infestation of bark beetles poses a challenging problem in forests, causing tree mortality, reduced timber quality, and ecosystem disruption. Multispectral imagery (MS) captured from unmanned aerial vehicles (UAVs) are increasingly employed for forest health assessment and have been extensively used to map the tree mortality caused by bark beetles. However, its utilisation for the identification of the initial stage of infestation known as &quot;green attack&quot; when infested trees do not display any visible symptoms yet, is still uncertain. This study is innovative in the ways it works with so far rare dense and comprehensive time series of MS UAV imagery starting in the initial month prior to the infestation and before the development of the Bark Beetle (BB) infestation visual symptoms, aiming to evaluate the detectability of bark beetle (Ips typographus L.) green attack. The study area predominantly covered by Norway spruce (Picea abies (L.) Karst.) is located in the Krkonoše Mountains National Park, in the Czech Republic. From May to August 2022, a total of nine MS UAV datasets were acquired with a DJI Phantom 4 MS sensor. The research question is whether infested trees exhibit significantly different changes in the spectra compared to healthy trees at an early stage. The detectability of green attacks was statistically investigated along with the underlying factors (flowering, growth of new shoots) that can affect the accuracy of detection. The results showed a distinct reduction in tree vitality of the infested trees in the late summer season and later stages of infestations. Based on our findings, we conclude that the proposed methods using UAV MS images can be employed to map local infestations and evaluate the tree vitality throughout the season, but the early detection of the green attack stage (in the absence of visible symptoms at the crown level) of Bark Beetle infestation from UAV MS data remains unfeasible. The precision of the detection using statistical methods can be further investigated using a time series of UAV hyperspectral images that offer a higher spectral resolution.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10508 - Physical geography

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Article name in the collection

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

ISBN

—

ISSN

2194-9050

e-ISSN

2194-9050

Number of pages

8

Pages from-to

1033-1040

Publisher name

Copernicus Gesellschaft MBH

Place of publication

Gottingen

Event location

Cairo, Egypt

Event date

Sep 2, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001185683800135