Early detection of bark beetle infestation in Norway spruce forests of Central Europe using Sentinel-2

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F21%3A00543252" target="_blank" >RIV/86652079:_____/21:00543252 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0303243421000428?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0303243421000428?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jag.2021.102335" target="_blank" >10.1016/j.jag.2021.102335</a>

Result language

angličtina

Original language name

Early detection of bark beetle infestation in Norway spruce forests of Central Europe using Sentinel-2

Original language description

In the past decade, massive outbreaks of bark beetles (Ips spp.) have caused large-scale decline of coniferousdominated, prevailingly managed forests of Central Europe. Timely detection of newly infested trees is important for minimizing economic losses and effectively planning forest management activities to stop or at least slow outbreaks. With the advancement of Copernicus services, a pair of Sentinel-2 satellites provides a unique remote sensing data source of multi-temporal observations in high spatial resolution on the scale of individual forest stands (although not allowing for individual tree detection). This study investigates the potential for using seasonal trajectories of Sentinel-2 bands and selected vegetation indices in early detection of bark beetle infestation (so?called green-attack stage detection) in Norway spruce monoculture forests in the Czech Republic. Spectral trajectories of nine bands and six vegetation indices were constructed for the 2018 vegetation season using 14 satellite observations from April to November to distinguish four infestation classes. We used a random forest algorithm to classify healthy (i.e., stands not infested) and infested trees with various trajectories of decay. The seasonal trajectories of vegetation indices separated the infestation classes better than did the individual bands. Among the most promising vegetation indices we identified the tasselled cap wetness (TCW) component and normalized difference index constructed from near and shortwave infrared bands. Analysing the inter-annual change of the indices was more promising for early detection than is single-date classification. It achieved 96% classification accuracy on day of year 291 for the tested data set. The algorithm for early detection of tree infestation based on the assessment of seasonal changes in TCW was applied on the time series of Sentinel-2 observations from 2019 and its outputs were verified using field observations of forest conditions conducted on 80 spruce forest plots (located in spruce monoculture stands). The overall accuracy of 78% was achieved for the separation of healthy and green-attack classes. Our study highlights the great potential of multi-temporal remote sensing and the use of shortwave infrared wavelengths for early detection of spruce forest decline caused by bark beetle infestation.

Czech name

—

Czech description

—

Type

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

CEP classification

—

OECD FORD branch

10511 - Environmental sciences (social aspects to be 5.7)

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)

Publication year

2021

Confidentiality

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

Name of the periodical

International Journal of Applied Earth Observation and Geoinformation

ISSN

0303-2434

e-ISSN

1872-826X

Volume of the periodical

100

Issue of the periodical within the volume

AUG

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

13

Pages from-to

102335

UT code for WoS article

000647733500001

EID of the result in the Scopus database

2-s2.0-85109770208