Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season

Abstract

Climate change is increasing pest insects’ ability to reproduce as temperatures rise, resulting in vast tree mortality globally. Early information on pest infestation is urgently needed for timely decisions to mitigate the damage. We investigated the mapping of trees that were in decline due to European spruce bark beetle infestation using multispectral unmanned aerial vehicles (UAV)-based imagery collected in spring and fall in four study areas in Helsinki, Finland. We used the Random Forest machine learning to classify trees based on their symptoms during both occasions. Our approach achieved an overall classification accuracy of 78.2% and 84.5% for healthy, declined and dead trees for spring and fall datasets, respectively. The results suggest that fall or the end of summer provides the most accurate tree vitality classification results. We also investigated the transferability of Random Forest classifiers between different areas, resulting in overall classification accuracies ranging from 59.3% to 84.7%. The findings of this study indicate that multispectral UAV-based imagery is capable of classifying tree decline in Norway spruce trees during a bark beetle infestation.

Early detection of Ips typographus infestations by using Sentinel-2 satellite images in windthrow affected Norway spruce forests in Smolyan region, Bulgaria

“…Multispectral UAV-based imagery is capable of classifying tree decline during a bark beetle infestations in Scots pine (Georgieva et al, 2022) and Norway spruce (Junttila et al, 2022). Recent studies of spruce bark beetle attacks using Sentinel-2 multispectral data and different vegetation indices (including NDVI) in the Italian Alps show that the two stages of the epidemic (i.e.…”

Heterobasidion root rot infections on Scots pine : A cryptic threat to sustainable forest management in Sweden

“…They have been used in a wide range of applications, including forest health monitoring (Leckie et al, 2010), monitoring regenerated forest stands, and tree species identification (Gini et al, 2014;Michez et al, 2016). Several studies have also used UAV imagery to detect bark beetle-infested trees (Junttila et al, 2022;Klouček et al, 2019;Näsi et al, 2015) (Näsi et al, 2015;Klouček et al, 2019;Junttila et al, 2022).…”

MART3D: A Multilayer Heterogeneous 3D Radiative Transfer Framework for Characterizing Forest Disturbances

“…However, there are large differences between different regions, different types of forest (e.g., temperate forest and tropical forest), plant formations (e.g., grass, shrubs, mediate trees and upper trees) and disturbance categories (e.g., wildfires, forest insects and diseases), making this method difficult to transfer and apply. Among the image pattern recognition methods, traditional machine learning algorithms such as random forests [15][16][17] and support vector machines [18,19], as well as deep learning algorithms like the UNet model [20], are often utilized to monitor forest disturbance areas. But such methods have high requirements on data quality and availability, and the accuracy is often limited by ground survey data [16].…”