SLU news

Assessing the detectability of European spruce bark beetle

Published: 05 May 2023
Drönarbild av infekterade träd, toppvy av träd.

Detecting disease- or insect-infested forests as early as possible is a classic application of remote sensing. Under conditions of climate change and global warming, outbreaks of the European spruce bark beetle are threatening spruce forests and the related timber industry across Europe, and early detection of infestations is important for damage control. Those are some findings that researchers at SLU have presented in a paper.

Langning Huo, researcher in forest remote sensing at the Department of Forest Resource Management, SLU, is the corresponding author to the paper.

What were the most interesting findings from the report?

Early detecting the infestations by bark beetles is important for damage control, and our group is developing methods for using remote sensing to identify infestations. Although some research has been going on on this topic, how early the infestations can be detected is still an unsolved question. So in our project, we answered that question by weekly monitored trees from being healthy to being attacked and gradually lose the vitality, and used revisiting drones to collect multispectral images covering healthy and different infestation stages.

The most interesting finding is that, at the first five weeks since attacked, the detectability of infested trees was only 15%, which means almost impossible to detect infestations at this stage during June. We can assume it is also challenging to detect infestations before July by satellite images, which have lower resolution than drone images. While after 10 weeks of infestation, using multispectral images can detect 90% of the infestations, while at this time, the fieldwork based on observing the crown color only detected 48% of the infestations, which means using multispectral drone imagery, we can detect infestations earlier, and it is also a useful tool to map all infestations in stands much faster than field inventory.

We also developed the methodology that analyzed the trees' spectrum and indicated the tree's health and vitality, and we suppose the similar methodology could be applied to other tree diseases and infestations.

Read the paper “Assessing the detectability of European spruce bark beetle green attack in multispectral drone images with high spatial- and temporal resolutions”

What were SLU Forest Damage Centre’s role in your research?

SLU Forest Damage Centre has been supporting the data collection in the published research, and is also supporting our ongoing research on developing hyperspectral imagery, which has more spectral bands and has the potential to detect the infestations even earlier. The Centre also supported the research exchange between SLU and the Finnish Geospatial Research Institute FGI, the world leading group on hyperspectral drone imagery, so we will gain competence in this research field and build up good network with other research groups.

The Centre is also co-supporting a new PhD student’s project on remote sensing of forest damage. The Centre has also been providing a platform for cross-discipline networking and collaboration, and also helps with result dissemination with the stockholders.

What’s new about this type of research?

It is the first study that answers how early the infestation can be detected after being attacked by bark beetles, and the first study proving full evidence on detecting green-attack. There are two indispensable parts in the published study that make it unique so far in this research field. The first is the weekly observation from the field that enables us to analyze the spectral response per infestation week. Many previous studies did not conduct continuous field data collection, so they could not know how long the trees have been infested, only assuming the infestation stages according to the season, thus not able to answer how early they can be detected after attacked.

The other is using multispectral drone images, which have very high resolution and we can use the spectrum only from individual tree crowns, therefore, the other information such as canopy gaps, forest structure, tree species compensation, will not disturb the model.

Can you describe the Horizon project some more?

The project name is “Network for novel remote sensing technologies in forest disturbance ecology (RESDINET)”

The project enhances networking activities between research institution in Widening country (Institute of Forest Ecology, Slovak Academy of Sciences, IFE SAS) and top-class counterparts at the EU level (Finnish Geospatial Research Institute, The University of Eastern Finland and Swedish University of Agricultural Sciences). The project builds on networking for excellence through knowledge transfer and exchange of best practices between involved institutions. Training workshops and research activities are planned in the project.

The project proposes establishment of initial network and development of a new joint research project in novel remote sensing technologies (RST) applications in forest disturbance ecology. Rigorous analyses of severe insect-induced disturbances using novel RST will be carried out in test areas representing different forest and climate types: mountain forests in Slovakia and boreal forests in Finland and Sweden. We will integrate in situ UAV and drone acquired remotely sensed data, existing multitemporal geospatial information and field data, particularly data on bark beetle population density, visible infestation symptoms linked to outbreak phases, and tree physiology parameters measured using electronic dendrometers or sapflow meters. The combined dataset will be used to develop new tools for multi-scale early identification and mapping of bark beetle attack.

This project also support a new PhD student in SLU remote sensing group, with co-funding from SLU Forest Damage Centre.

What’s next for you in your research?

For our research, we will develop multiscale remote sensing technique to identify forest disturbance and damages with higher spatial, spectral, and tempororal resolution, and cover laryge areas. We will extend the application of RS to different types of forest disturbances and damages such as other forest insects, diseases, drought, and storm damages. For bark beetle damages, we will have similar data collection as mentioned in the published paper, but with more frequent drone flights, and with hyperspectral sensors with more bands and larger spectral range. I am having a research visit to Finland to build competence in hyperspectral drone imagery, and will apply it to our research in Sweden. We will also have a new PhD student developing remote sensing of forest disturbance and damages.