Prediction of bark beetle outbreak spreading using TANABBO system
Briefly: Have you ever heard of the spruce bark beetle? It’s an insect that can cause serious damage to spruce forests in Europe, and with climate change on the rise, their population is increasing. But don’t worry — scientists are working hard to protect our forests! In our research project, we use remote sensing and GIS data to predict the spread of the spruce bark beetle. We focus on the Norway spruce and use a system called TANABBO to calculate the likelihood of a beetle outbreak. We also collect data about bark beetle attacks and the health of trees in the study area, which is ŠLP Kostelec nad Černými Lesy in the Czech Republic. By analyzing this data and creating maps of predicted infestation areas, we hope to take proactive steps to protect our forests from the damaging effects of the spruce bark beetle.
Climate change is a topic of much discussion in recent times, and one of its lesser-known consequences is the increased population of the spruce bark beetle. These beetles typically infest weakened trees in endemic conditions, but in the event of a major windstorm or extreme heat and drought, they can take over healthy trees and cause significant damage to spruce forests in Europe. So what can we do to protect our forests?
Fortunately, scientists have extensively studied the biology, ecology, and population dynamics of the spruce bark beetle, leading to effective measures for protecting coniferous forests in the early stages of an outbreak. Higher temperatures in spring and summer create ideal conditions for the faster development of larvae into beetles and the formation of subsequent generations during this season. However, drought and mechanical damage to trees (windfall, windthrow, ice storm, fire) weaken trees and make them more vulnerable to bark beetle attack.
To detect and monitor infested trees, scientists use various techniques, including satellite imagery of medium and high resolution, aerial photos, and remote sensing techniques like spectral imaging, time series analysis, and identification of infested areas using reference forest samples. Specific spectral ranges, such as the red edge, near-infrared, or short-wave infrared, are used to determine forest health.
Factors affecting bark beetle distribution include the size of windfall areas, distance from previous infestations, slope exposure, age structure, and density of spruce stands. By using these parameters, scientists can develop predictive models for predicting bark beetle spread and outbreak.
One such system is the TANABBO, which uses satellite data and forest inventory data, and the Normalized Difference Vegetation Index (NDVI) to predict bark beetle outbreaks. The TANABBO II system, which incorporates meteorological data, has been shown to improve the accuracy of predicting spruce bark beetle outbreaks. By optimizing measures for the protection of coniferous forests, such as selecting locations for pheromone traps and conducting ground surveys to detect bark beetles, we can better protect our forests from this damaging pest.
In our research project, we aim to improve and test the prediction of bark beetle spreading modules using different remote sensing sources of data in the TANABBO system. We collect data from satellite imagery, drone imagery, forest inventory data, meteorological data, and ecophysiology and tree health data, and use GIS and remote sensing software to process the data and calculate the prediction model of bark beetle spreading. We are testing machine learning techniques to determine if they can improve the accuracy of the TANABBO system.
By better understanding and predicting the spread of the spruce bark beetle, we can take proactive steps to protect our forests from this damaging pest and mitigate the effects of climate change.
