Over the last decades, climate change has triggered an increase in the frequency of bark in both Central Europe and Ukraine. Stem pests of coniferous plantations quickly took a dominant position, increasing the area of their cells in 5 years by 7.7 times leading to high ecological and economic losses. The available diagnosis of damage to pine trees by visual signs is ineffective. The exponential increase of bark beetle infestation hinders the implementation of costly field campaigns to prevent and mitigate its effects. Remote sensing may help to overcome such limitations as it provides frequent and spatially continuous data on vegetation condition. Using Sentinel-2 images as main input, two models have been developed to test the ability of this data source to test the ability of this data source to monitor the condition and damage of forests by bark beetle. The first approach was based on the selection (segmentation) of the affected areas using the method of automatic object recognition on satellite images, which is based on the pixel-oriented approach and artificial neural networks. And the second was based on the construction of a time trend of vegetation and forest moisture using cloud computing algorithms of Google Earth Engine. The proposed approaches on the example of a test plot of Tobolsk forestry Kamin-Kashirsky district of Volyn region with a total area of 54 ha were presented. The study period was from 2015 to 2020. These approaches of assessing damage based on Sentinel-2 can be set up for any location to derive regular forest vitality maps and inform of early damage.
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