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Deep Learning for Regular Change Detection in Ukrainian Forest Ecosystem With Sentinel-2

The logging is the leading cause for the reduction in the forest area in the world. At the same time, the number of forest clear-cuts continues to grow. However, despite the massive scale, such incidents are difficult to track in time. As a result, huge areas of forests are gradually being cut down. Therefore, there is a need for regular and effective monitoring of changes in forest cover. The multi-temporal data sources like Copernicus Sentinel-2 allow enhancing the potential of monitoring the Earth’s surface and environmental dynamics including forest plantations. In this article, we present a baseline U-Net model for deforestation detection in the forest-steppe zone. Training and evaluation are conducted on our own data-set created on Sentinel-2 imagery for the Kharkiv region of Ukraine (31 400 km2). As a part of the research, we present several models with the ability to work with time-dependent imagery. The main contribution of this article is to provide a baseline model for the forest change detection inside Ukraine and improve it adding the ability to use several sequential images as an input of the segmentation model.

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