U-Net Model for Logging Detection Based on the Sentinel-1 and Sentinel-2 Data

Illegal logging in Ukraine is a big problem that negatively affects both environmental and socio-economic indicators of the country. The main reason for this problem is the lack of independent control over the forest industry. Lack of control, in turn, makes it possible to provide inaccurate information about the permitted logging and to hide the fact of logging. The solution to this problem is the use of modern approaches of Remote Sensing and deep learning to implement mechanisms for forestry monitoring and logging detection based on the satellite data. Most researches on satellite-based logging detection technology are based on the optical satellite missions. However, for countries with temperate and cold climates, the use of such approaches is problematic in winter and autumn due to the lack of vegetative biomass and the high percentage of clouds and snow in satellite images. In this study, we assessed a methodology for detecting logging based on optical and radar images of Copernicus satellite missions, namely Sentinel-l and 2. The obtained results show that when using this approach, it is possible to monitor and detect logging with high accuracy both in summer and in winter with the frequency of data updates once a week. The basis of this methodology is a convolutional neural network with U -Net architecture, which input is a stack of optical and radar images in summer and spring, and works on radar images only in winter and autumn.