A visual analysis of agricultural land areas was conducted using openly available sub-meter resolution data to identify littering and contamination caused by damaged or burned military equipment and to determine the locations of its concentration. Multi-Sensor Data Fusion was applied based on three types of satellite data: optical burn index analysis (Sentinel-2, NBR), land surface thermal monitoring (Landsat, LST), and radar remote sensing (Sentinel-1, VV). The calculations were performed using cloud computing on the Google Earth Engine platform. Using a specific case study, it was demonstrated that cross-validation of time series obtained from the three sensors makes it possible to determine the temporal range of a military equipment destruction event with an accuracy of up to 10 days. The results were verified using commercial very high-resolution satellite imagery and data from open databases of destroyed military equipment. The proposed approach can be used to develop geospatial databases for monitoring contamination hotspots, integrate them into the land cadastre system for recording disturbed lands, and support the planning of land reclamation measures.
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