Currently, existing urban landscapes and their structure result from the modern military aggression of russian forces against Ukraine. It causesserious natural and anthropogenic, humanitarian disasters, and significant damage to urban infrastructure and landscape. Currently, the assessment of the impact on the Ukrainian environment caused by military aggression and the study of its consequences in Ukraine are lacking in-situ networks that is why remote sensing data assessment has become an important tool for environmental managers and decision-makers to understand changes across a range of sectors and for landscape studies for territory restoration needs. For this issue, remote sensing technologies and data, especially radar, can help in difficult environmental conditions with the detection, monitoring, and forecasting of military-caused environmental changes (an urbicide and ecocide). In this study, we were constrained due to the performance of Sentinel-1 and Sentinel-2 data for urban landscape damage assessment on the examples of the several model sites located to the northwest of Kyiv(Ukraine) such as the cities of Irpin, Bucha, Horenka, and the Irpin River valley. These areas were temporarily occupied and/or destroyed by russianforces in 2022, impacting semi-natural and anthropogenic landscape complexes. As a result, various war-affected systems and complexes have been formed. For their damage assessment, we employ multitemporal synthetic aperture radar (SAR) images, constructed based on information about texture reflection intensity and surface texture for the Sentinel-1, and involve differences between two images identification by comparing corresponding pixels in pairs accordingly to the «Application Radar Data for Determining Urban Landscape Destruction Zones Using Change Detection on Multitemporal Images» that was applied to detect urban changed areasand landscapes. The result is a single image that combines information from both input images in a certain way (e.g. binarized mask) indicating that the destroyed or severely damaged areas are widely distributed in the area of interest. The calculated NDVI and NDMI indexes obtained from Sentinel-2 images were compared with the damage coherence map, which shows a significant correlation. Thus, available remote sensing data (radar and optical) responds quickly to the war damage assessment providing usefulinformation for decision-makers dealing with landscape destruction and humanitarian disasters.
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