Background and objective: Subsidence is a crisis that modern societies are currently facing. It has the potential to inflict irreparable damage to the lives and properties of residents, as well as disrupt urban infrastructure, including water, oil, and gas transmission lines. While horizontal displacement is also possible, its extent is typically minor. Subsidence results in the formation of cracks and fissures in the ground, alterations in underground water quality, changes to the Earth's surface topography, and other related issues.Materials and methods: In this study, using the multi-criteria decision-making approach, the seven criteria have been taken into account to produce subsidence risk map. At first, expert opinion on this issue have been used to investigate the effect of different criteria on subsidence. Then the weight of each criterion was obtained using the geometric mean method. Then to combine the layers, VIKOR and TOPSIS fusion techniques were used. To evaluate the implemented method, Sentinel 1 radar images were used to prepare a subsidence map, and a comparison between the two maps has been made.Results and conclusion: The analysis indicated that land use, underground water, and rainfall had the most significant influence on subsidence, with weights of 0.4292, 0.2699, and 0.1473, respectively. In contrast, slope and elevation had the least impact, with weights of 0.0220 and 0.0375, respectively. A subsidence map was successfully produced using Sentinel-1 images and Differential Interferometric Synthetic-Aperture Radar (DInSAR) techniques, and this map was compared to those obtained through VIKOR and TOPSIS methods, demonstrating a favorable level of compatibility. Manuscript profile

Background and objective: In recent years, we have witnessed the growth of forest fires due to severe climate changes and increased human activities. These fires impose many destructive effects on the environment and human health. Therefore, it is necessary to identify and measure the intensity of forest fires and plan for the revitalization of vegetation.Materials and methods: This study aims to investigate the intensity of the fire in the forest areas of northern Ukraine using Sentinel 2 satellite images and using the indicators of different normalized burn ratios (dNBR), relatively different normalized burn ratios (RdNBR), and relativized burn ratio ( RBR) in the Google Earth Engine (GEE) cloud platform and comparing the results of the extent of the fire area extracted from the indicators with the data available by the European Forest Fire Information System (EFFIS). Also, the Normalized Difference Vegetation Index (NDVI) was used to investigate the process of forest cover restoration.Results and conclusion: The results showed that the RBR and RdNBR indices in study areas A and B have been able to estimate the fire extent with 1.43% and 5.96% differences compared to EFFIS data. Also, the results of the NDVI index showed that after two years of the fire, in study areas A and B, 76.06% and 58.86% of the damaged forest cover improved, respectively. Manuscript profile