Abstract
Abstract. Recently, the demand for nuclear power plants has been increasing in developing countries in line with global energy demands. Turkey, one of the developing economies, is also making plans for nuclear power generation since 1970. The Sinop Nuclear Power Plant was a planned nuclear plant located in the Turkey's most northern point in an area where 99% of the land is forest, in Sinop Peninsula. If disputes are resolved and its construction continues, the plant is expected to be put into service in 2028. On the other hand, due to the construction of the nuclear power plant, the land cover in and around the plant site has changed, potentially causing major environmental changes. As an example, more than 650000 trees have been cut down so far for the construction of a nuclear power plant, which may have a negative impact on the region's ecological balances by endangering biodiversity and causing ecological damage. The aim of this study is to detect changes in forest areas from the start of nuclear power plant construction through December 2020 using Sentinel 1 SAR and Sentinel 2 optical time series images. For this purpose, different radar and optical vegetation indices such as Modified Radar Vegetation Index (mRVI), Modified Radar Forest Degradation Index (mRFDI), and Normalized Difference Vegetation Index (NDVI) were applied using Google Earth Engine (GEE) Sentinel 1/2 satellite time series for 2015–2020 period. As a result, the indices used were found to yield findings consistent with the reported negative land cover change. In addition, correlation analysis were made between the radar vegetation indices used and a very high negative correlation (−0.99) was found. The annual distributions of the values of the three indices used were statistically evaluated using boxplots.
Highlights
In recent years, global energy demand has increased rapidly all over the world, especially in developing countries
Weather-independent radar vegetation indices derived from SAR data are an alternative to Normalized Difference Vegetation Index (NDVI) generated from optical data
To show forest destruction clearly indicated by the NDVI data, with these radar vegetation indices, Sentinel 1 Modified Radar Vegetation Index (mRVI) and Modified Radar Forest Degradation Index (mRFDI) time series were created using the Google Earth Engine (GEE) platform as in the NDVI time series (Figure 7)
Summary
Global energy demand has increased rapidly all over the world, especially in developing countries. Given the global energy demand, high power supply capability, and low fuel levels needed for service, nuclear energy is a viable power source for developing countries (Prăvălie and Bandoc, 2018; Cardin et al, 2017). Despite the continuing concerns about long-term waste disposal, nuclear power remains a viable source of electricity. According to International Energy Agency (2020), in emerging market and developing economies, nuclear power output will increase by over 60% from 2019 to 2030. The fleet of nuclear reactors in advanced economies will shrink over the decade, while emerging market and developing economies will gain the most additional capacity (International Energy Agency, 2020) (Figure 1)
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