The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 701–706, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-701-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 701–706, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-701-2021

  29 Jun 2021

29 Jun 2021

THE USE OF SENTINEL 1/2 VEGETATION INDEXES WITH GEE TIME SERIES DATA IN DETECTING LAND COVER CHANGES IN THE SINOP NUCLEAR POWER PLANT CONSTRUCTION SITE

E. Çolak1, M. Chandra1, and F. Sunar2 E. Çolak et al.
  • 1Chemnitz University of Technology, Germany
  • 2ITU, Civil Engineering Faculty, Geomatics Engineering Department, 80626 Maslak Istanbul, Turkey

Keywords: Nuclear Power Plant, Sentinel 1/2 Data, SAR Vegetation Indices, Google Earth Engine (GEE), Change Detection

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.