PSI - Issue 20

S.A. Tikhonova et al. / Procedia Structural Integrity 20 (2019) 230–235

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S.A. Tikhonova et al. / Structural Integrity Procedia 00 (2019) 000 – 000

The results of conducted studies show that the use of NDVI vegetation index makes it possible to identify and classify different types of vegetation using satellite images, as well as to trace seasonal changes in plant communities. This technique can be used in monitoring the state of natural environment of oil-producing areas, taking into account its features (swampiness, inaccessibility, lack of opportunities for ground-based research). Acknowledgements Landsat satellite images, which are in the public domain, were used in this work. The work is carried out within the framework of budget financing. References Adamovich, T.A., Kantor, G.Y., Ashikhmina, T. Y., Savinykh, V.P., 2018. Analysis of the seasonal and long-term dynamics of the NDVI vegetation index in the territory of the Nurgush State Nature Reserve. Vyatka State University, Institute of Biology, Komi Scientific Center, Ural Branch of the Russian Academy of Sciences, Moscow State University of Geodesy and Cartography. Theoretical and applied ecology 1, 18-24. Adams, J.B., Sabol, D.E., Kapos, V., Almeida, R. Filho, Roberts, D.A., Smith, M.O., Gillespie, A.R., 1995. Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brazilian Amazon. Remote Sens. Environ. 52(2), 137-154. Alekseeva, M.N., Peremitina, TO, Yaschenko, IG, 2013. Environmental risk assessment of emergency oil spills using satellite data. Federal State Budgetary Institution of Science Institute of Petroleum Chemistry, Siberian Branch of the Russian Academy of Sciences (IKHN SB RAS). Atmospheric and Ocean Optics 6, 525-530. Dzyuba, SA, 2006. Information-analytical system of geotechnical monitoring and control of the Yamal-Torzhok gas pipeline // abstract for the degree of candidate of technical sciences, Institute of Oil and Gas Problems of the Russian Academy of Sciences, Publishing and Printing Center MITHT them. V.Lomonosova, Moscow, 21. Dneprovskaya, V.P., Yashchenko, I.G., Peremitina, T.O., 2017. Comprehensive study of technological load using satellite and terrestrial data. Institute of Chemistry of Petroleum, Siberian Branch of the Russian Academy of Sciences (IKN SB RAS). The science. Equipment. Technologies 4, 134 – 42. Dubinin, M., 2002. GIS-Lab is an informal community of specialists in the field of GIS and remote sensing, developing themselves and helping to master spatial technologies for those who need help.” NDVI - theory and practice [Electronic resource]. Access mode: http://gis lab.info/qa/ndvi.html, free. Elmore, A.J., Mustard, J.F., Manning, S.J., Lobell, D.B., 2000. Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index Remote Sens. Environ. 73(1), 87-102. Harris Geospatial Documentation Center. Reference manuals and reference documents. Using ENVI. Vegetation index calculator [Electronic resource]. Access mode: http://www.harrisgeospatial.com/docs/VegetationIndexCalculator.html, free. Kapitonova, T.A., Struchkova, G.P., Tarskaya, L.E., Efremov, P.V., 2014. Analysis of risk factors of pipelines laid in cryolithozone using GIS technologies. Fundamental Research 5 – 5, 954 – 958. Luo Lihui, Ma Wei, Yanli Zhuang, Yaonan Zhang, Shuhua Yi, Jianwei Xu, Yinping Long, Di Ma, Zhongqiong Zhang, 2018. The impacts of climate change and human activities on alpine vegetation and permafrost in the Qinghai-Tibet Engineering Corridor, Ecological Indicators 93, 24-35. Peremitina, T.O., Yashchenko, I.G., Alekseeva, M.N., 2014. Comprehensive Environmental Risk Assessment of Oil Spills Institute of Chemistry of Petroleum SB RAS, Tomsk. Ecology and Industry of Russia 11, 22-25. Stroytransgaz. Projects Oil and gas construction [Electronic resource]. Access mode: http://www.stroytransgaz.ru/projects/oilgas_engineering/, free. Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation Remote Sens. Environ 8(2), 127-150. Verbesselt, J., Hyndman, R., Newnham, G., Culvenor, D., 2010. Detecting trend and seasonal changes in satellite image time series. Remote Sens. Environ 114(1), 106-115. Weather in 243 countries of the world. The site is developed and accompanied by the company (LLC) "Reliable Prognosis" [Electronic resource]. Access mode: https://rp5.ru/Pogoda_world, free.