ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 2, pp. 106-129

Characteristics of anthropogenic transformations of landscapes in the area of Bovanenkovo gas field based on Landsat satellite data

S.G. Kornienko 1 
1 Oil and Gas Research Institute RAS, Moscow, Russia
Accepted: 11.04.2022
DOI: 10.21046/2070-7401-2022-19-2-106-129
The results of the assessment of transformations of natural landscapes of the permafrost zone in the area of construction and operation of technical facilities of the Bovanenkovo oil-gas-condensate field on the Yamal Peninsula are presented. The study was conducted using 10 Landsat satellite images of summer surveys from 1988 to 2020 based on parameters characterizing the noon-time mean land surface temperature (LST), albedo (Alb), chlorophyll content (NDVI index), and moisture (NDWI index) of the ground cover. Analysis of long-term trends of mean values of LST parameters, Alb, NDVI, and NDWI to assess the influence of anthropogenic factors on the background of global and regional changes was conducted using the technique of relative radiometric normalization of the time series of multispectral space images. The coefficients of the equations for image transformation and normalization errors were determined based on the cross-validation method. The significance of the trends was assessed using the nonparametric Mann-Kendall test. The informativity of the LST, Alb, NDVI and NDWI parameters for characterizing landscape transformations was confirmed by assessing vegetation changes using the 2004 and 2016 ultra-high spatial resolution satellite images. Within the boundaries of the plot covering all facilities built by 2020, the trends are insignificant. In the local area of the longest technogenic load (on the southern arch of the field), there is a more evident (significant) LST growth and reduction of NDWI, indicating the dominance of surface drainage processes. Trends of Alb and NDVI are insignificant in this area, indicating no trends in vegetation cover changes associated with anthropogenic impact. It is noted that the observed increase in surface temperature against the background of the observed global climatic trend may be an additional factor in increase in the depth of the active layer and permafrost degradation. It is concluded that changes in LST, Alb, NDVI, and NDWI parameters characterizing transformations of natural landscapes are not recorded beyond the boundaries of industrial and infrastructure sites.
Keywords: anthropogenic impact, remote sensing, cryogenic landscape, radiometric normalization, cross-validation, surface temperature, albedo, NDVI, NDWI, transformations, tundra, Bovanenkovo gas field
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References:

  1. Vorontsov K. V., Combinatorial approach to assessing the quality of learning algorithms, Matematicheskie voprosy kibernetiki, 2004, Vyp. 13, pp. 5–36 (in Russian), available at: http://library.keldysh.ru/mvk.asp?id=2004-5.
  2. Elsakov V. V., Spectral differences in vegetation cover characteristics of tundra communities by Landsat sensors, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 4, pp. 92–101 (in Russian), DOI: 10.21046/2070-7401-2021-18-4-92-101.
  3. Kadnichanskii S. A., Contrast evaluation of digital aerial and satellite images, Geodeziya i kartografiya, 2018, No. 3, pp. 46–51 (in Russian), DOI: 10.22389/0016-7126-2018-933-3-46-5.
  4. Konishchev V. N., Response of permafrost to climate warming, Vestnik Moskovskogo universiteta. Ser. 5: Geografiya, 2009, No. 4, pp. 10–20 (in Russian).
  5. Kornienko S. G., Using thermal images from the Landsat 7 satellite for mapping tundra landscapes: the case of the Bovanenkovo – Baydaratskaya Bay gas pipeline section, Aktual’nye problemy nefti i gaza, 2020, Vyp. 3(30), pp. 51–63 (in Russian), DOI: 10.29222/ipng.2078-5712.2020-30.art6.
  6. Kriosfera neftegazokondensatnykh mestorozhdenii poluostrova Yamal, V 3 t., T. 2, Kriosfera Bovanenkovskogo neftegazokondensatnogo mestorozhdeniya (Cryosphere of oil and gas condensate fields of the Yamal Peninsula, In 3 vol., Vol. 2, Cryosphere of the Bovanenkovo oil and gas condensate field), Yu. B. Badu, N. A. Gafarov, E. E. Podbornyi (eds.), Moscow: OOO “Gazprom EKSPO”, 2013, 422 p. (in Russian).
  7. Kritsuk L. N., Dubrovin V. A., Maps of geocryological zoning as the basis of the geoecological assessment of the developed territory of the permafrost zone, Razvedka i okhrana nedr, 2003, No. 7, pp. 12–15 (in Russian).
  8. Lavrinenko I. A., Map of technogenic disturbance of Nenets Autonomous District, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 2, pp. 128–136 (in Russian), DOI: 10.21046/2070-7401-2018-15-2-128-136.
  9. Morozova L. M., Magomedova M. A., Struktura rastitel’nogo pokrova i rastitel’nye resursy poluostrova Yamal (Land cover structure and plant resources of the Yamal Peninsula), Ekaterinburg: Izd. Uralskogo universiteta, 2004, 63 p. (in Russian).
  10. Moskovchenko D. V., Peculiarities of long-term dynamics of vegetation at the Bovanenkovskoye field (Yamal Peninsula), Vestnik Tyumenskogo gosudarstvennogo universiteta, 2013, No. 12, pp. 57–66 (in Russian).
  11. Titkova T. B., Vinogradova V. V., The response of vegetation to climate change in boreal and subarctic landscapes at the beginning of XXI century, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 3, pp. 75–86 (in Russian).
  12. Tishkov A. A., Belonovskaya E. A., Vaisfel’d M. A., Glazov P. M., Krenke A. N., Tertitskii G. M., “The greening” of the tundra as a driver of the modern dynamics of Arctic biota, Arktika: ekologiya i ekonomika, 2018, No. 2(30), pp. 31–44 (in Russian), DOI: 10.25283/2223-4594-2018-2-31-44.
  13. Ardelean F., Onaca A., Chetan M.-A., Dornik A., Georgievski G., Hagemann S., Timofte F., Berzescu O., Assessment of spatio-temporal landscape changes from VHR images in three different permafrost areas in the Western Russian Arctic, Remote Sensing, 2020, Vol. 12, No. 23, Art. No. 3999, DOI: 10.3390/rs12233999.
  14. Beck P. S.A., Goetz S. J., Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: ecological variability and regional differences, Environmental Research Letters, 2011, Vol. 6, No. 4, Art. No. 045501, 10 p., https://doi.org/10.1088/1748-9326/6/4/049501.
  15. Bhatt U. S., Walker D. A., Raynolds M. K., Bieniek P. A., Epstein H. E., Comiso J. C., Pinzon J. E., Tucker C. J., Steele M., Ermold W., Zhang J., Changing seasonality of panarctic tundra vegetation in relationship to climatic variables, Environmental Research Letters, 2017, Vol. 12, No. 5, Art. No. 055003, 17 p., https://doi.org/10.1088/1748-9326/aa6b0b.
  16. Canty M. J., Nielsen A. A., Automatic radiometric normalization of multitemporal satellite imagery with the iteratively Re-weighted MAD transformation, Remote Sensing of Environment, 2008, Vol. 112, No. 3, pp. 1025–1036, https://doi.org/10.1016/j.rse.2007.07.013.
  17. Chander G., Markham B. L., Helder D. L., Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors, Remote Sensing of Environment, 2009, Vol. 113, pp. 893–903, https://doi.org/10.1016/j.rse.2009.01.007.
  18. Gao B., NDWI — A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, 1996, Vol. 58, pp. 257–266, DOI: 10.1016/S0034-4257(96)00067-3.
  19. Holloway J. E., Lewkowicz A. G., Douglas T. A., Li X., Turetsky M. R., Baltzer J. L., Jin H., Impact of wildfire on permafrost landscapes: A review of recent advances and future prospects, Permafrost and Periglacial Processes, 2020, Vol. 31, No. 3, pp. 371–382, https://doi.org/10.1002/ppp.2048.
  20. Jacob F., Olioso A., Gu X. F., Su Z., Seguin B., Mapping surface fluxes using airborne visible, near infrared, thermal infrared remote sensing data and a spatialized surface energy balance model, Agronomie, 2002, Vol. 22, pp. 669–680, DOI: 10.1051/agro:2002053.
  21. Kim T., Han Y., Integrated Preprocessing of Multitemporal Very-High-Resolution Satellite Images via Conjugate Points-Based Pseudo-Invariant Feature Extraction, Remote Sensing, 2021, Vol. 13, Art. No. 3990, https://doi.org/10.3390/rs13193990.
  22. Kornienko S. G., Radiometric normalization of Landsat thermal imagery for detection of tundra land cover changes: experience from West Siberia, Intern. J. Remote Sensing, 2021, Vol. 42, No. 4, pp. 1420–1449, DOI: 10.1080/01431161.2020.1832280.
  23. Kumpula T., Forbes B. C., Stammler F., Meschtyb N., Dynamics of a Coupled System: Multi-Resolution Remote Sensing in Assessing Social-Ecological Responses during 25 Years of Gas Field Development in Arctic Russia, Remote Sensing, 2012, Vol. 4, No. 4, pp. 1046–1068, DOI: 10.3390/rs4041046.
  24. Landsat 8 (L8) Data Users Handbook. Version 5.0, USGS, Department of the Interior, Sioux Falls, South Dakota: EROS, 2019, 114 p., available at: https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1574_L8_Data_Users_Handbook-v5.0.pdf.
  25. Liang S., Narrowband to broadband conversions of land surface albedo I — Algorithms, Remote Sensing of Environment, 2000, Vol. 76, pp. 213–238, DOI: 10.1016/S0034-4257(00)00205-4.
  26. Mann H. B., Nonparametric tests against trend, Econometrica, 1945, Vol. 13, pp. 245–259.
  27. Marcot B. G., Hanea A. M., What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? Computational Statistics, 2021, Vol. 36, pp. 2009–2031, https://doi.org/10.1007/s00180-020-00999-9.
  28. O’Donnell J. A., Romanovsky V. E., Harden J. W., McGuire A. D., The effect of moisture content on the thermal conductivity of moss and organic soil horizons from black spruce ecosystems in interior Alaska, Soil Science, 2009, Vol. 174, No. 12, pp. 646–651, DOI: 10.1097/SS.0b013e3181c4a7f8.
  29. Piralilou S. T., Einali G., Ghorbanzadeh O., Nachappa T. G., Gholamnia K., Blaschke T., Ghamisi P. A., Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions, Remote Sensing, 2022, Vol. 14, Art. No. 672, https://doi.org/10.3390/rs14030672.
  30. Rahman M. M., Hay G. J., Couloigner I., Hemachandran B., Bailin J., An Assessment of Polynomial Regression Techniques for the Relative Radiometric Normalization (RRN) of High-Resolution Multi-Temporal Airborne Thermal Infrared (TIR) Imagery, Remote Sensing, 2014, Vol. 6, pp. 11810–11828, https://doi.org/10.3390/rs61211810.
  31. Rahman M. M., Hay G. J., Couloigner I., Hemachandran B., Bailin J. A., A Comparison of four radiometric normalization techniques for mosaicing H-res multi-temporal thermal infrared flight lines of a complex urban scene, ISPRS J. Photogrammetry and Remote Sensing, 2015, Vol. 106, pp. 82–94, https://doi.org/10.1016/j.isprsjprs.2015.05.002.
  32. Scheidt S., Ramsey M., Lancaster N., Radiometric normalization and image mosaic generation of ASTER thermal infrared data: An application to extensive sand sheets and dune fields, Remote Sensing of Environment, 2008, Vol. 112, No. 3, pp. 920–933, https://doi.org/10.1016/j.rse.2007.06.020.
  33. Schieldge J. P., Kahle A. B., Alley R. E., Gillespie A. R., Use of thermal inertia properties for material identification, SPIE Image Processing for Missile Guidance, 1980, Vol. 238, pp. 350–357.
  34. Schott J. R., Salvaggio C., Vochok W. J., Radiometric scene normalization using pseudo-invariant features, Remote Sensing of Environment, 1988, Vol. 26, No. 1, pp. 1–14, https://doi.org/10.1016/0034-4257(88)90116-2.
  35. Sun Y., Gao C., Li J., Wang R., Liu J., Quantifying the Effects of Urban Form on Land Surface Temperature in Subtropical High-Density Urban Areas Using Machine Learning, Remote Sensing, 2019, Vol. 11, Art. No. 959, DOI: 10.3390/rs11080959.
  36. Tucker C. J., Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment, 1979, Vol. 8, pp. 127–150, DOI: 10.1016/0034-4257(79)90013-0.
  37. Urban M., Forkel M., Eberle J., Hüttich C., Schmullius C., Herold M., Pan-Arctic climate and land cover trends derived from multi-variate and multi-scale analyses (1981–2012), Remote Sensing, 2014, Vol. 6, pp. 2296–2316, DOI: 10.3390/rs6032296.
  38. Van de Griend A. A., Owe M., On the relationship between thermal emissivity and the normalized different vegetation index for natural surfaces, Intern. J. Remote Sensing, 1993, Vol. 14, No. 6, pp. 1119–1131, DOI: 10.1080/01431169308904400.
  39. Weng Q., Lu D., Schubring J., Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies, Remote Sensing of Environment, 2004, Vol. 89, pp. 467–483, DOI: 10.1016/j.rse.2003.11.005.
  40. Xu H., Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, Intern. J. Remote Sensing, 2006, Vol. 27, No. 14, pp. 3025–3033, DOI: 10.1080/01431160600589179.
  41. Xu H., Wei Y., Li X., Zhao Y., Cheng Q., A novel automatic method on pseudo-invariant features extraction for enhancing the relative radiometric normalization of high-resolution images, Intern. J. Remote Sensing, 2021, Vol. 42, pp. 6153–6183, https://doi.org/10.1080/01431161.2021.1934912.
  42. Yu Q., Epstein H. E., Engstrom R., Shiklomanov N., Strelestskiy D., Land cover and land use changes in the oil and gas regions of Northwestern Siberia under changing climatic conditions, Environmental Research Letters, 2015, Vol. 10, No. 12, Art. No. 124020, https://doi.org/10.1088/1748-9326/10/12/124020.
  43. Yuan D., Elvidge C. D., Comparison of relative radiometric normalization techniques, ISPRS J. Photogrammetry and Remote Sensing, 1996, Vol. 51, pp. 117–126.