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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, 2025, Vol. 22, No. 1, pp. 131-147

Analysis of informativeness of the temperature-vegetation index in solving the tasks of monitoring moisture content of tundra ground cover (on the example of Bovanenkovo field polygon, Yamal Peninsula)

S.G. Kornienko 1 
1 Oil and Gas Research Institute RAS, Moscow, Russia
Accepted: 14.01.2025
DOI: 10.21046/2070-7401-2025-22-1-131-147
Moisture content of tundra ground vegetation cover (GVC) is one of the main factors affecting the condition and degradation of frozen soils under climate warming and anthropogenic impact. On the example of Bovanenkovo field polygon (Yamal Peninsula), the possibility of using the temperature-vegetation index WI (Wet Index) to characterize long-term and short-term changes in moisture content of the GVC and substrates to the depth of penetration of daily temperature fluctuations has been considered for the first time. The analysis was based on Landsat satellite images of summer (July – August) surveys from 1984 to 2023 in areas of anthropogenic load, as well as at different geomorphologic levels and sections of the background area of the landfill. Time series of NDVI (Normalized Difference Vegetation Index) characterizing the concentration of green mass of vegetation, NDWI (Normalized Difference Water Index) estimating moisture on the cover surface, and a parameter characterizing total water body area (TWA) were additionally used in this work. In contrast to NDVI, NDWI and TWA, seasonal (summer) changes in WI are clearly independent of air temperature and are characterized by a gradual decrease in WI from the beginning of July to the end of August, which corresponds to the trends of gradual temperature rise and increase in the depth of the seasonally thawed layer. The site of longer anthropogenic impact and adjacent floodplain area of the background area show the most significant trends of decrease in WI normalized relative to the entire background area. This fact rather testifies to the influence of climatic warming, but the influence of anthropogenic factors cannot be excluded either. The revealed differences in multiyear changes of WI, NDWI and NDVI normalized relative to the background area at different geomorphological levels may be signs of different response of cryogenic landscape types to climate warming. Similar trends in the indices indicate their mutual relationship and timing to the transformations of the GVC. Examples of short-term anomalous changes in WI associated with water release from lakes and construction of objects, including formation of new reservoirs, are given, which confirms a sufficiently high informativeness of WI in solving the tasks of monitoring and dynamic mapping of moisture content in tundra GVC.
Keywords: Bovanenkovo field, moisture, ground vegetation cover, climate warming, temperature-vegetation index, trends, tundra, Landsat
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References:

  1. Anisimov O. A., Sherstiukov A. B., Evaluating the effect of environmental factors on permafrost in Russia, Earth’s Cryosphere, 2016, V. 20, No. 2, pp. 90–99 (in Russian).
  2. Vasil’ev A. A., Oblogov G. E., Shirokov R. S., Long-term monitoring of the depth of seasonal thawing of rocks in typical tundras of western Yamal, Earth’s Cryosphere, 2023, V. 27, No. 4, pp. 3–13 (in Russian), DOI: 10.15372/KZ20230401.
  3. Konishchev V. N., Permafrost response to climate warming, Vestnik Moskovskogo universiteta. Ser. 5. Geografiya, 2009, No. 4, pp. 10–20 (in Russian).
  4. Kornienko S. G., Characteristics of anthropogenic transformations of landscapes in the area of Bovanenkovo gas field based on Landsat satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, V. 19, No. 2, pp. 106–129 (in Russian), DOI: 10.21046/2070-7401-2022-19-2-106-129.
  5. Kornienko S. G., Studying the dynamics of thermokarst lakes in the area of the Yamburg gas field using Landsat satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, V. 20, No. 5, pp. 246–260 (in Russian), DOI: 10.21046/2070-7401-2023-20-5-246-260.
  6. Kornienko S. G., Elsakov V. V., Assessment of informativeness of temperature-vegetation index as an indicator of moisture content of tundra ground vegetation cover, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, V. 21, No. 3, pp. 155–170 (in Russian), DOI: 10.21046/2070-7401-2024-21-3-155-170.
  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. 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. Ural’skogo universiteta, 2004, 63 p. (in Russian).
  9. 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).
  10. Ardelean F., Onaca A., Chetan M.-A. et al., Assessment of spatio-temporal landscape changes from VHR images in three different permafrost areas in the Western Russian Arctic, Remote Sensing, 2020, V. 12, No. 23, Article 3999, DOI: 10.3390/rs12233999.
  11. Elsakov V. V., Kaverin D. A., Shchanov V. M., The study of seasonal vertical changes of ground surface in the Polar Ural foothills based on field measurements and Alos PALSAR radar interferometry, Earth’s Cryosphere, 2021, V. 25, No. 5, pp. 36–46, DOI: 10.15372/KZ20210505.
  12. Gao B., NDWI — A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, 1996, V. 58, pp. 257–266, DOI: 10.1016/S0034-4257(96)00067-3.
  13. Mann H. B., Nonparametric tests against trend, Econometrica, 1945, V. 13, pp. 245–259.
  14. Roy D. P., Kovalskyy V., Zhang H. K. et al., Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity, Remote Sensing of Environment, 2016, V. 185, pp. 57–70, DOI: 10.1016/j.rse.2015.12.024.
  15. 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, V. 238, pp. 350–357.
  16. Teillet P. M., Barker J. L., Markham B. L., Irish R. R., Fedosejevs G., Storey J. C., Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets, Remote Sensing of Environment, 2001, V. 78, pp. 39–54.
  17. Tucker C. J., Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment, 1979, V. 8, pp. 127–150, DOI: 10.1016/0034-4257(79)90013-0.
  18. Weng Q., Lu D., Schubring J., Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies, Remote Sensing of Environment, 2004, V. 89, pp. 467–483, DOI: 10.1016/j.rse.2003.11.005.
  19. Xu H., Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, Intern. J. Remote Sensing, 2006, V. 27, No. 14, pp. 3025–3033, DOI: 10.1080/01431160600589179.