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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, 2021, Vol. 18, No. 4, pp. 79-91

The seasonal changes investigation of Vize Island surface for the mapping purposes using a multi-temporal coherence composite (MTC)

V.Yu. Shirshova 1, 2 , E.A. Baldina 1 
1 Lomonosov Moscow State University, Moscow, Russia
2 Research Center for Earth Operative Monitoring, Moscow, Russia
Accepted: 16.06.2021
DOI: 10.21046/2070-7401-2021-18-4-79-91
Radar interferometry methods are widely used to create and update digital elevation maps and to study the movements of the Earth’s surface. However, radar interferometry can also be used in many other applications, in particular for monitoring seasonal variability of hard-to-reach areas. Small Arctic islands are “white spots” on maps, due to the fact that since the 1950s the cartographic information on them has not been updated, and its updating with the use of ground measurements or images in the optical range is very labor- and resource-intensive. Meanwhile, in the conditions of global climate change, such islands are the most susceptible to transformations. Small Arctic islands can therefore serve as markers of warming processes, for which it is necessary to designate indicators for monitoring environmental changes. The presented work demonstrates the application of interferometric coherence to study seasonal variability. The multitemporal coherence composite (MTC), widely used as a change indicator for agricultural areas, was applied to assess changes in Arctic Vise Island. With the help of the multitemporal composite, the characteristic features of seasonal surface variability at different times of the year were determined on the example of Vise Island. For this purpose, all available interferometric radar data from Sentinel-1 satellite on the territory of the island were processed. Vise (a total of 88 images). A detailed analysis of the MTC composites was performed for 2019, the year for which the largest number of coherence maps was obtained, which was 26. We obtained data on the periods of ice-free water area closest to the island and snow-free surface over the past five years, provided by Sentinel-1 surveys. The conducted study will serve as a basis for creating thematic maps for the territory of Vise Island in the future.
Keywords: satellite radar interferometry, Sentinel 1, coherence, MTC composite, Arctic
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References:

  1. Vize V. Yu., Moria Sovetskoi Arktiki: ocherki po istorii issledovaniia (Seas of the Soviet Arctic: Essays on the History of Research), Moscow: Glavsevmorput’, 1948, 296 p. (in Russian).
  2. Vinogradova N. S., Sosnovsky A. V., Coherence maps application for InSAR data accuracy improving, Ural Radio Engineering J., 2018, Vol. 2, No. 1, pp. 67–80 (in Russian), DOI: 10.15826/urej.2018.2.1.006.
  3. Dostovalov M. Yu., Troshko K. A., An experimental estimation of coherence using magnitude Sentinel-1 synthetic aperture radar images, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No 2, pp. 9–18 (in Russian), DOI: 10.21046/2070-7401-2020-17-2-9-18.
  4. Zakharov A. I., Yakovlev O. I., Smirnov V. M., Sputnikovyi monitoring Zemli. Radiolokatsionnoe zondirovanie poverkhnosti (Satellite Earth observation. Radar remote sensing of the surface), Moscow: Krasand, 2012, 248 p. (in Russian).
  5. https://litau.ru/2014/08/16/8946.
  6. Mikhaylyukova P. G., Zakharov A. I., Zakharova L. N., Zoning of the Tolbachinsky Dol based on InSAR coherence, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 2, pp. 85–98 (in Russian), DOI: 10.21046/2070-7401-2020-17-2-85-98.
  7. Pietranera L., Cesarano L., Britti F., Gentile V., Kantemirov Y., New MTC product based on COSMO-SkyMed data, Geomatika, 2012, No. 1, pp. 46–51 (in Russian).
  8. Romanenko F. A., The geomorphic processes intensiveness on the islands and coasts of the Kara and Laptev seas (on the base of polar stations data), Geomorfologiya, 2008, No. 1, pp. 56–64 (in Russian), DOI: 10.15356/0435-4281-2008-1-56-64.
  9. Chimitdorzhiev T. N., Dmitriev A. V., Dagurov P. N., Technology of joint analysis of Sentinel-1 interferometric coherence time series and vegetation index based on Sentinel-2 data for monitoring agricultural fields, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 4, pp. 61–72 (in Russian), DOI: 10.21046/2070-7401-2020-17-4-61-72.
  10. Amarsaikhan D., Ganzorig M., Ache P., Blotevogel H., The integrated use of optical and InSAR data for urban land‐cover mapping, Intern. J. Remote Sensing, 2007, Vol. 28, No. 6, pp. 1161–1171, DOI: 10.1080/01431160600784267.
  11. Baldina E. A., Shirshova V. Yu., Zhdanova E. Yu. Characteristics of the Small Arctic Island of Vise (Kara Sea) Basing on 2019 Multi-season Sentinel-1 Data, European Polar Science Week, ESA, 2020, 15 p., p. 7
  12. Bartsch A., Pointner G., Ingeman-Nielsen T., Lu W., Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2, Remote Sensing, 2020, Vol. 12, No. 15, Art. No. 2368. DOI: 10.3390/rs12152368.
  13. Engdahl M. E., Hyyppa J. M., Land-cover classification using multitemporal ERS-1/2 InSAR data, IEEE Trans. Geoscience and Remote Sensing, 2003, Vol. 41, No. 7, pp. 1620–1628, DOI: 10.1109/TGRS.2003.813271.
  14. Jacob A. W., Vicente-Guijalba F., Lopez-Martinez C., Lopez-Sanchez J. M., Litzinger M., Kristen H., Mestre-Quereda A., Ziółkowski D., Lavalle M., Notarnicola C., Suresh G., Antropov O., Ge S., Praks J., Ban Y., Pottier E., Mallorquí Franquet J. J., Duro J., Engdahl M. E., Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2020, Vol. 13, pp. 535–552, DOI: 10.1109/JSTARS.2019.2958847.
  15. Jiang M., Yong B., Tian X., Malhotra R., Hu R., Li Z., Yu Z., Zhang X., The potential of more accurate InSAR covariance matrix estimation for land cover mapping, ISPRS J. Photogrammetry and Remote Sensing, 2017, Vol. 126, pp. 120–128, DOI: 10.1016/j.isprsjprs.2017.02.009.
  16. Khalil R. Z., Haque S., InSAR coherence-based land cover classification of Okara, Pakistan, The Egyptian J. Remote Sensing and Space Science, 2018, Vol. 21, pp. S23–S28, DOI: 10.1016/j.ejrs.2017.08.005.
  17. Ma G., Zhao Q., Wang Q., Liu M., On the Effects of InSAR Temporal Decorrelation and Its Implications for Land Cover Classification: The Case of the Ocean-Reclaimed Lands of the Shanghai Megacity, Sensors, 2018, Vol. 18, No. 9, Art No. 2939. DOI: 10.3390/s18092939.
  18. Sharov A. I., Online Atlas of Glacier Fluctuations in the Eurasian High Arctic, 2012, available at: http://dib.joanneum.at/maires/index.php?page=atlas (accessed 25.12.2020).
  19. Srivastava H. S., Patel P., Navalgund R. R., Application potentials of synthetic aperture radar interferometry for land-cover mapping and crop-height estimation, Current Science, 2006, Vol. 90, No. 6, pp. 783–788.
  20. Touzi R., Lopes A., Bruniquel J., Vachon P. W., Coherence estimation for SAR imagery, IEEE Trans. Geoscience and Remote Sensing, 1999, Vol. 37, No. 1, pp. 135–149, DOI: 10.1109/36.739146.
  21. Vicente-Guijalba F., Jacob A., Lopez-Sanchez J. M., Lopez-Martinez C., Duro J., Notarnicola C., Ziolkowski D., Mestre-Quereda A., Pottier E., Mallorquí J. J., Lavalle M., Engdahl M., Sincohmap: Land-cover and vegetation mapping using multi-temporal Sentinel-1 interferometric coherence, 2018 IEEE Intern. Geoscience and Remote Sensing Symp. (IGARSS), 2018, pp. 6631–6634, DOI: 10.1109/IGARSS.2018.8517926.
  22. Wang L., Marzahn P., Bernier M., Ludwig R., Mapping permafrost landscape features using object-based image classification of multi-temporal SAR images, ISPRS J. Photogrammetry and Remote Sensing, 2018, Vol. 141, pp. 10–29, DOI: 10.1016/j.isprsjprs.2018.03.026.
  23. Yun H. W., Kim J. R., Choi Y. S., Lin S. Y., Analyses of Time Series InSAR Signatures for Land Cover Classification: Case Studies over Dense Forestry Areas with L-Band SAR Images, Sensors, 2019, Vol. 19, No. 12, Art No. 2830, DOI: /10.3390/s19122830.