Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
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, 2019, Vol. 16, No. 1, pp. 105-117

The use of remote sensing data in the study of river network reorganization (by example of the Upper Kama)

N.N. Nazarov 1 , S.V. Kopytov 1 
1 Perm State University, Perm, Russia
Accepted: 30.11.2018
DOI: 10.21046/2070-7401-2019-16-1-105-117
The modern landscape structure of the Upper Kama depression looks rather complicated. The geosystem pattern of this territory is a reflection of the history of drainage system formation in the Upper Kama basin. Study of river network reorganization in this region is an important element in understanding the course of natural events in the periglacial zone of the east of Russian Plain. The geosystem pattern is manifested in Landsat multiband satellite images. Typical marsh tracts are distinguished by a variety of landscape structure. Several types of “non-marsh” geosystems stand out against their background. Their configuration and variety of colors indirectly indicate, firstly, genetic heterogeneity of the natural complexes, and secondly, their belonging to landforms of different timing and subsequent development. The location of the ancient channels (spatio-temporal marks of the drainage system reorganization in the Neo-Pleistocene and Holocene) was determined using satellite images Landsat-8 OLI taken in 2017 and 2018 surveys. The process of determining the traces of channel influence on the surface of the lacustrine terrace consisted in selecting the most appropriate band combinations. These combinations best identify the contours of individual erosion systems. The satellite images decoding allowed establishing several generations of the lacustrine (first above-floodplain?) terrace in the Upper Kama depression. Three channel systems and one marshy-channel system play the role of separate generations within the depression except for the modern Kama floodplain. According to the results of the analysis of satellite images, the best geomorphological expression of the erosion relief was obtained for bands combinations of the SWIR and NIR 7–6–5 (indication of sandy bars and ridges, indication of the moistening degree of upper bog facies) and NIR, SWIR and RED bands —5–6–4 (indication of the vegetation species composition and the moistening degree of floodplain and swamp peat geosystems).
Keywords: multispectral satellite images, Landsat, digital elevation model, Late Pleistocene, Holocene, river network reorganization, Upper Kama depression, Upper Kama
Full text

References:

  1. Karpukhina N. V., Osobennosti degradatsii ostashkovskogo lednikovogo pokrova v predelakh Chudsko-Pskovskoi nizmennosti (Characteristic features of the Ostashkov ice sheet degradation within Chudsko-Pskovskaya Lowland), Geomorfologiya, 2013, Vol. 4, pp. 38–47.
  2. Knizhnikov Yu. F., Kravtsova V. I., Mnogozonal’naya aerokosmicheskaya s”emka i ee primenenie pri izuchenii okruzhayushchei sredy (Multiband aerospace sensing and its use in environmental research), Obninsk: VNIIGMI-MCD, 1978, 47 p.
  3. Kornienko S. G., Variatsii koeffitsientov otrazheniya v krasnoi, blizhnei infrakrasnoi oblasti spektra i indeksa NDVI obraztsov tundrovoi rastitel’nosti v zavisimosti ot vlazhnosti substratov (Variations of red and near-infrared reflectance and NDVI of tundra vegetation as a function of substrate moisture), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 3, pp. 225–234.
  4. Kravtsova V. I., Kosmicheskie metody issledovaniya pochv (Space methods of soil investigation), Moscow: Aspekt Press, 2005, 190 p.
  5. Krasnov I. I., Chetvertichnye otlozheniya i geomorfologiya Kamsko-Pechorsko-Vychegodskogo vodorazdela i prilegayushchikh territorii (Quaternary deposits and geomorphology of the Kama–Pechora–Vychegda watershed and adjacent territories), In: Materialy po geomorfologii Urala (Materials on the Urals geomorphology), Moscow, Leningrad: Gosgeolizdat, 1948, Vol. 1, pp. 47–87.
  6. Lavrov A. S., Potapenko L. M., Neopleistotsen severo-vostoka Russkoi ravniny (Neopleistocene of the Northeastern Russian Plain), Moscow: Aerogeologiya, 2005, 348 p.
  7. Lavrov A. S., Potapenko L. M., Neopleistotsen Pechorskoi nizmennosti i Zapadnogo Pritiman’ya (stratigrafiya, paleogeografiya, khronologiya) (Neopleistocene of the Pechora lowland and Western Pre-Timan (stratigraphy, paleogeography, chronology)), Moscow: OAO “Mozhaiskii poligraficheskii kombinat”, 2012, 191 p.
  8. Lyamina V. A., Korolyuk A. Yu., Zolnikov I. D., Smolentsev B. A., Lashinsky N. N., Generalizatsiya landshaftnykh obstanovok v spektral’nykh kharakteristikakh kosmicheskikh snimkov razlichnogo prostranstvennogo razresheniya (Reflection of landscape generalization in spectral characteristics of fine, middle and large scale space images), Issledovanie Zemli iz kosmosa, 2010, No. 4, pp. 77–84.
  9. Nazarov N. N., Pleistotsenovye perestroiki rechnykh rusel i sovremennoe razvitie poimenno-ruslovykh kompleksov verkhnei Kamy (Pleistocene reorganization and recent development of river channels in the upper Kama river basin), Geomorfologiya, 2017, No. 3, pp. 88–100.
  10. Nazarov N. N., Chernov A. V., Kopytov S. V., Perestroiki rechnoi seti Severnogo Predural’ya v pozdnem pleistotsene i golotsene (River’s network rearrangements of the northern Pre-Urals in the Late Pleistocene and Holocene), Geographical bulletin, 2015, No. 3, pp. 26–34.
  11. Ryabkov N. V., Drevnie prilednikovye basseiny mezhdurech’ya Kamy, Pechory, Vychegdy i ikh relikty (Ancient glacial basins between the Kama, Pechora, Vychegda and their relicts), Byulleten’ Komissii po izucheniyu chetvertichnogo perioda, 1976, No. 45, pp. 94–105.
  12. Sladkopevtsev S. A., Izuchenie i kartografirovanie rel’efa s ispol’zovaniem aerokosmicheskoi informatsii (Study and mapping of relief using aerospace information), Moscow: Nedra, 1982, 216 p.
  13. Sudakova N. G., Karpukhin S. S., Altynov A. E., Paleogeograficheskie rekonstruktsii lednikovykh morfolitostruktur Podmoskov’ya s ispol’zovaniem kosmicheskoi informatsii (Paleogeographic reconstructions of glacial morpholitic structures of the Moscow region using space information), Byulleten’ Komissii po izucheniyu chetvertichnogo perioda, 2015, No. 74, pp. 76–89.
  14. Amer R., Kolker A. S., Muscietta A., Propensity for erosion and deposition in a deltaic wetland complex: Implications for river management and coastal restoration, Remote Sensing of Environment, 2017, Vol. 199, pp. 39–50.
  15. Boles S. H., Xiao X., Liu J., Zhang Q., Munkhtuya S., Chen S., Ojima D., Land cover characterization of temperate East Asia using multi-temporal VEGETATION sensor data, Remote Sensing of Environment, 2004, Vol. 90, pp. 477–489.
  16. Breeze P. S., Drake N. A., Groucutt H. S., Parton A., Jennings R. P., White T. S., Clark-Balzan L., Shipton C., Scerri E. M. L., Stimpson C. S., Crassard R., Hilbert Y, Alsharekh A., Al-Omari A., Petraglia M., Remote sensing and GIS techniques for reconstructing Arabian palaeohydrology and identifying archaeological sites, Quaternary Intern., 2015, Vol. 382, pp. 98–119.
  17. Ceccato P., Gobron N., Flasse S., Pinty B., Tarantola S., Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1, Remote Sensing of Environment, 2002, Vol. 82, pp. 188–197.
  18. Ghoneim E., Benedetti M., El-Baz F., An integrated remote sensing and GIS analysis of the Kufrah Paleoriver, Eastern Sahara, Geomorphology, 2012, Vol. 139–140, pp. 242–257.
  19. Grosswald M. G., Late Weichselian Ice Sheets of Northern Eurasia, Quaternary Research, 1980, Vol. 13, pp. 1–32.
  20. Harris J. R., Wickert L., Lynds T., Behnia P., Rainbird R., Grunsky E., McGregor R., Schetselaar E., Remote Predictive Mapping 3. Optical Remote Sensing — A Review for Remote Predictive Geological Mapping in Northern Canada, Geoscience Canada, 2011, Vol. 38(2), pp. 49–83.
  21. Larsen E., Fredin O., Jensen M., Kuznetsov D., Lysa A., Subetto D., Subglacial sediment, proglacial lake-level and topographic controls on ice extent and lobe geometries during the Last Glacial Maximum in NW Russia, Quaternary Science Reviews, 2014, Vol. 92, pp. 369–387.
  22. Lysa A., Jensen M. A., Larsen E., Fredin O., Demidov I. N., Ice-distal landscape and sediment signatures evidencing damming and drainage of large pro-glacial lakes, northwest Russia, Boreas, 2011, Vol. 40, pp. 481–497.
  23. Lysa A., Larsen E., Buylaert J.-P., Fredin O., Jensen M., Kuznetsov D., Late Pleistocene stratigraphy and sedimentary environments of the Severnaya Dvina-Vychegda region in northwestern Russia, Boreas, 2014, Vol. 43, pp. 759–779.
  24. Mangerud J., Jacobsson M., Alexanderson H., Astakhov V., Clarke  G. C. K., Henriksen M., Hjort C., Krinnerm G., Lunkkja J.-P., Moller P., Murray A., Nikolskaya O., Saarnisto M., Svendsen J. I., Ice-dammed lakes and rerouting of the drainage of northern Eurasia during the Last Glaciation, Quaternary Science Reviews, 2004, Vol. 23, pp. 1313–1332.
  25. Peltier W. R., Global glacial isostasy and the surface of the ice-age Earth: the ICE-5g (VM2) model and GRACE, Annual Review of Earth and Planetary Sciences, 2004, Vol. 32, pp. 111–149.
  26. Roy D. P., Wulder M. A., Loveland T. R., Landsat-8: Science and product vision for terrestrial global change research, Remote Sensing of Environment, 2014, Vol. 145, pp. 154–172.
  27. Skakun R. S., Wulder M. A., Franklin S. E., Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage, Remote Sensing of Environment, 2003, Vol. 86, pp. 433–443.
  28. Veremeeva A. A., Glushkova N. V., Relief formation in the regions of the ice complex deposit occurrence: remote sensing and GIS-studies in the Kolyma lowland tundra, Earth’s Cryosphere, Vol. 20(1), pp. 15–25.
  29. Wilson E. H., Sader S. A., Detection of forest harvest type using multiple dates of Landsat TM imagery, Remote Sensing of Environment, 2002, Vol. 80, pp. 385–396.
  30. Xu H., Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensing imagery, Intern. J. Remote Sensing, 2006, Vol. 27, pp. 3025–3033.