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. 6, pp. 169-179

Mapping thermally heterogeneous tundra landscapes from satellite data: a case study of the Yamal Peninsula

S.G. Kornienko 1 
1 Oil and Gas Research Institute RAS, Moscow, Russia
Accepted: 24.10.2019
DOI: 10.21046/2070-7401-2019-16-6-169-179
The thermal and insulation properties of tundra landscapes control the parameters of permafrost, especially the active layer thickness (seasonal thaw and freezing depths). Experimental results suitable for the mapping of the tundra land cover thermal properties have been very limited so far. The possibility to map the thermally heterogeneous tundra land cover within the layer of diurnal temperature variations using normalized distributions of apparent thermal inertia (ATIN(/i)) has been considered for the first time in this study for the case of the central Yamal Peninsula. The reported results include comparison of ATIN(/i) distributions calculated from NOAA and MetOp-A/B (AVHRR scaner) data with the use of two different algorithms. Analysis of several ATIN(/i) distributions retrieved from images of different years and dates shows that they are not random and are applicable to map the thermal field of the Arctic and Subarctic tundras. The allowable misfit between different ATIN(/i) distributions caused by the effect of random factors is estimated using the criterion of root mean square deviation (RSMD) in the scattering pattern of two distributions based on scenes of the same date but different time of day. The average values and RSMD of ATIN(/i) generally decrease as the geomorphological levels heighten from layda and floodplain to marine terraces.
Keywords: remote sensing, apparent thermal inertia, mapping, land cover, tundra
Full text

References:

  1. Vechnaya merzlota i osvoenie neftegazonosnykh raionov (Permafrost and oil and gas development), E. S. Melnikova, S. E. Grechishcheva (eds.), Moscow: GEOS, 2002, 402 p.
  2. Gavril’ev R. I., Teplofizicheskie svoistva komponentov prirodnoi sredy v kriolitozone: (Thermophysical properties of the components of the natural environment in the permafrost zone), Novosibirsk: Izd. SO RAN, 2004, 146 p.
  3. Kornienko S. G., Izuchenie i modelirovanie neodnorodnostei teplofizicheskikh svoistv tundrovogo pochvenno-rastitel’nogo pokrova po dannym nazemnykh nablyudenii i kosmicheskoi sʺemki (Study and modeling of heterogeneities of the thermophysical properties of the tundra soil and vegetation cover according to ground-based observations and satellite imagery), 16-ya Vserossiiskaya otkrytaya konferentsiya “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (16th All-Russia Open Conf. “Current Problems in Remote Sensing of the Earth from Space”), Book of abstracts, Moscow, 12–16 Nov. 2018, Moscow: IKI RAN, 2018, p. 413.
  4. Kritsuk L. N., Dubrovin V. A., Karty geokriologicheskogo raionirovaniya kak osnova geoekologicheskoi otsenki osvaivaemoi territorii kriolitozony (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.
  5. 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.
  6. Pavlov A. V., Teplofizika landshaftov (Thermophysics of landscapes), Novosibirsk: Nauka, 1979, 284 p.
  7. Pendin V. V., Ganova S. D., Geoekologicheskii monitoring territorii raspolozheniya ob”ektov transporta gaza v kriolitozone (Geoecological monitoring of territories of location of objects of transport of gas in the permafrost zone), Moscow: OAO PNIIIS, 2009, 236 p.
  8. Kornienko S. G., Analysis of Errors in Estimating Changes in Water Body Areas by Satellite Data: Case Study of Thermokarst Lakes in Yamal Peninsula, Water Resources, 2016, Vol. 43, No. 6, pp. 180–191.
  9. Liang Sh., Narrowband to broadband conversions of land surface albedo I Algorithms, Remote Sensing of Environment, 2000, Vol. 76, pp. 213–238.
  10. Negm A., Capodici F., Ciraolo G., Maltese A., Provenzano G., Rallo G., Assessing the Performance of Thermal Inertia and Hydrus Models to Estimate Surface Soil Water Content, Applied Sciences, 2017, Vol. 7, 975, DOI: 10.3390/app7100975.
  11. Ramakrishnan D., Bharti R., Singh K. D., Nithya M., Thermal inertia mapping and its application in mineral exploration: results from Mamandur polymetal prospect, India, Geophysical J. Intern., 2013, Vol. 195(1), pp. 357–368, DOI: 10.1093/gji/ggt237.
  12. 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, available at: https://gis.ess.washington.edu/keck/Publications/Use%20of%20thermal-inertia%20properties%20for%20material%20identification.pdf.
  13. Song C., Jia L., A Method for Downscaling FengYun-3B Soil Moisture Based on Apparent Thermal Inertia, Remote Sensing, 2016, Vol. 8, 703, DOI:10.3390/rs8090703.
  14. Tucker C. J., Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment, 1979, Vol. 8, pp. 127–150.
  15. Ulivieri C., Castronuovo M. M., Francioni R., Cardillo A., A split window algorithm for estimating land surface temperature from satellites, Advances in Space Research, 1994, Vol. 14(3), pp. 59–65.
  16. 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.
  17. Van Doninck J., Peters J., Baets B. D., Clercq E. M., Ducheyne E., Verhoest N. E. C., The Potential of Multitemporal Aqua and Terra MODIS Apparent Thermal Inertia as a Soil Moisture Indicator, Intern. J. Applied Earth Observation and Geoinformation, 2011, Vol. 13, pp. 934–941.
  18. Verstraeten W. W., Veroustraete F., Van der Sande C. J., Grootaers I., Feyen J., Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests, Remote Sensing of Environment, 2006, Vol. 101, pp. 299–314.