Evaluation of the possibility of vegetation interpretation on thermal infrared satellite images, case of the Southern Urals and Kuznetsk Alatau

DOI: 10.35595/2414-9179-2022-1-28-496-507

View or download the article (Rus)

About the Authors

Mikhail Y. Grishchenko

Lomonosov Moscow State University, Faculty of Geography,
Leninskie Gory, 1, 119991, Moscow, Russia;

HSE University, Faculty of Geography and Geoinformatics,
Pokrovsky bvd., 11, 109028, Moscow, Russia;

E-mail: m.gri@geogr.msu.ru

Denis A. Lucher

Lomonosov Moscow State University, Faculty of Geography,
Leninskie Gory, 1, 119991, Moscow, Russia;
E-mail: denis.lucher@gmail.com

Maxim V. Bocharnikov

Lomonosov Moscow State University, Faculty of Geography,
Leninskie Gory, 1, 119991, Moscow, Russia;
E-mail: maxim-msu-bg@mail.ru

Abstract

The paper presents the results of the vegetation cover interpretation using multitemporal thermal satellite images of two mountain-steppe areas: in the Southern Urals (Abzelilovsky district of the Republic of Bashkortostan) and in Kuznetsk Alatau (Ust-Abakansky and Shirinsky districts of the Republic of Khakassia). These areas have a large amount of field data on vegetation, which allows for reliable verification of satellite data. On the basis of field data and images of high spatial resolution in the optical range, vegetation maps were compiled, which became the basis for further interpretation of thermal images—images of the TIRS sensor, Landsat 8 satellite, were used. Methods of controlled and uncontrolled classification were applied to multitemporal images. In the course of the study, it was possible to establish that, based on the results of vegetation interpretation using thermal satellite images for a site in the South Urals, it is possible to determine forest areas with good reliability (up to 50–70 %), and confidently draw the border between forest and treeless areas. With satisfactory accuracy (up to 44 %), petrophytic steppes are determined. The site in the Southern Urals is characterized by a small size of the territory, a low diversity of plant communities, and rather a large dependence of the intensity of thermal radiation on the exposure of slopes. The site in Kuznetsk Alatau showed more representative interpretation results. Larch and birch-larch forests (up to 70 %), fir and birch-fir forests (up to 56 %), dwarf birch and moss-lichen tundras (up to 49 %), and steppe vegetation (up to 45 %) are most confidently recognized.

Keywords

geographical imagery interpretation, Landsat, supervised classification, unsupervised classification, South Urals, Kuznetsk Alatau

References

  1. Alshaikh A. Vegetation Cover Density and Land Surface Temperature Interrelationship Using Satellite Data, Case Study of Wadi Bisha, South KSA. Advances in Remote Sensing, 2015. V. 4. No. 3 P. 248–262. DOI: 10.4236/ars.2015.43020.
  2. Grishchenko M.Y., Butorina S.A. Investigation of the possibilities of using thermal images for vegetation interpretation (case of the Bering and Kunashir Islands). Proceedings of the International Conference InterCarto. InterGIS, 2017. V. 23. No. 3. P. 71–81 (in Russian). DOI: 10.24057/2414-9179-2017-3-23-71-81.
  3. Grishchenko M.Y., Kalitka L.S. Study of the seasonal variability of the Krasnodar thermal field based on images from the Landsat 8 satellite. InterCarto. InterGIS, 2019. V. 25. No. 2. P. 101–111 (in Russian). DOI: 10.35595/2414-9179-2019-2-25-101-111.
  4. Hansen M.C., DeFries R.S., Townshend J.R.G., Sohlberg R., Dimiceli C., Carroll M. Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data. Remote Sensing of Environment, 2002. V. 83. Issues 1–2. P. 303–319. DOI: 10.1016/S0034-4257(02)00079-2.
  5. Knizhnikov Y.F. Dynamic aerospace sounding (content, problems, scope). Moscow University Bulletin. Series 5, Geography. 1985. No. 4. P. 7–14 (in Russian).
  6. Knizhnikov Y.F., Kravtsova V.I. Aerospace research of the dynamics of geographical phenomena. Moscow: Moscow University Press. 1991. 206 p. (in Russian).
  7. Kronberg P. Remote study of the Earth: Fundamentals and methods of remote research in geology. Moscow: Mir. 1988. 343 p. (in Russian).
  8. Neinavaz E., Schlerf M., Darvishzadeh R., Gerhards M., Skidmore A.K. Thermal infrared remote sensing of vegetation: Current status and perspectives. International Journal of Applied Earth Observation and Geoinformation, 2021. V. 102. P. 102415. DOI: 10.1016/j.jag.2021.102415.
  9. Rodriguez-Galiano V., Pardo-Iguzquiza E., Sanchez-Castillo M., Chica-Olmo M., Chica-Rivas M. Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images. International Journal of Applied Earth Observation and Geoinformation. 2012. V. 18. P. 515–527. DOI: 10.1016/j.jag.2011.10.002.
  10. Sinha S., Sharma L.K., Nathawat M.S. Improved Land-use/Land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing. The Egyptian Journal of Remote Sensing and Space Science, 2015. V. 18. Issue 2. P. 217–233. DOI: 10.1016/J.EJRS.2015.09.005.
  11. Southworth J. An assessment of Landsat TM band 6 thermal data for analysing land cover in tropical dry forest regions, International Journal of Remote Sensing, 2004. V. 25. No. 4. P. 689–706. DOI: 10.1080/0143116031000139917.
  12. Srivastava P.K., Majumdar T.J., Bhattacharya A.K. Surface temperature estimation in Singhbhum Shear Zone of India using Landsat 7 ETM+ thermal infrared data. Advances in Space Research, 2009. V. 43. No. 10. P. 1563–1574. DOI: 10.1016/j.asr.2009.01.023.

For citation: Grishchenko M.Y., Lucher D.A., Bocharnikov M.V. Evaluation of the possibility of vegetation interpretation on thermal infrared satellite images, case of the Southern Urals and Kuznetsk Alatau. InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: MSU, Faculty of Geography, 2022. V. 28. Part 1. P. 496–507. DOI: 10.35595/2414-9179-2022-1-28-496-507 (in Russian)