ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 3, pp. 153-168

Patterns of wind-induced forest damage in the European Russia and Ural: analysis with satellite data

A.N. Shikhov 1 , D.A. Dremin 1 
1 Perm State University, Perm, Russia
Accepted: 09.04.2021
DOI: 10.21046/2070-7401-2021-18-3-153-168
Windthrow is one of the most substantial forest disturbance agents of the boreal forest zone. At the same time, the patterns that determine the features and degree of wind-related damage in Russia’s forests remain poorly studied. In this study, we consider the relationships of wind-related forest damage with stands species composition and age, and also with the geomorphometric variables and clear-cut proximity. The analysis was performed based on publicly available satellite images and digital elevation models, on the example of 10 large-scale windthrows that occurred in the period 1995–2020 in different parts of the European Russia and Ural, and were caused by various weather events (squalls, tornadoes and heavy snowfall). It is found that forest species composition and age are the most important factors that determine their susceptibility to windthrow. Old-growth dark coniferous forests are most susceptible to windthrow; in some cases, old-growth mixed forests or pine forests were strongly damaged. The percentage of wind-damaged area in re-grown small-leaved forests was 10–50 times less than in old-growth forests, except for tornado-induced windthrow. For large-scale windthrow induced by squall events, a statistically significant relationship of the damaged area with geomorphometric variables is confirmed. Thus, windthrow area on the windward slopes is 3–6 times higher than on the leeward ones. A substantial (1.5–3 times) increase of wind-related damage was also revealed for forests located in close proximity with new logged area. The identified relationships may be used as a basis for windthrow exposure and risk assessment and modeling, but they are not universal for all windthrow events.
Keywords: windthrow, forest damage degree, forest species composition and age, site factors, digital elevation models, geomorphometric variables
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  1. Alesenkov Yu. M., Mishin A. S., Uspin A. A., Yakushev A. B., The impact of storm winds on the forests of the Ural’s natural reserves, Ekologicheskie issledovaniya v Visimskom biosfernom zapovednike, Ekaterinburg, 2006, pp. 41–47 (in Russian).
  2. Bartalev S. A., Egorov V. A., Ershov D. V., Isaev A. S., Loupian E. A., Plotnikov D. E., Uvarov I. A., Mapping of Russia’s vegetation cover using MODIS satellite spectroradiometer data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 4, pp. 285–302 (in Russian).
  3. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Shabanov N. V., Land cover mapping over Russia using Earth observation data, Moscow: Space Research Institute RAS, 2016, 208 p. (in Russian).
  4. Gavrilyuk E. A., Ershov D. V., Method of combined processing of multi-seasonal Landsat-TM images and creation of the map of terrestrial ecosystems of the Moscow region, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 4, pp. 15–23 (in Russian).
  5. Devyatova N. V., Ershov D. V., Lyamtsev N. I., Denisov B. S., Determination of the extent of desiccation of coniferous forests in the European North of Russia according to satellite observations, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2007, Vol. 4, No. 2, pp. 204–211 (in Russian).
  6. Denisova A. Yu., Kavelenova L. M., Korchikov E. S., Prokhorova N. V., Terentyeva D. A., Fedoseev V. A., Tree species classification in Samara Region using Sentinel-2 remote sensing images and forest inventory data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16(4), pp. 86–101 (in Russian), DOI: 10.21046/2070-7401-2019-16-4-86-101.
  7. Krylov A. M., Vladimirova N. A., Remote monitoring of forest health based on satellite imagery data, Geomatika, 2011, No. 3, pp. 53–58 (in Russian).
  8. Labintsev E., Metriki v zadachakh mashinnogo obucheniya (Metrics in Machine Learning Problems),, 12.05.2017 (in Russian, accessed: 31.03.2021).
  9. Petukhov I. N., Rol’ massovykh vetrovalov v formirovanii lesnogo pokrova v podzone yuzhnoi taigi (Kostromskaya oblast’): Diss. kand. biol. nauk (The role of massive windthrows in the forest cover formation in the southern taiga subzone (Kostroma region), Cand. biol. sci. thesis), Kostroma, 2016, 150 p. (in Russian).
  10. Shikhov A. N., Abdullin R. K., Semakina A. V., Mapping forest areas threatened by fires and windthrows (on the example of the Ural territory), Geodeziya i kartografiya, 2020, No. 4, pp. 19–30 (in Russian).
  11. Albrecht A. T., Jung C., Schindler D., Improving empirical storm damage models by coupling with high-resolution gust speed data, Agricultural and Forest Meteorology, 2019, Vol. 268, pp. 23–31.
  12. Boehner J., Antonic O., Land-surface parameters specific to topo-climatology, In: Geomorphometry — Concepts, Software, Applications. Developments in Soil Science, Hengl T., Reuter H. (eds.), 2009, Vol. 33, pp. 195–226.
  13. Bouchard M., Pothier D., Ruel J.-C., Stand-replacing windthrow in the boreal forests of eastern Quebec, Canadian J. Forest Research, 2009, Vol. 39(2), pp. 481–487.
  14. Dobbertin M., Influence of stand structure and site factors on wind damage comparing the storms Vivian and Lothar, Forest Snow and Landscape Research, 2002, Vol. 77(1–2), pp. 187–205.
  15. Gardiner B., Byrne K., Hale S., Kamimura K., Mitchell S. J., Peltola H., Ruel J-C., A review of mechanistic modelling of wind damage risk to forests, Forestry, 2008, Vol. 81(3), pp. 447‒463.
  16. Hanewinkel M., Kuhn T., Bugmann H., Lanz A., Brang P., Vulnerability of uneven-aged forests to storm damage, Forestry, 2014, Vol. 87, pp. 525–534.
  17. Hansen M. C., Potapov P. V., Moore R., Hancher M., Turubanova S. A., Tyukavina A., Thau D., Stehman S. V., Goetz S. J., Loveland T. R., Kommareddy A., Egorov A., Chini L., Justice C. O., Townshend J. R.G., High-Resolution Global Maps of 21st-Century Forest Cover Change, Science, 2013, Vol. 342, pp. 850–853.
  18. Hart E., Sim K., Kamimura K., Meredieu C., Guyon D., Gardiner B., Use of machine learning techniques to model wind damage to forests, Agricultural and Forest Meteorology, 2019, Vol. 265, pp. 16–29.
  19. Hovi A., Raitio P., Rautiainen M., A spectral analysis of 25 boreal tree species, Silva Fennica, 2017, Vol. 51(4), Art. No. 7753.
  20. Iwahashi J., Pike R., Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature, Geomorphology, 2007, Vol. 86, pp. 409–440.
  21. Klaus M., Holsten A., Hostert P., Kropp J., Integrated methodology to assess windthrow impacts on forest stands under climate change, Forest Ecology and Management, 2011, Vol. 261, pp. 1799–1810.
  22. Kramer M. G., Hansen A. J., Taper M. L. Kissinger E. J., Abiotic controls on long-term windthrow disturbance and temperate rain forest dynamics in southeast Alaska, Ecology, 2001, Vol. 82(10), pp. 2749–2768.
  23. Kupfer J. A., Myers A. T., McLane S. E., Melton G. N., Patterns of forest damage in a southern Mississippi landscape caused by Hurricane Katrina, Ecosystems, 2008, Vol. 11(1), pp. 45–60.
  24. Lässig R., Močalov S. A., Frequency and characteristics of severe storms in the Urals and their influence on the development, structure and management of the boreal forests, Forest Ecology and Management, 2000, Vol. 135, pp. 179–194.
  25. Lindemann J. D., Baker W. L., Using GIS to analyse a severe forest blowdown in the Southern Rocky Mountains, Intern. J. Geographical Information Science, 2002, Vol. 16(4), pp. 377–399.
  26. Mitchell S. J., Wind as a natural disturbance agent in forests: a synthesis, Forestry, 2013, Vol. 86, pp. 147–157.
  27. Peltola H., Kellomäki S., Väisänen H., Ikonen V. P., A mechanistic model for assessing the risk of wind and snow damage to single trees and stands of Scots pine, Norway spruce, and birch, Canadian J. Forest Research, 1999, Vol. 29(6), pp. 647–661.
  28. Potapov P. V., Turubanova S. A., Tyukavina A., Krylov A. M., McCarty J. L., Radeloff V. C., Hansen M. C., Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive, Remote Sensing of Environment, 2015, Vol. 159, pp. 28–43.
  29. Rodriguez-Galiano V. F., Ghimire B., Rogan J., Chica-Olmo M., Rigol-Sanchez J. P., An assessment of the effectiveness of a random forest classifier for land-cover classification, ISPRS J. Photogrammetry and Remote Sensing, 2012, Vol. 67(1), pp. 93–104.
  30. Schelhaas M. J., Nabuurs G. J., Schuck A., Natural disturbances in the European forests in the 19th and 20th centuries, Global Change Biology, 2003, Vol. 9, pp. 1620–1633.
  31. Schindler D., Grebhan K., Albrecht A., Schönborn J., Kohnle U., GIS-based estimation of the winter storm damage probability in forests: a case study from Baden-Wuerttemberg (Southwest Germany), Intern. J. Biometeorology, 2012, Vol. 56, pp. 57–69.
  32. Seidl R., Fernandes P. M., Fonseca T. F., Gillet F., Jönsson A.M, Merganičová K., Netherer S., Arpaci A., Bontemps J.-D., Bugmann H., González-Olabarria J. R., Lasch P., Meredieu C., Moreira F., Schelhaas M. J., Mohren F., Modelling natural disturbances in forest ecosystems: A review, Ecological Modelling, 2011, Vol. 22(4), pp. 903–924.
  33. Seidl R., Thom D., Kautz M., Martin-Benito D., Peltoniemi M., Vacchiano G., Wild J., Ascoli D., Petr M., Honkaniemi J., Lexer M. J., Trotsiuk V., Mairota P., Svoboda M., Fabrika M., Nagel T. A., Reyer, C. P. O., Forest disturbances under climate change, Nature Climate Change, 2017, Vol. 7, pp. 395–402.
  34. Shikhov A. N., Chernokulsky A. V., A satellite-derived climatology of unreported tornadoes in forested regions of northeast Europe, Remote Sensing of Environment, 2018, Vol. 204, pp. 553‒567.
  35. Shikhov A. N., Perminova E. S., Perminov S. I., Satellite based analysis of the spatial patterns of fire and storm related forest disturbances in the Ural region, Russia, Natural Hazards, 2019, Vol. 97(1), pp. 283–308.
  36. Shikhov A. N., Chernokulsky A. V., Azhigov I. O., Semakina A. V., A satellite-derived database for stand-replacing windthrow events in boreal forests of European Russia in 1986–2017, Earth System Science Data, 2020, Vol. 12, pp. 3489–3513.
  37. Suvanto S., Henttonen H. M., Nöjd P., Mäkinen H., Forest susceptibility to storm damage is affected by similar factors regardless of storm type: Comparison of thunder storms and autumn extra-tropical cyclones in Finland, Forest Ecology and Management, 2016, Vol. 381, pp. 17–28.
  38. Suvanto S., Peltoniemi M., Tuominen S., Strandström M., Lehtonen A., High-resolution mapping of forest vulnerability to wind for disturbance-aware forestry, Forest Ecology and Management, 2019, Vol. 453, Art. No. 117619.
  39. Ulanova N. G., The effects of windthrow on forests at different spatial scales: a review, Forest Ecology and Management, 2000, Vol. 135, pp. 155–167.