Skip to main content
SpringerLink
Log in

Health of Vegetation in the Area of Mass Outbreaks of Siberian Moth Based on Satellite Data

  • Published:
Contemporary Problems of Ecology Aims and scope

Abstract

This paper presents the results of analyzing the state and dynamics of damaged vegetation from satellite images of high and ultrahigh spatial resolution. This study was conducted based on the example of the site of coniferous forests in the Lower Angara region (Krasnoyarsk krai), where a large outbreak of the Siberian moth took place in 1944–1995. The remote assessment of the state of dark coniferous forests revealed the trends of the SWVI (or NDMI) and NDVI indices that characterize long-term changes in the vegetation cover over the period 2000–2018. The SWVI index is the most informative indicator: a sharp decrease in average values and increase in the coefficient of variation of the index are noted for dead and severely damaged wood stands (crown defoliation of more than 75%). The area of dead forests was calculated according to the difference images of the indices (ΔSWVI) with the threshold criterion lσ (the standard deviation). In 2000, the area of forests that died under the impact of the Siberian moth was approximately 19 200 ha. Alter two major fires in 2004 and 2011 and as a result of destructive factors combination, the area of dead forests increased up to 20 400 ha by 2017–2018. Reforestation within the boundaries of dead stands was estimated from the classification of Landsat images (June 20, 2017; June 23, 2018) by the Random Forest algorithm using the selection of templates from detailed Resurs-P images with a spatial resolution of 1 m (Geoton-LI—July 22, 2015 and December 3, 2018), which were taken during different seasons. The classification proved to be highly reliable (Kappa index is more than 0.9). The areas of classified deciduous and coniferous stands, deciduous and mixed stands with mainly coniferous regrowth, grass–shrub vegetation, and barren soil were calculated. Natural regeneration of mainly coniferous undergrowth occurred in 17% of the damaged area, and deciduous regrowth occurred in approximately 10% of the area 23 years after damage by pests. The area damaged by the moth affected reforestation: the larger the area of the outbreak, the higher the share of open lands with grass and shrub vegetation (it accounts for more than half of the area for the large outbreak and approximately 45% for smaller outbreaks). Regrowth was found in proximity of patches of stands and deadwood in the sites unaffected by large fires. Frequent fires in the territory of moth infestation limit the process of reforestation; therefore, most of the vegetation was at the initial stage of the succession cycle.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

Similar content being viewed by others

REFERENCES

  1. Baboi, S.D., Astapenko, S.A., Golubev, D.V., Yagunov, M.N., Evaluation of distribution and impact of pests on forests of Krasnoyarsk krai, Materialy VI Vserossiiskoi nauchno-prakticheskoi konferentsii “Monitoring, modelirovanie i prognozirovanie opasnykh prirodnykh yavlenii i chrezvychainykh situatsii,” Zheleznogorsk, 27 maya 2016 g. (Proc. VI All-Russ. Sci.-Pract. Conf. “Monitoring, Modeling, and Forecasting of Dangerous Nature Events and Energency Situations,” Zheleznogorsk, May 27, 2016), Zheleznogorsk: Sib. Pozharno-Spasatel’naya Akad., MChS Rossii, 2016, pp. 37–40.

  2. Bartalev, S.A., Ershov, D.V., and Isaev, A.S., Spectral de-composition-based assessment of forest defoliation from multispectral satellite images, Issled. Zemli Kosm., 1999, no. 4, pp. 78–86.

  3. Bartalev, S.A., Stytsenko, F.V., Egorov, V.A., and Lupyan, E.A., Satellite-based assessment of Russian forest fire mortality, Lesovedenie, 2015, no. 2, pp. 83–94.

  4. Belova, E.I. and Ershov, D.V., Preprocessing Landsat TM/ETM+ data sets for creating cloud-free composite imagery, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosm., 2011, vol. 8, no. 1, pp. 73–82.

    Google Scholar 

  5. Breiman, L., Random forests, Mach. Learn., 2001, vol. 45, no. 1, pp. 5–32.

    Article  Google Scholar 

  6. Chander, G., Markham, B.L., and Helder, D.L., Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 AL1 sensors, Remote Sens. Environ., 2009, vol. 113, no. 5, pp. 893–903.

    Article  Google Scholar 

  7. Cohen, W.B., Yang, Z., and Kennedy, R., Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—Tools for calibration and validation, Remote Sens. Environ., 2010, vol. 114, no. 12, pp. 2911–2924.

    Article  Google Scholar 

  8. Coops, N.C., Johnson, M., Wulder, M.A., and White, J.C., Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation, Remote Sens. Environ., 2006, vol. 103, no. 1, pp. 67–80.

    Article  Google Scholar 

  9. Ershov, D.V. and Devyatova, N.V., Application of satellite survey data to monitor mass outbreaks of Siberian moth, Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos’emka, 2008, no. 2, pp. 161–167.

  10. Franklin, S., Fan, H., and Guo, X., Relationship between Landsat TM and SPOT vegetation indices and cumulative spruce budworm defoliation, Int. J. Remote Sens., 2008, vol. 29, no. 4, pp. 1215–1220.

    Article  Google Scholar 

  11. Fraser, R. and Latifovic, R., Mapping insect-induced tree defoliation and mortality using coarse spatial resolution satellite imagery, Int. J. Remote Sens., 2005, vol. 26, no. 1, pp. 193–200.

    Article  Google Scholar 

  12. Furyaev, V.V., Shelkopryadniki taigi i ikh vyzhiganie (Burning the Centers of Silkmoth Outbreaks in Taiga), Moscow: Nauka, 1966.

  13. Gao, B.-C., NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ., 1996, vol. 58, no. 3, pp. 257–266.

    Article  Google Scholar 

  14. Goodwin, N.R., Coops, N.C., Wulder, M.A., Gillanders, S., Schroeder, T.A., and Nelson, T., Estimation of insect infestation dynamics using a temporal sequence of Landsat data, Remote Sens. Environ., 2008, vol. 112, no. 9, pp. 3680–3689.

    Article  Google Scholar 

  15. Grodnitskii, D.L., Siberian moth and destiny of fir taiga, Priroda (Moscow), 2004, no. 11 (1071), pp. 49–56.

  16. Grodnitskii, D.L., Raznobarskii, V.G., Remarchuk, N.P., and Soldatov, V.V., Decline of the wood stands in centers of mass outbreak of silk moths, Sib. Ekol. Zh., 2002, vol. 9, no. 1, pp. 3–11.

    Google Scholar 

  17. Im, S.T., Fedotova, E.V., and Kharuk, V.L., Analysis of the centers disturbed by Siberian moth in taiga with low-resolution satellite imagery, Vychisl. Tekhnol., 2007, vol. 12, suppl. 2, pp. 60–69.

    Google Scholar 

  18. Im, S.T., Fedotova, E.V., and Kharuk, V.I., Spectroradiometric satellite imagery application in analysis of Siberian moth outbreaks, Zh. Sib. Fed. Univ.,Ser.: Tekh. Tekhnol., 2008, vol. 1, no. 4, pp. 346–358.

    Google Scholar 

  19. Isaev, A.S., Raznoobrazie i dinamika lesnykh ekosistem Rossii (Forest Ecosystems of Russia: Diversity and Dynamics), Moscow: KMK, 2012, vol. 1.

  20. Isaev, A.S. and Kondakov, Yu.P., Principles and methods of the forest-entomological monitoring, Lesovedenie, 1986, no. 4, pp. 3–12.

  21. Isaev, A.S. and Korovin, G.N., Large-scale changes in Eurasian boreal forests and their assessment using satellite data, Lesovedenie, 2003, no. 2, pp. 3–9.

  22. Isaev, A.S. and Sukhikh, V.I., Aerospace monitoring of forest resources, Lesovedenie, 1986, no. 6, pp. 11–21.

  23. Isaev, A.S., Rozhkov, AS., and Kiselev, V.V., Chernyi pikhtovyi usach (Black Fir Sawyer Beetle Monochamus urussovi (Fisch.)), Novosibirsk: Nauka, 1988.

  24. Isaev, A.S., Sukhikh, V.I., and Kalashnikov, E.N., Aerokosmicheskii monitoring lesov (Aerospace Monitoring of Forests), Moscow: Nauka, 1991.

  25. Isaev, A.S., Kiselev, V.V., Kalashnikov, E.N., Pleshikov, F.I., and Cherkashin, V.P., GIS application to forecast and regulate the forest insect outbreaks, Lesovedenie, 1999a, no. 5, pp. 15–23.

  26. Isaev, A.S., Ovchinnikova, T.M., Pal’nikova, E.N., Sukhovol’skii, V.G., and Tarasova, O.V., Assessment of forest–insect relations in forests of boreal zone under probable climatic changes, Lesovedenie, 1999b, no. 6, pp. 39–44.

  27. Jepsen, J.U., Hagen, S.B., Høgda, K.A., Ims, R.A., Karlsen, S.R., Tømmervik, H., and Yoccoz, N.G., Monitoring the spatio-temporal dynamics of geometrid moth outbreaks in birch forest using MODIS-NDVI data, Remote Sens. Environ., 2009, vol. 113, no. 9, pp. 1939–1947.

    Article  Google Scholar 

  28. Kennedy, R.E., Yang, Z., and Cohen, W.B., Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms, Remote Sens. Environ., 2010, vol. 114, no. 12, pp. 2897–2910.

    Article  Google Scholar 

  29. Kerzhentsev, A.S. and Trashcheev, R.V., “Double carousel” of the succession process in a regional ecosystem, Russ. J. Ecol., 2011, vol. 42, no. 6, pp. 445–452.

    Article  Google Scholar 

  30. Kharuk, V.I., Demidko, D.A., Fedotova, E.V., Dvin-skaya, M.L., and Budnik, I.A., Spatial and temporal dynamics of Siberian silk moth large-scale outbreak in dark-needle coniferous tree stands in Altai, Contemp. Probl. Ecol., 2016, vol. 9, no. 6, pp. 711–720.

    Article  Google Scholar 

  31. Kharuk, V.I., Im, ST., Ranson, K.J., and Yagunov, M.N., Climate-induced northerly expansion of Siberian silkmoth range, Forests, 2017, vol. 8, no. 8, art. ID 301. https://doi.org/I0.3390/f8080301.

    Article  Google Scholar 

  32. Kharuk, V.I., Im, S.T., and Yagunov, M.N., Migration of the northern boundary of the Siberian silk moth, Contemp. Probl. Ecol., 2018, vol. 11, no. 1, pp. 26–34.

    Article  Google Scholar 

  33. Kharuk, V.I., Kozhukhovskaya, A.G., Pestunov, I.A., Ranson, K.J., and Tsibul’skii, G.M., NOAA/AVHRR imagery application to Siberian moth outbreaks monitoring, Issled. Zemli Kosm., 2001, no. 1, pp. 80–86.

  34. Kharuk, V.I., Renson, K.Dzh., Kuz’michev, V.V., Burenina, T.A., Tikhomirov, A.Yu., and Im, S.T., Landsat imagery in analysis of silkworm outburst sites in South Siberia, Issled. Zemli Kosm., 2002, no. 4, pp. 79–90.

  35. Koroleva, N.V., Tikhonova, E.V., Ershov, D.V, Salty-kov, A.N., Gavrilyuk, E.A., and Pugachevskii, A.V., Twenty-five years of reforestation on nonforest lands in Smolenskoe Poozerye National Park according to Landsat imagery assessment, Contemp. Probl. Ecol., 2018, vol. 2, no. 7, pp. 719–728.

    Article  Google Scholar 

  36. Krasnoshchekov, Yu.N. and Bezkorovainaya, I.N., Soil functioning in foci of Siberian moth population outbreaks in the southern taiga subzone of Central Siberia, Biol. Bull. (Moscow), 2008, vol. 35, no. 1, pp. 70–79.

    Article  Google Scholar 

  37. Lausch, A., Erasmi, S., King, D.J., Magdon, P., and Heurich, M., Understanding forest health with remote sensing: Part I—A review of spectral traits, processes and remote-sensing characteristics, Remote Sens., 2016, vol. 8, no. 12, art. ID 1029. https://doi.org/10.3390/rs8121029

    Article  Google Scholar 

  38. Meddens, A.J.H. and Hicke, J.A., Spatial and temporal patterns of Landsat-based detection of tree mortality caused by a mountain pine beetle outbreak in Colorado, USA, For. Ecol. Manage., 2014, vol. 322, pp. 78–88.

    Article  Google Scholar 

  39. Meddens, A.J.H., Hicke, J.A., Vierling, L.A., and Hudak, A.T., Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery, Remote Sens. Environ., 2013, vol. 132, pp. 49–58.

    Article  Google Scholar 

  40. Mikhailov, Yu.Z. and Sumina, N.Yu., Siberian moth Dendrolimus superans (Butter, 1877) and its control Irkutsk oblast, Baikal. Zool. Zh., 2012, no. 3 (11), pp. 25–29.

  41. Pavlov, I.N., Litovka, Yu.A., and Astapenko, S.A., Role of entomologically pathogenic fungi and bacteria in dynamics of Siberian silkmoth, Materialy mezhdunarodnoi konferentsii pamyati O.A. Kataeva “Dendrobiontnye bespozvonochnye zhivotnye i griby i ikh rol’ v lesnykh ekosistemakh,” Sankt-Peterburg, 23–25 noyabrya 2016 g. (Proc. Int. Conf. in Memoriam of O.A. Kataev “Dendrobiont Invertebrates and Fungi: Role in Forest Ecosystems,” St. Petersburg, November 23–25, 2016), St. Petersburg: S.-Peterb. Gos. Lesotekh. Univ., 2016, pp. 76–77.

  42. Potapov, P.V., Turubanova, S.A., Tyukavina, A., Krylov, A.M., McCarty, J.L., Radeloff, V.C., and Hansen, M.C., Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive, Remote Sens. Environ., 2015, vol. 159, pp. 28–43.

    Article  Google Scholar 

  43. Senf, C., Seidl, R., and Hostert, P., Remote sensing of forest insect disturbances: current state and future directions, Int. J. Appl. Earth Obs. Geoinf., 2017, vol. 60, pp. 49–60.

    Article  Google Scholar 

  44. Skakun, R.S., Wulder, M.A., and Franklin, S.E., Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage, Remote Sens. Environ., 2003, vol. 86, no. 4, pp. 433–443.

    Article  Google Scholar 

  45. Vogelmann, J.E., Comparison between two vegetation indices for measuring different types of forest damage in the north-eastern United States, Int. J. Remote Sens., 1990, vol. 11, no. 12, pp. 2281–2297.

    Article  Google Scholar 

  46. Vogelmann, J.E., Tolk, B., and Zhu, Z., Monitoring forest changes in the southwestern United States using mullitemporal Landsat data, Remote Sens. Environ., 2009, vol. 113, no. 8, pp. 1739–1748.

    Article  Google Scholar 

  47. White, J.C., Wulder, M.A., Brooks, D., Reich, R., and Wheate, R.D., Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery, Remote Sens. Environ., 2005, vol. 96, nos. 3–4, pp. 340–351.

    Article  Google Scholar 

  48. Wilson, E.H. and Sader, S.A., Detection of forest harvest type using multiple dates of Landsat TM imagery, Remote Sens. Environ., 2002, vol. 80, no. 3, pp. 385–396.

    Article  Google Scholar 

  49. Wulder, M.A., White, J.C., Bentz, B., Alvarez, M.F., and Coops, C., Estimating the probability of mountain pine beetle red-attack damage, Remote Sens. Environ., 2006, vol. 101, no. 2, pp. 150–166.

    Article  Google Scholar 

  50. Zhirin, V.M., Knyazeva, S.V., and Eydlina, S.P., Long-term dynamics of vegetation indices in dark coniferous forest after Siberian moth disturbance, Contemp. Probl. Ecol., 2016, vol. 9, no. 7, pp. 834–843.

    Article  Google Scholar 

Download references

Funding

The study was performed within the theme of the State Order of the Center for Forest Ecology and Productivity of the Russian Academy of Sciences for 2019, project no. AAAA-A18-118052400130-7.

Author information

Authors and Affiliations

  1. Center for Forest Ecology and Productivity, Russian Academy of Sciences, 117997, Moscow, Russia

    S. V. Knyazeva, N. V. Koroleva, S. P. Eidlina & E. N. Sochilova

Authors
  1. S. V. Knyazeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. V. Koroleva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. P. Eidlina
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. E. N. Sochilova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. V. Knyazeva.

Ethics declarations

Conflict of interests

The authors declare that they have no conflicts of interest.

Statement on the welfare of animals

This article does not contain any studies involving animals performed by any of the authors.

Additional information

Translated by L. Solovyova

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Knyazeva, S.V., Koroleva, N.V., Eidlina, S.P. et al. Health of Vegetation in the Area of Mass Outbreaks of Siberian Moth Based on Satellite Data. Contemp. Probl. Ecol. 12, 743–752 (2019). https://doi.org/10.1134/S1995425519070114

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1995425519070114

Keywords:

Search

Navigation