Applicability Of NBR And dNBR Indices In Assessment Of Pyrogenic Transformation And Post-Fire Forest Regeneration: Case Study Of Southeastern Siberia Coniferous Forests

Zhanna A. Atutova; Elena A. Rasputina

doi:10.24057/2071-9388-2025-3963

Applicability Of NBR And dNBR Indices In Assessment Of Pyrogenic Transformation And Post-Fire Forest Regeneration: Case Study Of Southeastern Siberia Coniferous Forests

Zhanna A. Atutova, Elena A. Rasputina

https://doi.org/10.24057/2071-9388-2025-3963

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Abstract

This study evaluated the reliability of the Normalised Burn Ratio (NBR) and its differenced variant (dNBR) for assessing burnt areas and post-fire forest recovery. The research was conducted in the pine forests of the Tunka Depression (Southwestern Cisbaikalia), focusing on areas affected by a 2010 wildfire. Field data consisted of annual geobotanical observations from 2014 to 2022, which documented plant community regeneration across varying degrees of fire severity. Remote sensing analysis utilised Landsat 7 imagery (30 m resolution) between 2009 and 2022. Approximately 500 cloudfree NBR values were extracted from the USGS Landsat 7 Level 2, Collection 2, Tier 1 dataset using the Google Earth Engine platform. We assessed the spatiotemporal dynamics of these indices alongside the geobotanical parameters. The results confirm the effectiveness of NBR and dNBR for mapping burnt areas and determining initial fire severity. For detecting recent burns, mid-growing season imagery was most informative. Regarding post-fire regeneration analysis, springtime dNBR data were most reliable, as the influence of herbaceous cover on the spectral signal is minimised compared to the peak growing season. However, field observations revealed that the recovery of NBR values to pre-2009 fire levels by 2021 does not indicate that plant communities have regenerated to a near-natural state. This trend of rapid NBR recovery underscores the limitation of using NBR/dNBR indices alone for assessing long-term regeneration prospects. In conclusion, the results of the synthesised analysis of geobotanical and geoinformation materials showed that while remote sensing data effectively corroborate landscape-forming processes in disturbed ecosystems, their utility in detailed regeneration studies requires calibration with field data. The findings contribute to refining the application of NBR/dNBR indices and highlight the necessity of integrated approaches for calibrating remote sensing data.

Keywords

burnt area, geobotanical observations, regeneration, monitoring, spectral index NBR, dNBR

About the Authors

Zhanna A. Atutova

V.B. Sochava Institute of Geography, Siberian Branch of Russian Academy of Sciences
Russian Federation

Ulan-Batorskaya 1, Irkutsk, 664033

Elena A. Rasputina

V.B. Sochava Institute of Geography, Siberian Branch of Russian Academy of Sciences
Russian Federation

Ulan-Batorskaya 1, Irkutsk, 664033

References

1. Atutova Zh.V. (2022). Post-pyrogenic reforestation of subtaiga light coniferous geosystems of the Tunkinskaya depression, Southwestern Cisbaikalia (the study of pine forests of the Badary area). Geographical bulletin, 4(63), 6-18, DOI: 10.17072/2079-7877-2022-4-6-18 (in Russian).

2. Atutova Zh.V. (2023). Post-fire regeneration of pine forests in the Badary area, Ttunkinskiy National Park, Russia. Nature Conservation Research. 8(2), 22-32, DOI:10.24189/ncr.2023.010.

3. Atutova Zh.V. (2024). Experience with Using Geoinformation Data in the Evaluation of Post-Fire Vegetation Coverage Regeneration. Proceedings of Voronezh state university. Series: geography, geoecology, 3, 4-13, DOI:10.17308/geo/1609-0683/2024/3/4-13 (in Russian).

4. Avetisyan D., Velizarova E. and Filchev L. (2022). Post-fire forest vegetation state monitoring through Satellite Remote Sensing and In Situ Data. Remote Sensing, 14, 6266, DOI:10.3390/rs14246266.

5. Ba R., Song W., Lovallo M., Zhang H. and Telesca L. (2022). Informational analysis of MODIS NDVI and EVI time series of sites affected and unaffected by wildfires. Physica A: Statistical Mechanics and its Applications, 604, 127911, DOI: 10.1016/j.physa.2022.127911.

6. Bartalev S.A., Egorov V.A., Krylov A.M., Stytsenko F.V. and Khovratovich T.C. (2010). The evaluation of possibilities to assess forest burnt severity using multi-spectral satellite data. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa, 3, 215-225 (in Russian).

7. Bastos A., Gouveia C., DaCamara C.C. and Trigo R.M. (2011). Modelling post-fire vegetation recovery in Portugal. Biogeosciences Discussions, 8, 4559-4601, DOI:10.5194/bgd-8-4559-2011.

8. Bratkov V.V. and Ataev Z.V. (2017). Vegetation indexes and their use for mapping mountain landscapes of the Russian Caucasus. APRIORI. Series: Natural and Technical Sciences, 1, 3-23 (in Russian).

9. Casady G.M. and Marsh S.E. (2010). Broad-Scale environmental conditions responsible for post-fire vegetation dynamics. Remote Sensing, 2, 2643-2664, DOI:10.3390/rs2122643.

10. Chu T., Guo X. and Takeda K. (2016). Remote sensing approach to detect post-fire vegetation regrowth in Siberian boreal larch forest. Ecological Indicators, 62, 32-46, DOI:10.1016/j.ecolind.2015.11.026.

11. Cuevas-González M., Gerard F., Balzter H. and Riaño D. (2009). Analysing forest recovery after wildfire damage in boreal Siberia using remotely sensed vegetation indices. Global Change Biology, 15(3), 561-577, DOI:10.1111/j.1365-2486.2008.01784.x.

12. Chugunova R.V. (1960). To the question about the classification of burnt areas. Scientific reports. Issue 3. 67-70. Yakutsk: Yakutsk Book Publishing House (in Russian).

13. Delcourt C.J.F., Combee A., Izbicki B., Mack M.C., Maximov T., Petrov R., Rogers B.M., Scholten R.C., Shestakova T.A., van Wees D. and Veraverbeke S. (2021). Evaluating the Differenced Normalized Burn Ratio for Assessing Fire Severity Using Sentinel-2 Imagery in Northeast Siberian Larch Forests. Remote Sensing, 13(12), 2311, DOI:10.3390/rs13122311.

14. Drude O. 1890. Handbuch der Pflanzengeographie. Stuttgart: J. Engelhorn.

15. Hao B., Xu X., Wu F. and Tan L. (2022). Long-term effects of fire severity and climatic factors on post-forest-fire vegetation recovery. Forests, 13, 883, DOI:10.3390/f13060883.

16. Ivanyo Ya.M., Lazareva A.A. and Stolpova Yu.V. (2017). Modeling the variability of fire characteristics in the territory of the Tunkinskiy National Park. The Bulletin of KrasGAU, 7, 44-50 (in Russian).

17. Khakim M.Y.N., Poerwono P., Affandi A.K., Anhar M.F., Indrawan F., Ardiansyah T. and Tsuji T. (2024). Land Cover and Burn Severity Dynamics of the Ogan Komering Ilir Peatlands from 2015 to 2023 Using Sar and Optical Datasets. Geography, Environment, Sustainability, 3(17), 6-18, DOI:10.24057/2071-9388-2024-3217/.

18. Kharitonova A.O. and Kharitonova T.I. (2021). The influence of the landscape structure of the Mordovian Nature Reserve (Russia) on the spread of the 2010 fire. Nature Conservation Research, 6(2), 29-41, DOI:10.24189/ncr.2021.022 (in Russian).

19. Kibler C.L., Parkinson A.-M.L., Peterson S.H., Roberts D.A., D’Antonio C.M., Meerdink S.K. and Sweeney S.H. (2019). Monitoring post-fire recovery of chaparral and conifer species using field Surveys and Landsat time series. Remote Sensing, 11(24), 2963, DOI:10.3390/rs11242963.

20. Miller J.D. and Thode A.E. (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment 109(1), 66-80, DOI:10.1016/j.rse.2006.12.006.

21. Ponomarev E., Zabrodin A. and Ponomareva T. (2022). Classification of Fire Damage to Boreal Forests of Siberia in 2021 Based on the dNBR Index. Fire, 5(1):19, DOI: 10.3390/fire5010019.

22. Pushkin A.A., Sidelnik N.Ya. and Kovalevsky S.V. (2015). The use of satellite imagery materials to assess fire danger in forests. Proceedings of BSTU, 1: Forestry, 174, 36-40 (in Russian).

23. Radjabova R.T., Alekseenko N.A., Kuramagomedov B.M., Tazhudinova Z.Sh. and Sultanov Z.M. (2020). The use of index images in decoding the vegetation cover of Inland Dagestan. South of Russia: ecology, development, 15(4), 126-136, DOI: 10.18470/1992-1098-2020-4-126-136 (in Russian).

24. Rodionova N.V., Vakhnina I.L. and Zhelibo T.V. (2020). Assessment of the dynamics of the post-fire state of vegetation in the territory of the Ivano-Arakhleisky Nature Park (Zabaikalsky Krai) using radar and optical data from Sentinel ½ satellites. Issledovanie Zemli iz Kosmosa, 3, 14-25, DOI: 10.31857/S0205961420030045 (in Russian).

25. Rozhkov Yu.F. and Kondakov M.Yu. (2017). Assessment of the process of forest regeneration after a fire using cluster analysis when decoding satellite images. Vestnik of North-Eastern Federal University, 2, 38-48 (in Russian).

26. Santos S.M.B.d., Bento-Gonçalves A., Franca-Rocha W. and Baptista G. (2020). Assessment of Burned Forest Area Severity and Postfire Regrowth in Chapada Diamantina National Park (Bahia, Brazil) Using dNBR and RdNBR Spectral Indices. Geosciences,10(3), 106. DOI: 10.3390/geosciences10030106.

27. Sidelnik N.Ya., Pushkin A.A. and Kovalevsky S.V. (2018). Mapping damaged forest stands and objects of forestry measures using space survey materials and GIS technologies. Proceedings of BSTU, 1: Forestry, 1, 5-12 (in Russian).

28. Shvetsov E.G. and Ponomarev E.I. (2020). Post-fire effects in Siberian larch forests on multispectral satellite data. Contemporary Problems of Ecology, 1, 129-140, DOI: 10.15372/SEJ20200110 (in Russian).

29. Soromotin A.V., Brodt L.V. and Prikhodko N.V. (2022). Transformation of NDVI and NBR indices in post-pyrogenic territories in the forest tundra zone of the Yamalo-Nenets Autonomous District. XVII All-Russian Scientific and Practical Conference «High Technologies, Science and education: current issues, achievements and innovations». Collection of articles. Penza: Science and Education, 262-265 (in Russian).

30. Tokareva O.S., Alshaibi A.D.A. and Pasko O.A. (2021). Assessment of the regenerative dynamics of vegetation cover of forest harems using data from Landsat satellites. Bulletin of Tomsk Polytechnic University. Geo Assets Engineering, 332(7), 191-199, DOI: 10.18799/24131830/2021/7 (in Russian).

31. Vasilenko O.V., Voropay N.N. (2015). Peculiarities of climate formation in the basins of the South-Western Baikal region. Izvestiya RAN. Seriya geograficheskaya, 2, 98-104. (in Russian).

32. Vorobiev O.N., Kurbanov E.A., Gubarev A.V., Lezhnin S.A. and Polevshchikova Yu.A. (2012). Remote monitoring of harems in the Mari Trans-Volga region. Vestnik of Volga State University of Technology. Series «Forest. Ecology. Nature management», 1, 12-22 (in Russian).

33. Xofis P., Spiliotis J.A., Chatzigiovanakis S. and Chrysomalidou A.S. (2022). Long-term monitoring of vegetation dynamics in the Rhodopi Mountain Range National Park-Greece. Forests, 13, 377, DOI:10.3390/f13030377.

Review

For citations:

Atutova Zh.A., Rasputina E.A. Applicability Of NBR And dNBR Indices In Assessment Of Pyrogenic Transformation And Post-Fire Forest Regeneration: Case Study Of Southeastern Siberia Coniferous Forests. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2025;18(4):36-47. https://doi.org/10.24057/2071-9388-2025-3963

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