Research Article
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Year 2025, Volume: 7 Issue: 2, 52 - 62

Zeynep Kiraz , Bahadır Kulavuz , Tolga Bakırman , Bülent Bayram

https://doi.org/10.53093/mephoj.1628753

Abstract

Project Number

122N254

References

  • Bowman, D.M., Kolden, C.A., Abatzoglou, J.T., Johnston, F.H., Werf, G.R. & Flannigan M. (2020). Vegetation fires in the Anthropocene. Nature Reviews Earth & Environment, 1, pages 500–515. https://doi.org/10.1038/s43017-020-0085-3
  • Bıçakcı, C., & Yıldız, S. S. (2024). Google Earth Engine ve Coğrafi Bilgi Sistemleri Kullanarak Orman Yangını Şiddetinin Belirlenmesinde Farklı İndekslerin Karşılaştırılması: 2023 Hatay-Belen Yangını Örneği. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 7(2), 708-719. https://doi.org/10.47495/okufbed.1404480
  • Chuvieco, E. et al. Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sens. Environ. 225, 45–64 (2019). https://doi.org/10.1016/j.rse.2019.02.013
  • dos Santos, S. M. B., Bento-Gonçalves, A., Franca-Rocha, W., & 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. https://doi.org/10.3390/geosciences10030106
  • Miller, J. D., & Thode, A. E. (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (RdNBR). Remote Sensing of Environment, 109(1), 66–80. https://doi.org/10.1016/j.rse.2006.12.006
  • Van Wagtendonk, J.W.; Root, R.R.; Key, C.H. Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sens. Environ. 2004, 92, 397–408. https://doi.org/10.1016/j.rse.2003.12.015
  • TAN Liuxia, ZENG Yongnian, ZHENG Zhong. An adaptability analysis of remote sensing indices in evaluating fire severity[J]. Remote Sensing for Natural Resources, 2016, (2): 84-90. https://doi.org/10.6046/gtzyyg.2016.02.14
  • Han, A., Qing, S., Bao, Y., Na, L., Bao, Y., Liu, X., Zhang, J., & Wang, C. (2021). Short-term effects of fire severity on vegetation based on Sentinel-2 satellite data. Sustainability, 13(1), 432. https://doi.org/10.3390/su13010432
  • Key, C. H., & Benson, N. C. (2006). Landscape assessment (LA). In: Lutes, Duncan C.; Keane, Robert E.; Caratti, John F.; Key, Carl H.; Benson, Nathan C.; Sutherland, Steve; Gangi, Larry J. 2006. FIREMON: Fire effects monitoring and inventory system. Gen. Tech. Rep. RMRS-GTR-164-CD. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. LA-1-55, 164. https://doi.org/10.2737/RMRS-GTR-164
  • Sinergise Ltd. (n.d.). Sentinel-2 L2A data documentation. Sentinel Hub. Retrieved April 20, 2025, from https://docs.sentinel-hub.com/api/latest/data/sentinel-2-l2a/
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
  • Baş, N.: Introducing a Learning Tool (QSVI): A QGIS Plugin for Computing Vegetation, Chlorophyll, and Thermal Indices with Remote Sensing Images, Geosci. Instrum. Method. Data Syst. Discuss. [preprint], https://doi.org/10.5194/gi-2024-8, in review, 2025.
  • Chen, X., Vogelmann, J. E., Rollins, M., Ohlen, D., Key, C. H., Yang, L., Huang, C., & Shi, H. (2011). Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest. International Journal of Remote Sensing, 32(23), 7905–7927. https://doi.org/10.1080/01431161.2010.524678
  • Chen, D., Loboda, T. V., & Hall, J. V. (2020). A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 63–77. https://doi.org/10.1016/j.isprsjprs.2019.11.011
  • Rouse, J.W., Jr.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation; Texas A & M University, Remote Sensing Center: College Station, TX, USA, 1973.
  • Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/s11676-020-01155-1
  • Strashok, O., Ziemiańska, M., & Strashok, V. (2022). Evaluation and correlation of Sentinel-2 NDVI and NDMI in Kyiv (2017–2021). Journal of Ecological Engineering, 23(9), 212–218. https://doi.org/10.12911/22998993/151884
  • Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119–126. https://doi.org/10.1016/0034-4257(94)90134-1
  • Lamaamri, M., Lghabi, N., Ghazi, A., El Harchaoui, N., Adnan, M. S. G., & Islam, M. S. (2023). Evaluation of desertification in the Middle Moulouya Basin (North-East Morocco) using Sentinel-2 images and spectral index techniques. Earth Systems and Environment, 7, 473–492. https://doi.org/10.1007/s41748-022-00327-9
  • Li, Q., Zhang, C., Shen, Y., Jia, W., & Li, J. (2016). Quantitative assessment of the relative roles of climate change and human activities in desertification processes on the Qinghai-Tibet Plateau based on net primary productivity. CATENA, 147, 789–796. https://doi.org/10.1016/j.catena.2016.09.005
  • Rodriguez, P. S., Schwantes, A. M., Gonzalez, A., & Fortin, M.-J. (2024). Monitoring changes in the Enhanced Vegetation Index to inform the management of forests. Remote Sensing, 16(16), 2919. https://doi.org/10.3390/rs16162919
  • Filipponi, F. (2018). BAIS2: Burned Area Index for Sentinel-2. Proceedings of the 2nd International Electronic Conference on Remote Sensing, 2(7), 364. https://doi.org/10.3390/ecrs-2-05177
  • Deshpande, M. V., Pillai, D., & Jain, M. (2022). Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite. MethodsX, 9, 101741. https://doi.org/10.1016/j.mex.2022.101741
  • Alcaras, E., Costantino, D., Guastaferro, F., Parente, C., & Pepe, M. (2022). Normalized Burn Ratio Plus (NBR+): A new index for Sentinel-2 imagery. Remote Sensing, 14(7), 1727. https://doi.org/10.3390/rs14071727
  • Miura, T., Huete, A. R., Yoshioka, H., & Holben, B. N. (2001). An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target‐based atmospheric correction. Remote Sensing of Environment, 78(3), 284–298. https://doi.org/10.1016/S0034-4257(01)00223-1

Beyond dNBR: Exploring Alternative Satellite Indices for Post-Fire Damage Assessment in Turkish Forests

Year 2025, Volume: 7 Issue: 2, 52 - 62

Zeynep Kiraz , Bahadır Kulavuz , Tolga Bakırman , Bülent Bayram

https://doi.org/10.53093/mephoj.1628753

Abstract

Difference Normalised Burn Ratio (dNBR) is used as a reliable reference index since it has accepted thresholds for determining fire severity. However, the use of the dNBR index is limited if pre-fire data cannot be obtained due to weather and other conditions. This situation necessitates the development of alternative indices that are calculated only with post-fire satellite imagery. This study was conducted on forest fires in Mersin, Izmir and Mugla provinces of Turkey while aiming to evaluate the effectiveness of alternative indices to the dNBR index, which is widely used in determining ecosystem damage and fire severity after forest fires. Using Sentinel-2 satellite data operated by the European Space Agency (ESA), the study analysed the performance of the NDVI, NDMI, NBR, MSAVI, EVI and BAIS2 indices calculated using only post-fire data against the dNBR index calculated from pre- and post-fire imagery. The main methods used in this study include data processing and analyses performed on the Google Earth Engine (GEE) platform and comparisons made on the QGIS platform. In this study, the extent to which these alternative indices can be effective in accurately and reliably assessing post-fire ecosystem damage was investigated. The results of the analyses showed that the NBR and BAIS2 indices have the highest accuracy in detecting post-fire ecosystem damage. While both indices produced results close to the dNBR index, MSAVI and EVI were found to be effective in monitoring vegetation changes but insufficient in determining fire severity. In conclusion, BAIS2 and NBR provide strong alternatives to dNBR in analyses based on post-fire data, while the other indices used in the study are considered as complementary tools.

Keywords

Forest Fire Burn Severity Sentinel-2 Google Earth Engine dNBR

Ethical Statement

The authors declare no conflict of interest.

Supporting Institution

This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) ARDEB 2549 (NCBR) Grant No: 122N254. The authors declare the utilization of AI-assisted technologies to enhance the readability and linguistic quality of the manuscript.

Project Number

122N254

References

  • Bowman, D.M., Kolden, C.A., Abatzoglou, J.T., Johnston, F.H., Werf, G.R. & Flannigan M. (2020). Vegetation fires in the Anthropocene. Nature Reviews Earth & Environment, 1, pages 500–515. https://doi.org/10.1038/s43017-020-0085-3
  • Bıçakcı, C., & Yıldız, S. S. (2024). Google Earth Engine ve Coğrafi Bilgi Sistemleri Kullanarak Orman Yangını Şiddetinin Belirlenmesinde Farklı İndekslerin Karşılaştırılması: 2023 Hatay-Belen Yangını Örneği. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 7(2), 708-719. https://doi.org/10.47495/okufbed.1404480
  • Chuvieco, E. et al. Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sens. Environ. 225, 45–64 (2019). https://doi.org/10.1016/j.rse.2019.02.013
  • dos Santos, S. M. B., Bento-Gonçalves, A., Franca-Rocha, W., & 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. https://doi.org/10.3390/geosciences10030106
  • Miller, J. D., & Thode, A. E. (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (RdNBR). Remote Sensing of Environment, 109(1), 66–80. https://doi.org/10.1016/j.rse.2006.12.006
  • Van Wagtendonk, J.W.; Root, R.R.; Key, C.H. Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sens. Environ. 2004, 92, 397–408. https://doi.org/10.1016/j.rse.2003.12.015
  • TAN Liuxia, ZENG Yongnian, ZHENG Zhong. An adaptability analysis of remote sensing indices in evaluating fire severity[J]. Remote Sensing for Natural Resources, 2016, (2): 84-90. https://doi.org/10.6046/gtzyyg.2016.02.14
  • Han, A., Qing, S., Bao, Y., Na, L., Bao, Y., Liu, X., Zhang, J., & Wang, C. (2021). Short-term effects of fire severity on vegetation based on Sentinel-2 satellite data. Sustainability, 13(1), 432. https://doi.org/10.3390/su13010432
  • Key, C. H., & Benson, N. C. (2006). Landscape assessment (LA). In: Lutes, Duncan C.; Keane, Robert E.; Caratti, John F.; Key, Carl H.; Benson, Nathan C.; Sutherland, Steve; Gangi, Larry J. 2006. FIREMON: Fire effects monitoring and inventory system. Gen. Tech. Rep. RMRS-GTR-164-CD. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. LA-1-55, 164. https://doi.org/10.2737/RMRS-GTR-164
  • Sinergise Ltd. (n.d.). Sentinel-2 L2A data documentation. Sentinel Hub. Retrieved April 20, 2025, from https://docs.sentinel-hub.com/api/latest/data/sentinel-2-l2a/
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
  • Baş, N.: Introducing a Learning Tool (QSVI): A QGIS Plugin for Computing Vegetation, Chlorophyll, and Thermal Indices with Remote Sensing Images, Geosci. Instrum. Method. Data Syst. Discuss. [preprint], https://doi.org/10.5194/gi-2024-8, in review, 2025.
  • Chen, X., Vogelmann, J. E., Rollins, M., Ohlen, D., Key, C. H., Yang, L., Huang, C., & Shi, H. (2011). Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest. International Journal of Remote Sensing, 32(23), 7905–7927. https://doi.org/10.1080/01431161.2010.524678
  • Chen, D., Loboda, T. V., & Hall, J. V. (2020). A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 63–77. https://doi.org/10.1016/j.isprsjprs.2019.11.011
  • Rouse, J.W., Jr.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation; Texas A & M University, Remote Sensing Center: College Station, TX, USA, 1973.
  • Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/s11676-020-01155-1
  • Strashok, O., Ziemiańska, M., & Strashok, V. (2022). Evaluation and correlation of Sentinel-2 NDVI and NDMI in Kyiv (2017–2021). Journal of Ecological Engineering, 23(9), 212–218. https://doi.org/10.12911/22998993/151884
  • Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119–126. https://doi.org/10.1016/0034-4257(94)90134-1
  • Lamaamri, M., Lghabi, N., Ghazi, A., El Harchaoui, N., Adnan, M. S. G., & Islam, M. S. (2023). Evaluation of desertification in the Middle Moulouya Basin (North-East Morocco) using Sentinel-2 images and spectral index techniques. Earth Systems and Environment, 7, 473–492. https://doi.org/10.1007/s41748-022-00327-9
  • Li, Q., Zhang, C., Shen, Y., Jia, W., & Li, J. (2016). Quantitative assessment of the relative roles of climate change and human activities in desertification processes on the Qinghai-Tibet Plateau based on net primary productivity. CATENA, 147, 789–796. https://doi.org/10.1016/j.catena.2016.09.005
  • Rodriguez, P. S., Schwantes, A. M., Gonzalez, A., & Fortin, M.-J. (2024). Monitoring changes in the Enhanced Vegetation Index to inform the management of forests. Remote Sensing, 16(16), 2919. https://doi.org/10.3390/rs16162919
  • Filipponi, F. (2018). BAIS2: Burned Area Index for Sentinel-2. Proceedings of the 2nd International Electronic Conference on Remote Sensing, 2(7), 364. https://doi.org/10.3390/ecrs-2-05177
  • Deshpande, M. V., Pillai, D., & Jain, M. (2022). Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite. MethodsX, 9, 101741. https://doi.org/10.1016/j.mex.2022.101741
  • Alcaras, E., Costantino, D., Guastaferro, F., Parente, C., & Pepe, M. (2022). Normalized Burn Ratio Plus (NBR+): A new index for Sentinel-2 imagery. Remote Sensing, 14(7), 1727. https://doi.org/10.3390/rs14071727
  • Miura, T., Huete, A. R., Yoshioka, H., & Holben, B. N. (2001). An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target‐based atmospheric correction. Remote Sensing of Environment, 78(3), 284–298. https://doi.org/10.1016/S0034-4257(01)00223-1
There are 25 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Article
Authors

Zeynep Kiraz 0009-0000-3204-1577

Bahadır Kulavuz 0009-0007-2320-6350

Tolga Bakırman 0000-0001-7828-9666

Bülent Bayram 0000-0002-4248-116X

Project Number 122N254
Publication Date December 13, 2025
Submission Date January 28, 2025
Acceptance Date July 1, 2025
Published in Issue Year 2025 Volume: 7 Issue: 2

Cite

APA Kiraz, Z., Kulavuz, B., Bakırman, T., Bayram, B. (n.d.). Beyond dNBR: Exploring Alternative Satellite Indices for Post-Fire Damage Assessment in Turkish Forests. Mersin Photogrammetry Journal, 7(2), 52-62. https://doi.org/10.53093/mephoj.1628753
AMA Kiraz Z, Kulavuz B, Bakırman T, Bayram B. Beyond dNBR: Exploring Alternative Satellite Indices for Post-Fire Damage Assessment in Turkish Forests. Mersin Photogrammetry Journal. 7(2):52-62. doi:10.53093/mephoj.1628753
Chicago Kiraz, Zeynep, Bahadır Kulavuz, Tolga Bakırman, and Bülent Bayram. “Beyond DNBR: Exploring Alternative Satellite Indices for Post-Fire Damage Assessment in Turkish Forests”. Mersin Photogrammetry Journal 7, no. 2 n.d.: 52-62. https://doi.org/10.53093/mephoj.1628753.
EndNote Kiraz Z, Kulavuz B, Bakırman T, Bayram B Beyond dNBR: Exploring Alternative Satellite Indices for Post-Fire Damage Assessment in Turkish Forests. Mersin Photogrammetry Journal 7 2 52–62.
IEEE Z. Kiraz, B. Kulavuz, T. Bakırman, and B. Bayram, “Beyond dNBR: Exploring Alternative Satellite Indices for Post-Fire Damage Assessment in Turkish Forests”, Mersin Photogrammetry Journal, vol. 7, no. 2, pp. 52–62, doi: 10.53093/mephoj.1628753.
ISNAD Kiraz, Zeynep et al. “Beyond DNBR: Exploring Alternative Satellite Indices for Post-Fire Damage Assessment in Turkish Forests”. Mersin Photogrammetry Journal 7/2 (n.d.), 52-62. https://doi.org/10.53093/mephoj.1628753.
JAMA Kiraz Z, Kulavuz B, Bakırman T, Bayram B. Beyond dNBR: Exploring Alternative Satellite Indices for Post-Fire Damage Assessment in Turkish Forests. Mersin Photogrammetry Journal.;7:52–62.
MLA Kiraz, Zeynep et al. “Beyond DNBR: Exploring Alternative Satellite Indices for Post-Fire Damage Assessment in Turkish Forests”. Mersin Photogrammetry Journal, vol. 7, no. 2, pp. 52-62, doi:10.53093/mephoj.1628753.
Vancouver Kiraz Z, Kulavuz B, Bakırman T, Bayram B. Beyond dNBR: Exploring Alternative Satellite Indices for Post-Fire Damage Assessment in Turkish Forests. Mersin Photogrammetry Journal. 7(2):52-6.