Earth remote sensing and using the satellite images play an important role when monitoring the effects of forest fires and assessing damage. Applying different methods of multispectral space images processing, we can determine the risk of fire distribution, define hot spots and determine thermal parameters, mapping the damaged areas and assess the consequences of fire. The purpose of the work is the severity assessment connected with the post-fire period on the example of the forests in the Chornobyl Exclusion Zone. The tasks of the study are to define the area of burned zones using space images of different time which were obtained from the Sentinel-2 satellite applying the method of a normalized burn ratio (NBR) and method of supervised classification. Space images taken from the Sentinel-2 satellite before and after the fire were the input data for the study. Copernicus Open Access Hub service is a source of images and its spatial resolution is 10 m for visible and near infrared bands of images, and 20 m for medium infrared bands of images. We used method of Normalized Burn Ratio (NBR) and automatically calculated the area damaged with fire. Using this index we were able to identify areas of zones after active combustion. This index uses near and middle infrared bands for the calculations. In addition, a supervised classification was performed on the study area, and signature files were created for each class. According to the results of the classification, the areas of the territories damaged by the fire were also calculated. The scientific novelty relies upon the application of a method of using the normalized combustion coefficient (NBR) and supervised classification for space images obtained before and after the fire in the Chernobyl Exclusion Zone. The practical significance lies in the fact that the studied methods of GIS technologies can be used to identify territories and calculate the areas of vegetation damaged by fires. These results can be used by local organizations, local governments and the Ministry of Emergency Situations to monitor the condition and to plan reforestation. The normalized burned ratio (NBR) gives possibility efficiently and operatively to define and calculate the area which were damaged by fires, that gives possibility operatively assess the consequences of such fires and estimate the damage. The normalized burned ratio allows to calculate the area of burned forest almost 2 times more accurately than the supervised classification. The calculation process itself also takes less time and does not require additional procedures (set of signatures). Supervised classification in this case gives worse accuracy, the process itself is longer, but allows to determine the area of several different classes.
1. Boschetti, L., Roy, D.P., Giglio, L., Huang, H., Zubkova, M., Humber, M.L. (2019). Global validation of the collection 6 MODIS burned area product. Remote Sensing Environments, Vol. 235, 111490.
https://doi.org/10.1016/j.rse.2019.111490
2. Bowman, D. (2018). Wildfire science is at a loss for comprehensive data. Nature. 560:7.
https://doi.org/10.1038/d41586-018-05840-4
3. Burshtynska, Kh., Denys, Yu., Polishchuk, B., & Tymchyshyn, M. (2018). Monitoring of forest fires by space images of medium resolution (on the example of Arizona, USA). Modern achievements of geodetic science and industry. No. 1 (35), 179-184. (in Ukrainian).
4. DaCamara, C., Libonati, R., Pinto, M. Hurduc, A. (2018). Near- and Middle-Infrared Monitoring of Burned Areas from Space. Satellite Information Classification and Interpretation.
https://doi.org/10.5772/intechopen.82444
5. Ertugrul, M., Ozel, H. B., Varol, T., Cetin, M., Sevik, H. (2019). Investigation of the relationship between burned areas and climate factors in large forest fires in the Canakkale region. Environmental Monitoring and Assessment, 191 (12), 737.
https://doi.org/10.1007/s10661-019-7946-6
6. Filipponi, F. (2018). BAIS2: Burned Area Index for Sentinel-2. Proceedings, 2 (7), 364.
https://doi.org/10.3390/ecrs-2-05177
7. Giglio, L., Boschetti, L., Roy, DP., Humber, ML., & Justice, CO. (2018). The collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment. 217, 72-85.
https://doi.org/10.1016/j.rse.2018.08.005
8. Hall, J., Argueta, F., & Giglio, L. (2021). Validation of MCD64A1 and FireCCI51 cropland burned area mapping in Ukraine. International Journal of Applied Earth Observations and Geoinformation, №102, 1-11.
https://doi.org/10.1016/j.jag.2021.102443
9. Kumar, S. S.; & Roy, D. P. (2018). Global operational land imager Landsat-8 reflectance-based active fire detection algorithm. International Journal of Digital Earth, Abingdon, Vol. 11, n. 2, 154-178.
https://doi.org/10.1080/17538947.2017.1391341
10. Lanorte, R. Lasaponara, M. Lovallo, and Telesca, L. (2015). Fisher-Shannon information plane analysis of SPOT/VEGETATION normalized difference vegetation index (NDVI) time series to characterize vegetation recovery after fire disturbance. International Journal of Applied Earth Observation and Geoinformation, Vol. 26, 441-446.
https://doi.org/10.1016/j.jag.2013.05.008
11. Lasaponara, R. and Tucci, B. (2019). Identification of Burned Areas and Severity Using SAR Sentinel-1. IEEE Geoscience and Remote Sensing Letters, Vol. 16, no. 6, 917-921.
https://doi.org/10.1109/LGRS.2018.2888641
12. Lasko, K. (2019). Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping Incorporating Sentinel-1 SAR imagery with the MODIS. Geocarto International.
https://doi.org/10.1080/10106049.2019.1608592
13. Ling, F., Du, Y., Zhang, Y., Li, X. Xiao, F. (2015). Burned-Area Mapping at the subpixel scale with MODIS Images. IEEE Geoscience and Remote Sensing Letters, Vol. 12, no. 9, 1963-1967. h
https://doi.org/10.1109/LGRS.2015.2441135
14. Padilla, M., Stehman, S. V., Ramo, R., Corti, D., Hantson, S., Oliva, P., ... & Chuvieco, E. (2015). Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. Remote sensing of environment, 160, 114-121.
https://doi.org/10.1016/j.rse.2015.01.005
15. Pereira. I. M. S. de Carvalho, E. V., Batista, A. C., Machado, I. E. S., Tavares, M. E. F., & Giongo, M. (2018). Identification of burned areas by special index in a cerrado region of the state of tocantins, Brazil. Floresta, 48(4), 553-562.
https://doi.org/10.5380/rf.v48i4.57362
16. Pleniou, M., & Koutsias, N. (2013). Sensitivity of spectral reflectance values to different burn and vegetation ratios: A multi- scale approach applied in a fire affected area. ISPRS Journal of Photogrammetry and Remote Sensing, Amsterdam, Vol. 79, 199-210.
https://doi.org/10.1016/j.isprsjprs.2013.02.016
17. Quintano, C.; Fernandez-Manso, A.; Fernandez-Manso, O. (2018). Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity. International Journal of Applied Earth Observation and Geoinformation, Vol. 64, 221-225.
https://doi.org/10.1016/j.jag.2017.09.014
18. Ramo, R., Roteta, E., Bistinas, I., Van Wees, D., Bastarrika, A., Chuvieco, E., Van der Werf, G. R. (2021). African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data. Proceedings of the National Academy of Sciences, 118 (9).
https://doi.org/10.1073/pnas.2011160118
19. Rasul, A., Ibrahim, G.R.F., Hameed, H.M., Tansey, K. (2021). A trend of increasing burned areas in Iraq from 2001 to 2019. Environ Dev Sustain 23, 5739-5755.
https://doi.org/10.1007/s10668-020-00842-7
20. Stroppiana, D., Azar, R., Calò, F., Pepe, A., Imperatore, P., Boschetti, M., ... & Lanari, R. (2015). Integration of optical and SAR data for burned area mapping in Mediterranean Regions. Remote Sensing, 7(2), 1320-1345.
https://doi.org/10.3390/rs70201320