Evaluating spectral indices for estimating burned areas in the case of Izmir / Turkey

Abstract

Mapping and determination of fire damaged areas in an accurate and prompt way is essential for identifying environmental losses caused by fires, post-fire management activities and planning strategies. In this context, this study aims to evaluate the performance spectral indices for discriminating burned and unburned areas in the immediate post-fire environment in the case of Gaziemir, Buca and Karabağlar districts of Izmir metropolitan city where one of the forest fires occurred in the 18rd August 2019. For this, whilst a Sentinel 2A (26th August 2019) was used to map burned / unburned areas as the reference dataset, two Landsat 8 satellite images (7th and 28th August 2019) were used for the calculation of spectral indices. The spectral indices of normalised difference vegetation index (NDVI), atmospherically resistant vegetation index (ARVI), two versions of normalised burn ratio (NBR and NBR2) and burnt area index (BAI) were calculated for the selected two dates as well as pre-fire and post-fire temporal differences in those indices. For the performance comparison of spectral indices, binary maps of burned and unburned areas were created and separability index (SI) was calculated for pre/post-fire differenced spectral indices. Our results suggest that NBR2, NDVI and ARVI had the highest potential for discriminating burned areas, respectively. Even though the value of separability indices was different from each other where NBR and BAI had the lowest values, that doesn’t necessarily mean these indices cannot discriminate burned areas, since the separation of burned and unburned areas highly depend on spatio-temporal circumstances like vegetation types and time lags between image acquisition dates.

Keywords

• Remote sensing,spectral indices,burned area mapping,Landsat 8,Sentinel 2A

References

1. Boschetti, M., Stroppiana, D., Brivio, P. A. (2010). Mapping burned areas in a Mediterranean environment using soft integration of spectral indices from high-resolution satellite images. Earth Interactions, 14(17), 1-20.
2. Chander, G., Markham, B. (2003). Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE Transactions on geoscience and remote sensing, 41(11), 2674-2677.
3. Chen, W., Cao, C., He, Q., Guo, H., Zhang, H., Li, R., Zheng, S., Xu, M., Gao, M., Zhao, J. and Li, S. (2010). Quantitative estimation of the shrub canopy LAI from atmosphere-corrected HJ-1 CCD data in Mu Us Sandland. Science China Earth Sciences, 53(1), 26-33.
4. Chen, X., Vogelmann, J.E., Rollins, M., Ohlen, D., Key, C.H., Yang, L., Huang, C. and 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.
5. Chuvieco, E., Congalton, R. G. (1988). Mapping and inventory of forest fires from digital processing of TM data. Geocarto International, 3(4), 41-53.
6. Chuvieco, E., Martin, M. P., Palacios, A. (2002). Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. International Journal of Remote Sensing, 23(23), 5103-5110.
7. Chuvieco, E., Riaño, D., Danson, F. M., Martin, P. (2006). Use of a radiative transfer model to simulate the postfire spectral response to burn severity. Journal of Geophysical Research: Biogeosciences, 111(G4).
8. Curtis, P. S., Gough, C. M. (2018). Forest aging, disturbance and the carbon cycle. New Phytologist, 219(4), 1188-1193.

9. Fairman, T. A., Nitschke, C. R., Bennett, L. T. (2016). Too much, too soon? A review of the effects of increasing wildfire frequency on tree mortality and regeneration in temperate eucalypt forests. International Journal of Wildland Fire, 25(8), 831-848.
10. Fornacca, D., Ren, G., Xiao, W. (2018). Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China. Remote Sensing, 10(8), 1196.
11. Gibbs, H. K., Brown, S., Niles, J. O., Foley, J. A. (2007). Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters, 2(4), 045023.
12. Hammill, K. A., Bradstock, R. A. (2006). Remote sensing of fire severity in the Blue Mountains: influence of vegetation type and inferring fire intensity. International Journal of Wildland Fire, 15(2), 213-226.
13. Hernandez, C., Drobinski, P., Turquety, S. (2015). How much does weather control fire size and intensity in the Mediterranean region?. Annales Geophysicae, European Geosciences Union, 2015, 33 (7), 931-939.
14. Kaufman, Y. J., Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE transactions on Geoscience and Remote Sensing, 30(2), 261-270.
15. Kavzoglu, T., Erdemir, M. Y., Tonbul, H. (2016). Evaluating performances of spectral indices for burned area mapping using object-based image analysis. In 12th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences (pp. 5-8).
16. Key, C. H., Benson, N. C. (2005). Landscape assessment: ground measure of severity, the Composite Burn Index; and remote sensing of severity, the Normalized Burn Ratio. FIREMON: Fire effects monitoring and inventory system, 2004.
17. Liu, W., Wang, L., Zhou, Y., Wang, S., Zhu, J., Wang, F. (2016). A comparison of forest fire burned area indices based on HJ satellite data. Natural Hazards, 81(2), 971-980.
18. Martín, M. P., Gómez, I., Chuvieco, E. (2006). Burnt Area Index (BAIM) for burned area discrimination at regional scale using MODIS data. Forest Ecology and Management, (234), S221.
19. Mathieu, R., Aryal, J., Chong, A. (2007). Object-based classification of Ikonos imagery for mapping large-scale vegetation communities in urban areas. Sensors, 7(11), 2860-2880.
20. Miller, J. D., 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.
21. Mitri, G. H., Gitas, I. Z. (2004). A performance evaluation of a burned area object-based classification model when applied to topographically and non-topographically corrected TM imagery. International Journal of Remote Sensing, 25(14), 2863-2870.
22. Mouillot, F., Schultz, M. G., Yue, C., Cadule, P., Tansey, K., Ciais, P., Chuvieco, E. (2014). Ten years of global burned area products from spaceborne remote sensing—A review: Analysis of user needs and recommendations for future developments. International Journal of Applied Earth Observation and Geoinformation, 26, 64-79.
23. Nurlu, Engin, Ü. Erdem, H. Doygun, H. Oğuz, Birsen Kesgin, N. Doygun, Isin Barut, and Eylul Malkoc. (2013). The Effects of Land Cover Change on Natural Ecosystems: The Case of İzmir, Turkey. Journal of Selcuk University Natural and Applied Science, 2(2), 371-378.
24. Pereira, J. M. (1999). A comparative evaluation of NOAA/AVHRR vegetation indexes for burned surface detection and mapping. IEEE Transactions on Geoscience and Remote Sensing, 37(1), 217-226.
25. Pillai, R. B., Weisberg, P. J., & Lingua, E. (2005), Object-oriented classification of repeat aerial photography for quantifying woodland expansion in central Nevada. In 20th Biennial Workshop on Aerial Photography, Videography, and High Resolution Digital Imagery for Resource Assessment, Waslaco, TX, October (pp. 2-6).
26. 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, 79, 199-210.
27. RTGDF. (2019). Forestry statistics, Republic of Turkey General Directorate of Forestry. https://www.ogm.gov.tr/ekutuphane/Sayfalar/Istatistikler.aspx?RootFolder=%2Fekutuphane%2FIstatistikler%2FOrmanc%C4%B1l%C4%B1k%20%C4%B0statistikleri&FolderCTID=0x012000301D182F8CB9FC49963274E712A2DC00&View={4B3B693B-B532-4C7F-A2D0-732F715C89CC}
28. Schepers, L., Haest, B., Veraverbeke, S., Spanhove, T., Vanden Borre, J., Goossens, R. (2014). Burned area detection and burn severity assessment of a heathland fire in Belgium using airborne imaging spectroscopy (APEX). Remote Sensing, 6(3), 1803-1826.
29. TSMS. (2019). Turkish State Meteorological Service. Official Statistics. Izmir. https://mgm.gov.tr/eng/forecast-cities.aspx?m=IZMIR
30. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), 127-150.
31. USGS. (2019). Landsat Surface Reflectance-Derived Spectral Indices. https://www.usgs.gov/land-resources/nli/landsat/landsat-normalized-burn-ratio
32. Veraverbeke, S., Verstraeten, W. W., Lhermitte, S., Goossens, R. (2010). Evaluating Landsat Thematic Mapper spectral indices for estimating burn severity of the 2007 Peloponnese wildfires in Greece. International Journal of Wildland Fire, 19(5), 558-569.