Spectral Analysis of Forest Fire Based on the +NBR Index and Its Comparison with Spectral Indices in Sentinel-2 Satellite (Case Study: Baharestan Village)

Document Type : Original Article

Author

Assistant Professor of Geomorphology, Faculty of Human Sciences, University of Zanjan, Zanjan, Iran

Abstract

Burned areas can be easily monitored using multispectral satellite images. Indices were provided to show the difference between healthy vegetation areas and burned areas. To avoid errors and optimize the results, based on the reflective condition of the Sentinel 2 satellite bands, the normalized burn ratio index +NBR was presented. The efficiency of this index was confirmed by comparing it with four other indices in an area of 47.4 square kilometers in the downstream part of the Hiran region on the border between Iran and the Republic of Azerbaijan, around the village of Baharestan. To achieve this goal, two single- and two-time approaches were adopted. To separate the pixels of burned and non-burned areas, the differentiation method was used between the periods before and after the fire on January 8, 2021, and January 25, 2021.  The NBR+ index achieved favorable results due to the removal of cloud masses and water areas that were wrongly classified in the other indices. Pearson's correlation coefficient values also showed that the NBR and NDSWIR index with values of 0.95 had the highest correlation with the NBR + index, and the MIRBI index with values of 0.16 had the lowest correlation. Meanwhile, the NBR+ index with the highest Kappa coefficient of 0.92 can detect areas affected by fire.

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References (in Persian)
Abdi, O., Shetaei, S., Shirvani, Z., Naghavi, M. (2012). Forest management impacts on forest fires in Golestan province by GIS application, Iranian Journal Forest and Range Protection Research, 9(18), pp 100- 108. [In Persian]
Abedi Gheshlaghi, H., Valizadeh, K. (2018). Evaluation and zoning of forest fire risk using multi-criteria decision-making techniques and GIS, Journal of Natural Environmental Hazards, 7(15), pp 49- 66. [In Persian]
Emami, Hassan., Shahriyari, H. (2020). Quantifying environmental and human factors affecting the occurrence and spread of wildfire using RS and GIS methods protected area of Arasbaran, Scientific- research Quarterly of Geographical Data, 28(112), pp 35-53. [In Persian]
Farajzadeh, M., Ghavidel, Y., Mokri, S. (2015). The analysis of forest fires with climatic approach using satellite data in Alborz area Iran, Journal of spatial analysis environmental hazards, 2(3), pp 83- 104. [In Persian]
Janbazghobadi, G. (2019). Investigation of forest fire hazard areas in Golestan province based on fire risk system index (FRSI) using the technique, Journal of Spatial Analysis environmental hazards, 6(3), pp 89-102. [In Persian]
Roodsarabi, Z., Sam Khaniani, A., Kiani, A. (2023), A review of remote sensing methods in identifying and monitoring forest fires, Iranian Journal of remote sensing & GIS, 4(56), pp 19- 52. [In Persian]
 
References (in English)
Arellano-Pérez, S., Ruiz-González, A. D., Álvarez-González, J. G., Vega-Hidalgo, J. A., Díaz-Varela, R., & Alonso-Rego, C. (2018). Mapping fire severity levels of burned areas in Galicia (NW Spain) by Landsat images and the dNBR index: Preliminary results about the influence of topographical, meteorological, and fuel factors on the highest severity level. Advances in Forest Fire Research, 1053-1060.
Amos, C., Petropoulos, G. P., & Ferentinos, K. P. (2019). Determining the use of Sentinel-2A MSI for wildfire burning & severity detection. International journal of remote sensing, 40(3), 905-930.
Barboza Castillo, E., Turpo Cayo, E.Y., de Almeida, C.M., Salas López, R., Rojas Briceño, N.B., Silva López, J.O., Espinoza-Villar, R. (2020). Monitoring wildfires in the northeastern Peruvian Amazon using Landsat-8 and sentinel-2 imagery in the GEE platform. International Journal of Geoinformation, 9, 564- 576.
Dibs, H., Hasab, H.A., Al-Rifaie, J.K., Al-Ansari, N. (2020). An optimal approach for land-use/land-cover mapping by integration and fusion of multispectral Landsat OLI images: A case study in Baghdad, Iraq. Water Air Soil Pollution, 231, 488- 498.
Epting, J., Verbyla, D., Sorbel, B. (2005). Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sensing Environment, 96, 328–339.
García, M.J.L., Caselles, V. (1991). Mapping burns and natural reforestation using thematic mapper data. Geocarto International, 6, 31–37.
Gerard, F., Plummer, S., Wadsworth, R., Sanfeliu, A.F., Iliffe, L., Balzter, H., Wyatt, B. (2003). Forest fire scar detection in the boreal forest with multitemporal spot-vegetation data. IEEE Trans. Geoscience. Remote Sensing, 41, 2575–2585.
Hasmadi, M., Pakhriazad, H.Z., Shahrin, M.F. (2009). Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data. Geogr. Malays. Journal of Society and Space, 5, 1–10.
Huang, H., Chen, Y., Clinton, N., Wang, J., Wang, X., Liu, C., Zhu, Z. (2017). Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sensing Environment, 202, 166–176.
Ip, F., Dohm, J.M., Baker, V.R., Doggett, T., Davies, A.G., Castano, B., Cichy, B. (2004). ASE floodwater classifier development for EO- 1 Hyperion imagery. Lunar Planet. Sci, 35, 1–2.
Liu, C., Frazier, P., Kumar, L. (2007). Comparative assessment of the measures of thematic classification accuracy. Remote Sensing Environment, 107, 606–616.
Liu, S., Zheng, Y., Dalponte, M., Tong, X. (2020). A novel fire index-based burned area change detection approach using Landsat-8 OLI data. Eur. Journal of Remote Sensing, 53, 104–112.
Mallinis, G., Gitas, I.Z., Giannakopoulos, V., Maris, F., Tsakiri-Strati, M. (2013). An object-based approach for flood area delineation in a transboundary area using ENVISAT ASAR and LANDSAT TM data. Journal of Digital Earth, 6, 124–136.
McFeeters, S.K., 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal Remote Sensing, 17, 1425–1432.
Meneses, B.M. (2021). Vegetation recovery patterns in burned areas were assessed with Landsat 8 OLI imagery and environmental biophysical data. Fire, 4, 76-92.
Nolde, M., Plank, S., Riedlinger, T. (2020). An adaptive and extensible system for satellite-based, large-scale burnt area monitoring in near-real time. Remote Sensing, 12, 2162- 2189.
Oliveira, E.R., Disperati, L., Alves, F.L. (2021). A new method (MINDED-BA) for automatic detection of burned areas using remote sensing. Remote Sensing, 13, 5164- 5191.
Ponomarev, E., Zabrodin, A., Ponomareva, T. (2022). Classification of fire damage to boreal forests of Siberia in 2021 based on the dNBR index. Fire, 5, 19- 35.
Pulvirenti, L., Squicciarino, G., Fiori, E., Fiorucci, P., Ferraris, L., Negro, D., Puca, S. (2020). An automatic processing chain for near real-time mapping of burned forest areas using sentinel-2 data. Remote Sensing, 12, 674- 691.
Roy, D.P., Zhang, H.K., Ju, J., Gomez-Dans, J.L., Lewis, P.E., Schaaf, C.B., Kovalskyy, V. (2016). A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance. Remote Sensing Environment, 176, 255–271.
Saidi, S., Younes, A.B., Anselme, B. (2021). A GIS-remote sensing approach for forest fire risk assessment: Case of Bizerte region, Tunisia. Applied Geomatic, 13, 587–603.
Sanchez, A.H., Picoli, M.C.A., Camara, G., Andrade, P.R., Chaves, M.E.D., Lechler, S., Queiroz, G.R. (2020). Comparison of cloud cover detection algorithms on sentinel-2–2 images of the Amazon tropical forest. Remote Sensing, 12, 1284- 1302.
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, 1803–1826.
Silva, J.M.N., Pereira, J.M.C., Cabral, A.I., Sa’, A.C.L., Vasconcelos, M.J.P., Mota, B., Gre’Goire, J.-M. (2003). An estimate of the area burned in southern Africa during the 2000 dry season using SPOT-VEGETATION satellite data. Journal of Geophysical Research, 108, 8498- 8522.
Seydi, S.T. Akhoondzadeh, M., Amani, M., Mahdavi, S. (2021). Wildfire damage assessment over Australia using sentinel-2 imagery and MODIS land cover product within the Google Earth Engine cloud platform. Remote Sensing, 13, 220- 246.
Story, M., Congalton, R.G. (1986). Accuracy assessment: A user’s perspective. Photogrammetric Engineering Remote Sensing, 52, 397–399.
Szpakowski, D.M., Jensen, J.L. (2019). A review of the applications of remote sensing in fire ecology. Remote Sensing, 11, 2638- 2652.
Tanase, M.A., Belenguer-Plomer, M.A., Roteta, E., Bastarrika, A., Wheeler, J., Fernández-Carrillo, Á., Chuvieco, E. (2020). Burned area detection and mapping: Intercomparison of sentinel-1 and sentinel-2 based algorithms over tropical Africa. Remote Sensing, 12, 334- 351.
Trigg, S., Flasse, S. (2001). An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah. International Journal of Remote Sens, 22, 2641–2647.
Veraverbeke, S., Hook, S., Hulley, G. (2012). An alternative spectral index for rapid fire severity assessments. Remote Sensing Environmental, 123, 72–80.
Wang, L., Qu, J.J., Hao, X. (2008). Forest fire detection using the normalized multi-band drought index (NMDI) with satellite measurements. Agricultural and forest Meteorological, 148, 1767–1776.

Articles in Press, Accepted Manuscript
Available Online from 14 August 2024
  • Receive Date: 22 May 2024
  • Revise Date: 12 July 2024
  • Accept Date: 14 August 2024