Research Article
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Year 2022, , 37 - 44, 09.09.2022
https://doi.org/10.26650/tjbc.1082039

Abstract

References

  • Adagbasa, G. E., Adelabu S.A, & Okello T.W. (2018). Spatio-temporal assessment of fire severity in a protected and mountainous ecosystem. IEEE Geoscience and Remote Sensing Symposium (IGARSS) Proceedings, 6572-6575. google scholar
  • 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. google scholar
  • Atun, R., Kalkan, K., & Gürsoy, Ö. (2020). Determining the forest fire risk with Sentinel 2 images. Turkish Journal of Geosciences, 1(1), 22-26. google scholar
  • Delegido, J., Pezzola, A., Casella, A., Winschel, C., Urrego, E.P., Jimenez, J.C., Sobrino, J.A., Soria, G., & Moreno, J. (2018) Fire severity estimation in southern of the Buenos Aires province, Argentina, using Sentinel-2 and its comparison with Landsat-8. Revista Teledeteccion, 51, 47-60. google scholar
  • 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 of Environment, 96(3), 328-339. google scholar
  • ESA Sentinel 2 User Handbook. 2015. Available online: https:// sentinels.copernicus.eu/documents/247904/685211/Sentinel-2_User_Handbook (accessed on May 5, 2022). google scholar
  • Escuin, S., Navarro R., & P Fernandez. (2008). Fire Severity Assessment by Using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) Derived from LANDSAT TM/ETM Images. International Journal of Remote Sensing, 29, 1053-1073. google scholar
  • Garda, M.J.L. & Caselles, V. (1991). Mapping burns and natural reforestation using thematic mapper data. Geocarto International, 6(1), 31-37. google scholar
  • Hirschberger, P. (2016). Forests ablaze: Causes and effects of global forest fires. S.Winter, Y. VonLaer, & T. Köberich, Eds. google scholar
  • Keeley, J.E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18(1), 116-126. google scholar
  • Key, C. (2006). Ecological and sampling constraints on defining landscape fire severity. Fire Ecology, 2, 34-59. google scholar
  • Key, C.H. & Benson, N.C. (2006). Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio. In ‘FIREMON: Fire Effects Monitoring and Inventory System’. (Eds DC Lutes, RE Keane, JF Caratti, CH Key, NC Benson, S Sutherland, LJ Gangi) USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-164-CD, p. LA-1-55. Fort Collins, CO. google scholar
  • Lentile, L., Smith, F., & Shepperd, W. (2005). Patch structure, fire-scar formation, and tree regeneration in a large mixed-severity fire in the South Dakota Black Hills, USA. Canadian Journal of Forest Research, 35, 2875-2885. google scholar
  • Liu, S., Zheng, Y., Dalponte, M. & Tong, X. (2020). A novel fire index-based burned area change detection approach using Landsat-8 OLI data. European Journal of Remote Sensing, 53(1), 104-112. google scholar
  • Mack, M.C., Walker, X.J., Johnstone, J.F., Alexander, H.D., Melvin, A.M., Jean, M. & Miller, S.N. (2021). Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees. Science, 372, 280-283. google scholar
  • Mallinis, G., Mitsopoulos, I., & Chysafi, I. (2018). Evaluating and comparing Sentinel 2A and Landsat 8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece. GIScience & Remote Sensing, 55, 1-18. google scholar
  • Mitri, G.H. & Gitas, I.Z. (2004). A semi-automated object-oriented model for burned area mapping in the Mediterranean region using Landsat-TM imagery. International Journal of Wildland Fire, 13(3), 367-376. google scholar
  • Moreira, F., Ascoli, D., Safford, H., Adams, M., Moreno, J.M., Pereira, J.C., Catry, F., Armesto, J., Bond, W.J., Gonzalez, M., Curt, T., Koutsias, N., McCaw, L., Price, O., Pausas, J.G., Rigolot, E., Stephens, S., Tavsanoglu, C., Vallejo, R., van Wilgen, B., Xanthopoulos, G., & Fernandes P. (2020). Wildfire management in Mediterranean-type regions: paradigm change needed. Environmental Research Letters, 15, 011001. google scholar
  • Mukherjee, J., Mukherjee, J., & Chakravarty, D. (2018). Detection of coal seam fires in summer seasons from Landsat 8 OLI/ TIRS in Dhanbad. National Conference on Computer Vision, Springer.In: Rameshan R., Arora C., Dutta Roy S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science. Springer, Singapore, 841, 529-539. google scholar
  • Pausas, J.G. & Keeley, J.E. (2021). Wildfires and global change. Frontiers in Ecology and Environment, 19(7), 387-395. google scholar
  • Roy, D., Boschetti, L., & Trigg, S. (2006). Remote Sensing of Fire Severity: Assessing the Normalized Burn Ratio. IEEE Geoscience and Remote Sensing Letters, 3(1), 112-120. google scholar
  • Ruffault, J. et al. (2020). Increased likelihood of heat-induced large wildfires in the Mediterranean Basin. Nature Scientific Reports, 10: 13790. https://doi.org/10.1038/s41598-020-70069-z google scholar
  • Saputra, A.D., et al. (2017) Burn scar analysis using normalized burning ratio (NBR) index during 2015 forest fire at Merang-Kepahyang peat forest, South Sumatra, Indonesia. in AIP Conference Proceedings. AIP Publishing. google scholar
  • 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. google scholar
  • Smith, A.M.S., Drake, N.A., Wooster, M.J., Hudak, A.T., Holden, Z.A., & Gibbons, C.J. (2007). Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: Comparison of methods and application to MODIS. International Journal of Remote Sensing, 28(12), 2753-2775. google scholar
  • Teodoro, A., & Amaral, A. (2019). A statistical and spatial analysis of Portuguese forest fires in summer 2016 considering Landsat 8 and Sentinel 2A data. Environments, 6(3), 36. https://doi. org/10.3390/environments6030036 google scholar
  • Turco, M., Rosa-Canovas, J. R., Bedia, J., Jerez, S., Montavez, J. P., Llasat, M. C., & Provenzale, A. 2018. Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models. Nature Communications, 9, 1-9. google scholar
  • Veraverbeke, S., Lhermitte, S., Verstraeten, W.W., & Goossens, R. (2010) The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: the case of the large 2007 Peloponnese wildfires in Greece. Remote Sensing of Environment, 114(11), 2548-2563. google scholar
  • Url 1: https://www.hurriyet.com.tr/gundem/geliboludaki-yangin-sonduruldu-mu-gelibolu-orman-yangininda-son-durum-41558884 google scholar
  • Url 2: https://www.canakkalehaber.com/yanginda-tarlalari-yanan-koyluler-uzgun/4634/ google scholar
  • Url 3: https://www.sozcu.com.tr/2020/gundem/geliboluyu-yangindan-sonra-ikinci-tehlike-bekliyor-5919575/ google scholar

Burned Area and Fire Severity Prediction of a Forest Fire Using a Sentinel 2-Derived Spectral Index in Çanakkale, Turkey

Year 2022, , 37 - 44, 09.09.2022
https://doi.org/10.26650/tjbc.1082039

Abstract

Objective: The objective of this study was to determine the extent and degree of severity of a burned area resulting from a forest fire using Sentinel 2 remote sensing data in Çanakkale, Turkey within the Mediterranean Basin, an area of the world where forest fire occurrence and severity are increasing.
Materials and Methods: Pre and postfire Sentinel images were obtained. The Normalized Burn Ratio (NBR) index was calculated for each scene. Then the difference NBR (dNBR) was calculated by subtracting the postfire NBR from the prefire NBR. dNBR ranges were classified into fire severity categories. A map with 20 m spatial resolution displaying the burned area and fire severity was generated from the classified dNBR image. Finally, a forest stand map of the burn area was laid over the fire severity map to examine the relationship between fire severity and stand and cover types.
Results: Approximately 1400 ha of area was predicted to have been burned. Twenty nine, 21, 42, and 8% of the burned area was identified as low, moderate low, moderate high, and severely burned using the dNBR index, respectively.
Conclusions: The overlay of the stand map on the burn severity map revealed that the forested areas were more severely burned compared to the agricultural sections. dNBR is an effective index to delineate fire area extent and identify fire severity. Sentinel 2 data provide a fast and accurate means to monitor forest fire extent and severity due to its improved spatial and temporal resolution.

References

  • Adagbasa, G. E., Adelabu S.A, & Okello T.W. (2018). Spatio-temporal assessment of fire severity in a protected and mountainous ecosystem. IEEE Geoscience and Remote Sensing Symposium (IGARSS) Proceedings, 6572-6575. google scholar
  • 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. google scholar
  • Atun, R., Kalkan, K., & Gürsoy, Ö. (2020). Determining the forest fire risk with Sentinel 2 images. Turkish Journal of Geosciences, 1(1), 22-26. google scholar
  • Delegido, J., Pezzola, A., Casella, A., Winschel, C., Urrego, E.P., Jimenez, J.C., Sobrino, J.A., Soria, G., & Moreno, J. (2018) Fire severity estimation in southern of the Buenos Aires province, Argentina, using Sentinel-2 and its comparison with Landsat-8. Revista Teledeteccion, 51, 47-60. google scholar
  • 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 of Environment, 96(3), 328-339. google scholar
  • ESA Sentinel 2 User Handbook. 2015. Available online: https:// sentinels.copernicus.eu/documents/247904/685211/Sentinel-2_User_Handbook (accessed on May 5, 2022). google scholar
  • Escuin, S., Navarro R., & P Fernandez. (2008). Fire Severity Assessment by Using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) Derived from LANDSAT TM/ETM Images. International Journal of Remote Sensing, 29, 1053-1073. google scholar
  • Garda, M.J.L. & Caselles, V. (1991). Mapping burns and natural reforestation using thematic mapper data. Geocarto International, 6(1), 31-37. google scholar
  • Hirschberger, P. (2016). Forests ablaze: Causes and effects of global forest fires. S.Winter, Y. VonLaer, & T. Köberich, Eds. google scholar
  • Keeley, J.E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18(1), 116-126. google scholar
  • Key, C. (2006). Ecological and sampling constraints on defining landscape fire severity. Fire Ecology, 2, 34-59. google scholar
  • Key, C.H. & Benson, N.C. (2006). Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio. In ‘FIREMON: Fire Effects Monitoring and Inventory System’. (Eds DC Lutes, RE Keane, JF Caratti, CH Key, NC Benson, S Sutherland, LJ Gangi) USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-164-CD, p. LA-1-55. Fort Collins, CO. google scholar
  • Lentile, L., Smith, F., & Shepperd, W. (2005). Patch structure, fire-scar formation, and tree regeneration in a large mixed-severity fire in the South Dakota Black Hills, USA. Canadian Journal of Forest Research, 35, 2875-2885. google scholar
  • Liu, S., Zheng, Y., Dalponte, M. & Tong, X. (2020). A novel fire index-based burned area change detection approach using Landsat-8 OLI data. European Journal of Remote Sensing, 53(1), 104-112. google scholar
  • Mack, M.C., Walker, X.J., Johnstone, J.F., Alexander, H.D., Melvin, A.M., Jean, M. & Miller, S.N. (2021). Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees. Science, 372, 280-283. google scholar
  • Mallinis, G., Mitsopoulos, I., & Chysafi, I. (2018). Evaluating and comparing Sentinel 2A and Landsat 8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece. GIScience & Remote Sensing, 55, 1-18. google scholar
  • Mitri, G.H. & Gitas, I.Z. (2004). A semi-automated object-oriented model for burned area mapping in the Mediterranean region using Landsat-TM imagery. International Journal of Wildland Fire, 13(3), 367-376. google scholar
  • Moreira, F., Ascoli, D., Safford, H., Adams, M., Moreno, J.M., Pereira, J.C., Catry, F., Armesto, J., Bond, W.J., Gonzalez, M., Curt, T., Koutsias, N., McCaw, L., Price, O., Pausas, J.G., Rigolot, E., Stephens, S., Tavsanoglu, C., Vallejo, R., van Wilgen, B., Xanthopoulos, G., & Fernandes P. (2020). Wildfire management in Mediterranean-type regions: paradigm change needed. Environmental Research Letters, 15, 011001. google scholar
  • Mukherjee, J., Mukherjee, J., & Chakravarty, D. (2018). Detection of coal seam fires in summer seasons from Landsat 8 OLI/ TIRS in Dhanbad. National Conference on Computer Vision, Springer.In: Rameshan R., Arora C., Dutta Roy S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science. Springer, Singapore, 841, 529-539. google scholar
  • Pausas, J.G. & Keeley, J.E. (2021). Wildfires and global change. Frontiers in Ecology and Environment, 19(7), 387-395. google scholar
  • Roy, D., Boschetti, L., & Trigg, S. (2006). Remote Sensing of Fire Severity: Assessing the Normalized Burn Ratio. IEEE Geoscience and Remote Sensing Letters, 3(1), 112-120. google scholar
  • Ruffault, J. et al. (2020). Increased likelihood of heat-induced large wildfires in the Mediterranean Basin. Nature Scientific Reports, 10: 13790. https://doi.org/10.1038/s41598-020-70069-z google scholar
  • Saputra, A.D., et al. (2017) Burn scar analysis using normalized burning ratio (NBR) index during 2015 forest fire at Merang-Kepahyang peat forest, South Sumatra, Indonesia. in AIP Conference Proceedings. AIP Publishing. google scholar
  • 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. google scholar
  • Smith, A.M.S., Drake, N.A., Wooster, M.J., Hudak, A.T., Holden, Z.A., & Gibbons, C.J. (2007). Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: Comparison of methods and application to MODIS. International Journal of Remote Sensing, 28(12), 2753-2775. google scholar
  • Teodoro, A., & Amaral, A. (2019). A statistical and spatial analysis of Portuguese forest fires in summer 2016 considering Landsat 8 and Sentinel 2A data. Environments, 6(3), 36. https://doi. org/10.3390/environments6030036 google scholar
  • Turco, M., Rosa-Canovas, J. R., Bedia, J., Jerez, S., Montavez, J. P., Llasat, M. C., & Provenzale, A. 2018. Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models. Nature Communications, 9, 1-9. google scholar
  • Veraverbeke, S., Lhermitte, S., Verstraeten, W.W., & Goossens, R. (2010) The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: the case of the large 2007 Peloponnese wildfires in Greece. Remote Sensing of Environment, 114(11), 2548-2563. google scholar
  • Url 1: https://www.hurriyet.com.tr/gundem/geliboludaki-yangin-sonduruldu-mu-gelibolu-orman-yangininda-son-durum-41558884 google scholar
  • Url 2: https://www.canakkalehaber.com/yanginda-tarlalari-yanan-koyluler-uzgun/4634/ google scholar
  • Url 3: https://www.sozcu.com.tr/2020/gundem/geliboluyu-yangindan-sonra-ikinci-tehlike-bekliyor-5919575/ google scholar
There are 31 citations in total.

Details

Primary Language English
Subjects Environmental Sciences
Journal Section Research Articles
Authors

Kemal Gökkaya 0000-0001-8980-5072

Publication Date September 9, 2022
Submission Date March 3, 2022
Acceptance Date August 9, 2022
Published in Issue Year 2022

Cite

APA Gökkaya, K. (2022). Burned Area and Fire Severity Prediction of a Forest Fire Using a Sentinel 2-Derived Spectral Index in Çanakkale, Turkey. Turkish Journal of Bioscience and Collections, 6(2), 37-44. https://doi.org/10.26650/tjbc.1082039
AMA Gökkaya K. Burned Area and Fire Severity Prediction of a Forest Fire Using a Sentinel 2-Derived Spectral Index in Çanakkale, Turkey. tjbc. September 2022;6(2):37-44. doi:10.26650/tjbc.1082039
Chicago Gökkaya, Kemal. “Burned Area and Fire Severity Prediction of a Forest Fire Using a Sentinel 2-Derived Spectral Index in Çanakkale, Turkey”. Turkish Journal of Bioscience and Collections 6, no. 2 (September 2022): 37-44. https://doi.org/10.26650/tjbc.1082039.
EndNote Gökkaya K (September 1, 2022) Burned Area and Fire Severity Prediction of a Forest Fire Using a Sentinel 2-Derived Spectral Index in Çanakkale, Turkey. Turkish Journal of Bioscience and Collections 6 2 37–44.
IEEE K. Gökkaya, “Burned Area and Fire Severity Prediction of a Forest Fire Using a Sentinel 2-Derived Spectral Index in Çanakkale, Turkey”, tjbc, vol. 6, no. 2, pp. 37–44, 2022, doi: 10.26650/tjbc.1082039.
ISNAD Gökkaya, Kemal. “Burned Area and Fire Severity Prediction of a Forest Fire Using a Sentinel 2-Derived Spectral Index in Çanakkale, Turkey”. Turkish Journal of Bioscience and Collections 6/2 (September 2022), 37-44. https://doi.org/10.26650/tjbc.1082039.
JAMA Gökkaya K. Burned Area and Fire Severity Prediction of a Forest Fire Using a Sentinel 2-Derived Spectral Index in Çanakkale, Turkey. tjbc. 2022;6:37–44.
MLA Gökkaya, Kemal. “Burned Area and Fire Severity Prediction of a Forest Fire Using a Sentinel 2-Derived Spectral Index in Çanakkale, Turkey”. Turkish Journal of Bioscience and Collections, vol. 6, no. 2, 2022, pp. 37-44, doi:10.26650/tjbc.1082039.
Vancouver Gökkaya K. Burned Area and Fire Severity Prediction of a Forest Fire Using a Sentinel 2-Derived Spectral Index in Çanakkale, Turkey. tjbc. 2022;6(2):37-44.