Research Article
PDF Mendeley EndNote BibTex Cite

Year 2020, Volume 1, Issue 2, 72 - 77, 15.12.2020

Abstract

References

  • Chuvieco, E., Mouillot, F., Van Der Werf, G.R., San Miguel, J., Tanasse, M., Koutsias, N., García, M., Yebra, M., Padilla, M., Gitas, I., Heil, A., Hawbaker, T. J., & Giglio, L. (2019). Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment, 225, 45-64.
  • De Santis, A., Chuvieco, E. (2009). GeoCBI: A modified version of the Composite Burn Index for the initial assessment of the short-term burn severity from remotely sensed data. Remote Sensing of Environment, 113(3), 554–562.
  • EOS (2019, February). 6 Spectral indexes to make vegetation analysis complete. Retrieved from https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/
  • ESA (2020, July). European Space Agency. Retrieved from https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/overview
  • Huete, A. (1988). A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 295-309.
  • 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.
  • Key, C.H., Benson, N.C. (2000). Measuring and remote sensing of burn severity. Joint Fire Science Conference and Workshop Proceedings, 284–285.
  • Key, C.H, Benson, N.C. (2006). Landscape assessment: Remote sensing of severity, the Normalized Burn Ratio. FIREMON: Fire Effects Monitoring and Inventory System. General Technical Report, RMRS-GTR-164-CD, 305–325.
  • Lanorte, A., Danese, M., Lasaponara, R., & Murgante, B. (2012). Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis. International Journal of Applied Earth Observation and Geoinformation, 20(1), 42–51.
  • Lutes, D., Keane, R., Caratti, J., Key, C., Benson, N., Sutherland, S., & Gangi, L. (2006). FIREMON: Fire Effects Monitoring and Inventory System. General Technical Report, United States Department of Agriculture. Retrieved from https://www.fs.fed.us/rm/pubs/rmrs_gtr164.pdf
  • 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.
  • Nasi, R., Dennis, R., Meijaard, E., Applegate, G., & Moore, P. (2002). Forest fire and biological diversity. Unasylva, 53(209), 36–40.
  • Pereira, P., Francos, M., Brevik, E.C., Ubeda, X., & Bogunovic, I. (2018). Post-fire soil management. Current Opinion in Environmental Science and Health, 5, 26–32.
  • Quintano, C., Fernandez-Manso, A., & Roberts, D. A. (2017). Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature. Remote Sensing of Environment, 190, 83–95.
  • 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.
  • Sazayya, K., Sugano, T., & Kuramitz, H. (2020). High-heat effects on the physical and chemical properties of soil organic matter and its water-soluble components in Japan’s forests: A comprehensive approach using multiple analytical methods. Analytical Sciences, 36(5), 601–609.
  • Veraverbeke, S., Lhermitte, S., Verstraeten, W.W., & Goossens, R. (2011). Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat thematic mapper. International Journal of Remote Sensing, 32(12), 3521–3537.
  • Xue, J., Su, B. (2017). Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors, 2017, 1-18.
  • Url-1: https://www.aa.com.tr/tr/turkiye/izmirde-yanan-ormanlik-alanlar-havadan-goruntulendi/1559816 (last accessed 1 July 2020)
  • Url-2: https://www.haberturk.com/son-dakika-izmir-deki-orman-yangini-45-saattir-devam-ediyor-2514596 (last accessed 5 May 2020)
  • Url-3: https://www.hurriyet.com.tr/gundem/izmirde-bu-yilin-en-buyuk-yangini-41306741 (last accessed 10 July 2020)
  • Url-4: https://www.izmir.bel.tr/tr/IzmirinCografyasi/220/255 (last accessed 12 May 2020)
  • Url-5: https://www.yenisafak.com/gundem/turkiyede-orman-yangininin-en-cok-yasandigi-3-il-2759389 (last accessed 5 July 2020)
  • Url-6: https://www.aa.com.tr/tr/turkiye/10-yilda-24-bin-264-orman-yangini-cikti/865449 (last accessed 3 July 2020)
  • Url-7: https://www.ogm.gov.tr/Sayfalar/OrmanYanginlari.aspx (last accessed 7 June 2020)
  • Url-8: https://www.sentinel-hub.com/eoproducts/ndvi-normalized-difference-vegetation-index (last accessed 20 June 2020)

Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey

Year 2020, Volume 1, Issue 2, 72 - 77, 15.12.2020

Abstract

Forest fires are serious environmental problems for ecosystem which destroys huge amount of forests every year across the world. Detecting burned areas is not the only important task, it is also very important to distinguish severity degrees of soil suffered for post-fire land management and vegetation regeneration. Remote sensing presents accurate and efficient methods for mapping burned areas and assessing burn severity levels. In this study we detected forest burn areas and assessed burn severity levels using Remote Sensing techniques and Sentinel 2 satellite data products in Karabağlar, Menderes and Seferihisar districts of İzmir, Turkey. A recent big forest fire occurred on 18 August 2019 is assessed in this study, which burned down about 500 hectares of the forest. Burned areas are detected using Normalized Burn Ratio (NBR) and Soil Adjusted Vegetation Index (SAVI) indices and burn area severity analysis is performed using Differenced Normalized Burn Ratio (dNBR) and Differenced Soil Adjusted Vegetation Index (dSAVI) indices. The results of dNBR index show that a total of 6909.708-hectare area is burned during forest fire while 11184.502-hectare is unburned. Areas with different levels of burn severity were detected: 9.3 % Low, 11.1% Moderate-low, 8.5% Moderate-high and 9.3% High. Furthermore, based on the results of dSAVI analysis, 6699.554-hectare area is burned and 11394.656-hectare is unburned; the following different levels of burn severity were detected in the area: 9.4 % Low, 6.4% Moderate-low, 8.7% Moderate-high and 12.5% High.

References

  • Chuvieco, E., Mouillot, F., Van Der Werf, G.R., San Miguel, J., Tanasse, M., Koutsias, N., García, M., Yebra, M., Padilla, M., Gitas, I., Heil, A., Hawbaker, T. J., & Giglio, L. (2019). Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment, 225, 45-64.
  • De Santis, A., Chuvieco, E. (2009). GeoCBI: A modified version of the Composite Burn Index for the initial assessment of the short-term burn severity from remotely sensed data. Remote Sensing of Environment, 113(3), 554–562.
  • EOS (2019, February). 6 Spectral indexes to make vegetation analysis complete. Retrieved from https://eos.com/blog/6-spectral-indexes-on-top-of-ndvi-to-make-your-vegetation-analysis-complete/
  • ESA (2020, July). European Space Agency. Retrieved from https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/overview
  • Huete, A. (1988). A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 295-309.
  • 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.
  • Key, C.H., Benson, N.C. (2000). Measuring and remote sensing of burn severity. Joint Fire Science Conference and Workshop Proceedings, 284–285.
  • Key, C.H, Benson, N.C. (2006). Landscape assessment: Remote sensing of severity, the Normalized Burn Ratio. FIREMON: Fire Effects Monitoring and Inventory System. General Technical Report, RMRS-GTR-164-CD, 305–325.
  • Lanorte, A., Danese, M., Lasaponara, R., & Murgante, B. (2012). Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis. International Journal of Applied Earth Observation and Geoinformation, 20(1), 42–51.
  • Lutes, D., Keane, R., Caratti, J., Key, C., Benson, N., Sutherland, S., & Gangi, L. (2006). FIREMON: Fire Effects Monitoring and Inventory System. General Technical Report, United States Department of Agriculture. Retrieved from https://www.fs.fed.us/rm/pubs/rmrs_gtr164.pdf
  • 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.
  • Nasi, R., Dennis, R., Meijaard, E., Applegate, G., & Moore, P. (2002). Forest fire and biological diversity. Unasylva, 53(209), 36–40.
  • Pereira, P., Francos, M., Brevik, E.C., Ubeda, X., & Bogunovic, I. (2018). Post-fire soil management. Current Opinion in Environmental Science and Health, 5, 26–32.
  • Quintano, C., Fernandez-Manso, A., & Roberts, D. A. (2017). Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature. Remote Sensing of Environment, 190, 83–95.
  • 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.
  • Sazayya, K., Sugano, T., & Kuramitz, H. (2020). High-heat effects on the physical and chemical properties of soil organic matter and its water-soluble components in Japan’s forests: A comprehensive approach using multiple analytical methods. Analytical Sciences, 36(5), 601–609.
  • Veraverbeke, S., Lhermitte, S., Verstraeten, W.W., & Goossens, R. (2011). Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat thematic mapper. International Journal of Remote Sensing, 32(12), 3521–3537.
  • Xue, J., Su, B. (2017). Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors, 2017, 1-18.
  • Url-1: https://www.aa.com.tr/tr/turkiye/izmirde-yanan-ormanlik-alanlar-havadan-goruntulendi/1559816 (last accessed 1 July 2020)
  • Url-2: https://www.haberturk.com/son-dakika-izmir-deki-orman-yangini-45-saattir-devam-ediyor-2514596 (last accessed 5 May 2020)
  • Url-3: https://www.hurriyet.com.tr/gundem/izmirde-bu-yilin-en-buyuk-yangini-41306741 (last accessed 10 July 2020)
  • Url-4: https://www.izmir.bel.tr/tr/IzmirinCografyasi/220/255 (last accessed 12 May 2020)
  • Url-5: https://www.yenisafak.com/gundem/turkiyede-orman-yangininin-en-cok-yasandigi-3-il-2759389 (last accessed 5 July 2020)
  • Url-6: https://www.aa.com.tr/tr/turkiye/10-yilda-24-bin-264-orman-yangini-cikti/865449 (last accessed 3 July 2020)
  • Url-7: https://www.ogm.gov.tr/Sayfalar/OrmanYanginlari.aspx (last accessed 7 June 2020)
  • Url-8: https://www.sentinel-hub.com/eoproducts/ndvi-normalized-difference-vegetation-index (last accessed 20 June 2020)

Details

Primary Language English
Subjects Geosciences, Multidisciplinary
Journal Section Research Articles
Authors

Suhrabuddin NASERY (Primary Author)
Eskişehir Teknik Üniversitesi
0000-0001-5846-2476
Türkiye


Kaan KALKAN
TÜBITAK Space Technologies Research Institute
0000-0002-2732-5425
Türkiye

Publication Date December 15, 2020
Application Date July 17, 2020
Acceptance Date September 7, 2020
Published in Issue Year 2020, Volume 1, Issue 2

Cite

Bibtex @research article { turkgeo770803, journal = {Turkish Journal of Geosciences}, eissn = {2717-7696}, address = {Aksaray Üniversitesi Mühendislik Fakültesi B Blok Kat:2 Harita Mühendisliği Bölümü Merkez/Aksaray}, publisher = {Süleyman Sefa BİLGİLİOĞLU}, year = {2020}, volume = {1}, number = {2}, pages = {72 - 77}, title = {Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey}, key = {cite}, author = {Nasery, Suhrabuddin and Kalkan, Kaan} }
APA Nasery, S. & Kalkan, K. (2020). Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey . Turkish Journal of Geosciences , 1 (2) , 72-77 . Retrieved from https://dergipark.org.tr/en/pub/turkgeo/issue/56822/770803
MLA Nasery, S. , Kalkan, K. "Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey" . Turkish Journal of Geosciences 1 (2020 ): 72-77 <https://dergipark.org.tr/en/pub/turkgeo/issue/56822/770803>
Chicago Nasery, S. , Kalkan, K. "Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey". Turkish Journal of Geosciences 1 (2020 ): 72-77
RIS TY - JOUR T1 - Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey AU - Suhrabuddin Nasery , Kaan Kalkan Y1 - 2020 PY - 2020 N1 - DO - T2 - Turkish Journal of Geosciences JF - Journal JO - JOR SP - 72 EP - 77 VL - 1 IS - 2 SN - -2717-7696 M3 - UR - Y2 - 2020 ER -
EndNote %0 Turkish Journal of Geosciences Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey %A Suhrabuddin Nasery , Kaan Kalkan %T Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey %D 2020 %J Turkish Journal of Geosciences %P -2717-7696 %V 1 %N 2 %R %U
ISNAD Nasery, Suhrabuddin , Kalkan, Kaan . "Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey". Turkish Journal of Geosciences 1 / 2 (December 2020): 72-77 .
AMA Nasery S. , Kalkan K. Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey. turkgeo. 2020; 1(2): 72-77.
Vancouver Nasery S. , Kalkan K. Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey. Turkish Journal of Geosciences. 2020; 1(2): 72-77.
IEEE S. Nasery and K. Kalkan , "Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey", Turkish Journal of Geosciences, vol. 1, no. 2, pp. 72-77, Dec. 2020