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DETECTION OF BURNED AREAS WITH SENTINEL-2 MSI AND LANDSAT-9 OLI SATELLITE IMAGES: 2022 MUĞLA/MARMARİS FOREST FIRE

Year 2023, , 866 - 880, 03.12.2023

Emre Yücer

https://doi.org/10.17780/ksujes.1303299

Abstract

Forest fires damage living creatures and vegetation, as well as cause air pollution. Therefore, the fight against forest fires is an important issue. Today, thanks to the developing technology, it is possible to detect the burned areas by using image processing algorithms, indexes used in remote sensing and satellite images. In this study, the forest fire that occurred on June 21, 2022 in Muğla province, Marmaris district, Küfre bay and Hisarönü neighborhood was analyzed with Sentinel-2 MSI and Landsat-9 OLI satellite images. For this purpose, the Normalized Difference Vegetation Index (NDVI), Normalized Moisture Index (NDMI), Normalized Burn Ratio Index (NBRI) and Burned Area Index (BAI) were calculated from the satellite data before and after the fire. As a result of the analysis, the burned areas were compared with the data of the General Directorate of Forestry. As a result of this comparison, it was determined that the closest results to the General Directorate of Forestry values were NDMI in the Landsat-9 OLI image and NDVI indices in the Sentinel-2 MSI image. The error matrix was calculated to evaluate the classification results. According to general accuracy and Kappa values, Sentinel-2 MSI image has higher values than Landsat-9 OLI image. The NBRI index obtained the highest values with 0,99 overall accuracy and 0,98 Kappa value in Sentinel-2 MSI image.

Keywords

Landsat-9 OLI forest fire Sentinel-2 MSI remote sensing indices

References

  • Botella-Martínez, M. A., & Fernández-Manso, A. (2017). Study of post-fire severity in the Valencia region comparing the NBR, RdNBR and RBR indexes derived from Landsat 8 images. Revista de Teledetección, (49), 33-47. https://doi.org/10.4995/raet.2017.7095
  • Chen G., Metz M.R., Rizzo D.M., & Meentemeyer R.K., (2015). Mapping burn severity in a disease-impacted forest landscape using Landsat and ASTER imagery. International Journal of Applied Earth Observation and Geoinformation, 40(2015), 91-99. https://doi.org/10.1016/j.jag.2015.04.005
  • 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. https://doi.org/10.1080/01431160210153129
  • Chung, M., Jung, M., & Kim, Y., (2019). Wildfire damage assessment using multi-temporal Sentinel-2 data, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(3/W8), 97-102. https://doi.org/10.5194/isprs-archives-XLII-3-W8-97-2019
  • Cihan, A., Cerit, K., & Erener, A., (2022). Yangın Alanında Uydu Görüntüleri ile Yer Yüzey Sıcaklık Değişimi Gözlemi ve Mekânsal Alan Tespiti. Doğal Afetler ve Çevre Dergisi, 8(1), 142-155. https://doi.org/10.21324/dacd.942724
  • Çolak E., & Sunar A.F., (2018). Remote sensing & GIS integration for monitoring the areas affected by forest fires: A case study in Izmir, Turkey. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences-ISPRS Archives, 42(3W4), 165-170. https://doi.org/10.5194/isprs-archives-XLII-3-W4-165-2018
  • Ertuğrul M., (2005). Orman Yangınlarının Dünyadaki ve Türkiye’deki Durumu. Bartın Orman Fakültesi Dergisi, 7(7), 43-45.
  • Feizizadeh, B., Darabi, S., Blaschke, T., & Lakes, T. (2022). QADI as a new method and alternative to kappa for accuracy assessment of remote sensing-based image classification. Sensors, 22(12), 4506. https://doi.org/10.3390/s22124506
  • 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. https://doi.org/10.3390/rs10081196
  • García-Llamas P., Suárez-Seoane S., Fernández-Guisuraga J. M., Fernández-García V., Fernández-Manso A., Quintano C., Taboada A., Marcos E., & Calvo L., (2019). Evaluation and comparison of Landsat-8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems. International Journal of Applied Earth Observation and Geoinformation, 80(2019), 137–144. https://doi.org/10.1016/j.jag.2019.04.006
  • Gonçalves, A. C., & Sousa, A. M. (2017). The fire in the Mediterranean region: a case study of forest fires in Portugal. Mediterranean Identities-Environment, Society, Culture; Fuerst-Bielis, B., Ed, 305-335. https://doi.org/10.5772/intechopen.69410
  • Isabel, M. P. M. (1999). Cartografía e inventario de incendios forestales en la Península Ibérica a partir de imágenes NOAA-AVHRR (Doctoral dissertation, Universidad de Alcalá).
  • Kesgin Atak B., & Tonyaloğlu E., (2020). Evaluating spectral indices for estimating burned areas in the case of Izmir/Turkey. Eurasian Journal of Forest Science, 8(1), 49-59. https://doi.org/10.31195/ejejfs.657253
  • Key, C. H., & Benson, N. C. (2005). Landscape assessment: remote sensing of severity, the normalized burn ratio and ground measure of severity, the composite burn index. FIREMON: Fire effects monitoring and inventory system Ogden, Utah: USDA Forest Service, Rocky Mountain Res. Station.
  • 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. https://doi.org/10.1071/WF07049
  • Khorshid, K. (2016). Impervious Surface Estimation and Mapping via Remotely Sensed Techniques, M.Sc. Thesis, İstanbul Technical University Graduate School Of Science Engineering And Technology, Department of Geomatics Engineering Geomatics Engineering Programme, İstanbul.
  • Landsat. (2023). Landsat Satellites. https://landsat.gsfc.nasa.gov/satellites/ Erişim tarihi: 24.07.2023
  • Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons. 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. https://doi.org/10.1080/22797254.2020.1738900
  • 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. https://doi.org/10.1016/j.rse.2006.12.006
  • Navarro G., Caballero I., Silva G., Parra P.C., Vázquez Á., & Caldeira R., (2017). Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. International Journal of Applied Earth Observation and Geoinformation, 58(2017), 97-106. https://doi.org/10.1016/j.jag.2017.02.003
  • O. G. M. (2023), T.C. Tarım ve Orman Bakanlığı Orman Genel Müdürlüğü 2022 Yılı Faaliyet Raporu, https://www.ogm.gov.tr/tr/faaliyet-raporu. Erişim tarihi: 24.07.2023 ÖZTÜRK, D. (2022). Sentinel-2A MSI ve Landsat-9 OLI-2 Görüntüleri Kullanılarak Farklı Geçirimsiz Yüzey İndekslerinin Karşılaştırmalı Değerlendirmesi: Samsun Örneği. Ege Coğrafya Dergisi, 31(2), 401-423. https://doi.org/10.51800/ecd.1175827
  • Quintano C., Fernández-Manso A., & Fernández-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, 64(2018), 221-225. https://doi.org/10.1016/j.jag.2017.09.014
  • Roy, D. P., Li, J., Zhang, H. K., Yan, L., Huang, H., & Li, Z. (2017). Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance. Remote Sensing of Environment, 199, 25-38. https://doi.org/10.1016/j.rse.2017.06.019
  • Rwanga S., & Ndambuki J., (2017), Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 8(04), 611-622. https://doi.org/10.4236/ijg.2017.84033
  • Sabuncu A., Özener H., (2019), Uzaktan algılama teknikleri ile yanmış alanların tespiti: İzmir Seferihisar orman yangını örneği, Doğal Afetler ve Çevre Dergisi, 5(2), 317-326. https://doi.org/10.21324/dacd.511688
  • Sarp, G., Temuçin, K., Aldırmaz, Y., & Baydoğan, E. (2018). Spatial analysis of forest fires using remote sensing technologies; a case of 2017 Mersin-Anamur forest fire. In 2018, Innovation and Global Issues Congress IV, pp 300-308.
  • Sentinels. (2023). Sentinel-2 MSI User Guide. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi Erişim tarihi: 24.07.2023
  • Taloor, A. K., Manhas, D. S., & Kothyari, G. C. (2021). Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data. Applied Computing and Geosciences, 9, 100051. https://doi.org/10.1016/j.acags.2020.100051
  • U.S. Geological Survey (2023a). Landsat 9 Data Users Handbook: https://www.usgs.gov/media/files/landsat-9-data-users-handbook. Erişim tarihi: 24.07.2023
  • U.S. Geological Survey (2023b). Landsat Collection 2 Level-2 Science Products: https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products. Erişim tarihi: 24.07.2023
  • U.N.E.P (2023). United Nations Environment Programme, Climate & Wildfire Information - A Report By UNEP, https://www.unep.org/resources/report/spreading-wildfire-rising-threat-extraordinary-landscape. Erişim tarihi: 24.07.2023
  • Yılmaz, B., Demirel, M., & Balçık, F. (2022). Yanmış alanların sentinel-2 msı ve landsat-8 olı ile tespiti ve analizi: Çanakkale/Gelibolu orman yangını. Doğal Afetler ve Çevre Dergisi, 8 (1). https://doi.org/76-8. 10.21324/dacd.941456

SENTINEL-2 MSI VE LANDSAT-9 OLI UYDU GÖRÜNTÜLERİYLE YANMIŞ ALANLARIN TESPİTİ: 2022 MUĞLA/MARMARİS ORMAN YANGINI

Year 2023, , 866 - 880, 03.12.2023

Emre Yücer

https://doi.org/10.17780/ksujes.1303299

Abstract

Orman yangınları canlılara ve bitki örtüsüne zarar vermekte, bunun yanında hava kirliliğine de neden olmaktadır. Bu nedenle orman yangınlarıyla mücadele önemli bir durum olarak karşımıza çıkmaktadır. Günümüzde gelişen teknoloji sayesinde görüntü işleme algoritmaları ve uzaktan algılamadaki farklı indeksler kullanılarak uydu görüntülerinden yanan alanların tespiti yapılabilmektedir. Bu çalışmada 21 Haziran 2022 tarihinde Muğla ili Marmaris ilçesinin Küfre koyu ve Hisarönü mahallesinde meydana gelen orman yangını Sentinel-2 MSI ve Landsat-9 OLI uydu görüntüleriyle analiz edilmiştir. Bu amaçla çalışma alanına ait yangın öncesi ve sonrasında ait uydu verilerinden Normalleştirilmiş Fark Bitki İndeksi (Normalized Difference Vegetation Index-NDVI), Normalize Edilmiş Nem İndeksi (Normalized Moisture Index-NDMI), Normalize Edilmiş Yanma Oranı İndeksi (Normalized Burn Ratio Index-NBRI) ve Yanmış Alan İndeksi (Burned Area Index-BAI) hesaplanmıştır. Analizler sonucunda elde edilen yanmış alanlar Orman Genel Müdürlüğü (OGM) verileri ile karşılaştırılmıştır. Bu karşılaştırma sonucunda OGM değerlerine en yakın sonuçların; Landsat-9 OLI görüntüsünde NDMI ve Sentinel-2 MSI görüntüsünde NDVI indekslerinin olduğu tespit edilmiştir. Sınıflandırma sonuçlarını değerlendirmek için hata matrisi hesaplanmıştır. Genel doğruluk ve Kappa değerlerine göre Sentinel-2 MSI görüntüsü, Landsat-9 OLI görüntüsüne göre daha yüksek değerlere sahip olduğu tespit edilmiştir. Sentinel-2 MSI görüntüsünde 0,99 genel doğruluk ve 0,98 Kappa değeri ile NBRI indeksi en yüksek değerleri elde etmiştir.

Keywords

Landsat-9 OLI orman yangını Sentinel-2 MSI uzaktan algılama indeksleri

References

  • Botella-Martínez, M. A., & Fernández-Manso, A. (2017). Study of post-fire severity in the Valencia region comparing the NBR, RdNBR and RBR indexes derived from Landsat 8 images. Revista de Teledetección, (49), 33-47. https://doi.org/10.4995/raet.2017.7095
  • Chen G., Metz M.R., Rizzo D.M., & Meentemeyer R.K., (2015). Mapping burn severity in a disease-impacted forest landscape using Landsat and ASTER imagery. International Journal of Applied Earth Observation and Geoinformation, 40(2015), 91-99. https://doi.org/10.1016/j.jag.2015.04.005
  • 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. https://doi.org/10.1080/01431160210153129
  • Chung, M., Jung, M., & Kim, Y., (2019). Wildfire damage assessment using multi-temporal Sentinel-2 data, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(3/W8), 97-102. https://doi.org/10.5194/isprs-archives-XLII-3-W8-97-2019
  • Cihan, A., Cerit, K., & Erener, A., (2022). Yangın Alanında Uydu Görüntüleri ile Yer Yüzey Sıcaklık Değişimi Gözlemi ve Mekânsal Alan Tespiti. Doğal Afetler ve Çevre Dergisi, 8(1), 142-155. https://doi.org/10.21324/dacd.942724
  • Çolak E., & Sunar A.F., (2018). Remote sensing & GIS integration for monitoring the areas affected by forest fires: A case study in Izmir, Turkey. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences-ISPRS Archives, 42(3W4), 165-170. https://doi.org/10.5194/isprs-archives-XLII-3-W4-165-2018
  • Ertuğrul M., (2005). Orman Yangınlarının Dünyadaki ve Türkiye’deki Durumu. Bartın Orman Fakültesi Dergisi, 7(7), 43-45.
  • Feizizadeh, B., Darabi, S., Blaschke, T., & Lakes, T. (2022). QADI as a new method and alternative to kappa for accuracy assessment of remote sensing-based image classification. Sensors, 22(12), 4506. https://doi.org/10.3390/s22124506
  • 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. https://doi.org/10.3390/rs10081196
  • García-Llamas P., Suárez-Seoane S., Fernández-Guisuraga J. M., Fernández-García V., Fernández-Manso A., Quintano C., Taboada A., Marcos E., & Calvo L., (2019). Evaluation and comparison of Landsat-8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems. International Journal of Applied Earth Observation and Geoinformation, 80(2019), 137–144. https://doi.org/10.1016/j.jag.2019.04.006
  • Gonçalves, A. C., & Sousa, A. M. (2017). The fire in the Mediterranean region: a case study of forest fires in Portugal. Mediterranean Identities-Environment, Society, Culture; Fuerst-Bielis, B., Ed, 305-335. https://doi.org/10.5772/intechopen.69410
  • Isabel, M. P. M. (1999). Cartografía e inventario de incendios forestales en la Península Ibérica a partir de imágenes NOAA-AVHRR (Doctoral dissertation, Universidad de Alcalá).
  • Kesgin Atak B., & Tonyaloğlu E., (2020). Evaluating spectral indices for estimating burned areas in the case of Izmir/Turkey. Eurasian Journal of Forest Science, 8(1), 49-59. https://doi.org/10.31195/ejejfs.657253
  • Key, C. H., & Benson, N. C. (2005). Landscape assessment: remote sensing of severity, the normalized burn ratio and ground measure of severity, the composite burn index. FIREMON: Fire effects monitoring and inventory system Ogden, Utah: USDA Forest Service, Rocky Mountain Res. Station.
  • 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. https://doi.org/10.1071/WF07049
  • Khorshid, K. (2016). Impervious Surface Estimation and Mapping via Remotely Sensed Techniques, M.Sc. Thesis, İstanbul Technical University Graduate School Of Science Engineering And Technology, Department of Geomatics Engineering Geomatics Engineering Programme, İstanbul.
  • Landsat. (2023). Landsat Satellites. https://landsat.gsfc.nasa.gov/satellites/ Erişim tarihi: 24.07.2023
  • Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons. 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. https://doi.org/10.1080/22797254.2020.1738900
  • 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. https://doi.org/10.1016/j.rse.2006.12.006
  • Navarro G., Caballero I., Silva G., Parra P.C., Vázquez Á., & Caldeira R., (2017). Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. International Journal of Applied Earth Observation and Geoinformation, 58(2017), 97-106. https://doi.org/10.1016/j.jag.2017.02.003
  • O. G. M. (2023), T.C. Tarım ve Orman Bakanlığı Orman Genel Müdürlüğü 2022 Yılı Faaliyet Raporu, https://www.ogm.gov.tr/tr/faaliyet-raporu. Erişim tarihi: 24.07.2023 ÖZTÜRK, D. (2022). Sentinel-2A MSI ve Landsat-9 OLI-2 Görüntüleri Kullanılarak Farklı Geçirimsiz Yüzey İndekslerinin Karşılaştırmalı Değerlendirmesi: Samsun Örneği. Ege Coğrafya Dergisi, 31(2), 401-423. https://doi.org/10.51800/ecd.1175827
  • Quintano C., Fernández-Manso A., & Fernández-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, 64(2018), 221-225. https://doi.org/10.1016/j.jag.2017.09.014
  • Roy, D. P., Li, J., Zhang, H. K., Yan, L., Huang, H., & Li, Z. (2017). Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance. Remote Sensing of Environment, 199, 25-38. https://doi.org/10.1016/j.rse.2017.06.019
  • Rwanga S., & Ndambuki J., (2017), Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 8(04), 611-622. https://doi.org/10.4236/ijg.2017.84033
  • Sabuncu A., Özener H., (2019), Uzaktan algılama teknikleri ile yanmış alanların tespiti: İzmir Seferihisar orman yangını örneği, Doğal Afetler ve Çevre Dergisi, 5(2), 317-326. https://doi.org/10.21324/dacd.511688
  • Sarp, G., Temuçin, K., Aldırmaz, Y., & Baydoğan, E. (2018). Spatial analysis of forest fires using remote sensing technologies; a case of 2017 Mersin-Anamur forest fire. In 2018, Innovation and Global Issues Congress IV, pp 300-308.
  • Sentinels. (2023). Sentinel-2 MSI User Guide. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi Erişim tarihi: 24.07.2023
  • Taloor, A. K., Manhas, D. S., & Kothyari, G. C. (2021). Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data. Applied Computing and Geosciences, 9, 100051. https://doi.org/10.1016/j.acags.2020.100051
  • U.S. Geological Survey (2023a). Landsat 9 Data Users Handbook: https://www.usgs.gov/media/files/landsat-9-data-users-handbook. Erişim tarihi: 24.07.2023
  • U.S. Geological Survey (2023b). Landsat Collection 2 Level-2 Science Products: https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products. Erişim tarihi: 24.07.2023
  • U.N.E.P (2023). United Nations Environment Programme, Climate & Wildfire Information - A Report By UNEP, https://www.unep.org/resources/report/spreading-wildfire-rising-threat-extraordinary-landscape. Erişim tarihi: 24.07.2023
  • Yılmaz, B., Demirel, M., & Balçık, F. (2022). Yanmış alanların sentinel-2 msı ve landsat-8 olı ile tespiti ve analizi: Çanakkale/Gelibolu orman yangını. Doğal Afetler ve Çevre Dergisi, 8 (1). https://doi.org/76-8. 10.21324/dacd.941456
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Geological Sciences and Engineering (Other)
Journal Section Geological Engineering
Authors

Emre Yücer 0000-0003-0417-9338

Publication Date December 3, 2023
Submission Date May 26, 2023
Published in Issue Year 2023

Cite

APA Yücer, E. (2023). SENTINEL-2 MSI VE LANDSAT-9 OLI UYDU GÖRÜNTÜLERİYLE YANMIŞ ALANLARIN TESPİTİ: 2022 MUĞLA/MARMARİS ORMAN YANGINI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(4), 866-880. https://doi.org/10.17780/ksujes.1303299