Research Article
BibTex RIS Cite

EVALUATION OF DIFFERENT REMOTE SENSING INDICES IN DETECTION OF FOREST BURNED AREA: A CASE STUDY OF 2022 MERSİN (GÜLNAR) WILDFIRE

Year 2022, Volume: 4 Issue: 2, 160 - 171, 31.12.2022
https://doi.org/10.57165/artgrid.1179074

Abstract

In recent years, with the effect of global warming, wildfires are causing increasingly destructive damages. Determining the destruction caused by wildfires in forest areas is a time-consuming and cost effective. The mapping of the forest areas burned as a result of wildfires and the area in terms of burn severity is of great importance in terms of rehabilitation activities. Remote sensing and GIS techniques are widely used in mapping and monitoring studies of forest areas. In terms of providing practical, cost-effective and sensitive results, remote sensing offers significant advantages in mapping the area in terms of burned area and burn severity after wildfires. In this study, the determination of the amount of burned area belonging to the wildfire that occurred in Mersin province Gülnar district in September 2022 and the performances of different remote sensing indices in determining the size of the burned area were compared. Sentinel-2 satellite image was used in the mapping of the study area. The amount of burned area was estimated according to the dNDVI (Differenced normalized difference vegetation index), dSAVI (Differenced soil adjusted vegetation index) and dNBR (Differenced normalized burn ratio) indices. The performance values of three different indices used within the scope of the study were revealed by accuracy analysis. The general accuracy values of the dNDVI, dSAVI and dNBR indices were found to be 75.56%, 84.44% and 88.89%, respectively, in determining the size of the burned area. While the accuracy of the dNDVI was acceptable, the dSAVI and dNBR indices performed very well in detecting the size of the burned area. Areas damaged by wildfires can be detected quickly and precisely with satellite images and remote sensing techniques.

References

  • Akay, A.E., Erdoğan, A. 2017. GIS-based multi-criteria decision analysis for forest fire risk mapping. In 4Th International Geoadvances Workshop-Geoadvances 2017: Isprs Workshop On Multi-Dimensional & Multi-Scale Spatial Data Modeling. Copernicus Gesellschaft Mbh. https://doi.org/10.5194/isprs-annals-IV-4-W4-25-2017
  • Arıcak, B., Enez, K., Küçük, Ö., 2012. Uydu Görüntüsü Kullanarak Yangın Potansiyelinin Belirlenmesi, KSU Mühendislik Bilimleri Dergisi, Özel Sayı, 220.
  • Atun, R., Kalkan, K., Gürsoy, Ö. (2020). Determining the forest fire risk with Sentinel 2 images. Turkish Journal of Geosciences, 1(1), 22-26.
  • Bilici, E., 2009. A Study on the Integration of Firebreaks and Fireline with Forest Roads Networks and It's Planning and Construction (A Case Study of Gallipoly National Park) Istanbul University. Faculty of Forestry Journal Series: A 59(2), pp. 86-102. https://doi.org/10.17099/jffiu.66756
  • 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
  • Coskuner, K.A. 2022. Assessing the performance of MODIS and VIIRS active fire products in the monitoring of wildfires: a case study in Turkey. iForest 15: 85-94. https://doi.org/10.3832/ifor3754-015
  • Çömert, R., Matcı, D.K., Emir, H., Avdan, U. 2017. Nesne Tabanlı Sınıflandırma ile Yanmış Orman Alanlarının Tespiti. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17(4), 27-34.
  • Duran, C. 2014. Mersin ilindeki orman yangınlarının başlangıç noktalarına göre mekânsal analizi (2001-2013). Ormancılık Araştırma Dergisi, 1(1 A), 38-49. https://doi.org/10.17568/oad.87328
  • Escuin, S., Navarro, R., Fernandez, P. 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(4), 1053-1073. https://doi.org/10.1080/01431160701281072
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
  • Gülci, S., Akay, A.E., Yüksel, K. 2016. Evaluating capabilities of using thermal imagery for detecting impacts of forest operations on residual forests. In Living Planet Symposium (Vol. 740, p. 193), Czech Republic, Prague.
  • Gülci, S., Yüksel, K., Gümüş, S., Wing, M.G. 2021. Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry. European Journal of Forest Engineering, 7(2), 57-66. https://doi.org/10.33904/ejfe.1031090
  • Halofsky, J. E., Peterson, D. L., Harvey, B.J. 2020. Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA. Fire Ecology, 16(1), 1-26. https://doi.org/10.1186/s42408-019-0062-8
  • İban, M.C., Şahin, E. 2021. Monitoring burn severity and air pollutants in wildfire events using remote sensing data: the case of Mersin wildfires in summer 2021. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 12(2), 487-497. https://doi.org/10.17714/gumusfenbil.1008242
  • Key, C.H., Benson, N.C. 2006. 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.
  • Koç, A., Selik, C. 1996. Belgrad ormanında arazi kullanımının uzaktan algılama yöntemleri ile belirlenmesi. Journal of the Faculty of Forestry Istanbul University, 46(1), 137-146.
  • Kurnar, D. 2011. Monitoring forest cover changes using remote sensing and GIS: a global prospective. Research Journal of Environmental Sciences, 5(2), 105. https://doi.org/10.3923/rjes.2011.105.123
  • Küçükosmanoğlu, A. 1990. Kızılçam-orman yangınları ilişkisi. Journal of the Faculty of Forestry Istanbul University, 40(4), 67-84.
  • Li, Z., Fraser, R., Jin, J., Abuelgasim, A.A., Csiszar, I., Gong, P., Pu, R. and Hao, W. 2003. Evaluation of algorithms for fire detection and mapping across North America from satellite. Journal of Geophysical Research: Atmospheres, 108(D2). https://doi.org/10.1029/2001JD001373.
  • 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.
  • Özel, H.B., Ateşoğlu, A., Kırdar, E. 2021. Orman Yangınları: Sebepleri, Etkileri, İzlenmesi, Alınması Gereken Önlemler ve Rehabilitasyon Faaliyetleri. (Editör: Taşkın Kavzaoğlu) Orman Yangınları Sonrası Yanan Alanların Ağaçlandırılması, İzleme ve Değerlendirme (275-300). Ankara: Türkiye Bilimleri Akademisi. https://doi.org/10.53478/TUBA.2021.050)
  • Sağlam, B., Bilgili, E., Durmaz, B.D., Kadıoğulları, A.İ., Küçük, Ö. 2008. Spatio-temporal analysis of forest fire risk and danger using LANDSAT imagery. Sensors, 8(6), 3970-3987. https://doi.org/10.3390/s8063970
  • Özdemir, F.B., Demir, N. 2022. 2019 İzmir Karabağlar İlçesi Orman Yangın Alanının Uydu Görüntüleri İle Analizi. Turkish Journal of Remote Sensing and GIS, 3(1), 20-33. https://doi.org/10.48123/rsgis.1009319
  • Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), 127-150. https://doi.org/10.1016/0034-4257(79)90013-0
  • URL-1. https://www.trthaber.com/foto-galeri/mersinde-kule-donen-ormanlik-alan-havadan-goruntulendi/50141/sayfa-8.html (Erişim Tarihi: 15.09.2022).
  • URL-2. https://sentinel.esa.int/web/sentinel/missions/sentinel-2. (Erişim Tarihi: 21.08.2022).
  • Veraverbeke, S., Harris, S., Hook, S. 2011. Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data. Remote Sensing of Environment, 115(10), 2702-2709. https://doi.org/10.1016/j.rse.2011.06.010
  • Williams, A. P., Abatzoglou, J. T., Gershunov, A., Guzman‐Morales, J., Bishop, D. A., Balch, J. K., Lettenmaier, D. P. 2019. Observed impacts of anthropogenic climate change on wildfire in California. Earth's Future, 7(8), 892-910. https://doi.org/10.1029/2019EF001210
  • Yılmaz, B., Demirel, M., Balçık, F. 2022. Yanmış Alanların Sentinel-2 MSI ve Landsat-8 OLI ile Tespiti ve Analizi: Çanakkale/Gelibolu Orman Yangını. Doğal Afetler ve Çevre Dergisi, 8(1), 76-86. https://doi.org/10.21324/dacd.941456

YANAN ORMAN ALANI TESPİTİNDE FARKLI UZAKTAN ALGILAMA İNDİSLERİNİN DEĞERLENDİRİLMESİ: 2022 YILI MERSİN (GÜLNAR) ORMAN YANGINI ÖRNEĞİ

Year 2022, Volume: 4 Issue: 2, 160 - 171, 31.12.2022
https://doi.org/10.57165/artgrid.1179074

Abstract

Son yıllarda küresel ısınmanın etkisi ile orman yangınları giderek yıkıcı tahribatlara neden olmaktadır. Orman yangınlarının, orman alanlarında meydana getirdiği tahribatın belirlenmesi zaman alıcı ve maliyetli bir iştir. Orman yangınları sonucunda yanan orman alanlarının ve yanma şiddeti açısından alanın haritalanması, rehabilitasyon çalışmaları açısından büyük önem taşımaktadır. Orman alanlarına ait haritalama ve izleme çalışmalarında uzaktan algılama ve CBS teknikleri yaygın bir şekilde kullanılmaktadır. Uzaktan algılama, pratik, uygun maliyetli ve hassas sonuçlar vermesi açısından orman yangınları sonrasında yanan alan büyüklüğü ve yanma şiddeti açısından alanın haritalanmasında önemli avantajlar sunmaktadır. Bu çalışmada, 2022 yılı eylül ayında Mersin ili Gülnar ilçesinde meydana gelen orman yangınına ait yanan alan miktarının belirlenmesi ve farklı uzaktan algılama indislerinin yanan alan büyüklüğünün belirlenmesindeki performansları karşılaştırılmıştır. Çalışma alanına ait haritalama kapsamında Sentinel-2 uydu görüntüsü kullanılmıştır. Yanan alan miktarı, dNDVI (Differenced normalized difference vegetation index), dSAVI (Differenced soil adjusted vegetation index) ve dNBR (Differenced normalized burn ratio) indislerine göre tahmin edilmiştir. Çalışma kapsamında kullanılan üç farklı indise ait performans değerleri doğruluk analizi ile ortaya konmuştur. Yanan alan büyüklüğünün tespit edilmesinde, dNDVI, dSAVI ve dNBR indislerine ait genel doğruluk değerleri sırasıyla % 75.56, % 84.44 ve % 88.89 olarak bulunmuştur. dNDVI indisine ait doğruluk oranı kabul edilebilir düzeydeyken, dSAVI ve dNBR indisleri yanan alan büyüklüğünün tespit edilmesinde oldukça iyi performans göstermiştir. Orman yangınları sonucu zarar gören alanlar, uydu görüntüleri ve uzaktan algılama teknikleri ile hızlı ve hassas bir şekilde tespit edilebilmektedir.

References

  • Akay, A.E., Erdoğan, A. 2017. GIS-based multi-criteria decision analysis for forest fire risk mapping. In 4Th International Geoadvances Workshop-Geoadvances 2017: Isprs Workshop On Multi-Dimensional & Multi-Scale Spatial Data Modeling. Copernicus Gesellschaft Mbh. https://doi.org/10.5194/isprs-annals-IV-4-W4-25-2017
  • Arıcak, B., Enez, K., Küçük, Ö., 2012. Uydu Görüntüsü Kullanarak Yangın Potansiyelinin Belirlenmesi, KSU Mühendislik Bilimleri Dergisi, Özel Sayı, 220.
  • Atun, R., Kalkan, K., Gürsoy, Ö. (2020). Determining the forest fire risk with Sentinel 2 images. Turkish Journal of Geosciences, 1(1), 22-26.
  • Bilici, E., 2009. A Study on the Integration of Firebreaks and Fireline with Forest Roads Networks and It's Planning and Construction (A Case Study of Gallipoly National Park) Istanbul University. Faculty of Forestry Journal Series: A 59(2), pp. 86-102. https://doi.org/10.17099/jffiu.66756
  • 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
  • Coskuner, K.A. 2022. Assessing the performance of MODIS and VIIRS active fire products in the monitoring of wildfires: a case study in Turkey. iForest 15: 85-94. https://doi.org/10.3832/ifor3754-015
  • Çömert, R., Matcı, D.K., Emir, H., Avdan, U. 2017. Nesne Tabanlı Sınıflandırma ile Yanmış Orman Alanlarının Tespiti. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17(4), 27-34.
  • Duran, C. 2014. Mersin ilindeki orman yangınlarının başlangıç noktalarına göre mekânsal analizi (2001-2013). Ormancılık Araştırma Dergisi, 1(1 A), 38-49. https://doi.org/10.17568/oad.87328
  • Escuin, S., Navarro, R., Fernandez, P. 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(4), 1053-1073. https://doi.org/10.1080/01431160701281072
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
  • Gülci, S., Akay, A.E., Yüksel, K. 2016. Evaluating capabilities of using thermal imagery for detecting impacts of forest operations on residual forests. In Living Planet Symposium (Vol. 740, p. 193), Czech Republic, Prague.
  • Gülci, S., Yüksel, K., Gümüş, S., Wing, M.G. 2021. Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry. European Journal of Forest Engineering, 7(2), 57-66. https://doi.org/10.33904/ejfe.1031090
  • Halofsky, J. E., Peterson, D. L., Harvey, B.J. 2020. Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA. Fire Ecology, 16(1), 1-26. https://doi.org/10.1186/s42408-019-0062-8
  • İban, M.C., Şahin, E. 2021. Monitoring burn severity and air pollutants in wildfire events using remote sensing data: the case of Mersin wildfires in summer 2021. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 12(2), 487-497. https://doi.org/10.17714/gumusfenbil.1008242
  • Key, C.H., Benson, N.C. 2006. 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.
  • Koç, A., Selik, C. 1996. Belgrad ormanında arazi kullanımının uzaktan algılama yöntemleri ile belirlenmesi. Journal of the Faculty of Forestry Istanbul University, 46(1), 137-146.
  • Kurnar, D. 2011. Monitoring forest cover changes using remote sensing and GIS: a global prospective. Research Journal of Environmental Sciences, 5(2), 105. https://doi.org/10.3923/rjes.2011.105.123
  • Küçükosmanoğlu, A. 1990. Kızılçam-orman yangınları ilişkisi. Journal of the Faculty of Forestry Istanbul University, 40(4), 67-84.
  • Li, Z., Fraser, R., Jin, J., Abuelgasim, A.A., Csiszar, I., Gong, P., Pu, R. and Hao, W. 2003. Evaluation of algorithms for fire detection and mapping across North America from satellite. Journal of Geophysical Research: Atmospheres, 108(D2). https://doi.org/10.1029/2001JD001373.
  • 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.
  • Özel, H.B., Ateşoğlu, A., Kırdar, E. 2021. Orman Yangınları: Sebepleri, Etkileri, İzlenmesi, Alınması Gereken Önlemler ve Rehabilitasyon Faaliyetleri. (Editör: Taşkın Kavzaoğlu) Orman Yangınları Sonrası Yanan Alanların Ağaçlandırılması, İzleme ve Değerlendirme (275-300). Ankara: Türkiye Bilimleri Akademisi. https://doi.org/10.53478/TUBA.2021.050)
  • Sağlam, B., Bilgili, E., Durmaz, B.D., Kadıoğulları, A.İ., Küçük, Ö. 2008. Spatio-temporal analysis of forest fire risk and danger using LANDSAT imagery. Sensors, 8(6), 3970-3987. https://doi.org/10.3390/s8063970
  • Özdemir, F.B., Demir, N. 2022. 2019 İzmir Karabağlar İlçesi Orman Yangın Alanının Uydu Görüntüleri İle Analizi. Turkish Journal of Remote Sensing and GIS, 3(1), 20-33. https://doi.org/10.48123/rsgis.1009319
  • Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), 127-150. https://doi.org/10.1016/0034-4257(79)90013-0
  • URL-1. https://www.trthaber.com/foto-galeri/mersinde-kule-donen-ormanlik-alan-havadan-goruntulendi/50141/sayfa-8.html (Erişim Tarihi: 15.09.2022).
  • URL-2. https://sentinel.esa.int/web/sentinel/missions/sentinel-2. (Erişim Tarihi: 21.08.2022).
  • Veraverbeke, S., Harris, S., Hook, S. 2011. Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data. Remote Sensing of Environment, 115(10), 2702-2709. https://doi.org/10.1016/j.rse.2011.06.010
  • Williams, A. P., Abatzoglou, J. T., Gershunov, A., Guzman‐Morales, J., Bishop, D. A., Balch, J. K., Lettenmaier, D. P. 2019. Observed impacts of anthropogenic climate change on wildfire in California. Earth's Future, 7(8), 892-910. https://doi.org/10.1029/2019EF001210
  • Yılmaz, B., Demirel, M., Balçık, F. 2022. Yanmış Alanların Sentinel-2 MSI ve Landsat-8 OLI ile Tespiti ve Analizi: Çanakkale/Gelibolu Orman Yangını. Doğal Afetler ve Çevre Dergisi, 8(1), 76-86. https://doi.org/10.21324/dacd.941456
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Forest Industry Engineering
Journal Section Articles
Authors

Kıvanç Yüksel 0000-0001-9660-5028

Publication Date December 31, 2022
Published in Issue Year 2022 Volume: 4 Issue: 2

Cite

APA Yüksel, K. (2022). YANAN ORMAN ALANI TESPİTİNDE FARKLI UZAKTAN ALGILAMA İNDİSLERİNİN DEĞERLENDİRİLMESİ: 2022 YILI MERSİN (GÜLNAR) ORMAN YANGINI ÖRNEĞİ. ArtGRID - Journal of Architecture Engineering and Fine Arts, 4(2), 160-171. https://doi.org/10.57165/artgrid.1179074