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Çok Bantlı Uydu Görüntüleriyle Orman Yangınlarında Hasar Tespiti

Year 2021, Volume: 23 Issue: 1, 172 - 181, 15.04.2021
https://doi.org/10.24011/barofd.837507

Abstract

Uydu verileri, yangın hakkında bilgi sağlayarak hasar tespiti ve iyileştirme çalışmalarına ciddi katkı sunmaktadır. Özellikle çok bantlı uydu sistemleri sayesinde yangın hasarlı alanların kesin bir şekilde belirlenmesi ve hızlı bir şekilde haritalanması mümkün olmaktadır. Özellikle sınıflandırma teknikleri ve spektral bilginin kullanılmasıyla bu tarz çalışmalar küresel ve bölgesel olarak gerçekleştirilmektedir. Bu çalışmada Avrupa Uzay Ajansı (ESA) tarafından işletilen Sentinel 2 uydu sistemiyle elde edilen görüntüler kullanılarak Harran Üniversitesi Osmanbey kampüsü ve civarında meydana gelen yangın incelenmiştir. Çalışmanın temel amacı yangın bölgesini belirlemek, bu bölgeyi hasar seviyesine göre sınıflandırmak ve her hasar sınıfındaki bitki varlığı değişimini tespit etmektir. Bu noktada klasik anlamda denetimli ya da denetimsiz sınıflandırma uygulamak yerine bitki indeksi ve yangın indeksi görüntüleri elde edilerek meydana gelen yangın alanı belirlenmiş ve hasar gören bu alanın kendi içinde maruz kaldıkları hasar seviyeleri belirlenmiştir. Daha sonra her hasar seviyesi ile bitki yoğunluğu incelenerek bitki yoğunluğu seviyelerindeki kayıp belirlenerek haritalandırılmıştır. Çalışma sonucunda toplamda 55 hektar alanın yangından farklı derecelerde hasar gördüğü ve buna bağlı olarak faklı bitki yoğunluğundaki alanlarda kayıplar olduğu belirlenmiştir.

References

  • Aksoy, H., Kaptan, S. (2020). Simulation of future forest and land use/cover changes (2019-2039) using the Cellular Automata-Markov Model. Geocarto International, (just-accepted), 1-17, DOI: https://doi.org/10.1080/10106049.2020.1778102.
  • Bıyıklı, D. (2019). Landsat-8 uydu görüntüleri kullanarak nesne-tabanlı sınıflandırma yöntemi ile ormanlık alanlardaki zamansal değişimin izlenmesi: Muğla ili örneği. TMMOB 6. Coğrafi Bilgi Sistemleri Kongresi,23-25 Ekim 2019, Ankara.
  • Boschetti, M., Stroppiana, D., Brivio, P.A. (2010). Mapping burned areas in a Mediterranean environment using soft integration of spectral indices from high-resolution satellite images. Earth Interaction, 14, 1-20.
  • Brivio, P.A., Maggi, M., Binaghi, E., Gallo, I. (2003). Mapping burned surfaces in Sub-Saharan Africa based on multi-temporal neural classification. International Journal of Remote Sensing, 24,4003-4016.
  • Chongo, D., Nagasawa, R., Ahmed, A.O.C., Perveen, M.F. (2007). Fire monitoring in savanna ecosystems using MODIS data: A case study of Kruger National Park, South Africa. Landscape and Ecological Engineering, 3, 79-88.
  • Chuvieco, E. (2009). Global impacts of fire. In Earth Observation of Wildland Fires in Mediterranean Ecosystems, Springer: Berlin/Heidelberg, Germany, 1-11.
  • Chuvieco, E., Lizundia-Loiola, J., Pettinari, M.L., Ramo, R., Padilla, M., Tansey, K., Mouillot, F., Laurent, P., Storm, T., Heil, A. (2018). Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth System Science Data, 10, 2015-2031.
  • Cocke, A.E., Fulé, P.Z., Crouse, J.E. (2005). Comparison of burn severity assessments using differenced normalized burn ratio and ground data. International Journal of Wildland Fire, 14, 189-198.
  • Comert, R., Matcı D K, Avdan U. (2019). Object Based Burned Area Mappıng With Random Forest Algorithm. International Journal of Engineering and Geosciences, 4(2), 78-87.
  • Dereli, M. A. (2019). Sentinel-2A Uydu görüntüleri ile giresun il merkezi için kısa dönem arazi örtüsü değişiminin belirlenmesi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 19(2), 361-368.
  • Durkaya, B., Kaptan, S., Durkaya, A. (2020). Socio-economic and cultural sources of conflict between forest villagers and forest; a case study from Black Sea region, Turkey. Crime, Law and Social Change, 74, 155-173.
  • Filipponi, F. B. (2018). Burned area ındex for Sentinel-2. Multidisciplinary Digital Publishing Institute Proceedings, 2, 364.
  • Filipponi, F., Valentini, E., Nguyen Xuan, A., Guerra, C.A., Wolf, F., Andrzejak, M., Taramelli, A. (2018). Global MODIS Fraction of Green Vegetation Cover for Monitoring Abrupt and Gradual Vegetation Changes. Remote Sensing, 10 (4), 653.
  • French, N.H.F., Kasischke, E.S., Williams, D.G. (2003). Variability in the emissions of carbon-based trace gases from wildfire in the Alaskan boreal forest. Journal of Geophysical Research, 107, 8151.
  • Giglio, L., Boschetti, L., Roy, D.P., Humber, M.L., Justice, C.O. (2018). The Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment, 217, 72-85.
  • Hudak, A.T., Brockett, B.H. (2004). Mapping fire scars in a southern African savannah using Landsat imagery. International Journal of Remote Sensing 25(16), 3231-3243.
  • Key, C., Benson, N. (2006). Landscape assessment: ground measure of severity, the composite burn ındex, and remote sensing of severity, the normalized burn ratio. In Fire Effects Monitoring and Inventory System, 219-279.
  • Kontoes, C.C., Poilvé, H., Florsch, G., Keramitsoglou, I., Paralikidis, S. (2009). A comparative analysis of a fixed thresholding vs. a classification tree approach for operational burn scar detection and mapping. International Journal of Applied Earth Observation and Geoinformation, 11, 299-316.
  • Koutsias, N., Karteris, M. (2000). Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 Thematic Mapper image. International Journal of Remote Sensing 21, 673-687.
  • 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, 367-376.
  • Patterson, M.W., Yool, S.R. (1998). Mapping fire-induced vegetation mortality using Landsat Thematic Mapper data: A comparison of linear transformation techniques. Remote Sensing of Environment 65, 132-142.
  • Paysen, T.E., Ansley, R.J., Brown, J.K., Gottfried, G.J., Haase, S.M. (2000). Fire in western shrubland, woodland, and grassland ecosystems. Wildland fire in ecosystems: Effects of fire on flora. RMRS General Technical Reports 42(2), 121-159.
  • Polychronaki, P., Gitas, I.Z. (2010). The development of an operational procedure for burned-area mapping using object-based classification and ASTER imagery. International Journal of Remote Sensing 31, 1113-1120.
  • 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.
  • Saylan, İ., Çömert, R. (2019). Sentinel-2A ürünlerinin yanmış orman alanlarının haritalanmasındaki başarının araştırılması. Türkiye Uzaktan Algılama Dergisi, 1(1),8-15.
  • Silva, J.M., Sá, A.C., Pereira, J.M. (2005). Comparison of burned area estimates derived from SPOT-vegetation and Landsat ETM+ data in Africa: Influence of spatial pattern and vegetation type. Remote Sensing of Environment 96, 188-201.
  • Smith, R., Adams, M., Maier, S., Craig, R., Kristina, A., Maling, I. (2007). Estimating the area of stubble burning from the number of active fires detected by satellite. Remote Sensing of Environment 109, 95-106.
  • Thonicke, K., Venevsky, S., Sitch, S., Cramer, W. (2001). The role of fire disturbance for global vegetation Dynamics Coupling fire into a Dynamic Global Vegetation Model. Global Ecology and Biogeography 10, 661-677.
  • Tonbul, H. (2015). Uydu görüntü verileri kullanılarak orman yangın şiddeti ve yangın sonrası durumun zamansal olarak incelenmesi: Akdeniz bölgesi örneği. Doktora Tezi (yayımlanmış), İTÜ Fen Bilimleri Enstitüsü, Geomatik Mühendisliği Anabilim Dalı, İstanbul, 88 s.
  • USGS (2016). Landsat—Earth Observation Satellites, Version 1.1 U.S. Geological Survey Fact Sheet 2015–3081, U.S. Geological Survey: Washingotn, DC, ABD.
  • Yiğit, A.Y., Kaya, Y (2020). Sentinel-2A uydu verileri kullanılarak sel alanlarının incelenmesi: Düzce örneği. Türkiye Uzaktan Algılama Dergisi, 2(1), 1-9.

Damage Detection in Forest Fires with Multi-Band Satellite Images

Year 2021, Volume: 23 Issue: 1, 172 - 181, 15.04.2021
https://doi.org/10.24011/barofd.837507

Abstract

Satellite data provides information about fire, making a serious contribution to damage assessment and improvement efforts. Especially thanks to the multi-band satellite systems, it is possible to determine the fire damaged areas precisely and to map them quickly. Especially with the use of classification techniques and spectral information, such studies are carried out globally and regionally. In this study, using the images obtained with the Sentinel-2 satellite system operated by the European Space Agency (ESA), Harran University Osmanbey campus and its fire have been examined. The main purpose of the study is to determine the fire zone, classify this zone according to the damage level and determine the change in plant existence in each damage class. At this point, instead of applying supervised or unsupervised classification methods, the fire area occurred by obtaining vegetation index and fire index images, and the damage levels were determined. Later, by examining each damage level and plant density, the loss in plant density levels were determined and mapped. As a result of the study, it was determined that a total of 55 hectares of land was damaged to different degrees from the fire and accordingly there are losses in areas with different vegetation densities.

References

  • Aksoy, H., Kaptan, S. (2020). Simulation of future forest and land use/cover changes (2019-2039) using the Cellular Automata-Markov Model. Geocarto International, (just-accepted), 1-17, DOI: https://doi.org/10.1080/10106049.2020.1778102.
  • Bıyıklı, D. (2019). Landsat-8 uydu görüntüleri kullanarak nesne-tabanlı sınıflandırma yöntemi ile ormanlık alanlardaki zamansal değişimin izlenmesi: Muğla ili örneği. TMMOB 6. Coğrafi Bilgi Sistemleri Kongresi,23-25 Ekim 2019, Ankara.
  • Boschetti, M., Stroppiana, D., Brivio, P.A. (2010). Mapping burned areas in a Mediterranean environment using soft integration of spectral indices from high-resolution satellite images. Earth Interaction, 14, 1-20.
  • Brivio, P.A., Maggi, M., Binaghi, E., Gallo, I. (2003). Mapping burned surfaces in Sub-Saharan Africa based on multi-temporal neural classification. International Journal of Remote Sensing, 24,4003-4016.
  • Chongo, D., Nagasawa, R., Ahmed, A.O.C., Perveen, M.F. (2007). Fire monitoring in savanna ecosystems using MODIS data: A case study of Kruger National Park, South Africa. Landscape and Ecological Engineering, 3, 79-88.
  • Chuvieco, E. (2009). Global impacts of fire. In Earth Observation of Wildland Fires in Mediterranean Ecosystems, Springer: Berlin/Heidelberg, Germany, 1-11.
  • Chuvieco, E., Lizundia-Loiola, J., Pettinari, M.L., Ramo, R., Padilla, M., Tansey, K., Mouillot, F., Laurent, P., Storm, T., Heil, A. (2018). Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth System Science Data, 10, 2015-2031.
  • Cocke, A.E., Fulé, P.Z., Crouse, J.E. (2005). Comparison of burn severity assessments using differenced normalized burn ratio and ground data. International Journal of Wildland Fire, 14, 189-198.
  • Comert, R., Matcı D K, Avdan U. (2019). Object Based Burned Area Mappıng With Random Forest Algorithm. International Journal of Engineering and Geosciences, 4(2), 78-87.
  • Dereli, M. A. (2019). Sentinel-2A Uydu görüntüleri ile giresun il merkezi için kısa dönem arazi örtüsü değişiminin belirlenmesi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 19(2), 361-368.
  • Durkaya, B., Kaptan, S., Durkaya, A. (2020). Socio-economic and cultural sources of conflict between forest villagers and forest; a case study from Black Sea region, Turkey. Crime, Law and Social Change, 74, 155-173.
  • Filipponi, F. B. (2018). Burned area ındex for Sentinel-2. Multidisciplinary Digital Publishing Institute Proceedings, 2, 364.
  • Filipponi, F., Valentini, E., Nguyen Xuan, A., Guerra, C.A., Wolf, F., Andrzejak, M., Taramelli, A. (2018). Global MODIS Fraction of Green Vegetation Cover for Monitoring Abrupt and Gradual Vegetation Changes. Remote Sensing, 10 (4), 653.
  • French, N.H.F., Kasischke, E.S., Williams, D.G. (2003). Variability in the emissions of carbon-based trace gases from wildfire in the Alaskan boreal forest. Journal of Geophysical Research, 107, 8151.
  • Giglio, L., Boschetti, L., Roy, D.P., Humber, M.L., Justice, C.O. (2018). The Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment, 217, 72-85.
  • Hudak, A.T., Brockett, B.H. (2004). Mapping fire scars in a southern African savannah using Landsat imagery. International Journal of Remote Sensing 25(16), 3231-3243.
  • Key, C., Benson, N. (2006). Landscape assessment: ground measure of severity, the composite burn ındex, and remote sensing of severity, the normalized burn ratio. In Fire Effects Monitoring and Inventory System, 219-279.
  • Kontoes, C.C., Poilvé, H., Florsch, G., Keramitsoglou, I., Paralikidis, S. (2009). A comparative analysis of a fixed thresholding vs. a classification tree approach for operational burn scar detection and mapping. International Journal of Applied Earth Observation and Geoinformation, 11, 299-316.
  • Koutsias, N., Karteris, M. (2000). Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 Thematic Mapper image. International Journal of Remote Sensing 21, 673-687.
  • 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, 367-376.
  • Patterson, M.W., Yool, S.R. (1998). Mapping fire-induced vegetation mortality using Landsat Thematic Mapper data: A comparison of linear transformation techniques. Remote Sensing of Environment 65, 132-142.
  • Paysen, T.E., Ansley, R.J., Brown, J.K., Gottfried, G.J., Haase, S.M. (2000). Fire in western shrubland, woodland, and grassland ecosystems. Wildland fire in ecosystems: Effects of fire on flora. RMRS General Technical Reports 42(2), 121-159.
  • Polychronaki, P., Gitas, I.Z. (2010). The development of an operational procedure for burned-area mapping using object-based classification and ASTER imagery. International Journal of Remote Sensing 31, 1113-1120.
  • 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.
  • Saylan, İ., Çömert, R. (2019). Sentinel-2A ürünlerinin yanmış orman alanlarının haritalanmasındaki başarının araştırılması. Türkiye Uzaktan Algılama Dergisi, 1(1),8-15.
  • Silva, J.M., Sá, A.C., Pereira, J.M. (2005). Comparison of burned area estimates derived from SPOT-vegetation and Landsat ETM+ data in Africa: Influence of spatial pattern and vegetation type. Remote Sensing of Environment 96, 188-201.
  • Smith, R., Adams, M., Maier, S., Craig, R., Kristina, A., Maling, I. (2007). Estimating the area of stubble burning from the number of active fires detected by satellite. Remote Sensing of Environment 109, 95-106.
  • Thonicke, K., Venevsky, S., Sitch, S., Cramer, W. (2001). The role of fire disturbance for global vegetation Dynamics Coupling fire into a Dynamic Global Vegetation Model. Global Ecology and Biogeography 10, 661-677.
  • Tonbul, H. (2015). Uydu görüntü verileri kullanılarak orman yangın şiddeti ve yangın sonrası durumun zamansal olarak incelenmesi: Akdeniz bölgesi örneği. Doktora Tezi (yayımlanmış), İTÜ Fen Bilimleri Enstitüsü, Geomatik Mühendisliği Anabilim Dalı, İstanbul, 88 s.
  • USGS (2016). Landsat—Earth Observation Satellites, Version 1.1 U.S. Geological Survey Fact Sheet 2015–3081, U.S. Geological Survey: Washingotn, DC, ABD.
  • Yiğit, A.Y., Kaya, Y (2020). Sentinel-2A uydu verileri kullanılarak sel alanlarının incelenmesi: Düzce örneği. Türkiye Uzaktan Algılama Dergisi, 2(1), 1-9.
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Conservation and Biodiversity
Journal Section Biodiversity, Environmental Management and Policy, Sustainable Forestry
Authors

Nizar Polat 0000-0002-6061-7796

Yunus Kaya 0000-0003-2319-4998

Publication Date April 15, 2021
Published in Issue Year 2021 Volume: 23 Issue: 1

Cite

APA Polat, N., & Kaya, Y. (2021). Çok Bantlı Uydu Görüntüleriyle Orman Yangınlarında Hasar Tespiti. Bartın Orman Fakültesi Dergisi, 23(1), 172-181. https://doi.org/10.24011/barofd.837507


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