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Yangın Şiddetinin Uzaktan Algılama ve Coğrafi Bilgi Sistemleri ile Hesaplanması: 2021 Yılı Milas-Karacahisar Yangını

Year 2022, Volume: 22 Issue: 3, 236 - 246, 23.12.2022

Kadir Alperen Coşkuner Ertuğrul Bilgili

https://doi.org/10.17475/kastorman.1215333
Cited By: 1

Abstract

Çalışmanın amacı: Bu çalışmanın amacı, uzaktan algılama ve coğrafi bilgi sistemlerini kullanarak yangın şiddetini hesaplamak, yangın şiddeti ile VIIRS aktif yangın verileri arasındaki ilişkiyi araştırmak ve pratik olarak kullanılabilecek bir yangın şiddeti tahmin modeli geliştirmektir.
Materyal ve yöntem: Yangın yayılma oranını tahmin etmek için Suomi Ulusal Kutup Yörünge Ortaklığı (S-NPP) ve Amerikan Ulusal Okyanus ve Atmosfer Dairesi (NOAA-20) uyduları tarafından sağlanan Görünür Kızılötesi Görüntüleme Radyometre Sensöründen (VIIRS) gelen aktif yangın/sıcak nokta verileri kullanılmıştır. Yanıcı madde tüketimini tahmin etmek için Sentinel-2 görüntüleri, meşcere tipi haritaları ve Kızılçam (Pinus brutia Ten.) için geliştirilmiş ölü örtü ve tepe yanıcı madde miktarı tahmin modelleri kullanılmıştır. Yangın şiddeti değerleri Byram (1959) tarafından geliştirilen denklemle hesaplanmıştır.
Temel sonuçlar: VIIRS aktif yangın verileri ile yangın şiddeti, yangın yayılma oranı ve yanıcı madde tüketimi arasında anlamlı pozitif korelasyon elde edilmiştir Yangında hesaplanan yangın şiddeti 175.0 ila 47597.2 kW/m arasında değişmiş, ortalama 9280.4 kW/m olarak hesaplanmıştır. Tek başına VIIRS aktif yangın veri sayısı ile yangın şiddetindeki değişimin %72'sini açıklanabilmiştir.
Araştırma vurguları: Uydu tabanlı ürünler, yanan alanlarda yangının yayılma oranı ve yanıcı madde tüketiminin kolay ve etkin bir şekilde tahmin edilmesi yoluyla, yangın şiddetinin hesaplanmasında kullanılabilir.

Keywords

Orman Yangınları Yangın Yayılma Oranı Yanıcı Madde Tüketimi Yangın Şiddeti VIIRS, Sentinel-2

References

  • Alexander, M.E. (1982). Calculating and Interpreting Forest Fire Intensities. Canadian Journal of Botany-Revue Canadienne De Botanique, 60(4), 349-357.
  • Alexander, M.E., & Cruz, M.G. (2018). Fireline Intensity. In S. L. Manzello (Ed.), Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, Springer International Publishing, 1-8.
  • Andela, N., van der Werf, G. R., Kaiser, J. W., van Leeuwen, T. T., Wooster, M. J., & Lehmann, C. E. R. (2016). Biomass burning fuel consumption dynamics in the tropics and subtropics assessed from satellite. Biogeosciences, 13(12), 3717-3734.
  • Baysal, I. (2021). Vertical Crown Fuel Distributions in Natural Calabrian Pine (Pinus brutia Ten.) Stands. Croatian Journal of Forest Engineering, 42(13), 301-312.
  • Baysal, İ., Yurtgan, M., Küçük, Ö., & Öztürk, N. (2019). Estimation of Crown Fuel Load of Suppressed Trees in Non-treated Young Calabrian Pine (Pinus brutia Ten.) Plantation Areas. Kastamonu University Journal of Forestry Faculty, 19(3), 350-359.
  • Bradstock, R.A., & Auld, T.D. (1995). Soil Temperatures During Experimental Bushfires in Relation to Fire Intensity: Consequences for Legume Germination and Fire Management in South-Eastern Australia. Journal of Applied Ecology, 32(1), 76-84.
  • Byram, G.M. (1959). Combustion of forest fuels. In K. P. Davis (Ed.), Forest Fire: Control and Use, MCGraw-Hill, 61-89.
  • Chen, J., Li, R., Tao, M., Wang, L.T., Lin, C., Wang, J., . . . Chen, L. (2022). Overview of the performance of satellite fire products in China: Uncertainties and challenges. Atmospheric Environment, 268, 118838.
  • Cole, F.V., & Alexander, M.E. (1995). Head fire intensity class graph for FBP System Fuel Type C-2 (Boreal Spruce). Alaska Department of Natural Resources, Division of Forestry, Fairbanks, AK and Natural Resources Canada, Canadian Forest Service, Edmonton, AB.
  • Coskuner, K.A. (2022a). Assessing the performance of MODIS and VIIRS active fire products in the monitoring of wildfires: a case study in Turkey. iForest - Biogeosciences and Forestry, 15(2), 85-94.
  • Coskuner, K.A. (2022b). Land use/land cover change as a major driver of current landscape flammability in Eastern Mediterranean region: A case study in Southwestern Turkey Bosque (Valdivia), 43(2), 157-167.
  • de Groot, W.J., Landry, R., Kurz, W., Anderson, K., Englefield, P., Fraser, R., . . . Pritchard, J. (2007). Estimating direct carbon emissions from Canadian wildland fires. International Journal of Wildland Fire, 16, 593–606.
  • Fernández-Alonso, J.M., Alberdi, I., Álvarez-González, J.G., Vega, J.A., Cañellas, I., & Ruiz-González, A.D. (2013). Canopy fuel characteristics in relation to crown fire potential in pine stands: analysis, modelling and classification. European Journal of Forest Research, 132(2), 363-377.
  • FIRMS-NOAA-20. (2021). NRT VIIRS 375 m Active Fire product VJ114IMGTDL_NRT distributed from NASA FIRMS. Available on-line [https://earthdata.nasa.gov/firms]. 10.5067/FIRMS/VIIRS/VJ114IMGT_NRT.002.
  • FIRMS-SUOMI-NPP. (2021). NRT VIIRS 375 m Active Fire product VNP14IMGT distributed from NASA FIRMS. Available on-line [https://earthdata.nasa.gov/firms]. doi:10.5067/FIRMS/VIIRS/VNP14IMGT_NRT.002
  • Fusco, E.J., Finn, J.T., Abatzoglou, J.T., Balch, J.K., Dadashi, S., & Bradley, B.A. (2019). Detection rates and biases of fire observations from MODIS and agency reports in the conterminous United States. Remote Sensing of Environment, 220, 30-40.
  • Johnston, J.M., Wooster, M.J., Paugam, R., Wang, X.W., Lynham, T.J., & Johnston, L.M. (2017). Direct estimation of Byram’s fire intensity from infrared remote sensing imagery International Journal of Wildland Fire, 26(8), 668-684.
  • Keeley, J.E. (2008). Fire. In S. E. Jørgensen & B. D. Fath (Eds.), Encyclopedia of Ecology (pp. 1557-1564). Academic Press.
  • Küçük, Ö., & Bilgili, E. (2007). Crown Fuel Load for Young Calabrian Pine (Pinus brutia Ten.) Trees. Kastamonu University Journal of Forestry Faculty, 7(2), 180-189.
  • Küçük, Ö., Bilgili, E., Durmaz, B.D., Sağlam, B., & Baysal, İ. (2009). Örtü Yangınının Tepe Yangınına Geçişinde Etkili Olan Faktörler. Kastamonu University Journal of Forestry Faculty, 9(2), 80-85.
  • Mallinis, G., Mitsopoulos, Ι., Stournara, P., Patias, P., & Dimitrakopoulos, A.P. (2013). Canopy Fuel Load Mapping of Mediterranean Pine Sites Based on Individual Tree-Crown Delineation. Remote Sensing, 5(12), 6461-6480.
  • Mitsopoulos, I.D., & Dimitrakopoulos, A.P. (2007). Canopy fuel characteristics and potential crown fire behavior in Aleppo pine (Pinus halepensis Mill.) forests. Annals of Forest Science, 64(3), 287-299.
  • NASA-FIRMS. (2021). Fire Information for Resource Management System (FIRMS), Active Fire Data.
  • Ononye, A.E., Vodacek, A. & Saber, E. (2007). Automated extraction of fire line parameters from multispectral infrared images. Remote Sensing of Environment, 108(2), 179-188.
  • Ottmar, R.D. (2014). Wildland fire emissions, carbon, and climate: Modeling fuel consumption. Forest Ecology and Management, 317, 41-50.
  • Paugam, R., Wooster, M.J., & Roberts, G. (2013). Use of Handheld Thermal Imager Data for Airborne Mapping of Fire Radiative Power and Energy and Flame Front Rate of Spread. IEEE Transactions on Geoscience and Remote Sensing, 51(6), 3385-3399.
  • Pinto, M.M., Trigo, R.M., Trigo, I.F., & DaCamara, C. C. (2021). A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS. 13(9), 1608.
  • Pinto, R., Benali, A., Sá, A., Fernandes, P., Soares, P.M.M., Cardoso, R., . . . Pereira, J.F. (2016). Probabilistic fire spread forecast as a management tool in an operational setting. SpringerPlus, 5, 1205.
  • Ruecker, G., Leimbach, D., & Tiemann, J. (2021). Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape. Fire, 4(4), 65.
  • Schroeder, W., & Giglio, L. (2018). NASA VIIRS Land Science Investigator Processing System (SIPS) Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m & 750 m Active Fire Products. Product User’s Guide Version 1.4, NASA. 23p.
  • Scott, J.H., & Reinhardt, E.D. (2002). Estimating canopy fuels in conifer forests. Fire Management Today, 62, 45-50.
  • Sofan, P., Bruce, D., Schroeder, W., Jones, E., & Marsden, J. (2020). Assessment of VIIRS 375 m active fire using tropical peatland combustion algorithm applied to Landsat-8 over Indonesia's peatlands. International Journal of Digital Earth, 13(12), 1695-1716.
  • SPSS, I.C. (2019). IBM SPSS Statistics for Windows, Version 26.0. In IBM Corp.
  • Stefanidou, A., Gitas, I. Z., & Katagis, T. (2020). A national fuel type mapping method improvement using sentinel-2 satellite data. Geocarto International, 1-21.
  • Stocks, B.J., Alexander, M.E., Wotton, B.M., Stefner, C.N., Flannigan, M.D., Taylor, S.W., . . . Lanoville, R.A. (2004). Crown fire behaviour in a northern jack pine black spruce forest. Canadian Journal of Forest Research, 34(8), 1548-1560.
  • TOVAG. (2011). Towards Turkish National Fire Danger Rating System. Part 1: Fire Behavior Prediction System (TOVAG 108O327). The Scientific and Technological Research Council of Türkiye (TUBITAK). Project Report, 89p. TUBITAK.
  • Urbanski, S., Nordgren, B., Albury, C., Schwert, B., Peterson, D.L., Quayle, B., & Hao, W.M. (2018). A VIIRS direct broadcast algorithm for rapid response mapping of wildfire burned area in the western United States. Remote Sensing of Environment, 219, 271-283.
  • Van Wagner, C.E. (1973). Height of Crown Scorch in Forest Fires. Canadian Journal of Forest Research, 3(3), 373-378.
  • Yavuz, M., Sağlam, B., Küçük, Ö., & Tüfekçioğlu, A. (2018). Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey. Kastamonu University Journal of Forestry Faculty, 18(2), 171-188.
  • Zheng, Y., Liu, J., Jian, H., Fan, X., & Yan, F. (2021). Fire Diurnal Cycle Derived from a Combination of the Himawari-8 and VIIRS Satellites to Improve Fire Emission Assessments in Southeast Australia. 13(15), 2852.

Calculation of Fireline Intensity Using Remote Sensing and Geographic Information Systems: 2021 Milas-Karacahisar Fire

Year 2022, Volume: 22 Issue: 3, 236 - 246, 23.12.2022

Kadir Alperen Coşkuner Ertuğrul Bilgili

https://doi.org/10.17475/kastorman.1215333
Cited By: 1

Abstract

Aim of the study: The objective of this study is to calculate fireline intensity using remote sensing and geographic information systems, to investigate relationship between fireline intensity and VIIRS active fire data, and to develop a practical fireline intensity estimation model.
Material and methods: The Visible Infrared Imaging Radiometer Suite (VIIRS) active fire/hotspot data provided by Suomi National Polar Orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration (NOAA-20) satellites were used to estimate the rate of fire spread. Fuel consumption was estimated using Sentinel-2 images, stand type maps and surface and available crown fuel loading models for Turkish red pine (Pinus brutia Ten.). The fireline intensity was then calculated using Byram’s (1959) fireline intensity equation.
Main results: The results indicated that the number of VIIRS active fire data was well correlated with fireline intensity, rate of fire spread and fuel consumption. The calculated fireline intensity ranged between 175.0 and 47597.2 kW/m with an average value of 9280.4 kW/m. The number of VIIRS active fire data alone explained 72% of the variation in fireline intensity.
Highlights: Satellite based products can be effectively used to calculate fireline intensity through estimating rate of fire spread and fuel consumption easily and effectively in burned areas.

Keywords

Forest fires VIIRS Sentinel-2 rate of spread fuel consumption fireline intensity

References

  • Alexander, M.E. (1982). Calculating and Interpreting Forest Fire Intensities. Canadian Journal of Botany-Revue Canadienne De Botanique, 60(4), 349-357.
  • Alexander, M.E., & Cruz, M.G. (2018). Fireline Intensity. In S. L. Manzello (Ed.), Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, Springer International Publishing, 1-8.
  • Andela, N., van der Werf, G. R., Kaiser, J. W., van Leeuwen, T. T., Wooster, M. J., & Lehmann, C. E. R. (2016). Biomass burning fuel consumption dynamics in the tropics and subtropics assessed from satellite. Biogeosciences, 13(12), 3717-3734.
  • Baysal, I. (2021). Vertical Crown Fuel Distributions in Natural Calabrian Pine (Pinus brutia Ten.) Stands. Croatian Journal of Forest Engineering, 42(13), 301-312.
  • Baysal, İ., Yurtgan, M., Küçük, Ö., & Öztürk, N. (2019). Estimation of Crown Fuel Load of Suppressed Trees in Non-treated Young Calabrian Pine (Pinus brutia Ten.) Plantation Areas. Kastamonu University Journal of Forestry Faculty, 19(3), 350-359.
  • Bradstock, R.A., & Auld, T.D. (1995). Soil Temperatures During Experimental Bushfires in Relation to Fire Intensity: Consequences for Legume Germination and Fire Management in South-Eastern Australia. Journal of Applied Ecology, 32(1), 76-84.
  • Byram, G.M. (1959). Combustion of forest fuels. In K. P. Davis (Ed.), Forest Fire: Control and Use, MCGraw-Hill, 61-89.
  • Chen, J., Li, R., Tao, M., Wang, L.T., Lin, C., Wang, J., . . . Chen, L. (2022). Overview of the performance of satellite fire products in China: Uncertainties and challenges. Atmospheric Environment, 268, 118838.
  • Cole, F.V., & Alexander, M.E. (1995). Head fire intensity class graph for FBP System Fuel Type C-2 (Boreal Spruce). Alaska Department of Natural Resources, Division of Forestry, Fairbanks, AK and Natural Resources Canada, Canadian Forest Service, Edmonton, AB.
  • Coskuner, K.A. (2022a). Assessing the performance of MODIS and VIIRS active fire products in the monitoring of wildfires: a case study in Turkey. iForest - Biogeosciences and Forestry, 15(2), 85-94.
  • Coskuner, K.A. (2022b). Land use/land cover change as a major driver of current landscape flammability in Eastern Mediterranean region: A case study in Southwestern Turkey Bosque (Valdivia), 43(2), 157-167.
  • de Groot, W.J., Landry, R., Kurz, W., Anderson, K., Englefield, P., Fraser, R., . . . Pritchard, J. (2007). Estimating direct carbon emissions from Canadian wildland fires. International Journal of Wildland Fire, 16, 593–606.
  • Fernández-Alonso, J.M., Alberdi, I., Álvarez-González, J.G., Vega, J.A., Cañellas, I., & Ruiz-González, A.D. (2013). Canopy fuel characteristics in relation to crown fire potential in pine stands: analysis, modelling and classification. European Journal of Forest Research, 132(2), 363-377.
  • FIRMS-NOAA-20. (2021). NRT VIIRS 375 m Active Fire product VJ114IMGTDL_NRT distributed from NASA FIRMS. Available on-line [https://earthdata.nasa.gov/firms]. 10.5067/FIRMS/VIIRS/VJ114IMGT_NRT.002.
  • FIRMS-SUOMI-NPP. (2021). NRT VIIRS 375 m Active Fire product VNP14IMGT distributed from NASA FIRMS. Available on-line [https://earthdata.nasa.gov/firms]. doi:10.5067/FIRMS/VIIRS/VNP14IMGT_NRT.002
  • Fusco, E.J., Finn, J.T., Abatzoglou, J.T., Balch, J.K., Dadashi, S., & Bradley, B.A. (2019). Detection rates and biases of fire observations from MODIS and agency reports in the conterminous United States. Remote Sensing of Environment, 220, 30-40.
  • Johnston, J.M., Wooster, M.J., Paugam, R., Wang, X.W., Lynham, T.J., & Johnston, L.M. (2017). Direct estimation of Byram’s fire intensity from infrared remote sensing imagery International Journal of Wildland Fire, 26(8), 668-684.
  • Keeley, J.E. (2008). Fire. In S. E. Jørgensen & B. D. Fath (Eds.), Encyclopedia of Ecology (pp. 1557-1564). Academic Press.
  • Küçük, Ö., & Bilgili, E. (2007). Crown Fuel Load for Young Calabrian Pine (Pinus brutia Ten.) Trees. Kastamonu University Journal of Forestry Faculty, 7(2), 180-189.
  • Küçük, Ö., Bilgili, E., Durmaz, B.D., Sağlam, B., & Baysal, İ. (2009). Örtü Yangınının Tepe Yangınına Geçişinde Etkili Olan Faktörler. Kastamonu University Journal of Forestry Faculty, 9(2), 80-85.
  • Mallinis, G., Mitsopoulos, Ι., Stournara, P., Patias, P., & Dimitrakopoulos, A.P. (2013). Canopy Fuel Load Mapping of Mediterranean Pine Sites Based on Individual Tree-Crown Delineation. Remote Sensing, 5(12), 6461-6480.
  • Mitsopoulos, I.D., & Dimitrakopoulos, A.P. (2007). Canopy fuel characteristics and potential crown fire behavior in Aleppo pine (Pinus halepensis Mill.) forests. Annals of Forest Science, 64(3), 287-299.
  • NASA-FIRMS. (2021). Fire Information for Resource Management System (FIRMS), Active Fire Data.
  • Ononye, A.E., Vodacek, A. & Saber, E. (2007). Automated extraction of fire line parameters from multispectral infrared images. Remote Sensing of Environment, 108(2), 179-188.
  • Ottmar, R.D. (2014). Wildland fire emissions, carbon, and climate: Modeling fuel consumption. Forest Ecology and Management, 317, 41-50.
  • Paugam, R., Wooster, M.J., & Roberts, G. (2013). Use of Handheld Thermal Imager Data for Airborne Mapping of Fire Radiative Power and Energy and Flame Front Rate of Spread. IEEE Transactions on Geoscience and Remote Sensing, 51(6), 3385-3399.
  • Pinto, M.M., Trigo, R.M., Trigo, I.F., & DaCamara, C. C. (2021). A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS. 13(9), 1608.
  • Pinto, R., Benali, A., Sá, A., Fernandes, P., Soares, P.M.M., Cardoso, R., . . . Pereira, J.F. (2016). Probabilistic fire spread forecast as a management tool in an operational setting. SpringerPlus, 5, 1205.
  • Ruecker, G., Leimbach, D., & Tiemann, J. (2021). Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape. Fire, 4(4), 65.
  • Schroeder, W., & Giglio, L. (2018). NASA VIIRS Land Science Investigator Processing System (SIPS) Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m & 750 m Active Fire Products. Product User’s Guide Version 1.4, NASA. 23p.
  • Scott, J.H., & Reinhardt, E.D. (2002). Estimating canopy fuels in conifer forests. Fire Management Today, 62, 45-50.
  • Sofan, P., Bruce, D., Schroeder, W., Jones, E., & Marsden, J. (2020). Assessment of VIIRS 375 m active fire using tropical peatland combustion algorithm applied to Landsat-8 over Indonesia's peatlands. International Journal of Digital Earth, 13(12), 1695-1716.
  • SPSS, I.C. (2019). IBM SPSS Statistics for Windows, Version 26.0. In IBM Corp.
  • Stefanidou, A., Gitas, I. Z., & Katagis, T. (2020). A national fuel type mapping method improvement using sentinel-2 satellite data. Geocarto International, 1-21.
  • Stocks, B.J., Alexander, M.E., Wotton, B.M., Stefner, C.N., Flannigan, M.D., Taylor, S.W., . . . Lanoville, R.A. (2004). Crown fire behaviour in a northern jack pine black spruce forest. Canadian Journal of Forest Research, 34(8), 1548-1560.
  • TOVAG. (2011). Towards Turkish National Fire Danger Rating System. Part 1: Fire Behavior Prediction System (TOVAG 108O327). The Scientific and Technological Research Council of Türkiye (TUBITAK). Project Report, 89p. TUBITAK.
  • Urbanski, S., Nordgren, B., Albury, C., Schwert, B., Peterson, D.L., Quayle, B., & Hao, W.M. (2018). A VIIRS direct broadcast algorithm for rapid response mapping of wildfire burned area in the western United States. Remote Sensing of Environment, 219, 271-283.
  • Van Wagner, C.E. (1973). Height of Crown Scorch in Forest Fires. Canadian Journal of Forest Research, 3(3), 373-378.
  • Yavuz, M., Sağlam, B., Küçük, Ö., & Tüfekçioğlu, A. (2018). Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey. Kastamonu University Journal of Forestry Faculty, 18(2), 171-188.
  • Zheng, Y., Liu, J., Jian, H., Fan, X., & Yan, F. (2021). Fire Diurnal Cycle Derived from a Combination of the Himawari-8 and VIIRS Satellites to Improve Fire Emission Assessments in Southeast Australia. 13(15), 2852.
There are 40 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Kadir Alperen Coşkuner This is me

Ertuğrul Bilgili This is me

Publication Date December 23, 2022
Published in Issue Year 2022 Volume: 22 Issue: 3

Cite

APA Coşkuner, K. A., & Bilgili, E. (2022). Calculation of Fireline Intensity Using Remote Sensing and Geographic Information Systems: 2021 Milas-Karacahisar Fire. Kastamonu University Journal of Forestry Faculty, 22(3), 236-246. https://doi.org/10.17475/kastorman.1215333
AMA Coşkuner KA, Bilgili E. Calculation of Fireline Intensity Using Remote Sensing and Geographic Information Systems: 2021 Milas-Karacahisar Fire. Kastamonu University Journal of Forestry Faculty. December 2022;22(3):236-246. doi:10.17475/kastorman.1215333
Chicago Coşkuner, Kadir Alperen, and Ertuğrul Bilgili. “Calculation of Fireline Intensity Using Remote Sensing and Geographic Information Systems: 2021 Milas-Karacahisar Fire”. Kastamonu University Journal of Forestry Faculty 22, no. 3 (December 2022): 236-46. https://doi.org/10.17475/kastorman.1215333.
EndNote Coşkuner KA, Bilgili E (December 1, 2022) Calculation of Fireline Intensity Using Remote Sensing and Geographic Information Systems: 2021 Milas-Karacahisar Fire. Kastamonu University Journal of Forestry Faculty 22 3 236–246.
IEEE K. A. Coşkuner and E. Bilgili, “Calculation of Fireline Intensity Using Remote Sensing and Geographic Information Systems: 2021 Milas-Karacahisar Fire”, Kastamonu University Journal of Forestry Faculty, vol. 22, no. 3, pp. 236–246, 2022, doi: 10.17475/kastorman.1215333.
ISNAD Coşkuner, Kadir Alperen - Bilgili, Ertuğrul. “Calculation of Fireline Intensity Using Remote Sensing and Geographic Information Systems: 2021 Milas-Karacahisar Fire”. Kastamonu University Journal of Forestry Faculty 22/3 (December 2022), 236-246. https://doi.org/10.17475/kastorman.1215333.
JAMA Coşkuner KA, Bilgili E. Calculation of Fireline Intensity Using Remote Sensing and Geographic Information Systems: 2021 Milas-Karacahisar Fire. Kastamonu University Journal of Forestry Faculty. 2022;22:236–246.
MLA Coşkuner, Kadir Alperen and Ertuğrul Bilgili. “Calculation of Fireline Intensity Using Remote Sensing and Geographic Information Systems: 2021 Milas-Karacahisar Fire”. Kastamonu University Journal of Forestry Faculty, vol. 22, no. 3, 2022, pp. 236-4, doi:10.17475/kastorman.1215333.
Vancouver Coşkuner KA, Bilgili E. Calculation of Fireline Intensity Using Remote Sensing and Geographic Information Systems: 2021 Milas-Karacahisar Fire. Kastamonu University Journal of Forestry Faculty. 2022;22(3):236-4.

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