Araştırma Makalesi
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Analysis of the Effects of Forest Fires Using Remote Sensing and GIS: The Case of the 2020 Taşköprü Forest Fire

Yıl 2025, Cilt: 6 Sayı: 2, 168 - 180, 27.09.2025

Tolga Kaynak

https://doi.org/10.48123/rsgis.1643526

Öz

Forest fires are among the disasters that create significant environmental impacts on ecosystems and threaten the sustainability of natural resources. Difference Normalized Burn Ratio (dNBR), Difference Normalized Difference Vegetation Index (dNDVI), and Difference Land Surface Temperature (dLST) maps derived from Sentinel-2 and Landsat 8 satellite imagery were used to determine ecological changes before and after the forest fire that occurred in Taşköprü district, Kastamonu province, in 2020. Accuracy analysis was performed by comparing these maps with data from the European Forest Fire Information System (EFFIS). dNBR results identified fire areas with high accuracy, with an accuracy rate of 84.44% for Sentinel-2 and 81.53% for Landsat 8. dNDVI analysis provided accuracy rates of 77.84% and 74.81% for Sentinel-2 and Landsat 8, respectively. In addition, dLST map achieved an accuracy rate of 77.26%. The results showed that Sentinel-2 imagery produced more accurate results in determining fire areas due to their higher spatial resolution. However, factors such as spectral bandwidths of satellite data, imaging timings and atmospheric effects have also had a significant impact on accuracy. This study highlights the effectiveness of Remote Sensing (RS) and Geographic Information Systems (GIS) techniques in detecting forest fires; it also presents a scientific contribution to the development of fire management and ecosystem restoration strategies.

Anahtar Kelimeler

Forest fire GIS Remote sensing

Kaynakça

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  • Angelino, C. V., Cicala, L., Parrilli, S., Fiscante, N., & Ullo, S. L. (2020, September 26–October 2). Post-fire assessment of burned areas with Landsat-8 and Sentinel-2 imagery together with MODIS and VIIRS active fire Products [Symposium presentation]. IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), Waikoloa, HI, USA.
  • Avetisyan, D., Stankova, N., & Dimitrov, Z. (2023). Assessment of spectral vegetation indices performance for post-fire monitoring of different forest environments. Fire, 6(8), Article 290. https://doi.org/10.3390/fire6080290
  • Barnadi, I., Osawa, T., & Nuarsa, I. W. (2023). The accuracy of Gililawa Darat wildfire spread estımation using burn severity and WRF-SFIRE MODEL. Ecotrophic, 17(1), 119–136.
  • Bowman, D. M., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M., Cochrane, M. A., ... & Pyne, S. J. (2009). Fire in the Earth system. Science, 324(5926), 481–484. https://doi.org/10.1126/science.1163886
  • 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
  • Çelik, M. Ö., Fidan, D., Ulvi, A., & Yakar, M. (2024). Akdeniz bölgesi’ndeki orman yangınlarının uzaktan algılama ve coğrafi bilgi sistemleri kullanılarak değerlendirilmesi: Mersin ili Silifke ilçesi örneği. Anadolu Orman Araştırmaları Dergisi, 9(2), 116–125. https://doi.org/10.53516/ajfr.1302553
  • Çolak, E., & Sunar, F. (2018, 18–21 Eylül). Yüzey sıcaklığı ve spektral yanma indekslerinin orman yangını analizinde kullanımı [Bildiri sunumu]. VII. Uzaktan Algılama–CBS Sempozyumu, Eskişehir, Türkiye.
  • Çolak, E., & Sunar, F. (2020). Spatial pattern analysis of post-fire damages in the Menderes District of Turkey. Frontiers of Earth Science, 14(2), 446–461.
  • Çolak, E., & Sunar, F. (2023). Investigating the usefulness of satellite-retrieved land surface temperature (LST) in pre-and post-fire spatial analysis. Earth Science Informatics, 16(1), 945–963. https://doi.org/10.1007/s12145-022-00883-8
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  • Ezzaher, F. E., Ben Achhab, N., Raissouni, N., Naciri, H., & Chahboun, A. (2023). Normalized Burn Ratio and land surface temperature pre-and post-Mediterranean forest fires. Environmental Sciences Proceedings, 29(1), Article 3. https://doi.org/10.3390/ecrs2023-15829
  • Fernández-García, V., Santamarta, M., Fernández-Manso, A., Quintano, C., Marcos, E., & Calvo, L. (2018). Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery. Remote Sensing of Environment, 206, 205–217. https://doi.org/10.1016/j.rse.2017.12.029
  • Fernández-Manso, A., & Quintano, C. (2022, June 27–July 1). Nuevas metodologías basadas en teledetección para estimar la severidad de afección de un incendio [Conference presentation]. 8° Congreso Forestal Español, Catalunya, Spain.
  • García-Llamas, P., Suárez-Seoane, S., Fernández-Guisuraga, J. M., Fernández-García, V., Fernández-Manso, A., Quintano, C., ... & 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, 137–144. https://doi.org/10.1016/j.jag.2019.04.006
  • García, M. J., & Caselles, V. (1991). Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International, 6(1), 31–37. https://doi.org/10.1080/10106049109354290
  • Guo, L., Li, S., Wu, Z., Parsons, R. A., Lin, S., Wu, B., & Sun, L. (2022). Assessing spatial patterns and drivers of burn severity in subtropical forests in southern China based on Landsat 8. Forest Ecology and Management, 524, Article 120515. https://doi.org/10.1016/j.foreco.2022.120515
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Uzaktan Algılama ve CBS Kullanılarak Orman Yangınlarının Etkilerinin Analizi: Taşköprü 2020 Orman Yangını Örneği

Yıl 2025, Cilt: 6 Sayı: 2, 168 - 180, 27.09.2025

Tolga Kaynak

https://doi.org/10.48123/rsgis.1643526

Öz

Orman yangınları, ekosistem üzerinde önemli çevresel etkiler yaratan ve doğal kaynakların sürdürülebilirliğini tehdit eden afetler arasında yer almaktadır. 2020 yılında Kastamonu ilinin Taşköprü ilçesinde meydana gelen orman yangınının yangın öncesi ve sonrası ekolojik değişimleri belirlemek için Sentinel-2 ve Landsat 8 uydu görüntülerinden elde edilen Fark Normalize Edilmiş Yanma Oranı (dNBR), Fark Normalize Edilmiş Bitki örtüsü İndeksi (dNDVI) ve Fark Arazi Yüzey Sıcaklığı (dLST) haritaları kullanılmıştır. Bu haritalar Avrupa Orman Yangın Bilgi Sistemi (EFFIS) verileriyle karşılaştırılarak doğruluk analizi yapılmıştır. dNBR sonuçları Sentinel-2 için %84,44, Landsat 8 için %81,53 doğruluk oranı ile yangın alanlarını yüksek doğrulukla belirlemiştir. dNDVI analizleri ise Sentinel-2 ve Landsat 8 için sırasıyla %77,84 ve %74,81 doğruluk sağlamıştır. Ayrıca, dLST haritası %77,26 doğruluk oranına ulaşmıştır. Sonuçlar, Sentinel-2 görüntülerinin daha yüksek mekânsal çözünürlüğe sahip olması nedeniyle yangın alanlarını belirlemede daha doğru sonuçlar verdiğini göstermiştir. Bununla birlikte, uydu verilerinin spektral bant genişlikleri, görüntüleme zamanlamaları ve atmosferik etkiler gibi faktörler de doğruluk üzerinde önemli etkiye sahip olmuştur. Bu çalışma, orman yangınlarının tespitinde Uzaktan Algılama (UA) ve Coğrafi Bilgi Sistemleri (CBS) tekniklerinin etkinliğini vurgulamakta; ayrıca yangın yönetim ve ekosistem restorasyon stratejilerinin geliştirilmesine yönelik bir bilimsel katkı sunmaktadır.

Anahtar Kelimeler

Orman yangını CBS Uzaktan algılama

Etik Beyan

Bu çalışma kapsamında kullanılan tüm veriler, analizler ve sonuçlar akademik dürüstlük ve etik kurallar çerçevesinde üretilmiştir. Çalışmada, bilimsel araştırma etiğine tam uyum sağlanmış olup, herhangi bir veri manipülasyonu, yanlış yönlendirme veya çıkar çatışması bulunmamaktadır. Çalışmada kullanılan tüm kaynaklara uygun şekilde atıfta bulunulmuş, intihal ve etik ihlalleri önlemek adına gerekli önlemler alınmıştır.

Kaynakça

  • Abdikan, S., Bayik, C., Sekertekin, A., Bektas Balcik, F., Karimzadeh, S., Matsuoka, M., & Balik Sanli, F. (2022). Burned area detection using multi-sensor SAR, optical, and thermal data in Mediterranean pine forest. Forests, 13(2), Article 347. https://doi.org/10.3390/f13020347
  • Alexander, D. E. (2002). Principles of emergency planning and management. Terra Publishing.
  • Angelino, C. V., Cicala, L., Parrilli, S., Fiscante, N., & Ullo, S. L. (2020, September 26–October 2). Post-fire assessment of burned areas with Landsat-8 and Sentinel-2 imagery together with MODIS and VIIRS active fire Products [Symposium presentation]. IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), Waikoloa, HI, USA.
  • Avetisyan, D., Stankova, N., & Dimitrov, Z. (2023). Assessment of spectral vegetation indices performance for post-fire monitoring of different forest environments. Fire, 6(8), Article 290. https://doi.org/10.3390/fire6080290
  • Barnadi, I., Osawa, T., & Nuarsa, I. W. (2023). The accuracy of Gililawa Darat wildfire spread estımation using burn severity and WRF-SFIRE MODEL. Ecotrophic, 17(1), 119–136.
  • Bowman, D. M., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M., Cochrane, M. A., ... & Pyne, S. J. (2009). Fire in the Earth system. Science, 324(5926), 481–484. https://doi.org/10.1126/science.1163886
  • 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
  • Çelik, M. Ö., Fidan, D., Ulvi, A., & Yakar, M. (2024). Akdeniz bölgesi’ndeki orman yangınlarının uzaktan algılama ve coğrafi bilgi sistemleri kullanılarak değerlendirilmesi: Mersin ili Silifke ilçesi örneği. Anadolu Orman Araştırmaları Dergisi, 9(2), 116–125. https://doi.org/10.53516/ajfr.1302553
  • Çolak, E., & Sunar, F. (2018, 18–21 Eylül). Yüzey sıcaklığı ve spektral yanma indekslerinin orman yangını analizinde kullanımı [Bildiri sunumu]. VII. Uzaktan Algılama–CBS Sempozyumu, Eskişehir, Türkiye.
  • Çolak, E., & Sunar, F. (2020). Spatial pattern analysis of post-fire damages in the Menderes District of Turkey. Frontiers of Earth Science, 14(2), 446–461.
  • Çolak, E., & Sunar, F. (2023). Investigating the usefulness of satellite-retrieved land surface temperature (LST) in pre-and post-fire spatial analysis. Earth Science Informatics, 16(1), 945–963. https://doi.org/10.1007/s12145-022-00883-8
  • European Forest Fire Information System. (2023). Data and services. Copernicus Europe’s Eyes on Earth. 27 Kasım 2024’te https://effis.jrc.ec.europa.eu/applications/data-and-services adresinden alındı.
  • Erener, A., & Sarp, G. (2017, 18–20 October). Forest fire monitoring by using satellite images and information Technology [Symposium presentation]. ISFOR 2017 International Symposium on New Horizons in Forestry, Isparta, Turkey.
  • European Space Agency. (2021). Sentinel-2 downloads. 27 Kasım 2024’te https://www.esa.int/Applications/ Observing_the_Earth/Copernicus/Sentinel-2/Sentinel-2_downloads adresinden alındı.
  • Ezzaher, F. E., Ben Achhab, N., Raissouni, N., Naciri, H., & Chahboun, A. (2023). Normalized Burn Ratio and land surface temperature pre-and post-Mediterranean forest fires. Environmental Sciences Proceedings, 29(1), Article 3. https://doi.org/10.3390/ecrs2023-15829
  • Fernández-García, V., Santamarta, M., Fernández-Manso, A., Quintano, C., Marcos, E., & Calvo, L. (2018). Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery. Remote Sensing of Environment, 206, 205–217. https://doi.org/10.1016/j.rse.2017.12.029
  • Fernández-Manso, A., & Quintano, C. (2022, June 27–July 1). Nuevas metodologías basadas en teledetección para estimar la severidad de afección de un incendio [Conference presentation]. 8° Congreso Forestal Español, Catalunya, Spain.
  • García-Llamas, P., Suárez-Seoane, S., Fernández-Guisuraga, J. M., Fernández-García, V., Fernández-Manso, A., Quintano, C., ... & 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, 137–144. https://doi.org/10.1016/j.jag.2019.04.006
  • García, M. J., & Caselles, V. (1991). Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International, 6(1), 31–37. https://doi.org/10.1080/10106049109354290
  • Guo, L., Li, S., Wu, Z., Parsons, R. A., Lin, S., Wu, B., & Sun, L. (2022). Assessing spatial patterns and drivers of burn severity in subtropical forests in southern China based on Landsat 8. Forest Ecology and Management, 524, Article 120515. https://doi.org/10.1016/j.foreco.2022.120515
  • İbret, Ü. B. (2018). Kastamonu İlinin Coğrafi Özellikleri. In M. Eriş (Ed.), 81 İlde Kültür ve Şehir–KASTAMONU (pp. 16–25). T.C. Kastamonu Valiliği.
  • Junaidi, S. N., Khalid, N., Othman, A. N., Hamid, J. R. A., & Saad, N. M. (2021). Analysis of the relationship between forest fire and land surface temperature using Landsat 8 OLI/TIRS imagery. IOP Conference Series: Earth and Environmental Science, 767(1), Article 012005. https://doi.org/10.1088/1755-1315/767/1/012005
  • Key, C. H., & Benson, N. C. (2006). Landscape assessment (LA) sampling and analysis methods. In D. C. Lutes (Ed.), FIREMON: Fire Effects Monitoring and Inventory System (pp. 219–269). United States Department of Agriculture.
  • Kuzey Anadolu Kalkınma Ajansı. (2012). 2012 Yılı Faaliyet Raporu. 2 Ocak 2025’te https://www.kuzka.gov.tr/dosya/ 2012_yillik_faaliyet_raporu.pdf adresinden alındı.
  • Lasaponara, R., Proto, A. M., Aromando, A., Cardettini, G., Varela, V., & Danese, M. (2019). On the mapping of burned areas and burn severity using self-organizing map and Sentinel-2 data. IEEE Geoscience and Remote Sensing Letters, 17(5), 854–858.
  • Lentile, L. B., Holden, Z. A., Smith, A. M. S., Falkowski, M. J., Hudak, A. T., Morgan, P., Lewis, S. A., Gessler, P. E., & Benson, N. C. (2006). Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire, 15(3), 319–345. https://doi.org/10.1071/WF05097
  • Llorens, R., Sobrino, J. A., Fernández, C., Fernández-Alonso, J. M., & Vega, J. A. (2021). A methodology to estimate forest fires burned areas and burn severity degrees using Sentinel-2 data: Application to the October 2017 fires in the Iberian Peninsula. International Journal of Applied Earth Observation and Geoinformation, 95, Article 102243. https://doi.org/10.1016/j.jag.2020.102243
  • Lv, Q., Liu, Z., Li, K., Guo, W., Zhou, S., Guan, R., & Wang, W. (2024). Influence of forest cover loss on land surface temperature differs by drivers in China. Journal of Geophysical Research: Biogeosciences, 129(11), Article e2024JG008103. https://doi.org/10.1029/2024JG008103
  • Maffei, C., Alfieri, S. M., & Menenti, M. (2018). Relating spatiotemporal patterns of forest fires burned area and duration to diurnal land surface temperature anomalies. Remote Sensing, 10(11), Article 1777. https://doi.org/10.3390/rs10111777
  • Mallinis, G., Mitsopoulos, I., & Chrysafi, I. (2018). Evaluating and comparing Sentinel-2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece. GIScience & Remote Sensing, 55(1), 1–18. https://doi.org/10.1080/15481603.2017.1354803
  • Mehmood, K., Anees, S. A., Luo, M., Akram, M., Zubair, M., Khan, K. A., & Khan, W. R. (2024). Assessing Chilgoza Pine (Pinus gerardiana) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies. Trees, Forests and People, 16, Article 100521. https://doi.org/10.1016/j.tfp.2024.100521
  • Mohammed, Khan, A., Kuri, A., Ahammed, S., Al Muqtadir Abir, K., & Arfin-Khan, M. A. (2025). A Google Earth Engine approach for anthropogenic forest fire assessment with remote sensing data in Rema-Kalenga Wildlife Sanctuary, Bangladesh. Geology, Ecology, and Landscapes, 9(1), 45–66. https://doi.org/10.1080/24749508.2023.2165297
  • Moharir, K., Singh, M., Pande, C., Singh, S. K., & Gelete, G. (2024). Mapping forest fire-affected areas using advanced machine learning techniques in Damoh District of Central India. Knowledge-Based Engineering and Sciences, 5(1), 62–80. https://doi.org/10.51526/kbes.2024.5.1.62-80
  • Orman Genel Müdürlüğü. (2021). Ormancılık istatistikleri. 27 Kasım 2024’te https://www.ogm.gov.tr/tr/e-kutuphane/ resmi-istatistikler adresinden alındı.
  • Parks, S. A., Holsinger, L. M., Miller, C., & Nelson, C. R. (2015). Wildland fire as a self-regulating mechanism: The role of previous burns and weather in limiting fire progression. Ecological Applications, 25(6), 1478–1492. https://doi.org/10.1890/14-1430.1
  • Pérez-Cabello, F., Montorio, R., & Alves, D. B. (2021). Remote sensing techniques to assess post-fire vegetation recovery. Current Opinion in Environmental Science & Health, 21, Article 100251. https://doi.org/10.1016/j.coesh.2021.100251
  • Qarallah, B., Othman, Y. A., Al-Ajlouni, M., Alheyari, H. A., & Qoqazeh, B. A. A. (2022). Assessment of small-extent forest fires in semi-arid environment in Jordan using Sentinel-2 and Landsat sensors data. Forests, 14(1), Article 41. https://doi.org/10.3390/f14010041
  • 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
  • San-Miguel-Ayanz, J., Moreno, J. M., & Camia, A. (2013). Analysis of large fires in European Mediterranean landscapes: Lessons learned and perspectives. Forest Ecology and Management, 294, 11–22. https://doi.org/10.1016/j.foreco.2012.10.050
  • Sarp, G., Temurçin, K., Aldırmaz, Y., & Baydoğan, E. (2018, November 22–24). Spatial analysis of forest fires using remote sensing technologies; a case of 2017 Mersin–Anamur forest fire [Conference presentation]. Innovation and Global Issues Congress IV, Antalya, Türkiye.
  • Smith, C. W., Panda, S. K., Bhatt, U. S., Meyer, F. J., Badola, A., & Hrobak, J. L. (2021). Assessing wildfire burn severity and its relationship with environmental factors: A case study in interior Alaska boreal forest. Remote Sensing, 13(10), Article 1966. https://doi.org/10.3390/rs13101966
  • Smith, K., Fearnley, C. J., Dixon, D., Bird, D. K., & Kelman, I. (2023). Environmental hazards: Assessing risk and reducing disaster. Routledge.
  • Sobrino, J. A., Llorens, R., Fernández, C., Fernández-Alonso, J. M., & Vega, J. A. (2019). Relationship between soil burn severity in forest fires measured in situ and through spectral indices of remote detection. Forests, 10(5), Article 457. https://doi.org/10.3390/f10050457
  • Srivastava, S. K., Lewis, T., Behrendorff, L., & Phinn, S. (2021). Spatial databases and techniques to assist with prescribed fire management in the south-east Queensland bioregion. International Journal of Wildland Fire, 30(2), 90–111. https://doi.org/10.1071/WF19105
  • 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
  • U.S. Geological Survey. (2016). Landsat 8 Data Users Handbook. United States Geological Survey – Science for a Changing World. 13 Aralık 2024’te https://www.usgs.gov/landsat-missions/landsat-8-data-users-handbook adresinden alındı.
  • U.S. Geological Survey. (2021). Earth Explorer. United States Geological Survey – Science for a Changing World. 5 Aralık 2024’te https://earthexplorer.usgs.gov/ adresinden alındı.
  • Van Leeuwen, W. J. (2008). Monitoring the effects of forest restoration treatments on post-fire vegetation recovery with MODIS multitemporal data. Sensors, 8(3), 2017–2042. https://doi.org/10.3390/s8032017
  • 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. https://doi.org/10.1080/01431161003752430
  • Vlassova, L., Pérez-Cabello, F., Mimbrero, M. R., Llovería, R. M., & García-Martín, A. (2014). Analysis of the relationship between land surface temperature and wildfire severity in a series of Landsat images. Remote Sensing, 6(7), 6136–6162.
  • Yao, H., Yang, Z., Zhang, G., & Liu, F. (2024). Forest fire detection based on spatial characteristics of surface temperature. Remote Sensing, 16(16), Article 2945. https://doi.org/10.3390/rs16162945
  • Yüksel, K. (2022). Yanan orman alanı tespitinde farklı uzaktan algılama indislerinin değerlendirilmesi: 2022 yılı Mersin (Gülnar) orman yangını örneği. ArtGRID – Journal of Architecture Engineering and Fine Arts, 4(2), 160–171. https://doi.org/10.57165/artgrid.1179074
  • Zheng, Z., Wang, J., Zou, B., Gao, Y., Yang, S., & Wang, Y. (2022). Initial assessment of burn severity using the transfer learning model. National Remote Sensing Bulletin, 26(10), 2001–2013.
Toplam 53 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme, Fotogrametri ve Uzaktan Algılama
Bölüm Araştırma Makaleleri
Yazarlar

Tolga Kaynak 0000-0002-0718-9091

Yayımlanma Tarihi 27 Eylül 2025
Gönderilme Tarihi 20 Şubat 2025
Kabul Tarihi 5 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: 2

Kaynak Göster

APA Kaynak, T. (2025). Uzaktan Algılama ve CBS Kullanılarak Orman Yangınlarının Etkilerinin Analizi: Taşköprü 2020 Orman Yangını Örneği. Türk Uzaktan Algılama ve CBS Dergisi, 6(2), 168-180. https://doi.org/10.48123/rsgis.1643526
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Turkish Journal of Remote Sensing and GIS (Türk Uzaktan Algılama ve CBS Dergisi), Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanlanmıştır.