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Akdeniz bölgesi’ndeki orman yangınlarının uzaktan algılama ve coğrafi bilgi sistemleri kullanılarak değerlendirilmesi: Mersin ili Silifke ilçesi örneği

Year 2023, Volume: 9 Issue: 2, 116 - 125, 01.01.2024

Mehmet Özgür Çelik Doğa Fidan Ali Ulvi Murat Yakar

https://doi.org/10.53516/ajfr.1302553

Abstract

Orman yangınları çevreyi ve canlıları olumsuz etkileyen olaylardır. Bu yangınların önlenmesi ile yangın sonrası ağaçlandırma ve koruma stratejilerinin geliştirilmesi için, hasarın boyutunun belirlenmesi ve yanma şiddetinin hızlı bir şekilde araştırılması gereklidir. Uzaktan algılama (UA) yangından etkilenen bölgelerin ve yanma şiddetinin haritalanmasında Coğrafi Bilgi Sistemleri (CBS) ile birlikte sıklıkla kullanılmaktadır. Bu çalışmada, 2021 yılında Mersin ili Silifke içesinde meydana gelen orman yangını incelenmiştir. Sahanın yangın öncesi ve sonrasına ait Sentinel-2A ve Landsat 8 OLI uydu görüntüleri yardımıyla NDVI (Normalize Fark Vejetasyon İndeksi) ve NBR (Normalize Yanma Şiddeti) indeksleri hesaplanmıştır. Elde edilen indeks haritalarından fark haritaları oluşturulmuş, yangın sonrasındaki arazi örtüsündeki değişim ve yanma şiddeti belirlenmiştir. Buna göre toplam yanan alanlar 2324,71 hektardır. Yangına “yüksek” derecede maruz kalan alanlar çalışma alanın %27,72’sini (644,44 ha), “orta” derecede yanan alanlar %66,72’sini (1538,16 ha) ve “düşük” seviyede yanan alanlar ise %6,11’ini (142,11 ha) oluşturmaktadır. Ayrıca, EFFIS veri tabanından elde edilen çalışma alanına ait yangın verisiyle de yapılan analizin doğrulaması gerçekleştirilmiştir. Bu işlem için alıcı işletim karakteristik (receiver operating characteristic – ROC) eğrisi kullanılmış ve eğri altındaki alan (area under the curve - AUC) değeri 0,973 olarak hesaplanmıştır. Çıkan sonuçlar, Orman Genel Müdürlüğü (OGM) yetkililerine ve diğer karar vericilere sürdürülebilir arazi yönetimi uygulamaları konusunda yardımcı olmayı amaçlamaktadır.

Keywords

Orman yangını Landsat 8 OLI Sentinel-2A Sürdürülebilir orman yönetimi

References

  • Amjad, D., Kausar, S., Waqar, R., Sarwar, F., 2019. Land cover change analysis and impacts of deforestation on the climate of district Mansehra, Pakistan. Journal of Biodiversity and Environmental Sciences 14(6), 103-113.
  • Arca, D., Hacısalihoğlu, M., Kutoğlu, Ş. H., 2020. Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods. Natural Hazards 104, 73-89.
  • Arisanty, D., Adyatma, S., Muhaimin, M., Nursaputra, A., 2019. Landsat 8 OLI TIRS Imagery Ability for Monitoring Post Forest Fire Changes. Pertanika Journal of Science & Technology, 27(3), 1105-1120.
  • Arunachalam, M., Joshua, R. M., Kochuparampil, A. J., Saravanavel, J., 2023. ArcOLITIRS: A toolbox for radiometric calibration and surface temperature estimation from Landsat 8 products in ArcGIS environment. Journal of the Indian Society of Remote Sensing, 51(3), 453-468.
  • Bekçi, R. N., Kuşak, L., 2022. Mekânsal çözünürlüğün güneşlenme potansiyeline etkisi. Türkiye İnsansız Hava Araçları Dergisi 4(1), 46-51.
  • Bekçi, R. N., Zorlu, Ö., Menekşe, E., 2022. Regression analysis and use of artificial neural networks in housing valuation forecasting: case example of Güvenevler neighbourhood in Mersin. Estate Development with Risk Analysis, Advanced GIS 2(1), 24-32.
  • Bentekhici, N., Bellal, SA., Zegrar, A., 2020. Contribution of remote sensing and GIS to mapping the fire risk of Mediterranean forest case of the forest massif of Tlemcen (North-West Algeria). Natural Hazards 104(1), 811–831.
  • Bustillo Sánchez, M., Tonini, M., Mapelli, A., Fiorucci, P., 2021. Spatial assessment of wildfires susceptibility in SantaCruz (Bolivia) using random forest. Geosciences 11(5), 224.
  • Chuvieco, E., 2009. Earth Observation of Wildland Fires in Mediterranean Ecosystems (p. 257). Springer, Berlin / Heidelberg.
  • Coruhlu, Y. E., Baser, V., Yildiz, O., 2021. Object-based geographical data model for determination of the cemetery sites using SWOT and AHP integration. Survey Review 53(377), 108-121.
  • Coruhlu, Y. E., Uzun, B., Yildiz, O., 2020. Zoning plan-based legal confiscation without expropriation in Turkey in light of ECHR decisions. Land use Policy 95, 104598.
  • Çakır, M., 2017. Toprak faunasının kurak ekosistemlerdeki görevleri. Anadolu Orman Araştırmaları Dergisi,3(1),67-78.
  • Çelik, M. A., Gülersoy, A. E., 2018. Climate classification and drought analysis of Mersin. Manisa Celal Bayar University Journal of Social Sciences 16(1), 1-26.
  • Çolak, E., Sunar, F., 2020. Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, Izmir. International journal of disaster risk reduction, 45, 101479.
  • Çoruhlu, Y. E., Çelik, M. Ö., 2022. Protected area geographical management model from design to implementation for specially protected environment area. Land Use Policy 122, 106357.
  • Das, J., Mahato, S., Joshi, P. K., Liou, Y. A., 2023. Forest fire susceptibility zonation in Eastern India using statistical and weighted modelling approaches. Remote Sensing, 15(5), 1340.
  • Dilekçi, S., Marangoz, A. M., Ateşoğlu, A., 2021). Zonguldak ve Ereğli Orman İşletme Müdürlükleri orman yangını risk alanlarının belirlenmesi. Geomatik, 6(1), 44-53.
  • Doğan, Y., Yakar, M., 2018. GIS and three-dimensional modeling for cultural heritages. International Journal of Engineering and Geosciences 3(2), 50-55.
  • Down to Earth, 2022. Down to Earth state of the world’s forests https://www.downtoearth.org.in/news/forests/state-of-the-world-s-forests-2022-10-of-total-forest-area-on-earth-lost-in-30-years-82658. Duran, C., 2014. Mersin ilindeki orman yangınlarının başlangıç noktalarına göre mekânsal analizi (2001-2013). Ormancılık Araştırma Dergisi 1(1 A), 38-49.
  • EFFIS, 2023a. European Forest Fire Information System. effis.jrc.ec.europa.eu/apps/effis.statistics/estimates (Erişildi 03.01.2023).
  • EFFIS, 2023b. European Forest Fire Information System. https://effis.jrc.ec.europa.eu/applications/data-and-services (Erişildi 16.11.2023).
  • Ercan, B., Özdilim, S., Avcı, M. G., 2023. Orman yangınlarına ilk müdahale ekiplerinin yerleşim planlaması: Aliağa-İzmir örneği. Anadolu Orman Araştırmaları Dergisi, 9(1), 96-103.
  • ESA, 2023. Sentinel. sentinels.copernicus.eu/web/ sentinel/missions/sentinel-2 (Erişildi 03.01.2023). ESA. 2021. ESA. http://www.esa.int/ (Erişildi 15.10.2021).
  • Escuin, S., Navarro, R., Fernández, 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.
  • FAO. 2022a. EFFIS statistic estimates https://effis.jrc.ec.europa.eu/apps/effis.statistics/estimates (Erişildi 29.12.2022).
  • FAO, 2022b. The State of the World’s Forests (SOFO) https://www.fao.org/publications/sofo/2022/en/ (Erişildi 30.12.2022).
  • Fidanboy, M., Adar, N., Okyay, S., 2022. Derin öğrenmeye dayalı orman yangını tahmin modeli geliştirilmesi ve Türkiye yangın risk haritasının oluşturulması. Ormancılık Araştırma Dergisi 9(2), 206-218.
  • Garbolino, E., Sanseverino-Godfrin, V., Hinojos-Mendoza, G., 2017. Describing and predicting of the vegetation development of Corsicadue to expected climate change and its impact on forest fire risk evolution. Safety Science 88, 180-186.
  • Ghorbanzadeh, O., Valizadeh Kamran, K., Blaschke, T., Aryal, J., Naboureh, A., Einali, J., Bian, J., 2019. Spatial prediction of wildfire susceptibility using field surveygpsdata and machine learning approaches. Fire 2(3).
  • Giannakopoulos, C., Le Sager, P., Bindi, M., Moriondo, M., Kostopoulou, E., Goodess, C. M., 2009. Climatic changes and associated impacts in the Mediterranean resulting from a 2 C global warming. Global and Planetary Change 68(3), 209-224.
  • Golkarian, A., S. A. Naghibi, B. Kalantar, Pradhan, B., 2018. Groundwater Potential Mapping Using C5.0, Random Forest, and Multivariate Adaptive Regression Spline Models in GIS.Environmental Monitoring and Assessment, 190,149.
  • Gürsoy, M. İ., Orhan, O., Tekin, S., 2023. Creation of wildfire susceptibility maps in the Mediterranean Region (Turkey) using convolutional neural networks and multilayer perceptron techniques. Forest Ecology and Management, 538, 121006.
  • Iban, M. C., Sekertekin, A., 2022. Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey. Ecological Informatics 69, 101647.
  • Isaev, A. S., Korovin, G. N., Bartalev, S. A., Ershov, D. V., Janetos, A., Kasischke, E. S., Shugart, H. H., French B. E. O. Murphy, T. L., 2002. Using remote sensing to assess Russian forest fire carbon emissions. Climatic Change, 55, 235-249.
  • İban, M. C., Şahin, E., 2022. Monitoring burn severity and air pollutants in wildfire events using remotesensing data: the case of Mersin wildfires in summer 2021. Gümüşhane Üniversitesi Fen Bilimleri Dergisi 12(2), 487-497.
  • Jin, R., Lee, K. S., 2022. Investigation of forest fire characteristics in north korea using remote sensing data and GIS. Remote Sensing 14(22), 5836.
  • Kalantar, B., Ueda, N., Idrees, M. O., Janizadeh, S., Ahmadi, K., Shabani, F., 2020. Forest fire susceptibility prediction based on machine learning models with resampling algorithms on remote sensing data. Remote Sensing, 12(22), 3682.
  • Karabulut, M., Karakoç, A., Gürbüz, M., Kızılelma, Y., 2013. Coğrafi bilgi sistemleri kullanarak başkonuş dağında (Kahramanmaraş) orman yangını risk alanlarının belirlenmesi. Uluslararası Sosyal Araştırmalar Dergisi, 6(24), 171-179.
  • Kimengsi, J. N., Owusu, R., Djenontin, I. N., Pretzsch, J., Giessen, L., Buchenrieder, G., Pouliot, M., Acosta, A. N., 2022. What do we (not) know on forest management institutions in sub-Saharan Africa A regional comparative review. Land Use Policy 114, 105931.
  • Lapola, D. M., Pinho, P., Barlow, J., Aragão, L. E., Berenguer, E., Carmenta, R., Liddy, H. M., Walker, W. S., 2023. The drivers and impacts of Amazon Forest degradation. Science, 379(6630), eabp8622.
  • Lavanya, B., Padmaja, B., 2014. A Novel approach for identification of forest fires using land surface temperature images. IOSR Journal of Computer Engineering 16(5), 78-83.
  • Li, W., Guo, W. Y., Pasgaard, M., Niu, Z., Wang, L., Chen, F., Qin Y., Svenning, J. C., 2023. Human fingerprint on structural density of forests globally. Nature Sustainability 1-12.
  • Lībiete, Z., Jansons, Ā., Ruņis, D., Donis, J., 2023. Forest resources and sustainable management. In Forest Microbiology (pp. 3-31). Academic Press.
  • 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 remotesensing 53(1), 104-112.
  • Mersin Valiliği, 2022. Nüfus ve dağılım. http://www.mersin.gov.tr/nufus-ve-dagilim(Erişildi 15.11.2022) MGM, 2022a. Kuraklık analizi, https://www.mgm.gov.tr/veridegerlendirme/kuraklik-analizi.aspx?d=yillik#sfB (Erişildi 27.12.2022).
  • MGM, 2022b. İl ve ilçe veri değerlendirme. https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=A (Erişildi 27.12.2022).
  • Moayedi, H., Mehrabi, M., Bui, D. T., Pradhan, B., Foong, L. K., 2020. Fuzzy-metaheuristicensembles for spatial assessment of forest fire susceptibility. Journal of Environmental Management 260, 109867.
  • Mwaniki, M. W., Kuria, D. N., Boitt, M. K., Ngigi, T. G., 2017. Image enhancements of Landsat 8 (OLI) and SAR data for preliminary landslide identification and mapping applied to the central region of Kenya. Geomorphology, 282, 162-175.
  • Naghibi, S. A., H. R. Pourghasemi, Dixon, B., 2016. GIS-based Groundwater Potential Mapping Using Boosted Regression Tree, Classification and Regression Tree, and Random Forest Machine Learning Models in Iran. Environmental Monitoring and Assessment, 188,44.
  • Navarro, G., Caballero, I., Silva, G., Parra, P., 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, 97-106.
  • Nguyen, Q. H., Nguyen, H. D., Le, D. T., Bui, Q. T., 2023. Fine-tuning LightGBM using an artificial ecosystem-based optimizer for forest fire analysis. Forest Science, 69(1), 73-82.
  • OBM, 2022. Silifke Orman bilgileri https://mersinobm.ogm.gov.tr/SilifkeOIM/Sayfalar/default.aspx (Erişildi 08.12.2022).
  • OGM, 2022. Orman Genel Müdürlüğü. Ormancılık ve Yangın İstatistikleri. https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistikler (Erişildi 17.08.2023).
  • Oğuz, E., Oğuz, K., Öztürk, K., 2021.Determination of flood susceptibility areas of Düzce region. Journal of Geomatics 7(3), 220-234.
  • Orhan, O., 2021. Land suitability determination for citrus cultivation using a GIS-based multi-criteria analysis in Mersin, Turkey. Computers and Electronics in Agriculture, 190, 106433.
  • Orhan, O., Yakar, M., Ekercin, S., 2020. An application on sinkhole susceptibility mapping by integrating remote sensing and geographic information systems. Arabian Journal of Geosciences 13, 886.
  • Our World in Data, 2022. Our World in Data forest area. https://ourworldindata.org/forest-area (Erişildi 29.12.2022).
  • Öztürk, D., 2022. Sentinel-2A MSI ve Landsat-9 OLI-2 görüntüleri kullanılarak farklı geçirimsiz yüzey indekslerinin karşılaştırmalı değerlendirmesi: Samsun Örneği. Ege Coğrafya Dergisi, 31(2), 401-423.
  • Öztürk, T., Gürsoy, F., 2022. Küresel iklim değişikliğinin Arktik Okyanusu’na jeopolitik etkisi. Akdeniz Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 22(1), 117–31.
  • Pourtaghi, Z. S., Pourghasemi, H. R., Aretano, R., Semeraro, T., 2016. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological indicators, 64, 72-84.
  • Pourtaghi, Z. S., Pourghasemi, H. R., Rossi, M., 2015. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environmental Earth Sciences 73(4), 1515-1533.
  • Pouyan, S., Pourghasemi, H. R., Bordbar, M., Rahmanian, S., Clague, J. J., 2021. A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran. Scientific Reports, 11(1), 1-19.
  • Rege, J.E.O., Ochieng, J.W., 2022. The state of capacities, enabling environment, applications and ımpacts of biotechnology in the forestry sector. Agricultural Biotechnology in Sub-Saharan Africa, 123-143.
  • 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.
  • Saglam, B., Bilgili, E., Dincdurmaz, B. D., Kadiogulları, A. İ., Kücük, Ö., 2008. Spatio-temporal analysis of forest fire risk and danger using LANDSAT imagery. Sensors 8(6), 3970-3987.
  • Sari, F., 2021. Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Muğla, Turkey: A comparative analysis of VIKOR and TOPSIS. Forest Ecology and Management 480, 118644.
  • Sargıncı, M., Beyazyüz, F, 2022. İklim değişikliğinin ormanlar üzerindeki etkisi: İklim akılcı ormancılık bakış açısı. Anadolu Orman Araştırmaları Dergisi, 8(2), 142-149.
  • Satir, O., Berberoglu, S., Donmez, C., 2016. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomatics, Natural Hazards and Risk 7(5), 1645-1658.
  • Seleem, T., Bafi, D., Karantzia, M., Parcharidis, I., 2022. Water quality monitoring using Landsat 8 and Sentinel-2 satellite data (2014–2020) in Timsah Lake, Ismailia, Suez Canal Region (Egypt). Journal of the Indian Society of Remote Sensing, 50(12), 2411-2428.
  • Shin, J. I., Seo, W. W., Kim, T., Park, J., Woo, C. S., 2019. Using UAV multispectralimages for classification of forest burnseverity—A case study of the 2019 Gangneung forest fire. Forests 10(11), 1025.
  • Si, L., Shu, L., Wang, M., Zhao, F., Chen, F., Li, W., Li, W., 2022. Study on forest fire danger prediction in plateau mountainous forest area. Natural Hazards Research, 2(1), 25-32.
  • Silva, I. D. B.,Valle, M. E., Barros, L. C., Meyer, J. F. C., 2020. A wildfirewarning system applied to the state of Acre in the Brazilian Amazon. Applied Soft Computing 89, 106075.
  • Sivrikaya, F., Küçük, Ö., 2022. Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecological Informatics 68, 101537.
  • The Global Economy, 2022. The Global Economy rankings forest area. https://www.theglobaleconomy.com/rankings/forest_area/ (Erişildi 29.12.2022).
  • Tonbul, H., Kavzoglu, T., Kaya, S., 2016. Assessment of fire severity and post-fire regeneration based on topographical features using multitemporal Landsat imagery: A case study in Mersin, Turkey. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 763-769.
  • USGS FIREMON, 2023. United States Geological Survey, Fire Effects Monitoring and Inventory Protocol. https://www.frames.gov/firemon/home Erişildi 17.08.2023.
  • USGS, 2021. United States Geological Survey. 20201. https://earthexplorer.usgs.gov/ (Erişildi 15.10.2021).
  • USGS, 2023. United States Geological Survey https://www.usgs.gov/landsat-missions(Erişildi 1.03.2023).
  • WB, 2023. The World Bank. https://data.worldbank.org/indicator/AG.LND.FRST.ZS?end=2020&start=1990&view=chart (Erişildi 27.02.2023). Warren, M. A., Simis, S. G., Martinez-Vicente, V., Poser, K., Bresciani, M., Alikas, K., Spyrakos, E., Giardino, C., & Ansper, A., 2019. Assessment of atmospheric correction algorithms for the Sentinel-2A multispectral ımager over coastal and inland waters. Remote sensing of environment, 225, 267-289.
  • Weather Spark, 2021. Weather Spark hava tahmini https://tr.weatherspark.com/h/d/98267/2021/7/28/28-Temmuz-2021-%C3%87ar%C5%9Famba-tarihinde-inMersin-T%C3%BCrkiye-Ortalama-Hava-Durumu#metar-04-50 (Erişildi 12.12.2022).
  • Worldometer, 2022. Worldometers Turkey food agriculture https://www.worldometers.info/food-agriculture/turkey-food-agriculture/ (Erişildi 01.12.2022).
  • Yakubu, I.,Mireku-Gyimah, D., &Duker, A. A. (2015). Review of methods for modelling forest fire risk and hazard. African Journal of Environmental Science and Technology 9(3), 155-165.
  • 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), 76-86.

Evaluation of forest fires using remote sensing and geographic information systems: a case study of Mersin province, Silifke district

Year 2023, Volume: 9 Issue: 2, 116 - 125, 01.01.2024

Mehmet Özgür Çelik Doğa Fidan Ali Ulvi Murat Yakar

https://doi.org/10.53516/ajfr.1302553

Abstract

Forest fires are events that negatively affect the environment and living creatures. In order to prevent these fires, and to develop post-fire regeneration techniques, it is vital to promptly evaluate the damage amount and to investigate the fire's severity. Remote sensing (RS) is frequently used with Geographic Information Systems (GIS) to map fire-affected areas and fire intensity. In this study, the forest fire in Silifke district in Mersin took place in 2021 was examined. Before and following the fire, NDVI (Normalized Difference Vegetation Index) and NBR (Normalized Burn Ratio) indexes were derived using Sentinel-2A and Landsat 8 OLI satellite images. The index maps were used to generate difference maps, and the change in land cover after the fire, as well as the intensity of the fire, was determined. Accordingly, the total area burned is 2324.71 hectares. The study area is made up of 27.72% (644.44 ha) of "high" fire areas, 66.72% (1538.16 ha) of "moderate" fire areas, and 6.11 (142.11 ha) of "low" fire areas. Furthermore, the analysis was validated using fire data from the EFFIS database for the research area. The receiver operating characteristic (ROC) curve was employed for this operation, and area under the curve (AUC) value was calculated at 0.973. The conclusions are intended to assist the General Directorate of Forestry (GDF) and other decision-makers to practice sustainable land management.

Keywords

Forest fire Landsat 8 OLI Sentinel-2A Sustainable forest management

References

  • Amjad, D., Kausar, S., Waqar, R., Sarwar, F., 2019. Land cover change analysis and impacts of deforestation on the climate of district Mansehra, Pakistan. Journal of Biodiversity and Environmental Sciences 14(6), 103-113.
  • Arca, D., Hacısalihoğlu, M., Kutoğlu, Ş. H., 2020. Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods. Natural Hazards 104, 73-89.
  • Arisanty, D., Adyatma, S., Muhaimin, M., Nursaputra, A., 2019. Landsat 8 OLI TIRS Imagery Ability for Monitoring Post Forest Fire Changes. Pertanika Journal of Science & Technology, 27(3), 1105-1120.
  • Arunachalam, M., Joshua, R. M., Kochuparampil, A. J., Saravanavel, J., 2023. ArcOLITIRS: A toolbox for radiometric calibration and surface temperature estimation from Landsat 8 products in ArcGIS environment. Journal of the Indian Society of Remote Sensing, 51(3), 453-468.
  • Bekçi, R. N., Kuşak, L., 2022. Mekânsal çözünürlüğün güneşlenme potansiyeline etkisi. Türkiye İnsansız Hava Araçları Dergisi 4(1), 46-51.
  • Bekçi, R. N., Zorlu, Ö., Menekşe, E., 2022. Regression analysis and use of artificial neural networks in housing valuation forecasting: case example of Güvenevler neighbourhood in Mersin. Estate Development with Risk Analysis, Advanced GIS 2(1), 24-32.
  • Bentekhici, N., Bellal, SA., Zegrar, A., 2020. Contribution of remote sensing and GIS to mapping the fire risk of Mediterranean forest case of the forest massif of Tlemcen (North-West Algeria). Natural Hazards 104(1), 811–831.
  • Bustillo Sánchez, M., Tonini, M., Mapelli, A., Fiorucci, P., 2021. Spatial assessment of wildfires susceptibility in SantaCruz (Bolivia) using random forest. Geosciences 11(5), 224.
  • Chuvieco, E., 2009. Earth Observation of Wildland Fires in Mediterranean Ecosystems (p. 257). Springer, Berlin / Heidelberg.
  • Coruhlu, Y. E., Baser, V., Yildiz, O., 2021. Object-based geographical data model for determination of the cemetery sites using SWOT and AHP integration. Survey Review 53(377), 108-121.
  • Coruhlu, Y. E., Uzun, B., Yildiz, O., 2020. Zoning plan-based legal confiscation without expropriation in Turkey in light of ECHR decisions. Land use Policy 95, 104598.
  • Çakır, M., 2017. Toprak faunasının kurak ekosistemlerdeki görevleri. Anadolu Orman Araştırmaları Dergisi,3(1),67-78.
  • Çelik, M. A., Gülersoy, A. E., 2018. Climate classification and drought analysis of Mersin. Manisa Celal Bayar University Journal of Social Sciences 16(1), 1-26.
  • Çolak, E., Sunar, F., 2020. Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, Izmir. International journal of disaster risk reduction, 45, 101479.
  • Çoruhlu, Y. E., Çelik, M. Ö., 2022. Protected area geographical management model from design to implementation for specially protected environment area. Land Use Policy 122, 106357.
  • Das, J., Mahato, S., Joshi, P. K., Liou, Y. A., 2023. Forest fire susceptibility zonation in Eastern India using statistical and weighted modelling approaches. Remote Sensing, 15(5), 1340.
  • Dilekçi, S., Marangoz, A. M., Ateşoğlu, A., 2021). Zonguldak ve Ereğli Orman İşletme Müdürlükleri orman yangını risk alanlarının belirlenmesi. Geomatik, 6(1), 44-53.
  • Doğan, Y., Yakar, M., 2018. GIS and three-dimensional modeling for cultural heritages. International Journal of Engineering and Geosciences 3(2), 50-55.
  • Down to Earth, 2022. Down to Earth state of the world’s forests https://www.downtoearth.org.in/news/forests/state-of-the-world-s-forests-2022-10-of-total-forest-area-on-earth-lost-in-30-years-82658. Duran, C., 2014. Mersin ilindeki orman yangınlarının başlangıç noktalarına göre mekânsal analizi (2001-2013). Ormancılık Araştırma Dergisi 1(1 A), 38-49.
  • EFFIS, 2023a. European Forest Fire Information System. effis.jrc.ec.europa.eu/apps/effis.statistics/estimates (Erişildi 03.01.2023).
  • EFFIS, 2023b. European Forest Fire Information System. https://effis.jrc.ec.europa.eu/applications/data-and-services (Erişildi 16.11.2023).
  • Ercan, B., Özdilim, S., Avcı, M. G., 2023. Orman yangınlarına ilk müdahale ekiplerinin yerleşim planlaması: Aliağa-İzmir örneği. Anadolu Orman Araştırmaları Dergisi, 9(1), 96-103.
  • ESA, 2023. Sentinel. sentinels.copernicus.eu/web/ sentinel/missions/sentinel-2 (Erişildi 03.01.2023). ESA. 2021. ESA. http://www.esa.int/ (Erişildi 15.10.2021).
  • Escuin, S., Navarro, R., Fernández, 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.
  • FAO. 2022a. EFFIS statistic estimates https://effis.jrc.ec.europa.eu/apps/effis.statistics/estimates (Erişildi 29.12.2022).
  • FAO, 2022b. The State of the World’s Forests (SOFO) https://www.fao.org/publications/sofo/2022/en/ (Erişildi 30.12.2022).
  • Fidanboy, M., Adar, N., Okyay, S., 2022. Derin öğrenmeye dayalı orman yangını tahmin modeli geliştirilmesi ve Türkiye yangın risk haritasının oluşturulması. Ormancılık Araştırma Dergisi 9(2), 206-218.
  • Garbolino, E., Sanseverino-Godfrin, V., Hinojos-Mendoza, G., 2017. Describing and predicting of the vegetation development of Corsicadue to expected climate change and its impact on forest fire risk evolution. Safety Science 88, 180-186.
  • Ghorbanzadeh, O., Valizadeh Kamran, K., Blaschke, T., Aryal, J., Naboureh, A., Einali, J., Bian, J., 2019. Spatial prediction of wildfire susceptibility using field surveygpsdata and machine learning approaches. Fire 2(3).
  • Giannakopoulos, C., Le Sager, P., Bindi, M., Moriondo, M., Kostopoulou, E., Goodess, C. M., 2009. Climatic changes and associated impacts in the Mediterranean resulting from a 2 C global warming. Global and Planetary Change 68(3), 209-224.
  • Golkarian, A., S. A. Naghibi, B. Kalantar, Pradhan, B., 2018. Groundwater Potential Mapping Using C5.0, Random Forest, and Multivariate Adaptive Regression Spline Models in GIS.Environmental Monitoring and Assessment, 190,149.
  • Gürsoy, M. İ., Orhan, O., Tekin, S., 2023. Creation of wildfire susceptibility maps in the Mediterranean Region (Turkey) using convolutional neural networks and multilayer perceptron techniques. Forest Ecology and Management, 538, 121006.
  • Iban, M. C., Sekertekin, A., 2022. Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey. Ecological Informatics 69, 101647.
  • Isaev, A. S., Korovin, G. N., Bartalev, S. A., Ershov, D. V., Janetos, A., Kasischke, E. S., Shugart, H. H., French B. E. O. Murphy, T. L., 2002. Using remote sensing to assess Russian forest fire carbon emissions. Climatic Change, 55, 235-249.
  • İban, M. C., Şahin, E., 2022. Monitoring burn severity and air pollutants in wildfire events using remotesensing data: the case of Mersin wildfires in summer 2021. Gümüşhane Üniversitesi Fen Bilimleri Dergisi 12(2), 487-497.
  • Jin, R., Lee, K. S., 2022. Investigation of forest fire characteristics in north korea using remote sensing data and GIS. Remote Sensing 14(22), 5836.
  • Kalantar, B., Ueda, N., Idrees, M. O., Janizadeh, S., Ahmadi, K., Shabani, F., 2020. Forest fire susceptibility prediction based on machine learning models with resampling algorithms on remote sensing data. Remote Sensing, 12(22), 3682.
  • Karabulut, M., Karakoç, A., Gürbüz, M., Kızılelma, Y., 2013. Coğrafi bilgi sistemleri kullanarak başkonuş dağında (Kahramanmaraş) orman yangını risk alanlarının belirlenmesi. Uluslararası Sosyal Araştırmalar Dergisi, 6(24), 171-179.
  • Kimengsi, J. N., Owusu, R., Djenontin, I. N., Pretzsch, J., Giessen, L., Buchenrieder, G., Pouliot, M., Acosta, A. N., 2022. What do we (not) know on forest management institutions in sub-Saharan Africa A regional comparative review. Land Use Policy 114, 105931.
  • Lapola, D. M., Pinho, P., Barlow, J., Aragão, L. E., Berenguer, E., Carmenta, R., Liddy, H. M., Walker, W. S., 2023. The drivers and impacts of Amazon Forest degradation. Science, 379(6630), eabp8622.
  • Lavanya, B., Padmaja, B., 2014. A Novel approach for identification of forest fires using land surface temperature images. IOSR Journal of Computer Engineering 16(5), 78-83.
  • Li, W., Guo, W. Y., Pasgaard, M., Niu, Z., Wang, L., Chen, F., Qin Y., Svenning, J. C., 2023. Human fingerprint on structural density of forests globally. Nature Sustainability 1-12.
  • Lībiete, Z., Jansons, Ā., Ruņis, D., Donis, J., 2023. Forest resources and sustainable management. In Forest Microbiology (pp. 3-31). Academic Press.
  • 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 remotesensing 53(1), 104-112.
  • Mersin Valiliği, 2022. Nüfus ve dağılım. http://www.mersin.gov.tr/nufus-ve-dagilim(Erişildi 15.11.2022) MGM, 2022a. Kuraklık analizi, https://www.mgm.gov.tr/veridegerlendirme/kuraklik-analizi.aspx?d=yillik#sfB (Erişildi 27.12.2022).
  • MGM, 2022b. İl ve ilçe veri değerlendirme. https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=A (Erişildi 27.12.2022).
  • Moayedi, H., Mehrabi, M., Bui, D. T., Pradhan, B., Foong, L. K., 2020. Fuzzy-metaheuristicensembles for spatial assessment of forest fire susceptibility. Journal of Environmental Management 260, 109867.
  • Mwaniki, M. W., Kuria, D. N., Boitt, M. K., Ngigi, T. G., 2017. Image enhancements of Landsat 8 (OLI) and SAR data for preliminary landslide identification and mapping applied to the central region of Kenya. Geomorphology, 282, 162-175.
  • Naghibi, S. A., H. R. Pourghasemi, Dixon, B., 2016. GIS-based Groundwater Potential Mapping Using Boosted Regression Tree, Classification and Regression Tree, and Random Forest Machine Learning Models in Iran. Environmental Monitoring and Assessment, 188,44.
  • Navarro, G., Caballero, I., Silva, G., Parra, P., 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, 97-106.
  • Nguyen, Q. H., Nguyen, H. D., Le, D. T., Bui, Q. T., 2023. Fine-tuning LightGBM using an artificial ecosystem-based optimizer for forest fire analysis. Forest Science, 69(1), 73-82.
  • OBM, 2022. Silifke Orman bilgileri https://mersinobm.ogm.gov.tr/SilifkeOIM/Sayfalar/default.aspx (Erişildi 08.12.2022).
  • OGM, 2022. Orman Genel Müdürlüğü. Ormancılık ve Yangın İstatistikleri. https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistikler (Erişildi 17.08.2023).
  • Oğuz, E., Oğuz, K., Öztürk, K., 2021.Determination of flood susceptibility areas of Düzce region. Journal of Geomatics 7(3), 220-234.
  • Orhan, O., 2021. Land suitability determination for citrus cultivation using a GIS-based multi-criteria analysis in Mersin, Turkey. Computers and Electronics in Agriculture, 190, 106433.
  • Orhan, O., Yakar, M., Ekercin, S., 2020. An application on sinkhole susceptibility mapping by integrating remote sensing and geographic information systems. Arabian Journal of Geosciences 13, 886.
  • Our World in Data, 2022. Our World in Data forest area. https://ourworldindata.org/forest-area (Erişildi 29.12.2022).
  • Öztürk, D., 2022. Sentinel-2A MSI ve Landsat-9 OLI-2 görüntüleri kullanılarak farklı geçirimsiz yüzey indekslerinin karşılaştırmalı değerlendirmesi: Samsun Örneği. Ege Coğrafya Dergisi, 31(2), 401-423.
  • Öztürk, T., Gürsoy, F., 2022. Küresel iklim değişikliğinin Arktik Okyanusu’na jeopolitik etkisi. Akdeniz Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 22(1), 117–31.
  • Pourtaghi, Z. S., Pourghasemi, H. R., Aretano, R., Semeraro, T., 2016. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological indicators, 64, 72-84.
  • Pourtaghi, Z. S., Pourghasemi, H. R., Rossi, M., 2015. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environmental Earth Sciences 73(4), 1515-1533.
  • Pouyan, S., Pourghasemi, H. R., Bordbar, M., Rahmanian, S., Clague, J. J., 2021. A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran. Scientific Reports, 11(1), 1-19.
  • Rege, J.E.O., Ochieng, J.W., 2022. The state of capacities, enabling environment, applications and ımpacts of biotechnology in the forestry sector. Agricultural Biotechnology in Sub-Saharan Africa, 123-143.
  • 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.
  • Saglam, B., Bilgili, E., Dincdurmaz, B. D., Kadiogulları, A. İ., Kücük, Ö., 2008. Spatio-temporal analysis of forest fire risk and danger using LANDSAT imagery. Sensors 8(6), 3970-3987.
  • Sari, F., 2021. Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Muğla, Turkey: A comparative analysis of VIKOR and TOPSIS. Forest Ecology and Management 480, 118644.
  • Sargıncı, M., Beyazyüz, F, 2022. İklim değişikliğinin ormanlar üzerindeki etkisi: İklim akılcı ormancılık bakış açısı. Anadolu Orman Araştırmaları Dergisi, 8(2), 142-149.
  • Satir, O., Berberoglu, S., Donmez, C., 2016. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomatics, Natural Hazards and Risk 7(5), 1645-1658.
  • Seleem, T., Bafi, D., Karantzia, M., Parcharidis, I., 2022. Water quality monitoring using Landsat 8 and Sentinel-2 satellite data (2014–2020) in Timsah Lake, Ismailia, Suez Canal Region (Egypt). Journal of the Indian Society of Remote Sensing, 50(12), 2411-2428.
  • Shin, J. I., Seo, W. W., Kim, T., Park, J., Woo, C. S., 2019. Using UAV multispectralimages for classification of forest burnseverity—A case study of the 2019 Gangneung forest fire. Forests 10(11), 1025.
  • Si, L., Shu, L., Wang, M., Zhao, F., Chen, F., Li, W., Li, W., 2022. Study on forest fire danger prediction in plateau mountainous forest area. Natural Hazards Research, 2(1), 25-32.
  • Silva, I. D. B.,Valle, M. E., Barros, L. C., Meyer, J. F. C., 2020. A wildfirewarning system applied to the state of Acre in the Brazilian Amazon. Applied Soft Computing 89, 106075.
  • Sivrikaya, F., Küçük, Ö., 2022. Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecological Informatics 68, 101537.
  • The Global Economy, 2022. The Global Economy rankings forest area. https://www.theglobaleconomy.com/rankings/forest_area/ (Erişildi 29.12.2022).
  • Tonbul, H., Kavzoglu, T., Kaya, S., 2016. Assessment of fire severity and post-fire regeneration based on topographical features using multitemporal Landsat imagery: A case study in Mersin, Turkey. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 763-769.
  • USGS FIREMON, 2023. United States Geological Survey, Fire Effects Monitoring and Inventory Protocol. https://www.frames.gov/firemon/home Erişildi 17.08.2023.
  • USGS, 2021. United States Geological Survey. 20201. https://earthexplorer.usgs.gov/ (Erişildi 15.10.2021).
  • USGS, 2023. United States Geological Survey https://www.usgs.gov/landsat-missions(Erişildi 1.03.2023).
  • WB, 2023. The World Bank. https://data.worldbank.org/indicator/AG.LND.FRST.ZS?end=2020&start=1990&view=chart (Erişildi 27.02.2023). Warren, M. A., Simis, S. G., Martinez-Vicente, V., Poser, K., Bresciani, M., Alikas, K., Spyrakos, E., Giardino, C., & Ansper, A., 2019. Assessment of atmospheric correction algorithms for the Sentinel-2A multispectral ımager over coastal and inland waters. Remote sensing of environment, 225, 267-289.
  • Weather Spark, 2021. Weather Spark hava tahmini https://tr.weatherspark.com/h/d/98267/2021/7/28/28-Temmuz-2021-%C3%87ar%C5%9Famba-tarihinde-inMersin-T%C3%BCrkiye-Ortalama-Hava-Durumu#metar-04-50 (Erişildi 12.12.2022).
  • Worldometer, 2022. Worldometers Turkey food agriculture https://www.worldometers.info/food-agriculture/turkey-food-agriculture/ (Erişildi 01.12.2022).
  • Yakubu, I.,Mireku-Gyimah, D., &Duker, A. A. (2015). Review of methods for modelling forest fire risk and hazard. African Journal of Environmental Science and Technology 9(3), 155-165.
  • 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), 76-86.
There are 83 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Mehmet Özgür Çelik 0000-0003-4569-888X

Doğa Fidan 0000-0003-0856-5594

Ali Ulvi 0000-0003-3005-8011

Murat Yakar 0000-0002-2664-6251

Early Pub Date December 30, 2023
Publication Date January 1, 2024
Submission Date May 25, 2023
Published in Issue Year 2023 Volume: 9 Issue: 2

Cite

APA 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
AMA Çelik MÖ, Fidan D, Ulvi A, Yakar M. Akdeniz bölgesi’ndeki orman yangınlarının uzaktan algılama ve coğrafi bilgi sistemleri kullanılarak değerlendirilmesi: Mersin ili Silifke ilçesi örneği. AJFR. January 2024;9(2):116-125. doi:10.53516/ajfr.1302553
Chicago Çelik, Mehmet Özgür, Doğa Fidan, Ali Ulvi, and Murat Yakar. “Akdeniz bölgesi’ndeki Orman yangınlarının Uzaktan algılama Ve coğrafi Bilgi Sistemleri kullanılarak değerlendirilmesi: Mersin Ili Silifke ilçesi örneği”. Anadolu Orman Araştırmaları Dergisi 9, no. 2 (January 2024): 116-25. https://doi.org/10.53516/ajfr.1302553.
EndNote Çelik MÖ, Fidan D, Ulvi A, Yakar M (January 1, 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.
IEEE M. Ö. Çelik, D. Fidan, A. Ulvi, and M. Yakar, “Akdeniz bölgesi’ndeki orman yangınlarının uzaktan algılama ve coğrafi bilgi sistemleri kullanılarak değerlendirilmesi: Mersin ili Silifke ilçesi örneği”, AJFR, vol. 9, no. 2, pp. 116–125, 2024, doi: 10.53516/ajfr.1302553.
ISNAD Çelik, Mehmet Özgür et al. “Akdeniz bölgesi’ndeki Orman yangınlarının Uzaktan algılama Ve coğrafi Bilgi Sistemleri kullanılarak değerlendirilmesi: Mersin Ili Silifke ilçesi örneği”. Anadolu Orman Araştırmaları Dergisi 9/2 (January 2024), 116-125. https://doi.org/10.53516/ajfr.1302553.
JAMA Çelik MÖ, Fidan D, Ulvi A, Yakar M. Akdeniz bölgesi’ndeki orman yangınlarının uzaktan algılama ve coğrafi bilgi sistemleri kullanılarak değerlendirilmesi: Mersin ili Silifke ilçesi örneği. AJFR. 2024;9:116–125.
MLA Çelik, Mehmet Özgür et al. “Akdeniz bölgesi’ndeki Orman yangınlarının Uzaktan algılama Ve coğrafi Bilgi Sistemleri kullanılarak değerlendirilmesi: Mersin Ili Silifke ilçesi örneği”. Anadolu Orman Araştırmaları Dergisi, vol. 9, no. 2, 2024, pp. 116-25, doi:10.53516/ajfr.1302553.
Vancouver Çelik MÖ, Fidan D, Ulvi A, Yakar M. Akdeniz bölgesi’ndeki orman yangınlarının uzaktan algılama ve coğrafi bilgi sistemleri kullanılarak değerlendirilmesi: Mersin ili Silifke ilçesi örneği. AJFR. 2024;9(2):116-25.
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