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Uzaktan algılama ve CBS yöntemleri ile orman yangını risk alanlarının haritalanması: Manisa örneği

Year 2020, , 15 - 24, 26.03.2020
https://doi.org/10.18182/tjf.649747

Abstract

Bu araştırmanın amacı, çeşitli peyzaj analiz tekniklerini kullanarak Manisa ilindeki potansiyel orman yangın risk bölgelerini haritalamaktır. Doğal olarak veya insan faaliyetlerinin sonucunda ortaya çıkan orman yangını, ekolojik bağlantılılığın azalmasına ve peyzajın parçalanmasına neden olur. Bu nedenle, yangın sıklığını en aza indirmek, hasarı önlemek, yangına neden olabilecek sorunlara yönelik tahmin yapmak ve çözüm yöntemlerinde karar mekanizması olarak kullanılabilecek yangın risk bölgesi haritasından yararlanılmaktadır. Bu araştırmanın materyalini, Avrupa Birliği tarafından koordine edilen ve yönetilen Yer Gözlem Programı olan Copernicus Programı çerçevesinde üretilen CORINE 2018 verisi, NASA Earthdata web sitesinden elde edilen ASTER Global DEM sayısal yükseklik modeli verisi, MODIS uydu görüntülerine dayanan yangın arşiv kayıtları, ağaç türlerinin mekânsal dağılımını gösteren sayısal meşcere haritası ve mevcut yol ağını haritalamak için kullanılan OpenStreetMap (OSM) verileri oluşturmaktadır. Risk bölgelerini tanımlamak için bitki örtüsü, eğim, bakı, yükseklik, yerleşim yerine ve yola uzaklık değişkenleri kullanılmıştır. Orman yangını çıkma potansiyeline göre her bir tematik harita katmanındaki öznitelik değerlerine belirli ağırlıklar atanmıştır. Sayısal yükseklik modelinden eğim, bakı ve yükseklik haritaları oluşturulmuştur. Yerleşim haritasına olan mesafe CORINE veri tabanından, yol haritasına olan mesafe ise OSM'den üretilmiştir. Orman yangın riski bölgelerini belirlemek için Yangın Riski Bölge Endeksi kullanılmıştır. Oluşturulan yangın riski haritasına göre, çalışma alanının %25.8’inin çok yüksek ve yüksek seviyede yangın riski taşıdığı ortaya çıkmıştır. MODIS görüntülerinden elde edilen 2001 ve 2018 yılları arasındaki orman yangını verileri ile yüksek ve çok yüksek yangın riski taşıyan alanlar çakıştırılmış, 149 orman yangının 97’sinin yüksek ve çok yüksek risk altındaki alanlarda meydana geldiği ortaya çıkmıştır. Bu bulgu, uzaktan algılama ve CBS tekniklerine dayanan metodolojinin güvenilir olduğunu ve orman yangın riski bölgelerinin tanımlanma sürecinde etkin bir şekilde kullanılabileceğini desteklemektedir.

References

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  • Adab, H., Kanniah, K.D., Solaimani, K., 2013. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65(3):1723-1743.
  • Akkas, M.E., Bucak, C., Boza, Z., Eronat, H., Bekereci, A., Erkan, A., Cebeci, C., 2008. Investigation of Large Forest Fires in the Light of Meteorological Data. Republic of Turkey Ministry of Environment and Forestry, Aegean Forestry Research Institute, 36.
  • Avcı, M., Korkmaz, M., Alkan, H., 2009. Türkiye’de orman yangınlarının nedenleri üzerine bir değerlendirme. I. Orman Yangınları ile Mücadele Sempozyumu, 07–10 Ocak, Antalya, s. 33–45.
  • Bahadır, M., 2010. Türkiye’de (1998-2007) Görülen orman yangınlarının yüzey ve rakamsal sorgulama analizi. Nature Sciences, 5(3): 146-162.
  • Belgherbi, B., Benabdeli, K., Mostefai, K., 2018. Mapping the risk forest fires in Algeria: Application of the forest of Guetarnia in Western Algeria. Ekológia (Bratislava), 37(3): 289-300.
  • Bingol, B., 2017. Determination of forest fire risk areas in Burdur Province using Geographical Information Systems. Turkish Journal of Forest Science, 1(2):169-182.
  • Cardille, J.A., Ventura, S.J., 2001. Occurrence of wildfire in the northern Great Lakes Region: effects of land cover and land ownership assessed at multiple scales. International Journal of Wildland Fire, 10(2):145-154.
  • Catry, F.X., Rego, F.C., Bação, F.L., Moreira, F., 2010. Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire, 18(8):921-931.
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  • Chuvieco, E., Congalton, R.G., 1989. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote sensing of Environment, 29(2):147-159.
  • Coban, H.O., Eker, M., 2010. Analysis of forest road network conditions before and after forest fire. 43rd International Symposium on Forestry Mechanisation, July 11-14, Padova Italy, pp. 11-14.
  • Coban, H.O., Erdin, C., 2020. Forest fire risk assessment using GIS and AHP integration in Bucak forest enterprise, Turkey. Applied Ecology and Environmental Research, 18(1):1567-1583.
  • Coban, H.O., Ozdamar, S., 2014. Mapping forest fire in relation to land-cover and topographic characteristics. Journal of Environmental Biology, 35(1): 217.
  • Colak, 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, 101479.
  • Díaz-Delgado, R., Lloret, F., Pons, X., 2004. Spatial patterns of fire occurrence in Catalonia, NE, Spain. Landscape Ecology, 19(7):731-745.
  • Dong, X.U., Li-min, D., Guo-fan, S., Lei, T., Hui, W., 2005. Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China. Journal of forestry research, 16(3): 169-174.
  • Duran, C., 2014. Mersin ilindeki orman yangınlarının başlangıç noktalarına göre mekânsal analizi (2001-2013). Ormancılık Araştırma Dergisi, 1(1): 38-49.
  • Fischer, A.P., Vance-Borland, K., Jasny, L., Grimm, K. E., Charnley, S., 2016. A network approach to assessing social capacity for landscape planning: The case of fire-prone forests in Oregon, USA. Landscape and Urban Planning, 147:18-27.
  • GDF, 2017. Activity Report of Strategy Development Department. Republic of Turkey General Directorate of Forestry, Ankara.
  • Ghobadi, G.J., Gholizadeh, B., Dashliburun, O.M., 2012. Forest fire risk zone mapping from geographic information system in Northern Forests of Iran (Case study, Golestan province). International Journal of Agriculture and Crop Sciences, 4(12): 818-824.
  • Gigović, L., Jakovljević, G., Sekulović, D., Regodić, M., 2018. GIS Multi-Criteria Analysis for Identifying and Mapping Forest Fire Hazard: Nevesinje, Bosnia and Herzegovina. Tehnički vjesnik, 25(3):891-897.
  • Göktepe, S., Avcı, M., 2015. Muğla-Fethiye ormanlarında yangın sorunu, yangınların dağılımı ve yangınlar üzerinde etkili olan faktörler. Türkiye Ormancılık Dergisi, 16(2): 130-140.
  • Gugliette, D., Conedera, M., Mazzolenıs, S., Ricotta, C., 2011. Mapping fire ignition risk in a complex anthropogenic landscape. Remote Sensing Letters, 2(3):213-219.
  • Guney, C.O., Ozkan, K., Sentürk, O., 2016. Modelling of spatial prediction of fire ignition risk in the Antalya-Manavgat district. Journal of the Faculty of Forestry Istanbul University, 66(2): 459-470.
  • Gungoroglu, C., 2017. Determination of forest fire risk with fuzzy analytic hierarchy process and its mapping with the application of GIS: The case of Turkey/Cakırlar. Human and Ecological Risk Assessment, 23(2):388-406.
  • Hacisalihoglu, M., 2018. Forest fire risk mapping using multi criteria decision analysis method: The case of Karabuk. In Turkish. MSc Dissertation, Zonguldak Bulent Ecevit University, Zonguldak, Turkey.
  • Jaiswal, R.K., Mukherjee, S., Raju, K.D., Saxena, R., 2002. Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation, 4(1): 1-10.
  • Joaquim, G.S., Bahaaeddin, A., Josep, R.C., 2007. Remote Sensing Analysis to Detect Fire Risk Locations. Proceedings of GéoCongrès, 2-5 October, Québec, Canada.
  • Karabulut, M., Karakoc, A., Gurbuz, M., Kizilelma, Y., 2013. Determination of forest fire risk areas using geographical information systems in Baskonus Mountain (Kahramanmaras). The Journal of International Social Research, 6(24):171-179.
  • Keifer, M., Caprio, E.A., Lineback, P., 2000. Incorporating a GIS model of ecological need into fire management planning. Joint Fire Sciences Conference and Workshop, 15-17 June, Idaho, USA.
  • Kumari, B., Pandey, A.C., 2020. MODIS based forest fire hotspot analysis and its relationship with climatic variables. Spatial Information Research, 28(1): 87-99.
  • Kuter, S., Usul, N., Kuter, N., 2011. Bandwidth determination for kernel density analysis of wildfire events at forest sub-district scale. Ecological Modelling, 222(17): 3033-3040.
  • Lin, J., Sergio, R., 2009. A derivation of the statistical characteristics of forest fires. Ecological Modelling, 220:898-903.
  • Malczewski, J., 1999. GIS and Multicriteria Decision Analysis. ISBN: 0-471-32944-4, John Wiley and Sons, New York.
  • Malik, T., Rabbani, G., Farooq, M., 2013. Forest fire risk zonation using remote sensing and GIS technology in Kansrao Forest Range of Rajaji National Park, Uttarakhand, India. India. International Journal of Advanced RS and GIS, 2(1):86-95.
  • Martínez-Fernández, J., Chuvieco, E., Koutsias, N., 2013. Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression. Natural Hazards and Earth System Sciences, 13(2):311-327.
  • MOI, 2019. E-Interior Project. Ministry of Interior, Ankara.
  • MOM, 2019. Geography of Manisa. Information Processing Department of Manisa Municipality, Manisa.
  • MPCTD, 2019. General Information, Manisa Provincial Culture and Tourism Directorate, Geography of Manisa.
  • Neyisci, T., 1987. A Study on Slow-Burning Plant Species for Preventing Forest Fires. Tubitak Journal of Nature, 11: 595-604.
  • Nuthammachot, N., Stratoulias, D., 2019. A GIS-and AHP-based approach to map fire risk: A case study of Kuan Kreng peat swamp forest, Thailand. Geocarto International, 1-14.
  • Pradhan, B., Dini Hairi Bin Suliman, M., Arshad Bin Awang, M., 2007. Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS). Disaster Prevention and Management: An International Journal, 16(3):344-352.
  • Renard, Q., Pélissier, R., Ramesh, B.R., Kodandapani, N., 2012. Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. International Journal of Wildland Fire, 21(4): 368-379.
  • Roy, P.S., 2003. Forest fire and degradation assessment using satellite remote sensing and geographic information system. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, 361-400.
  • Senturk, N., 2018. Assessment of relationship between locations and distances to roadside of forest fires in Istanbul, Turkey. Applied Ecology and Environmental Research, 16(5): 6195-6204.
  • Sowmya, S.V., Somashekar, R.K., 2010. Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India. Journal of Environmental Biology, 31(6): 969.
  • Tian, X., Zhao, F., Shu, L., Wang, M., 2013. Distribution characteristics and the influence factors of forest fires in China. Forest Ecology and Management, 310:460-467.
  • Türkeş, M., Altan, G., 2012. Analysis of the year 2008 fires in the forest lands of the Muğla Regional Forest Service by using drought indices. Journal of Human Sciences, 9(1): 912-931.
  • Yang, J., He, H.S., Shifley, S.R., Gustafson, E.J., 2007. Spatial patterns of modern period human-caused fire occurrence in the Missouri Ozark Highlands. Forest science, 53(1): 1-15.

Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey

Year 2020, , 15 - 24, 26.03.2020
https://doi.org/10.18182/tjf.649747

Abstract

The aim of this research is to map the potential forest fire risk zones using various landscape analysis techniques in Manisa province, Turkey. Forest fire, which is defined as an ecological disaster caused by natural processes or as a result of human activities, causes environmental degradation and fragmentation of the landscape. Therefore, it is very important to produce a fire risk zone map that can be used to minimize the frequency of fire, to prevent damage, to provide a prediction for the problems that may cause fire and to form a decision mechanism for the solution methods. This research utilized CORINE 2018 produced under the framework of the Copernicus Program which is the European Union's Earth Observation Programme coordinated and managed by the European Commission, ASTER Global DEM digital elevation model data obtained from the website of NASA Earthdata, fire archive records based on MODIS satellite images, digital stand map displaying the spatial distribution of tree species, and the OpenStreetMap (OSM) which were used for mapping the existing road network. Vegetation cover, slope, aspect, elevation, distance to settlement, and distance to road variables were used to determine risk zones. The specific weights were assigned to each thematic map layer according to their capacity on fire ignition. The slope, aspect, and elevation maps were generated from the digital elevation model. The distance to settlement map was generated from the CORINE database while the distance to road map was produced from OSM. The Fire Risk Zone Index (FRZI) was utilized to determine forest fire risk zones. According to the generated final fire risk map, almost 25.8% of the study area was predicted to be under very high and highrisk zones. The final forest fire risk model was validated with past fire incidents data that was acquired from MODIS images as fire points. The result of this research showed that out of 149 fire incidents in Manisa between 2001 and 2018, 97 incidents had occurred in very high and highrisk areas. This finding supports that the presented methodology based on RS and GIS techniques is reliable and can be effectively used in the process of delineation of the forest fire risk zones.

Thanks

We would like to express our special thanks to Assoc. Prof. Dr. Cumhur Gungoroglu for his guidance to this research paper and sharing his knowledge and experience with us, for his contributions. We also thank the academic staff of Forest Engineering Department of Suleyman Demirel University who organized the scientific activity entitled “Preparing Environmental Databases Using GIS and Satellite Imagery for Natural Ecosystems” (Project No: 1059B291500065), supported by TUBITAK.

References

  • Abedi Gheshlaghi, H., Feizizadeh, B., Blaschke, T., 2019. GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. Journal of Environmental Planning and Management, 63(3):481-499.
  • Adab, H., Kanniah, K.D., Solaimani, K., 2013. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65(3):1723-1743.
  • Akkas, M.E., Bucak, C., Boza, Z., Eronat, H., Bekereci, A., Erkan, A., Cebeci, C., 2008. Investigation of Large Forest Fires in the Light of Meteorological Data. Republic of Turkey Ministry of Environment and Forestry, Aegean Forestry Research Institute, 36.
  • Avcı, M., Korkmaz, M., Alkan, H., 2009. Türkiye’de orman yangınlarının nedenleri üzerine bir değerlendirme. I. Orman Yangınları ile Mücadele Sempozyumu, 07–10 Ocak, Antalya, s. 33–45.
  • Bahadır, M., 2010. Türkiye’de (1998-2007) Görülen orman yangınlarının yüzey ve rakamsal sorgulama analizi. Nature Sciences, 5(3): 146-162.
  • Belgherbi, B., Benabdeli, K., Mostefai, K., 2018. Mapping the risk forest fires in Algeria: Application of the forest of Guetarnia in Western Algeria. Ekológia (Bratislava), 37(3): 289-300.
  • Bingol, B., 2017. Determination of forest fire risk areas in Burdur Province using Geographical Information Systems. Turkish Journal of Forest Science, 1(2):169-182.
  • Cardille, J.A., Ventura, S.J., 2001. Occurrence of wildfire in the northern Great Lakes Region: effects of land cover and land ownership assessed at multiple scales. International Journal of Wildland Fire, 10(2):145-154.
  • Catry, F.X., Rego, F.C., Bação, F.L., Moreira, F., 2010. Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire, 18(8):921-931.
  • Chou, Y.H., Minnich, R.A., Chase, R.A., 1993. Mapping probability of fire occurrence in San Jacinto Mountains, California, USA. Environmental Management, 17(1):129-140.
  • Chuvieco, E., Congalton, R.G., 1989. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote sensing of Environment, 29(2):147-159.
  • Coban, H.O., Eker, M., 2010. Analysis of forest road network conditions before and after forest fire. 43rd International Symposium on Forestry Mechanisation, July 11-14, Padova Italy, pp. 11-14.
  • Coban, H.O., Erdin, C., 2020. Forest fire risk assessment using GIS and AHP integration in Bucak forest enterprise, Turkey. Applied Ecology and Environmental Research, 18(1):1567-1583.
  • Coban, H.O., Ozdamar, S., 2014. Mapping forest fire in relation to land-cover and topographic characteristics. Journal of Environmental Biology, 35(1): 217.
  • Colak, 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, 101479.
  • Díaz-Delgado, R., Lloret, F., Pons, X., 2004. Spatial patterns of fire occurrence in Catalonia, NE, Spain. Landscape Ecology, 19(7):731-745.
  • Dong, X.U., Li-min, D., Guo-fan, S., Lei, T., Hui, W., 2005. Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China. Journal of forestry research, 16(3): 169-174.
  • Duran, C., 2014. Mersin ilindeki orman yangınlarının başlangıç noktalarına göre mekânsal analizi (2001-2013). Ormancılık Araştırma Dergisi, 1(1): 38-49.
  • Fischer, A.P., Vance-Borland, K., Jasny, L., Grimm, K. E., Charnley, S., 2016. A network approach to assessing social capacity for landscape planning: The case of fire-prone forests in Oregon, USA. Landscape and Urban Planning, 147:18-27.
  • GDF, 2017. Activity Report of Strategy Development Department. Republic of Turkey General Directorate of Forestry, Ankara.
  • Ghobadi, G.J., Gholizadeh, B., Dashliburun, O.M., 2012. Forest fire risk zone mapping from geographic information system in Northern Forests of Iran (Case study, Golestan province). International Journal of Agriculture and Crop Sciences, 4(12): 818-824.
  • Gigović, L., Jakovljević, G., Sekulović, D., Regodić, M., 2018. GIS Multi-Criteria Analysis for Identifying and Mapping Forest Fire Hazard: Nevesinje, Bosnia and Herzegovina. Tehnički vjesnik, 25(3):891-897.
  • Göktepe, S., Avcı, M., 2015. Muğla-Fethiye ormanlarında yangın sorunu, yangınların dağılımı ve yangınlar üzerinde etkili olan faktörler. Türkiye Ormancılık Dergisi, 16(2): 130-140.
  • Gugliette, D., Conedera, M., Mazzolenıs, S., Ricotta, C., 2011. Mapping fire ignition risk in a complex anthropogenic landscape. Remote Sensing Letters, 2(3):213-219.
  • Guney, C.O., Ozkan, K., Sentürk, O., 2016. Modelling of spatial prediction of fire ignition risk in the Antalya-Manavgat district. Journal of the Faculty of Forestry Istanbul University, 66(2): 459-470.
  • Gungoroglu, C., 2017. Determination of forest fire risk with fuzzy analytic hierarchy process and its mapping with the application of GIS: The case of Turkey/Cakırlar. Human and Ecological Risk Assessment, 23(2):388-406.
  • Hacisalihoglu, M., 2018. Forest fire risk mapping using multi criteria decision analysis method: The case of Karabuk. In Turkish. MSc Dissertation, Zonguldak Bulent Ecevit University, Zonguldak, Turkey.
  • Jaiswal, R.K., Mukherjee, S., Raju, K.D., Saxena, R., 2002. Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation, 4(1): 1-10.
  • Joaquim, G.S., Bahaaeddin, A., Josep, R.C., 2007. Remote Sensing Analysis to Detect Fire Risk Locations. Proceedings of GéoCongrès, 2-5 October, Québec, Canada.
  • Karabulut, M., Karakoc, A., Gurbuz, M., Kizilelma, Y., 2013. Determination of forest fire risk areas using geographical information systems in Baskonus Mountain (Kahramanmaras). The Journal of International Social Research, 6(24):171-179.
  • Keifer, M., Caprio, E.A., Lineback, P., 2000. Incorporating a GIS model of ecological need into fire management planning. Joint Fire Sciences Conference and Workshop, 15-17 June, Idaho, USA.
  • Kumari, B., Pandey, A.C., 2020. MODIS based forest fire hotspot analysis and its relationship with climatic variables. Spatial Information Research, 28(1): 87-99.
  • Kuter, S., Usul, N., Kuter, N., 2011. Bandwidth determination for kernel density analysis of wildfire events at forest sub-district scale. Ecological Modelling, 222(17): 3033-3040.
  • Lin, J., Sergio, R., 2009. A derivation of the statistical characteristics of forest fires. Ecological Modelling, 220:898-903.
  • Malczewski, J., 1999. GIS and Multicriteria Decision Analysis. ISBN: 0-471-32944-4, John Wiley and Sons, New York.
  • Malik, T., Rabbani, G., Farooq, M., 2013. Forest fire risk zonation using remote sensing and GIS technology in Kansrao Forest Range of Rajaji National Park, Uttarakhand, India. India. International Journal of Advanced RS and GIS, 2(1):86-95.
  • Martínez-Fernández, J., Chuvieco, E., Koutsias, N., 2013. Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression. Natural Hazards and Earth System Sciences, 13(2):311-327.
  • MOI, 2019. E-Interior Project. Ministry of Interior, Ankara.
  • MOM, 2019. Geography of Manisa. Information Processing Department of Manisa Municipality, Manisa.
  • MPCTD, 2019. General Information, Manisa Provincial Culture and Tourism Directorate, Geography of Manisa.
  • Neyisci, T., 1987. A Study on Slow-Burning Plant Species for Preventing Forest Fires. Tubitak Journal of Nature, 11: 595-604.
  • Nuthammachot, N., Stratoulias, D., 2019. A GIS-and AHP-based approach to map fire risk: A case study of Kuan Kreng peat swamp forest, Thailand. Geocarto International, 1-14.
  • Pradhan, B., Dini Hairi Bin Suliman, M., Arshad Bin Awang, M., 2007. Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS). Disaster Prevention and Management: An International Journal, 16(3):344-352.
  • Renard, Q., Pélissier, R., Ramesh, B.R., Kodandapani, N., 2012. Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. International Journal of Wildland Fire, 21(4): 368-379.
  • Roy, P.S., 2003. Forest fire and degradation assessment using satellite remote sensing and geographic information system. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, 361-400.
  • Senturk, N., 2018. Assessment of relationship between locations and distances to roadside of forest fires in Istanbul, Turkey. Applied Ecology and Environmental Research, 16(5): 6195-6204.
  • Sowmya, S.V., Somashekar, R.K., 2010. Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India. Journal of Environmental Biology, 31(6): 969.
  • Tian, X., Zhao, F., Shu, L., Wang, M., 2013. Distribution characteristics and the influence factors of forest fires in China. Forest Ecology and Management, 310:460-467.
  • Türkeş, M., Altan, G., 2012. Analysis of the year 2008 fires in the forest lands of the Muğla Regional Forest Service by using drought indices. Journal of Human Sciences, 9(1): 912-931.
  • Yang, J., He, H.S., Shifley, S.R., Gustafson, E.J., 2007. Spatial patterns of modern period human-caused fire occurrence in the Missouri Ozark Highlands. Forest science, 53(1): 1-15.
There are 50 citations in total.

Details

Primary Language English
Journal Section Orijinal Araştırma Makalesi
Authors

Derya Gülçin 0000-0001-7118-0174

Bülent Deniz

Publication Date March 26, 2020
Acceptance Date March 10, 2020
Published in Issue Year 2020

Cite

APA Gülçin, D., & Deniz, B. (2020). Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey. Turkish Journal of Forestry, 21(1), 15-24. https://doi.org/10.18182/tjf.649747
AMA Gülçin D, Deniz B. Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey. Turkish Journal of Forestry. March 2020;21(1):15-24. doi:10.18182/tjf.649747
Chicago Gülçin, Derya, and Bülent Deniz. “Remote Sensing and GIS-Based Forest Fire Risk Zone Mapping: The Case of Manisa, Turkey”. Turkish Journal of Forestry 21, no. 1 (March 2020): 15-24. https://doi.org/10.18182/tjf.649747.
EndNote Gülçin D, Deniz B (March 1, 2020) Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey. Turkish Journal of Forestry 21 1 15–24.
IEEE D. Gülçin and B. Deniz, “Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey”, Turkish Journal of Forestry, vol. 21, no. 1, pp. 15–24, 2020, doi: 10.18182/tjf.649747.
ISNAD Gülçin, Derya - Deniz, Bülent. “Remote Sensing and GIS-Based Forest Fire Risk Zone Mapping: The Case of Manisa, Turkey”. Turkish Journal of Forestry 21/1 (March 2020), 15-24. https://doi.org/10.18182/tjf.649747.
JAMA Gülçin D, Deniz B. Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey. Turkish Journal of Forestry. 2020;21:15–24.
MLA Gülçin, Derya and Bülent Deniz. “Remote Sensing and GIS-Based Forest Fire Risk Zone Mapping: The Case of Manisa, Turkey”. Turkish Journal of Forestry, vol. 21, no. 1, 2020, pp. 15-24, doi:10.18182/tjf.649747.
Vancouver Gülçin D, Deniz B. Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey. Turkish Journal of Forestry. 2020;21(1):15-24.

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