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Batı Karadeniz Bölgesinde FlamMap yazılımı ve uzaktan algılama teknikleri kullanılarak orman yangın davranışı simülasyonunun değerlendirilmesi

Yıl 2018, Cilt: 18 Sayı: 2, 171 - 188, 15.09.2018
https://doi.org/10.17475/kastorman.459698

Öz

Çalışmanın amacı: Yanıcı madde tipleri ve tüketilebilir yanıcı madde miktarı yangın davranışının modellenmesinde, yangın şiddetinin hesaplanmasında ve yangın tehlike riskinin haritalanmasında çok önemlidir. Yanıcı madde tipi, yanıcı madde nem içeriği, rüzgâr hızı ve eğim arasındaki ilişki, belirli bir bölgenin yangın davranışının tahmin edilmesinde kullanılan önemli parametrelerden bazılarıdır. Bu çalışmada, Bayam Orman İşletme Şefliğine ait ormanlarda yanıcı madde özellikleri, hava halleri ve bazı topoğrafik özellikler kullanılarak yangın şiddeti ve yangın yayılma oranı haritaları ile yangın risk haritaları uzaktan algılama teknikleri ve FlamMap yazılımı yardımıyla geliştirilmiştir.
Çalışma alanı: Çalışma alanı Türkiye’nin batı Karadeniz bölgesinde bulunan Kastamonu ili, Taşköprü ilçesi, Bayam Orman İşletme Şefliği sınırlarını kapsamaktadır.
Materyal ve Yöntem: Çalışma alanının yanıcı madde miktarını tahmin etmek ve haritalamak için yanıcı madde modelleri kullanılmıştır. FlamMap yazılımı kullanılarak yanıcı madde miktarı, yanıcı madde nem içeriği, rüzgâr hızı ve eğim parametrelerine bağlı olarak yayılma oranı ve yangın şiddeti tahmin edilmiştir. Geçmişte çıkan yangın verileri ile baş yangın şiddeti haritaları çakıştırılarak potansiyel yangın tehlikesi olan yerler CBS ve uzaktan algılama teknikleri kullanarak belirlenmiştir.
Sonuçlar: Bölgenin %20,0’sının düşük (<2 m dakika-1), %43,2 'inin orta (2-15 m dakika-1), %12,0' ü yüksek (15-30 m dakika-1) ve %24,8’si sırasıyla, çok yüksek (> 30 m.dakika-1) yayılma oranına sahiptir. Yangın şiddeti haritasına göre, alanın %60,7’unun düşük (0-350 kW m-1), %24,9’nin orta (350-1700 kW m-1), %1,3'ü yüksek (1700-3500 kW m -1) ve %13,0'si çok yüksek (> 3500 kW m-1) yangın şiddeti sınıfında yer almaktadır.
Önemli Vurgular: Bu çalışmayla çalışma alanına ait yanıcı madde türlerinin mekansal dağılımı haritalanmış ve yangın davranışı parametrelerinden üçü (baş yangın şiddeti, yayılma oranı ve alev yüksekliği) uzaktan algılama ve CBS teknikleri kullanılarak tahmin edilmiştir. Daha önce yanan alanların yangın davranış modeli çıktı verileriyle uzamsal olarak çakıştırılması sonucunda, en çok yangına maruz kalan alanların % 40-70 kapalılığındaki karışık genç Anadolu karaçamı ile sarıçam meşcereleri ile kapalılığı %70'den fazla olan genç Anadolu karaçam meşcerelerinin bulunduğu alanlarda olduğu sonucuna varılmıştır.

Kaynakça

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Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey

Yıl 2018, Cilt: 18 Sayı: 2, 171 - 188, 15.09.2018
https://doi.org/10.17475/kastorman.459698

Öz

Aim of study: Forest fuels are very critical for fire behavior models and hazard maps. Relationship among wind speed, fuel moisture content, slope, and fuel type directs us to predict fire behavior of a given region. For this study, we evaluated fire behavior parameters such as fireline intensity and rate of fire spread using the fuel moisture content, slope, fuel load, and wind speed for the Bayam Forest District with the help of remote sensing techniques and FlamMap software.
Area of study: The study area is located in Bayam Forest District in the city of Taskopru, Kastamonu, a Western Black Sea region of Turkey.
Material and Methods: In order to estimate and map forest fuel load of the study area, fuel models were developed using the parameters of the average vegetation height, 1-hr, 10-hr, and 100-hr fuel load, foliage, total fuel load, litter load and litter depth. Three basic fire descriptors (fireline intensity, rate of fire spread, and flame length) were calculated using FlamMap software with the parameters fuel load, wind speed, fuel moisture, and slope. Using the descriptors above, the historical fire data was overlaid with the fireline intensity maps to determine fire potential areas within the remote sensing and GIS framework.
Main results: The results of this study showed that 20.0% of the region had low (<2 m min-1), 43.2% had moderate (2-15 m min-1), 12.0% had high (15-30 m min-1), and 24.8% had very high (>30 m min-1) rate of fire spread, respectively. The fireline intensity map showed that 60.7% of the area was in low (0-350 kW m-1), 24.9% was in moderate (350-1700 kW m-1), 1.3% was in high (1700-3500 kW m-1), and 13.0% was in very high (>3500 kW m-1) fireline intensity.
Highlights: The spatial extent of fuel types was observed and three of the potential fire behavior predictors (fire intensity, rate of fire spread and flame length) were estimated using remote sensing and GIS techniques. The overlaid historical fire data showed that the most fire-prone areas are in the mixed young Anatolian black pine - Scots pine tree stands that have 40-70% canopy cover and that are in the young Anatolian black pine tree stands that have more than 70% canopy cover.

Kaynakça

  • Yavuz, M., Saglam, B. (2012). Use of remote sensing and geographic information systems techniquies in forest fires. KSU Journal of Engineering Science (Special Issue), 235-242.
  • Yavuz, M., Saglam, B., Kucuk, O., Tufekcioglu, A. (2015). Assessing fuel load and fireline intensity in Bayam forest district, Turkey using Flam Map software and remote sensing techniquies. Paper presented at the International forest fire conference in Black Sea Region.
  • Zaimes, G. N., Tufekcioglu, M., Tufekcioglu, A., Zibtsev, S., Kaziolas, D., Yavuz, M., Trombitsky, I., Emmanouloudis, D., Uratu, R., Ghulijanyan, A. (2013). Innovative and sustainable use of stream water to suppress fires in protected areas: overview of the streams-2-suppress-fires project. Paper presented at the International Caucasia Forestry Symposium, Artvin, Turkey.
  • Zibtsev, S. V., Goldammer, J. G., Robinson, S., Borsuk, O. A. (2015). Fires in nuclear forests: silent threats to the environment and human security. Unasylva, 66(243/244), 40.
  • Yavuz, M., Saglam, B. (2012). Use of remote sensing and geographic information systems techniquies in forest fires. KSU Journal of Engineering Science (Special Issue), 235-242.
  • Mitsopoulos, I., Mallinis, G., Zibtsev, S., Yavuz, M., Saglam, B., Kucuk, O., Bogomolov, V., Borsuk, A., Zaimes, G.(2017). An integrated approach for mapping fire suppression difficulty in three different ecosystems of Eastern Europe. Journal of Spatial Science, 62(1), 139-155.
  • Nelson, R. M. (2001). Water relations of forest fuels Forest fires (pp. 79-149): Elsevier.
  • Pearce, H. G. (2009). Review of fire growth simulation models for application in New Zealand. Scion Fire Research Group, Christchurch, NZ. Client Report(16246).
  • Peterson, D. L., Evers, L., Gravenmier, R. A., Eberhardt, E. (2007). Analytical and decision support for managing vegetation and fuels: A consumer guide. Gen. Tech. Rep. PNW-GTR-690. Portland, OR: USDA Forest Service, Pacific Northwest Research Station.
  • Pyne, S. J., Andrews, P. L., Laven, R. D. (1996). Introduction to wildland fire (2nd ed.). New York: Wiley.
  • Rebain, S. A., Reinhardt, E. D., Crookston, N. L., Beukema, S. J., Kurz, W. A., Greenough, J. A., Robinson, D. C. E., Lutes, D. C. (2010). The fire and fuels extension to the forest vegetation simulator: updated model documentation. USDA For. Serv. Int. Rep, 408.
  • Rothermel, R. C. (1972). A mathematical model for predicting fire spread in wildland fuels. USDA For. Serv. Res. Pap. INT-115.
  • Rothermel, R. C. (1983). How to predict the spread and intensity of forest and range fires. USDA For. Serv. Gen. Tech. Rep. INT-143.
  • Roussopoulos, P. J., Loomis, R. M. (1979). Weights and dimensional properties of shrubs and small trees of the Great Lakes conifer forest.
  • Saglam, B., Bilgili, E., Dincdurmaz, B., Kadiogulari, A. I., Küçük, Ö. (2008). Spatio-temporal analysis of forest fire risk and danger using LANDSAT imagery. Sensors, 8(6), 3970-3987.
  • Sağlam, B., Küçük, Ö., Bilgili, E., Durmaz, B. D., Baysal, I. (2008). Estimating fuel biomass of some shrub species (Maquis) in Turkey. Turkish Journal of Agriculture and Forestry, 32(4), 349-356.
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  • Burgan, R. E., Rothermel, R. C. (1984). BEHAVE: fire behavior prediction and fuel modeling system--FUEL subsystem. The Bark Beetles, Fuels, and Fire Bibliography, 47.
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  • Coleman, J. R., Sullivan, A. L. (1996). A real-time computer application for the prediction of fire spread across the Australian landscape. Simulation, 67(4), 230-240.
  • Cruz, M. G., Alexander, M. E. (2010). Assessing crown fire potential in coniferous forests of western North America: a critique of current approaches and recent simulation studies. International Journal of Wildland Fire, 19(4), 377-398.
  • Deeming, I. E., Lancaster, I. W., Fosberg, M. A., Furman, R. W., Schroeder, M. (1972). The National Fire-Danger Rating System USDA Forest Service Research Paper RM-84.
  • Dimitrakopoulos, A. P. (2002). Mediterranean fuel models and potential fire behaviour in Greece. International Journal of Wildland Fire, 11(2), 127-130.
  • Dimitrakopoulos, A. P., Bemmerzouk, A. M. (2003). Predicting live herbaceous moisture content from a seasonal drought index. International Journal of Biometeorology, 47(2), 73-79.
  • Dimitrakopoulos, A. P., Panov, P. I. (2001). Pyric properties of some dominant Mediterranean vegetation species. International Journal of Wildland Fire, 10(1), 23-27.
  • ERDAS. (2008). ERDAS field guide. Georgia. USA: ERDAS Inc. .
  • ESRI. (2014). ArcGIS for Desktop: Release 10.2.1 (Version 10.2.1). Redlands ,CA: Environmental Systems Research Institute.
  • Farris, C. A., Pezeshki, C., Neuenschwander, L. F. (1999). A comparison of fire probability maps derived from GIS modeling and direct simulation techniques. Paper presented at the Proceedings of the Joint Fire Science Conference and Workshop: Crossing the Millenium: Integrating Spatial Technologies and Ecological Principles for a New Age in Fire Management.
  • Fernandes, P. M., Botelho, H. S., Rego, F. C., Loureiro, C. (2009). Empirical modelling of surface fire behaviour in maritime pine stands. International Journal of Wildland Fire, 18(6), 698-710. doi:https://doi.org/10.1071/WF08023
  • Finney, M. A. (1998). FARSITE, Fire Area Simulator--model development and evaluation (Vol. 3): US Department of Agriculture, Forest Service, Rocky Mountain Research Station Ogden, UT.
  • Finney, M. A. (2005). The challenge of quantitative risk analysis for wildland fire. Forest Ecology and Management, 211(1), 97-108. doi:https://doi.org/10.1016/j.foreco.2005.02.010
  • Finney, M. A. (2006). An overview of FlamMap fire modeling capabilities. Paper presented at the Fuels management—how to measure success: conference proceedings, 28-30 March 2006; Portland, OR.
  • Finney, M. A., Cohen, J. D., McAllister, S. S., Jolly, W. M. (2013). On the need for a theory of wildland fire spread. International Journal of Wildland Fire, 22(1), 25-36. doi:https://doi.org/10.1071/WF11117
  • Alexander, M. E. (1982). Calculating and Interpreting Forest Fire Intensities. Canadian Journal of Botany-Revue Canadienne De Botanique, 60(4), 349-357. doi:DOI 10.1139/b82-048
  • Anderson, H. E. (1982). Aids to determining fuel models for estimating fire behavior. USDA For. Serv. Gen. Tech. Rep. INT-122. lntermt. For. and Range Exp. Stn., Ogden, Utah 84401., 22.
  • Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964): US Government Printing Office. USGS Professional Paper 964. A revision of the land use classification system as presented in the USGS Circular 671.
  • Andrews, P., Finney, M., Fischetti, M. (2007). Predicting wildfires. Scientific American, 297(2), 46-55.
  • Andrews, P. L. (2007). BehavePlus fire modeling system: past, present, and future. Paper presented at the Proceedings of 7th symposium on fire and forest meteorology.
  • Andrews, P. L., Heinsch, F. A., Schelvan, L. (2011). How to generate and interpret fire characteristics charts for surface and crown fire behavior: United States Department of Agriculture, Forest Service, Rocky Mountain Research Station.
  • Andrews, P. L., Rothermel, R. C. (1982). Charts for interpreting wildland fire behavior characteristics. Gen. Tech. Rep. INT-131. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 21 p, 131.
  • Arca, B., Duce, P., Laconi, M., Pellizzaro, G., Salis, M., Spano, D. (2007). Evaluation of FARSITE simulator in Mediterranean maquis. International Journal of Wildland Fire, 16(5), 563-572.
  • Aricak, B., Kucuk, O., Enez, K. (2014). Determining a fire potential map based on stand age, stand closure and tree species, using satellite imagery (Kastamonu central forest directorate sample). Croatian Journal of Forest Engineering: Journal for Theory and Application of Forestry Engineering, 35(1), 101-108.
  • Arroyo, L. A., Pascual, C., Manzanera, J. A. (2008). Fire models and methods to map fuel types: the role of remote sensing. Forest Ecology and Management, 256(6), 1239-1252.
  • Bilgili, E., Durmaz, B. D., Saglam, B., Kucuk, O., Baysal, I. (2006). Fire behaviour in immature calabrian pine plantations. Forest Ecology and Management, 234, S112. doi:https://doi.org/10.1016/j.foreco.2006.08.148
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  • Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Queiroz Feitosa, R., van der Meer, F., van der Werff, H., van Coillie, F., Tiede, D. (2014). Geographic Object-Based Image Analysis – Towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87(Supplement C), 180-191. doi:https://doi.org/10.1016/j.isprsjprs.2013.09.014
  • Borsuk, A., Zibtsev, S. (2013). Fire History in Mountain Forests of the Crimean Peninsula of Ukraine. Paper presented at the International Caucasia Forestry Symposium.
  • Brown, J. K. (1982). Fuel and fire behavior prediction in big sagebrush: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station.
  • Brown, J. K., Oberheu, R. D., Johnston, C. M. (1982). Handbook for inventorying surface fuels and biomass in the Interior West. USDA Forest Service, Intermountain Forest and Range Experiment Station General Technical Report INT-129. Ogden.
  • Burgan, R. E. (1987). Concepts and interpreted examples in advanced fuel modeling. Gen. Tech. Rep. INT-238. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Research Station. 40 p., 238, 40.
  • Agee, J. K., Bahro, B., Finney, M. A., Omi, P. N., Sapsis, D. B., Skinner, C. N., Van Wagtendonk, J. W., Weatherspoon, C. P. (2000). The use of shaded fuelbreaks in landscape fire management. Forest Ecology and Management, 127(1-3), 55-66.
  • Agee, J. K., Skinner, C. N. (2005). Basic principles of forest fuel reduction treatments. Forest Ecology and Management, 211(1-2), 83-96.
  • Ager, A. A., Finney, M. A. (2009). Application of wildfire simulation models for risk analysis. Paper presented at the EGU General Assembly Conference Abstracts.
  • Ager, A. A., Vaillant, N. M., Finney, M. A. (2011). Integrating fire behavior models and geospatial analysis for wildland fire risk assessment and fuel management planning. Journal of Combustion, 2011, 19. doi:10.1155/2011/572452
  • Aguado, I., Chuvieco, E., Boren, R., Nieto, H. (2007). Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment. International Journal of Wildland Fire, 16(4), 390-397.
  • Alcasena, F. J., Salis, M., Ager, A. A., Arca, B., Molina, D., Spano, D. (2015). Assessing landscape scale wildfire exposure for highly valued resources in a Mediterranean area. Environmental management, 55(5), 1200-1216
Toplam 87 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Mehmet Yavuz

Bülent Sağlam Bu kişi benim

Ömer Küçük Bu kişi benim

Aydın Tüfekçioğlu Bu kişi benim

Yayımlanma Tarihi 15 Eylül 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 18 Sayı: 2

Kaynak Göster

APA 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. https://doi.org/10.17475/kastorman.459698
AMA Yavuz M, Sağlam B, Küçük Ö, Tüfekçioğlu A. Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey. Kastamonu University Journal of Forestry Faculty. Eylül 2018;18(2):171-188. doi:10.17475/kastorman.459698
Chicago Yavuz, Mehmet, Bülent Sağlam, Ömer Küçük, ve Aydın Tüfekçioğlu. “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, sy. 2 (Eylül 2018): 171-88. https://doi.org/10.17475/kastorman.459698.
EndNote Yavuz M, Sağlam B, Küçük Ö, Tüfekçioğlu A (01 Eylül 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.
IEEE M. Yavuz, B. Sağlam, Ö. Küçük, ve A. Tüfekçioğlu, “Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey”, Kastamonu University Journal of Forestry Faculty, c. 18, sy. 2, ss. 171–188, 2018, doi: 10.17475/kastorman.459698.
ISNAD Yavuz, Mehmet vd. “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 (Eylül 2018), 171-188. https://doi.org/10.17475/kastorman.459698.
JAMA Yavuz M, Sağlam B, Küçük Ö, Tüfekçioğlu A. Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey. Kastamonu University Journal of Forestry Faculty. 2018;18:171–188.
MLA Yavuz, Mehmet vd. “Assessing Forest Fire Behavior Simulation Using FlamMap Software and Remote Sensing Techniques in Western Black Sea Region, Turkey”. Kastamonu University Journal of Forestry Faculty, c. 18, sy. 2, 2018, ss. 171-88, doi:10.17475/kastorman.459698.
Vancouver Yavuz M, Sağlam B, Küçük Ö, Tüfekçioğlu A. Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey. Kastamonu University Journal of Forestry Faculty. 2018;18(2):171-88.

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