Spatial pattern analysis of post-fire damages in the Menderes District of Turkey

  • Emre ÇolakEmail author
  • Filiz Sunar
Research Article


Forest fires, whether caused naturally or by human activity can have disastrous effects on the environment. Turkey, located in the Mediterranean climate zone, experiences hundreds of forest fires every year. Over the past two decades, these fires have destroyed approximately 308000 ha of forest area, threatening the sustainability of its ecosystem. This study analyzes the forest fire that occurred in the Menderes region of Izmir on July 1, 2017, by using pre- and post-fire Sentinel 2 (10 m and 20 m) and Landsat 8 (30 m) satellite images, MODIS and VIIRS fire radiative power (FRP) data (1000 m and 375 m, respectively), and reference data obtained from a field study. Hence, image processing techniques integrated with the Geographic Information System (GIS) database were applied to a satellite image data set to monitor, analyze, and map the effects of the forest fire. The results show that the land surface temperature (LST) of the burned forest area increased from 1 to 11°C. A high correlation (R = 0.81) between LST and burn severity was also determined. The burned areas were calculated using two different classification methods, and their accuracy was compared with the reference data. According to the accuracy assessment, the Sentinel (10 m) image classification gave the best result (96.43% for Maximum Likelihood, and 99.56% for Support Vector Machine). The relationship between topographical/forest parameters, burn severity and disturbance index was evaluated for spatial pattern distribution. According to the results, the areas having canopy closure between 71%–100% and slope above 35% had the highest burn incidence. As a final step, a spatial correlation analysis was performed to evaluate the effectiveness of MODIS and VIIRS FRP data in the post-fire analysis. A high correlation was found between FRP-slope, and FRP-burn severity (0.96 and 0.88, respectively).


remote sensing GIS spectral indices disturbance index land surface temperature burn severity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Akkaş M E, Bucak C, Boza Z, Eronat H, Bekereci A, Erkan A, Cebeci C (2006). The investigation of the great wild fires based on meteorological data. Ege Forestry Res I T, 36 (in Turkish)Google Scholar
  2. Boschetti L, Roy D P (2009). Strategies for the fusion of satellite fire radiative power with burned area data for fire radiative energy derivation. J Geophys Res, 114(D20)Google Scholar
  3. Chaparro D, Vall-llossera M, Piles M, Camps A, Rüdiger C, Riera-Tatche R (2016). Predicting the extent of wildfires using remotely sensed soil moisture and temperature trends. IEEE J Sel Top Appl Earth Obs Remote Sens, 9(6): 2818–2829CrossRefGoogle Scholar
  4. Chen W, Cao C, Koyama L (2012). Detection of forest disturbance in the Greater Hinggan Mountain of China based on Landsat time-series data. In: 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)Google Scholar
  5. Chowdhury E H, Hassan Q K (2015). Operational perspective of remote sensing-based forest fire danger forecasting systems. ISPRS J Photogramm Remote Sens, 104: 224–236CrossRefGoogle Scholar
  6. Chuvieco E, Congalton R G (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sens Environ, 29(2): 147–159CrossRefGoogle Scholar
  7. Dağlıyar A, Avdan U, Uça Avcı Z D (2015). Determination of land surface temperature of Kahramanmaras and its environment with the help of remote sensing data. In: TUFUAB VIII. Technical Symposium, Konya (in Turkish)Google Scholar
  8. Díaz-Delgado R, Lloret F, Pons X (2004). Spatial patterns of fire occurrence in Catalonia. Landsc Ecol, 19(7): 731–745CrossRefGoogle Scholar
  9. Flannigan M D, Stocks B J, Wotton B M (2000). Climate change and forest fires. Sci Total Environ, 262(3): 221–229CrossRefGoogle Scholar
  10. Fraser R H, Li Z, Cihlar J (2000). Hotspot and NDVI differencing Synergy (HANDS): a new technique for burned area mapping over boreal forest. Remote Sens Environ, 74(3): 362–376CrossRefGoogle Scholar
  11. Gençay G, Birben Ü (2018). Legal process of the mining permits and rehabilitation in the state forests in Turkey—a case of Bartın Forest enterprise. Anatolian Journal of Forest Research, 4(1): 11–12Google Scholar
  12. Giannini M B, Belfiore O R, Parente C, Santamaría R (2015). Land surface temperature from Landsat 5 TM images: comparison of different methods using airborne thermal data. J Eng Sci Technol Re, 8(3): 83–90CrossRefGoogle Scholar
  13. Giglio L, Schroeder W, Hall J V, Justice C O (2018). MODIS Collection 6 Active Fire Product User’s Guide Revision BGoogle Scholar
  14. Gonçalves A C, Sousa A M O (2017). The fire in the Mediterranean region: a case study of forest fires in Portugal. Mediterranean Identi: 305–335Google Scholar
  15. Heward H, Smith A M S, Roy D P, Tinkham W T, Hoffman C M, Morgan P, Lannom K O (2013). Is burn severity related to fire intensity? Observations from landscape scale remote sensing. Int J Wildland Fire, 22(7): 910–918CrossRefGoogle Scholar
  16. Holden Z A, Smith A M S, Morgan P, Rollins M G, Gessler P E (2005). Evaluation of novel thermally enhanced spectral indices for mapping fire perimeters and comparisons with fire atlas data. Int J Remote Sens, 26(21): 4801–4808CrossRefGoogle Scholar
  17. Jaiswal R K, Mukherjee S, Raju K D, Saxena R (2002). Forest fire risk zone mapping from satellite imagery and GIS. Int J Appl Earth Obs Geoinf, 4(1): 1–10CrossRefGoogle Scholar
  18. Jimenez-Munoz J C, Cristobal J, Sobrino J A, Soria G, Ninyerola M, Pons X, Pons X (2009). Revision of the single-channel algorithm for land surface temperature retrieval from landsat thermal-infrared data. IEEE Geosci Remote Sens Lett, 47(1): 339–349CrossRefGoogle Scholar
  19. Key C H, Benson N C (2005). Landscape assessment (LA) sampling and analysis methods. In: Lutes D C, Keane R E, Caratti J F, Key C H, Benson N C, Sutherland S, Gangi LJ eds. FIREMON: Fire Effects Monitoring and Inventory System. Ogden: USDA Forest Service, Rocky Mountain Research Station, 1–55Google Scholar
  20. Norton J (2008). The use of remote densing indices to determine wildland burn severity in semiarid sagebrush steppe rangelands ssing Landsat ETM + and SPOT 5. Dissertation for Doctoral Degree, Pocatello: Idaho State UniversityGoogle Scholar
  21. Richards J A (2013). Supervised classification techniques. In: Remote Sensing Digital Image Analysis. Berlin: SpringerCrossRefGoogle Scholar
  22. Peterson D L, Liittell J S (2013). Risk assessment for wildfire in the western United States. In: VVose J M, Peterson D L, Patel-Weynand, eds. Effects of Climatic Variability and Change on Forest Ecosystems: A Comprehensive Science Synthesis for the U.S. Forest SectorGoogle Scholar
  23. Platt W J, Orzell S L, Slocum M G (2015). Seasonality of fire weather strongly influences fire regimes in South Florida savanna-grassland landscapes. PLoS One, 10(1): e0116952CrossRefGoogle Scholar
  24. Schepers L, Haest B, Veraverbeke S, Spanhove T, Borre J V, Goossens R (2014). Burned area detection and burn severity assessment of a heathland fire in Belgium using airborne imaging spectroscopy (apex). Remote Sens, 6(3): 1803–1826CrossRefGoogle Scholar
  25. Seidl R, Thom D, Kautz M, Martin-Benito D, Peltoniemi M, Vacchiano G, Wild J, Ascoli D, Petr M, Honkaniemi J, Lexer M J, Trotsiuk V, Mairota P, Svoboda M, Fabrika M, Nagel T A, Reyer C P O (2017). Forest disturbances under climate change. Nat Clim Chang, 7(6): 395–402CrossRefGoogle Scholar
  26. Sivrikaya F, Sağlam B, Akay A E, Bozali N (2014). Evaluation of forest fire risk with GIS. Pol J Environ Stud, 23(1): 187–194Google Scholar
  27. Sonti S H (2015). Application of Geographic Information System (GIS) in forest management. J Geogr Nat Disaster, 5(3): 2167–0587Google Scholar
  28. Sunar F, Özkan Ç (2001). Forest fire analysis with remote sensing data. Int J Remote Sens, 22(12): 2265–2277CrossRefGoogle Scholar
  29. Tucker C J (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ, 8(2): 127–150CrossRefGoogle Scholar
  30. US Geological Survey (2016). Landsat 8 (L8) Data Users Handbook, 2016Google Scholar
  31. Vadrevu K, Lasko K (2018). Intercomparison of MODIS AQUA and VIIRS I-Band fires and emissions in an agricultural landscape-implications for air pollution research. Remote Sens (Basel), 10(7): 978CrossRefGoogle Scholar
  32. Valero M M, Rios O, Mata C, Pastor E, Plannas E (2018). GIS-based integration of spatial and remote sensing data for wildfire monitoring. In: Earth Resources and Environmental Remote Sensing/GIS Applications IX, Vol. 10790, 107900RGoogle Scholar
  33. Vlassova L, Pérez-Cabello F, Mimbrero M, Llovería R, García-Martín A (2014). Analysis of the relationship between land surface temperature and wildfire severity in a series of landsat images. Remote Sens, 6(7): 6136–6162CrossRefGoogle Scholar
  34. Yu X, Guo X, Wu Z (2014). Land surface temperature retrieval from Landsat 8 TIRS comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sens, 6(10): 9829–9852CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Civil Engineering FacultyIstanbul Technical UniversityMaslakTurkey

Personalised recommendations