Advertisement

Natural Hazards

, Volume 87, Issue 3, pp 1807–1825 | Cite as

Landfire hazard assessment in the Caspian Hyrcanian forest ecoregion with the long-term MODIS active fire data

  • Hamed AdabEmail author
Original Paper

Abstract

Relatively little is known about the causes of landfire assembly in Golestan Province that are subject to environmental and anthropogenic factors. The present study investigated how the landfire hazard is influenced by the environmental and anthropogenic parameters in the fire-prone Hyrcanian forest. The MODIS hotspot data of the past 15 years were collected and analyzed in Golestan Province. The frequencies and distributions of landfires were investigated with 13 environmental and anthropogenic factors selected to construct landfire hazard maps by BLR and ANN methods. The comparison between MODIS active fire detections collected between 2000 and 2015 of the Golestan Province and landfire hazard areas, as predicted by the BLR and ANN, showed satisfactory results for ANN. The results of this study confirmed that anthropogenic variables were important predictors of landfire hazard and showed nonlinear relationships. Vegetation moisture, climate, and topography were also significant variables in the study area.

Keywords

Landfire Artificial neural network Binary logistic regression Geographic information system 

Notes

Acknowledgements

The author thanks the Ministry of Science, Research and Technology of Iran and the Office of Research and Technology, Hakim Sabzevari University (HSU) for supporting this work (Research Grants 937 and 1166). The author acknowledges the USGS and NASA dataset for providing the ASTER DEM and MODIS fire hotspots data, and the European Space Agency (ESA) for providing GLOBCOVER Portal access. The author thanks both anonymous reviewers for their constructive comments on manuscript.

References

  1. Adab H, Kanniah KD, Solaimani K (2013) Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Nat Hazards 65:1723–1743. doi: 10.1007/s11069-012-0450-8 CrossRefGoogle Scholar
  2. Adab H, Kanniah KD, Solaimani K, Sallehuddin R (2015) Modelling static fire hazard in a semi-arid region using frequency analysis. Int J Wildland Fire 24:763–777. doi: 10.1071/WF13113 CrossRefGoogle Scholar
  3. Adel M, Pourbabaei H, Omidi A, Dey D (2013) Forest structure and woody plant species composition after a wildfire in beech forests in the north of Iran. J For Res 24:255–262. doi: 10.1007/s11676-012-0316-7 CrossRefGoogle Scholar
  4. Agee JK (1996) Fire ecology of Pacific Northwest forests. Island press, Washington, D.C, USAGoogle Scholar
  5. Allard GB (2003) Fire situation in the Islamic Republic of Iran. Int For Fire News 28:88–91Google Scholar
  6. Arino O, Casadio S, Serpe D (2012) Global night-time fire season timing and fire count trends using the ATSR instrument series. Remote Sens Environ 116:226–238. doi: 10.1016/j.rse.2011.05.025 CrossRefGoogle Scholar
  7. Ayoubi S, Khormali F, Sahrawat K, Rodrigues de Lima A (2011) Assessing impacts of land use change on soil quality indicators in a loessial soil in Golestan Province. Iran J Agric Sci Technol 13:727–742Google Scholar
  8. Babrauskas V (2003) Ignition handbook: principles and applications to fire safety engineering, fire investigation, risk management and forensic science. Fire Science Publishers, Issaquah, Washington, D.C, USAGoogle Scholar
  9. Bai Y, Feng M, Jiang H, Wang J, Zhu Y, Liu Y (2014) Assessing consistency of five global land cover data sets in China. Remote Sens 6:8739–8759CrossRefGoogle Scholar
  10. Baranovskiy NV, Yankovich EP (2015) Geoinformation system for prediction of forest fire danger caused by solar radiation using remote sensing data. In: Comerón A, Kassianov EI, Schäfer K, Picard RH, Weber K (eds) SPIE Remote Sensing, Toulouse, France, International Society for Optics and Photonics, pp 96400Z-96406ZGoogle Scholar
  11. Bennie J, Hill MO, Baxter R, Huntley B (2006) Influence of slope and aspect on long-term vegetation change in British chalk grasslands. J Ecol 94:355–368CrossRefGoogle Scholar
  12. Bhople AD, Tijare P (2012) Fast fourier transform based classification of epileptic seizure using artificial neural network. Int J Adv Res Comput Sci Softw Eng 2:228–231Google Scholar
  13. Boger Z, Guterman H (1997) Knowledge extraction from artificial neural network models. In: 1997 IEEE international conference on systems, man, and cybernetics, 1997. Computational cybernetics and simulation, vol 3034, 12–15 Oct 1997, pp 3030–3035. doi: 10.1109/ICSMC.1997.633051
  14. Böhner J, Antonić O (2009) Chapter 8 land-surface parameters specific to topo-climatology. In: Tomislav H, Hannes IR (eds) Developments in soil science, vol 33. Elsevier, Amsterdam, pp 195–226. doi: 10.1016/S0166-2481(08)00008-1 Google Scholar
  15. Brown PM, Kaye MW, Huckaby LS, Baisan CH (2001) Fire history along environmental gradients in the Sacramento Mountains, New Mexico: influences of local patterns and regional processes. Ecoscience 8:115–126CrossRefGoogle Scholar
  16. Buchanan B, Fleming M, Schneider R, Richards B, Archibald J, Qiu Z, Walter M (2013) Evaluating topographic wetness indices across central New York agricultural landscapes. Hydrol Earth Syst Sci Dis 10:14041–14093CrossRefGoogle Scholar
  17. Chafer CJ, Noonan M, Macnaught E (2004) The post-fire measurement of fire severity and intensity in the Christmas 2001 Sydney wildfires. Int J Wildland Fire 13:227–240CrossRefGoogle Scholar
  18. Chandler C, Cheney P, Thomas P, Trabaud L, Williams D (1983) Fire in forestry. Forest fire behaviour and effects, vol 1. Wiley, London, p 450Google Scholar
  19. Chaparro D, Vall-llossera M, Piles M, Camps A, Rudiger C (2015) Low soil moisture and high temperatures as indicators for forest fire occurrence and extent across the Iberian Peninsula. In: 2015 IEEE international on geoscience and remote sensing symposium (IGARSS), 26–31 July 2015, pp 3325–3328. doi: 10.1109/IGARSS.2015.7326530
  20. Choi J, Oh H-J, Won J-S, Lee S (2010) Validation of an artificial neural network model for landslide susceptibility mapping. Environ Earth Sci 60:473–483CrossRefGoogle Scholar
  21. Chuvieco E, Congalton RG (1989) Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sens Environ 29:147–159CrossRefGoogle Scholar
  22. Conrad O et al (2015) System for automated geoscientific analyses (SAGA) v. 2.1. 4. Geosci Model Dev Discuss 8:2271–2312CrossRefGoogle Scholar
  23. Davies DK, Ilavajhala S, Min Minnie W, Justice CO (2009) Fire information for resource management system: archiving and distributing MODIS active fire data. IEEE Trans Geosci Remote Sens 47:72–79CrossRefGoogle Scholar
  24. Defourny P, Vancutsem C, Bicheron P, Brockmann C, Nino F, Schouten L, Leroy M (2006) GLOBCOVER: a 300 m global land cover product for 2005 using Envisat MERIS time series. In: Proceedings of the ISPRS commission VII mid-term symposium, remote sensing: from pixels to processes, Enschede, pp 8–11Google Scholar
  25. Dong X, 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. J For Res 16:169–174. doi: 10.1007/bf02856809 CrossRefGoogle Scholar
  26. Ercanoglu M, Weber KT, Langille J, Neves R (2006) Modeling wildland fire susceptibility using fuzzy systems. GISci Remote Sens 43:268–282. doi: 10.2747/1548-1603.43.3.268 CrossRefGoogle Scholar
  27. Eskandari S, Chuvieco E (2015) Fire danger assessment in Iran based on geospatial information. Int J Appl Earth Obs Geoinf 42:57–64. doi: 10.1016/j.jag.2015.05.006 CrossRefGoogle Scholar
  28. Eva H, Fritz S (2003) Examining the potential of using remotely sensed fire data to predict areas of rapid forest change in South America. Appl Geogr 23:189–204. doi: 10.1016/j.apgeog.2003.08.009 CrossRefGoogle Scholar
  29. Eva H, Lambin EF (2000) Fires and land-cover change in the tropics: a remote sensing analysis at the landscape scale. J Biogeogr 27:765–776. doi: 10.1046/j.1365-2699.2000.00441.x CrossRefGoogle Scholar
  30. González JR, Pukkala T (2007) Characterization of forest fires in Catalonia (north–east Spain). Eur J Forest Res 126:421–429. doi: 10.1007/s10342-006-0164-0 CrossRefGoogle Scholar
  31. Hawbaker TJ, Radeloff VC, Stewart SI, Hammer RB, Keuler NS, Clayton MK (2013) Human and biophysical influences on fire occurrence in the United States. Ecol Appl 23:565–582. doi: 10.1890/12-1816.1 CrossRefGoogle Scholar
  32. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  33. Jackson RD, Huete AR (1991) Interpreting vegetation indices. Prev Vet Med 11:185–200. doi: 10.1016/S0167-5877(05)80004-2 CrossRefGoogle Scholar
  34. Jahdi R et al (2014) Calibration of FARSITE fire area simulator in Iranian northern forests. Nat Hazards Earth Syst Sci Discuss 2:6201–6240. doi: 10.5194/nhessd-2-6201-2014 CrossRefGoogle Scholar
  35. Jaiswal RK, Mukherjee S, Raju KD, Saxena R (2002) Forest fire risk zone mapping from satellite imagery and GIS. Int J Appl Earth Obs Geoinf 4:1–10. doi: 10.1016/S0303-2434(02)00006-5 CrossRefGoogle Scholar
  36. Jolly WM (2005) 2.1 Sensitivity of a fire behavior model to changes in live fuel moistureGoogle Scholar
  37. Jolly WM, Cochrane MA, Freeborn PH, Holden ZA, Brown TJ, Williamson GJ, Bowman DMJS (2015) Climate-induced variations in global wildfire danger from 1979 to 2013. Nat Commun. doi: 10.1038/ncomms8537 Google Scholar
  38. Jones B, Sall J (2011) JMP statistical discovery software. Wiley Interdiscip Rev Comput Stat 3:188–194. doi: 10.1002/wics.162 CrossRefGoogle Scholar
  39. Justice CO et al (2002) The MODIS fire products. Remote Sens Environ 83:244–262. doi: 10.1016/s0034-4257(02)00076-7 CrossRefGoogle Scholar
  40. Ketterings QM, Tri Wibowo T, van Noordwijk M, Penot E (1999) Farmers’ perspectives on slash-and-burn as a land clearing method for small-scale rubber producers in Sepunggur, Jambi Province, Sumatra, Indonesia. For Ecol Manag 120:157–169. doi: 10.1016/S0378-1127(98)00532-5 CrossRefGoogle Scholar
  41. Kipfmueller KF, Baker WL (2000) A fire history of a subalpine forest in south-eastern Wyoming, USA. J Biogeogr 27:71–85. doi: 10.1046/j.1365-2699.2000.00364.x CrossRefGoogle Scholar
  42. Knorr W, Kaminski T, Arneth A, Weber U (2014) Impact of human population density on fire frequency at the global scale. Biogeosciences 11:1085–1102. doi: 10.5194/bg-11-1085-2014
  43. Krawchuk MA, Moritz MA (2011) Constraints on global fire activity vary across a resource gradient. Ecology 92:121–132. doi: 10.1890/09-1843.1 CrossRefGoogle Scholar
  44. Lead C, de Guenni LB, Cardoso M, Ebi K (2005) Regulation of natural hazards: floods and fires. Ecosyst Hum Well Being Curr State Trends Find Cond Trends Work Group Millenn Ecosyst Assess 1:441Google Scholar
  45. Liu Z, Wimberly MC (2015) Climatic and landscape influences on fire regimes from 1984 to 2010 in the western United States. PLoS ONE 10:e0140839CrossRefGoogle Scholar
  46. Maeda EE, Formaggio AR, Shimabukuro YE, Arcoverde GFB, Hansen MC (2009) Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks. Int J Appl Earth Obs Geoinf 11:265–272. doi: 10.1016/j.jag.2009.03.003 CrossRefGoogle Scholar
  47. Maingi JK, Henry MC (2007) Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA. Int J Wildland Fire 16:23–33. doi: 10.1071/WF06007 CrossRefGoogle Scholar
  48. Matthews S (2006) A process-based model of fine fuel moisture. Int J Wildland Fire 15:155–168. doi: 10.1071/WF05063 CrossRefGoogle Scholar
  49. Mohd Razali S, Marin Atucha AA, Nuruddin AA, Abdul Hamid H, Mohd Shafri HZ (2016) Monitoring vegetation drought using MODIS remote sensing indices for natural forest and plantation areas. J Spat Sci 61:157–172. doi: 10.1080/14498596.2015.1084247 CrossRefGoogle Scholar
  50. Muhammad Ehsan R, Simon SP, Venkateswaran PR (2016) Day-ahead forecasting of solar photovoltaic output power using multilayer perceptron. Neural Comput Appl. doi: 10.1007/s00521-016-2310-z Google Scholar
  51. Munn I, Zhai Y, Evans DL (2003) Modeling forest fire probabilities in the South Central United States using FIA data. South J Appl For 27:11–17Google Scholar
  52. Narayanaraj G, Wimberly MC (2011) Influences of forest roads on the spatial pattern of wildfire boundaries. Int J Wildland Fire 20:792–803. doi: 10.1071/WF10032 CrossRefGoogle Scholar
  53. Nelson A, Chomitz KM (2011) Effectiveness of strict vs. multiple use protected areas in reducing tropical forest fires: a global analysis using matching methods. PLoS ONE 6:e22722CrossRefGoogle Scholar
  54. Olson DL, Agee JK (2005) Historical fires in Douglas-fir dominated riparian forests of the southern Cascades, Oregon. Fire Ecol 1:50–74CrossRefGoogle Scholar
  55. Piñol J, Terradas J, Lloret F (1998) Climate warming, wildfire hazard, and wildfire occurrence in coastal eastern Spain. Clim Change 38:345–357. doi: 10.1023/a:1005316632105 CrossRefGoogle Scholar
  56. Prasad VK, Badarinath KVS, Eaturu A (2008) Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India. J Environ Manag 86:1–13. doi: 10.1016/j.jenvman.2006.11.017 CrossRefGoogle Scholar
  57. Press SJ, Wilson S (1978) Choosing between logistic regression and discriminant analysis. J Am Stat As 73:699–705. doi: 10.1080/01621459.1978.10480080 CrossRefGoogle Scholar
  58. Price O, Bradstock R (2014) Countervailing effects of urbanization and vegetation extent on fire frequency on the Wildland Urban Interface: disentangling fuel and ignition effects. Landsc Urban Plan 130:81–88. doi: 10.1016/j.landurbplan.2014.06.013 CrossRefGoogle Scholar
  59. Renard Q, Pélissier R, Ramesh BR, Kodandapani N (2012) Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. Int J Wildland Fire 21:368–379. doi: 10.1071/WF10109 CrossRefGoogle Scholar
  60. Rollins MG, Morgan P, Swetnam T (2002) Landscape-scale controls over 20th century fire occurrence in two large Rocky Mountain (USA) wilderness areas. Landsc Ecol 17:539–557. doi: 10.1023/a:1021584519109 CrossRefGoogle Scholar
  61. Romero-Calcerrada R, Novillo CJ, Millington JDA, Gomez-Jimenez I (2008) GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (Central Spain). Landsc Ecol 23:341–354. doi: 10.1007/s10980-008-9190-2 CrossRefGoogle Scholar
  62. Sall J, Lehman A, Stephens ML, Creighton L (2012) JMP start statistics: a guide to statistics and data analysis using JMP. SAS Institute, Cary, North Carolina, USAGoogle Scholar
  63. Scharnweber T, Rietschel M, Manthey M (2007) Degradation stages of the Hyrcanian forests in southern Azerbaijan. Archiv für Naturschutz und Landschaftsforschung 46:133–156Google Scholar
  64. Schroeder MJ, Buck CC (1970) Fire weather. Agriculture handbook 360. Department of Agriculture, Forest Service, Washington, DCGoogle Scholar
  65. Schwartz NB, Uriarte M, Gutiérrez-Vélez VH, Baethgen W, DeFries R, Fernandes K, Pinedo-Vasquez MA (2015) Climate, landowner residency, and land cover predict local scale fire activity in the Western Amazon. Glob Environ Change 31:144–153. doi: 10.1016/j.gloenvcha.2015.01.009 CrossRefGoogle Scholar
  66. Shafiei AB, Akbarinia M, Jalali G, Hosseini M (2010) Forest fire effects in beech dominated mountain forest of Iran. For Ecol Manag 259:2191–2196. doi: 10.1016/j.foreco.2010.02.025 CrossRefGoogle Scholar
  67. Sharples JJ (2008) Review of formal methodologies for wind–slope correction of wildfire rate of spread. Int J Wildland Fire 17:179–193. doi: 10.1071/WF06156 CrossRefGoogle Scholar
  68. Siljander M (2009) Predictive fire occurrence modelling to improve burned area estimation at a regional scale: a case study in East Caprivi, Namibia. Int J Appl Earth Obs Geoinf 11:380–393. doi: 10.1016/j.jag.2009.06.004 CrossRefGoogle Scholar
  69. Swingler K (1996) Applying neural networks: a practical guide. Morgan Kaufmann, Los AltosGoogle Scholar
  70. Syphard AD, Radeloff VC, Keuler NS, Taylor RS, Hawbaker TJ, Stewart SI, Clayton MK (2008) Predicting spatial patterns of fire on a southern California landscape. Int J Wildland Fire 17:602–613. doi: 10.1071/WF07087 CrossRefGoogle Scholar
  71. Tachikawa T, Hato M, Kaku M, Iwasaki A (2011a) Characteristics of ASTER GDEM version 2. In: 2011 IEEE international on geoscience and remote sensing symposium (IGARSS), 24–29 July 2011, pp 3657–3660. doi: 10.1109/IGARSS.2011.6050017
  72. Tachikawa T, Kaku M, Iwasaki A et al (2011b) ASTER Global digital elevation model version 2—summary of validation results. Technical report, NASA Jet Propulsion Laboratory, California Institute of Technology, USAGoogle Scholar
  73. Tanpipat V, Honda K, Nuchaiya P (2009) MODIS hotspot validation over Thailand. Remote Sens 1:1043–1054CrossRefGoogle Scholar
  74. Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and Naïve Bayes Models. Math Probl Eng 2012:26. doi: 10.1155/2012/974638 CrossRefGoogle Scholar
  75. Vázquez A, Moreno JM (2001) Spatial distribution of forest fires in Sierra de Gredos (Central Spain). For Ecol Manag 147:55–65. doi: 10.1016/S0378-1127(00)00436-9 CrossRefGoogle Scholar
  76. Wang S, Zhou Y, Wang L, Zhang P (2003) A research on fire automatic recognition using MODIS data. Paper presented at the geoscience and remote sensing symposium, 2003. IGARSS’03. Proceedings. 2003 IEEE InternationalGoogle Scholar
  77. Wittenberg L, Malkinson D (2009) Spatio-temporal perspectives of forest fires regimes in a maturing Mediterranean mixed pine landscape. Eur J Forest Res 128:297–304. doi: 10.1007/s10342-009-0265-7 CrossRefGoogle Scholar
  78. Wu Z, He HS, Yang J, Liu Z, Liang Y (2014) Relative effects of climatic and local factors on fire occurrence in boreal forest landscapes of northeastern China. Sci Total Environ 493:472–480. doi: 10.1016/j.scitotenv.2014.06.011 CrossRefGoogle Scholar
  79. Xu T, Wang J, Fang Y (2014) A model-free estimation for the covariate-adjusted Youden index and its associated cut-point. Stat Med 33:4963–4974CrossRefGoogle Scholar
  80. Yadegarnejad SA, Dylam Jafarabad M, Mohammadi Savadkoohi N (2012) Surface wildfire in temperate forests of the Golestan Province, northern Iran. Int Res J Appl Basic Sci 3:2243–2247Google Scholar
  81. Yang Y, Watanabe M, Li F, Zhang J, Zhang W, Zhai J (2006) Factors affecting forest growth and possible effects of climate change in the Taihang Mountains, northern China. Forestry 79:135–147. doi: 10.1093/forestry/cpi062 CrossRefGoogle Scholar
  82. Yang J, He HS, Shifley SR, Gustafson EJ (2007) Spatial patterns of modern period human-caused fire occurrence in the Missouri Ozark Highlands. For Sci 53:1–15Google Scholar
  83. Yin H-W, Kong F-H, Li X-Z (2004) RS and GIS-based forest fire risk zone mapping in da hinggan mountains. Chin Geogr Sci 14:251–257. doi: 10.1007/s11769-003-0055-y CrossRefGoogle Scholar
  84. Zumbrunnen T, Menéndez P, Bugmann H, Conedera M, Gimmi U, Bürgi M (2012) Human impacts on fire occurrence: a case study of hundred years of forest fires in a dry alpine valley in Switzerland. Reg Environ Change 12:935–949. doi: 10.1007/s10113-012-0307-4 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Faculty of Geography and Environmental SciencesHakim Sabzevari UniversitySabzevarIran

Personalised recommendations