Erratum to: Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran [Environmental Earth Sciences, DOI:10.1007/s12665-014-3502-4];Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran

Environmental Earth Sciences

Volumn 73, Issue 4, 2015, Pages 1515-1533

  Pourtaghi, Zohre Sadat ^a   Pourghasemi, Hamid Reza ^b,c   Rossi, Mauro ^d,e  

^a YAZD UNIVERSITY   (Iran)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

^c TARBIAT MODARES UNIVERSITY   (Iran)

^d CNR   (Italy)

^e UNIVERSITY OF PERUGIA   (Italy)

Author keywords

Forest fire mapping; Frequency ratio; Geographic information system (GIS); Iran; Minudasht forests; Shannon s entropy

Indexed keywords

CURVES (ROAD); DEFORESTATION; ECOSYSTEMS; ENTROPY; FIRE HAZARDS; GEOGRAPHIC INFORMATION SYSTEMS; INFORMATION SYSTEMS; INFORMATION USE; LAND USE; MAPPING; REMOTE SENSING; RISK ASSESSMENT; TEXTURES; WIND EFFECTS;

FOREST FIRES; FREQUENCY RATIOS; IRAN; MINUDASHT FORESTS; SHANNON;

FIRES;

EID: 84922001870     PISSN: 18666280     EISSN: 18666299     Source Type: Journal    
DOI: 10.1007/s12665-014-3589-7     Document Type: Erratum

References (95)

1. Adab H, Kanniah K, Soleimani K (2011) GIS-based probability assessment of fire risk in grassland and forested landscapes of Golestan Province, Iran. International Conference on Environmental and Computer Science. Vol. 19, IACSIT Press, Singapore
2. Adab H, Devi Kanniah K, Solaimani K (2013) Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Nat Hazards 65:1723–1743
3. Aleemahmoodi Sarab S, Feghhi J, Goshtasb H (2013) Determining the main parameters affecting on forest fire using MLP neural network (forests of western Iran: Izeh). Int J Mol Evol Biodivers 3(4):15–23
4. Alexander DE (1995) A survey of the field of natural hazards and disaster studies. In Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer Academic, Dordrecht, pp 1–19
5. Alexandrian D, Esnault F (1998) Public policies affecting forest fires in the Mediterranean Basin. FAO Forestry, Rome
6. Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service, Intermountain Forest and Range Experiment Station General Technical Report INT-122, Ogden, UT, p. 22
7. Antoninetti M, Binagli E, Rampini A, D’Angelo M (1993) The integrated use of satellite and topographic data for forest fire hazard map. In: Winkler P, Balkema AA, Rotterdam B (eds) Remote sensing for monitoring the changing environment of Europe, pp 179–184
8. Ardakani A, Valadanzooj MJ, Mansourian A (2010) Spatial analysis of fire potential in Iran using RS and GIS. J Environ Stud 35 (52):7–9
9. Artsybashev ES (1983) Forest fires and their control. Oxonian New Delhi (lst ed. in Russian, 1974)
10. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko mountains, Central Japan. Geomorphology 65(1–2):15–31
11. Banj Shafiei A, Akbarinia M, Jalali G, Hosseini H (2010) Forest fire effects in beech dominated mountain forest of Iran. Forest Ecol Manag 259:2191–2196
12. Bednarik M, Magulová B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kraľovany-Liptovský Mikuláš railway case study. Phys Chem Earth, Parts A/B/C 35(3):162–171
13. Bednarik M, Yilmaz I, Marschalko M (2012) Landslide hazard and risk assessment: a case study from the Hlohovec–Sered’ landslide area in south-west Slovakia. Nat Hazards 64(1):547–575
14. Bonham-Carter GF (1994) Geographic information systems for geoscientists: modeling with GIS. Pergamon Press, Ottawa
15. Braun WJ, Jones BL, Lee JSW, Woolford DG, Wotton BM (2010) Forest fire risk assessment: an illustrative example from Ontario, Canada. J Probab Stat. doi:10.1155/2010/823018
16. Brown AA, Davis KP (1973) Forest fire: control and use. McGraw-Hill, New York
17. Burgan RE (1988) 1988 revisons to the 1978 national fire-danger rating system. Research paper SE-273. Asheville, NC: US Department of Agriculture, Forest Service, Southeastern Forest Experiment Station.
18. Castro R, Chuvieco E (1998) Modeling forest fire danger from geographic information systems. GI 13:15–23
19. Chatterjee S, Hadi AS (2006) Regression analysis by example, 4th edn. Wiley, New York, p 366
20. Chuvieco E (2003) Wildland fire danger estimation and mapping: the role of remote sensing data. Series in Remote Sensing. World Scientific, Singapore
21. Chuvieco E, Congalton RG (1989) Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sens Environ 29:147–159
22. Chuvieco E, Sales J (1996) Mapping the spatial distribution of forest fire danger using GIS. Int J Geogr Inf Syst 10:333–345
23. Chuvieco E, Martinez Vega J, Page AL (1989) Multi temporal analysis of TM images: application to forest fire mapping and inventory in a Mediterranean environment. ESA, European Coordinated Effort for Monitoring the Earth’s Environment. A Pilot Project Campaign on Landsat Thematic Mapper Applications (1985–1987) pp 279–285 (SEE N 89-28055 22-43)
24. Chuvieco E, Coceroa D, Riano D, Martinc P, Martıiez-Vega J, Dela Riva J, Perez F (2004) Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sens Environ 92:322–331
25. Chuvieco E, Aguadoa I, Yebraa M (2010) Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecol Model 221:46–58
26. Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63:397–406
27. Cortez P, Morais A (2007) Data mining approach to predict forest fires using meteorological data. In Proceedings of the 13th EPIA 2007—Portuguese Conference on Artificial Intelligence, December, 2007. http://www.dsi.uminho.pt/~pcortez/fires.pdf
28. DeBano LF, Neary DG, Ffolliott PF (1998) Fire’s effects on ecosystems. Wiley, New York
29. Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2013) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Nat Hazards 65(1):135–165
30. Dong CE (2006) Seasonal variation in moisture content of Eastern Canadian Tree foliage and the possible effect on crown fires. Departmental Publication number 1204, Forestry Branch, Canada 227–229
31. Egan JP (1975) Signal detection theory and ROC analysis. NY Acad 195:266–268
32. Erten E, Kurgun V, Musaoglu N (2004) Forest fire risk zone mapping from satellite imagery and GIS: A case study. XXth Congress of the International Society for Photogrammetry and Remote Sensing, Istanbul, Turkey, 222–230
33. Eskandari S, Ghadikolaei J, Jalilvand H, Saradjian MR (2013) Detection of fire high-risk areas in northern forests of Iran using Dong model. World Appl Sci J 27(6):770–773. doi:10.5829/idosi.wasj.2013.27.06.503
34. García M, Chuvieco E, Nieto H, Aguado I (2008) Combining AVHRR and meteorological data for estimating live fuel moisture content. Remote Sens Environ 112:3618–3627
35. Giglioa L, Descloitresa J, Justicec ChO, Kaufman YJ (2003) An enhanced contextual fire detection algorithm for MODIS. Remote Sens Environ 87:273–282
36. Ghomi Motazeh A, Farahi Ashtiani E, Baniasadi R, Masoumpoor Choobar F (2013) Rating and mapping mire hazard in the hardwood Hyrcanian forests using GIS and expert choice software. For Ideas 19(2(46)): 141–150
37. Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78(1–2):11–27
38. Gorgan Natural Resources Burea (2010) http://golestan.frw.org.ir/01/En/
39. Hernandez-Leal PA, Arbelo M, Gonzalez-Calvo A (2006) Fire risk assessment using satellite data. Adv Space Res 37(4):741–746. doi:10.1016/j.asr.2004.12.053
40. Iwan S, Mahmud AR, Mansor S, Shariff ARM, Nuruddin AA (2004) GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia. Disaster Prev Manage 13(5):379–386
41. Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11(4):909–926
42. 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
43. Jamshidi Bakhtar A, Sagheb-Talebi Kh, Marvi Mohajer MR, Haidari M (2013) The impact of fire on the forest and plants diversity in Iranian Oak forest. Int J Adv Biol Biomed Res 1(3):273–284
44. Janbaz Ghobadi Gh, Gholizadeh B, Majidi Dashliburun O (2012) Forest fire risk zone mapping from geographic information system in Northern Forests of Iran (Case study, Golestan province). Int J Agri Crop Sci 4(12):818–824
45. Jenness JS (2000) The Effects of fire on Mexican spotted Owls in Arizona and New Mexico. MSc thesis. Northern Arizona University, p137
46. Krasnow K, Schoennagel T, Veblen TT (2009) Forest fuel mapping and evaluation of LANDFIRE fuel maps in Boulder County, Colorado, USA. Forest Ecol Manage 257:1603–1612
47. Krivtsov V, Vigy O, Legg C, Curt T, Rigolot E, Lecomte I, Jappiot M, Lampin-Maillet C, Fernandes P, Pezzatti GB (2009) Fuel modeling in terrestrial ecosystems: an overview in the context of the development of an object-orientated database for wild fire analysis. Ecol Model 220(21):2915–2926. doi:10.1016/j.ecolmodel.2009.08.019
48. Kushla JD, Ripple WJ (1997) The role of terrain in a fire mosaic of a temperate coniferous forest. Forest Ecol Manage 95(2):97–107. doi:10.1016/s0378-1127(97)82929-5
49. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41. doi:10.1007/s10346-006-0047-y
50. Mandallaz D, Ye R (2011) Prediction of forest fires with Poisson models. Can J Forest Res 27(10):1685–1694
51. Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236
52. Moore ID, Grayson RB, Ladson A (1991) Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30
53. Movaghati S, Samadzadegan F, Azizi A (2008) A comparative study of three algorithms for forest fire detection in Iran. The international archives of the photogrammetry, remote sensing and spatial information sciences, vol XXXVII. Part B8. Beijing
54. Negnevitsky M (2002) Artificial intelligence: a guide to intelligent systems. Addison–Wesley/Pearson Education, Harlow England 394
55. Noonan EK (2003) A coupled model approach for assessing fire hazard at point Reyes national seashore: Flam Map and GIS. In: Second international wild land fire ecology and fire management congress and fifth symposium on fire and forest meteorology, Orlando, FL. American Meteorological Society, 127–128
56. O’Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41(5):673–690
57. Oh HJ, Lee S, Soedradjat G (2010) Quantitative landslide susceptibility mapping at Pemalang area, Indonesia. Environ Earth Sci 60:1317–1328
58. Oh HJ, Kim YS, Choi JK, Lee S (2011) GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. J Hydrol 399:158–172
59. Ozdemir A (2011) Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir. J Hydrol, Turkey). doi:10.1016/j.jhydrol.2011.05.015
60. Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197
61. Özkana C, Sunarb F, Berberoğluc S, Dönmezc C (2008) Effectiveness of boosting algorithms in forest fire classification. The international archives of the photogrammetry, remote sensing and spatial information sciences, vol XXXVII. Part B7. Beijing
62. Pourghasemi HR, Mohammady M, Pradhan B (2012a) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84
63. Pourghasemi HR, Pradhan B, Gokceoglu C (2012b) Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon’s entropy and GIS. Appl Mech Mater 225:486–491
64. Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2012c) Application of weights-of evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci. doi:10.1007/s12517-012-0532-7
65. Pourghasemi HR, Pradhan B, Gokceoglu C (2012d) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat hazards 63(2):965–996
66. Pourghasemi HR, Moradi HR, Fatemi Aghda SM, Gokceoglu C, Pradhan B (2013a) GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi criteria evaluation models (North of Tehran, Iran). Arab J Geosci. doi:10.1007/s12517-012-0825-x
67. Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2013b) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69(1):749–779
68. Pourtaghi ZS, Pourghasemi HR (2014) GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeol J 22(3):643–662. doi:10.1007/s10040-013-1089-6
69. Pradhan B, Suliman MDHB, Awang MAB (2007) Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS). Disaster Prev Manage 16(3):344–352
70. Pradhan B, Lee S (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60:1037–1054
71. Pradhan B, Lee S (2010b) Landslide susceptibility assessment and factor effect analysis: back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759
72. Prasad VK, Badarinath KVS, Anuradha E (2008) Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India. J Environ Manage 86:1–13
73. Prosper-Laget V, Douguedroitl A, Guinot JP (1995) Mapping the risk of forest fire occurrence using NOAA satellite information. EAR seL Adv Remote Sens 4(3-Xll):30–38
74. Preisler HK, Brillinger DR, Burgan RE, Benoit JW (2006) Probability based models for estimation of wildfire risk. Int J Wildland Fire 13:133–142
75. Rawat GS (2003) Fire Risk Assessment for forest fire control management in Chilla forest range of Rajaji National Park Uttaranchal (India). MSc Thesis. International Institute for Geo-information Science and Earth Observation Enschede of the Netherlands. p 74
76. Razali SBM (2007) Forest fire hazard rating assessment in peat swamp forest using integrated remote sensing and geographical information system. MSc thesis. University Putra Malaysia. p 52
77. Renard Q, Pelissier R, Ramesh BR, Kodandapani N (2012) Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. Int J Wild land Fire. doi:10.1071/WF10109, p 15
78. Rouse JW Jr, Haas RH, Deering DW, Schell JA, Harlan JC (1974) Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA/GSFC type 3 final report green belt. pp 371
79. Saklani P (2008) Forest fire risk zonation, a case study Pauri Garhwal, Uttarakhand, India. MSc Thesis. International Institute for Geo-information Science and Earth Observation Enschede of the Netherlands and Indian Institute of Remote Sensing (NRSA) Dehradun India. p 71
80. Shadman Roodposhti M, Rahimi S, Jafar Beglou M (2012) PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Nat Hazards. doi:10.1007/s11069-012-0523-8
81. Singh VS (1997) The use of entropy in hydrology and water resource. Hydrol Process 11:587–626
82. Stojanova D, Panov P, Kobler A, Džeroski S, Taškova K (2006) Learning to Predict Forest Fires with Different Data Mining Techniques. In: Proceedings of the 9th International multi conference Information Society IS 2006, 9–3th October 2006, Jožef Stefan Institute, Ljubljana
83. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293
84. Turner AK, Schuster RL (eds) (1996) Landslides: Investigation and mitigation. Special Report 247 Transportation Research Board and National Research Council. National Academy Press, Washington DC, p 673
85. Van Wagner CE (1987) Development and structure of the Canadian Forest Fire Weather Index System. Canadian Forest Serv Ottawa Ontario Forest Tech Rep.35
86. Vasconcelos MJP, Pereira JMC, Zeigler BP (1995) Simulation of fire growth using discrete event hierarchical modular models. EARSeL Adv Remote Sens 4(3):54–62
87. Vasilakos C, Kalabokidis K, Hatzopoulos J, Matsinos I (2009) Identifying wild land fire ignition factors through sensitivity analysis of a neural network. Nat Hazards 50(1):125–143
88. Weise DR, Biging GS (1997) A qualitative comparison of fire spread models incorporating wind and slope effects. Forest Sci 43(2):170–180
89. Whelan RJ (1995) The ecology of fire. Cambridge University Press, Cambridge
90. Wulder MA, Franklin SE (2006) Understanding forest disturbance and spatial pattern: remote sensing and GIS approaches. CRC Press, Boca Raton 246
91. Xiangwei G, Xianyun F, Hongquan X (2011) Forest fire risk zone evaluation based on high spatial resolution RS image in Liangyungang Huaguo Mountain Scenic Spot. In: IEEE International conference on spatial data mining and geographical knowledge services (ICSDM), 2011, June 29 2011–July 1 2011. pp 593–596
92. Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey, Ph.D Thesis. Department of Geomatics the University of Melbourne, pp 423
93. Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat -Turkey). Comput Geosci 35:1125–1138
94. Yufeng S, Fengxiang J (2009) Landslide stability analysis based on generalized information entropy. ESIAT (2):83–85
95. Zhu L, Huang J (2006) GIS-based logistic regression method for landslide susceptibility mapping in regional scale. J Zhejiang Univ Sci A 7:2007–2017