Modelling static fire hazard in a semi-arid region using frequency analysis
Hamed Adab A , Kasturi Devi Kanniah B E , Karim Solaimani C and Roselina Sallehuddin D
+ Author Affiliations
- Author Affiliations
A Department of Physical Geography, Faculty of Geography and Environmental Science, Hakim Sabzevari University, Sabzevar, Khorasan Razavi 9617976487, Iran.
B Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia.
C GIS Center, Sari Agriculture and Natural Resources University, Sari, Mazandaran 4817844718, Iran.
D Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia.
E Corresponding author. Email: kasturi@utm.my
International Journal of Wildland Fire 24(6) 763-777 https://doi.org/10.1071/WF13113
Submitted: 16 July 2013 Accepted: 8 April 2015 Published: 15 June 2015
Abstract
Various fire hazard rating systems have been used by many countries at strategic and tactical levels for fire prevention and fire safety programs. Assigning subjective weight to parameters that cause fire hazard has been widely used to model wildland fire hazard. However, these methods are sensitive to experts’ judgements because they are independent of any statistical approaches. Therefore, in the present study, we propose a wildland fire hazard method based on frequency analysis (i.e. a probability distribution model) to identify the locations of fire hazard in north-eastern Iran, which has frequent fire. The proposed methodology uses factors that do not change or change very slowly over time to identify static fire hazard areas, such as vegetation moisture, slope, aspect, elevation, distance from roads and proximity to settlements, as essential parameters. Several probability distributions are assigned to each factor to show the possibility of fire using non-linear regressions. The results show that approximately 86% of MODerate-resolution Imaging Spectroradiometer (MODIS) hot spot data are located truly in the high fire hazard areas as identified in the present study and the most significant contributing factor to fire in Golestan Province, Iran, is elevation. The present study also reveals that approximately 14% of the total study area (~20 368 km2) has a fire hazard of 66%, which can be considered very high. Therefore, this area – located mostly in the central, west and north-east regions of Golestan Province – should be considered for an effective conservation strategy of wildland fire.
Additional keywords: forest, probabilistic model, remote sensing.
References
Adab H, Kanniah K, Solaimani K (2013) Modelling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards 65, 1723–1743.| Modelling forest fire risk in the northeast of Iran using remote sensing and GIS techniques.Crossref | GoogleScholarGoogle Scholar |
Allard GB (2003) Fire situation in the Islamic Republic of Iran. International Forest Fire News (IFFN) 28, 88–91. [Verified 6 May 2015]http://www.fire.uni-freiburg.de/iffn/iffn_28/Iran.pdf
Ardakani AS, Zoej MJV, Mohammadzadeh A, Mansourian A (2011) Spatial and temporal analysis of fires detected by MODIS data in Northern Iran from 2001 to 2008. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4, 216–225.
| Spatial and temporal analysis of fires detected by MODIS data in Northern Iran from 2001 to 2008.Crossref | GoogleScholarGoogle Scholar |
Avila-Flores D, Pompa-Garcia M, Antonio-Nemiga X, Rodriguez-Trejo D, Vargas-Perez E, Santillan-Perez J (2010) Driving factors for forest fire occurrence in Durango State of Mexico: a geospatial perspective. Chinese Geographical Science 20, 491–497.
| Driving factors for forest fire occurrence in Durango State of Mexico: a geospatial perspective.Crossref | GoogleScholarGoogle Scholar |
Badia A, Serra P, Modugno S (2011) Identifying dynamics of fire ignition probabilities in two representative Mediterranean wildland–urban interface areas. Applied Geography (Sevenoaks, England) 31, 930–940.
| Identifying dynamics of fire ignition probabilities in two representative Mediterranean wildland–urban interface areas.Crossref | GoogleScholarGoogle Scholar |
Badia-Perpinyà A, Pallares-Barbera M (2006) Spatial distribution of ignitions in Mediterranean periurban and rural areas: the case of Catalonia. International Journal of Wildland Fire 15, 187–196.
| Spatial distribution of ignitions in Mediterranean periurban and rural areas: the case of Catalonia.Crossref | GoogleScholarGoogle Scholar |
Bali A, Tohidi M (2008) Role of monitoring and conservation management in Hyrcanian forests biodiversity. In ‘Mainstreaming Biodiversity Issues into Forestry and Agriculture: Abstracts of Poster Presentations at the 13th Meeting of the Subsidiary Body on Scientific, Technical and Technological Advice of the Convention on Biological Diversity’, 18–22 February 2008, Rome, Italy. (Eds L. Janishevski and K. J. Mulongoy) CBD Secretariat of the Convention on Biological Diversity, CBD Technical Series No. 34, pp. 112–114. (Rome, Italy.)
Beringer J, Hacker J, Hutley LB, Leuning R, Arndt SK, Amiri R, Bannehr L, Cernusak LA, Grover S, Hensley C, Hocking D, Isaac P, Jamali H, Kanniah K, Livesley S, Neininger B, U KTP, Sea W, Straten D, Tapper N, Weinmann R, Wood S, Zegelin S (2011) SPECIAL: savanna patterns of energy and carbon integrated across the landscape. Bulletin of the American Meteorological Society 92, 1467–1485.
| SPECIAL: savanna patterns of energy and carbon integrated across the landscape.Crossref | GoogleScholarGoogle Scholar |
Beringer J, Hutley LB, Abramson D, Arndt SK, Briggs P, Bristow M, Canadell JG, Cernusak LA, Eamus D, Edwards AC, Evans BJ, Fest B, Goergen K, Grover SP, Hacker J, Haverd V, Kanniah K, Livesley SJ, Lynch A, Maier S, Moore C, Raupach M, Russell-Smith J, Scheiter S, Tapper NJ, Uotila P (2015) Fire in Australian savannas: from leaf to landscape. Global Change Biology 21, 62–81.
| Fire in Australian savannas: from leaf to landscape.Crossref | GoogleScholarGoogle Scholar | 25044767PubMed |
Bowman DMJS, Balch JK, Artaxo P, Bond WJ, Carlson JM, Cochrane MA, D’Antonio CM, DeFries RS, Doyle JC, Harrison SP, Johnston FH, Keeley JE, Krawchuk MA, Kull CA, Marston JB, Moritz MA, Prentice IC, Roos CI, Scott AC, Swetnam TW, van der Werf GR, Pyne SJ (2009) Fire in the Earth system. Science 324, 481–484.
| Fire in the Earth system.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXkvVGmtb8%3D&md5=acf0f16875fe2576863ea8a4f6d043c7CAS |
Bowman DM, Balch J, Artaxo P, Bond WJ, Cochrane MA, D’Antonio CM, DeFries R, Johnston FH, Keeley JE, Krawchuk MA (2011) The human dimension of fire regimes on Earth. Journal of Biogeography 38, 2223–2236.
| The human dimension of fire regimes on Earth.Crossref | GoogleScholarGoogle Scholar | 22279247PubMed |
Catry FX, Rego FC, Bação FL, Moreira F (2009) Modelling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire 18, 921–931.
| Modelling and mapping wildfire ignition risk in Portugal.Crossref | GoogleScholarGoogle Scholar |
Certini G (2005) Effects of fire on properties of forest soils: a review. Oecologia 143, 1–10.
| Effects of fire on properties of forest soils: a review.Crossref | GoogleScholarGoogle Scholar | 15688212PubMed |
Chuvieco E, Salas FG, Vega C (1997) Remote sensing and G.I.S. for long-term fire risk mapping. In ‘A ‘Review of remote sensing methods for the study of large wildland fires: megafires project ENC-CT96-0256’. (Ed. E Chuvieco.) pp. 91–108. (Universidad de Alcalá, Departamento de Geografía: Alcalá de Henares, Spain.)
Conese C, Bonora L, Romani M, Checcacci E, Tesi E (2005) Forest fire hazard model definition for local land user (Tuscany Region). In ‘Geophysical Research Abstracts, Vol. 7, 06804’, European Geosciences Union. (Munich, Germany)
Courtney Mustaphi CJ, Pisaric MFJ (2013) Varying influence of climate and aspect as controls of montane forest fire regimes during the late Holocene, south-eastern British Columbia, Canada. Journal of Biogeography 40, 1983–1996.
| Varying influence of climate and aspect as controls of montane forest fire regimes during the late Holocene, south-eastern British Columbia, Canada.Crossref | GoogleScholarGoogle Scholar |
Davies DK, Ilavajhala S, Min Minnie W, Justice CO (2009) Fire information for resource management system: archiving and distributing MODIS active fire data. IEEE Transactions on Geoscience and Remote Sensing 47, 72–79.
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’, 8–11 May, 2006. Available at http://postel.obs-mip.fr/IMG/pdf/RAQRS2006_S9.1_Globcover_Bicheron.pdf [Verified 6 May 2015]
Dimuccio LA, Ferreira R, Cunha L, Campar de Almeida A (2011) Regional forest-fire susceptibility analysis in central Portugal using a probabilistic ratings procedure and artificial neural network weights assignment. International Journal of Wildland Fire 20, 776–791.
Eskandari S, Ghadikolaei JO, Jalilvand H, Saradjian MR (2013) Evaluation of reliability of MODIS fire product in detection of active fires in northern forests of Iran. World Applied Sciences Journal 27, 1065–1070.
Eva H, Lambin EF (2000) Fires and land-cover change in the tropics:a remote sensing analysis at the landscape scale. Journal of Biogeography 27, 765–776.
| Fires and land-cover change in the tropics:a remote sensing analysis at the landscape scale.Crossref | GoogleScholarGoogle Scholar |
Food and Agriculture Organization of the United Nations (2009) ‘Forest fires and the law, a guide for national drafters based on the fire management voluntary guidelines.’ (FAO/UN The Forest Resources Assessment Programme: Rome.)
Farzipour B (2011) Climate change: impacts on forest fires in Iran. Available from https://behzzad.files.wordpress.com/2011/04/jpg-14011.pdf [Verified 6 May 2015]
Federal Emergency Management Agency (2002) Guidelines and specifications for flood hazard mapping partners, Appendix D: guidance for coastal flooding analyses and mapping. Available from https://www.fema.gov/media-library/assets/documents/13948 [Verified 6 May 2015]
Fernandes R, Geeven G, Soetens S, Klontza-Jaklova V (2011) Deletion/substitution/addition (DSA) model selection algorithm applied to the study of archaeological settlement patterning. Journal of Archaeological Science 38, 2293–2300.
| Deletion/substitution/addition (DSA) model selection algorithm applied to the study of archaeological settlement patterning.Crossref | GoogleScholarGoogle Scholar |
Forests Range and Watershed Management Organization of Iran (FRWOI) (2005) Statistical report of forest. [in Persian] Available at: http://www.frw.org.ir/ [Verified 1 January 2011]
Gilleland E, Ribatet M, Stephenson AG (2013) A software review for extreme value analysis. Extremes 16, 103–119.
| A software review for extreme value analysis.Crossref | GoogleScholarGoogle Scholar |
Giovanni L, Jahjah M, Fabrizio F, Fabrizio B (2011) The development of a fire vulnerability index for the Mediterranean region. Available at http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6050145 [Verified 6 May 2015]
GLOBCOVER (2009) ‘Global land cover map.’ Available at http://due.esrin.esa.int/page_globcover.php [Verified 30 April 2011]
Graña M, Duro RJ (2008) ‘Computational intelligence for remote sensing.’ (Springer: San Sebastian, Spain)
Grubbs FE (1950) Sample criteria for testing outlying observations. Annals of Mathematical Statistics 21, 27–58.
Hall PG, Hyndman RJ (2003) Improved methods for bandwidth selection when estimating ROC curves. Statistics & Probability Letters 64, 181–189.
| Improved methods for bandwidth selection when estimating ROC curves.Crossref | GoogleScholarGoogle Scholar |
Hashim M, Kanniah KD, Ahmad S, Rasib AW, Ibrahim AL (2004) The use of AVHRR data to determine the concentration of visible and invisible tropospheric pollutants originating from a 1997 forest fire in South East Asia. International Journal of Remote Sensing 25, 4781–4794.
| The use of AVHRR data to determine the concentration of visible and invisible tropospheric pollutants originating from a 1997 forest fire in South East Asia.Crossref | GoogleScholarGoogle Scholar |
Hernandez-Leal PA, Arbelo M, Gonzalez-Calvo A (2006) Fire risk assessment using satellite data. Advances in Space Research 37, 741–746.
| Fire risk assessment using satellite data.Crossref | GoogleScholarGoogle Scholar |
Holden ZA, Jolly WM (2011) Modelling topographic influences on fuel moisture and fire danger in complex terrain to improve wildland fire management decision support. Forest Ecology and Management 262, 2133–2141.
| Modelling topographic influences on fuel moisture and fire danger in complex terrain to improve wildland fire management decision support.Crossref | GoogleScholarGoogle Scholar |
Jahdi R, Salis M, Darvishsefat AA, Alcasena Urdiroz FJ, Etemad V, Mostafavi MA, Lozano OM, Spano D (2014) Calibration of FARSITE fire area simulator in Iranian northern forests. Natural Hazards and Earth System Sciences 2, 6201–6240.
| Calibration of FARSITE fire area simulator in Iranian northern forests.Crossref | GoogleScholarGoogle Scholar |
Kanniah KD, Lim HQ, Kaskaoutis DG, Cracknell AP (2014) Investigating aerosol properties in Peninsular Malaysia via the synergy of satellite remote sensing and ground-based measurements. Atmospheric Research 138, 223–239.
| Investigating aerosol properties in Peninsular Malaysia via the synergy of satellite remote sensing and ground-based measurements.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXjtVGhur8%3D&md5=a5e7118d3842ebcea6446273fc74bd85CAS |
Keane RE, Drury SA, Karau EC, Hessburg PF, Reynolds KM (2010) A method for mapping fire hazard and risk across multiple scales and its application in fire management. Ecological Modelling 221, 2–18.
| A method for mapping fire hazard and risk across multiple scales and its application in fire management.Crossref | GoogleScholarGoogle Scholar |
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. Forest Ecology and Management 120, 157–169.
| Farmers’ perspectives on slash-and-burn as a land clearing method for small-scale rubber producers in Sepunggur, Jambi Province, Sumatra, Indonesia.Crossref | GoogleScholarGoogle Scholar |
Killip S, Mahfoud Z, Pearce K (2004) What is an intracluster correlation coefficient? Crucial concepts for primary care researchers. Annals of Family Medicine 2, 204–208.
| What is an intracluster correlation coefficient? Crucial concepts for primary care researchers.Crossref | GoogleScholarGoogle Scholar | 15209195PubMed |
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.
| Impact of human population density on fire frequency at the global scale.Crossref | GoogleScholarGoogle Scholar |
Kotz S, Nadarajah S (2004) Kotz-type distribution. In ‘Encyclopedia of statistical sciences.’ (John Wiley & Sons: Washington, DC)
Koutsias N, Allgöwer B, Conedera M, Viegas D (2002) What is common in wildland fire occurrence in Greece and Switzerland: statistics to study fire occurrence pattern. In ‘Forest fire research and wildland fire safety: proceedings of IV International Conference on Forest Fire Research 2002 Wildland Fire Safety Summit’, 18–23 November 2002, Coimbra, Portugal. (Ed. D. X. Viegas.) p. 15. (Millpress Science Publishers: Luso, Portugal)
Lashkari M, Nouri Ganbalani G, Mozaffarian F, Ghorbani K, Fathi A (2009) Faunistic study of planthoppers infraorder Fulgoromorpha (Hem., Auchenorrhyncha) in different climatic regions of Gorgan, Iran. Journal of Entomological Research 1, 119–133.
Linn R, Winterkamp J, Edminster C, Colman JJ, Smith WS (2007) Coupled influences of topography and wind on wildland fire behaviour. International Journal of Wildland Fire 16, 183–195.
| Coupled influences of topography and wind on wildland fire behaviour.Crossref | GoogleScholarGoogle Scholar |
Maingi JK, Henry MC (2007) Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA. International Journal of Wildland Fire 16, 23–33.
| Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA.Crossref | GoogleScholarGoogle Scholar |
Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics 11, 431–441.
| An algorithm for least-squares estimation of nonlinear parameters.Crossref | GoogleScholarGoogle Scholar |
Merlo M, Rojas Briales E (2000) Public goods and externalities linked to Mediterranean forests: economic nature and policy. Land Use Policy 17, 197–208.
| Public goods and externalities linked to Mediterranean forests: economic nature and policy.Crossref | GoogleScholarGoogle Scholar |
Munn I, Zhai Y, Evans DL (2003) Modeling forest fire probabilities in the south central United States using FIA data. Southern Journal of Applied Forestry 27, 11–17.
Narayanaraj G, Wimberly MC (2011) Influences of forest roads on the spatial pattern of wildfire boundaries. International Journal of Wildland Fire 20, 792–803.
| Influences of forest roads on the spatial pattern of wildfire boundaries.Crossref | GoogleScholarGoogle Scholar |
Noroozi J, Akhani H, Breckle S-W (2008) Biodiversity and phytogeography of the alpine flora of Iran. Biodiversity and Conservation 17, 493–521.
| Biodiversity and phytogeography of the alpine flora of Iran.Crossref | GoogleScholarGoogle Scholar |
Nunes AN (2012) Regional variability and driving forces behind forest fires in Portugal an overview of the last three decades (1980–2009). Applied Geography (Sevenoaks, England) 34, 576–586.
| Regional variability and driving forces behind forest fires in Portugal an overview of the last three decades (1980–2009).Crossref | GoogleScholarGoogle Scholar |
Olson DL, Agee JK (2005) Historical fires in Douglas-fir dominated riparian forests of the southern Cascades, Oregon. Fire Ecology 1, 50–74.
| Historical fires in Douglas-fir dominated riparian forests of the southern Cascades, Oregon.Crossref | GoogleScholarGoogle Scholar |
Park S, Jeon S, Kim S, Choi C (2011) Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea. Landscape and Urban Planning 99, 104–114.
| Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea.Crossref | GoogleScholarGoogle Scholar |
Pausas JG, Llovet J, Rodrigo A, Vallejo R (2008) Are wildfires a disaster in the Mediterranean basin? A review. International Journal of Wildland Fire 17, 713–723.
| Are wildfires a disaster in the Mediterranean basin? A review.Crossref | GoogleScholarGoogle Scholar |
Pelizzari A, Goncalves R, Caetano M (2008) Information extraction for forest fires management. In ‘Computational intelligence for remote sensing’. Eds M Graña, R Duro) Vol. 133, pp. 295–312. (Springer: Berlin, Heidelberg.)
Peterson AT, Papeş M, Soberón J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modelling. Ecological Modelling 213, 63–72.
Pfeifer M, Disney M, Quaife T, Marchant R (2012) Terrestrial ecosystems from space: a review of Earth observation products for macroecology applications. Global Ecology and Biogeography 21, 603–624.
| Terrestrial ecosystems from space: a review of Earth observation products for macroecology applications.Crossref | GoogleScholarGoogle Scholar |
Pyne SJ, Andrews PL, Laven RD (1996) ‘Introduction to wildland fire.’ (John Wiley & Sons: New York, NY)
Quah E, Johnston D (2001) Forest fires and environmental haze in Southeast Asia: using the ‘stakeholder’ approach to assign costs and responsibilities. Journal of Environmental Management 63, 181–191.
Ray S, Turi RH (1999) Determination of number of clusters in k-means clustering and application in colour image segmentation. In ‘Proceedings of the 4th international conference on advances in pattern recognition and digital techniques’, 27–29 December, Calcutta, India. (Eds N. R. Pal, A. K. De, J. Das) pp. 137–143. (Narosa Publishing House: New Delhi, India)
Reddy CK, Vinzamuri B (2013) A survey of partitional and hierarchical clustering algorithms. In ‘Data clustering: algorithms and applications.’ (Eds CC Aggarwal, CK Reddy.) pp. 87–107. (Chapman and Hall/CRC: Boca Raton, FL)
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). Landscape Ecology 23, 341–354.
Sadasivuni R, Cooke WH, Bhushan S (2013) Wildfire risk prediction in southeastern Mississippi using population interaction. Ecological Modelling 251, 297–306.
| Wildfire risk prediction in southeastern Mississippi using population interaction.Crossref | GoogleScholarGoogle Scholar |
Sader SA, Jin S (2006) Feasibility and accuracy of MODIS 250m imagery for forest disturbance monitoring. In ‘ASPRS Annual Conference: Prospecting for Geospatial Information Ingegration’, 1–5 May 2006, Reno, Nevada. Available at http://www.asprs.org/a/publications/proceedings/reno2006/0131.pdf [Verified 6 May 2015]
Setiawan I, Mahmud AR, Mansor S, Mohamed Shariff AR, Nuruddin AA (2004) GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia. Disaster Prevention and Management 13, 379–386.
| GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia.Crossref | GoogleScholarGoogle Scholar |
Shafiei AB, Akbarinia M, Jalali G, Hosseini M (2010) Forest fire effects in beech dominated mountain forest of Iran. Forest Ecology and Management 259, 2191–2196.
| Forest fire effects in beech dominated mountain forest of Iran.Crossref | GoogleScholarGoogle Scholar |
Shakesby RA, Boakes DJ, Coelho C, Gonçalves AJB, Walsh RPD (1996) Limiting the soil degradational impacts of wildfire in pine and eucalyptus forests in Portugal: a comparison of alternative post-fire management practices. Applied Geography 16, 337–355.
| Limiting the soil degradational impacts of wildfire in pine and eucalyptus forests in Portugal: a comparison of alternative post-fire management practices.Crossref | GoogleScholarGoogle Scholar |
Sharifi Hashjin S, Hoseinpoor Milaghardan A, Esmaeily A, Mojaradi B, Naseri F (2012) Forest fire hazard modelling using hybrid AHP and fuzzy AHP methods using MODIS sensor. In ‘Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International’, 22–27 July 2012, Munich, Germany. Available at http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6351403 [Verified 6 May 2015]
Snyder RL, Spano D, Duce P, Baldocchi D, Xu L, Paw UKT (2006) A fuel dryness index for grassland fire-danger assessment. Agricultural and Forest Meteorology 139, 1–11.
| A fuel dryness index for grassland fire-danger assessment.Crossref | GoogleScholarGoogle Scholar |
Stephenson A, Gilleland E (2005) Software for the analysis of extreme events: the current state and future directions. Extremes 8, 87–109.
| Software for the analysis of extreme events: the current state and future directions.Crossref | GoogleScholarGoogle Scholar |
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. International Journal of Wildland Fire 17, 602–613.
| Predicting spatial patterns of fire on a southern California landscape.Crossref | GoogleScholarGoogle Scholar |
Tachikawa T, Hato M, Kaku M, Iwasaki A (2011) Characteristics of ASTER GDEM version 2, Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International, 24–29 July 2011, Canada.
Taylor AH (2000) Fire regimes and forest changes in mid and upper montane forests of the southern Cascades, Lassen Volcanic National Park, California, U.S.A. Journal of Biogeography 27, 87–104.
| Fire regimes and forest changes in mid and upper montane forests of the southern Cascades, Lassen Volcanic National Park, California, U.S.A.Crossref | GoogleScholarGoogle Scholar |
Vadrevu K, Eaturu A, Badarinath KVS (2010) Fire risk evaluation using multicriteria analysis: a case study. Environmental Monitoring and Assessment 166, 223–239.
| Fire risk evaluation using multicriteria analysis: a case study.Crossref | GoogleScholarGoogle Scholar | 19472063PubMed |
Vasilakos C, Kalabokidis K, Hatzopoulos J, Kallos G, Matsinos Y (2007) Integrating new methods and tools in fire danger rating. International Journal of Wildland Fire 16, 306–316.
| Integrating new methods and tools in fire danger rating.Crossref | GoogleScholarGoogle Scholar |
Vélez R (2009) The causing factors: a focus on economic and social driving forces. In ‘Living with wildfires: what science can tell us’. European Forest Institute Discussion Paper 15 (Ed. Y. Birot) pp. 21–25. (European Forest Institute: Joensuu, Finland
Vohora VK, Sykas D, Vafiadis G, Moulos V, Baglatzi A (2013) Integrating GIS and remote sensing techniques for studying forest fires. In ‘34th Asian conference on remote sensing (ACRS 2013)’, 20–24 October 2013, Bali, Indonesia. (Ed. G. H. Pramono) pp. 2–7. (Asian Association on Remote Sensing (AARS): Tokyo, Japan)
Wang S, Zhou Y, Wang L, Zhang P (2003) A research on fire automatic recognition using MODIS data. Available at http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1294489 [Verified 5 May 2015]
Wilcoxon F, Katti S, Wilcox RA (1963) ‘Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test.’ (American Cyanamid Company: Columbia, MO)
Wilson EH, Sader SA (2002) Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment 80, 385–396.
Wittenberg L, Malkinson D (2009) Spatio-temporal perspectives of forest fires regimes in a maturing Mediterranean mixed pine landscape. European Journal of Forest Research 128, 297–304.
| Spatio-temporal perspectives of forest fires regimes in a maturing Mediterranean mixed pine landscape.Crossref | GoogleScholarGoogle Scholar |
Wright R, Flynn L, Garbeil H, Harris A, Pilger E (2002) Automated volcanic eruption detection using MODIS. Remote Sensing of Environment 82, 135–155.
| Automated volcanic eruption detection using MODIS.Crossref | GoogleScholarGoogle Scholar |
Yadegarnejad SA, Dylam Jafarabad M, Mohammadi Savadkoohi N (2012) Surface wildfire in temperate forests of the Golestan Province, northern Iran. International Research Journal of Applied and Basic Sciences 3, 2243–2247.
Yang J, He HS, Shifley SR, Gustafson EJ (2007) Spatial patterns of modern period human-caused fire occurrence in the Missouri Ozark Highlands. Forest Science 53, 1–15.
Yoe C (2011). ‘Principles of risk analysis: decision making under uncertainty.’ (CRC Press: Boca Raton, FL)
