Subscribe to our Newsletter and get informed about new publication regulary and special discounts for subscribers!

ILNS > Volume 67 > Estimation of the Meteorological Forest Fire Risk...
< Back to Volume

Estimation of the Meteorological Forest Fire Risk in a Mountainous Region by Using Remote Air Temperature and Relative Humidity Data

Full Text PDF


The occurrence of forest fires is frequent phenomenon in Greece, especially during the warmest period of the year, the summer. Timely and reliable estimation of the meteorological risk for their onset is of crucial importance for their prevention. Thus, the purpose of our current work was firstly the estimation of the values of a suitable relevant index for Greece, meteorological forest fire risk index (MKs,t), derived from actual air temperature (T) and relative humidity data (RH) as well as from regressed T and RH, in a mountainous region (MR) for the most dangerous period of the year (July-August) and day (11:00 h-16:00 h), for five successive years (2006-2010) and secondly the comparison of the two ways of MKs,t values estimation (from actual and regressed T and RH), based on MKs,t classes. Regressed T and RH data were estimated with the aid of simple linear regression models from remote T and RH data, respectively, of an urban region, 175 Km away from MR, taking into account firstly the warmest (2007) and the coldest (2006) year of the examined year period. It was confirmed that MKs,t values (based on regressed T and RH data) coincided in their classification to the respective ones resulted from actual T and RH data, that is, there was absolute success (100%). Using common regression lines and applying them to estimate separately T and RH at MR, for the most dangerous period of year and day concerning the whole examined year period, it was found that almost all the estimated MKs,t values coincided, regarding their classification, with those estimated from actual T and RH data (97% success), which was considered very satisfactory. Therefore, our research methodology contributes a new perspective to a reliable estimation of MKs,t from remote T and RH data using simple statistical models.


International Letters of Natural Sciences (Volume 67)
A. Matsoukis et al., "Estimation of the Meteorological Forest Fire Risk in a Mountainous Region by Using Remote Air Temperature and Relative Humidity Data", International Letters of Natural Sciences, Vol. 67, pp. 1-8, 2018
Online since:
February 2018

[1] C. Tsiouvaras et al., Forests and grazing forest ecosystems of burned regions: Suggestions for raising and restoration, in Fires 2007, From devastation to development, Athens, Greece, 2008, pp.169-190.

[2] C. Kosmas et al., Erosion of soils after fires, measures of handling, in Fires 2007, From devastation to development, Athens, Greece, 2008, pp.125-149.

[3] A. Chronopoulou-Sereli et al., Evaluation of the meteorological danger for the prevention of fires, in Fires 2007, From devastation to development, Athens, Greece, 2008, pp.191-213.

[4] A. Holsten et al., Evaluation of the performance of meteorological forest fire indices for German federal states, Forest Ecology and Management. 287 (2013) 123-131.

[5] A. Chronopoulou-Sereli et al., General and Specific Topics of Bioclimatology, Applications-Exercises, Ziti Publ., Thessaloniki, Greece, (2012).

[6] C. Chandler et al., Fire in Forestry. Volume 1. Forest fire behavior and effects, John Wiley & Sons Inc., New York, (1983).

[7] A. Arpaci et al., A collection of possible fire weather indices (FWI) for alpine landscapes, ALP FFIRS project report, (2010).

[8] B. Sol, Estimation du risque météorologique d'incendies de forêts dans le Sud-est de la France, Revue Forestière Française. XLII (1990) 263-271.

[9] J.-C. Drouet, B. Sol, Mise au point d'un indice numérique de risque météorologique d'incendies de forêts, Forêt Méditerranéenne. 14(2) (1993) 155-162.

[10] A.G. McArthur, Weather and grassland fire behaviour, Department of National Development, Forestry and Timber Bureau Leaflet No. 100, Canberra, Australia, (1966).

[11] A.G. McArthur, Forest fire danger meter, Mk 5, Forest Research Institute, Forestry and Timber Bureau, Canberra, Australia, (1973).

[12] A.G. McArthur, Grassland fire danger meter, Mk 5, Country Fire Authority of Victoria, Melbourne, Australia, (1977).

[13] I.R. Noble, G.A.V. Bary, A.M. Gill, McArthur's fire-danger meters expressed as equations, Australian Journal of Ecology. 5 (1980) 201-203.

[14] C.E. Van Wagner, Development and structure of the Canadian forest fire weather index system, Technical Report 35, Canadian Forestry Service, Canada, (1987).

[15] V. Gouma, A methodology for the spatial-temporal assessment of forest fire meteorogical risk in mountainous areas: application in mountain Parnes, Ph.D. dissertation, Dept. Gen. Sci., Agric. Univ., Athens, Greece, (2001).

[16] M. Zorro Gonçalves, L. Lourenço, Meteorological index of forest fire risk in the Portuguese mainland territory, in: International Conference on Forest Fire Research, Coimbra, Portugal, 1990, pp.1-14.

[17] I. Aguado et al., Estimation of meteorological fire danger indices from multitemporal series of NOAA-AVHRR data, in 3rd International Conference on Forest Fires Research, 14th Conference on Fire and Forest Meteorology, Coimbra, Portugal, 1998, pp.1131-1147.

[18] Y. Liu et al., Analysis of the impact of the forest fires in August 2007 on air quality of Athens using multi-sensor aerosol remote sensing data, meteorology and surface observations, Atmospheric Environment. 43(21) (2009) 3310-3318.

[19] J. Chalatsis, Mountainous Nafpaktia, 2017. Available:

[20] Anonymous, Google Earth, 2017. Available:

[21] K. Chronopoulos et al., An artificial neural network model application for the estimation of thermal comfort conditions in mountainous regions, Greece, Atmósfera. 25(2) (2012) 171-181.

[22] P. Roussos et al., Relations of environmental factors with the phenol content and oxidative enzyme activities of olive explants, Scientia Horticulturae. 113(1) (2007) 100-102.

[23] A. Matsoukis, K. Chronopoulos, Estimating inside air temperature of a glasshouse using statistical models, Current World Environment. 12(1) (2017) 1-5.

[24] G. Feng et al., Two paradoxes in linear regression analysis, Shanghai Archives of Psychiatry. 28(6) (2016) 355-360.

[25] A.C. Rencher, G.B. Schaalje, Linear Models in Statistics, 2nd ed., John Wiley & Sons Inc., Hoboken, New Jersey, (2008).

[26] P.I. Kaltsikes, Simple Experimental Designs, Stamoulis Publ., Athens, Greece, (1997).

[27] J.H. Zar, Biostatistical Analysis, 4th ed., Prentice Hall, Upper Saddle River, New Jersey, (1999).

[28] A. Kamoutsis et al., A comparative study of human thermal comfort conditions in two mountainous regions in Greece during summer, Global Nest Journal. 12(4) (2010) 401-408.

[29] Ø. Hodnebrog et al., Impact of forest fires, biogenic emissions and high temperatures on the elevated Eastern Mediterranean ozone levels during the hot summer of 2007, Atmospheric Chemistry and Physics. 12(18) (2012) 8727-8750.

Show More Hide
Cited By:

[1] N. Badmaev, A. Bazarov, R. Sychev, Predicting, Monitoring, and Assessing Forest Fire Dangers and Risks, p. 183, 2020