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

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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

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