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The Development of a Fire Vulnerability Index for the Mediterranean Region

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  1. University of Rome “La Sapienza” Centro Ricerca Progetto San Marco - CRPSM, Italy The Development of a Fire Vulnerability Index for the Mediterranean Region G. Laneve, M. Jahjah, F. Ferrucci1, F. Batazza2 Munzer JAHJAH munzer.jahjah@uniroma1.it University of Rome “La Sapienza” 1 Università della Calabria, Department of Earth Sciences, Rends (CS), Italy 2 Agenzia Spaziale Italiana, Rome, Italia 2011 IEEE International Geoscience and Remote Sensing Symposium

  2. Outline • Objectives • SIGRI, Fire risk indices • Data specification • Methodology • Results • Conclusion 2011 IEEE International Geoscience and Remote Sensing Symposium

  3. Objectives 1 To develop a daily Fire Risk Index with the objective of showing the total risk level for the area of interest and the zones of major concern within such area. • 2 To develop maps able to show the fire risk considering the tight relationship between fire and: • fuel characteristics (vegetation type, density, humidity content); • topography (slope, altitude, solar aspect angle); • meteorological conditions (rainfall, wind direction and speed, air humidity, surface and air temperature). 3 Comparing the daily computed indices with the fire distribution obtained by using a fire detection algorithm based on SEVIRI/MSG images. 2011 IEEE International Geoscience and Remote Sensing Symposium

  4. SIGRI, Fire Risk Indices ‘1’ The SIGRI pilot project, funded by ASI, aims at developing an Integrated System for the Management of the Wild Fire Events. The system should provide satellite based products capable to help fire contrasting activities during all phases: prevision, detection, and damage assessment/recovering. The fact that 90% of fires is of human origin does not diminish the interest of the fire risk index, which however gives an assessment of the possibility of its spread and possible associated risks. 2011 IEEE International Geoscience and Remote Sensing Symposium

  5. SIGRI, Fire risk Indices ‘2’ Methods to estimate fire risk 1- Statistical or Structural (long-term fire risk index) Methodsdefining forecast models based on the utilization of slowly changing parameters, like topography or other variables that can be considered constant along the year and statistical information on the frequency of the phenomenon. 2- Dynamical Methods (short-term fire risk index) based on data measured continuously (i.e. daily), on characteristics territorial data (orography and vegetation) and on forecast models of the meteorological parameters 2011 IEEE International Geoscience and Remote Sensing Symposium

  6. SIGRI, Fire risk Indices ‘3’ The short-term fire indices are able to provide information on the danger of the event defining: areas of possible ignition, propagation direction and speed, irradiated energy, etc. This index represents: Ignition probability Forest fire propagation Daily level of risk definition By combining this daily fire risk index with that information typical of the Likely Probability Index (infrastructures, protected areas, etc.), we can compute the Fire Vulnerability Index that would be one of the products provided by the SIGRI project. 2011 IEEE International Geoscience and Remote Sensing Symposium

  7. Methodology ‘1’ FPI Burgan 1998 Extinction moisture FPI Burgan 2000 Died vegetation Relative greenness JRC To take into account the effect of solar illumination in determining the existing humidity in the died vegetation Evapotranspiration On going activity Changes in water content in plants tissues have a large effect on leave reflectance To improve the performance in the Mediterranean area Vegetation water content 2011 IEEE International Geoscience and Remote Sensing Symposium

  8. Methodology ‘2’ 16/16 20/18 17/19 11/12 19/20 9/10 15/15 7/7 6/12 18/11 14/14 13/6 5/8 11/13 8/5 4/4 10/9 1/3 2/2 3/1 2011 IEEE International Geoscience and Remote Sensing Symposium

  9. Methodology ‘3’ Daily NDVI 2006 2010 Min Min Min (7/8) RG NDVI 16 days LLFM Max (3/4) Max Max DLFM MXD Green veg. fraction Green veg. fraction linked to fuel type LL Dead veg. fraction Dead veg. fraction linked to fuel type Min humidity DL Dead veg. ext. moisture MX TNF Temp/humid EMC FM Evapotranspiration Relative greenness Fuel type Different fuel type have different fuel loads, we have 12 categories (Calabria). Different weight factors were chosen according to live and dead fuel loads for each fuel type Corine Fuel Min hum FPI Fire prob. index FM is a very important parameter for FPI, representative for MC. FM was calculated Empirically considering three intervals: H <10, 50> H>10, H >50 Ten hour lag fuel moisture Fraction of ten hour lag fuels moisture 2011 IEEE International Geoscience and Remote Sensing Symposium

  10. Methodology ‘4’ Y/m/15d T&H Solar incident Radiation Evapotranspiration Thornthwaite Hargreaves Penman-Monteith On going activity duration of the illumination time of the average elevation Day/30’’ Sun declination Aspect DEM Local sun elevation Sun elev. at H / sun Az Slope Sunset local time Potential sunshine Period x day Solar radiation TOA 2011 IEEE International Geoscience and Remote Sensing Symposium

  11. Methodology ‘5’ Risk map 1 1=0-20 2=20-40 3=40-50 4=50-65 5 >65 Risk map 2 Av of ET4 Std5 of ET Av of ET5 Std4 of ET IF Risk map 1= 4 AND ET >Av+std4 Class 4 =class 4 +1 IF Risk map 1= 5 AND ET >Av+std5 Class 5 =class 5 +1 Differences= Risk map 1 - Risk map 2 + values - value 2011 IEEE International Geoscience and Remote Sensing Symposium

  12. Results ‘1’ July 2011 IEEE International Geoscience and Remote Sensing Symposium

  13. Results ‘2’ 2011 IEEE International Geoscience and Remote Sensing Symposium

  14. Results ‘3’ 2011 IEEE International Geoscience and Remote Sensing Symposium

  15. Results ‘4’ pre post 2011 IEEE International Geoscience and Remote Sensing Symposium

  16. Results ‘5’ Performance of pixels with value FPI= 5 Performance of pixels with value FPI= 4 Fires Fires FPI= 4 FPI= 5 Performance of pixels with value FPI= 2 Performance of pixels with value FPI= 3 Fires Fires FPI= 2 FPI= 4 FPI=5 give an indication on the effective risk of fires in the area 2011 IEEE International Geoscience and Remote Sensing Symposium

  17. Conclusion ‘1’ 1- The FPI was computed using the three methods (Burgan 2000 was adopted). The index was tested in the Calabria Region using fire hot spot which were obtained by SFIDE algorithm; 2- The FPI index was improved by introducing the Evapotranspiration parameter; 2011 IEEE International Geoscience and Remote Sensing Symposium

  18. Conclusion ‘2’ 3- The FPI index range < 30, as expected, shows no correlation with the number of hotspots, while FPI > 55 clearly increases with the increase of the fire occurrences; 4- The objective is to compute the FPI index for 5 years (2006-2010) in order to evaluate the performance including other parameters like EWT; 2011 IEEE International Geoscience and Remote Sensing Symposium

  19. Questions & Comments Thank You! 2011 IEEE International Geoscience and Remote Sensing Symposium