The Development of a Fire Vulnerability Index for the Mediterranean Region University of Rome “La Sapienza” Centro Ricerca Progetto San Marco - CRPSM, Italy University of Rome “La Sapienza” G. Laneve, M. Jahjah , F. Ferrucci 1 , F. Batazza 2 Munzer JAHJAH [email protected]2011 IEEE International Geoscience and Remote Sensing Symposium 1 Università della Calabria, Department of Earth Sciences, Rends (CS), Italy 2 Agenzia Spaziale Italiana, Rome, Italia
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The Development of a Fire Vulnerability Index for the Mediterranean Region
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. Ferrucci 1 , F. Batazza 2 Munzer JAHJAH munzer.jahjah @ uniroma1.it. University of Rome “La Sapienza”. - PowerPoint PPT Presentation
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The Development of a Fire Vulnerability Index for the Mediterranean Region
University of Rome “La Sapienza”Centro Ricerca Progetto San Marco - CRPSM, Italy
2011 IEEE International Geoscience and Remote Sensing Symposium
1 Università della Calabria, Department of Earth Sciences, Rends (CS), Italy2 Agenzia Spaziale Italiana, Rome, Italia
Conclusion
Results
Data specification
SIGRI, Fire risk indices
Objectives
Methodology
2011 IEEE International Geoscience and Remote Sensing Symposium
Outline
2011 IEEE International Geoscience and Remote Sensing Symposium
Objectives1 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.
SIGRI, Fire Risk Indices ‘1’
2011 IEEE International Geoscience and Remote Sensing Symposium
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
1- Statistical or Structural (long-term fire risk index) Methods defining 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.
SIGRI, Fire risk Indices ‘2’Methods to estimate fire risk
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
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:
SIGRI, Fire risk Indices ‘3’
2011 IEEE International Geoscience and Remote Sensing Symposium
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.
Ignition probability
Forest fire propagation Daily level of risk definition
Methodology ‘1’
2011 IEEE International Geoscience and Remote Sensing Symposium
FPI Burgan 1998
FPI Burgan 2000
JRC
Died vegetationExtinction moisture
Relative greenness
EvapotranspirationTo take into account the effect of solar illumination indetermining the existing humidity in the died vegetation
Vegetation water contentTo improve the performancein the Mediterranean area
Changes in water content in plants tissues have a large effect on leave reflectanceOn going activity
1/3
9/10
11/1219/20
16/16
6/12
5/8
4/410/9
18/11
13/6
8/5
17/1920/18
15/157/7
14/14
11/13
3/1
2/2
Methodology ‘2’
2011 IEEE International Geoscience and Remote Sensing Symposium
Methodology ‘3’
2011 IEEE International Geoscience and Remote Sensing Symposium
Temp/humid EMC FM TNF
FPIMX
DL
LL
NDVI 16 days
Min (7/8)
Max (3/4)
MinMin
MaxMax
2006 2010Evapotranspiration
Fire prob. index
Relative greenness
Daily NDVI
RG
Corine
Fueltype
Fuel Minhum
Ten hour lag fuel moistureFraction of ten hour lag fuels moisture
Green veg. fraction
Dead veg. fractionMin humidity
LLFM
DLFM
MXDDead veg. fraction linked to fuel type
Green veg. fraction linked to fuel type
Dead veg. ext. moisture
Different fuel type have different fuel loads, we have 12 categories (Calabria). Differentweight factors were chosen according to live and dead fuel loads for each fuel type
FM is a very important parameter for FPI, representative for MC. FM was calculatedEmpirically considering three intervals: H <10, 50> H>10, H >50
Methodology ‘4’
2011 IEEE International Geoscience and Remote Sensing Symposium
DEMAspect
Slope
Sun declination
Day/30’’
Sun elev. at H / sun Az
duration of the illumination time of the average elevation
Sunset local time
Potential sunshinePeriod x day
Solar radiation TOA
Local sun elevation
Evapotranspiration
T&H Solar incidentRadiation
Y/m/15d
Hargreaves Thornthwaite Penman-Monteith
On going activity
2011 IEEE International Geoscience and Remote Sensing Symposium
Methodology ‘5’Risk map 1
1=0-20 2=20-40 3=40-50 4=50-65 5 >65
Std4 of ET Std5 of ET
Risk map 2
Av of ET4 Av of ET5
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
Performance of pixels with value FPI= 3 Performance of pixels with value FPI= 2
FPI= 4
Fires
FPI= 5
Fires
FPI= 4
FiresFPI= 2Fires
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
Conclusion ‘2’
2011 IEEE International Geoscience and Remote Sensing Symposium
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;
Questions & Comments
2011 IEEE International Geoscience and Remote Sensing Symposium