the development of a fire vulnerability index for the mediterranean region

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. Ferrucci1, F. Batazza2

Munzer [email protected]

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


Outline


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’


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.


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’


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’


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

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Methodology ‘2’


Methodology ‘3’


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’


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


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

Differences= Risk map 1 - Risk map 2- value + values

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1 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221 232 243 254 265 276 287 298 309 320 331 342 353 364 375 386 397 408

Post FPIPre FPIDiff

July

Results ‘1’


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Results ‘2’


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Results ‘3’

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Results ‘4’

Results ‘5’


FPI=5 give an indication on the effective risk of fires in the area

Performance of pixels with value FPI= 5

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Performance of pixels with value FPI= 4

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;


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;

Questions & Comments


the development of a fire vulnerability index for the mediterranean region

Documents

risk index methods

vulnerability index

remote sensing symposiumby

remote sensing symposiumf

total risk level

sigri project

daily computed indices

statistical information