Project no. FP6-018505
Project Acronym FIRE PARADOX
Project Title FIRE PARADOX: An Innovative Approach of Integrated Wildland Fire
Management Regulating the Wildfire Problem by the Wise Use of Fire: Solving the
Fire Paradox
Instrument Integrated Project (IP)
Thematic Priority Sustainable development, global change and ecosystems
Deliverable 5.1-1-33
Method to assess with good spatial accuracy the meteorological and
fuel moisture components of the fire risk
Due date of deliverable: Month 33
Actual submission date: Month 34
Start date of project: 1st March 2006
48months
Duration:
Organisation name of lead contractor for this deliverable:
Omikron Ltd (Greece) (P15)
Revision (1000)
Project co-funded by the European Commission within the Sixth Framework Programme
(2002-2006)
Dissemination Level
PU
Public
PP
Restricted to other programme participants (including the Commission Services)
RE
Restricted to a group specified by the consortium (including the Commission Services)
CO
Confidential, only for members of the consortium (including the Commission Services)
D5.1.1-0100
X
1
Authors:
Mantzavelas, Antonis; Apostolopoulou, Iossifina; Lazaridou, Thalia;
Thanassis; Topaloudis, Thanassis (P30: OMIKRON Ltd)
Partozis,
Lampin, Corinne; Borgniet, Laurent; Bouillon, Christophe; Brewer, Simon; Curt,
Thomas; Ganteaume, Anne; Jappiot, Marielle; (P15 : Cemagref)
Defossé, Guillermo; Gómez Fernán, Mariano; Lencinas Daniel, Jose; (P32 CIEFAP)
Scientifique consultant for drought indexes: Ganatsas, Petros (Aristotle
University of Thessaloniki)
D5.1-1-33-1000-1
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TABLE OF CONTENTS
1
INTRODUCTION ....................................................................................6
2
STATE OF THE ART ................................................................................6
2.1
Meteorological parameters and fire risk assessment .....................6
2.2
Fuel moisture and fire risk assessment ..........................................6
2.3
Drought indices and fire risk assessment ......................................7
2.3.1
Drought indices used as sub-models in forest fire risk rating systems .... 8
2.3.2
Stand-alone empirical drought indices ................................................ 9
2.3.2.1
Keetch-Byram drought index .......................................................... 9
2.3.2.2
Nesterov index ............................................................................ 12
2.3.2.3
Modified Nesterov Index .............................................................. 13
2.3.2.4
Zhdanko index ............................................................................ 14
2.3.2.5
Angstrom index I ........................................................................ 15
2.3.2.6
Baumgartner Index ..................................................................... 16
2.3.2.7
Drought indices recently developed .............................................. 17
2.3.3
Performance of the drought indices throughout the world .................. 17
3
METHODOLOGY
FOR
ENHANCING
SPATIAL
ACCURACY
OF
METEOROLOGICAL VARIABLES..................................................................18
3.1
The small scale approach .............................................................18
3.1.1
Introduction ................................................................................... 18
3.1.2
Interpolation of temperature ............................................................ 19
3.1.3
Interpolation of daily total precipitation............................................. 22
Interpolation of daily total precipitation: Flowchart .................................24
3.1.4
Interpolation of relative humidity ..................................................... 25
Interpolation of relative humidity: Flowchart............................................27
3.1.5
3.2
Interpolation of wind speed ............................................................. 27
The medium scale approach.........................................................28
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3.2.1
Introduction ................................................................................... 28
3.2.2
Limitation of the meteorological data ................................................ 30
3.2.3
Methods to data incorporation ......................................................... 30
3.2.4
Testing methods ............................................................................. 32
3.2.4.1
Air Temperature .......................................................................... 32
3.2.4.2
Wind speed ................................................................................ 37
3.2.4.3
Relative Humidity ........................................................................ 39
3.2.4.4
Precipitation................................................................................ 40
3.2.5
4
Conclusions .................................................................................... 40
METHODOLOGY FOR REMOTE SENSE FUEL MOISTURE CONTENT ......42
4.1
INTRODUCTION ...........................................................................42
4.2
- METHODOLOGY .........................................................................42
4.2.1
- Atmospheric corrections ................................................................ 43
4.2.1.1
- Theory of Atmospheric corrections with ENVI’s FLAASH model
(MODTRAN4) 43
4.2.1.2
- Implementation of Atmospheric corrections with ENVI’s FLAASH
model (MODTRAN4) ......................................................................................... 45
4.2.2
Vegetation indices........................................................................... 48
4.2.2.1
- Normalized Difference Vegetation Index...................................... 48
4.2.2.2
- Normalized Difference Infrared Index ......................................... 48
4.2.2.3
- Reduced Sample Ratio ............................................................... 49
4.3
FMC evaluation on plots targets with field measurements and
image segmentation ..................................................................................49
4.3.1
Upscaling of FMC values .................................................................. 51
4.3.2
Calculation of FMC on a plot taking in account the percentage cover of
each species and soil. ....................................................................................... 52
4.4
Results .........................................................................................53
4.4.1
Comparison of estimated ground FMC and vegetation indices. ............ 53
4.4.2
- Multiple Regression ....................................................................... 53
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4.5
Conclusion....................................................................................54
5
METHODOLOGY FOR THE DEVELOPMENT OF A DROUGHT INDEX
APPLICABLE IN THE MEDITERRANEAN CONDITIONS ...............................55
5.1
Analysis of the empirical drought indices concept .......................55
5.2
Drought indices evaluation ..........................................................56
5.3
Methodology ................................................................................57
5.3.1
Questions setting ............................................................................ 57
5.3.2
Spatial accuracy of meteorological data ............................................ 57
5.3.3
Indices selection for testing in the Mediterranean conditions .............. 57
5.3.4
Validation methods of the selected models ........................................ 58
5.3.4.1
Field campaign and data analysis.................................................. 58
5.3.4.2
Description of the study area ....................................................... 58
5.3.4.3
Data collection and process .......................................................... 59
5.3.4.4
Variables selected to be monitored ............................................... 59
5.3.4.5
Data screening ............................................................................ 60
5.4
Results .........................................................................................61
5.4.1
Performance of the Keetch-Byram drought index (KBDI) .................... 63
5.4.2
Performance of the Nesterov index................................................... 73
5.4.3
Performance of the Modified Nesterov index ..................................... 73
5.4.4
Performance of the Zhdanko index ................................................... 74
5.4.5
Performance of the Angstrom index.................................................. 75
6
TOWARDS AN ADAPTED EMPIRICAL DROUGHT INDEX TO
MEDITERRANEAN CONDITIONS ................................................................75
6.1
Models improvement....................................................................76
6.2
New model construction ..............................................................80
7
REFERENCES .......................................................................................82
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1
INTRODUCTION
Drought is considered a recurring phenomenon that affects natural ecosystems, as
well as many economical and social sectors (Heim 2002). Forest fires are greatly
affected by weather conditions while the relationship between meteorological
variables and fire occurrence is well known. Thus, forest fires tend to be
concentrated during the dry summer period when temperature is high, air humidity is
low and fuel moisture is reduced (Pinol et al. 1998).
The overall objective of the deliverable is to enable the improvement of the quality of
the daily performed fire hazard previsions, by combining the structural components
of the hazard (i.e. fuel models, topography), with the “dynamic” factors such as the
current meteorological parameters and the fuel moisture or drought conditions.
The specific objectives of the methodology presented hereafter, are to elaborate a
consistent tool that improves the spatial accuracy of the meteorological variables
recorded from existing meteorological stations’ network, and provide a remote
sensed fuel moisture content index, as well as an empirical drought index.
2
2.1
STATE OF THE ART
Meteorological parameters and fire risk assessment
Good knowledge of the weather is a critical issue in the assessment of fire risk
(Feidas et al. 2002). Meteorological conditions affect the probability of fire either by
determining the amount of energy required for an ignition (temperature), or by
influencing fuel moisture status (solar irradiance, rainfall, air relative humidity, dew,
solar humidity, wind speed). Air temperature, relative humidity and wind speed have
been used as inputs in several fire risk systems to estimate meteorological risk (e.g.
Gouma and Chronopoulou-Sereli 1998). Meteorological conditions vary in time and
space, thus resulting in a variation of the fire risk.
2.2
Fuel moisture and fire risk assessment
The moisture content of vegetation in fire prone regions determines the flammability
of the vegetation and therefore the potential for the outbreak and spread of wildfires
(Castro et al. 2003). It is assumed that the dryer the vegetation is, the more prone it
is to be burnt (San-Miguel-Ayanz et al. 2003). At ground level vegetation water
status is measured as Fuel Moisture Content (FMC), which is defined as the ratio
between the quantity of water in live and/or dead vegetation and either the fresh or
dry weight (mainly) of this vegetation (Viegas et al. 2001; Chuvieco et al. 2003;
Verbesselt et al. 2006). In forest fire risk literature, the estimation of the fuel
moisture content is considered to be one of the key variables affecting fire ignition
and fire propagation and therefore is widely used in fire risk rating systems (Burgan
1988; Chuvieco et al. 2003).
Live fuel moisture is regarded as one of the most important variables in fire risk
modeling and therefore is incorporated in most fire risk systems worldwide, like the
US National Fire Danger Rating System and the Canadian Forest Fire Weather Index
System (San-Miguel-Ayanz et al. 2003; Verbesselt et al. 2006). Since most fires are
initiated in the litter layer, a series of models are required to simulate wetting and
drying process in the ground fuel layer during changing weather conditions
(Venevsky et al. 2002). Thus, moisture content in the duff-litter layer is an
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important parameter for forest fire danger rating systems (Van Wagner 1987, Burgan
1988).
The moisture content of the upper soil, as well as that of the covering layer of duff,
has an important effect on the fire suppression effort in forest and wildland areas
(Keetch and Byram 1968), and it is considered in many models used in fire risk
systems (e.g. Keetch and Byram drought index (KBDI), Drought Code of the
Canadian Fire Weather Fire Danger System).
Various approaches and models have been developed to determine moisture content
for all fuel levels (Spano et al. 2005). Several authors have proposed the use of
indices derived from satellite data and geographic information systems as a method
to monitor FMC for fire risk assessment (Camia et al. 1999; Chuvieco et al. 2003;
Dennison et al. 2003; Verbesselt et al. 2006).
All the above mentioned models and approaches for fuel moisture estimation are
basic elements for fire suppression systems worldwide. Several fire rating systems
have been proposed which estimate the ignition potential of fuel, mainly as
variations of the Canadian Forest Fire Weather Index System (Van Wagner 1987),
the American National Fire Danger Rating System (Burgan 1988) or the Nesterov
Index which is widely used in Russia (Venevsky et al. 2002).
2.3
Drought indices and fire risk assessment
Drought index is defined as a number representing the net effect of
evapotranspiration and precipitation in producing cumulative moisture deficiency in
deep duff or upper soil layers. Drought index is, thus, a quantity that relates to the
flammability of organic material in the ground (Keetch and Byram 1968).
Many indices have been developed to estimate a variety of scales, types and impacts
of drought and moisture deficiency (Byun and Wilhite 1999; Heim 2002; Janis et al.
2002). Because of the complexity of drought, no single index has been able to
adequately capture the intensity and severity of drought and its potential impacts on
such a diverse group of users (Heim 2002).
The American Meteorological Society (1997) groups drought definitions and types
into four categories: meteorological or climatological, agricultural, hydrological, and
socioeconomic (Heim 2002). Perhaps the best known drought index worldwide is the
Palmer Drought Severity Index (Palmer 1965), while other famous indices are the
Standardized Precipitation index, the Vegetation Conditions Index and the US
Drought Monitor (see Heim 2002, for a detailed review).
In forestry and especially in wildfire risk assessment a lot of fire risk-rating systems
in use include several drought indices, specifically designed for fire potential
assessment (Viegas et al. 1999). In many cases the drought or dryness indices are
used as estimators of fuel components moisture. To characterize the level of
potential fire risk numerous indices have been suggested (Groisman et al. 2005b).
However, some of the drought indices are used as stand-alone indices directly
correlated to fire potential, while some others are included as sub-models in
integrated rating systems such as the Canadian Forest Fire Weather Index (FWI), the
US National Fire Danger System (NFDRS), and the McArthur’s Forest Fire Danger
Index.
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2.3.1 Drought indices used as sub-models in forest fire risk rating systems
The Canadian Forest Fire Danger Rating System includes three moisture indices (the
fine fuel moisture, the duff moisture and drought codes) that are used in the
estimation of the Fire Weather Index (FWI), and the fire risk in Canada (Van Wagner
1987; San-Miguel-Ayanz et al. 2003; Spano et al. 2005). The Canadian Fire Weather
Index (CFWI) comprises one of the two major modules of the Canadian Forest Fire
Danger Rating System (CFFDRS). The CFWI requires daily (noon) temperature,
relative humidity, wind speed and 24-hour accumulated rainfall inputs for its
calculation (Van Wagner 1987).
The Canadian Fire Weather Index (CFWI) has six components from which the first
three are the fuel moisture codes, the Fine Fuel Moisture Code (FFMC), Duff Moisture
Code (DMC) and the Drought Code (DC). Details are available online at
hhtp://cwfis.cfs.nrcan.gc.ca/en/background/bi_FWI_summary_e.php.
The
fuel
moisture codes of the Canadian Fire Danger Rating System, have been successfully
tested in many regions around the word. In the text bellow the fuel moisture codes
of the Canadian Fire System are briefly presented.
Fine Fuel Moisture Code (FFMC) represents the moisture of the uppermost layer
of litter in a pine forest, approximately 1.2 cm deep. The FFMC has a time lag of
approximately 2/3 of a day, and is the fastest changing component of the CFWI. It is
a relative indicator of the easiness of ignition of the fine fuels, ranging from 0 (moist
litter and low flammability) to 100 (dry litter and high flammability). FFMC is
computed from rainfall, relative humidity, wind speed and temperature data.
Duff Moisture Code (DMC) represents the moisture in the 7 cm deep layer below
the Fine Fuel layer, assumed to be a layer of loosely compacted organic material.
The DMC has a time lag of approximately 12 days. It is an indicator for the fire
consumption of a moderate duff layer or medium woody debris. The DMC is always
positive, but has no maximum. High values, indicate drier litter and higher fire
spread/risk. DMC is computed from rainfall, relative humidity and temperature data
Drought Code (DC) represents the moisture in a layer of compact organic matter
extending 18 cm below the Duff Moisture Code layer. The DC has a time lag of 52
days (the slowest changing of the three CFWI components). It is an indicator of the
smouldering potential in deep duff layers and large logs, and of the seasonal
drought. Like the DMC, it is positive and has no maximum value. High values of the
DC indicate a high degree of smouldering and burning of deep duff or large logs. DC
is calculated only from rainfall and temperature data.
The Canadian FFDRS model has also been tested, adopted or adapted in New
Zealand, Fiji, Alaska, Venezuela, Mexico, Chile, Argentina and Europe. This is one of
this system’s desirable traits. Also, it was found by Viegas et al. (2001) that the
Drought Code of the sub-model Forest Fire Weather Index can be used to estimate
the moisture content of live fine fuel of shrub type fuels during the summer period in
Central Portugal and Catalunya (NE Spain). The Drought Code of the system was
also selected by Aguado et al. (2003) to investigate the spatial correlation between
meteorological fire risk indices and satellite derived variables in Andalucia, southern
Spain.
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The drought factor and the fuel moisture sub-model of the McArthur Fire Danger
Index (FDI) are used in one of the Australian fire danger system (Willis et al. 2001;
San-Miguel-Ayanz et al. 2003; Spano et al. 2005).
The ICONA index is used in Spain for the estimation of the flammability of the fine
dead fuel and it is obtained from tabulated data on relative humidity and
temperature (Pinol et al. 1998; Viegas et al. 1999).
There is also the drought index of the French Fire Danger Rating that is combined
with wind speed as a measure of fire risk in France (Viegas et al. 1999; Willis et al.
2001).
However, all the above fire risk rating systems are quite sophisticated (Buchholz and
Weidemann 2000), and they are often very difficult to implement worldwide, since
they are based on a lot of meteorological data and need complicated calculations.
2.3.2 Stand-alone empirical drought indices
The Keetch-Byram drought index (Keetch and Byram 1968) was developed by Keetch
and Byram for use by fire control managers and is probably the most widely used
worldwide in wildfire monitoring and prediction (Heim 2002). This index was
developed and is in use in the USA, but it was also incorporated and used in other
systems such as in the McArthur Fire Danger Index (FDI).
The Nesterov Index is an empirical drought index widely used in Russia and other
parts of the former Soviet Union for fire risk rating (Groisman et al. 2005a,b; McRae
et al. 2006). The Modified Nesterov Index and the Zhdanko index are also wellknown drought indices used in Russia (Vensvsky et al. 2002; Groisman et al. 2005a,
b).
Other empirical drought indices used in fire risk assessment are the Angstrom index
(Willis et al. 2001) that was developed in Sweden and has been used all over the
Scandinavian peninsula and the German Baumgartner index used in Germany
(Skvarenina et al. 2003).
The characteristics of each of the above mentioned empirical drought indices are
presented analytically in the following text, as well as the theory of their model
construction and the variables included in each model.
2.3.2.1 Keetch-Byram drought index
Description
The Keetch/Byram drought index is a cumulative estimate of moisture deficiency (fire
potential) based on meteorological parameters and an empirical approximation for
moisture depletion in the upper soil and surface litter levels (Keetch and Byram
1968; Janis et al. 2002). It is a drought index specifically designed for fire potential
assessment (Keetch and Byram 1968; Heim 2002; Dimitrakopoulos and Bemmerzouk
2003) and it is considered as a conventional tool for estimating fire potential (Janis et
al. 2002). It requires only few meteorological data, maximum daily temperature,
total daily precipitation and the normal (average annual) precipitation. The index is
initialized when the soil is near saturation (close to field capacity). Soil saturation
varies by geographic region but may be reached during prolonged precipitation
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events (Janis et al. 2002). Keetch and Byram (1968) suggested that 150-200 mm of
precipitation on a week is sufficient for initialization.
However, for the development of the equation, which describes the degree of
drought or moisture deficiency which exists in a forested or wildland area, it is
assumed that (Keetch and Byram 1968):
a) From the stand point of fire control, the significant moisture relationships are
those which exist in an upper layer of soil and a covering layer of duff. The
filed capacity of the soil-duff layer will be taken as 8.0 inches of water in
excess of the moisture which the layer holds at the wilding point. For a heavy
soil at field capacity, 8.0 inches of free water require a soil layer about 30 to 35
inches deep. In a lighter sandy soil the depth would be somewhat greater.
b) The soil-duff layer gains moisture from rainfall and loses moisture by
evapotranspiration. Its lowest level of moisture content occurs at the wilting
point.
c) The evapotransipration rate will be a function of the weather variables and the
vegetation density.
d) The vegetation density, and hence the rate at which the vegetation can
remove moisture from the soil-duff layer when the weather variables are
constant, is a function of the amount of mean annual rainfall. This rate will be
characterized by a single parameter defined as the evapotraspiration timelag.
e) As a first approximation, simple exponential functions can be used to express
the relationships between essential variables in the basic equations.
The KBDI is calculated from the following equation (Keetch and Byram 1968; Janis et
al. 2002; Dennison et al. 2003):
KBDIt = KBDIt-1 + DF (Drought factor)
while:
[800-KBDIt-1] [0.968 exp(0.0875T+1.5552)-8.30] dt
DF =----------------------------------------------------------------x 10-3
1 + 10.88 exp(-0.001736R)
Where T is the daily maximum temperature (oC), R is the mean annual rainfall (mm),
dt is the time increment (days) and KBDIt-1 is the Keetch-Byram Drought index for
time t-1. Daily precipitation decreases KBDI when 24-h precipitation is greater than 5
mm (0.2 inches).
KBDI is a stand-alone index that can be used to measure the effects of seasonal
drought on fire potential (Roads et al. 2005). It can be related to a five-stage
descriptive fire-potential scale (Table 1; USDA 2002).
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Table 1: General description of moisture conditions and fire potential for relative
KBDI (USDA 2002; Janis et al. 2002).
KBDI range
General description
Forest fire potential
0-150
Upper soil and surface litter are wet
Fire
potential
minimal
is
150-300
Upper soil and surface litter are moist and Fire
behaviour
on not contribute to fire intensity
predictable
is
300-500
Upper soil and surface litter are dry and
may contribute to fire intensity
500-700
Upper soil and surface litter are very dry. Fire suppression is a
Surface litter and organic soil material significant undertaking
contribute to fire intensity
700-800
Upper soil and surface litter are extremely Fire
behaviour
dry. Live understory vegetation
burns unpredictable
actively and contributes to fire potential
Fire
behaviour
is
somewhat predictable
is
Application
o
It is a widely used and accepted, in the wildland fire community, drought
index.
o
KBDI is an integral component of the US National Fire Rating System since
1988 (Burgan 1988). It has been applied in a wide variety of environments
(Janis et al. 2002), including the United States, south eastern Australia
(Hatton et al. 1998), and Malaysia (Linington 1974).
o
It is included in the Australian Fire Rating Systems, as a measure of soil
(duff) moisture content (San-Miguel-Ayanz et al. 2003).
o
It has been tested successfully in the Hawaiian islands (Dolling et al. 2005)
by examining its relation with fire activity and in Greek conditions (Crete) by
testing its relation to moisture content of grass vegetation and upper soil
horizons (Dimitrakopoulos and Bemmerzouk 2003), as well as with plant
water potentials of three Mediterranean species, Pinus halepensis, Quercus
coccifera and Cistus creticus (Xanthopoulos et al. 2006)
o
It was comparatively tested with other indices (Nesterov, Modified Nesterov
and Zhdanko index) over northern Eurasia (Groisman et al. 2005b, Groisman
et al. 2007) by testing their values versus forest fire statistics, as well as
comparatively with Nesterov index in East Kalimantan, Indonesia (Buchholz
and Weidemann 2000). In both cases it was proved applicable and a useful
tool for early warning.
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It was also comparatively tested with satellite indices (normalized difference
vegetation index NDVI and normalized difference water index NDWI) for fire risk
assessment in savanna ecosystems in South Africa (Verbesselt et al. 2006). The
results showed that the index can be used to predict the fire season.
2.3.2.2 Nesterov index
Description
Professor V.G. Nesterov invented the Nesterov empirical drought Index in 1967. The
index uses synoptic daytime data of temperature and humidity and daily precipitation
(Groisman et al. 2005a, b). The index was derived as an empirical function reflecting
the relationship between fire and weather based on historical data (Venevsky 2002).
There was devised in the former Soviet Union and it is calculated as follows (Willis et
al. 2001; Skvarenina et al. 2003).
W
NI = Σ Ti x (Ti – Di)
i=1
Where:
NI = Nesterov Index
W = number of days since last rainfall > 3 mm
T = mid-day temperature (oC)
D = dew point temperature (oC)
Its computation begins on the first spring day when the height of temperature is
above freezing, after snow melting, and continues until the rainfall of 3 mm. The
total is calculated for positive temperatures for a sequence of days with precipitation
less than 3 mm. Rainfall above 3 mm resets the index NI to zero. It is a cumulative
index and reflects drying potential for fuels. High values of the index indicate long
periods without rain. For Central Russia, days with NI below 300 are the days
without substantial forest fire risk while days with NI above 1000 are characterized
as days with high forest fire risk.
However, the index requires sub-daily meteorological information that may not easily
available for many cases. On the other hand it is a relatively simple equation and
does not require variables such as wind speed or daily humidity, for which accurate
data are practically unobtainable over large periods (Venevsky et al. 2002). Five
different fire risk classes are used depending on the value of the index (Skvarenina
et al. 2003):
1.
N<300
no fire risk
2.
301<N<1,000
low risk
3.
1,001<N<4,000
medium risk
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4.
4,001<N<10,000
high risk
5.
N>10,001
extremely high risk
Application
o
It is widely used in the Russian fire-risk rating system, as well in other parts
of the former Soviet Union.
o
A modified version of the index has been incorporated in the Portuguese
Method for forest fire risk estimation (Viegas et al. 1999).
o
It was comparatively tested with other indices (Keetch-Byram drough index,
Modified Nesterov and Zhdanko index ) over northern Eurasia (Groisman et
al. 2005b, Groisman et al. 2007), by testing their values versus forest fire
statistics as well as with Keetch-Byram drought index in East Kalimantan,
Indonesia (Buchholz and Weidemann 2000). In both cases it was proved
applicable and a useful tool for early warning.
o
It was selected by Venevsky et al. (2002) for a new fire model construction
for estimates areas burnt on a macro scale (10-100 km) in human-dominated
ecosystems in the Iberian Peninsula that was proved to produce realistic
results, which were well correlated, both spatially and temporally, with the
fire statistics.
o
It was also comparatively evaluated (Skvarenina et al. 2003) with two other
indices (Angstrom and Baumgartner) in the Slovak Paradise National Park
during two large forest fire events. In these local conditions, it preliminary
seemed to be comparatively the less sensitive indicator of fire risk level
during the dry summer season of July 1976, while it has approached the
higher risk level during the dry continental weather in October 2000.
2.3.2.3 Modified Nesterov Index
Description
The Modified Nesterov index is the Nesterov index with a reduction factor similar to
that used by Zhdanko index (see below) (Groisman et al. 2005a,b). The index is
calculating as follows:
W
MNI =K Σ Ti x (Ti – Di)
i=1
Where:
MNI = Modified Nesterov Index
W = number of days since last rainfall > 3 mm
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T = mid-day temperature (oC)
D = dew point temperature (oC)
K takes the values of the table below, in dependence of the current rainfall.
R (mm)
0
0.1-0.9
1.0-.2.9
3.0-5.9
6.0-14.9
15.0-19.0
>19
K
1
0.8
0.6
0.4
0.2
0.1
0
The Fire Risk level is them determined from the Modified Mesterov Index using the
following classifications.
Fire
levels
Risk Modified
Index
Nesterov Forest Fire Risk
I
100 – 1000
Very Low
II
1001 – 2500
Low
III
2501 – 5 000
Moderate
IV
5 001 – 10 000
High
V
> 10 000
Extreme
Application
o
It is widely used in the Russian fire-rating system together with the Nesterov
index.
o
It has comparatively tested with other indices (Keetch-Byram drough index,
Nesterov and Zhdanko index) over northern Eurasia (Groisman et al. 2005b,
Groisman et al. 2007), by testing their values versus forest fire statistics
where it was proved applicable and a useful tool for early warning.
2.3.2.4 Zhdanko index
Description
Zhdanko (1965) suggested a recurrent index of potential forest fire risk for the warm
snow-free period of the year, which is similar to Nesterov Index (Groisman et al.
2005a, b). The index is calculated as follows:
Zh (N) = [Zh (N-1)+d] x K(N)
Where:
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d is the dew point deficit and K(N) is a scale coefficient in a [0,1] that controls the
index change when precipitation occurs on day N (Table 2). This reduction factor is
equal to 1 when no rainfall occurs, is equal to 0 when daily rainfalls is 20 mm or
more and gradually decreases between these thresholds (e.g. it is equal 0.2 when
daily rainfall is in the range of 6 to 14 mm, Table 2). Note that Nesterov index
assumes that K(N)=0 for rainfall as low as 3 mm without accounting for the level of
dry conditions prior to this event.
Table 2: Scale coefficient K used in Zhdanko index to account for daily precipitation
impact on accumulated drought indices (Groisman et el. 2005).
R (mm)
0
0.1-0.9
1.0-.2.9
3.0-5.9
6.0-14.9
15.0-19.0
>19
K
1
0.8
0.6
0.4
0.2
0.1
0
Application
o
It is widely used in the Russian fire-rating system together with Nesterov and
Modified Nesterov index.
o
It has comparatively tested with other indices (Keetch-Byram drough index,
Nesterov index and Modified Nesterov) over northern Eurasia (Groisman et al.
2005b, Groisman et al. 2007), by testing their values versus forest fire
statistics where it was proved applicable.
2.3.2.5 Angstrom index I
One of the simplest drought index used in the fire risk assessment is the Swedish
Angstrom Index (Willis et al. 2001). The Angstrom Index uses only air temperature
and relative humidity in its calculation and provides an indication of the likely number
of fires on any given day. The Angstrom Index is calculated according to the
following equation (Skvarenina et al. 2003):
I = [R/20] + [(27-T)/10]
Where:
R = relative humidity (%)
T = air temperature (oC)
The values for I translate into fire risk as follow:
1.
I>4.0
fire occurrence unlikely
2.
4.0<I<3.0
fire occurrence unfavourable
3.
3.0<I<2.5
fire conditions favourable
4.
2.5<I<2.0
fire conditions more favourable
5.
I<2.0
fire occurrence very likely
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Application
o
The index was devised in Sweden and has been used all over the
Scandinavia.
o
It was comparatively tested (Skvarenina et al. 2003), with two other
indices (Nesterov and Baumgartner) in the Slovak Paradise National Park
during two large forest fire events, where it seemed to be the most
sensitive measure of fire occurrence risk forecasting.
2.3.2.6 Baumgartner Index
This index is used in Germany (Skvarenina et al., 2003). The calculation of the index
is based on the amount of precipitation and the potential evapotranspiration with the
following equation model.
BI = P – PE (sum of 5 days)
Where:
P = precipitation (mm)
PE = potential evapotranspiration (mm)
The system is divided into the following fire risk classes as follows (Skvarenina et al.
2003):
Fire
risk 1
classes/Month
2
3
4
5
(mm)
March
+5>
+5 to -3
-3 to -9
-9 to -15
-15<
April
+3>
+3 to -8
-8 to -16
-16 to -27
-27<
May
-3>
-3 to -16
-16 to -25
-25 to -35
-35<
June
-12>
-12 to -24
-24 to -32
-32 to -41
-41<
July
-12>
-12 to -24
-24 to -31
-31 to -40
-40<
August
-8>
-8 to -20
-20 to -28
-28 to -37
-37<
September
-6>
-6 to -18
-18 to -26
-26 to -35
-35<
October
-6>
-6 to -18
-18 to -26
-26 to -35
-35<
Application
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o
It was comparatively evaluated (Skvarenina et al. 2003), with two other
indices (Nesterov and Angstrom) in the Slovak Paradise National Park during
two large forests fire events. In these local conditions, it preliminary seemed
to only seldom approach the highest fire risk levels (class 5).
2.3.2.7 Drought indices recently developed
Last years many efforts have been made aiming at the development of new drought
indices (or fuel dryness) that could be applied more easily and successfully in fire risk
assessment.
Dennison et al. (2003) suggested a cumulative water balance index (CWBI) for
measuring regional drought stress. The index cumulatively sums precipitation and
reference evaortranspiration over time so that CWBI at time T is calculated as
CWBIT = Σ (Pt – ET0t)
Where t is the time interval Pt is the precipitation over each interval and ET0t is the
reference evapotranspiration over each interval. A modified Penman equation is used
to calculate reference evapotranspiration using inputs of solar irradiance, air
temperature, vapor pressure, and wind speed. The index was tested in California
conditions and, based on its temporal and spatial attributes, offers a complementary
methodology for monitoring live fuel moisture for fire risk assessment. After testing
the index (CWBI) with live fuel conditions, Dennison et al. (2003) found that live fuel
moisture demonstrated a strong, nonlinear relationship with the index.
Spano et al. (2005) suggested a Fuel dryness index (Fd) for Mediterranean
vegetation following a model proposed by Snyder et al. (2003) for grassland fire risk
assessment. The Fd index was also proved to give useful information on fuel dryness
conditions of a vegetation grassland in California (Snyder et al. 2006). The index is
based on the surface energy balance, where available energy (Rn-G) is partitioned
into sensible and latent heat exchanges (H+LE). When soil water is not limited, then
H is typically small and (Rn–G) is a measure of the potential or maximum possible
LE. When the surface is dry and soil water is limited, evaporation from the surface is
reduced, LE decreases and (Rn-G) and H increases. Therefore the fuel dryness
index can be calculated as:
Fd = 1- LE/Rn-G = H/Rn-G
However, the index requires special instruments and complex computation and thus,
it is considered difficult to replace the widely used drought indices that are based on
simple and easily available meteorological data.
2.3.3 Performance of the drought indices throughout the world
Based on the large number of available data concerning the application and testing
of drought indices in fire risk assessment worldwide, some useful conclusions may be
extracted. Except for the drought indices that are used for sub-models in holistic Fire
Danger Rating Systems, a widely used stand-alone drought index directly correlated
with fire potential, is the Keetch-Byram drought index (KBDI). The index is
considered strongly related to live fuel moisture content (FMC), since most cases of
moisture stress in plants (grass and shrub species) are caused by soil moisture
deficiencies (Aguado et al. 2003; Verbesselt et al. 2006).
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The KBDI index is also a basic component of US National Fire Rating System while it
has been applied in a wide variety of environments (Janis et al. 2002), including
USA, south eastern Australia (Hatton et al. 1998), and Malaysia (Linington 1974). It
is also included in the Australian Fire rating Systems, as a measure of soil (duff)
moisture content (San-Miguel-Ayanz et al. 2003). It has been tested successfully in
the Hawaiian Islands (Dolling et al. 2005), and in Greek conditions (Crete)
(Dimitrakopoulos and Bemmerzouk 2003), as well as with plant water potentials of
three Mediterranean species, Pinus halepensis, Quercus coccifera and Cistus creticus
(Xanthopoulos et al. 2006). It has also been comparatively tested with other indices
(Nesterov, Modified Nesterov and Zhdanko index) over northern Eurasia (Groisman
et al. 2005b, Groisman et al. 2007), as well as with Nesterov index in East
Kalimantan, Indonesia (Buchholz and Weidemann 2000). In both cases it was proved
applicable and a useful tool for early warning. It was also comparatively tested with
satellite indices (normalized difference vegetation index NDVI and normalized
difference water index NDWI), for fire risk assessment in savanna ecosystems in
South Africa (Verbesselt et al. 2006); the results showed that it can be used to
predict the fire season.
The Nesterov index (NI), the Modified Nesterov index (MNI) and the Zhdanko index
are widely used in Russia and other parts of the former Soviet Union. These indices
were found also applicable in many other regions worldwide. They were
comparatively tested with the Keetch-Byram drought index over northern Eurasia
(Groisman et al. 2005b, Groisman et al. 2007), by testing their values versus forest
fire statistics, as well as with the Keetch-Byram drought index in East Kalimantan,
Indonesia (Buchholz and Weidemann 2000). In both cases they were proved
applicable and a useful tool for early warning. Nesterov index was also used by
Venevsky et al. (2002) for a new fire model construction for estimating burnt areas
on a macro scale (10-100 km) in human-dominated ecosystems in the Iberian
Peninsula that was proved to produce realistic results, which were well correlated,
both spatially and temporally, with the fire statistics.
3 METHODOLOGY
FOR
ENHANCING
METEOROLOGICAL VARIABLES
3.1
SPATIAL
ACCURACY
OF
The small scale approach
3.1.1 Introduction
Meteorological data usually derives from weather stations or sensors. This pointsource data has to be interpolated in order to provide information about the spatial
distribution of meteorological variables. Finally, a dimensionless number (index) is
computed from the interpolated values, expressing the impact of weather on fire
potential (Aguado et al. 2003, San-Miguel-Ayanz et al. 2003). The meteorological
variables of interest are:
(a) air temperature
(b) daily total precipitation
(c) relative humidity
(d) wind speed
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Several techniques have been used in order to estimate a variable in space from
nearby point measurements. The method proposed here is a combination of Kriging
and the inverse distance weighting (IDW). The method addresses the need for
accurate prediction of the spatial distribution of meteorological variables, in order to
estimate fire risk. Real-time or near real-time point measurements of the variables of
interest have to be available.
Alternatively, forecasted meteorological conditions are used to assess fire risk. The
forecasts derive from the combined operation of global scale (GCMs) and mesoscale
(RCMs) meteorological prediction models. This approach, although promising, still
requires a lot of computational resources.
3.1.2 Interpolation of temperature
The dependence of temperature upon elevation is generally accepted (Daly et al.
1994, Thornton et al. 1997). Daily temperature Tp at any location p can be
considered as the combination of two components: a vertical component Tvp (trend
varying with the elevation) and a horizontal random component Thp.
Thus:
Tp = Tvp + Thp
(1)
Where Tvp is the vertical trend
Thp is the horizontal residual
The vertical trend is considered to be a function of elevation.
Tvp = f(Zp)
(2)
Where Zp is the elevation at location p
To approximate f(Zp) we perform linear regression of temperature against elevation
using temperature and elevation data from the observation points (stations).
f(Zp) = aZp + b
(3)
thus,
(4)
Tvp = aZp + b
Where a and b are regression coefficients.
The horizontal residual of temperature at an observation point i is the difference
between the observed temperature and the estimated trend from equation (4).
Thi = Ti – Tvi = Ti – aZi + b
(5)
Since the trend has been removed from the data, detrended observations of
temperature can be interpolated using ordinary Kriging. The horizontal residual of
temperature at any point p can be calculated as a weighted mean of surrounding
observations:
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Page 19 of 89
Thp =
n

wiThi
(6)
i 1
It is clear from equations (1), (4) and (6) that temperature at any location p can be
estimated as:
Tp = aZp + b +
n

wiThi
(7)
i 1
Where p is any location within the area
i is the ith observation point (station)
n is the total number of observations whose measurements are spatially
correlated to the value at location p
wi is the weight of the ith observation point (station)
Zp is the elevation at point p.
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Interpolation of temperature: Flowchart
Begin
Input temperature data
from stations or sensors
Calculate vertical component of
temperature with linear regression
(temperature against elevation)
Calculate horizontal
(detrented) component of
temperature (Ti – Tvi)
Interpolate horizontal
component of temperature
(ordinary kriging)
Add the vertical component of
temperature to the interpolated
horizontal component
End
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Figure 1: Temperature map
of Thessaloniki area (Greece)
at 2.6.2006.
3.1.3 Interpolation of daily total precipitation
The estimation of precipitation at a location p is a two-step process: first we have to
predict precipitation occurrence and conditional on that to estimate total precipitation
(Sun et all. 2003).
The first step of this process is in fact the calculation of the probability of
precipitation occurrence at a location p, given that it rains (or not) at the surrounding
observation points (stations).
We are going to calculate the probability of precipitation occurrence POPp at a
location p using indicator kriging. We start with the transformation of rainfall amount
observations Pi into binary variables POi. :
0; Pi  0
1; Pi  0
POi. = 
(8)
Where POi = 1 means that there is rain at observation location i, and POi = 0 that
there is not.
The probability of precipitation occurrence at a location p can be calculated as
following:
POPp =
n

i 1
wiPOi + (1-
n

mi)
(9)
i 1
Where p is any location within the area
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Page 22 of 89
i is the ith observation point (station)
n is the total number of observations whose measurements are spatially
correlated to the value at location p
wi is the weight of the ith observation point (station)
POi is the indicator value at point i.
mi is the cumulative probability of rainfall at location point i.
The estimated value of POPp is a probability with its value lying between zero and
one. In order to separate rain and no rain estimations, a threshold value needs to be
selected. This value is usually selected between 0.45 and 0.55. We select a threshold
value of 0.5.
Having delineated the rainfall area and for every point p with POPp > 0.5, we can
proceed with the estimation of the total daily precipitation Pp at any location p
likewise we did for the estimation of temperature. Total daily precipitation can be
calculated as:
Pp = aZp + b +
n

wiPi
(10)
i 1
Where p is any location within the area
i is the ith observation point (station)
Pi is the observed daily total precipitation at location i.
n is the total number of observations whose measurements are spatially
correlated to the value at location p
wi is the weight of the ith observation point (station)
Zp is the elevation at point p.
a and b are regression coefficients.
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Interpolation of daily total precipitation: Flowchart
Begin
INTERPOLATE HORIZONTAL
COMPONENT OF
PRECIPITATION (ORDINARY
KRIGING)
Input precipitation data
from stations or sensors
Transform rainfall amount data into
rainfall occurrence (rain/no rain)
values
ADD THE VERTICAL COMPONENT
OF PRECIPITATION TO THE
INTERPOLATED HORIZONTAL
COMPONENT
Calculate the probability of
occurrence POPp for the
whole area (Indicator kriging)
Separate rain and no rain
estimations using a threshold
value of 0.5
End
ONLY FOR AREAS WITH RAIN: CALCULATE
VERTICAL COMPONENT OF PRECIPITATION
WITH LINEAR REGRESSION (PRECIPITATION
AGAINST ELEVATION)
Calculate horizontal
(detrented) component of
precipitation (Pi – Pvi)
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Page 24 of 89
Figure 2: Precipitation map
Thessaloniki
area,
(Greece)
2.6.2006.
of
at
3.1.4 Interpolation of relative humidity
Relative humidity (RH) is defined as the ratio of the amount water vapor in air to the
maximum amount of water vapor that could be in the air. Mathematically:
e
RH =   100
 es 
%
(11)
Where e is the water vapor pressure and
es is the saturation water vapor pressure
But it is known (Murray, 1967) that water vapor pressures are functions of
temperature. Namely:
 17.27T

d
 (kPa) (12)
e = 0.6108exp 
T

237
.3 
 d
 17.27T 
 (kPa) (13)
 T  237.3 


es = 0.6108exp 
where T is the current air temperature in 0C and
Td is the dew point temperature in 0C
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At an observation point (station), relative humidity (RH) is measured and so is
temperature (T). In case that dew point temperature (Td) at an observation point is
not measured, we can calculate it as a function of water vapor pressure e. From
(12):
e
237.3 0.6108
17,27
Td =
e
ln
1  0.6108
17,27
ln
0
C
(14)
 es 
 RH from equation (11)
 100 
Where e can be substituted by 
es can be calculated from equation (13)
Having calculated (or measured) dew point temperature Td at all observation points,
we are going to interpolate this variable using the methodology elaborated for
temperature (see Interpolation of temperature).
Obviously, after the interpolations of current temperature and dew point
temperature, at any location p we can calculate water vapor pressures (e and es)
using equations (12) & (13) and relative humidity (RH) using equation (11).
Figure 3: Relative humidity
map of Thessaloniki area,
(Greece) at 2.6.2006.
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Interpolation of relative humidity: Flowchart
Begin
Input temperature and
relative humidity data
from stations or sensors
Are dew point
temperature
data available?
No
Calculate dew point temperature
from relative humidity and
temperature at observation points
Yes
Interpolate dew point
temperature (ordinary kriging)
Interpolate temperature
Calculate water vapor pressures and relative
humidity at any point from (interpolated)
temperature and dew point temperature
End
3.1.5 Interpolation of wind speed
We choose to use the inverse distance weighting method (IDW) to interpolate the
wind speed data. According to this method, wind speed at a location p (Up) is
defined as the weighted average of measured wind speeds at the observation points
(Goodin et al. 1979).
A weight is defined as the inverse of the squared horizontal distance between a point
p and an observation point.
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Page 27 of 89
n
Up =
W U
i 1
n
i
i
W
i 1
(15)
i
Where Ui is the measured wind speed at observation point i
Wi is the weight associated with the observation point i
The weights are calculated as:
Wi =
1
d i2
(16)
Where di is the distance between point p and an observation point i.
Figure 4: Wind velocity map
of Thessaloniki area, (Greece)
at 2.6.2006.
3.2
The medium scale approach
3.2.1 Introduction
Weather conditions are among the main factors that affect fire occurrence and
behavior (Pyne, 1984). These conditions affect fire behavior in a direct way by
generating ignition sources such as lightning, and in an indirect way by influencing
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fuel moisture content, affecting also other variables such as fire propagation or
intensity (Dentoni, 2003). The majority of the fire danger evaluation systems have
an important meteorological component, and some of them are only based on the
evaluation of weather variables (Dentoni and Muñoz, 2000). Particularly,
meteorological danger indices have a long tradition in fire danger estimation,
because they comprise different critical variables related to fire ignition and fire
propagation (Aguado et al., 2003). One of the most particular properties of the
meteorological phenomena related to other variables is their high spatial and
temporal variability (Pyne, 1984). These two aspects are important to be accounted
for when fire danger needs to be forecasted for certain areas within a region. In this
case, the spatial and temporal qualities of the input data are of paramount
importance.
To estimate the meteorological variables that influence the fire danger situation in a
region, the data point obtained in a particular meteorological station must be
distributed continuously in the field. A way to obtain meteorological data for a
region between or among stations is by interpolating the values of the variable of
interest obtained in each particular station. Another way to do so is the use of non
punctual methods to measure the variables, or the association between
meteorological index of danger and vegetation index derived by satellite data
(Aguado and Camia, 1998). A development of continuous fields from discrete data
sets is generally used in different disciplines. Interpolation techniques have been
used extensively in meteorology since 1950. The interpolation of previously
calculated fire danger index is another method used by various authors with variable
success (Flannigan and Wotton, 1989). The accuracy achieved by those methods
depends on the quality and distribution of data that permit addressing the behavior
of the measured variable. For example, stations located in non-homogeneous areas
might not be representative of their surroundings, in particular with respect to
elevation and fuels. When data are sparse, the underlying assumptions about the
variation among sampled points often differed and the choice of interpolation
method and parameters then became critical (Czaijkowski, et al 2000). Satellite
images may be then considered a good alternative for interpolation of danger values,
since they perform a spatially exhaustive observation of the territory (Aguado et al.,
2003). In comparison to ground based meteorological observations which have
traditionally been used, satellites provide high spatial resolution data over large areas
of the earth. This is especially important on isolated areas where meteorological
observations are sparse (Czajkowski et al., 2000).
The use of satellite data to determine fire danger indexes could have two
approaches: the use of satellites to achieve spatial continuous values of the
necessary weather data to calculate an index (Flannigan et al., 1998; Charney et al.,
2007), or the use of satellite data to determine the fuel condition that is affected by
the weather behavior that a particular index intent to reveal (Aguado et al., 2003;
Guangmeng and Mei, 2004; Schneider et al., 2008).
Argentina began in the year 2000 the implementation of a National Fire Danger
Evaluation System with the adaptation of the Canadian FWI. This allowed the
unification of all provincial organisms responsible of fire management around a
unique fire danger system and fire language. This implementation was led by the
National Fire Management Plan. The Patagonian region of Argentina (comprising the
5 southernmost provinces) is one of the areas of the country where the
implementation of that system is more advanced. The codes and indices were
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adjusted for the fire climate of this region (Dentoni et al., 2006). The Fire whether
Index (FWI), as part of the Canadian Forest Fire Danger Rating System (CFFDRS),
depicts relative fire potential based solely on whether observations. The components
of the FWI system depends on daily measurements of dry-bulb temperature, relative
humidity, a 10-metre height open wind speed and 24-hour accumulated precipitation
(Beck, 2006). Today, the estimation of the different components of this index in
Patagonia is done by meteorological data gathered from stations located in different
brigades of the Provincial Fire Management Services and also from stations
belonging to the National Meteorological Service. Actually neither method is used to
calculate fire danger between these whether stations, assuming an invariable fire
whether situation for entire areas administratively defined. The absence of reliable
and well distributed meteorological stations in the region is a great limitation to
assess the fire whether conditions with a good spatial accuracy. It is necessary, with
the present availability of meteorological data and other resources, to achieve a
method that would permit to reflect, with acceptable accuracy, the spatial variation
in the values achieved by the meteorological indexes in the different areas.
The objective of this work was to inquire into different methods of mapping daily
meteorological variables with scattered availability of meteorological stations, and
explore the availability of methods to acquire meteorological variables in a
continuous field including those methods using satellite data that could, in a future,
improve the availability of data for fire danger evaluation. A secondary objective of
this work was to test some of those methods and evaluate their limitations for
forecasting meteorological data involved in the fire danger evaluation in an extensive
territory.
3.2.2 Limitation of the meteorological data
The scarcity of meteorological stations and the low availability of existent data were
among the main problems that we faced in this study. In the Patagonian Andes of
Chubut, Argentina, the National Meteorological Service has only one meteorological
station in an area which cover more than 60.000 km2. Because of that limitation,
other source of data were searched and used in this work: 1) Data of five
meteorological stations property of a Hydroelectric Company called Futaleufú. The
advantage of those stations is the availability of the information, which could be
downloaded daily via internet. The location of the meteorological stations, however,
pose some limitation to the usefulness of their data for our purposes, since their
objective is to bring data about rains, snow, or other meteorological data useful for
the operation and control of water input in that basin. Many of those stations are
located near lakes, rivers, creeks, etc. so the data obtained may not be
representative of the weather conditions of wider areas. Similar situation occur with
another prospected station, belonging to the Provincial Fire Management Service.
This station doesn’t comply with some of the basic requisites for installing a weather
station, since it has some buildings nearby that interfere with the variables to be
measured. The spatial distribution of the stations within the study area is not
homogeneous, showing a higher concentration in the west sector (Fig. 1). This fact
makes that the west to east gradient existent on the precipitation values in the
region are not well detected by those stations.
3.2.3 Methods to data incorporation
When the availability of stations is a restriction to map weather variables, there are
alternative methods that permit to interpolate or somehow calculate the missing data
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for certain areas. Some of these methods have been described in different studies
and are presented below.
Air temperature. It is possible to calculate air temperature by using the Land
Surface Temperature derived from values of thermal reflectance captured from
remote sensing. Two main methods are used for this calculation. The LST-NDVI-Tº
method permits the inference of air temperature based on the hypothesis that the
bulk temperature of an infinitely thick vegetation canopy is close to the ambient air
temperature (Prihodko and Goward, 1997). The temperature of the canopy could be
estimated with the LST derived from thermal infrared data and the density of the
canopy with a vegetation index like the NDVI. The method consists in the
correlation of the LST- with the vegetation index and then it evaluates the result
function with a value of the index that represents a full canopy. The LST–Air
Temperature regression method, calculates the air temperature from a function
derived from the lineal regression between the LST, calculated from satellite data,
and the air temperature measured in the same area by meteorological stations at the
same time the satellite passes. For using this method, it is necessary the availability
of a set of air temperature data from meteorological stations corresponding to the
moment the satellite overpass the area, and the daily values of LST derived from
satellite data to define the relation between the LST and air temperature that will
depend basically of the land coverage. Both methods have shown acceptable
success in previous works using MODIS (Cossetin, 2005; Colombi et al., 2007; Jones
et al. 2004) and AVHRR data (Czajkowski et al., 2000). The second methodology
(LST-air Temperature) needs a previous work that defines a representative
correlation function that related the Air Temperature and the LST derived from the
sensor measurements.
The use of satellite data to determine atmospheric moisture to derive Relative
Humidity has been informed by several authors (Prince et al., 1998; Hay and
Lennon, 1999; Czajkowski et al., 2000; Czajkowski et al., 2002). The fundaments of
this method are the different influence of water vapor in the brightness values
acquired by the two thermal channels of the AVHRR sensor. Hay and Lennon (1999)
used differences in brightness temperatures recorded simultaneously by the AVHRR
sensor (Channel 4 and 5), which has been shown to be linearly related to total
precipitation water in atmospheric column, U (kg/m2, Eck and Holben, 1994) where:
U = A + B (Ch4 – Ch5),
Where A and B are constants of 1.337 and 0.837, respectively. The estimated
precipitation water content, was then converted to a near surface dew point
temperature, by
Td = (ln U – ((0.113 – ln (λ +1)))/0.0393
where λ is a variable that is a function of the latitude and the time of the year.
Water Precipitation is another variable that is possible to be estimated with the
use of satellite data. With Infrared (IR) satellite imagery from geostationary
satellites, it is possible the identification of clouds that are producing rains in a
determined area. Sun et al. (2003) described this methodology for Australia, in
zones with scarce distribution of meteorological stations. The IR data is used as
auxiliary information to determine the areas in where it is actually raining. The
relation between the IR information and the rain gauge is a pre requisite to the
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application of this method and implied the availability of historic data. Other sources
to determine the amounts of precipitation in certain areas is by the use of radar
images. This source of data performs better results than the IR data, but it
availability is more restricted than IR, and the costs are more elevated.
3.2.4 Testing methods
In this section we evaluated the interpolation methods proposed in paragraph 3.1
and some of the alternative methods using satellite data, especially to determine air
temperature. The Relative Humidity determination method using satellite data was
not tested here because previous works did not achieve very good results, this
implicates much more time to achieve good results and largely exceeded the time of
this work. The methods to evaluate the rain occurrence and its amounts using
satellite data collided with the absence of previous works in the region, necessary to
define the IR range of raining clouds of and also without the possibility of obtaining
radar data.
3.2.4.1 Air Temperature
Satellite Data
The products used were 1) Daily Land Surface Temperature MODIS product
(MOD11A1) of 1 km resolution. This product was selected because the Terra satellite
(which has the MODIS product inside) overpasses daily the area of interest near
midday. The dates used were from December 1, 2007, to January 31, 2008. 2) The
MODIS Terra NDVI 16 days composite product (MOD13A1) was used in two dates:
December 3 and 19, 2007. 3) The NDVI derived from MODIS Terra was used daily
from January 1 to 31, 2008.
Meteorological data
Meteorological data from 8 stations distributed in the study area were used. Five
stations, belonging to Futaleufu Hydroelectric Dam, provided hourly data (Huemul,
Bustillo, Rio Percy, Puesto Ríos and Futaleufú). One station, purchased by FIRE
Paradox Project, provided data every half an hour (although it begun collecting data
January 7, 2008 (called Berwyn station from now on). The two other stations were
the one belonging to the National Meteorological Service (located in Esquel airport)
and the other property of the Provincial Fire Management Service (SMF Trevelin).
These two stations provided data only at midday (12 h). The spatial distribution of
the stations is shown in Figure 1. In the evaluation of the methods in which satellite
data were used, we only took into account data from the stations that recorded
hourly data, to match the time the satellite overpass the site.
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Figure 5: Geographic localisation of the eights meteorological stations used in this
work
3.2.4.1.1 Residual Method
The method proposed in paragraph 3.1.2 to interpolate values of temperature was
tested with the data of the meteorological stations deployed in the area. The change
tested was the use of the IDW method to interpolate the residuals of the regression
between altitude and temperature. That change was based on the assumption that
stochastic interpolation methods, like Kriging, needs more availability of data to
reach correct results prediction than that of what we have in this area. For these
situations, deterministic local methods are more adequate. The approaching tests
reinforce this, having better final results using the IDW method. The meteorological
data used was the air temperature of the 12 PM arising from the eight stations
depicted above. The period of time corresponded from 7 to 31 January, 2008. The
results of the interpolation were verified estimating the temperature at each station
based on observations from all the other stations and comparing them to with the
measurements.
Resukts
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The correlation between air temperatures and elevation was very strong during the
evaluated period. This was reflected in the short difference between the error found
with the incorporation of the residual and the error having accounted only the
vertical component of temperature. A particularly high error was found in the
estimation of temperatures at the Trevelin station (Fire Management Service) and
the temperature measured by that station. The probable deficiency above mentioned
in the measured data of this station is probably the cause of this difference.
Table 3. Results of the estimation of air temperature methods test. The number
between parentheses in the Average error Columns represents the account of
evaluated days.
Meteorological
Station
Standart
deviation
Average error ºC
1
2
3
and
1
2
Futaleufu
1,7(25)
2,5(47) 2,1(45) 1,2 1,9 1,6
8,6 - 22,2 (19,7)
Bustillo
1,9(25)
3,7(45) 2,0(42) 1,9 2,0 2,0
8,6 - 24,5 18,5)
Puerto Rios
1,7(25)
2,5(46) 2,7(46) 1,2 2,5 1,8
11,8 - 29 (21,3)
SMF Trevelin
4,6(25)
Berwyn
3,2(25)
3,3(17) 2,4(19) 2,9 2,8 1,7
13,9 - 26,7 (20,4)
Huemul
1,7(25)
4,6(36) 2,2(42) 1,2 1,9 1,9
10 - 29,9 (20,7)
Aeropuerto
1,4(25)
Percy
1,1(25)
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3
Range
average
1,7
1,0
2,2(44) 3,1(47) 1,2 2,0 2,6
of measured T ºC
12,0 - 33 (24,8)
6,4 - 27,6 (19,5)
5,8 - 31,6 (19,1)
Page 34 of 89
Figure 6. Temperature map of January 24 2008 at 12 h derived from the Residual
Method.
3.2.4.1.2 LST-NDVI-Tº method
We took, for evaluation, measurements of six meteorological stations that had
continued disposition of data. Around each meteorological station was selected a
window of 11x11 pixels to perform the regression between LST-NDVI. The process
that involves the generation of the LST Modis product eliminates from the analysis
those pixels that have the interference of clouds or water bodies. The regression was
made with the remained pixels and taking into account only days with almost 60
useful pixels within the 11x11 window. Each function was evaluated with a NDVI
value of 0.9 suggested by Prihodko (1992) and then compared with the value of
temperature measured in the meteorological stations at the time the satellite
overpass the area.
Results
In 4 of the sites the analysis was possible between 44 and 47 days and in the other
station in 36 days. With the station of Fire Paradox the analysis was possible only for
17 days because it begun to collect the data at 07 January of 2008. For the rest of
days the usable data within the 11 x 11 window around the station made possible
the regression analysis. Only for two days, in the lapse of the time studied, LST data
all over the area were not available because of the clouds.
The highest differences between estimated and measured air temperature occurred
in those areas where the station was close to big water bodies (Huemul and
Bustillo).
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Rio Percy January 8-2008
35
30
LST ºC
25
20
y = -20,844x + 37,046
R2 = 0,6609
15
10
5
0
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
NDVI
Figure 7. Example of the relation between the values of NDVI and LST for one day
in the surroundings of Río Percy station.
3.2.4.1.3 LST –Air Temperature regression
With the same data used in the LST-NDVI method, a correlation was performed with
the LST data of the pixel at each meteorological station with the temperature
measured at the time of satellite overpass. The function derived at each
meteorological station was evaluated with the daily value of LST and the result
compared with the registered temperature.
Results
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The regression was possible for more than 40 days, from December 2007 to January
2008 for five of the six stations used. In the other one the restriction was the time
in where the station started to take data. In the rest of the days the area around the
stations had no LST data because of the presence of clouds. The linear correlation
for the selected stations had a determination coefficient R2 between 0.4 and 0.68
(Fig 4). If the correlation is determined for all of the stations together the
determination coefficient descends to 0.31. In the case of RMSE determined by the
evaluation of the function to each station for all days, the averaging values vary from
PUERTO RIOS
35,00
35,00
30,00
30,00
25,00
25,00
Air Temperature ºC
Air Temperature ºC
BUSTILLO
20,00
15,00
10,00
y = 0,7672x + 6,7242
R2 = 0,6667
5,00
0,00
0,00
5,00
10,00
15,00
20,00
20,00
15,00
10,00
y = 0,6321x + 5,4198
R2 = 0,5538
5,00
25,00
0,00
0,00
30,00
5,00
10,00
15,00
LST ºC
25,00
30,00
Air Temperature ºC
Air Temperature ºC
35,00
20,00
15,00
10,00
y = 0,6525x + 7,285
R2 = 0,6774
5,00
10,00
40,00
15,00
10,00
y = 0,5657x + 3,5775
R2 = 0,4002
15,00
20,00
25,00
30,00
0,00
0,00
35,00
5,00
10,00
15,00
20,00
25,00
30,00
35,00
40,00
45,00
LST ºC
BERWYN
HUEMUL
35,00
30,00
30,00
25,00
25,00
Air Temperature ºC
Air Temperature ºC
35,00
20,00
5,00
5,00
30,00
25,00
LST ºC
20,00
15,00
y = 0,645x + 10,822
R2 = 0,6619
10,00
20,00
15,00
10,00
y = 0,3745x + 11,8
R2 = 0,4018
5,00
5,00
0,00
0,00
25,00
RIO PERCY
FUTALEUFU
30,00
0,00
0,00
20,00
LST ºC
5,00
10,00
15,00
20,00
25,00
2.08 to 3.14 as is shown in table 1.
LST ºC
30,00
0,00
0,00
5,00
10,00
15,00
20,00
25,00
30,00
35,00
LST ºC
Figure 8 Relationship between LST and Air temperature for the six evaluated
locations
3.2.4.2 Wind speed
The methodology proposed in paragraph 3.1.5 to map wind speed was tested with
the data of the 8 meteorological stations used in the temperature test. The results of
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the interpolation were verified estimating the wind speed at each station based on
observations from all the other stations.
Results
During the evaluated periods a large variation between stations in the measured
wind velocity was registered. This variation occurred in stations located very close
one to another, such as Puerto Rios, Berwyn and Futaleufu. The variation of data
was traduced in a deficient representation of the wind spatial distribution with the
interpolate method.
Table 4. Percentage of error in the estimation of windspped with the IDW
interpolation method for four of the 8 stations used in the calculus
Meteorological Station
Evaluated
days
Average error %
Futaleufú
25
50
Bustillo
25
87
Puerto Ríos
25
108
SMF Trevelin
25
45
Figure 9. Wind speed map of January 24-08 at 12 hours derived from IDW Method.
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3.2.4.3 Relative Humidity
The methodology proposed in paragraph 3.1.4 to map relative humidity was tested
with the data of the 8 meteorological stations described above. The changes tested
respect to the original methodology was the use of IDW method to interpolate dew
point temperature in change of the Kriging method. The results of the interpolation
were verified estimating the relative humidity at each station based on observations
from all the other stations. The difference between the calculated and measured
relative humidity to each day were averaged for the 8 sites uses to determinate the
error in each one.
Results
During the 25 days of the evaluated period, the measured relative humidity at
midday varied between 33 and 92%. Puesto Rios was the station in what the
maximums values of humidity were registered and the maximums error values too.
For the rest of the stations the error of the estimated value was significantly lower.
The days with a major difference between the measured values and the estimations
were those in which precipitations were occurring in some of the stations. The direct
interpolation of the relative humidity values was tested too and compared with these
results with an increment in error values.
Figure 10. Temperature map of January 24 2008 at 12 h derived from FP proposed
Method.
Table 5. Error corresponding to the estimation of Relative Humidity with the
mentioned stations between 7 and 31 January of 2008
Meteorological
Station
Evaluated days
Average error %
Bustillo
25
4,08
Puerto Ríos
25
19,30
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Futaleufú
25
9,70
SMF Trevelin
25
5,58
Río Percy
25
8,81
Huemúl
25
9,28
Aeropuerto
25
13,68
Berwyn
25
19,38
3.2.4.4 Precipitation
The methodology proposed in paragraph 3.1.3 to map precipitation was impossible
to be applied with the meteorological data available to this area of Patagonia. In a
great amount of days the Indicator Kriging didn’t work with the data used. This was
due by the scarce availability of data. The Kriging method is a probabilistic method.
This complicates the determination of the raining areas with this method. We tested,
instead, the use of the IDW to interpolate the probability of rains. The better results
to determine the raining areas was obtained with the use of IDW interpolation
method by using hourly calculations and then averaging the 24 hours results for
each calculated pixel
The interpolation of the rain amounts for the raining areas didn’t give good results
too. This is difficult to solve due to the variability of the rains in the area during the
summer, which coincide with the fire season. During the summer fire session in
western Patagonia, rainfall is very scarce and highly variable. For this reason, it is
very difficult to count with the availability of data with the probability raining areas
and the amount of rains too.
3.2.5 Conclusions
In the Andean region of Patagonia where this study was conducted, the interpolation
methods to obtain a continuous set of reliable meteorological data necessary for the
daily evaluation of fire danger have produced dissimilar results. The difficulties in
applying interpolation methods were not only related to the scarcity of
meteorological stations, but also to the location of every station, which produced
punctual data that represented valid meteorological values only in a small fraction of
the terrain around that station. Nevertheless, the use of alternative methods allowed
the estimation of some meteorological variables using satellite data that could
replace the presence of meteorological stations. Some of these methodologies need
previous work in the area in which they will be used, and may have some operative
problems too. However and with some improvements, the mapping of fire danger
evaluation could be done with relative accuracy by using satellite tools, with the
advantages this tool have in areas with low density of meteorological stations.
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Of the parameters calculated, the best results were achieved with air temperature
calculation. The interpolation method using the relationship between temperature
and altitude, showed acceptable results with a medium density of data like it was
used in this area of Patagonia. For areas where the availability of meteorological
stations is scarcer, the method using the relation between LST –NDVI and Air Tº
could be acceptable for temperature estimation. This method has the advantage
that needs no data from meteorological stations, although one of its weaknesses is
that since it uses optical data, its estimations are restricted to areas without clouds.
However, for days with continuous cloud cover, almost some areas with available
data could be used like new information that could be interpolated with the data
acquired from a meteorological station.
The error in the relative humidity calculation by the interpolation method varied
depending on the evaluated station. The use of stations near big water bodies
presented the problem that produced biases in the estimations. The incorporation of
data acquired by satellite is an option to be analyzed and tested with existent data,
but the time this may take is far beyond the objectives of this study.
The interpolation of precipitation with the data available did not present good
results. Some problems were found in the interpolation of the probabilities and the
amounts of rain too. The use of IR data to improve the map of daily precipitations is
an option when there is a scarcity of available data. The use of radar data may help
solve this problem in the near future.
The interpolation methods used to map the daily wind speed did not give very good
results, too. The variability of the wind in the area is very high, and impossible to
map with good accuracy with the data available in the region. This variable is an
entry data to the calculation of one of the three codes included in the FWI, and
refers to the state of the fine fuels and then is used in the propagation velocity
calculus too. The use of a model to derive the wind velocity was not evaluated, but
with the scarcity of stations that use the National Meteorological Service it is
impossible to infer wind velocity of more local winds.
There are some studies that related the calculated values of the Canadian Drought
Code (DC) with Vegetation Index derived from satellite data. Aguado et al. (2003)
found a good correlation between that code with NDVI and NDVI/ST derived from
NOAA images. The Drought Code is related to the water content of the large fuels
and the live fuels and is determined by seasonal meteorological conditions. That
danger condition influenced by the daily variability of the meteorological situation,
like that evaluated with the Canadian Fine Fuel Moisture Code (FFMC), are impossible
to be evaluated only with satellite data. The determination of wind velocity is
necessary and this is only possible using data of meteorological stations.
The resulting values of the different interpolated variables must be used to calculate
the different codes of the FWI and then analyze which are the biases respect to the
non interpolated values. This could be compared with the result achieved with the
correlation between the Drought Codes and Vegetation Indexes.
The efficient use of danger indexes or danger maps to define strategies of
prevention and a rational assignation of resources to the fire suppression needs that
the variables involved in the danger determination, had a good correlation with
reality. The scarcity of meteorological stations is a restriction that attempts
negatively for a good spatial forecast of meteorological danger conditions. This is
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more critical with those variables with a high spatial variation like wind and
precipitation, where more density of meteorological stations for amore reliable
mapping with interpolation techniques is necessary. The use of satellite data could
improve the mapping of some conditions.
4
4.1
METHODOLOGY FOR REMOTE SENSE FUEL MOISTURE CONTENT
INTRODUCTION
Risk of wildfire ignition is based on two main factors: meterological variables and
plant fuel moisture content (FMC). FMC is a key-parameter for ignition risk, due to its
important role in the ignition mechanism. As field measurements of FMC are
relatively time-consuming, and cannot be obtained for large areas, a method that
allows the estimation of these values from satellite or other remote sensing images
has a potentially important use in ignition risk mapping. This is, however, difficult,
and requires a combination of field measures of FMC values, together with a set of
remote sensed images.
4.2
- METHODOLOGY
We have focused initially on a single fire-prone vegetation type : the shrub-like
garrigue on calcareous soils of southern France.
Figure 11: localisation map of the study sites and shrublands distribution in
Provence
Values of FMC and point measures have been obtained during the fire-risk period in
2007. We have developed a method for downscaling these point measurements to a
resolution roughly equivalent to that of remote sensing images. The current aim is to
attempt to establish a link between these FMC values and remotely sensed images.
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We acquired a set of Spot images to establish a method of mapping risk from
satellite images (Images Spot: copyright CNES, Distribution Spot image, ISIS
programme 2008).
Sensor
date
Incidence angle
SPOT 4
2007-05-19
-22.32°
SPOT 4
2007-05-24
-16.5°
SPOT 5
2007-06-13
-28.18°
SPOT 4
2007-06-29
-4.92°
SPOT 4
2007-07-05
-27.17°
SPOT 4
2007-08-05
-21.63°
SPOT 4
2007-09-20
-1.53°
Table 6: Available Spot images for summer 2007 on test area around Aix-enProvence
These raw images can not be compared directly: The nature of remote sensing
requires that solar radiation pass through the atmosphere before it is collected by
the instrument. Because of this, remotely sensed images include information about
the atmosphere and the earth’s surface. If we are interested in quantitative analysis
of surface reflectance, removing the influence of the atmosphere becomes a critical
pre-processing step.
4.2.1 - Atmospheric corrections
To compensate for atmospheric effects, properties such as the amount of water
vapour, distribution of aerosols, and scene visibility must be known (Adler-Golden et
al 1999). Because direct measurements of these atmospheric properties are rarely
available, there are techniques that infer them from their imprint on multispectral
radiance data (Berk et al. 1998). These properties are then used to constrain highly
accurate models of atmospheric radiation transfer to produce an estimate of the true
surface reflectance. Moreover, atmospheric corrections of this type can be applied on
a pixel-by-pixel basis because each pixel in a multispectral image contains an
independent measurement of atmospheric water vapour absorption bands.
4.2.1.1 - Theory of Atmospheric corrections with ENVI’s FLAASH model
(MODTRAN4)
We choose an "easy to use" first-principles atmospheric correction modelling tool for
retrieving spectral reflectance from hyperspectral radiance images the ENVI®’s Fast
Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module.
FLAASH model can accurately compensate for atmospheric effects, correcting
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wavelengths in the visible through near-infrared and short-wave infrared regions, up
to 3000 nm.
Unlike many other atmospheric correction programs that interpolate radiation
transfer properties from a pre-calculated database of modelling results, FLAASH
incorporates the MODTRAN4 radiation transfer code. FLAASH starts from a standard
equation for spectral radiance at a sensor pixel, L, that applies to the solar
wavelength range (thermal emission is neglected) and flat, Lambertian materials or
their equivalents. The equation is as follow:
(1)
Where:
ρe is an average surface reflectance for the pixel and a surrounding region;
S is the spherical albedo of the atmosphere;
La is the radiance back scattered by the atmosphere;
A and B are coefficients that depend on atmospheric and geometric
conditions but not on the surface.
Each of these variables depends on the spectral channel.
The first term in Equation (1) corresponds to radiance that is reflected from the
surface and travels directly into the sensor, while the second term corresponds to
radiance from the surface that is scattered by the atmosphere into the sensor. The
distinction between ρ and ρe accounts for the adjacency effect (spatial mixing of
radiance among nearby pixels) caused by atmospheric scattering. However, this
correction can result in significant reflectance errors at short wavelengths, especially
under hazy conditions and when strong contrasts occur among the materials in the
scene.
The values of A, B, S and La are determined from MODTRAN4 calculations that use
the viewing and solar angles and the mean surface elevation of the measurement,
and they assume a certain model atmosphere, aerosol type, and visible range.
The values of A, B, S and La are strongly dependent on the water vapour column
amount, which is generally not well known and may vary across the scene. To
account for unknown and variable column water vapour, the MODTRAN4 calculations
are looped over a series of different column amounts, then selected wavelength
channels of the image are analyzed to retrieve an estimated amount for each pixel.
For images that do not contain bands in the appropriate wavelength positions to
support water retrieval (like SPOT), the column water vapour amount is determined
by the user-selected atmospheric model.
After the water retrieval is performed, Equation (1) is solved for the pixel surface
reflectance in all of the sensor channels. The solution method involves computing a
spatially averaged radiance image Le, from which the spatially averaged reflectance
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ρe is estimated using the approximate equation:
(2)
Spatial averaging is performed using a point-spread function that describes the
relative contributions to the pixel radiance from points on the ground at different
distances from the direct line of sight.
The FLAASH model includes a method for retrieving an estimated aerosol/haze
amount from selected dark land pixels in the scene. The method is based on
observations by Kaufman et al. (1997) of a nearly fixed ratio between the
reflectances for such pixels at 660 nm and 2100 nm.
FLAASH retrieves the aerosol amount by iterating Equations (1) and (2) over a series
of visible ranges, (17 km to 200 km). For each visible range, it retrieves the scene
average 660 nm and 2100 nm reflectances for the dark pixels, and it interpolates the
best estimate of the visible range by matching the ratio to the average ratio of ~0.45
that was observed by Kaufman et al. (1997). Using this visible range estimate,
FLAASH performs a second and final MODTRAN4 calculation loop over water.
4.2.1.2 - Implementation of Atmospheric corrections with ENVI’s FLAASH model
(MODTRAN4)
The input image for FLAASH must be a radiometrically calibrated radiance image and
input data has to be floating-point values in units of μW/cm2 * nm* sr. After
normalization (level 1A or more) and without digital dynamic stretching, the
numerical level in the image Xk (DN) is proportional to the input radiance Lk:
Lk=
Xk
Ak Gmk
Where: k is the spectral band,
Ak is the absolute calibration coefficient,
Gmk is the analogue gain (on-board amplifier), depending on the gain
number m.
The gain number used to address Gmk is provided with the auxiliary data of the
image and ranges 1 to 6 on SPOT4 and from 1 to 10 on SPOT5. In the DIMAP format
(SPOT Scene) it is named ‘GAIN_NUMBER’. Gmk is also provided in the metadata
when the format is DIMAP SPOT Scene under the name ‘GAIN_ANALOG_VALUE’.
When the image is programmed, the gain number is generally optimized using a
statistical estimate of the observed reflectance based on SPOT images previously
taken over the same target (Meygret, 2007).
Scene and sensor information include the scene centre location (latitude/longitude),
the average ground elevation of the scene, the sensor type, the sensor altitude, and
the flight date and time. These data let FLAASH determine where the sun was in the
sky and the path of sunlight through the atmosphere to the ground and back to the
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sensor. We choose one of the standard MODTRAN model atmospheres whose
standard column water vapour amount is similar to that expected for each scene.
The standard column water vapour amounts (from sea level to space) for each
model atmosphere are given in Table 7.
Table7: Column Water Vapor Amounts and Surface Temperatures for the ODTRAN
Model Atmospheres.
The “atm-cm” unit is specific to the atmospheric science community, which typically
uses one of two units to measure the total amount of a gas in the atmospheric
column from the ground to the top of the atmosphere (where 200 to 300 km is
generally a good number for the location of the top). Using units of “atm-cm,” Is
equivalent to bring all the water molecules down to a thin layer of pure water vapour
at the Earth's surface, at 1 atm of pressure and 0 degrees C. That layer has a
thickness measured in centimetres, so the water column is described in atmospherecentimetres. If the pressure were doubled, then the thickness would be halved.
Thus, the units of atm-cm (not just cm) are used to describe the amount of gas in
the atmospheric column to emphasize that the height and pressure are
interdependent. The second set of units, g/ cm2, is more easily understood as the
mass of water molecules in the atmospheric column over each cm2 of ground
surface. Since liquid water has a 1 g/cm2 density, this value is numerically equal to
the number of centimetres of water on the ground if all the atmospheric water rained
out at once (ENVI FLAASH tutorial).
To solve the radiative transfer equations that allow apparent surface reflectance to
be computed, the column water vapour amount for each pixel in the image must be
determined. We used a constant column water vapour amount for all pixels in the
image determined according to the standard column water vapour amount for the
selected Atmospheric Model, multiplied by an optional Water Column Multiplier (see
Table 2). The selected Aerosol Model is an Urban one: a mixture of 80% rural
aerosol with 20% soot-like aerosols, appropriate for high-density urban/industrial
areas. For each image, we enter an estimate of the scene visibility in kilometers. The
initial visibility value is assumed for the atmospheric correction if the aerosol is not
being retrieved. The visibility, V is defined as the 550 nm meteorological range and is
extinction coefficient R is defined as the horizontal optical depth per km. A related
value, the aerosol optical depth (AOD) is measured vertically (from the ground to
space). To convert the AOD to R, the AOD must be divided by the effective aerosol
thickness layer, which typically has a value of around 2 km, but varies with the
visibility and elevation.
The whole process is described and synthesised in the following scheme (figure 12).
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Image pre-processing
Physical gain
Bands re-order
IMAGES
Scene orientation
Incidence angle
Sun azimuth
Sun elevation
Input data parameters for ENVI FLAASH
atmospheric correction module (MODTRAN4 Model)
Atmospheric
corrections
Radiance
IMAGES
Radiance
conversion
Corrected
image
Visibility
Elevation
Imaging date & time
Sensor type
Figure 12: Synthetic scheme of multi-date image processing using ENVI® FLAASH
(MODTRAN4 model)
For each image we acquired meteorological data around the centre point thanks to
an alternate network of meteorological stations giving information hourly (figure 13).
Figure 13: Localisation map of free meteorological stations available in the images
area
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Table 8: meteorological data of Salon de Provence station for date 2007/05/19
4.2.2 Vegetation indices
4.2.2.1 - Normalized Difference Vegetation Index
Teillet et al. (1997) demonstrated that Vegetation Indices derived from satellite
image data have become one of the primary information sources for monitoring
vegetation conditions and mapping land cover change. The most widely used
vegetation index in this context is NDVI, the normalized difference vegetation index,
which is a function of red and near-infrared spectral bands. Given that the spectral
and spatial of imagery in the red and near-infrared vary from sensor to sensor, NDVI
values based on data from different instruments will not be directly comparable.
The Normalized Difference Vegetation Index (NDVI) is one of the oldest, most well
known, and most frequently used VIs. The combination of its normalized difference
formulation and use of the highest absorption and reflectance regions of chlorophyll
make it robust over a wide range of conditions. It can, however, saturate in dense
vegetation conditions when LAI becomes high. NDVI is defined by the following
equation:
NDVI = NIR-Red / NIR+Red
The value of this index ranges from -1 to 1. The common range for green vegetation
is 0.2 to 0.8.
4.2.2.2 - Normalized Difference Infrared Index
Canopy water content has been estimated by various vegetation indices. It is well
known that shortwave infrared reflectances (SWIR) are negatively related to the leaf
water content due to the large absorption by leaf water (Ceccato et al., 2001).
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However, a SWIR band alone is not adequate and must be contrasted with a NIR
band to estimate the VWC, since the other leaf parameters (e.g.internal leaf
structure) also affect the SWIR reflectance (Gao, 1996; Ceccato et al., 2001, Yilmaz
et al. 2008). For this study, a combination of SWIR and NIR bands was used to
calculate Normalized Difference Infrared Index (NDII) from Hardisky et al. (1983).
They showed NDII was related to canopy water content, and provided a name (used
here) that does not to refer specifically to a single sensor.
The Normalized Difference Infrared Index (NDII) is a reflectance measurement that
is sensitive to changes in water content of plant canopies. The NDII uses a
normalized difference formulation instead of a simple ratio, and the index values
increase with increasing water content. Applications include crop agricultural
management, forest canopy monitoring, and vegetation stress detection. NDII is
defined by the following equation:
NDII = NIR-SWIR / NIR+SWIR
The value of this index ranges from -1 to 1. The common range for green vegetation
is 0.02 to 0.6.
4.2.2.3 - Reduced Sample Ratio
The reduced simple ratio (RSR) is a vegetation index containing an additional
shortwave infrared (SWIR) term for better derivation of LAI (Chen et al., 2002). It is
calculated by using the red, near infrared and shortwave infrared reflectance (Brown
et al., 2000) so that:
Where: ρ(λred), ρ(λNIR), and ρ(λSWIR) are the reflectances in red, near
infrared, and shortwave infrared bands.
ρ (λSWIRmin ) and ρ (λSWIRmax) are the 1% minimum and maximum
reflectance in the whole scene respectively (Chen et al., 2002).In calculating
RSR, the SWIR (Spot band 4) was used to normalize the influence of
vegetation cover types and the background
(e.g. understory, soil) so that RSR greatly improved LAI retrieval in mixed
forest (Chen et al., 2002).
4.3 FMC evaluation on plots targets with field measurements and image
segmentation
Field sampling method is the same as this already described in Deliverable 3.4.1 &
3.4.2. 45 plots on calcareous soils were extensively described. Plots were chosen in
homogeneous site conditions, corresponding to the medium fertility class (Ladier and
Ripert, 1993) to prevent any difference of litter biomass due to site fertility. Plots
were located with a GPS.
Fuel macrostructure was described on those plots of 20x20 meters: fuel height
(trees, shrubs), tree diameter, fuel heterogeneity (garrigue), vegetation composition
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(dominant species in the upper and lower strata), specific measurements for trees
(height, crown volume, crown base height, crown diameter, vertical distribution of
biomass using direct estimation).
Figure 14: fuel types sampled in calcareous Provence: pine stand and garrigue
shrubs (France).
The detailed measurements are listed below:
o
Covering percentage by each vegetation stratum (visual estimation, 1/10):
dominant or subdominant trees (height > 10 m), dominated trees (height 610 m), understory (height 3-6 m, 1-3 m, < 1 m), herbaceous covering, litter
covering.
o
Horizontal and vertical fuel structure:

For each tree (diameter at breast height > 7.5 cm): precise location of the
centre of the stem (using x and y coordinates along the decametre strips),
precise coordinates of the edges of the crown (x and y for the north-south
aspect and the eats-west aspect), species, dead/alive/damaged, diameter at
breast height, height (using a pole or a Vertex dendrometer)

For clusters composed of young trees and/or shrubs: clusters are groups of
individuals which are clearly distinct in the field (fuels of low/heterogeneous
biomass). Each cluster is described as indicated above for the trees (location
of centre, crown diameters, species, and height)

For continuous and fully closed fuels (e.g. continuous Quercus coccifera
fuel bed) with fully connected and intermingled individuals, we used a series
of transects within plots. In each 10x10 m subplot, we locate 3 transects. The
first edge transect is located at 2 meters from the plot edge to prevent any
edge effect. Each of the following transect is located at 3 meters from the
precedent. The coordinates of transects are 2, 5, 8, 12, 15, and 18 meters in
x and in y. On each point of measurement, we note the dominant species, it
height and the covering percentage by the whole fuel bed.

For patches of bare soil or rock outcrop, we locate the coordinates of the
centre and the edges of the patch, the nature (bare soil, rock, and litter)
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Please note: the superposition of several vegetation strata is common (e.g. isolated
pines on matorrals cluster and patches of bare soils). In this case, each successive
stratum will be described as indicated above.
4.3.1 Upscaling of FMC values
There is an important difference in the spatial resolution of the field measures of
FMC and reflectance (point measures) and remote sensed image (pixel resolution).
Each pixel will integrate the variation in vegetation over an area that may vary from
several metres to a kilometre, depending on the captor. A method is needed that can
upscale the point samples of FMC to a given image resolution. In light of the
individual species responses described above, the method should retain as much
information as possible on the variation of FMC values within a pixel.
Figure 15: Map of the five sampled species within a 20x20 m sampling plot.
The method proposed here establishes an overall distribution of FMC values for an
area equivalent to the size of an image pixel, by combining the individual species
distributions (figure 15). These individual distributions are weighted by the
percentage coverage of each species in the study area, obtained from a detailed
description of the vegetation (figure 14). This enables us to provide an integrated
FMC value for the pixel (the most probable value) as well as the possible range of
values.
Pr[FMCp] 
W i* Pr[i]
i 1,n
W i* n
Where FMCp is the integrated FMC for a pixel,
Pr[i] is the distribution of values for species i, and
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Wi is the proportion of coverage of that species.
Values obtained may be mapped as a most probable FMC mean value by using
accurate fuel coverage (see D 3.4.2).
4.3.2 Calculation of FMC on a plot taking in account the percentage cover
of each species and soil.
In plots composition accurately described, we took in account soil as a very
important factor in the pixel radiometry. We applied a segmentation method with
ENVI® software on very high resolution images (Quickbird june 2006) in order to
calculate the soil percentage on the 9 plots areas.
The results on the plots composition (percentage vegetation cover on 5 species and
soil) shows that inside our garrigue shrubs fuel type in calcareous Provence, soil is
varying from 4% to 28 % (table4).
Plot
SPECIES cover
Rosmarinus
Officinalis
Quercus
Coccifera
Juniperus
Oxycedrus
Ulex
Parviflorus
(%)
Pinus
Halepensis
(%)
Soil
(%)
(%)
(%)
(%)
FMC1
19,09
18,41
26,27
24,33
3,90
8,00
FMC2
10,2
56,9
2
0,5
2,4
28
FMC3
19
47,3
10
7
1,5
15,2
FMC4
35,88
4,78
23,78
18,48
8,78
8,3
FMC5
26,85
6,11
33,72
19,88
7,94
5,50
FMC6
20,44
49,84
11,24
8,83
2,74
6,91
FMC7
20,7
51,5
12,8
8,68
2,4
3,92
PC29
8,23
68,16
8,23
8,23
0
7,15
PC31
3
86,1
1,9
4
0
5
Table 9: FMC plots characteristics in term of vegetation cover
For each date, we calculate a mean value of FMC on the 9 garrigue shrub plots.
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4.4
Results
4.4.1 Comparison of estimated ground FMC and vegetation indices.
First we use a simple regression in order to correlate each Vegetation Index
(paragraph 4.2.2) with the estimated FMC calculated with the field measurements
(see paragraph 4.3.2). The best fit model correlated NDII with estimated FMC (figure
16) with Pearson r2 of 0.47 for NDVI, 0.65 for NDII, and 0.44 for RSR
0,15
0,13
20
20
18
18
16
16
14
14
0,07
0,06
0,05
0,11
0,09
12
0,07
10
y = -1,5572x + 19,788
R2 = 0,8829
8
0,05
0,04
12
MOYNDII
FMCTmoy
10
0,03
Linéaire (MOYNDII)
Linéaire (FMCTmoy)
8
MOYFMCT
MOYNDII
0,02
6
6
0,01
0,03
4
4
y = -0,0069x + 0,0647
R2 = 0,571
0,01
0
2
2
0
-0,01
1
2
3
4
5
6
7
-0,01
19/05/07 24/05/07 13/06/07 29/06/07 05/07/07 05/08/07 20/09/07
0
Figure 16: estimated FMC and NDII curves
4.4.2 - Multiple Regression
We performed a multiple regression to explain estimated FMC as a combination of
NDII and RSR vegetation indices (see tables 10 and 11)
Table 10 : Box-Cox Transformation : puissance = 1,0 decalage = 0,0
Parameter
Estimation Error type T
Probability
CONSTANTE
1,71051
6,89394
0,248118 0,8201
RSR
4,35591
5,39277
0,807732 0,4784
NDII
155,406
39,2587
3,95851
0,0288
Table 11: Variance Analysis
Source
Somme
carrés
des Ddl mean square F
Model
65,5933
2
32,7966
Residue
8,10222
3
2,70074
Total (Corr.) 73,6955
5
Probability
12,14 0,0365
NDII has a larger effect than RSR and the whole model predict 81,7% of the
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variance (adjusted r2) with the mean absolute error of 1.05. The model is able to
predict estimated FMC value as (with F=12.14 and P=0.0365):
FMC = 1, 71051 + 4, 35591*RSR + 155,406*NDII
The model shows a positive trend between estimated FMC and the vegetation indices
and a good correlation level.
However, cautiousness is necessary when using the model for prediction due to the
limited dataset.
4.5
Conclusion
A method to assess the relation between vegetation indices calculated on Spot4
images and estimated FMC on the ground has been successfully designed. However,
results have to be improved by multiplying the dates of image acquisition during the
spring and summer 2006.
The method could be transposed to available sensors with similar resolution
(Landsat, Aster). The next phase consists in testing the method with available daily
sensor (modis) but with medium resolution.
The objective is to define a good pixel indicator for each fuel type on pixel of 250m
(NIR Band) or 500 m (SWIR Band) resolution inside a 1 km2 buffer, in order to avoid
mistakes in geolocalisation:
 Within Very homogeneous Garrigue shrub like (0.7 to 1.5m),
 Within Homogeneous scrubs (0.1 to 0.5m),
 Within Mature low density Pine stand mixed understory garrigue fuel.
The sampling methodology must be adapted to medium resolution sensors : a
regular sampling must be done every meter on perimeter of the plot, and also on the
2 diagonals ; image segmentation information on very high resolution sensor must
be added in order to calculate soil coverage percentage within the plot.
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5 METHODOLOGY FOR THE DEVELOPMENT OF A DROUGHT INDEX
APPLICABLE IN THE MEDITERRANEAN CONDITIONS
5.1
Analysis of the empirical drought indices concept
By analyzing the model equations and the parameters included in the indices
examined, we can distinguish two indices categories; the cumulative indices and the
daily ones. Most of the indices are cumulative and follow a similar pattern in their
evolution over time, i.e. they increase steadily with no rain and fall down or are
reduced when rain occurs. The most widely used indices such as the Keetch-Byram
drought index KBDI, the Nesterov index NI, the Modified Nesterov index MNI, the
Zhdanko index ZI belong to this category. Also, the cumulative water balance index
(CWBI) of Dennison et al. (2003) belongs to this category.
Only few indices belong in the second category, being the most representative is the
Sweden Angstrom Index. However, since a wildland is not a static, closed system,
where inputs and losses from the annex systems continuously occur, a cumulative
index seems to be more representative for the system status. This explains why most
of the drought indices are cumulative and thus, why our effort aims at the
development of an index that should have a cumulative concept. The index will be
developed based on daily observations, especially during the summer since most
wildfires occur during this period of the year. The problem of the accurate estimation
of the potential evapotranspiration is also important, since it has been proven that it
is difficult to estimate PET accurately and thus it should be used with caution for
estimating actual water loss from natural systems (Lu et al. 2005).
We should note that the indices that include this parameter such as KBDI, German
Baumgartner index and CWBI, use different methods for PET estimation. Also, when
Spano et al. (2005) replaced the current estimation method of PET in the KBDI index
with the Hangreaves and Samani method, the seasonal trend obtained seems
consistent with the actual seasonal changes in fuel availability and fire danger.
Improvement of KBDI calculation was also found by Snyder et al. (2006) when they
substitute the Samani-Hangreaves evapotranspiration estimation method in the
equation of KBDI. Samani-Hangreaves method for PET estimation was also chosen
by Pinol et al. (1998) to calculate wildfire hazard indices based on daily
meteorological data. On the contrary, Lu et al. (2005) based on the criteria of
availability of input data and correlations with AET values, recommend the PriestleyTaylor, Turc and Hamon methods for evapotranspiration estimation in the
southeastern United States. Spatial variability of rain should be also considered since
the local differentiation of summer rains is a common phenomenon. However, some
specific comments are made on the most widely used indices.
Since KBDI is a drought index based on the capacity of soil to hold water it is limited
by the filed capacity (Buchholz and Weidemann 2000); this capacity is assumed to be
200 mm, but this is not always true. Spano et al. (2005) reported also that KBDI
seemed to be underestimated during the most of the year, when it was tested under
Mediterranean conditions in Italy. The index also, due to its cumulative concept,
presents especially high values during the end of September (Spano et al. 2005),
whereas fire activity is normally reduced, due to atmospheric conditions.
The Nesterov Index and the Modified Nesterov Index do not have this limitation and
thus they have no upper limit; this usually causes extraordinary values (ex. 18.000)
by the end of September (Buchholz and Weidemann 2000), which shows an extreme
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fire risk that it is not usually the case. The Nesterov Index, by definition, falls down
to zero if a rain with more than 3 mm precipitation occurs. This is a clear limitation of
the index since it assumes that no fire risk on a day with more than 3 mm
precipitation (Buchholz and Weidemann 2000).
The Angstrom index is a daily index that does not take into consideration the rainfall;
this seems to be a weakness for the index use.
5.2
Drought indices evaluation
Generally, the relative literature indicates that in almost all cases more than one
index can be successfully used, while the observed differences between the indices
tested depend a lot on the current weather conditions. However, it is well known
that fire risk indicators yield dissimilar results when they are applied to different
biomes or geographic regions, a fact that creates confusion concerning their
effectiveness (Viegas et al. 1999).
The main crucial and yet unresolved problem is the indices performance evaluation
(Verbesselt et al. 2006). Two well-established methods are generally used to
evaluate such indices. These methods consist of correlating indices with:
o
Fire activity data
o
Fuel moisture data
The direct determination of live fuels moisture is complex and requires field sample
collection (Castro et al. 2003), while field sampling is very costly in order to assure
spatial significance, and is seldom performed (Chuvieco et al. 2003). As a result most
of the efforts for testing drought indices are based on (historical) fire activity data
either as the number of wildfires or surface of burned area (Pinol et al.1998;
Buchholz and Weidemann 2000; Skvarenina et al. 2003; Groisman et al. 2005b;
Dolling et. al. 2005, Groisman et al. 2007).
It is pointed out that fire ignition and rate of spread are not depending only on the
weather conditions but they are also related to the amount and type of fuels,
topography, fire suppression systems and human activities (Chuvieco et al. 2003).
However, many efforts have been addressed to evaluate indices performance based
on the correlation with real fuel moisture data (Viegas et al. 2001; Dimitrakopoulos
and Bemmerzouk 2003; Castro et al. 2003; Dennison et al. 2003, Pellizzaro et al.
2007).
For example, several studies have shown that KBDI is related to vegetation water
status dynamics, especially for shrub species (Verbesselt et al. 2006), tree species
(Xanthopoulos et al. 2006), or for grass species (Dimitrakopoulos and Bemmerzouk
2003), while the use of KBDI for pine needles moisture estimation has failed
(Dimitrakopoulos and Bemmerzouk 2003). KBDI was also used by Olson (1980) and
Brown et al. (1989) as predictor of Live Fine Fuel Moisture Content (LFFMC) for
several plant species in California and Wyoming, respectively, with dissimilar results
(Viegas et al. 2001). Pellizzaro et al (2007) also found that KBDI was strongly
correlated with live fuel moisture content of the evergreen Mediterranean shrubs
Pistacia lentiscus and Phillyrea angustifolia. Viegas et al. (2001) found that one
output of the Canadian Forest Fire Weather Index, the Drought Code, can be used to
estimate the moisture content of live fine fuel of shrub type fuels during the summer
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period in Central Portugal and Catalunya (NE Spain). Dennison et al. (2003) found
that the suggested cumulative water balance index (CWBI) demonstrated a strong,
nonlinear relationship with the live fuel moisture in California conditions. On the
other hand the Nesterov index (NI) was derived as an empirical function reflecting
the relationship between fire and weather based on historical data.
5.3
Methodology
5.3.1
Questions setting
In fact, the general sense is that most of the above mentioned indices can be
appropriate used with satisfactory results in many areas around the world. However,
some parameters can be further discussed such as:
1.
Which meteorological data can be used when there are no meteorological
stations in the reference area?
2.
Which ones from the existed models are applicable in the Mediterranean
conditions?
3.
Could the selected models be improved either by including additional
meteorological parameters or by modifications of the equations already used?
4.
Is it possible to elaborate a new drought index applicable in the
Mediterranean conditions?
5.3.2 Spatial accuracy of meteorological data
The first question concerns the spatial accuracy of the meteorological data needed
for the calculation of drought indices. Thus, determining an interpolation method for
estimating a drought index at specific locations is another important factor for
consideration. Spatial techniques for mapping drought indices include, among others,
inverse-distance weighting (IDW) and the kriging method presented in previous
chapters.
5.3.3 Indices selection for testing in the Mediterranean conditions
After the analysis of the available information derived from the literature we selected
those drought indices that, based on the literature, could be tested for fire risk
assessment in the European Mediterranean conditions. These indices are the KeetchByram drought index, the Nesterov index, the Modified Nesterov index, the Zhdanko
index and the Angstrom index.
The KBDI was selected since it is a widely used index in fire risk assessment and
widely accepted in the wildland fire community, even though it is reported that
climate regions that do not typically meet initialization conditions may be less suited
for a KBDI use (Janis et al. 2002). However, the index was successfully correlated
with grass moisture content and upper soil moisture (Dimitrakopoulos and
Bemmerzouk 2003) and it is considered strongly related to live fuel moisture content
(Aguado et al. 2003; Verbesselt et al. 2006, Xanthopoulos et al. 2006). Finally,
experience over the years has established the close relations existing between
difficult fire suppression and cumulative dryness or drought expressed by the KBDI
(Groisman et al. 2005a, Groisman et al. 2007).
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The Nesterov index, the Modified Nesterov index and the Zhdanko index are indices
with similar approach and development pattern, as the KBDI, and they were selected
aiming to investigate their applicability in the Mediterranean conditions. These
indices as well as the KBDI index follow the cumulative pattern and belong to the
first index category.
The Angstrom index was selected as a representative index of the second category
(daily indices) and because it is an index that includes relative humidity in its model
equation.
5.3.4 Validation methods of the selected models
In order to test and validate the selected models we followed the method of
correlating indices with real fuel moisture data. The method of correlation with fire
activity historical data, either as number of wildfires or as burned area surface, was
not selected since fire ignition and rate of spread are not depending only on the
weather conditions but they are also related to the amount and type of fuels,
topography, fire suppression systems and human presence.
Firstly, we gathered the available meteorological data from the nearby
meteorological stations and we constructed a time series of the drought indices
during the testing period (summer 2006 and 2007 respectively). The time series was
based on daily meteorological data. Secondly we designed and applied a field
campaign to collect the necessary field data in order to compute the real fuel
moisture content during the testing period.
5.3.4.1 Field campaign and data analysis
A work plan for the field data collection was organized for the study area
(Thessaloniki, northern Greece) in spring 2006. The aim of the field data collection
was: i) to test the selected indices with real fuel moisture data and ii) to use the data
in order to develop a new or modified drought index applicable in the Mediterranean
conditions. Thus, after field campaign the collected data were processed in order to:
o
test the selected drought indices in the specific conditions and
o
support the effort to produce a new or a modified index.
5.3.4.2
Description of the study area
The study area is the periurban forest of Thessaloniki, in northern Greece. This forest
is of high interest, since it constitutes the unique source of oxygen of the city, a city
that is developing with high rates. Additionally, approximately half of the forest was
burnt in a great wildfire in 1997. The forest extends at the NE part of the city and
occupies an area of 2,979 ha. It is composed mostly of Pinus brutia plantations but
after the fire of 1997 an effort of transformation to mixed stands with broadleaved
species has been undertaken by the Forest Service (Tsitsoni et al. 2004).
The altitude of the study area ranges from 50m to 450m. The climate is
Mediterranean with 135 dry days; the dry period lasts from the middle of May to the
end of September. The mean annual precipitation is 397 mm and the mean annual
temperature is 15.6 oC. Mean minimum temperature of the coldest month is 6.2 oC
and the equivalent mean maximum temperature of the warmest month is 26.0 oC
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according to the data from the meteorological station of the University of
Thessaloniki (period 1997-2007). Geologically, the area belongs to the magmatic
series of Chortiatis and consists mainly of green-schists. The soils are slightly acid up
to neutral, shallow up to middle depth, poor of nutritious ingredients having a high
percentage of stones and pebbles.
5.3.4.3
Data collection and process
Three locations were selected for field data collection with the following main
characteristics:
o
the first in a pine stand (Pinus brutia) in a north aspect
o
the second in a pine stand but in south aspect and
o
the third in an evergreen shrubland (Quercus coccifera dominated) in south
aspect
Three sampling replications per location were applied at a distance of 50 m.
Figure 17: Field plots at Thessaloniki (Greece) area
5.3.4.4
Variables selected to be monitored
Three variables were selected to be monitored.
o
Surface soil moisture content
o
Litter moisture content, as the dead fuel moisture content
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o
Grass moisture content, as the live (ground) fuel moisture content
Soil samples were taken by excavating the surface horizons and taking a soil quantity
of approximately 0.5 kg from each sampling point. Samples of litter were collected
by cutting square blocks (≈ 12 cm x 12 cm) from the forest floor (Wotton et al.
2005). Samples of grass were collected by cutting all the above ground part of the
grasses found in the forest floor, from square blocks (≈ 25 cm x 25 cm).
For all sampling cases three samples (which they were intermixed) in each
replication for each location were taken. The monitoring schedule was twice per
week from July to end of October for 2006 and from May to June for 2007, even
though the sampling in some cases was carried out at more rare periods, due to
unexpected reasons.
Field samples were taken and their moisture content was found as follows:
o
Fresh weight measurement
o
Oven-dry at 72 0C for 48 hours
o
Dry weight measurement
o
Moisture content as: (Fw - Dw)*100 / Dw
Where:
Fw = the fresh weight
Dw = the dry weight
5.3.4.5
Data screening
The collected field data were used to calculate the moisture content of the upper soil
layer, the litter (dead fuel) moisture content, and the grass (live fuel) moisture
content, and to construct a time series for the above variables during the testing
periods.
Then a correlation procedure was performed for determining any significance
relationship between the selected drought indices and the real fuel moisture data.
Furthermore, we examined the possible relationship among all the available
meteorological variables (mean, maximum and minimum temperature), relative
humidity, rainfall and wind speed with the tested drought indices and the real
moisture data. The results of the statistical analysis then were used in order to:
o
evaluate the applicability of the selected empirical drought indices
o
identify the spatial variability of moisture content and the factors responsible
o
to identify the significant (key) factors that could be used in further statistical
analysis in order to improve the indices applicability
o
to develop a procedure for a elaboration of new or modified drought index
applicable more successfully in the Mediterranean conditions
D5.1-1-33-1000-1
Page 60 of 89
5.4
Results
Generally, the statistical analysis of the collected field data and their correlation with
the tested drought indices calculated from the local meteorological data shows that
almost all the selected drought indices are applicable in the area since they are
significantly correlated with real field moisture data.
However, concerning the real moisture data, only slight spatial differentiation was
observed between the three sampling locations. The observed values of the live fuel
moisture, the litter moisture and the upper soil moisture content for the three
sampling locations are shown in Figure 18. The lowest values of fuel moisture
content, and thus the higher risk potential, were recorded during the period from the
beginning of August till approximately 20th of September. The relative heavy rainfall
during the end of September increased the moisture content of the fuels and the
upper soil layer.
140
Soil N P
Litter N P
Moisture content (%) .
120
Grasses N P
Soil S P
100
Litter S P
Grasses S P
80
Soil S Q
60
Litter S Q
Grasses S Q
40
20
01
/0
7
08 /06
/0
7
15 /06
/0
7
22 /06
/0
7
29 /06
/0
7
05 /06
/0
8
12 /06
/0
8
19 /06
/0
8
26 /06
/0
8
02 /06
/0
9
09 /06
/0
9
16 /06
/0
9
23 /06
/0
9
30 /06
/0
9
07 /06
/1
0
14 /06
/1
0
21 /06
/1
0
28 /06
/1
0
04 /06
/1
1/
06
0
Date
Figure 18a: Observed values of the live fuel, litter and upper soil moisture content for the three
sampling locations, during summer 2006 (P: pine forest, Q: Quercus coccifera
shrubland, N: north aspect, S: south aspect).
D5.1-1-33-1000-1
Page 61 of 89
320
Soil N P
280
Moisture content (%)
Litter N P
240
Grasses N P
Soil S P
200
Litter S P
160
Grasses S P
Soil S Q
120
Litter S Q
Grasses S Q
80
40
23
/4
/2
00
7
30
/4
/2
00
7
7/
5/
20
07
14
/5
/2
00
7
21
/5
/2
00
7
28
/5
/2
00
7
4/
6/
20
07
11
/6
/2
00
7
18
/6
/2
00
7
25
/6
/2
00
7
0
Date
Figure 18b: Observed values of the live fuel, litter and upper soil moisture content,
during spring and early summer 2007 (P: pine forest, Q: Quercus coccifera
shrubland, N: north aspect, S: south aspect).
It must be also noted the very low moisture values of the upper soil and litter during
the year 2007, that reach at 2-3% for the upper soil and 5-6% for the litter. The
high variation also of the grasses moisture during the spring can be attributed to the
different phonological stages that the grass species are during this period, so they
present a high fluctuation in moisture status.
The daily fluctuation of the meteorological variables (mean temperature, maximum
temperature, mean soil temperature, relative humidity and precipitation) at the study
area, during years 2006-2007, is depicted in Figure 19.
Mean air temp
Max air temp
Mean Soil Temp
Rain(mm)
RH%
Daily meteorological variables
100
90
80
70
60
50
40
30
20
10
0
/ 20
1/1
06
/20
1/3
06
/20
06
1/5
/20
1/7
06
/20
1/9
06
6
00
1/ 2
1/1
/20
1/1
07
/20
1/3
07
7
0
/20
1/5
/20
1/7
07
/20
1/9
07
7
00
1/ 2
1/1
Day of the year (DOY)
Figure 19: Daily fluctuation of the meteorological variables at the study area.
D5.1-1-33-1000-1
Page 62 of 89
Table 12 shows that almost all the selected empirical drought indices present
significant correlations with the real fuel and soil moisture content. The soil moisture
content is the most highly correlated variable while the litter variable the less
correlated. By far the best fitted model is that of KBDI that is highly correlated with
grasses moisture content (r = 0.92), soil moisture (r = 0.83) and quite lower with
litter moisture content (r = 0.56), but even in that case, the correlation is significant
at the 0.05 level.
The Modified Nesterov and Zhdanko indices present similar results with quite high
correlations with real moisture data; on the contrary, the Nesterov index shows the
lowest degree of applicability under the Mediterranean conditions.
The Angstrom index failed to be correlated with grass moisture content, but it
presented the highest correlation with the litter moisture content (r = 0.70), and
quite high correlation with soil moisture content.
Concerning the relationship between the meteorological variables and the real
moisture data, from the data shown in Table 13 it is observed that there is a quite
high correlation. Soil and litter moisture content are significantly correlated with
temperature and relative humidity while grass moisture is correlated only with
relative humidity. Rain seems to be not linearly correlated with any field data.
The correlation between the selected drought indices and the meteorological
variables is depicted in Table 14. Angstrom index is the highest correlated index with
meteorological variables while due to its form the KBDI index shows the lowest
correlation with meteorological variables.
In the text bellow the performance of each tested drought index in the study area is
analyzed.
5.4.1 Performance of the Keetch-Byram drought index (KBDI)
KBDI presents the highest correlation values with real moisture data (Table 12). The
correlation coefficient is very high (r = 0.92) in the case of grass (live fuel) moisture
content.
However, similar good fitting results were found by Dimitrakopoulos and
Bemmerzouk (2003) for three grass species in southern Greece (Creta). Soil
moisture is also highly correlated with KBDI (r = 0.83), while the litter moisture was
surprisingly the less correlated variable (r = 0.56) with KBDI; however, even in this
case the correlation is significant at the 0.05 level.
The time series of the KBDI during the testing period (years 2006-2007) is depicted
in Figure 20.
D5.1-1-33-1000-1
Page 63 of 89
KBDI
Keetch-Byram Drought index .
140
120
100
80
60
40
20
0
1/1
06
/20
1/3
06
/20
1/5
06
/20
1/7
06
/20
1/9
06
/20
1/1
0
1/2
06
1/1
07
/20
1/3
07
/20
1/5
07
/20
1/7
07
/20
1/9
07
/20
1/1
0
1/2
07
Day of the year (DOY)
Figure 20: Time series of the KBDI during years 2006-2007.
Looking at the estimated values of KBDI we can observe that the values are
relatively low during the studied period, since the index does not exceed the value
130 (520 of the original 800 scale), which may mean that the method for the
estimation of PET underestimates the actual water loss (Spano et al. 2005). The
index takes lower values during the year 2007 which means lower fire danger, in
contrast to what was observed for the other four indices, according to which, the fire
danger was estimated higher for the year 2007. Additionally, as it was observed in
many other cases, the index presents the highest values during the middle of
September, due to its development pattern.
However, it must be noted that the lowest values of moisture content were observed
at the same period.
D5.1-1-33-1000-1
Page 64 of 89
Table 12. Correlations between the selected empirical drought indices and real fuel and soil moisture content.
KBDI
Pearson Correlation
KBDI
Nesterov
Modified
Nest
Zhdanko
Angstrom
Soil
Moist(%)
Grass
Litter Moist(%) Moist(%)
1
.467(**)
.633(**)
.612(**)
-.546(**)
-.830(**)
-.558(*)
-.920(**)
.000
.000
.000
.000
.000
.020
.000
Sig. (2-tailed)
Nesterov
Modified
Nest
Zhdanko
D5.1-1-33-1000-1
N
129
129
129
129
129
53
53
53
Pearson Correlation
.467(**)
1
.929(**)
.939(**)
-.582(**)
-.597(*)
-.440
-.600(*)
Sig. (2-tailed)
.000
.000
.000
.000
.011
.077
.011
N
129
129
129
129
129
53
53
53
.633(**)
.929(**)
1
.998(**)
-.737(**)
-.753(**)
-.531(*)
-.765(**)
Sig. (2-tailed)
.000
.000
.000
.000
.000
.028
.000
N
129
129
129
129
129
53
53
53
Pearson Correlation
.612(**)
.939(**)
.998(**)
1
-.717(**)
-.756(**)
-.540(*)
-.760(**)
Pearson Correlation
Page 65 of 89
Angstrom
Soil
Moist(%)
Litter
Moist(%)
Grass
Moist(%)
D5.1-1-33-1000-1
Sig. (2-tailed)
.000
.000
.000
.000
.000
.025
.000
N
129
129
129
129
129
53
53
53
Pearson Correlation
-.546(**)
-.582(**)
-.737(**)
-.717(**)
1
.753(**)
.700(**)
.440
Sig. (2-tailed)
.000
.000
.000
.000
.000
.002
.077
N
129
129
129
129
129
53
53
53
Pearson Correlation
.830(**)
-.597(*)
-.753(**)
-.756(**)
.753(**)
1
.875(**)
.719(**)
Sig. (2-tailed)
.000
.011
.000
.000
.000
.000
.001
N
53
53
53
53
53
53
53
53
-.558(*)
-.440
-.531(*)
-.540(*)
.700(**)
.875(**)
1
.398
Sig. (2-tailed)
.020
.077
.028
.025
.002
.000
N
53
53
53
53
53
53
53
53
Pearson Correlation
.920(**)
-.600(*)
-.765(**)
-.760(**)
.440
.719(**)
.398
1
Pearson Correlation
Page 66 of 89
.113
Sig. (2-tailed)
.000
.011
.000
.000
.077
.001
.113
N
53
53
53
53
53
53
53
53
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
Table 13. Correlations between meteorological variables and real fuel and soil moisture content.
Mean
temp
Pearson Correlation
1
Sig. (2-tailed)
D5.1-1-33-1000-1
air Max
temp
air
Rain(mm)
RH%
Soil
Mois(%)
Litter
Mois(%)
Grass Mois(%)
.974(**)
-.146
-.414(**)
-.648(**)
-.653(**)
-.210
.000
.099
.000
.005
.004
.418
N
129
129
129
129
53
53
53
Pearson Correlation
.974(**)
1
-.202(*)
-.455(**)
-.730(**)
-.704(**)
-.344
Sig. (2-tailed)
.000
.022
.000
.001
.002
.177
N
129
129
129
129
53
53
53
Pearson Correlation
-.146
-.202(*)
1
.327(**)
.082
.289
-.164
Page 67 of 89
Sig. (2-tailed)
.099
.022
.000
.755
.261
.530
N
129
129
129
129
53
53
53
Pearson Correlation
-.414(**)
-.455(**)
.327(**)
1
.708(**)
.634(**)
.493(*)
Sig. (2-tailed)
.000
.000
.000
.001
.006
.045
N
129
129
129
129
53
53
53
Pearson Correlation
-.648(**)
-.730(**)
.082
.708(**)
1
.875(**)
.719(**)
Sig. (2-tailed)
.005
.001
.755
.001
.000
.001
N
53
53
53
53
53
53
53
-.653(**)
-.704(**)
.289
.634(**)
.875(**)
1
.398
Sig. (2-tailed)
.004
.002
.261
.006
.000
N
53
53
53
53
53
53
53
-.210
-.344
-.164
.493(*)
.719(**)
.398
1
.418
.177
.530
.045
.001
.113
Pearson Correlation
Pearson Correlation
Sig. (2-tailed)
D5.1-1-33-1000-1
Page 68 of 89
.113
N
53
53
53
53
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
D5.1-1-33-1000-1
Page 69 of 89
53
53
53
Table 14. Correlations between the selected empirical drought indices and meteorological variables.
KBDI
Pearson Correlation
KBDI
Nesterov
Modified
Nest
Zhdanko
Angstrom
Mean
temp
1
.467(**)
.633(**)
.612(**)
-.546(**)
.524(**)
.000
.000
.000
.000
Sig. (2-tailed)
Nesterov
Modified Nest
Zhdanko
air Max
temp
air
Rain(mm)
RH%
.569(**)
-.001
-.410(**)
.000
.000
.988
.000
N
129
129
129
129
129
129
129
129
129
Pearson Correlation
.467(**)
1
.929(**)
.939(**)
-.582(**)
.499(**)
.505(**)
-.194(*)
-.484(**)
Sig. (2-tailed)
.000
.000
.000
.000
.000
.000
.028
.000
N
129
129
129
129
129
129
129
129
129
Pearson Correlation
.633(**)
.929(**)
1
.998(**)
-.737(**)
.646(**)
.668(**)
-.224(*)
-.602(**)
Sig. (2-tailed)
.000
.000
.000
.000
.000
.000
.011
.000
N
129
129
129
129
129
129
129
129
129
Pearson Correlation
.612(**)
.939(**)
.998(**)
1
-.717(**)
.616(**)
.639(**)
-.234(**)
-.596(**)
Sig. (2-tailed)
.000
.000
.000
.000
.000
.000
.008
.000
D5.1-1-33-1000-1
Page 70 of 89
Angstrom
Mean
temp
RH%
129
129
129
129
129
129
129
129
129
Pearson Correlation
-.546(**)
-.582(**)
-.737(**)
-.717(**)
1
-.799(**)
-.812(**)
.293(**)
.878(**)
Sig. (2-tailed)
.000
.000
.000
.000
.000
.000
.001
.000
N
129
129
129
129
129
129
129
129
129
.524(**)
.499(**)
.646(**)
.616(**)
-.799(**)
1
.974(**)
-.146
-.414(**)
Sig. (2-tailed)
.000
.000
.000
.000
.000
.000
.099
.000
N
129
129
129
129
129
129
129
129
129
Pearson Correlation
.569(**)
.505(**)
.668(**)
.639(**)
-.812(**)
.974(**)
1
-.202(*)
-.455(**)
Sig. (2-tailed)
.000
.000
.000
.000
.000
.000
.022
.000
N
129
129
129
129
129
129
129
129
129
Pearson Correlation
-.001
-.194(*)
-.224(*)
-.234(**)
.293(**)
-.146
-.202(*)
1
.327(**)
Sig. (2-tailed)
.988
.028
.011
.008
.001
.099
.022
N
129
129
129
129
129
129
129
129
129
Pearson Correlation
-.410(**)
-.484(**)
-.602(**)
-.596(**)
.878(**)
-.414(**)
-.455(**)
.327(**)
1
air Pearson Correlation
Max air temp
Rain(mm)
N
D5.1-1-33-1000-1
Page 71 of 89
.000
Sig. (2-tailed)
.000
.000
.000
.000
.000
.000
.000
.000
N
129
129
129
129
129
129
129
129
** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).
D5.1-1-33-1000-1
Page 72 of 89
129
5.4.2 Performance of the Nesterov index
The time series of Nesterov index during years 2006-2007 is depicted in figure 21. The index
takes its highest value (18 813) on the 9th of August. According to the index values the
forest fire risk is extreme (values > 10 000) during the period from 25 of July until 9 of
August and in the period 8-18 of September, during the year 2006. While, during the year
2007 the index takes higher values and reaches the value 23,860 on 4th of August.. The
index presents comparatively the lowest correlation values with the real moisture data.
30000
Nesterov
Nesterov index
25000
20000
15000
10000
5000
0
6
7
06
06
06
06
06
07
07
07
07
07
00
00
/20 /3/20
/20
/20
/20
/20 /3/20
/20
/20
/20
1/2
1/2
1/1
1
1/5
1/7
1/9
1/1
1
1/5
1/7
1/9
1/1
1/1
Days of the year (DOY)
Figure 21: Time series of the Nesterov drought index during years 2006-2007.
5.4.3 Performance of the Modified Nesterov index
The values of the Modified Nesterov index during years 2006 and 2007, are shown in Figure
22. The index follows a similar development pattern as the Nesterov index. The index, during
the summer of 2006, takes its highest value on the 9th of August (19 389) while the period
when the forest fire risk is extreme (values > 10 000) is longer than that according to the
Nesterov index. This period is from 24 of July up to 9 of August and 4-18 of September.
Similar to the Nesterov index, the index takes higher values during the year 2007 and
reaches the value 24,309 on 4th of August, while (in contrast to Nesterov index), the
Modified Nesterov index presents improved correlation values with real moisture data. The
correlation values range from 0.76 for grass moisture content to 0.53 for litter moisture
content.
D5.1-1-33-1000-1
Page 73 of 89
30000
Modified Nesterov index
25000
20000
15000
10000
5000
0
06
/20
1/1
06
/20
1/3
06
/20
1/5
06
/20
1/7
06
/20
1/9
6
00
1/2
1/1
07
/20
1/1
07
/20
1/3
07
/20
1/5
07
/20
1/7
07
/20
1/9
7
00
1/2
1/1
Day of the year (DOY)
Figure 22. Time series of the Modified Nesterov index during years 2006-2007.
5.4.4 Performance of the Zhdanko index
The values of the Zhdanko index during years 2006 and 2007, are shown in Figure 23.
According to the data analysis, Zhdanko index seems to work like the Modified Nesterov
index. The index takes its highest value (596) in the same day as the Modified Nesterov
index (9 of August). Like the Modified Nesterov index, the index presents significant
correlation values with real moisture data. The correlation values range from 0.76 for grass
and soil moisture content to 0.54 for litter moisture content (quite similar to those of the
Modified Nesterov index).
30000
Modified Nesterov index
25000
20000
15000
10000
5000
0
06
/20
1/1
06
/20
1/3
06
/20
1/5
06
/20
1/7
06
/20
1/9
6
00
1/2
1/1
07
/20
1/1
07
/20
1/3
07
/20
1/5
07
/20
1/7
07
/20
1/9
7
00
1/2
1/1
Day of the year (DOY)
Figure 23: Time series of the Zhdanko drought index during years 2006-2007.
D5.1-1-33-1000-1
Page 74 of 89
5.4.5 Performance of the Angstrom index
The daily fluctuation of the Angstrom index during years 2006-2007 is depicted in Figure 24.
According to the index values the fire risk was high (values of the index between 2.0 - 2.5)
during the dates 17 of July, 7-9, 14-15, 17-20, 22 and 29-31 of August, and 6 of September.
However, during the year 2006, the lowest value 2.05 was observed on 7 of August, while
no values under the limit of 2.0 (extreme fire potential) was observed. On the contrary,
during the year 2007, the index takes lower values and reaches on the lowest value (0.88)
on 25th of July. Is should be mentioned that during the period 28/6 to 26/7 of 2007 the
index often takes values lower than the limit of 2.0 (extreme fire potential). The index
presented the lowest correlation values with grass moisture content, but the highest
correlation with the litter moisture content (r = 0.70), and quite high correlation with soil
moisture content (Table 3).
8
Angstrom index
7
6
5
4
3
2
1
0
/
1/1
06
06
06
06
06
07
07
07
07
07
06
07
20 3/20 5/20 7/20 9/20 1/20 1/20 3/20 5/20 7/20 9/20 1/20
/
/
/
/
/
1
1
1/
1/
1/
1/
1
1
1
1
1
/
/
1
1
Day of the year
Figure 24: Time series of the Angstrom index during years 2006-2007.
6 TOWARDS AN ADAPTED EMPIRICAL DROUGHT INDEX TO MEDITERRANEAN
CONDITIONS
Precipitation is the main water input to a natural system, and thus is the predominant factor
controlling the formation and persistence of drought conditions. Reliable rainfall observations
became available about two centuries ago, and as a result, practically all drought indices
included this variable either alone or in combination with other meteorological elements
(Heim 2002).
Temperature is a basic component of current weather conditions. High temperature affects
evapotranspiration rate and the drying speed of fuels and soil moisture.
Relative humidity also plays an important role in evapotranspiration rate.
High temperatures, low relative humidity, and desiccating winds usually add to the impact of
lack of rainfall (Heim 2002). Surface soil moisture status determines the water absorption
and the moisture content of the plants.
D5.1-1-33-1000-1
Page 75 of 89
Evapotranspiration is an important variable that expresses the water loss from a natural
system. However, difficulties in quantifying evapotranspiration rates, suggest that a general
classification scheme is best if it is limited to a simple measure of rainfall (Lloyd-Hughes and
Saunders 2002).
All the drought indices analyzed before, as well as those found in the literature, use the
following meteorological parameters: precipitation, air temperature (maximum, average,
dew-point and differences between maximum and dew-point temperature), relative humidity
and upper soil layer moisture. Additionally, many indices use an estimation of PET. However,
the variables most commonly used are precipitation and temperature. The drought indices
used in fire risk assessment were empirically developed to estimate the fuel dryness from
easily available meteorological data. However, many other parameters, such type and
amount of fuels, land topography, elevation and latitude can contribute to drought
conditions, but they are less important that the primary meteorological factors. The
assumption is that the meteorological variables determine the input and the loss of water
from a natural system.
From the above mentioned indices only the KBDI has been developed on a well stated
theoretical background of water losses from an ecosystem even if it is an empirical index. On
the contrary, all other indices are based on general assumptions that probably are not
always valid. Thus, the models of all the above mentioned indices are based on common
meteorological variables usually available by nearby meteorological stations. They were
mainly built on temperature values (usually daily maximum values and in some cases the
dew point temperature or the differences between them), and the amount of precipitation
during the last day(s); annual precipitation was also used in some cases such in the case of
KBDI. Only the Angstrom index includes air relative humidity (RH) values in its model
construction. The German Baumgartner index requires precipitation data and an estimation
of potential evapotranspiration. KBDI also uses an estimation of potential evapotranspiration
based on annual rainfall and maximum air temperature.
Wind speed, solar radiation and other parameters are not included in the indices mainly due
to the scarcity of the available data for many stations. Perhaps more questionable is the
apparent omission of sunshine intensity, wind and relative humidity (Keetch and Byram
1968). Concerning the precipitation there is an assumption in many models that an amount
less than 3 mm or 5 mm must not be considered for index estimation. Thus, precipitation
less than 5 mm is not enough to increase soil moisture on the calculation of KBDI, while a
rainfall less than 3 mm does not affect the Nesterov index but a rainfall more than 3 mm
falls down the index to zero!!. However, in the last case the addition of the K parameter in
the Modified Nesterov index seems to correct the ‘anomaly’.
Canopy interception thresholds of 1.5 mm have been defined for a range of forest cover
types in rainfall interception studies (Wotton et al. 2005).
Taking into consideration all the above mentioned analysis, there are two possibilities for a
development of an adapted empirical drought index to Mediterranean conditions: i) to
improve an existed model and ii) to construct a new index.
6.1
Models improvement
Analyzing the indices performance in the studied area, we see that the best fitting model is
by far the Keetch-Byram drought index. However, in the case we would suggest this index
application in the Mediterranean conditions some improvements have to be made, for a
better adaptation of the index in the area.
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1.
According to the previous analysis, and based on the literature data and the analysis
of our data, the index works well in the Mediterranean conditions.
2.
However, we have to point out some cautiousness during its application in the
Mediterranean region, such as the low estimated values during the summers (Spano
et al. 2005), as well as in our case, which may mean that the method used for the
estimation of PET underestimates the actual water loss. Additionally, the index
presents the highest values during the middle of September due to its development
pattern. However, it must be noted that according to our data analysis, the lowest
values of moisture content were observed at the same period.
3.
Thus, the following points in the index calculation may need some improvement:
4.
a.
In the original work of Keetch and Byram (1968), the equations 14, 15, 16,
17 and 18 assumes values of R (precipitation) 50 inches. This is not always
a fact in the Mediterranean areas that suffered from great wildfires; a
suggested value could be 20 or 30 inches (508 and 762 mm respectively).
b.
The units used; the initial values of T and R were in degree of Fahrenheit
and in hundreds of inch respectively; these units should be modified in
degree of Celcius and mm respectively. Some modifications have been
already carried out but a few others not; for example the upper limit of the
index (800, in hundredths of an inch) is still in use in the Wildland Fire
Assessment System in USA. A good explanation for unit modification is
presented in Snyder et al. (2006).
c.
In accordance to the previous point, a value of 200 for the upper limit is
reasonable instead of 203.2, since this value is an approximation of the soil
field capacity.
d.
The value (threshold) of 5 mm for precipitation that is ignored in the
system may have to be modified. Canopy interception thresholds of 1.5
mm have been defined for a range of forest cover types in rainfall
interception studies (Wotton et al. 2005). The threshold of R = 3 mm used
by Nesterov and Zhanko indices is suggested.
A basic assumption of the KBDI model concerns the value of field capacity. This is
approximately estimated as 200 mm, but this is not generally true and may not
match well the conditions of the Mediterranean Basin. Especially, in Greece and in the
lower Mediterranean floristic zone (Quercetalia ilicis), based on many available data,
the soil depth is generally low in many cases and the field capacity may not exceed
the value of 130-140mm (two thirds of the value used in the KBDI equation). For
example, studies on the referred area (peri-urban area of Thessaloniki) have shown
that the soil depth is less than 50 cm while the field capacity is estimated less than
100-120 mm (Radoglou 1987). However, when we tried to improve the model by
modifying the field capacity in the equation, the results showed that there was not
any improvement. Thus, it is suggested to remain as it is, based on the Keetch and
Byram (1968) sound argument that “eight inches of available moisture appears
reasonable for use in forest fire control because in many areas it takes all summer for
vegetation cover to transpire that much water”.
5.
That PET estimation also is an interesting point. As Spano et al. (2005) reported,
based on the current estimation of PET the index takes generally low values in the
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Page 77 of 89
Mediterranean conditions. This is also true in our case; the index during the dry
summer period took values lower than 520 (of the original 800 scale) which means
that during the testing period the fire potential was moderate (USDA 2002; Janis et
al. 2000). Consequently, an improvement of PET estimation method could be
possible. The Samani-Hangreaves method tested by Spano et al. (2005) and Synder
et al. (2006) could be used, but, we have to consider the available meteorological
data. However, based on our trial outputs, after the index improvement by
substituting the equations coefficients with those for R = 30 (see below the details),
this problem seems to be mitigated.
Based on the above concept, the KBDI adaptation to the Mediterranean conditions follows
the below steps. We follow the development procedure in the original paper of Keetch and
Byram (1968):
In equation 16, we set as R0 = 30 inches in order to adapt the index in the Mediterranean
conditions. In fact, this substitution does not affect the approach of Keetch and Byram since
the “potential evapotranspiration ratio in the right member of equation 14 will be the same
for all values of R and it can be expressed in terms of the curve of Figure 8 or equation 13”.
Then, the equation 16 gives tT,  = 0.2565 tT, 30
Assuming that T = T0 = 80’ F (26.7 0C) and wc = 800 then the equation
wc
tT, 30 = ----------------------------0.352 exp (0.0486T) – 3.015
gives t80, 30 = 56.47 days.
Thus, from equation 17, it follows that
t80,  = 0.2565 * 56.47 = 14.48 days.
Then, setting the new evaluated numerical constants in the equation 18, this gives the
modified drought factor for the Mediterranean conditions as follows
(800-Q)(1.713 exp (0.0486T) – 14.59)
dQ = ------------------------------------------------- X 10-3
1 + 10.88 exp (-0.04409 R)
and if we set T in oC and R in mm then
(800-Q)(1.713 exp (0.0875T + 1.5552) – 14.59)
dQ = ----------------------------------------------------------- X 10-3
1 + 10.88 exp (-0.001736 R)
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Page 78 of 89
If we set 200 mm instead 800 hundredths in an inch, for field capacity, the final equation of
drought factor takes the form
(200-Q)(1.713 exp (0.0875T + 1.5552) – 14.59)
dQ = ----------------------------------------------------------- X 10-3
1 + 10.88 exp (-0.001736 R)
and finally if we set the R threshold equal to 3 mm, the final equation of the modified KB
drought index takes the form
Mod KBDIt = Mod KBDIt-1 + dQ - (R - 3) (if there is any rain R > 3 mm).
Based on the above analysis we calculated the Modified KBDI for the study area during the
years 2006 and 2007 (Figure 25). While, Figure 26 shows the differences in the courses of
the Modified KBDI and KBDI. According to the analysis of the Modified index values, and in
comparison with the calculated values of KBDI, we have to point out:
1. There is a faster response of the Modified KBDI index to weather data in
comparison to the response of KBDI.
2. The index takes higher values during the summer months of both years, and
thus, the reported from others (Spano et al. 2005, Snyder et al. 2006) problem
of underestimation of actual water loss is overcome.
According to the calculated values of the Modified index, the estimated fire danger is higher
for the study area, that is in accordance with the very low values observed in field moisture
(e.g. soil moisture was found approximately 2% during the summer months).
Mod KBDI
Keetch-Byram Drought index .
200
180
160
140
120
100
80
60
40
20
0
1/1
06
/20
1/3
06
/20
1/5
06
/20
1/7
06
/20
1/9
06
/20
00
1/2
1/1
6
1/1
07
/20
1/3
07
/20
1/5
07
/20
1/7
07
/20
1/9
07
/20
1/1
00
1/2
7
Day of the year (DOY)
Figure 25. Time series of the Modified KBDI during the years 2006 and 2007.
D5.1-1-33-1000-1
Page 79 of 89
Mod KBDI
KBDI
Keetch-Byram drought index
200
180
160
140
120
100
80
60
40
20
0
6
7
6
6
6
6
6
7
7
7
7
7
00
00
00
00
00
00
00
00
00
00
00
00
/2
/2
/2
/2
/2
/2
/2
/2
/2
/2
/2
/2
1
1
1
3
5
7
9
1
3
5
7
9
1
1
1/
1/
1/
1/
1/
1/
1/
1/
1/
1/
1/
1/
Day of the year
Figure 26. Comparison between the courses of the Modified KBDI and KBDI, during the
years 2006 and 2007.
6.2
New model construction
Taking into consideration the drought index performance in Thessaloniki study area as well
as the theoretical basis of the most widely used drought indices in fire risk assessment, the
general principles that an empirical drought index should follow are:
o
The drought index (DI) should have a cumulative form.
o
The drought index (DI) should be based on the drought conditions of the previous
period (day) DIi-1.
o
Incorporation of an additional drought factor (DF) similar to that of KBDI that will
express the water losses of the system since the previous day. This factor can be
expressed as a function of PET. However, since PET is difficult to estimate
accurately and should be used with caution for estimating actual water loss from
natural systems (Lu et al. 2005), a simple function of basic and easily available
meteorological variables can be used. Maximum air temperature and relative
humidity are suggested since data analysis showed the larger correlation with real
fuel moisture data.
o
If rain occurs, the respective amount of water should be removed from the index.
This can be the value of the precipitation (like the KBDI index) or better a function
of precipitation (like the Zhdanko and Modified Nesterov index).
o
Index calculation may be initialized based on the weather data. KBDI is initialized
when the soil is near saturation and Keetch and Byram (1968) suggested after a
rainfall close to field capacity (200 mm), NI suggests 3 days after snow melting,
while the Canadian FWI suggests that start up occurs when the mean daily
temperature is 6oC for three consecutive days (Canadian Wildland Fire Information
System). This temperature represents the approximate limit for plant growth and
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Page 80 of 89
thus this condition could be initially suggested, although it is not a crucial factor for
the index calculation.
Thus, the proposed drought index could be expressed with the following equation:
DIi = DIi-1 + Drought Factor (DF) – Precipitation
or
DIi = DIi-1 + f (T, RH) – f (P)
Where:
DIi = Drought index
DIi-1 = Drought index in the previous day
T = Temperature
RH = Relative humidity
P = Precipitation
However, based on the preliminary data, we have to point out that there is a differentiation
in litter moisture estimation. As it is shown in Table 12, the highest correlation (r = 0.70)
between the litter moisture content and the tested drought indices was observed in the case
of Angstrom index. This may show that the water conditions in the litter layer are more
sensitive and reflect the current (daily) weather conditions.
Index initialization could be based on the weather data, as the Canadian FWI suggests. The
index starts up when the mean daily temperature is 6oC for three consecutive days
(Canadian Wildland Fire Information System).
The exactly equation model that can be applied is not precisely known. This can be
estimated based on an inventory of field data along the Mediterranean basin that will be
used for the determination of the model and the coefficients needed. Until further, the
Modified KBDI for Mediterranean conditions can be used, as it was previously described.
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7
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