www.elsevier.com/locate/jag

International Journal of Applied Earth Observation

and Geoinformation 9 (2007) 225–234

Remotely sensed characterization of forest fuel types

by using satellite ASTER data

Rosa Lasaponara *, Antonio Lanorte

National Research Council, Institute of Methodologies of Environmental Analysis, C. da S. Loja, Tito Scalo, Potenza 85050, Italy

Received 9 January 2006; accepted 11 August 2006

Abstract

The characterization of fuel types is very important for computing spatial fire hazard and risk and simulating fire growth and

intensity across a landscape. However, due to the complex nature of fuel characteristic a fuel map is considered one of the most

difficult thematic layers to build up. The advent of sensors with increased spatial resolution may improve the accuracy and reduce

the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced

Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. In order to ascertain how well ASTER data

can provide an exhaustive classification of fuel properties a sample area characterized by mixed vegetation covers was analysed.

The selected sample areas has an extension at around 60 km2 and is located inside the Sila plateau in the Calabria Region (South of

Italy). Fieldwork fuel type recognitions, performed before, after and during the acquisition of remote sensing ASTER data, were

used as ground-truth dataset to assess the results obtained for the considered test area. The method comprised the following three

steps: (I) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel

types and properties in the considered Mediterranean ecosystems; (II) model construction for the spectral characterization and

mapping of fuel types based on a maximum likelihood (ML) classification algorithm; (III) accuracy assessment for the performance

evaluation based on the comparison of ASTER-based results with ground-truth. Results from our analysis showed that the use

ASTER data provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was

higher than 90%.

# 2006 Elsevier B.V. All rights reserved.

Keywords: Fire; Fuel type; ASTER data

1. Introduction

Wildland fires are considered one of the most

important disturbance factors in natural ecosystems of

the Mediterranean regions, where every year, around

45,000 forest fires break out causing the destruction of

about 2.6 million ha (FAO, 2001). Several studies (see,

for example, Vila et al., 2001) dealing with the effects of

* Corresponding author.

E-mail address: lasaponara@imaa.cnr.it (R. Lasaponara).

0303-2434/$ – see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.jag.2006.08.001

fires on the vegetation within the Mediterranean basin

found that fires induce significant alterations in short as

well as long-term vegetation dynamics.

Prevention measures, together with early warning

and fast extinction, are the only methods available that

can support fire fighting and limit damages caused by

fires especially in regions with high ecological value,

dense populations, etc. In order to limit fire damage, fire

agencies need to have effective decision support tools

that are able to provide timely information for

quantifying fire risk. In particular, fire managers need

information concerning the distribution, amount and

R. Lasaponara, A. Lanorte / International Journal of Applied Earth Observation and Geoinformation 9 (2007) 225–234226

condition of fuels in order to improve fire prevention

and modelling fire spreading and intensity. Geographic

information systems (GIS) and remote sensing (RS) are

considered useful tools to support different phase of fire

monitoring, prevention, detection and post-fire damage

evaluation. In particular, satellite remote sensing can

successfully cope with different aspects of fire

management problems, such as danger estimation

(see, for example, review in Lasaponara, in press), fire

detection (see, for example reviews performed by

Cuomo et al., 2001; Lasaponara et al., 2003) burned

area mapping (Lasaponara, 2006, papers therein

quoted) and post-fire vegetation recovery (Telesca

and Lasaponara, 2006-a,b,c; Telesca and Lasaponara,

2005; Telesca et al., 2006-d).

In particular, remote sensing can provide valuable

data on type (namely distribution and amount of fuels)

and status of vegetation in a consistent way at different

spatial and temporal scales, see, for example, (Lasa-

ponara and Lanorte, 2006a,b,c). Since the description of

fuel proprieties is usually very complex, fire managers

have tried to summarize the physical parameters and

spatial distribution of fuel in different classes also

known as ‘‘fuel models’’ (Anderson, 1982; Burgan and

Rothermal, 1984). More specifically, a fuel model has

been defined as ‘‘an identifiable association of fuel

elements of distinctive species, form, size, arrangement,

and continuity that will exhibit characteristic fire

behaviour under defined burning conditions’’ (Merrill

and Alexander, 1987). The spatial distribution of the

fuel characteristics can be displayed as fuel type maps.

Northern Forest Fire Laboratory (NFFL) system

(Albini, 1976) is the most common and well-know fuel

model that was developed taking the vegetation

structure and characteristic of the North-American

floras in to account. Recently, in the framework of the

Prometheus project (1999), a new fuel type classifica-

tion system was specifically developed to better

represent the fuel characteristic of the Mediterranean

ecosystems (http://kentauros.rtd.algo.com.gr/promet/

index.htm, Algosystems SA, Greece). This classifica-

tion is principally based on the height and density of

fuel, which directly influence the intensity and

propagation of wildfire.

Due to the complex nature of fuel characteristic a

fuel map is considered one of the most difficult thematic

layers to build up (Keane et al., 2000) especially for

large areas. Aerial photos have been the most common

remote sensing data source traditionally used (Morris,

1970; Muraro, 1970; Oswald et al., 1999) for mapping

fuel types distribution. Nevertheless, remote sensing

data can be an effective data source available at different

temporal and spatial scales. For example, Lidar and

have been successfully used for estimating forest

canopy fuel parameters (Andersen et al., 2005).

Airborne hyperspectral sensors, such as multispectral

infrared and visible imaging spectrometer (MIVIS) and

airborne visible/infrared imaging spectrometer

(AVIRIS), demonstrated high reliability for fuel typing.

Several studies have been performed for assessing

spatial pattern of forest fuel using AVIRIS imagery by

Jia et al. (2006), Boardman (1998) and Boardman et al.

(1995). High accuracy levels (higher than 90%) were

obtained from MIVIS even for extremely heteroge-

neous areas (see, Lasaponara et al., 2006-a,b; Lasapo-

nara et al., in press).

Satellite multispectral data can be fruitfully adopted

for building up fuel type maps from global, region down

to local scale. For this purpose, up to now, several

satellite sensors have been used in last decades. For

example, NOAA-AVHRR (Advanced Very High Reso-

lution Radiometer) data were used by Mckinley et al.

(1985) for mapping fuel types in Western United States.

Landsat Thematic Mapper data were used for mapping

fuels models in Yosemite National Park, USA (Van

Wagtendonk and Root, 2003); and in Spain (Cohen,

1989 in California; Riano et al., 2002; Salas and

Chuvieco, 1994). A multisensor approach based on Spot

and Landsat imager was adopted by Castro and

Chuvieco (1998) to perform a classification of fuel

types for Chile by using an adapted version of

Anderson’s system.

The accuracies obtained from these researches

generally ranged from 65% to 90% (Chuvieco, 1999;

Lasaponara and Lanorte, 2006a,b,c). The higher

accuracy values were obtained using airborne hyper-

spectral MIVIS data (Lasaponara, 2006). The accuracy

level is strongly related with fuel presence and spatial

distribution (how many and where) and with specific

environmental conditions (topography, land cover

heterogeneity, etc.). The importance of using multi-

sensor data source to map fuel model was emphasised

by many authors (see, for example, Keane et al., 2001).

Although the recognized feasibility of satellite

sensors traditionally used for the remote characteriza-

tion of fuel types, the advent of new sensors with

improved spatial and spectral resolutions may improve

the accuracy (Chuvieco and Congalton, 1989) and

reduce the cost (Zhu and Blumberg, 2001) of forest fire

fuel mapping. Up to now, fire researchers did not paid

enough attention to the potentiality of using remote

sensing ASTER (Advanced Spaceborne Thermal

Emission and Reflection Radiometer) data to map fuel

types and properties.

R. Lasaponara, A. Lanorte / International Journal of Applied Earth Observation and Geoinformation 9 (2007) 225–234 227

This research aims to investigate the usefulness of

ASTER data to characterize and map fuel types in

fragmented ecosystems, such as those found in the

Mediterranean basin. This objective is achieved by

using Prometheus model coupled with ASTER data that

were analysed by using maximum likelihood (ML)

classifier for a test case (located in the south of Italy)

that is highly representative of Mediterranean like

ecosystems.

2. Study area

The study area has an extension of about 6.400 ha

and it is located inside the Sila plateau in the central part

of the Calabria Region. Fig. 1 shows the ASTER picture

for the study area.

The altitude of the study area is ranging from 1000 to

1600 m above sea level (a.s.l.). The site faces the

watershed between the basins of the Arco river and its

affluent. From a geological point of view the study area

Fig. 1. ASTER picture

is within the Monte Garigliano unit made up of high

grade metamorphic rocks, such as graniodiorites and

magmatites. The soils are mainly composed of siliceous

sands, products of the chemical and mechanical

alteration of acid igneous rocks, with a minor part of

clays made up of kaolins and vermiculites.

The more diffuse and important especially is the

Calabrian Laricio pine (Pinus Nigra Var. Calabrica) that

is to form pure woods and seldom mixture with other

especially, caducous leaves oaks (Quercus pubescens in

lower areas and Quercus cerris and Quercus frainetto in

higher areas up to 1000–1200), black and Neapolitan

Ontano (Alnus glutinosa, Alnus cordata) to altitudes he

understood between 1000 and the 1400 m of share. In

the highest altitudinal strip, above the 1400 m, they are

present mixed woods of oaks and beech (Fagus

sylvatica). Evident degradation forms are present, in

the form of xerophytic prairies and substitution bushes.

The deforested areas in this strip are generally engaged

by mesophytic prairies used for pasture.

for the study area.

R. Lasaponara, A. Lanorte / International Journal of Applied Earth Observation and Geoinformation 9 (2007) 225–234228

Table 1

Characteristic of the ASTER spectral bands

Bands Lower edge (mm) Upper edge (mm)

1 0.52 0.60

2 0.63 0.69

3N 0.76 0.86

3B 0.76 0.86

4 1.60 1.70

5 2.145 2.185

6 2.185 2.225

7 2.235 2.285

8 2.295 2.365

9 2.36 2.43

10 8.125 8.475

11 8.475 8.825

12 8.925 9.275

13 10.25 10.95

14 10.95 11.65

3. Data analysis

3.1. Dataset

ASTER is a high resolution imaging instrument that

is flying on the earth observing system (EOS) Terra

satellite. It has the highest spatial resolution of all five

sensors on Terra and collects data in the visible/near

infrared (VNIR), short wave infrared (SWIR), and

thermal infrared bands (TIR). Each subsystem is

pointable in the crosstrack direction. The VNIR

subsystem of ASTER is quite unique. One telescope

of the VNIR system is nadir looking and two are

backward looking, allowing for the construction of

three-dimensional digital elevation models (DEM) due

to the stereo capability of the different look angles.

ASTER has a revisit period of 16 days, to any one

Table 2

Fuel types classification developed (Prometheus S.V. Project 1999) for Me

1 Ground fuels (cover >50%)

2 Surface fuels (shrub cover >60%, tree cover <50%)

3 Medium-height shrubs (shrub cover >60%, tree cover <50%)

4 Tall shrubs (shrub cover >60%, tree cover <50%)

5 Tree stands (>4 m) with a clean ground surface

(shrub cover <30%)

6 Tree stands (>4 m) with medium surface fuels

(shrub cover >30%)

7 Tree stands (>4 m) with heavy surface fuels

(shrub cover >30%)

location on the globe, with a revisit time at the equator

of every four days. ASTER collects approximately

8 min of data per orbit (rather than continuously).

Among the 14 ASTER bands (see Table 1) we only

considered the three channels in the VNIR region and

six channels in the SWIR region, while the TIR

channels were excluded. The ASTER data used for this

study were acquired on 4 June 2001.

Additionally, photos and air photos were obtained for

the investigated area immediately before and after the

acquisition of satellite ASTER data. Fieldwork fuel

typing were performed using a global position system

(GPS) for collecting geo-position data (latitude and

longitude). Air photos and fieldwork fuel types were

used as a ground-truth dataset firstly to identify the fuel

types defined in the context of Prometheus system, and

secondly, to evaluate performance and results obtained

for the considered test area from the ASTER data

processing. A total of 35 plots (corresponding to almost

1000 pixels) were selected and sampled in the field

using a GPS. The selected plots were located in

homogeneous fuel areas, characterized by the same

vegetation species with similar density, height, and

coverage. Moreover, the plots were selected also

considering similar topographical features, such as

slope and elevation. The acquisition of the field data

before and after the acquisition of the image, maids to

verify possible changes in the land cover.

3.2. Prometheus adaptation

The seven fuel type classes standardized in the

context of Prometheus system (see Table 2) were

detailed identified and carefully verified for the study

area on the basis of field works performed before,

diterranean ecosystems in framework of the Prometheus project

Grass

Grassland, shrubland (smaller than 0.3–0.6 m and with a

high percentage of grassland), and clearcuts, where slash

was not removed

Shrubs between 0.6 and 2.0 m

High shrubs (between 2.0 and 4.0 m) and young trees

resulting from natural regeneration or forestation

The ground fuel was removed Either by prescribed burning

or by mechanical means. This situation may also occur

in closed canopies in which the lack of sunlight inhibits

the growth of surface vegetation

The base of the canopies is well above the surface fuel

layer (>0.5 m). The fuel consists essentially of small

shrubs, grass, litter, and duff

Stands with a very dense surface fuel layer and with a very

small vertical gap to the canopy base (<0.5 m)

R. Lasaponara, A. Lanorte / International Journal of Applied Earth Observation and Geoinformation 9 (2007) 225–234 229

during and after the acquisition of ASTER remote

sensing data. In particular, photos and air photos, taken

for the investigated region before, after and during the

acquisition of ASTER data, along with the fuel types

recognized in the field were used for this purpose.

Table 3 shows the results obtained from the adaptation

of Prometheus system to the characteristics and

properties of fuel types present in the investigated test

Table 3

Fuel type and vegetation typologies

Fuel types Fuel type

No fuel Roads and houses

Plowed and bare soils

Woody cultivations

Calcareous cliffs and detritus

Course and water bodies

Fuel type 1 Xerophytic Prairies

Mesophytic Prairies

Fuel type 2 Substitution bushes

Uncultivated soils, ferns, field boundary

vegetation, woods, meadows, roads

Fuel type 3 Substitution bushes, broom vegetation

Mediterranean scrubs, garigues

and shrubby prairies

Fuel type 4 Mediterranean scrubs

Beech woods and mixed oak woods

Ilex woods

Fuel type 5 Beech woods

Reforestation conifer woods

Fuel type 6 Oak woods

Chestnut woods

Fuel type 7 Mixed oak woods and reforesting trees

area. Significant patches corresponding to areas

representative for each fuel class were carefully

identified over the ASTER images by using geo-

position data (latitude and longitude) collected during

the ground surveys by means of a GPS positioning

system. Pixels relating to these areas were exploited for

performing the selection of adequate region of interest

point (ground-truth dataset) for the seven classes (fuel

Fuel type and vegetation typologies

Areas at present or permanently presenting soils with

no vegetation cover because of plowing or erosion phenomena

Arboreal cultivations (mainly orchards, olive-groves and vineyards)

Emergine rocks, landslides and generally melted sediments

Natural water and artificial courses besides natural water stretches

and artificial basins

Grassy vegetation of primary and secondary origin

with a prevalence of Bromus sp.

Grassy vegetation developing when water availability is good.

Middle altitude prairies and peak prairies a prevalence of

Cynosurus cristatus

Small shrubs with predominant presence of Cistus sp.

Vegetation growing in areas no longer agriculturally used or

in any case at borders of lands used for a definite and

consolidate purpose

Mediterranean formations of secondary origin with predominant

presence of Prunus sp., Spartium junceum transition states toward

forestry vegetation referable to mixed oak woods

Vegetation types referable to evergreen Sclerophylls mainly

degradated, Mediterranean formations with prevalence of

medium-height shrubs and secondary formations scarcely

covering areas no longer used for pasture

Vegetation types referable to evergreen Sclerophylls Mediterranean

formations with prevalence of tall shrubs

Young trees or small height formations of beech woods and

mixed oak woods

Wood formations with a prevalent presence of Quercus ilex,

of which there are in the study area scarce pure stands at

altitude lower than 600 m

Forestry formations with predominant presence of Fagus

sylvatica of great relevance in the study area since these

trees cover most part of the belt between 1200 and 1800 m

Areas which have been reforested mainly with Pinus nigra

and Pinus halepensis

Forestry formations in which, according to the altitude, with

prevalence of Quercus pubescens in lower areas and Quercus

cerris and Quercus frainetto in higher areas up to 1000–1200

Chestnut tree formations (Castanea sativa)

Forestry formations in which Quercus sp. is associated with

numerous other forestry species belonging to Acer, Fraxinus,

Ostrya, Alnus, Sorbus, Malus, Crataegus, Pinus, Picea, Abies

R. Lasaponara, A. Lanorte / International Journal of Applied Earth Observation and Geoinformation 9 (2007) 225–234230

type) with the addition of one class concerning areas

having no fuel and fuel type 1 class concerning areas

having unclassified pixels. The sample points of

ground-truth dataset were selected in the same areas

subject to direct check on field in order to be used firstly

to identify the fuel types defined in the context of

Prometheus system, and secondly, to evaluate perfor-

mance and results obtained for the considered test area

from the ASTER data processing. For this reason, pixels

corresponding to the given ground-truth areas were

subdivided in to testing data and training data through

randomization of the pixels to 50% for every class.

3.3. Model construction and comparison

The mapping of fuel types was obtained by using a

supervised classification based on maximum likelihood

(ML) algorithm. The ML classifier is considered one of

the most important and well-known image classification

methods due to its robustness and simplicity. It is wide

used in vegetation and land cover mapping. Moreover, it

was also tested for fuel model distribution (Riano and

Chuvieco, 2002). The ML method quantitatively

evaluates the variance and covariance of the spectral

signatures when classifying an unknown pixel assuming

at the same time a Gaussian distribution of points

Fig. 2. Selection of the ROI w

forming a cluster of a vegetation class. Under this

assumption the distribution of a class is described by the

mean vector and covariance matrix which is used to

compute the statistical probability of a given pixel value

being a member of a particular class. The probability for

each class is calculated and the class with the highest

probability is assigned the pixel (Lillesand and Kiefer,

2000). As above reported, the ML classifier is based on

the assumption that different variables used in the

computation are normally distributed. This assumption

is generally considered acceptable for common spectral

response distribution.

In our case, on the basis of ground surveys and air

photos, we selected the region of interest (ROI)

corresponding to the considered seven fuel types, plus

two additional classes related to no fuel and unclassified

regions (see Fig. 2). Pixels belonging to each of the

considered ROI were randomly separated into training

data and testing data, used for the ML and accuracy

evaluation, respectively.

4. Result

ML classifier was performed twice, firstly consider-

ing solely the ASTER spectral bands, secondly also

adding the normalized difference vegetation index

ithin ASTER imagery.

R. Lasaponara, A. Lanorte / International Journal of Applied Earth Observation and Geoinformation 9 (2007) 225–234 231

Fig. 3. Fuel type characterization obtained from ASTER data using maximum likelihood classification.

(NDVI). The accuracy levels obtained from the ASTER

data processing performed with and without NDVI were

substantially close each other. Fig. 3 shows the mapping

of fuel type obtained for the investigated test area from

the ASTER images. Fig. 4 plots the spectral patterns of

the seven fuel types as obtained from ASTER spectral

bands.

Fuel type map presents very high user’s accuracy.

For the accuracy assessment we consider the producer

accuracy, user accuracy, and overall accuracy that are

defined as follows.

The producer’s accuracy is a measure indicating the

probability that the classifier has correctly labelled an

image pixel, for example, into fuel type 1 class given

Fig. 4. ASTER-based spectral patterns of the seven fuel types.

that, on the basis of ground recognition such a pixel

belongs to fuel type 1 class. The user’s accuracy is a

measure indicating the probability that a pixel belongs

to a given class and the classifier has labelled the pixel

correctly into the same given class. The overall

accuracy is calculated by summing the number of

pixels classified correctly and dividing by the total

number of pixels. Finally, the kappa statistics (K) was

also considered. It measures the increase in classifica-

tion accuracy over that of pure chance by accounting for

omission and commission error (Congalton and Green,

1998). Overall accuracy is computed as the sum of the

number of observations correctly classified (class 1, as

class 1, class 2 as class 2, etc.) divided by the total

number of observations. This is equivalent to the

‘‘diagonal’’ of a square contingency table matrix

divided by the total number of observations described

in that contingency table (Congalton and Green, 1998).

Table 4 shows the accuracy coefficients as well as the

omission and commission errors. The achieved overall

accuracy was 90.39%. Thus, showing that the use of

remotely sensed ASTER data at high spatial and

spectral resolution provided a valuable characterization

and mapping of fuel types.

In particular, fuel type 7 (62.12%) shows the worst

producer accuracy; whereas fuel type 4 (72.73%) and

R. Lasaponara, A. Lanorte / International Journal of Applied Earth Observation and Geoinformation 9 (2007) 225–234232

Table 4

Confusion matrix

Class User Acc.

ASTER (%)

Prod. Acc.

Aster (%)

Fuel type 1 74.64 85.12

Fuel type 2 84.52 85.54

Fuel type 3 82.14 84.15

Fuel type 4 72.73 86.15

Fuel type 5 100.00 100.00

Fuel type 6 99.29 99.29

Fuel type 7 87.23 62.12

No fuel 95.09 88.57

Unclass 100.00 98.91

ASTER overall Acc. = 90.73; ASTER kappa coefficient = 0.89.

Table 5

Numerical description of the fuel

tonne/ha kg/m2 kcal/kg

FT1 5 0.5 4500

FT2 9 0.9 4700

FT3 11 1.1 5200

FT4 30 3 5200

FT5 11 1.1 4500

FT6 8 0.8 4500

FT7 32.5 3.25 4500

fuel type 1 (74.64%) show the worst user accuracy. That

is mainly due to the high mixture that is present between

fuel types 4 and 7. Additionally, a low mixing is also

present between fuel types 1 and 2, 3.

As regards fuel types 7 and 4 the mixing between the

two classes consists in a very strong pixel ‘‘transfer’’

from fuel type 7 toward fuel type 4 and much weaker

from fuel type 4 toward fuel type 7. The ML classifier

‘‘moves’’ 27% of the pixels of fuel type 7 toward fuel

type 4 and less than 8% of fuel type 4 toward fuel type 7.

This fact is mainly linked to the complexity of fuel types

distribution that makes impossible an accurate selection

of ROI at the spatial resolution of satellite ASTER data.

The level of classification accuracy is substantially

determined by the light mixing between fuel type 1 with

fuel types 2, 3 and the class corresponding to the No

fuel. Between fuel types 1 and 2 there is a nearly

analogous ‘‘exchange’’ of a pixels number and the same

it happens between fuel types 1 and 3. The complexity

of fuel types distribution makes an accurate selection of

ROI impossible.

As regards the mixing between fuel type 1 and no

fuel class, ML classifier ‘‘moves’’ 10% of the pixels

from no fuel toward fuel type 1 class. This is due to the

presence of typical grass species in cultivated areas.

The main finding of our analysis is that the

characterization of fuel type obtained from ASTER

data can be performed with very high accuracy levels

even for heterogeneous areas characterized by complex

topography features and a mixture of vegetation covers.

The high classification accuracy is due to the ability to

distinguish several classes that should be easily

confused. In particular, fuel type 3 versus fuel type

2; fuel type 4 versus fuel type 6; fuel type 7 versus fuel

type 6. The high accuracy levels achievable using

ASTER data showed that such imagery can be capable

to give an exhaustive classification of fuel properties,

with accuracy levels very close to those obtained from

airborne hypestecral MIVIS data (Lasaponara and

Lanorte, 2006a,b).

In order to directly adopt fuel map types in

operational contexts, it is important to develop accurate

ways to translate forest types and density into fuel

source parameters, namely fuel load could be estimated

in joule or other energy measure. Table 5 shows energy

measures expressed as metric tonne per ha (as well as

kg/m2 and kcal/kg) of the fuel load computed for the

seven fuel types of the study area, performed following

Velez Munoz (1990).

5. Conclusions

ASTER data were analysed for a test area of

southern Italy to ascertain how well remote sensing

data can characterize fuel type and map fuel properties.

Fieldwork fuel type recognitions, performed at the

same time as remote sensing data acquisitions, were

used as ground-truth dataset to assess the results

obtained for the considered test area. Results from our

analysis showed that the spatial and spectral resolution

of ASTER imagery provided a valuable characteriza-

tion of fuel types being that the classification accuracy

was higher than 90%. The high accuracy obtained from

ASTER data showed that such imagery are able to

provide an exhaustive classification of fuel properties

even for heterogeneous areas characterized by a

complex topography and mixed vegetation covers.

Results obtained from these investigations can be

directly extended to other Mediterranean like ecosys-

tems. The main finding of the performed investigation

is that satellite ASTER dataset assures an high

capability for fuel characterization, with accuracy

levels very close to those obtained from airborne

hyperspectral data (see Lasaponara et al., 2006-a, b;

Lasaponara et al., in press), but strongly reduces the

cost of data acquisition currently lower than $ 90 for

each ASTER image.

R. Lasaponara, A. Lanorte / International Journal of Applied Earth Observation and Geoinformation 9 (2007) 225–234 233

References

Albini, F.A., 1976. Estimating wildfire behaviour and effects. General

Technical Report. USDA Forest Service, Intermountain Forest and

Range Experiment Station INT-30. Ogden, UT, pp. 1–92.

Andersen, H.E., McGaughey, R.J., Robert, J., Reutebuch, S.E., 2005.

Estimating forest canopy fuel parameters using Lidar data.

Remote Sens. Environ. 4, 441–449.

Anderson, H.E., 1982. Aids to determining fuels models for estimat-

ing fire behaviour. USDA Forest Service, Intermountain Forest

and Range Experiment Station General Technical Report INT-122.

Ogden, UT, 22 pp.

Boardman, J.W., 1998. Leveraging the high dimensionality of AVIRIS

data for improved sub-pixel target unmixing and rejection of false

positives: mixture tuned matched filtering. Summaries of the

Seventh JPL Airborne Geoscience Workshop, JPL Publication

97-1. NASA Jet Propulsion Lab., Pasadena, CA, pp. 55–56.

Boardman, J.W., Kruse, F.A., Green, R.O., 1995. Mapping target

signatures via partial unmixing of AVIRIS data. Summaries of the

Fifth JPL Airborne Geoscience Workshop, JPL Publication 95-1.

NASA Jet Propulsion Lab., Pasadena, CA, pp. 23–26.

Burgan, R., Rothermal, R.C., 1984. BEHAVE: fire behaviour predic-

tion and fuel modelling system-FUEL subsystem. USDA Forest

Service General Technical Report INT-167, 126 pp.

Castro, R., Chuvieco, E., 1998. Modelling forest fire danger from

geographic information system. Geocarto Int. 13, 15–23.

Chuvieco, E., Congalton, R.G., 1989. Application of remote sensing

and geographic information systems to forest fire hazard mapping.

Remote Sens. Environ. 29, 147–159.

Chuvieco, E., 1999. Remote Sensing of Large Wildfires in European

Mediterranean Basin. Springer-Verlag, Berlin, p. 122.

Cohen, W.B., 1989. Potential utility of the TM tasseled cap multi-

spectral data transformation for crown fire hazard assessment. In:

ASPRS/ACSM Annual Convention Proceedings: Agenda for the

90s, vol. 3, Baltimore, MA, pp. 118–127.

Congalton, R.G., Green, K., 1998. Assessing the accuracy of remotely

sensed data. CRC Press, Lewis Publishers, Boca Raton, Florida,

USA.

Cuomo, V., Lasaponara, R., Tramutoli, V., 2001. Evaluation of a new

satellite-based method for forest fire detection. Int. J. Remote

Sens. 22 (9), 1799–1826.

FAO, 2001. Global forest fire assessment 1990–2000. Forest

Resources Assessment Programme, Working Paper No. 55,

http://www.fao.org/forestry/fo/fra/docs/Wp55_eng.pdf.

Jia, G.J., Ingrid, I.C., Goetz, A.F.H., Kaufmann, M.R., Kindel, B.C.,

2006. Assessing spatial patterns of forest fuel using AVIRIS data.

Remote Sens. Environ. 3/4, 318–327.

Keane, R.E., Mincemoyer, S.A., Schmidt, K.A., Long, D.G., Garner,

J.L., 2000. Mapping vegetation and fuel for fire management on

the Gila National Forest Complex. USDA Forest Service General

Technical Report RMRS-GTR-46-CD, New Mexico.

Keane, R.E., Burgan, R., Van Wagtendonk, J., 2001. Mapping wild-

land fuels for fire management across multiple scales: integrating

remote sensing, GIS, and biophysical modelling. Int. J. Wildl. Fire

10 (3/4), 301–319.

Lasaponara, R., in press. Inter-comparison of AVHRR-based fire

danger estimation methods. International J. Remote Sens. 26

(5), 853–870.

Lasaponara, R., 2006. Estimating Spectral separability of satellite

derived parameters for burned areas mapping in the Calabria

Region by using SPOT-vegetation data. Ecol. Model. 196, 265–

270.

Lasaponara, R., Lanorte, A. 2006a Multiscale fuel type characteriza-

tion by using multisensor remote sensing data for the Mediterra-

nean ecosystems of Southern Italy. IEMSS conference July, 9–13,

Burlington, Vermont USA, in press.

Lasaponara R., Lanorte, A. 2006b Remote characterization of fuel

type using satellite data, SPIE conference, Stoccolma-12–14

September, in press.

Lasaponara R, A Lanorte 2006b Multispectral fuel type characteriza-

tion based on remote sensing data and Prometheous model ICFFR

Conference, Coimbra, November 2006.

Lasaponara, R., Lanorte, A., Pignatti, S., 2006-a. Characterization and

mapping of fuel types for the Mediterranean ecosystems of Pollino

National Park in the Southern Italy by using Hyperspectral MIVIS

data. Earth Interactions 10, 1–11.

Lasaponara, R., Lanorte, A., Pignatti, S., 2006-b. Multiscale fuel type

mapping in fragmented ecosystems: preliminary results from

Hyperspectral MIVIS and Multispectral Landsat TM data. Int.

J. Remote Sens. 27 (3), 587–593.

Lasaponara R., Lanorte, A., Pignatti, S., in press. Hyperspectral

analysis of fuel types for Mediterranean ecosystems, Bull. Am.

Meteorol. Soc.

Lasaponara, R., Cuomo, V., Macchiato, M.F., Simoniello, T., 2003. A

self-adaptive algorithm based on AVHRR multitemporal data

analysis for small active fire detection. Int. J. Remote Sens. 24

(8), 1723–1749.

Lillesand, T.M., Kiefer, R.W., 2000. Remote Sensing and Image

Interpretation. Wiley, New York.

Mckinley, R.A., Chine, E.P., Werth, L.F., 1985. Operational Fire

Fuels Mapping with NOAA-AVHRR data. American Society for

Photogrammetry and Remote Sensing, Falls Church, VA, pp.

295–304.

Merrill, D.F., Alexander, M.E., 1987. Glossary of Forest Fire Manage-

ment Terms. National Research Council of Canada, Committee for

Forest Fire Management, Ottawa.

Morris, W.G., 1970. Photo inventory of fine logging slash. Photo-

grammet. Eng. 36, 1252–1256.

Muraro, S.J., 1970. Slash Fuel Inventories from 70 mm Low-level

Photography. Canadian Forest Service, Ottawa, Ontario, p. 63.

Oswald, B.P., Fancher, J.T., Kulhavy, D.L., Reeves, H.C., 1999.

Classifying fuels with aerial photography in East Texas. Int. J.

Wildl. Fire 9 (2), 301–319.

Prometheus, S.V., Project, 1999. Management techniques for optimi-

sation of suppression and minimization of wildfire effects. System

Validation. European Commission, contract number ENV4-CT98-

0716.

Riano, D., Chuvieco, E., 2002. Generation of fuel type maps from

Landsat-TM images and auxiliary data in Mediterranean ecosys-

tem. Department of Geography, Alcala de Henares University,

Alcala de Henares, Spain.

Riano, D., Chuvieco, E., Salas, J., Palacios-Orueta, A., Bastarrika, A.,

2002. Generation of fuel type maps from Landsat TM images and

ancillary data in Mediterranean ecosystems. Canadian Journal of

Forest Resources 32, 1301–1315.

Salas, J., Chuvieco, E., 1994. Geographic information system for

wildland fire risk mapping. Wildfire 3 (2), 7–13.

Telesca, L., Lasaponara, R., 2005. Discriminating dynamical patterns

in burned and unburned vegetational covers by using SPOT-VGT

NDVI data. Geophys. Res. Lett. 32, L21401 2005, doi:10.1029/

2005GL024391.

Telesca, L., Lasaponara, R., 2006-a. Pre-and post-fire behavioural

trends revealed in satellite NDVI time series. Geophys. Res. Lett.,

33, L14401 2006, doi:10.1029/2006GL026630

R. Lasaponara, A. Lanorte / International Journal of Applied Earth Observation and Geoinformation 9 (2007) 225–234234

Telesca, L., Lasaponara, R., 2006-b. Vegetational patterns in burned

and unburned areas investigated by using the detrended fluctuation

analysis. Physic. A. 368, 531–535.

Telesca, L., Lasaponara, R., 2006-c. Fire-induced variability in satel-

lite SPOT-VGT NDVI vegetational data. Int. J. Remote Sens. 27,

3087–3095.

Telesca, L., Lasaponara, R., Lanorte, A., 2006-d. Discrimination of

vegetational patterns in burned and unburned areas. Int. J. Non-

linear Sci. Numer. Simul. 7 (3), 279–284.

Van Wagtendonk, J.W., Root, R.R., 2003. The USE of multitemporal

Landsat normalized difference vegetation index (NDVI) data for

mapping fuels models in Yosemite National Par k, USA. Int. J.

Remote Sens. 24, 1639–1651.

Velez Munoz, R., 1990. Algunas observaciones para una selvicoltura

preventive de incendios forestales. Ecologia, Fuera de Serie 1,

561–571.

Vila, M., Lloret, F., Ogheri, E., Terradas, J., 2001. Positive fire–grass

feedback in Mediterranean basin shrubland. For. Ecol. Manage.

147, 3–14.

Zhu, G., Blumberg, D.G., 2001. Classification using ASTER data and

SVM algorithms; the case study of Beer Sheva, Israel. Remote

Sens. Environ. 80, 233–240.