Multispectral and LiDAR data fusion for fuel type mapping using

Remote Sensing of Environment 115 (2011) 1369–1379
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Remote Sensing of Environment
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e
Multispectral and LiDAR data fusion for fuel type mapping using Support Vector
Machine and decision rules
Mariano García a,⁎, David Riaño b,c, Emilio Chuvieco a, Javier Salas a, F. Mark Danson d
a
Department of Geography, University of Alcalá, Alcalá de Henares, 28801 Madrid, Spain
Institute of Economics and Geography, Spanish National Research Council (CSIC), Albasanz 26-28 28037 Madrid, Spain
Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, 250-N, The Barn, One Shields Avenue, Davis, CA 95616-8617, USA
d
Centre for Environmental Systems Research, School of Environment and Life Sciences, University of Salford, Manchester M5 4WT, UK
b
c
a r t i c l e
i n f o
Article history:
Received 14 September 2010
Received in revised form 25 January 2011
Accepted 29 January 2011
Keywords:
LiDAR
Data fusion
Support Vector Machine
Decision rules
Fuel types
Prometheus Classification System
a b s t r a c t
This paper presents a method for mapping fuel types using LiDAR and multispectral data. A two-phase
classification method is proposed to discriminate the fuel classes of the Prometheus classification system,
which is adapted to the ecological characteristics of the European Mediterranean basin. The first step mapped
the main fuel groups, namely grass, shrub and tree, as well as non-fuel classes. This phase was carried out
using a Support Vector Machine (SVM) classification combining LiDAR and multispectral data. The overall
accuracy of this classification was 92.8% with a kappa coefficient of 0.9. The second phase of the proposed
method focused on discriminating additional fuel categories based on vertical information provided by the
LiDAR measurements. Decision rules were applied to the output of the SVM classification based on the mean
height of LiDAR returns and the vertical distribution of fuels, described by the relative LiDAR point density in
different height intervals. The final fuel type classification yielded an overall accuracy of 88.24% with a kappa
coefficient of 0.86. Some confusion was observed between fuel types 7 (dense tree cover presenting vertical
continuity with understory vegetation) and 5 (trees with less than 30% of shrub cover) in some areas covered
by Holm oak, which showed low LiDAR pulses penetration so that the understory vegetation was not correctly
sampled.
© 2011 Elsevier Inc. All rights reserved.
1. Introduction
Fires are a major disturbance factor for Mediterranean forests and
play a critical role in the cycle of vegetation succession as well as
ecosystem structure and function (Koutsias & Karteris, 2003).
Although fires can be considered as a natural process in Mediterranean regions, the increase in their frequency, size and severity has led
to fires being considered as a natural hazard both, for the environment
and society. The loss of traditional activities in the Mediterranean
basin, such as extensive grazing or wood harvesting (ScarasciaMugnozza et al., 2000), together with management actions that
exclude fire, has contributed to modification of the composition and
structure of fuels. Resulting fuel loadings directly influence emissions
from both wildland and prescribed fires, and affect the vulnerability of
landscapes to more intense fire behaviour and crown fires (Ottmar &
Alvarado, 2004). Therefore, having accurate and spatially explicit
information on fuel properties is critical in order to improve fire
danger assessment, fire behaviour modelling and fire management
⁎ Corresponding author.
E-mail addresses: [email protected] (M. García), [email protected]
(D. Riaño).
0034-4257/$ – see front matter © 2011 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2011.01.017
decision-support systems, since fuels affect both fire ignition and
propagation (Chuvieco et al., 2009; Ottmar & Alvarado, 2004).
The structural complexity of fuels, which can vary greatly in their
physical attributes, results in a wide range of potential fire behaviour
and effects, as well as the options that fuels present for their treatment, fire control and use (Ottmar & Alvarado, 2004; Sandberg et al.,
2001). This complexity is a consequence of ecological processes,
natural disturbance events and even human manipulation of fuels
over time (Ottmar & Alvarado, 2004; Sandberg et al., 2001). Fuel
characteristics are commonly summarized by the concept of fuel types,
which are classification schemes of fuel properties that group
vegetation classes with similar combustion behaviour (Pyne et al.,
1996). More specifically, Merril and Alexander (1987) defined a fuel
type 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”. Fire
behaviour programs such as Behave (Andrews, 1986) or FARSITE
(Finney, 1998) require as input numerical descriptions of the fuel
properties, which are known as fuel models (Chuvieco et al., 2009).
Several fuel classification systems have been developed to be used in
fire behaviour modelling. Two widely used systems are the Northern
Forest Fire Laboratory (NFFL) system (Albini, 1976), extended by Scott
and Burgan (2005) from the initial 11 models to 40 models, and the
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Canadian Forest Fire Behaviour Prediction (FBP) system (Lawson
et al., 1985). In Europe, within the framework of the Prometheus
project, a new system based upon the NFFL system was adapted to
better describe fuels found in the Mediterranean ecosystems. This
system is mainly based on the type and height of the propagation
elements and it identifies 7 fuel types (Fig. 1), which are further
described in Table 1.
The inherent complexity and high dynamic nature of fuels make
field survey methods very limited for fuel type mapping in terms of
spatial and temporal coverage, and hence, methods based on aerial
photography and remotely sensed data have been developed (see
Chuvieco et al. (2003) and Arroyo et al. (2008) for a thorough revision
of remote sensing methods for fuel type mapping).
Most studies using remote sensing methods have been based on
medium-to-high resolution sensors, especially Landsat-TM data,
given its good compromise between spectral and temporal resolutions (Castro & Chuvieco, 1998; Riaño et al., 2002; Salas & Chuvieco,
1995). More recently, several studies have shown the suitability of the
Advanced Spaceborne Thermal Emission and Reflection Radiometer
(ASTER) for fuel type mapping based on either the VNIR system alone
(Falkowski et al., 2005) or in conjunction with the data collected by
the SWIR system (Lasaponara & Lanorte, 2007a). The advent of very
high spatial resolution sensors such as IKONOS or QuickBird has
allowed for finer scale mapping of fuels, which is particularly
important for the urban–wildland interface. Several studies have
been carried out in Mediterranean environments based on these
sensors using digital image classification techniques commonly
applied to remotely sensed data (Lasaponara & Lanorte, 2007b) and
object-oriented classification methods (Arroyo et al., 2006; Gitas et al.,
2006). The latter approach overcomes the limitation of the increase in
the spectral within-field variability that is common in very high
spatial resolution imagery.
The main limitation of passive optical data is their inability to
detect surface fuels when canopy cover is high, because they are
unable to penetrate forest canopies (Keane et al., 2001). Moreover,
reflectance is not directly related to vegetation height, which is a
critical variable to discriminate between some fuel types (Riaño et al.,
2002). This latter problem can be overcome by the use of Light
Detection and Ranging (LiDAR) data. LiDAR data have been successfully used to estimate important fuel parameters such as canopy bulk
density (Andersen et al., 2005; Erdody & Moskal, 2010; Riaño et al.,
2004), canopy base height (Erdody & Moskal, 2010; Popescu & Zhao,
2008; Riaño et al., 2003), canopy cover (Hall et al., 2005; Riaño et al.,
2003), shrub height (Riaño et al., 2007) or foliage biomass (García et
al., 2010; Hall et al., 2005). Although LiDAR has been proved suitable
to estimate fuel properties, fewer studies have tested the usefulness of
these data to map fuel types. Koetz et al. (2008) fused LiDAR and
hyperspectral data to map land cover for fire risk assessment by using
Support Vector Machines (SVM) in a characteristic wildland–urban
interface of the Mediterranean area of France. Mutlu et al. (2008)
integrated LiDAR and QuickBird data applying the minimum noise
fraction (MNF), and subsequently performed a supervised classification (Mahalanobis Distance decision rules) to map fuel models in
Texas. These studies showed how the synergy of LiDAR and optical
data improved the results of the classifications compared to the
results obtained by using a single data source alone. Because fusion of
multispectral and LiDAR data takes advantage of the information
provided by LiDAR data on the vertical structure of the fuels and the
Fig. 1. Scheme used to identify the Prometheus fuel types (adapted from Chuvieco et al., 2003).
M. García et al. / Remote Sensing of Environment 115 (2011) 1369–1379
1371
Table 1
Description of the fuel models considered in the Prometheus fuel classification system (adapted from Riaño et al., 2002).
Fuel type Primary fire carrier
FT
FT
FT
FT
FT
1
2
3
4
5
FT 6
FT 7
Description
Grassland
Ground fuels with a cover N50%
Shrubs (shrub cover N 60%. Grassland. Shrubland (smaller than 0.3–0.6 m and with a high percentage of grassland) and clearcuts where slash was not removed.
tree cover b 50%)
Shrubs between 0.6 and 2.0 m as well as young trees resulting from natural regeneration or forestation.
High shrubs (between 2.0 and 4.0 m) and regenerating trees.
Trees (tree height N4.0 m) Shrub cover b30%. 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
Medium surface fuels (shrub cover N 30%): the base of the canopies is well above the surface fuel layer (N 0.5 m). The fuel consists
essentially of small shrubs, grass, litter, and duff.
Heavy surface fuels (shrub cover N30%): stands with a very dense surface fuel layer and with a very small vertical gap to the canopy
base (b 0.5 m).
capability of multispectral data to capture the horizontal distribution
of fuels, as well as to differentiate vegetation types based on their
spectral response, the integration of remotely sensed imagery and
LiDAR data provides a unique opportunity to map fuel types.
The main objective of this research is to assess the potential of
integrating multispectral and LiDAR data to map characteristic
Mediterranean fuels based on the Prometheus classification system,
as well as to develop a robust methodology suitable for the integration
of multisource datasets for fuel type mapping.
2. Methods
2.1. Study area and dataset
This study was carried out in the Natural Park of the Alto Tajo in
Guadalajara, in central Spain (UL: 40° 56′ 49″ N; 2° 14′ 49″ W; LR: 40°
48′ 25″ N; 2° 13′ 21″ W) (Fig. 2). The area has rough topography with
a mean elevation of 1200 m, and a range of 895 to 1403 m, as well as a
mean slope of 24.25% with a standard deviation of 18.7%. The study
site is characterized by heterogeneous fuel complexes typical of
Mediterranean environments. The shrub stratum is mainly composed
of young Holm oak (Quercus ilex L.) and young pine (Pinus nigra Arn.;
Pinus sylvestris L.; and Pinus pinaster Ait.) as well as Spanish cedar
(Juniperus oxycedrus L.) and evergreen shrubs (Genista scorpius L.,
Erica arborea). The tree stratum is formed by mature Holm oak, pine
and Spanish juniper (Juniperus thurifera L.). A small plantation of
poplar (Populus alba) was also present in the area covered by the
dataset. The ground was mainly covered with herbaceous species,
although in some parts it was quite sparse with bare ground exposed.
The study area was flown twice in spring 2006 (May 16th and June
3rd), by the United Kingdom Natural Environment Research Council
(NERC) Airborne Research and Survey Facility. The mean flying
heights were 750 m and 775 m above ground level for the first and
second flights respectively, with a maximum scan angle of ±12°, and
a beam divergence of 0.2 mrad resulting in a footprint diameter at
nadir of approximately 18 cm. A multisensor campaign was conducted that included an Airborne Thematic Mapper (ATM) sensor
along with a LiDAR Optech-ALTM3033 system (Table 2). At each date,
three strips were flown in a North–South direction, without overlap
and the total area covered was about 382 km2. The data provided by
the NERC included raw ATM data with no geometric correction.
Regarding the LiDAR data, they were provided in ASCII format
including X, Y, Z coordinates and intensities of first and last returns.
LiDAR data from both flights were used together after verification
and adjustment of a small relative spatial offset between dates (García
et al., 2009) resulting in an effective increase in point density. After
integrating both datasets, the point density ranged from 1.5 to
6 points m− 2.
As for the optical data, only the image corresponding to the first
date was selected because of the short time-gap between the two
flights and lack of evidence of phenological changes.
For this research a subset of one of the flight lines was selected,
covering an area of 9 × 0.3 km2. This subset was representative of the
existent fuel types in the study area, so the methodology could be
extended to the area covered by the whole dataset.
2.2. Field-based fuel type reconnaissance
Potential reference plots were selected before-hand using 0.5 m
orthoimages, along with the LiDAR and ATM data available. After
identifying suitable areas, a field campaign was carried out in 2010 to
identify fuel types according to the Prometheus system. 84 plots were
selected and assigned to fuel type. In addition 19 plots used in a
previous study for biomass estimation (García et al., 2010) were
located within the area selected for this research and so were also
used and assigned to a fuel type. Despite most of the plots were
surveyed 4 years after the remotely sensed data acquisition, the study
area did not suffer any disturbance between the airborne and the field
campaigns such as fires, insect attack or clearance, that could have
caused significant changes and so, the fuel types remained the same.
For each plot tree species, coverage and mean height of shrubs were
recorded, and 4 to 8 photographs were taken to assign a fuel type.
Each plot location was recorded using a hand-held Trimble GeoTX GPS
system which allowed for post-processing, yielding an accuracy of
plots location better than the pixel size used. Fig. 3 shows a spreadsheet generated from field data for fuel type assignment.
2.3. ATM data processing
The ATM data was georeferenced based on the GPS/IMU data
collected during the flight. Additionally, in order to assure an
appropriate co-registration of the optical data to the LiDAR data, some
control points were collected using an intensity image generated from
the LiDAR data. The RMSE obtained was less than 1 pixel (2 m).
Since the objective was to fuse the ATM and LiDAR to map fuel types
and given the relatively low density of the LiDAR dataset, the ATM image
was resampled from 2 m to a 6 m pixel size. In doing so, it was also
ensured that a sufficient number of points (more than 54 points) would
be included within each pixel to derive subsequent LiDAR variables.
Moreover, by coarsening the resolution of the optical data the high
spectral variability within the field of view common to high resolution
sensors was reduced. The spatial resampling was performed by
considering the mean value of all pixels included within each 6 m pixel.
In order to remove the effect of the terrain slope and aspect on
the signal recorded by the sensor, a topographic correction was
performed. Numerous empirical and photometric methods have been
developed to remove the effect of topography, such as the cosine
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Fig. 2. Study area. The enlarged image shows the overlap area of the ATM and LiDAR data used for this study.
correction, Minnaert or the C correction (Smith et al., 1980; Teillet
et al., 1982). The main limitation of the previous corrections when
applied over forested areas is that they do not consider the geotropic
nature of trees, that is that the tree growth is driven by the
Table 2
Characteristics of the sensors used.
ATM
Optech-ALTM3033
Band
Spectral
Spatial resolution (m)
range (nm)
1
2
420–450
450–520
3
4
520–600
600–620
5
630–690
6
690–750
7
8
9
10
Spectral
indices
760–900
910–1050
1550–1750
2080–2350
2
NDVI
SAVI
Definition
ρB7 −ρB5 =ρB7 + ρB5
ðρB7 −ρB5 Þð1 + LÞ = ðρB7 + ρB5 + LÞ
NDII_1
NDII_2
L = 0.5
ρB7 −ρB9 =ρB7 + ρB9
ρB7 −ρB10 =ρB7 + ρB10
Pulse rate
Beam
divergence
Scan angle
Footprint
size
Returns
recorded
Mean point
density
33 kHz
0.2 mrad
± 12°
18 cm
First and
last
1.5 p/m2
(for each
flight)
gravitational field and therefore, it is not perpendicular to an inclined
surface (Soenen et al., 2005). Therefore, the correction developed by
Soenen et al. (2005) was applied, which is based on the Sun-CanopySensor (SCS) correction proposed by Gu and Gillespie (1998). The
correction used is based on the following formula:
ρn = ρ
cosα cosθ + C
:
cosi + C
ð1Þ
where ρn is the normalized reflectance, ρ is the uncorrected
reflectance, α is the slope terrain, θ is the solar zenith angle, i is the
illumination angle, and C is an empirical parameter introduced by
Soenen et al. (2005) to moderate the overcorrection of the SCS
correction at large incidence angles. The empirical parameter (C) is a
function of the slope (b) and the intercept (a) of the regression line
derived from the relationship between the reflectance and the cosine
of the illumination angle:
ρ = a + b cos i⇒C =
a
:
b
ð2Þ
Once the ATM image was corrected several spectral indices were
derived, namely the Normalized Difference Vegetation Index (NDVI),
the Soil Adjusted Vegetation Index (SAVI), and the Normalized
Difference Infrared Index (NDII). Since the ATM sensor has two
bands within the SWIR region, two NDII indices were computed,
NDII_1 and NDII_2.
M. García et al. / Remote Sensing of Environment 115 (2011) 1369–1379
1373
Fig. 3. Card generated from field work for fuel type assignment.
2.4. LiDAR data processing
After filtering the point cloud into ground and non-ground returns,
a digital elevation model (DEM) was created from the ground points
by applying a spline interpolation method. The height above the
ground of each vegetation point was computed as the difference
between the Z coordinate of the point, and the Z value of the DEM at
the same X, Y position:
hi = Zi −Zinterpolated :
ð3Þ
Afterwards several variables were derived from the height distribution of the first and last returns within each 6 × 6 m grid cell. These
variables included the maximum height, which was considered as the
99th percentile to avoid the noise caused by any possible outlier, the
mean height and the median height. Moreover, several metrics that
have been proved to provide a summary of the vegetation structure
based on the vertical distribution of the heights of the laser return were
computed, namely the standard deviation, the range, the skewness, the
kurtosis and the coefficient of variation (Donoghue et al., 2007; Jensen
et al., 2008). Similarly to Koetz et al. (2008) and Popescu and Zhao
(2008), the whole point cloud within each grid was used to represent
the relative point density in different height intervals. Considering the
percentage of points found in each interval the effect of the variable
point density along the flight line is removed (Mutlu et al., 2008). A total
of 15 height intervals were considered. Up to 4 m the height bins were
created every 0.5 m to achieve a better characterization of the surface
fuels, whereas for the canopy layer (above 4 m) it was considered that
1 m bins were enough to characterize the tree cover. The last bin
included all points with a height above 10 m.
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After generating raster layers with the LiDAR derived variables,
they were stacked with the ATM bands and the spectral indices into a
single image.
2.5. Fuel discrimination and mapping
The fuel type classification was carried out using a two phase
approach. The first part was intended to classify the main fuel groups,
namely grasslands (two spectral classes), shrublands (two spectral
classes) and trees (pine, Holm oak and poplar) as well as an additional
class corresponding to non-fuel covers (roads and bare soil), whereas the
second phase attempted to map fuel types according to the Prometheus
system.
Remotely sensed data have been traditionally classified using
parametric methods such as maximum likelihood; however, satisfying the assumptions that underlie these methods such as the normal
distribution of the data, is difficult in remote sensing applications, and
has led to the investigation of non-parametric methods such as
Artificial Neural Networks (ANN) or more recently, Support Vector
Machine (SVM) (Foody & Mathur, 2004). Another drawback of
parametric methods is the fact that they may perform worse for
classifying multisource data since these data cannot be modelled by a
convenient multivariate model (Benediktsson et al., 1990).
SVM is based on the principle of statistical learning theory and its
foundations were developed by Vapnik (1995). SVM attempts to fit an
optimal separating hyperplane to the training data in the multidimensional feature space. Contrary to other approaches such as ANN
that are based on empirical risk minimization, that is, on the
minimization of the error on the training data, SVM applies structural
risk minimization, which tries to maximize the margin between the
optimal separating hyperplane and the closest training samples,
known as support vectors (Vapnik, 1998). If two classes cannot be
linearly separated, SVM is able to represent the non-linearity by
projecting the input data into a higher-dimensional feature space,
where the classes may be linearly separated, by means of a kernel
function to address the ‘curse of dimensionality’ or Hughes effect
(Gunn, 1998). Although it was originally developed as a binary
classifier, two approaches have been suggested for using SVM in
multiclass classifications that reduce the multiclass problem to a set of
binary problems, namely “one against all” and “one against one”
(Foody & Mathur, 2004). The former approach was used where a set of
binary classifiers is trained so each class is separated from the rest. The
final class label is assigned by selecting the largest decision value
(Foody & Mathur, 2004). As for the kernel used, a radial basis function
was selected. This type of kernel has been widely used in remote
sensing applications (Foody & Mathur, 2004; Koetz et al., 2008;
Melgani & Bruzzone, 2004; Waske et al., 2007) and it is controlled by
two parameters that will determine the accuracy of the classification,
specifically C and γ. The latter determines the width of the Gaussian
kernel, while the former controls the penalty associated with training
samples that lie on the wrong side of the decision boundary. Thus, a
low C value will cause an increase in the number of support vectors
derived and consequently larger errors, whereas a large value of C
reduces the errors but also the generalization ability, and may result
in overfitting the SVM to the training data (Foody & Mathur, 2004).
Given the importance of these two parameters a grid search approach
was performed using LIBSVM library by Chen and Lin (2009), with a
five-fold cross validation. In this search, pairs of (C, γ) are tested on
the training data and the one with the best cross validation accuracy is
selected. The search was carried out in two steps (Oommen et al.,
2004). First a coarser grid was used with an exponentially growing
sequence (C = 2− 5, 2− 3 … 215 and γ = 2− 15, 2− 13 … 23), followed by
a finer search which slightly increased the accuracy.
Training data was collected over the image using prior knowledge
of the area acquired during the field work and 0.5 m orthoimages of
the area. Additionally, for discrimination of vertical properties for
certain classes, especially to discriminate between shrubs of young
Holm oak (≤4 m) and trees of Holm oak (N4 m), LiDAR heights were
used. SVM have been shown to achieve good results even with small
training data sets in high dimensional feature spaces (Melgani &
Bruzzone, 2004). The number of samples used for training varied
between 35 for poplar, which was only present in a very small area of
the image, and 325 for shrubs, which were widely present in the
image. Approximately, 70% of the samples were used to train the SVM
and the 30% remaining was used for validation. In this first phase of
the classification, the initial feature space was determined by the ATM
data and spectral indices derived from them, as well as the following
LiDAR-derived metrics: maximum height, mean height, median
height, standard deviation, range, kurtosis, skewness and the
coefficient of variation. Subsequently, those bands showing good
discrimination between the different classes were selected to carry
out the SVM classification.
Input data were scaled to avoid attributes in greater numeric
ranges dominating those in smaller numeric ranges, thus biasing the
results (Hsu et al., 2009). Another advantage is that it avoids
numerical difficulties that can arise as consequence of large attribute
values during the inner products of the feature vectors on which the
kernel values depend. Taking into account the previous considerations, the ATM and LiDAR data were scaled to a range of 0–1 before the
search of the optimum parameters by considering the maximum and
minimum values of each band.
Xscaled =
X−X min
X max −X min
ð4Þ
Where Xscaled represents the scaled values for the optical and
LiDAR-derived bands, Xi represents the value of a given pixel at each
band, and Xmin and Xmax are the minimum and maximum value for
each band respectively.
After classifying the main groups of fuels, the second phase tried to
refine the discrimination of those fuel types that are related to
vegetation vertical properties. To achieve this classification, a set of
decision rules were applied according to the Prometheus classification
system. The input data were the output of the SVM classification
carried out in phase one, the LiDAR mean height since it is a key
variable to differentiate shrub fuel types, and the LiDAR relative point
density images derived to represent the vertical distribution of
vegetation and, therefore the continuity of fuels. Fig. 4 shows the
decision rules used to classify the fuel types.
The accuracy of the classification was assessed through the use of
confusion matrix and the Cohen's kappa coefficient (Congalton &
Green, 2008), using as reference data 103 plots that had been assigned
to a fuel type after field reconnaissance.
3. Results
Fig. 5 shows the signatures of the different spectral classes
identified on the image. These curves were obtained from the mean
value of the samples collected for each class after scaling the data
between 0 and 1, and were used to identify the bands with the
greatest separability that were subsequently used in the SVM
classification. Based on Fig. 5, the following bands were selected as
input data in the classification: ATM bands 2 to 10, SAVI index and
NDII_1, the maximum height, the median height instead of the mean,
since it is less affected by extreme values, the standard deviation and
the range. Band 1 (blue) was not included in the analysis given the
high atmospheric scattering that affected this band. The SAVI index
was selected instead to NDVI since it is highly sensitive to differences
in vegetation changes cover, while it is less sensitive to soil
background than the NDVI.
The grid search approach provided initially the following values
for the two parameters of the Gaussian kernel used, C = 128 and
M. García et al. / Remote Sensing of Environment 115 (2011) 1369–1379
1375
Fig. 4. Decision rules applied to classify fuel types based on the Prometheus classification system.
γ = 4, which were subsequently changed to C = 207.94 and γ = 3.25
after a finer search of the optimal parameters to avoid overfitting to
the training data. The accuracy obtained over the training sample
using a fivefold cross validation was 94.22%.
After performing the SVM classification on the image, the accuracy
assessment was carried out using the 30% of the samples collected.
The classes considered initially for the analysis were: non-fuel (roads
and bare soil), grasslands (2 spectral classes), shrubland (bushes and
1,00
0,90
0,80
Scaled band values
0,70
0,60
0,50
0,40
0,30
0,20
0,10
0,00
ROADS
BARESOIL
PASTURE1
PASTURE2
PINE
OLD OAK 1
OLD OAK 2
YOUNG OAK
BUSHES
Poplar
Fig. 5. Signatures of the spectral classes found in the study area.
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Table 3
Confusion matrix obtained after applying SVM classification method.
Reference data
Classified data
Non-fuel
Bushes
Poplar
Holm oak
Grass
Pine
Total
Producer's accuracy
Error of omission (%)
Non-fuel
Bushes
Poplar
Holm oak
Grass
Pine
112
1
0
1
0
0
114
98.25
1.75
0
150
0
10
5
0
165
90.91
9.09
0
0
9
0
0
2
11
81.82
18.18
0
8
0
148
1
1
158
93.67
6.33
4
13
0
1
102
0
120
85
15
0
3
0
6
0
75
84
89.28
10.72
young Holm oaks), Holm oak (two spectral classes), pine and poplar.
The overall accuracy for these 6 classes was 91.4%, with a kappa
statistic of 0.89. Subsequently, the spectral classes representing the
same informational class were merged. Also the different tree species
were group into one class since the Prometheus classification system
only considers the propagation element (tree) and does not
distinguish tree species although they could present different
combustion properties. After merging the Holm oak, pine and poplar
classes into one class, and so to consider the three main fire carriers
(grass, shrub and trees) and the non-fuel classes, a non-significant
increase was observed with an overall accuracy of 92.8% and a kappa
statistic of 0.9.
SVM classification has been suggested as more suitable for a
multisensor approach than parametric methods commonly used in
remote sensing (Benediktsson et al., 1990). To verify the better
performance of the SVM compared to parametric methods, a maximum
likelihood classification (MLC) was conducted and its results were
compared to those obtained using the SVM classification. The overall
accuracy achieved with the MLC was 85.7% for the 6 initial classes, which
was slightly lower than that obtained by SVM. Tables 3 and 4 show the
confusion matrices obtained after applying the SVM and MLC methods
respectively, which relate the reference and the classified data allowing
for the identification of the main sources of error.
The vegetation vertical distribution was represented by the
percentage of returns in 0.5 m height intervals below 4 m (shrubs)
and 1 m height intervals above 4 m (canopy). This information was
mainly used to distinguish between fuel types involving trees. Fig. 6
shows an example of the vertical continuity of three characteristic
plots of fuel types 5, 6 and 7. Fuel types 5 (left) and 6 (centre) are
characterized by vertical gaps and the fact that fuel type 5 presents no
shrub cover whereas for fuel type 7 (right), which is characterized by
vertical continuity, all height intervals are occupied.
The decision rules applied to the SVM classification, the mean
height and the height bin images, yielded a good agreement with the
Total
User's accuracy
(%)
Error of commission
(%)
116
175
9
166
108
78
652
96.55
85.71
100
89.17
94.44
96.16
3.45
14.29
0
10.83
5.56
3.84
103 validation plots used, with an overall accuracy of 88.24% and a
kappa statistic of 0.86. Table 5 shows the confusion matrix obtained
for the fuel type classification after applying the decision rules shown
in Fig. 5. Fig. 7 represents the final fuel types map generated from the
ATM and LiDAR data for the study area.
4. Discussion
The grid search of the optimum parameters for the Gaussian kernel
used was intended to avoid overfitting to the training data, which
would reduce the generalization ability of the SVM; nevertheless,
some researchers have shown the robustness of the SVM classification
to variation of the parameters (Foody & Mathur, 2004).The change of
the parameters C and γ between the coarser and the finer search of
the parameters yielded a negligible increase of the accuracy over the
training data of less than 0.5%, although larger changes in the accuracy
were observed during the search of the parameters across the whole
range of values tested. Thus, although time consuming, the gridsearch approach is a suitable procedure to assure a higher accuracy of
the SVM classification instead of applying arbitrary values to the
parameters C and γ.
The SVM classification provided very good agreement with the
reference data. Some confusion was observed between shrubs and
Holm oaks. Since the shrublands included the spectral class Holm oak
with a height lower than 4 m, this confusion could be expected since
the difference between a shrub and a tree is given by a height threshold
of 4 m and in some areas young Holm oaks presented a similar height.
On the other hand, shrubs presented some confusion with pastures.
This occurred in areas where the shrub cover was low but its presence
still affected the statistics derived from the LiDAR data, especially for
some of the metrics such as the maximum height, the standard
deviation or the range, which were used in the classification.
Comparison of the SVM classification to the MLC classification
showed a higher accuracy of about 7% for the former method. This
Table 4
Confusion matrix obtained after applying ML classification method.
Reference data
Classified data
Non-fuel
Bushes
Poplar
Holm oak
Grass
Pine
Total
Producer's accuracy
Error of omission (%)
Non-fuel
Bushes
Poplar
Holm oak
Grass
Pine
100
11
0
1
2
0
114
87.72
12.28
0
154
0
6
5
0
165
93.33
6.67
0
0
5
1
0
5
11
45.46
54.54
0
26
0
130
1
1
158
82.28
17.72
2
21
0
1
96
0
120
80
20
0
1
0
9
0
74
84
88.1
11.9
Total
User's accuracy
(%)
Error of commission
(%)
102
213
5
148
104
80
652
98.04
72.3
100
87.84
92.31
92.5
1.96
27.7
0
12.16
7.69
7.5
M. García et al. / Remote Sensing of Environment 115 (2011) 1369–1379
FT 6
10
20
30
40
FT 7
> 10.0
9.0 to 10.0
8.0 to 9.0
7.0 to 8.0
6.0 to 7.0
5.0 to 6.0
4.0 to 5.0
3.5 to 4.0
3.0 to 3.5
2.5 to 3.0
2.0 to 2.5
1.5 to 2.0
1.0 to 1.5
0.5 to 1.0
0 to 0.5
50
Height Intervals (m)
Height Intervals (m)
Height Intervals (m)
FT 5
> 10.0
9.0 to 10.0
8.0 to 9.0
7.0 to 8.0
6.0 to 7.0
5.0 to 6.0
4.0 to 5.0
3.5 to 4.0
3.0 to 3.5
2.5 to 3.0
2.0 to 2.5
1.5 to 2.0
1.0 to 1.5
0.5 to 1.0
0 to 0.5
0
1377
0
5
10
15
> 10.0
9.0 to 10.0
8.0 to 9.0
7.0 to 8.0
6.0 to 7.0
5.0 to 6.0
4.0 to 5.0
3.5 to 4.0
3.0 to 3.5
2.5 to 3.0
2.0 to 2.5
1.5 to 2.0
1.0 to 1.5
0.5 to 1.0
0 to 0.5
0
20
Percentage of returns
Percentage of returns
5
10
15
20
25
30
Percentage of returns
Fig. 6. Vertical vegetation continuity of three characteristic plots of fuel type 5 (left), fuel type 6 (centre) and fuel type 7 (right).
performance of SVM is confirmed by the similar results found by other
researchers using both multispectral and hyperspectral data (Oommen
et al., 2004), and when using a multisensor approach combining optical
and synthetic aperture radar (SAR) data (Waske & Benediktsson, 2007).
An analysis of the confusion matrices obtained for both classification
methods showed that in general terms, SVM yielded slightly higher
user's and producer's accuracies than the MLC. It is worth noting that
for the poplar category the SVM classification yielded a producer's
accuracy of 81.82% whereas for the MLC it was only 46.46%, this
confirms the fact that SVM can provide accurate results even with small
sample sizes whereas the MLC is more dependent on the size of the
training sample.
The final fuel type classification obtained after applying the
decision rules showed very good agreement with the 103 plots used
for validation. Although the number of plots used to validate our
results can be considered small for some classes (10–31 plots for each
class), they were considered to be sufficient given the small area of the
subset data used (9 × 0.3 km2). In addition, these plots were not used
either to train the classifier in the first phase or to develop the decision
rules in the second phase since the thresholds were defined by the
Prometheus classification system. The analysis of the confusion matrix
obtained for the fuel types classification showed that user's and
producer's accuracies were very high except for FT 5 (scattered shrubs
under trees), which presented a commission error of about 33%. This
error was particularly due to some pixels classified as FT 5 which
actually corresponded to FT 7. This confusion mainly occurred in areas
that presented a mixture of shrubs of Holm oak and mature Holm
oaks. These areas present a dense cover at different height intervals
that reduces the penetration of LiDAR pulses through the canopy, so
the lower parts of the canopy and the understory are missed.
Inspection of the cloud points on pixels presenting this confusion
showed that most of returns were concentrated on the higher parts of
the canopy and few of them were able to penetrate down to the
ground or even the lower parts of the canopy. Although the height bin
layers generated provided an adequate description of the vertical
distribution of fuels within each pixel, results are affected by the
penetration of LiDAR pulses through the canopy. The low penetrability found in some areas was represented in the canopy height bins as
gaps between the fuel strata, that is, as vertical discontinuity causing
confusion between FT 7 and FT 5. This confusion could be partly
avoided using higher density LiDAR data since the mean point density
of data used in this study was 2.5 points m− 2 and in some areas it was
lower than 2 points m− 2. Considering a larger size (e.g. 1 m) for the
lower height bins could also help to reduce the number of gaps
resulting as consequence of canopy occlusion. Nevertheless, this could
increase the error of fuel types 5 and 6 since the Prometheus system
consider fuel discontinuity when the gap between shrubs and the
canopy is greater than 0.5 m (Fig. 1) and so, using a height bin larger
than 0.5 m would hamper the identification of vertical gaps. In fact, for
this study area, using a size of 1 m for the lower height bins reduced
the overall accuracy by 10% and largely reduced the user's and
producer's accuracies for fuel types 5 and 6.
Of particular interest is the confusion found between models FT 0
(non-fuel) and FT 5. A detailed analysis of the points where this
confusion occurred showed that it was due to roads that were covered
by trees. Arroyo et al. (2006) pointed out the benefits of applying
contextual methods to classify linear objects such as roads. Therefore,
this approach could avoid the confusion found between FT 0 and FT 5.
The accuracy of the results yielded by the method presented here
can be considered high given the heterogeneity of the study area.
Compared to other studies carried out in Mediterranean areas and
based on the Prometheus classification system, the overall accuracy as
well as the kappa coefficient obtained in this study was higher than
that obtained by Riaño et al. (2002) who achieved an overall accuracy
of 82.8% with a kappa statistic of 0.79 in a Mediterranean forest using
multitemporal Landsat-TM data and auxiliary information. In their
Table 5
confusion matrix of the fuel types classification after applying decision rules.
Reference data
Classified data
Total
Producer's accuracy
Error of omission (%)
FT 0
FT 1
FT 3
FT 4
FT 5
FT 6
FT 7
FT 0
FT 1
FT 3
FT 4
FT 5
FT 6
FT 7
10
1
0
0
2
0
0
13
76.92
23.08
0
12
0
0
0
0
1
13
92.31
7.69
0
0
9
0
0
0
1
10
90
10
0
0
0
10
0
0
1
11
90.91
9.09
0
0
0
0
15
0
0
15
100
0
0
0
0
0
1
8
1
10
80
20
0
0
0
0
4
0
27
31
87.1
12.9
Total
User's accuracy
(%)
Error of commission
(%)
10
13
9
10
22
8
31
103
100
92.31
100
100
66.67
100
87.1
0
7.69
0
0
33.33
0
12.9
1378
M. García et al. / Remote Sensing of Environment 115 (2011) 1369–1379
producer's accuracies were slightly lower, particularly for fuel types 2
to 4. The exceptions are FT 5, for which they obtained a 100% accuracy
and FT 6, for which the producer's accuracy was 99%.
The overall accuracy yielded by the method proposed in this
research was slightly higher than that obtained by Arroyo et al. (2006)
using an object-oriented classification approach (81.5%) applied using
a QuickBird image. User's and producer's accuracies were also
generally higher, but the user's accuracy for FT 5 and the producer's
accuracy for FT 7 were slightly lower.
Compared to previous studies, the inclusion of LiDAR data has
allowed better discrimination between shrub fuel types, that is
between fuel types 2 to 4, which are only distinguished by the mean
height and, therefore, these models are difficult to discriminate using
optical data alone because there is not a direct relationship between
height and reflectance. The use of LiDAR data also allowed for better
discrimination of fuel types involving trees, that is, FT 5, FT 6 and FT 7
which are distinguished by the shrub cover beneath the canopies as
well as the existence of vertical continuity between surface and
canopy fuel strata.
5. Conclusions
Fig. 7. Fuel type map of the study area based on the Prometheus classification system.
case, to increase accuracy, the surrounding pixels of all validation sites
were used for training.
The accuracy is also higher than that obtained by Lasaponara and
Lanorte (2007b) using QuickBird data over a Mediterranean area of
Italy based upon the Prometheus fuel types. These authors obtained
an overall accuracy of 75.83% and a kappa coefficient of 0.72. The
user's and producer's accuracies obtained by these authors were also
lower than that achieved in this study, especially for shrubs categories
and fuel type 7. Using ASTER data Lasaponara and Lanorte (2007a)
obtained similar results to those of this study, with an overall accuracy
of 90.73% and a kappa statistic of 0.89, although the user's and
In this paper the potential of fusing LiDAR and multispectral data
to map fuel types has been demonstrated. Since fuel models are an
input layer for fuel behaviour modelling, having accurate descriptions
of different fuels is critical for fire behaviour simulations. Fusing
optical and LiDAR data allows for a detailed characterization of fuel
type distribution by exploiting the spectral information provided by
the optical data and the three-dimensional information provided by
LiDAR data. Therefore the combined used of both datasets is well
suited to be used in complex areas as the wildland–urban interface
and in heterogeneous areas typical of Mediterranean environments
like the one used in this study, where the composition and structure of
fuels is very complex presenting different fuel types mixed. The used
of LiDAR data allowed overcoming the limitation of multispectral data
to distinguish certain surface types that present similar spectral
response by providing information on the vertical structure of the
vegetation, which is a critical attribute of fuels.
The two-phase approach proposed has been shown to provide
accurate results and could be applied to other Mediterranean
ecosystems since the decision rules applied are based on fixed
thresholds defined by the Prometheus classification system. The
methodology could also be applied to different environments and
with different classification systems, since the first phase attempts to
map the main fuel groups whereas the second phase discriminates
fuel types according to a set of rules based on a given fuel classification
scheme. Thus, the decision rules would have to be redefined
accordingly to the fuel classification system adopted.
Integration of LiDAR and multispectral data has been successfully
achieved through SVM, which has shown higher potential than MLC
for integration of different data sources. An important factor when
applying SVM is to find the optimal values for the two parameters
needed for the Gaussian kernel, namely C and γ, which was carried
out in a two-step grid search procedure. The vertical distribution of
fuels has been effectively described by the relative point density in
different height intervals or height bins, which allowed identification
of fuel vertical continuity (FT 7) or discontinuity (FT 5 or FT 6);
however, its accuracy is dependent on the penetration of LiDAR pulses
through the canopy and the understory.
The use of LiDAR data together with optical data has been shown to
be useful to reduce the confusion commonly found between fuel types
2 to 4 in other researches, based on optical data alone. Some confusion
still remained between fuel types 5 and 7, which was a consequence of
low penetration of LiDAR pulses in some areas with dense canopy
cover.
M. García et al. / Remote Sensing of Environment 115 (2011) 1369–1379
Acknowledgements
Data were acquired by the UK Natural Environment Research
Council (Airborne Remote Sensing Facility 2006 Mediterranean
Campaign, grant WM06-04). We would also like to thank the help
provided by John Gajardo during the field work carried out for fuel
type reconnaissance. We greatly appreciate the invaluable help of
Elena Prado from the Remote Sensing Area of the National Institute of
Aerospacial Technology (INTA) for her help with the pre-processing of
the ATM data. We greatly appreciate the comments on the manuscript
made by the anonymous reviewers.
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