Remote Sensing of Environment 115 (2011) 1369–1379

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Remote Sensing of Environment

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Multispectral and LiDAR data fusion for fuel type mapping using Support VectorMachine 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, Spainb Institute of Economics and Geography, Spanish National Research Council (CSIC), Albasanz 26-28 28037 Madrid, Spainc Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, 250-N, The Barn, One Shields Avenue, Davis, CA 95616-8617, USAd Centre for Environmental Systems Research, School of Environment and Life Sciences, University of Salford, Manchester M5 4WT, UK

⁎ Corresponding author.E-mail addresses: mariano.garcia@uah.es (M. García

(D. Riaño).

0034-4257/$ – see front matter © 2011 Elsevier Inc. Aldoi:10.1016/j.rse.2011.01.017

a b s t r a c t

a r t i c l e i n f o

Article history:Received 14 September 2010Received in revised form 25 January 2011Accepted 29 January 2011

Keywords:LiDARData fusionSupport Vector MachineDecision rulesFuel typesPrometheus Classification System

This paper presents a method for mapping fuel types using LiDAR and multispectral data. A two-phaseclassification 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 mappedthe main fuel groups, namely grass, shrub and tree, as well as non-fuel classes. This phase was carried outusing a Support Vector Machine (SVM) classification combining LiDAR and multispectral data. The overallaccuracy of this classification was 92.8% with a kappa coefficient of 0.9. The second phase of the proposedmethod focused on discriminating additional fuel categories based on vertical information provided by theLiDAR measurements. Decision rules were applied to the output of the SVM classification based on the meanheight of LiDAR returns and the vertical distribution of fuels, described by the relative LiDAR point density indifferent height intervals. The final fuel type classification yielded an overall accuracy of 88.24% with a kappacoefficient of 0.86. Some confusion was observed between fuel types 7 (dense tree cover presenting verticalcontinuity with understory vegetation) and 5 (trees with less than 30% of shrub cover) in some areas coveredby Holm oak, which showed low LiDAR pulses penetration so that the understory vegetationwas not correctlysampled.

), david.riano@cchs.csic.es

l rights reserved.

© 2011 Elsevier Inc. All rights reserved.

1. Introduction

Fires are a major disturbance factor for Mediterranean forests andplay a critical role in the cycle of vegetation succession as well asecosystem structure and function (Koutsias & Karteris, 2003).Although fires can be considered as a natural process in Mediterra-nean regions, the increase in their frequency, size and severity has ledto fires being considered as a natural hazard both, for the environmentand society. The loss of traditional activities in the Mediterraneanbasin, such as extensive grazing or wood harvesting (Scarascia-Mugnozza et al., 2000), together with management actions thatexclude fire, has contributed to modification of the composition andstructure of fuels. Resulting fuel loadings directly influence emissionsfrom bothwildland and prescribed fires, and affect the vulnerability oflandscapes to more intense fire behaviour and crown fires (Ottmar &Alvarado, 2004). Therefore, having accurate and spatially explicitinformation on fuel properties is critical in order to improve firedanger assessment, fire behaviour modelling and fire management

decision-support systems, since fuels affect both fire ignition andpropagation (Chuvieco et al., 2009; Ottmar & Alvarado, 2004).

The structural complexity of fuels, which can vary greatly in theirphysical attributes, results in a wide range of potential fire behaviourand effects, as well as the options that fuels present for their treat-ment, 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 fuelsover time (Ottmar & Alvarado, 2004; Sandberg et al., 2001). Fuelcharacteristics are commonly summarized by the concept of fuel types,which are classification schemes of fuel properties that groupvegetation classes with similar combustion behaviour (Pyne et al.,1996). More specifically, Merril and Alexander (1987) defined a fueltype as “an identifiable association of fuel elements of distinctivespecies, form, size, arrangement, and continuity that will exhibitcharacteristic fire behaviour under defined burning conditions”. Firebehaviour programs such as Behave (Andrews, 1986) or FARSITE(Finney, 1998) require as input numerical descriptions of the fuelproperties, which are known as fuel models (Chuvieco et al., 2009).Several fuel classification systems have been developed to be used infire behaviour modelling. Two widely used systems are the NorthernForest Fire Laboratory (NFFL) system (Albini, 1976), extended by Scottand Burgan (2005) from the initial 11 models to 40 models, and the

1370 M. García et al. / Remote Sensing of Environment 115 (2011) 1369–1379

Canadian Forest Fire Behaviour Prediction (FBP) system (Lawsonet al., 1985). In Europe, within the framework of the Prometheusproject, a new system based upon the NFFL system was adapted tobetter describe fuels found in the Mediterranean ecosystems. Thissystem is mainly based on the type and height of the propagationelements and it identifies 7 fuel types (Fig. 1), which are furtherdescribed in Table 1.

The inherent complexity and high dynamic nature of fuels makefield survey methods very limited for fuel type mapping in terms ofspatial and temporal coverage, and hence, methods based on aerialphotography and remotely sensed data have been developed (seeChuvieco et al. (2003) and Arroyo et al. (2008) for a thorough revisionof remote sensing methods for fuel type mapping).

Most studies using remote sensing methods have been based onmedium-to-high resolution sensors, especially Landsat-TM data,given its good compromise between spectral and temporal resolu-tions (Castro & Chuvieco, 1998; Riaño et al., 2002; Salas & Chuvieco,1995). More recently, several studies have shown the suitability of theAdvanced 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 bythe SWIR system (Lasaponara & Lanorte, 2007a). The advent of veryhigh spatial resolution sensors such as IKONOS or QuickBird hasallowed for finer scale mapping of fuels, which is particularlyimportant for the urban–wildland interface. Several studies havebeen carried out in Mediterranean environments based on thesesensors using digital image classification techniques commonlyapplied to remotely sensed data (Lasaponara & Lanorte, 2007b) andobject-oriented classificationmethods (Arroyo et al., 2006; Gitas et al.,

Fig. 1. Scheme used to identify the Prometheus fu

2006). The latter approach overcomes the limitation of the increase inthe spectral within-field variability that is common in very highspatial resolution imagery.

The main limitation of passive optical data is their inability todetect surface fuels when canopy cover is high, because they areunable to penetrate forest canopies (Keane et al., 2001). Moreover,reflectance is not directly related to vegetation height, which is acritical variable to discriminate between some fuel types (Riaño et al.,2002). This latter problem can be overcome by the use of LightDetection and Ranging (LiDAR) data. LiDAR data have been success-fully used to estimate important fuel parameters such as canopy bulkdensity (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 etal., 2010; Hall et al., 2005). Although LiDAR has been proved suitableto estimate fuel properties, fewer studies have tested the usefulness ofthese data to map fuel types. Koetz et al. (2008) fused LiDAR andhyperspectral data to map land cover for fire risk assessment by usingSupport Vector Machines (SVM) in a characteristic wildland–urbaninterface of the Mediterranean area of France. Mutlu et al. (2008)integrated LiDAR and QuickBird data applying the minimum noisefraction (MNF), and subsequently performed a supervised classifica-tion (Mahalanobis Distance decision rules) to map fuel models inTexas. These studies showed how the synergy of LiDAR and opticaldata improved the results of the classifications compared to theresults obtained by using a single data source alone. Because fusion ofmultispectral and LiDAR data takes advantage of the informationprovided by LiDAR data on the vertical structure of the fuels and the

el types (adapted from Chuvieco et al., 2003).

Table 1Description of the fuel models considered in the Prometheus fuel classification system (adapted from Riaño et al., 2002).

Fuel type Primary fire carrier Description

FT 1 Grassland Ground fuels with a cover N50%FT 2 Shrubs (shrub cover N60%.

tree cover b50%)Grassland. Shrubland (smaller than 0.3–0.6 m and with a high percentage of grassland) and clearcuts where slash was not removed.

FT 3 Shrubs between 0.6 and 2.0 m as well as young trees resulting from natural regeneration or forestation.FT 4 High shrubs (between 2.0 and 4.0 m) and regenerating trees.FT 5 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 vegetationFT 6 Medium surface fuels (shrub cover N30%): the base of the canopies is well above the surface fuel layer (N0.5 m). The fuel consists

essentially of small shrubs, grass, litter, and duff.FT 7 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 (b0.5 m).

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capability of multispectral data to capture the horizontal distributionof fuels, as well as to differentiate vegetation types based on theirspectral response, the integration of remotely sensed imagery andLiDAR data provides a unique opportunity to map fuel types.

The main objective of this research is to assess the potential ofintegrating multispectral and LiDAR data to map characteristicMediterranean fuels based on the Prometheus classification system,as well as to develop a robustmethodology suitable for the integrationof 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 inGuadalajara, 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 witha mean elevation of 1200 m, and a range of 895 to 1403 m, as well as amean slope of 24.25% with a standard deviation of 18.7%. The studysite is characterized by heterogeneous fuel complexes typical ofMediterranean environments. The shrub stratum is mainly composedof 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, pineand Spanish juniper (Juniperus thurifera L.). A small plantation ofpoplar (Populus alba) was also present in the area covered by thedataset. 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 June3rd), by the United Kingdom Natural Environment Research Council(NERC) Airborne Research and Survey Facility. The mean flyingheights were 750 m and 775 m above ground level for the first andsecond flights respectively, with a maximum scan angle of ±12°, anda beam divergence of 0.2 mrad resulting in a footprint diameter atnadir of approximately 18 cm. A multisensor campaign was con-ducted that included an Airborne Thematic Mapper (ATM) sensoralong with a LiDAR Optech-ALTM3033 system (Table 2). At each date,three strips were flown in a North–South direction, without overlapand the total area covered was about 382 km2. The data provided bythe NERC included raw ATM data with no geometric correction.Regarding the LiDAR data, they were provided in ASCII formatincluding X, Y, Z coordinates and intensities of first and last returns.

LiDAR data from both flights were used together after verificationand adjustment of a small relative spatial offset between dates (Garcíaet al., 2009) resulting in an effective increase in point density. Afterintegrating both datasets, the point density ranged from 1.5 to6 points m−2.

As for the optical data, only the image corresponding to the firstdate was selected because of the short time-gap between the twoflights 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 theexistent fuel types in the study area, so the methodology could beextended 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 morthoimages, along with the LiDAR and ATM data available. Afteridentifying suitable areas, a field campaign was carried out in 2010 toidentify fuel types according to the Prometheus system. 84 plots wereselected and assigned to fuel type. In addition 19 plots used in aprevious study for biomass estimation (García et al., 2010) werelocated within the area selected for this research and so were alsoused and assigned to a fuel type. Despite most of the plots weresurveyed 4 years after the remotely sensed data acquisition, the studyarea did not suffer any disturbance between the airborne and the fieldcampaigns such as fires, insect attack or clearance, that could havecaused significant changes and so, the fuel types remained the same.For each plot tree species, coverage and mean height of shrubs wererecorded, and 4 to 8 photographs were taken to assign a fuel type.Each plot locationwas recorded using a hand-held Trimble GeoTX GPSsystem which allowed for post-processing, yielding an accuracy ofplots location better than the pixel size used. Fig. 3 shows a spread-sheet generated from field data for fuel type assignment.

2.3. ATM data processing

The ATM data was georeferenced based on the GPS/IMU datacollected during the flight. Additionally, in order to assure anappropriate co-registration of the optical data to the LiDAR data, somecontrol points were collected using an intensity image generated fromthe LiDAR data. The RMSE obtained was less than 1 pixel (2 m).

Since the objective was to fuse the ATM and LiDAR tomap fuel typesand given the relatively lowdensity of the LiDARdataset, theATMimagewas resampled from 2 m to a 6 m pixel size. In doing so, it was alsoensured that a sufficient number of points (more than 54 points)wouldbe included within each pixel to derive subsequent LiDAR variables.Moreover, by coarsening the resolution of the optical data the highspectral variability within the field of view common to high resolutionsensors was reduced. The spatial resampling was performed byconsidering themean value of all pixels includedwithin each 6 m pixel.

In order to remove the effect of the terrain slope and aspect onthe signal recorded by the sensor, a topographic correction wasperformed. Numerous empirical and photometric methods have beendeveloped to remove the effect of topography, such as the cosine

Fig. 2. Study area. The enlarged image shows the overlap area of the ATM and LiDAR data used for this study.

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correction, Minnaert or the C correction (Smith et al., 1980; Teilletet al., 1982). The main limitation of the previous corrections whenapplied over forested areas is that they do not consider the geotropicnature of trees, that is that the tree growth is driven by the

Table 2Characteristics of the sensors used.

ATM Optech-ALTM3033

Band Spectralrange (nm)

Spatial resolution (m)

1 420–450 2 Pulse rate 33 kHz2 450–520 Beam

divergence0.2 mrad

3 520–600 Scan angle ±12°4 600–620 Footprint

size18 cm

5 630–690 Returnsrecorded

First andlast

6 690–750 Mean pointdensity

1.5 p/m2

(for eachflight)

7 760–9008 910–10509 1550–175010 2080–2350Spectralindices

DefinitionNDVI ρB7−ρB5=ρB7+ρB5SAVI ρB7−ρB5ð Þ 1+Lð Þ = ρB7+ρB5+Lð Þ

L=0.5NDII_1 ρB7−ρB9=ρB7+ρB9NDII_2 ρB7−ρB10=ρB7+ρB10

gravitational field and therefore, it is not perpendicular to an inclinedsurface (Soenen et al., 2005). Therefore, the correction developed bySoenen et al. (2005) was applied, which is based on the Sun-Canopy-Sensor (SCS) correction proposed by Gu and Gillespie (1998). Thecorrection used is based on the following formula:

ρn = ρcosα cosθ + C

cosi + C: ð1Þ

where ρn is the normalized reflectance, ρ is the uncorrectedreflectance, α is the slope terrain, θ is the solar zenith angle, i is theillumination angle, and C is an empirical parameter introduced bySoenen et al. (2005) to moderate the overcorrection of the SCScorrection at large incidence angles. The empirical parameter (C) is afunction of the slope (b) and the intercept (a) of the regression linederived from the relationship between the reflectance and the cosineof the illumination angle:

ρ = a + b cos i⇒C =ab: ð2Þ

Once the ATM image was corrected several spectral indices werederived, namely the Normalized Difference Vegetation Index (NDVI),the Soil Adjusted Vegetation Index (SAVI), and the NormalizedDifference Infrared Index (NDII). Since the ATM sensor has twobands within the SWIR region, two NDII indices were computed,NDII_1 and NDII_2.

Fig. 3. Card generated from field work for fuel type assignment.

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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 pointsby applying a spline interpolation method. The height above theground of each vegetation point was computed as the differencebetween the Z coordinate of the point, and the Z value of the DEM atthe same X, Y position:

hi = Zi−Zinterpolated: ð3Þ

Afterwards several variables were derived from the height distribu-tion of the first and last returns within each 6×6 m grid cell. Thesevariables included the maximum height, which was considered as the99th percentile to avoid the noise caused by any possible outlier, the

mean height and the median height. Moreover, several metrics thathave been proved to provide a summary of the vegetation structurebased on the vertical distribution of the heights of the laser return werecomputed, namely the standard deviation, the range, the skewness, thekurtosis and the coefficient of variation (Donoghue et al., 2007; Jensenet al., 2008). Similarly to Koetz et al. (2008) and Popescu and Zhao(2008), the whole point cloud within each grid was used to representthe relative point density in different height intervals. Considering thepercentage of points found in each interval the effect of the variablepoint density along theflight line is removed (Mutlu et al., 2008). A totalof 15 height intervals were considered. Up to 4 m the height bins werecreated every 0.5 m to achieve a better characterization of the surfacefuels, whereas for the canopy layer (above 4 m) it was considered that1 m bins were enough to characterize the tree cover. The last binincluded 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 asingle image.

2.5. Fuel discrimination and mapping

The fuel type classification was carried out using a two phaseapproach. The first part was intended to classify the main fuel groups,namely grasslands (two spectral classes), shrublands (two spectralclasses) and trees (pine, Holm oak and poplar) as well as an additionalclass corresponding tonon-fuel covers (roadsandbare soil),whereas thesecond phase attempted to map fuel types according to the Prometheussystem.

Remotely sensed data have been traditionally classified usingparametric methods such as maximum likelihood; however, satisfy-ing the assumptions that underlie these methods such as the normaldistribution of the data, is difficult in remote sensing applications, andhas led to the investigation of non-parametric methods such asArtificial Neural Networks (ANN) or more recently, Support VectorMachine (SVM) (Foody & Mathur, 2004). Another drawback ofparametric methods is the fact that they may perform worse forclassifying multisource data since these data cannot be modelled by aconvenient multivariate model (Benediktsson et al., 1990).

SVM is based on the principle of statistical learning theory and itsfoundations were developed by Vapnik (1995). SVM attempts to fit anoptimal separating hyperplane to the training data in the multidi-mensional feature space. Contrary to other approaches such as ANNthat are based on empirical risk minimization, that is, on theminimization of the error on the training data, SVM applies structuralrisk minimization, which tries to maximize the margin between theoptimal separating hyperplane and the closest training samples,known as support vectors (Vapnik, 1998). If two classes cannot belinearly separated, SVM is able to represent the non-linearity byprojecting the input data into a higher-dimensional feature space,where the classes may be linearly separated, by means of a kernelfunction to address the ‘curse of dimensionality’ or Hughes effect(Gunn, 1998). Although it was originally developed as a binaryclassifier, two approaches have been suggested for using SVM inmulticlass classifications that reduce themulticlass problem to a set ofbinary problems, namely “one against all” and “one against one”(Foody &Mathur, 2004). The former approachwas usedwhere a set ofbinary classifiers is trained so each class is separated from the rest. Thefinal class label is assigned by selecting the largest decision value(Foody &Mathur, 2004). As for the kernel used, a radial basis functionwas selected. This type of kernel has been widely used in remotesensing applications (Foody & Mathur, 2004; Koetz et al., 2008;Melgani & Bruzzone, 2004; Waske et al., 2007) and it is controlled bytwo parameters that will determine the accuracy of the classification,specifically C and γ. The latter determines the width of the Gaussiankernel, while the former controls the penalty associated with trainingsamples that lie on the wrong side of the decision boundary. Thus, alow C value will cause an increase in the number of support vectorsderived and consequently larger errors, whereas a large value of Creduces the errors but also the generalization ability, and may resultin overfitting the SVM to the training data (Foody & Mathur, 2004).Given the importance of these two parameters a grid search approachwas performed using LIBSVM library by Chen and Lin (2009), with afive-fold cross validation. In this search, pairs of (C, γ) are tested onthe training data and the onewith the best cross validation accuracy isselected. The search was carried out in two steps (Oommen et al.,2004). First a coarser grid was used with an exponentially growingsequence (C=2−5, 2−3 … 215 and γ=2−15, 2−13 … 23), followed bya finer search which slightly increased the accuracy.

Training data was collected over the image using prior knowledgeof the area acquired during the field work and 0.5 m orthoimages ofthe area. Additionally, for discrimination of vertical properties for

certain classes, especially to discriminate between shrubs of youngHolm oak (≤4 m) and trees of Holm oak (N4 m), LiDAR heights wereused. SVM have been shown to achieve good results even with smalltraining data sets in high dimensional feature spaces (Melgani &Bruzzone, 2004). The number of samples used for training variedbetween 35 for poplar, which was only present in a very small area ofthe image, and 325 for shrubs, which were widely present in theimage. Approximately, 70% of the samples were used to train the SVMand the 30% remaining was used for validation. In this first phase ofthe classification, the initial feature space was determined by the ATMdata and spectral indices derived from them, as well as the followingLiDAR-derived metrics: maximum height, mean height, medianheight, standard deviation, range, kurtosis, skewness and thecoefficient of variation. Subsequently, those bands showing gooddiscrimination between the different classes were selected to carryout the SVM classification.

Input data were scaled to avoid attributes in greater numericranges dominating those in smaller numeric ranges, thus biasing theresults (Hsu et al., 2009). Another advantage is that it avoidsnumerical difficulties that can arise as consequence of large attributevalues during the inner products of the feature vectors on which thekernel values depend. Taking into account the previous considera-tions, the ATM and LiDAR datawere scaled to a range of 0–1 before thesearch of the optimum parameters by considering the maximum andminimum values of each band.

Xscaled =X−X min

X max−X minð4Þ

Where Xscaled represents the scaled values for the optical andLiDAR-derived bands, Xi represents the value of a given pixel at eachband, and Xmin and Xmax are the minimum and maximum value foreach band respectively.

After classifying themain groups of fuels, the second phase tried torefine the discrimination of those fuel types that are related tovegetation vertical properties. To achieve this classification, a set ofdecision rules were applied according to the Prometheus classificationsystem. The input data were the output of the SVM classificationcarried out in phase one, the LiDAR mean height since it is a keyvariable to differentiate shrub fuel types, and the LiDAR relative pointdensity images derived to represent the vertical distribution ofvegetation and, therefore the continuity of fuels. Fig. 4 shows thedecision rules used to classify the fuel types.

The accuracy of the classification was assessed through the use ofconfusion matrix and the Cohen's kappa coefficient (Congalton &Green, 2008), using as reference data 103 plots that had been assignedto a fuel type after field reconnaissance.

3. Results

Fig. 5 shows the signatures of the different spectral classesidentified on the image. These curves were obtained from the meanvalue of the samples collected for each class after scaling the databetween 0 and 1, and were used to identify the bands with thegreatest separability that were subsequently used in the SVMclassification. Based on Fig. 5, the following bands were selected asinput data in the classification: ATM bands 2 to 10, SAVI index andNDII_1, the maximum height, the median height instead of the mean,since it is less affected by extreme values, the standard deviation andthe range. Band 1 (blue) was not included in the analysis given thehigh atmospheric scattering that affected this band. The SAVI indexwas selected instead to NDVI since it is highly sensitive to differencesin vegetation changes cover, while it is less sensitive to soilbackground than the NDVI.

The grid search approach provided initially the following valuesfor the two parameters of the Gaussian kernel used, C=128 and

Fig. 4. Decision rules applied to classify fuel types based on the Prometheus classification system.

1375M. García et al. / Remote Sensing of Environment 115 (2011) 1369–1379

γ=4, which were subsequently changed to C=207.94 and γ=3.25after a finer search of the optimal parameters to avoid overfitting tothe training data. The accuracy obtained over the training sampleusing a fivefold cross validation was 94.22%.

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

0,90

1,00

Sca

led

ban

d v

alu

es

ROADS BARESOIL

OLD OAK 1 OLD OAK 2

Fig. 5. Signatures of the spectral cl

After performing the SVM classification on the image, the accuracyassessment was carried out using the 30% of the samples collected.The classes considered initially for the analysis were: non-fuel (roadsand bare soil), grasslands (2 spectral classes), shrubland (bushes and

PASTURE1 PASTURE2 PINE

YOUNG OAK BUSHES Poplar

asses found in the study area.

Table 3Confusion matrix obtained after applying SVM classification method.

Reference data Total User's accuracy(%)

Error of commission(%)

Non-fuel Bushes Poplar Holm oak Grass Pine

Classified data Non-fuel 112 0 0 0 4 0 116 96.55 3.45Bushes 1 150 0 8 13 3 175 85.71 14.29Poplar 0 0 9 0 0 0 9 100 0Holm oak 1 10 0 148 1 6 166 89.17 10.83Grass 0 5 0 1 102 0 108 94.44 5.56Pine 0 0 2 1 0 75 78 96.16 3.84Total 114 165 11 158 120 84 652

Producer's accuracy 98.25 90.91 81.82 93.67 85 89.28Error of omission (%) 1.75 9.09 18.18 6.33 15 10.72

1376 M. García et al. / Remote Sensing of Environment 115 (2011) 1369–1379

young Holm oaks), Holm oak (two spectral classes), pine and poplar.The overall accuracy for these 6 classes was 91.4%, with a kappastatistic of 0.89. Subsequently, the spectral classes representing thesame informational class were merged. Also the different tree specieswere group into one class since the Prometheus classification systemonly considers the propagation element (tree) and does notdistinguish tree species although they could present differentcombustion properties. After merging the Holm oak, pine and poplarclasses into one class, and so to consider the three main fire carriers(grass, shrub and trees) and the non-fuel classes, a non-significantincrease was observed with an overall accuracy of 92.8% and a kappastatistic of 0.9.

SVM classification has been suggested as more suitable for amultisensor approach than parametric methods commonly used inremote sensing (Benediktsson et al., 1990). To verify the betterperformance of the SVM compared to parametric methods, amaximumlikelihood classification (MLC) was conducted and its results werecompared to those obtained using the SVM classification. The overallaccuracy achievedwith theMLCwas85.7% for the6 initial classes,whichwas slightly lower than that obtained by SVM. Tables 3 and 4 show theconfusion matrices obtained after applying the SVM and MLC methodsrespectively, which relate the reference and the classified data allowingfor the identification of the main sources of error.

The vegetation vertical distribution was represented by thepercentage of returns in 0.5 m height intervals below 4 m (shrubs)and 1 m height intervals above 4 m (canopy). This information wasmainly used to distinguish between fuel types involving trees. Fig. 6shows an example of the vertical continuity of three characteristicplots of fuel types 5, 6 and 7. Fuel types 5 (left) and 6 (centre) arecharacterized by vertical gaps and the fact that fuel type 5 presents noshrub cover whereas for fuel type 7 (right), which is characterized byvertical continuity, all height intervals are occupied.

The decision rules applied to the SVM classification, the meanheight and the height bin images, yielded a good agreement with the

Table 4Confusion matrix obtained after applying ML classification method.

Reference data

Non-fuel Bushes Poplar Holm oak

Classified data Non-fuel 100 0 0 0Bushes 11 154 0 26Poplar 0 0 5 0Holm oak 1 6 1 130Grass 2 5 0 1Pine 0 0 5 1Total 114 165 11 158

Producer's accuracy 87.72 93.33 45.46 82.28Error of omission (%) 12.28 6.67 54.54 17.72

103 validation plots used, with an overall accuracy of 88.24% and akappa statistic of 0.86. Table 5 shows the confusion matrix obtainedfor the fuel type classification after applying the decision rules shownin Fig. 5. Fig. 7 represents the final fuel types map generated from theATM and LiDAR data for the study area.

4. Discussion

The grid search of the optimum parameters for the Gaussian kernelused was intended to avoid overfitting to the training data, whichwould reduce the generalization ability of the SVM; nevertheless,some researchers have shown the robustness of the SVM classificationto variation of the parameters (Foody & Mathur, 2004).The change ofthe parameters C and γ between the coarser and the finer search ofthe parameters yielded a negligible increase of the accuracy over thetraining data of less than 0.5%, although larger changes in the accuracywere observed during the search of the parameters across the wholerange of values tested. Thus, although time consuming, the grid-search approach is a suitable procedure to assure a higher accuracy ofthe SVM classification instead of applying arbitrary values to theparameters C and γ.

The SVM classification provided very good agreement with thereference data. Some confusion was observed between shrubs andHolm oaks. Since the shrublands included the spectral class Holm oakwith a height lower than 4 m, this confusion could be expected sincethe difference between a shrub and a tree is given by a height thresholdof 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 presencestill affected the statistics derived from the LiDAR data, especially forsome of the metrics such as the maximum height, the standarddeviation or the range, which were used in the classification.

Comparison of the SVM classification to the MLC classificationshowed a higher accuracy of about 7% for the former method. This

Total User's accuracy(%)

Error of commission(%)

Grass Pine

2 0 102 98.04 1.9621 1 213 72.3 27.70 0 5 100 01 9 148 87.84 12.16

96 0 104 92.31 7.690 74 80 92.5 7.5

120 84 65280 88.120 11.9

FT 5

0 10 20 30 40 50

0 to 0.50.5 to 1.01.0 to 1.51.5 to 2.0

2.0 to 2.52.5 to 3.03.0 to 3.53.5 to 4.04.0 to 5.05.0 to 6.06.0 to 7.07.0 to 8.08.0 to 9.0

9.0 to 10.0> 10.0

Hei

gh

t In

terv

als

(m)

Percentage of returns

FT 6

0 5 10 15 20

0 to 0.5

0.5 to 1.0

1.0 to 1.5

1.5 to 2.02.0 to 2.5

2.5 to 3.0

3.0 to 3.5

3.5 to 4.04.0 to 5.0

5.0 to 6.0

6.0 to 7.0

7.0 to 8.08.0 to 9.0

9.0 to 10.0

> 10.0

Hei

gh

t In

terv

als

(m)

Percentage of returns

FT 7

0 5 10 15 20 25 30

0 to 0.50.5 to 1.01.0 to 1.51.5 to 2.02.0 to 2.52.5 to 3.03.0 to 3.53.5 to 4.04.0 to 5.05.0 to 6.06.0 to 7.07.0 to 8.08.0 to 9.0

9.0 to 10.0> 10.0

Hei

gh

t In

terv

als

(m)

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).

1377M. García et al. / Remote Sensing of Environment 115 (2011) 1369–1379

performance of SVM is confirmed by the similar results found by otherresearchers using both multispectral and hyperspectral data (Oommenet al., 2004), andwhen using amultisensor approach combining opticaland synthetic aperture radar (SAR) data (Waske & Benediktsson, 2007).An analysis of the confusion matrices obtained for both classificationmethods showed that in general terms, SVM yielded slightly higheruser's and producer's accuracies than the MLC. It is worth noting thatfor the poplar category the SVM classification yielded a producer'saccuracy of 81.82% whereas for the MLC it was only 46.46%, thisconfirms the fact that SVM can provide accurate results evenwith smallsample sizes whereas the MLC is more dependent on the size of thetraining sample.

The final fuel type classification obtained after applying thedecision rules showed very good agreement with the 103 plots usedfor validation. Although the number of plots used to validate ourresults can be considered small for some classes (10–31 plots for eachclass), theywere considered to be sufficient given the small area of thesubset data used (9×0.3 km2). In addition, these plots were not usedeither to train the classifier in the first phase or to develop the decisionrules in the second phase since the thresholds were defined by thePrometheus classification system. The analysis of the confusionmatrixobtained for the fuel types classification showed that user's andproducer's accuracies were very high except for FT 5 (scattered shrubsunder trees), which presented a commission error of about 33%. Thiserror was particularly due to some pixels classified as FT 5 whichactually corresponded to FT 7. This confusionmainly occurred in areasthat presented a mixture of shrubs of Holm oak and mature Holmoaks. These areas present a dense cover at different height intervalsthat reduces the penetration of LiDAR pulses through the canopy, sothe lower parts of the canopy and the understory are missed.Inspection of the cloud points on pixels presenting this confusionshowed that most of returns were concentrated on the higher parts ofthe canopy and few of them were able to penetrate down to the

Table 5confusion matrix of the fuel types classification after applying decision rules.

Reference data

FT 0 FT 1 FT 3 FT 4 FT 5

Classified data FT 0 10 0 0 0 0FT 1 1 12 0 0 0FT 3 0 0 9 0 0FT 4 0 0 0 10 0FT 5 2 0 0 0 15FT 6 0 0 0 0 0FT 7 0 1 1 1 0

Total 13 13 10 11 15Producer's accuracy 76.92 92.31 90 90.91 100Error of omission (%) 23.08 7.69 10 9.09 0

ground or even the lower parts of the canopy. Although the height binlayers generated provided an adequate description of the verticaldistribution of fuels within each pixel, results are affected by thepenetration of LiDAR pulses through the canopy. The low penetrabil-ity found in some areas was represented in the canopy height bins asgaps between the fuel strata, that is, as vertical discontinuity causingconfusion between FT 7 and FT 5. This confusion could be partlyavoided using higher density LiDAR data since the mean point densityof data used in this study was 2.5 points m−2 and in some areas it waslower than 2 points m−2. Considering a larger size (e.g. 1 m) for thelower height bins could also help to reduce the number of gapsresulting as consequence of canopy occlusion. Nevertheless, this couldincrease the error of fuel types 5 and 6 since the Prometheus systemconsider fuel discontinuity when the gap between shrubs and thecanopy is greater than 0.5 m (Fig. 1) and so, using a height bin largerthan 0.5 mwould hamper the identification of vertical gaps. In fact, forthis study area, using a size of 1 m for the lower height bins reducedthe overall accuracy by 10% and largely reduced the user's andproducer'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 thisconfusion occurred showed that it was due to roads that were coveredby trees. Arroyo et al. (2006) pointed out the benefits of applyingcontextual 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 herecan be considered high given the heterogeneity of the study area.Compared to other studies carried out in Mediterranean areas andbased on the Prometheus classification system, the overall accuracy aswell as the kappa coefficient obtained in this study was higher thanthat obtained by Riaño et al. (2002) who achieved an overall accuracyof 82.8% with a kappa statistic of 0.79 in a Mediterranean forest usingmultitemporal Landsat-TM data and auxiliary information. In their

Total User's accuracy(%)

Error of commission(%)

FT 6 FT 7

0 0 10 100 00 0 13 92.31 7.690 0 9 100 00 0 10 100 01 4 22 66.67 33.338 0 8 100 01 27 31 87.1 12.9

10 31 10380 87.120 12.9

Fig. 7. Fuel type map of the study area based on the Prometheus classification system.

1378 M. García et al. / Remote Sensing of Environment 115 (2011) 1369–1379

case, to increase accuracy, the surrounding pixels of all validation siteswere used for training.

The accuracy is also higher than that obtained by Lasaponara andLanorte (2007b) using QuickBird data over a Mediterranean area ofItaly based upon the Prometheus fuel types. These authors obtainedan overall accuracy of 75.83% and a kappa coefficient of 0.72. Theuser's and producer's accuracies obtained by these authors were alsolower than that achieved in this study, especially for shrubs categoriesand fuel type 7. Using ASTER data Lasaponara and Lanorte (2007a)obtained similar results to those of this study, with an overall accuracyof 90.73% and a kappa statistic of 0.89, although the user's and

producer's accuracies were slightly lower, particularly for fuel types 2to 4. The exceptions are FT 5, for which they obtained a 100% accuracyand FT 6, for which the producer's accuracy was 99%.

The overall accuracy yielded by the method proposed in thisresearchwas slightly higher than that obtained by Arroyo et al. (2006)using an object-oriented classification approach (81.5%) applied usinga QuickBird image. User's and producer's accuracies were alsogenerally higher, but the user's accuracy for FT 5 and the producer'saccuracy for FT 7 were slightly lower.

Compared to previous studies, the inclusion of LiDAR data hasallowed better discrimination between shrub fuel types, that isbetween fuel types 2 to 4, which are only distinguished by the meanheight and, therefore, these models are difficult to discriminate usingoptical data alone because there is not a direct relationship betweenheight and reflectance. The use of LiDAR data also allowed for betterdiscrimination of fuel types involving trees, that is, FT 5, FT 6 and FT 7which are distinguished by the shrub cover beneath the canopies aswell as the existence of vertical continuity between surface andcanopy fuel strata.

5. Conclusions

In this paper the potential of fusing LiDAR and multispectral datato map fuel types has been demonstrated. Since fuel models are aninput layer for fuel behaviour modelling, having accurate descriptionsof different fuels is critical for fire behaviour simulations. Fusingoptical and LiDAR data allows for a detailed characterization of fueltype distribution by exploiting the spectral information provided bythe optical data and the three-dimensional information provided byLiDAR data. Therefore the combined used of both datasets is wellsuited to be used in complex areas as the wildland–urban interfaceand in heterogeneous areas typical of Mediterranean environmentslike the one used in this study, where the composition and structure offuels is very complex presenting different fuel types mixed. The usedof LiDAR data allowed overcoming the limitation of multispectral datato distinguish certain surface types that present similar spectralresponse by providing information on the vertical structure of thevegetation, which is a critical attribute of fuels.

The two-phase approach proposed has been shown to provideaccurate results and could be applied to other Mediterraneanecosystems since the decision rules applied are based on fixedthresholds defined by the Prometheus classification system. Themethodology could also be applied to different environments andwith different classification systems, since the first phase attempts tomap the main fuel groups whereas the second phase discriminatesfuel types according to a set of rules based on a given fuel classificationscheme. Thus, the decision rules would have to be redefinedaccordingly to the fuel classification system adopted.

Integration of LiDAR and multispectral data has been successfullyachieved through SVM, which has shown higher potential than MLCfor integration of different data sources. An important factor whenapplying SVM is to find the optimal values for the two parametersneeded for the Gaussian kernel, namely C and γ, which was carriedout in a two-step grid search procedure. The vertical distribution offuels has been effectively described by the relative point density indifferent height intervals or height bins, which allowed identificationof fuel vertical continuity (FT 7) or discontinuity (FT 5 or FT 6);however, its accuracy is dependent on the penetration of LiDAR pulsesthrough the canopy and the understory.

The use of LiDAR data together with optical data has been shown tobe useful to reduce the confusion commonly found between fuel types2 to 4 in other researches, based on optical data alone. Some confusionstill remained between fuel types 5 and 7, whichwas a consequence oflow penetration of LiDAR pulses in some areas with dense canopycover.

1379M. García et al. / Remote Sensing of Environment 115 (2011) 1369–1379

Acknowledgements

Data were acquired by the UK Natural Environment ResearchCouncil (Airborne Remote Sensing Facility 2006 MediterraneanCampaign, grant WM06-04). We would also like to thank the helpprovided by John Gajardo during the field work carried out for fueltype reconnaissance. We greatly appreciate the invaluable help ofElena Prado from the Remote Sensing Area of the National Institute ofAerospacial Technology (INTA) for her helpwith the pre-processing ofthe ATM data.We greatly appreciate the comments on themanuscriptmade by the anonymous reviewers.

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