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Remote Sensing of Environment 112 (2008) 2988–2998

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Remote Sensing of Environment

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Object-based land cover classification using airborne LiDAR

A.S. Antonarakis a,⁎, K.S. Richards a, J. Brasington b

a Department of Geography, University of Cambridge, Downing Place, Cambridge CB2 3EN, UKb Institute of Geography and Earth Sciences, The University of Wales, Aberystwyth, Llandinam Building, Penglais Campus, Aberystwyth, Ceredigion SY23 3DB, Wales, UK

a r t i c l e i n f o

⁎ Corresponding author.E-mail addresses: [email protected] (A.S. Antonaraki

[email protected] (K.S. Richards), james.bra(J. Brasington).

0034-4257/$ – see front matter © 2008 Elsevier Inc. Aldoi:10.1016/j.rse.2008.02.004

a b s t r a c t

Article history:Received 31 October 2007Received in revised form 22 January 2008Accepted 16 February 2008

Light Detection and Ranging (LiDAR) provides high resolution horizontal and vertical spatial point cloud data,and is increasingly being used in a number of applications and disciplines, which have concentrated on theexploit andmanipulation of the data usingmainly its three dimensional nature. LiDAR information potential ismade even greater though, with its consideration of intensity.Elevation and intensity airborne LiDAR data are used in this study in order to classify forest and ground typesquickly and efficiently without the need for manipulating multispectral image files, using a supervised object-orientated approach. LiDAR has the advantage of being able to create elevation surfaces that are in 3D, whilealso having information on LiDAR intensity values, thus it is a spatial and spectral segmentation tool. Thisclassification method also uses point distribution frequency criteria to differentiate between land cover types.Classifications were performed using two methods, one that included the influence of the ground in heavilyvegetated areas, and the other which eliminated the ground points before classification. The classification ofthreemeanders of the Garonne and Allier rivers in France has demonstrated overall classification accuracies of95% and 94% for the methods including and excluding the ground influence respectively. Five types of riparianforest were classified with accuracies between 66 and 98%. These forest types included planted and naturalforest stands of different ages. Classifications of short vegetation and bare earth also produced high accuraciesaveraging above 90%.

© 2008 Elsevier Inc. All rights reserved.

Keywords:Airborne LiDARImage classificationRiparian forestVegetation roughnessPoint cloudIntensityHydraulic modelling

1. Introduction

Better information on roughness of various types of vegetation isneeded for use in resistance equations and eventually in floodmodelling. These types include woody riparian species with differentstructural characteristics. Remote Sensing information such as 3Dpoint cloud data from LiDAR can be used as a tool for extracting simpleroughness information relevant for the condition of below canopyflow, as well as roughness relevant for more complex tree morphologythat affects the flowwhen it enters the canopy levels. One strategy forextracting roughness parameters from remote sensing techniques is touse a data fusion object classification model. This means that multipledatasets such as LiDAR, digital aerial photography, ground data andsatellite data can be combined to produce roughness parametersestimated for different vegetative patches, which can subsequently bemapped spatially using a classification methodology.

Airborne LiDAR is used in this study in order to classify forest andground types quickly and efficiently without the need for manipulat-ing multispectral image files. Classifications have until recently beenattempted with multispectral imagery (Duda et al., 1999; Sun et al.,

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2003). LiDAR has the advantage of being able to create elevationsurfaces that are in 3D, while also having information on LiDARintensity values, thus it is a spatial and spectral segmentation tool.

LiDAR intensity information has not been greatly used either in thecommercial sector or in academia, yet it could be an important factor forfeature extraction or land cover classification (Flood, 2001). LiDAR hastraditionally been used spatially to separate ground points fromaboveground points (Cobby et al., 2001; Vosselman, 2000; Lohmannet al., 2000), or classify LiDAR into features such as buildings (Axelsson,1999) and vegetation (Mason et al., 2003; Cobby et al., 2003). Until veryrecently, not much had been achieved by using LiDAR point cloud datawith elevations and intensity for land cover classifications.

Classifications have been attempted by Brennan and Webster(2006) who used derived LiDAR surfaces to differentiate betweendifferent layers. Their study used four layers (Mean Intensity,Normalised Height, Digital Surface Model, Multiple Waveform LiDARReturns) to differentiate between ten land features. These classifica-tions included water, vegetation, roads, saturated and unsaturatedsoils, coniferous and deciduous trees, and building structures. OtherLiDAR based classifications have also been attempted, for example byCharaniya et al. (2004) using LiDAR point cloud elevation and intensitydata to classify roofs, grass, trees and roads. Bartels and Wei (2006)performed LiDAR based maximum likelihood classifications fusedwith co-registered spectral bands, extracting land types includingbuilding, vegetation, and ground (i.e. all features at low elevationranges) from a small 800 m2 urban area.

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http://dx.doi.org/10.1016/j.rse.2008.02.004

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2989A.S. Antonarakis et al. / Remote Sensing of Environment 112 (2008) 2988–2998

For this study, it was desired to classify known land cover types onriver reaches of the Rivers Garonne and Allier, with specialconsideration given to three meanders. The land cover types havebeen defined in the field, and relevant parameters have been extractedfrom them to be able to estimate their roughness at different scales ofcomplexity. Thus these desired land types exist from ground truthingdata, aerial photography and from previous research on the study sitesincluding Muller et al. (2002) and De Jong (2005). These desired landtypes are gravel bars, bare earth, short vegetation, three different agesof planted forests, and two ages of natural riparian forest. Includingthe river water, this makes nine classes. Each land type was classifiedusing exclusive criteria, in order to classify only known land types.These criteria were chosen based on different rasterised surfaceinterpolations of LiDAR point cloud data. The characteristics of theland types thus need to be known, in order to develop the appropriateLiDAR derived surface models.

The objectives of this study were to:

- Explore the possibility of identifying and classifying land types intwo floodplains, using airborne LiDAR intensity and elevation data.- Accurately identify and classify the different forest types andforest ages.

2. Data description

The airborne LiDAR data and digital aerial photographs used in thisstudy were obtained from flights organised by the Natural Environ-ment Research Council Airborne Research & Survey Facility. The flightscollected information on the reaches of the two rivers on the 6th ofJune 2006 for the Garonne, and the 8th of June 2006 for the Allier. ASPOT image of the Garonne River from Toulouse to Montauban wasalso collected on the 6th of June 2006, with four spectral bandsincluding near infrared (NIR) with a resolution of 10 m. The airborneLiDAR had an average flying height of 1300 m collecting first and lastpulse data with an average point density of 1.9 m, and an averagespatial resolution of 1.0–2.5 m. The data were provided by theCambridge University based Unit for Landscape Modelling, where thepoint clouds had an x–y position accuracy of less than 1 m and anelevation accuracy of less than 15 cm, with the highest pointaccuracies of 0.020 m, 0.009 m, 0.052 m in x, y, z, resulting from ahigh fixed point GPS accuracy of 0.0002m. Apart from xyz coordinates,first and last pulse information on intensity were also collected. LiDARintensity can be defined as the ratio of the strength of the lightreflected from an object related to the light emitted (Song et al., 2002).Song et al. (2002) further stated that different objects can have adifferent reflectance, and can be related to the on-site light conditionsand the spectral band being used by the LiDAR emitter.

Three meanders are considered in this study, the first two beingfrom the heavily managed Garonne River, and the second from thealmost unmanaged Allier River. The first two meanders consideredwere near the village of Verdun-sur-Garonne (UTM31; 359500E

Fig. 1. Distribution of LiDAR raw points in

4854000N), and the second near the village of Monbequi (UTM31;356000E 4861500N). Both the Verdun and Monbequi sites consist of alarge proportion of commercial planted poplar clones of all ages, whichare heavily pruned. The ground in the summer consists of ploughedearth and dry, prone grass distributed sparsely around the site, as wellas gravel near the edges of the low flow river edge. Vegetation on thesemeanders also includes natural black poplar (Populus nigra), which canbe very dense and are situated on the immediate bank of the river. Asecondary natural species was Salix alba, but its distributionwas limited(Muller et al., 2002). For the Garonne River, three planted poplar ages aswell as mature dense natural riparian forests were considered andmeasured. The youngest planted poplar was 1–3 years old, withintermediate aged planted poplar at 3–8 years, and mature plantedpoplar from 8–12 years. One meander section was examined on theAllier near the village of Châtel-de-Neuvre (UTM31; 525250E5140350N). In this meandermost of the surfacewas bare and consistingof bar forms with variously sized gravel but also included sparselyvegetated areas. The main species was again P. nigra with a limitednumber of S. alba. For this river, the younger natural riparian forestswere between one to five years, and mature natural riparian forestswere older than five years.

3. Land types

There are eight main land cover types that were previously definedwith a total of nine layers including water surfaces.

Water surfaces are usually recognised as areas that absorb muchof the incoming radiation. Of course this depends on the velocity,turbulence and depth of the water as these can cause the surface tobe brighter, thus absorbing less radiation (i.e. from ‘white water’ oremerging gravel bars in rivers). Therefore water in remote sensinghas traditionally been considered as having the lowest intensitypixels (Harris, 1987; Nedeljkovic, 2006). Song et al. (2002) definedthe reflectivity of airborne LiDAR pulses in relation to varioussurfaces. In the near infrared, clear water was defined as having thelowest reflectivity (0–10% reflectance), pebbles having a very lowreflectance similar to water (17%), grass and short vegetation havingthe highest (50% reflectance), and high reflectances for sand (41–57%for wet to dry soil). Using airborne LiDAR, all these surfaces can bedifferentiated from trees by simply considering a low height range.Thus water can be defined as having low intensity values as well ashaving a low range in height, or a small deviation from an averageterrain model. Three more low height range surfaces are described inthis study, and are related to those stated here in the research doneby Song et al. (2002). Short vegetation is described as any land typethat is dominated by tall grass and non-woody agricultural crops.Due to the chlorophyll content of the grass and short vegetation, itshould have a high intensity value when reflecting pulses in the nearinfrared. Bare earth is un-vegetated land that is on the wholedisturbed agricultural fallow land as well as soils. Gravel bars aredescribed as the riparian sediments that are continuously prone to

natural and planted forests at Verdun.

2990 A.S. Antonarakis et al. / Remote Sensing of Environment 112 (2008) 2988–2998

erosion and migration, and are located near the edges of the watersurface for flows that do not exceed bankfull discharge. As suggestedby Song et al. (2002), gravel has a similar intensity signal to water.This may be explained by the high moisture content present on thegravel related to other un-vegetated surfaces such as soils. Some-times, though, the intensity values from the un-vegetated surfacescould be confounded, therefore a further criterion for gravel bars hasto be used. This is the difference in intensity values from the first andlast pulse. Here, the difference in the intensity signal should not be aslarge as that of bare earth. Gravel is deemed a more homogeneoussurface than bare earth as gravel consists of weathered rocks withaverage diameters estimated to 10–20 cm, while bare earth isrougher ploughed and disturbed earth with regular occurrences ofpits and soil mounds, with differences in moisture. Specific ranges inintensity values for these four land cover types are presented in theresults section. Indeed water and gravel had the lowest NIR values inthis dataset averaging below 70–75; bare earth and soils hadintensity values averaged at around 70 to 90; and short vegetationand grasses had values of around 90–150.

A more complex surface to classify is that of riparian forest types.Natural and planted forests cannot be classified just from aggregatedelevation values or from intensity values, especially when dealingwith the same species in different spatial formations. The verticaldistribution of points in these different forest types could be a key inclassifying them. Fig. 1 shows the distribution of points in both naturaland planted forests from a section of the meander at Verdun.

The natural forests have a more uniform distribution of points withheight than the planted forests. Also in the natural forested section, theconcentrations of points have a near uniform distribution withelevation, while there seem to bemore concentrated point distributionswith height for the planted forest. Therefore, the skewness and kurtosis

Fig. 2. Frequency distribution of points in planted poplars of different ages (mature [graph B],

of the LiDAR elevation points could provide a way of differentiatingbetween these two layers, as they illustrate deviations from a normaldistribution of points. The skewness of a probability frequencydistribution is a measure of its asymmetry compared to a normaldistribution. In other words, if the mass of the distribution isconcentrated away from the centre of tendency, then it is consideredskewed. Skewness is characterised by the ratio between the thirdmoment about the mean (κ3) divided by the second moment about themean (κ2), and is defined by the equation:

Skewness ¼ j3

j3=22

¼Pn

i¼1 xi � Pxð Þ3Pn

i¼1 xi �Pxð Þ2� �3=2 : ð1Þ

Each moment about the mean is defined by the sum of a power ofthe deviation of the individual point (xi) from the mean ( xP). Thekurtosis in a probability frequency distribution is the measure of itspeakedness. Mathematically it is described by the ratio between thefourth moment about the mean (κ4) and the second moment aboutthe mean (κ2), and is defined as:

Kurtosis ¼ j4j22

� 3 ¼Pn

i¼1 xi �Pxð Þ4Pn

i¼1 xi �Pxð Þ2� �2 � 3: ð2Þ

Four frequency histogram plots of the forest types are shownbelow in Fig. 2, three being planted polar of different ages, and the firstdescribing the distribution of points in a natural forested section.These elevations are from the original LiDAR point cloud data with anaverage spatial resolution of 1.5–2.5 m.

From Fig. 2, it can be seen that the frequency distributions ofelevations for the planted and natural forests are very different. The

intermediate-aged [graph C], young [graph D]), and natural poplars of all ages (graph A).

Fig. 3. Frequency distribution of points in planted poplars of different ages (mature [graph B], intermediate-aged [graph C], young [graph D]), and natural poplars of all ages (graph A)without the influence of ground hits.


intermediate and young poplar plantations have a largely positiveskew, due to the very irregular distribution of points with a highproportion of them belonging to the ground. The distribution of pointsfor the natural and mature planted forest shows less of a deviation interms of its skewness, and a large number of points exist as hits in theplanted poplar's canopy. The kurtosis of these, though, is quitedifferent. Due to the strong influence of the canopy compared to theground hits in the mature planted forests, the kurtosis will be largelyplatykurtic, while the distribution for the natural forest will have akurtosis closer to zero. A combination of kurtosis and skewness couldaid in differentiating between planted and natural forests. Yet thedistributions illustrated in Fig. 2 are bimodal if the ground andundergrowth is included. The problem is that skewness and kurtosisare based on calculations of the moments about the mean that aredesigned for unimodal distributions. The deviations from the meanare measured in relation to a mean, which in the case of Fig. 2 are thegaps between the two modes. Fig. 3 describes the same areas andvegetation types as Fig. 2 without the influence of the ground or near-ground hits. Here, the distributions seem to be more unimodal exceptfor the younger planted forests. Themoremature planted forests seemto be more negatively skewed, while the natural forests and theyounger planted forests are more positively skewed.

Due to the varied point distribution with height of the youngerplanted forests, a further factor could be used to differentiatebetween this forest type and natural forest. The percentage ofcanopy hits could be used to identify the porosity of a spaciousyounger planted forest and a dense natural forest. This measuremay be somewhat discriminatory in itself, as canopy hits couldconsider points belonging to ground flora, and vice versa. Thepercentage of canopy hits could also be useful in differentiatingbetween planted and natural forests as a whole if the cell size

defining each distribution is large enough to account for habitualgaps in the planted forests.

4. Surface derivations

From the information provided above, six LiDAR derived surfaceswere used in order to classify the desired land types for the threemeanders described in Section 2. These surfaceswere a vegetation heightmodel, a percentage canopy model, an average intensity model, anintensity difference model and probability distribution skewness andkurtosismodels. The development of these layerswas aided by using C++code. This code inputs raw point cloud data, and outputs the desiredinformation on points and their position such as maxima and averageheights, as well as moments about the mean and skewness and kurtosisvalues, within a defined cell resolution. This code has the advantage ofkeeping the original positioning of the output points, depending on theelevation or intensity criteria assigned. The resolution of cells in the codewas chosen to be5m inorder to aggregate areas of similar characteristics.

4.1. Vegetation height model

The VHM is derived from the difference between a terrain model(TM; Fig. 4B) and the original point cloud canopy surface model (CSM;Fig. 4A). The terrain model was determined as the point in each 5m cellwith the lowest elevation. The canopy surfacemodelwas determined asthe point in each 5 m cell with the highest elevation. Cells of 5 mwerechosen, as thiswas theminimumwidthof tree canopiesmeasured in thefield, and thus limits the effects of considering cells with forested andnon-forested land. For both of these models, the first and last pulse rawairborne LiDAR information was used. First pulse returns were used inconjunctionwith last pulse returns because some last return values of a

Fig. 4. Airborne LiDAR surfaces for the Verdun meander on the Garonne: A) canopy surface model (CSM); B) terrain model (TM); C) vegetation height model (VHM); D) intensitymodel (IM); E) intensity difference model (IDM); F) skewness model (SkM); G) kurtosis model (KrM); H) percentage canopy model (PCM).


single pulse returned a higher value than the first pulse return, due tonoise in the LiDAR receiver. The terrain model and surfacemodel pointswere then used to construct triangulated irregular networks (TINs)based on the elevations and this was then interpolated to a rasterisedimage with a 5 m resolution. The difference of these two rasteriseddatasets formed thevegetationheightmodel (Fig. 4C). It shouldbenotedthough that the problem of accurately defining the ground and canopytops using LiDAR point cloud data is unresolved, with suggestions ofmoving into calibrating multiple signal airborne LiDAR (Harding et al.,2001; Lefsky et al., 2002). Nevertheless, Antonarakis (in press) foundthat LiDAR derived tree heights from crown apices using these surfaceswere around 81% of the field measured values.

4.2. Percentage canopy hits model

The Percentage Canopy hits Model (PCM) was defined as thepercentage of LiDAR hits that were reflected from the canopy comparedto the total LiDAR hits in a 10m cell resolution. This resolution is differentthan the other LiDAR derived surfaces and was chosen for two reasons.First, this resolutionwas chosen in order to be able to identify more thanjust one pointwhen considering spacious youngplanted forests. Second, a10 m resolution would account for gaps in the planted forests, causingsharpercontrastsbetweenmatureplantedandmaturenatural forests. Thegroundand thegroundflorawere consideredasbeing in the lower thirdofthe forest's elevation range. Thiswas chosen from the limiting factor beingthe youngest planted poplar raw LiDAR point frequency distributionwithelevation (Fig. 2D). ATINwas subsequently created relating each resultingpercentage per 10 m cell, and a raster image was finally interpolation.

4.3. Intensity models

The average intensitymodel (IM; Fig. 4D) was constructed from theLiDAR point intensity values using an amalgamation of both raw first

and last pulse points. The average intensity was used only for landcover types with a low elevation range. Keeping that in mind, the totaldifference in intensity values for short vegetation and bare earth wasonly 15–20,with each cell having an average standard deviation of onlyaround 4. Thus it was considered that averaging intensities did notaffect the eventual classification of a surface, and did not significantlyconfuse land cover types. The same method of surface developmentwas deployed for the mean intensity layer as the terrain and surfacemodels. This was by constructing a TIN based on the average intensityreturns, and subsequently interpolating the triangular network into arasterised image with a 5 m resolution. The intensity difference model(IDM; Fig. 3E) was constructed using the differences of the maximavalues obtained from the first pulses and the minima values fromthe last pulse intensities. Again the maximum first pulse points andminimum last pulse points were interpolated separately intorasterised images, and subsequently the difference between the twowas extracted.

4.4. Skewness and kurtosis models

The skewness model (SkM; Fig. 4F) was constructed using Eq. (1), andthekurtosismodel (KrM; Fig. 4G)was calculatedusing Eq. (2) in this study.These layers were calculated for each 5 m cell of the C++ code using thecombination of first and last pulse point clouds, considering the elevationvalues to construct the moments about the mean. Raw LiDAR data pointsin the specified cellwere grouped, andeach individual point elevationwassubtracted from the mean elevation value of the cell. The exponent wasappliedaccording to themomentdegree required, and the resultingvalueswere summed. TIN and raster interpolations were subsequentlyperformed at the desired resolution to create the two surfaces. Theskewness and kurtosis models were created for both ground-on andground-off conditions, and used in twodifferentmethodologies describedin the subsequent section. All surfaces for themeander at the Verdun site

Fig. 5. Flow chart of method 1 classification logic.


on the Garonne River are shown below (Fig. 4). From these surfaces, theclassification criteria can now be described.

5. Classification methods

The classifications of each land type were performed using threesoftware packages, C++ programming, ArcGIS, and MATLAB. The firsttwo are used to develop the surfaces and the class separation isperformed through MATLAB. Two classification methods were per-formed, and differ in the consideration of the forested classifications.

The first classification method describes criteria to differentiatebetween forest types that do include the ground hits in their pointfrequency distribution as shown in Fig. 2. This first method focuses onthe use of bimodal distribution skewness and kurtosis models todifferentiate between the natural and planted poplars. Fig. 5 is a flowchart describing the classification hierarchy for the first method. Thesecond classification method describes a technique to differentiateforest types without the inclusion of ground hits as shown in thefrequency distributions in Fig. 3. This method considers the differ-entiation between natural and planted poplars using unimodaldistribution skewness and kurtosis models, and includes a furtherlayer of the percentage of canopy hits compared to ground hits. Fig. 6 isaflowchart describing the forest classification hierarchy for the secondmethod. Descriptions of definition criteria for water, gravel, bare earth,and short vegetation are the same for both methods. Both of thesemethods are used and compared. The use of the second method isbetter justified because the skewness and kurtosis models createdhave unimodal frequency point distributions with height. The firstmethod isworth considering aswell, as the resulting forest types could

Fig. 6. Method 2 classification logic excluding the influence of the ground. The skewne

have very high classification accuracies insinuating the strength ofincluding the ground influence. Also, the comparison of both thesemethods should demonstrate the need for omitting ground hits in thepresent or any future airborne LiDAR classification techniques.

All classifications are defined as belonging to one or multiplecriteria thresholds. The spatial location and extent of each class areknown before the classification algorithm is applied. Accordingly,multiple pixels of a known class were examined for their value rangesfrom a certain surface model type, and threshold values weresubsequently chosen. For example skewness and kurtosis thresholdvalues for planted poplar forests were defined as a representativerange of values from a large sample of their pixels values.

5.1. Water

The first classification was of open water surfaces, in this caserivers. River water was classified as having a height range of less than0.5 m (VHMb0.5), and average intensity values of less than 55(IMb55). Potentially in some cases, the river may not have returnedany points, due to water absorption and no backscatter. In these cases,the SPOT image obtained on the 6th June 2006 could be used in theNIR to determined water bodies as having very dark pixel values.

5.2. Land feature elevations

Land features such as agricultural land or forests are first consideredfrom their elevation deviations from the local minimum (as defined bythe terrain model). To classify low features and tall features, twoelevations need to be determined and separated. Hence, near-ground

ss and kurtosis models are now defined from their canopy as SkM(c) and KrM(c).


features were determined as having a deviation from the terrain modelof less than 3.5 m (VHMb3.5), while elevated features (namely tallvegetation) is considered as having a deviation from the terrain modelofmore than 3.5m (VHMN3.5). Low features are differentiated from thewater layer by the difference in intensity values attributed (intensityvalues described in Section 5.3). This elevationwas chosen to signify thewitnessed cut-off point between the tallest nettles and shortest trees.

5.3. Short vegetation and bare earth

Short vegetation such as nettles, grasses, and agricultural crops iscategorised as those pixels that have a deviation from the terrain of lessthan 3.5m, and an average intensity of greater than 90 (IMN90). Bare earthis subsequently characterised as pixels with the same deviation from theterrain model, but with an average intensity of between 75 and 90(75b IMb90). These classification criteria are supervised andwere selectedfrom knowledge of the land types and where they belonged spatially.

5.4. Gravel

Gravel is considered as having a very low height range (VHMb0.5),with a witnessed intensity between 55 and 75 (55b IMb75). Gravelalso should produce a difference in the maxima and minima intensityvalues that is less than bare earth and short vegetation, and this couldsharpen the classification of this land type. For this a difference of lessthan 30 was chosen (IDMb30). Also gravel was considered as beingclose to the river, so any gravel-defined pixels that were not directlyconnected to the low flow river's edge were omitted.

5.5. Forests (inclusion of ground hits)

Both skewness and kurtosis surface models were used to define thenatural and planted poplar riparian forests. From Fig. 2 and fromsupervisions of the two layers, planted poplars were defined as thosewith skewness values of more than 1.3 m (SkMN1.3) and kurtosis valuesof less than −1.7 (KrMb−1.7). Here, the skewness criterion helps definepixels that potentially belong to younger planted forests, and the kurtosiscriterion helps define the more mature planted poplar forest. The valueswere chosen to be as extreme as possible in order to best differentiate

Fig. 7. Classification of th

between planted and natural forests. Natural forests were defined fromskewness and kurtosis values thatwere less negative and less platykurticrespectively. The frequency distribution of points is more normal thanthose of planted forest sections, so values were chosen for skewness ofbetween0.5 and−0.5 (−0.5bSkMb0.5), and forkurtosis of between0and−0.5 (−0.5bKrMb0). Some post-processing needed to be performed onthese resulting classifications. Plantations and natural forested sectionswere unified through a fewsteps. First, in some instances, small areas of afew pixels in size were classified even if they did not belong to a forest.Thus areas that were defined by being smaller than 3–4 pixels weredeleted. A filtering technique was subsequently applied to even out theedges of defined areas. This was achieved by first eroding the binaryimage using a 3×3 kernel, and subsequently dilating it using the samekernel size. Theerodingprocess using thesamekernel size acted todeletejagged or thinpeninsular edges,while not erodingpixels that belonged toa consolidated area. Using non-consistent kernel sizes would ultimatelychange the fabric and resolution of the image. Finally, gaps in a classifiedforest were filled in to further consolidate the main areas of the forestedsections. The gaps were defined as zero values in the binary image thatwere enclosed by forest values defined with values of 1.

5.6. Forests (omission of ground hits)

The second classification method (Fig. 6) was performed withskewness and kurtosis layers that did not include the influence of theground or ground flora. To create a more accurate classification, thepercentage of canopy hits model (PCM) was also included. As above,the values chosen for each of the two forested land types were asextreme as possible for the most accurate differentiation betweenthem. Planted forests were defined with large negative skewness(SkMb−0.8) and large leptokurtic values (KrMN2). Natural forestswere defined as having skewness and kurtosis values being near zero(−0.15bSkMb0.15; −0.1bKrMb0.1). The percentage of canopy pointswere used to identify the planted forests that would have a smallproportion of the points being intercepted by their canopies(0bPCMb0.2), and identify the denser natural forests as areaswhere most of the LiDAR points are intercepted by the canopy(0.4bPCMb0.9). Values between 0.2bPCMb0.4 were not included asthis would create spatial overlapping of planted and natural poplars,

e Verdun meander.

Fig. 8. Classification of the Monbequi meander.

Fig. 9. Classification of the Chatel meander.


Table 1Method 1 accuracy indices (AI) of forested areas in the two Garonne sites

Type Verdunclassification

Monbequiclassification

Chatelclassification

Class pixels(km2)

AI (%) Class pixels(km2)

AI (%) Class pixels(km2)

AI (%)

Water 0.23 99.19% 0.18 98.72% 0.14 95.20%Gravel 0.05 70.04% 0.05 84.08% 0.17 80.14%Short vegetation 0.88 99.96% 0.31 99.72% 1.13 99.89%Bare earth 1.15 99.12% 0.73 99.07% 0.26 84.08%Young plantedpoplar

0.23 84.23% 0.18 97.67% 0.004 –

Inter plantedpoplar

0.23 95.33% 0.21 98.22% 0.001 –

Mature plantedpoplar

0.36 94.57% 0.28 97.38% 0.0001 –

Young naturalpoplar

0.19 82.93% 0.15 97.87% 0.38 94.60%

Mature naturalpoplar

0.18 80.99% 0.16 83.84% 0.18 81.46%

Total 3.50 95.44% 2.25 97.21% 2.25 93.76%


as this range includes both dense mature planted poplar and naturalpoplar. Again, plantations and natural forested sections were unifiedas noted in Section 5.5.

5.7. Forest ages

Three ages of planted forests and two ages of natural forests weredefined. Natural Forests were thus segregated into Young NaturalForest (VHMb12) and Mature Natural Forests (VHMN12). Plantedforests were further divided into Young Planted Forests (VHMb10),Intermediate Planted Forests (10bVHMb18), and Mature PlantedForests (VHMN18). These heights were chosen from previous fieldmeasurements of individual tree characteristics in June 2006 andFebruary 2007. More classes of forest age could be defined if desiredusing further segmentations of the canopy heights. It is important tonote that trees with the same height in planted and natural poplarmay not necessarily be of the same age due to the antecedentconditions in each vegetation stand.

6. Results and classification accuracy

Classifications were produced for the three meander areas on theGaronne and Allier Rivers. The resulting classified images arepresented in Figs. 7, 8 and 9.

At first glance, it can be noticed that very few pixels were classifiedas planted forests in the Chatel meander site, while the majority offorested land cover in the two Garonne meanders show planted forestdominance. Prior knowledge of the three sites is enough to confirmthat the Allier River has no or very few planted forests in theimmediate riparian zone, while the Garonne river is characterised byat least 81% planted forests in the whole floodplain with naturalforests occupying the below bankfull discharge river edges (Mulleret al., 2002). In fact for both of the Garonne meanders, around 70% ofthe classified forest cover was found to be occupied by poplarplantations, just in these small reaches. Estimates on the Allier Riverfrom Clermont-Ferrand to Moulins have indicated that live forestcover can account for around one third of the total floodplain landtype, while pioneer vegetation and undergrowth can account for 32%of the total area (Peters et al., 2000). The forest cover estimated in thetwo classification methods amounted to around 33% and 35% of thetotal meander areas investigated, while the shorter vegetationaccounted for 29% and 28% of the total areas. Even if these meandersdo not take into account the full floodplain in the sectionsinvestigated, the percentages can give a good indication of landcover ratios. These accuracies that have been stated here are allgeneralised spatially for the three meander sites, resulting in broadaccuracies of the forest cover types discussed.

Both methods developed in this study show good to very highlevels of accuracy when concerning both the non-vegetation andvegetation land cover types, with the accuracy values presentedbelow. There are some principle differences that can be distin-guished when first comparing the classified images for the twomethods. The largest differences are on the Verdun meander,especially in the northeast corner. The first method classifies apatch of young poplar forest, while the second classifies it as youngnatural poplar. There are also some other sections inside the Verdunmeander in the southeast of the classified image that are classified asmature planted poplar and natural forest for the first and secondmethod respectively. Hence, when removing the influence of theground for these areas, the point cloud distributions of the areabecome more platykurtic, and more evenly skewed, resulting inpixels in these regions to be classified as natural poplar forest ratherthan planted poplar forest. There are also some regions in theMonbequi meander that exhibit the same pattern. Further specula-tions on this land cover type divergences are discussed in theconcluding section.

The accuracy of the classification results was assessed primarily byusing aerial digital photographs acquired on the same date as theairborne LiDAR data (6th June 2006). The three sites were visited aswell, so memory of the land types was also useful in defining theaccuracy of each meander. The classified image was visited in relationto the digital aerial photographs, and each area that seemed to befalsely classified was noted with the number of pixels. This wasperformed for the entirety of each of the three meanders. Omissionsand commissions of all pixels were investigated for all land types forthe three meanders and for both classification methods. The accuracyindex (AI) was used to take commission and omission pixels intoaccount for each land type. This accuracy method is defined by Pouliotet al. (2002). Overall classification accuracy can be defined as:

AI kð Þ ¼ n� Oþ Cð Þð Þn

� �� 100: ð3Þ

AI is an accuracy index in percent, O and C represent the number ofomission and commission errors, and n is the total number of trees in theimage to be detected. Thepurpose of the index is to count all error againstthe correct number of trees to be detected. Commission errors arewherepixels are falsely assigned to another class, and omission errors arewherepixels were not assigned to their correct land type. The classificationaccuracy results for the first method are show below in Table 1.

The accuracy results in Table 1 show that the first classificationmethod had overall accuracies of around 95%, with the best overallclassification accuracy being for the Monbequi site with 97.2%, and thelowest for the Chatel site with 93.8% accuracy. The total accuracy for allthree sites combined was 95.5%. Just considering the total woodyvegetation, this method accurately classified 91.5% of all trees. The treecategory that was least accurately classified was mature natural riparianforest with accuracies for the three sites ranging from around 81–84%,whilemature and intermediate planted poplar were accurately classifiedwith percentages between 95 and 98%. The young planted poplar at theVerdun site seemed to be under-classified compared to the other plantedpoplar forest ages (84.2% accuracy). Comparing the planted and naturalpoplar forests, the natural forest had 88% classification accuracy, and theplanted forests had 94% classification accuracy. The weakest land coverclassification was for the gravel bars with values as low as 70%.

The weaker classification of the natural forest compared to theplanted forest for this first methodmay be attributed to the unclassifiedpixels. Around half of the pixels omitted from the mature and youngnatural riparian forest were left unclassified. A small number ofunclassified pixels also belonged to the gravel bar surface. Only smallareas were classified as gravel for the Garonne meanders, while the

Table 2Method 2 accuracy indices (AI) of forested areas in the two Garonne sites

Type Verdunclassification

Monbequiclassification

Chatelclassification

Classpixels(km2)

AI (%) Classpixels(km2)

AI (%) Classpixels(km2)

AI (%)

Water 0.23 99.08% 0.18 98.72% 0.14 95.20%Gravel 0.05 74.03% 0.04 80.52% 0.16 80.73%Short vegetation 0.87 99.96% 0.31 99.72% 1.13 99.89%Bare earth 1.15 99.12% 0.73 99.07% 0.26 84.08%Young planted poplar 0.19 74.56% 0.19 97.11% 0.01 –

Inter planted poplar 0.21 89.92% 0.21 94.41% 0.00 –

MaturePlantedPoplar 0.33 81.32% 0.28 91.21% 0.01 –

Young natural poplar 0.29 79.83% 0.16 93.00% 0.40 98.33%Mature natural poplar 0.23 66.37% 0.17 84.99% 0.18 95.36%Total 3.55 91.77% 2.27 95.69% 2.27 95.17%


Chatel site on the other hand contained large areas of gravel bars spreadthrough the meander. In the Chatel site, many pixels classified as bareearth actually were of gravel bars. The secondmethod had fewer overallunclassified pixels, with more in the Monbequi section. Classificationaccuracy results for the second method are shown below in Table 2.

This second classification method had overall accuracies of around94% with the highest average accuracy index being for the Monbequisite with 95.7%, and the lowest for the Verdun site with 91.8%accuracy. The total woody vegetation accurately classified from thissecond method was lower that the first, with an average accuracyindex of 86.8%. Most to all of the individual forest categories hadlower classification accuracies associated with this second methodwith the least accurately classified forest type being again the naturalriparian forest with accuracies for the three sites from 66–95%. In thismethod, the planted and natural riparian forests had the sameclassification accuracy of 86.8%. This is because there were less un-classified pixels than in the first method, and much of the error wascaused through overlap of natural forest into planted forest, or viceversa. The gravel land cover type increased in classification accuracyfor the second method mainly through the correct classification ofpixels as forest.

Using intensity and elevation information from airborne LiDARdata, Charaniya et al. (2004) were able to classify four land types withsuccessful classification accuracy ranging from 66–84%. The fourclasses derived from LiDAR datawere trees, grass, roads and roofs, andthe terrain model used was not derived using LiDAR points, butobtained from the United States Geological Survey (USGS). Usingmultiple LiDAR derived surfaces, Brennan and Webster (2006)classified ten land types with 94% overall accuracy. This classificationmethod was based on identifying bright and dark vegetation,coniferous and deciduous forests, defining building structures, andalso the saturation of soils. The LiDAR derived surfaces that were usedwere based on elevation values and intensity, but the study was alsoable to obtain data of multiple LiDAR returns (greater than 2 returns).

The results from this study can also be compared to recentclassifications performed using multispectral imagery only. Lu et al.(2004) used Thematic Mapper imagery to classify different stages ofrainforest succession andagricultural landwith an overall accuracy rangeof 70–86%. Buddenbaum et al. (2005) used hyperspectral remote sensingdata (HyMap) to classify forests, and reported an overall accuracy of 74%.Stow et al. (2007) used Landsat TM/ETM+ data to classify forests andshrubland with overall accuracies of 64% and forest classificationaccuracies of around 80%. Vohland et al. (2007) used Landsat TM toclassify 8 land cover types including forests, and reported an averageaccuracy of 87.5%. Bork and Su (2007) defined 8 vegetation types, fromgrasses to shrubs and forest. They defined classification accuracies using3-bandmultispectral dataof 59.4%. CombinedwithLiDARelevation rangevalues, this classification went up to 80.3%.

7. Conclusions

This classification method first demonstrates that airborne LiDARis an effective tool in classifying land types, and has been successful inaccurately classifying 95% and 94% of the land types in three sites inthe Garonne and Allier floodplains for the first and second methodsrespectively. These methods also demonstrate the possibility of usingfrequency distribution parameters such as skewness and kurtosis toaccurately identify planted and natural poplar riparian forests in two-dimensional interpolated LiDAR surfaces. Natural poplar forests wereclassified with 88% accuracy, and planted poplar forests wereclassified with 94% accuracy in the first method, and all forests wereclassified with 86.8% accuracy using the second method. Thecombination of intensity and elevation data from the LiDAR pointclouds can be enough to classify multiple land types. Both intensityand elevation data played a prominent role in defining regions, andthe combination of both could aid in the better classification of a landtype. Both methods demonstrated their effectiveness in classifyingforests with high accuracies, and the effect of removing the groundinfluence did not hinder this classification process significantly. Thesecond method though, justifiably used skewness and kurtosis withunimodal canopy point frequency distributions, while the firstmethod used kurtosis and skewness of forests dominated by groundhits for sparser forests and canopy hits for denser forests. Therefore, itmay be expected that the second method would produce higherclassification accuracies. A reason for this not being the case may bethat removing the ground decreases the polarity of the kurtosis andskewness ranges in the forests, resulting in more overlapping of oneforest type or the other. A second reason may be that for youngerpoplars, the distributions of points were not truly unimodal as themature forest distributions were, resulting in skewness and kurtosisrange errors. Although the overall accuracy of both methods was veryhigh, there were some issues with the method, as discussed below.

One source of error could almost certainly result from the TINinterpolation of raw LiDAR point cloud data. In certain areas on thefloodplain, especially on the river surface, points were absent eitherthrough pulses not returning, or through the fault of the flight pathcoverage. This could have caused unnaturally high elevations for theriver surface. Secondly, some of the areas have been aggregated in orderto follow the desired choices stated in Section 3, arising from theneed torepresent land types with a previously calculated roughness associatedwith it. For example, the short vegetation land type most likely is anaggregation of distinctive surfaces such as pioneer vegetation in ariparian zone, grasses and short shrubs. Roads and buildings were alsonot considered in this classification method, although they have beenincluded in other studies. On the three sites investigated, the land areaoccupied by buildings was relatively small, but they could potentially bedefinable. Different species of woody vegetation were not identified inthismethod either. One area defined as planted forest in the northeast ofthe Verdun site for example is occupied by orchards rather than plantedpoplar. Potentially different tree species could be identified from theirdifferent point cloud elevation frequency distributions, but this could bedifficult to implement on the floodplain scale. It was considered that theeffects of calibration errors of the airborne LiDAR point clouds on thefinal classifications were minimal. First this is because the spatial(0.001–4%) and elevation (0.005–6%) errors of the cell size (5×5 m) andheight range (3.5–30m) consideredwere too small to affect the creationof the surfaces used in classification. The calibration errors would onlydirectly affect the choice of height range values to represent the differentforest ages, yet these chosen values were not centimetric and broadlyrepresented a stage in the maturity of a tree.

Finally this information could be used for many applicationsincluding monitoring of vegetation change through time. In thebroader sense of this research, forest classification information couldbe very useful when considering 2D hydraulic modelling. In somecomputational fluid dynamics (CFD) packages such as grid based flow


model HYDRO2DE, 2D spatial GIS type information can be used as aninput with a lumped roughness value associated with each land type.This would ultimately involve LiDAR point cloud data eventually beingused in flood simulations.

Acknowledgments

This paper is the result of airborne LiDAR data obtained from a NERCproject in June 2006. This research was supported by the British Societyfor Geomorphology (BSG) and from the William Vaughan Lewis andPhillip Lake Funds.

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