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ANNALS OF GEOPHYSICS, VOL. 49, N. 1, February 2006

Key words hyperspectral imaging – species distri-bution – LAI – semi-empirical model – POPs

1. Introduction

The study and understanding of the fate ofPersistent Organic Pollutants (POPs) in the en-vironment is nowadays necessary to managetheir risk of adverse effect to humans or to the

ecosystem (Mackay et al., 1996). POPs aregenerally able to be bioaccumulated in the foodchain and affect living organisms due to theirlong persistence and toxicity. Recent publica-tions showed that forests play a key role in theenvironmental distribution of POPs, since theyact as filters of these chemicals, trapping themin the air compartment and transferring to for-est soils consequently decreasing their atmos-pheric half-lives (McLachlan and Horstmann,1998). Therefore it is necessary to include thevegetation compartment in multimedia models,to understand and predict the fate of these sub-stances in the environment.

Many persistent organic pollutants are knownas global contaminants because they have been

The contribution of hyperspectral remotesensing to identify vegetation characteristics

necessary to assess the fate of PersistentOrganic Pollutants (POPs)

in the environment

Mirco Boschetti (1), Pietro Alessandro Brivio (1), Daniela Carnesale (1) and Antonio Di Guardo (2)(1) Istituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), CNR, Milano, Italy

(2) Dipartimento di Scienze Chimiche ed Ambientali, Università degli Studi dell’Insubria, Varese, Italy

AbstractDuring recent years hyperspectral remote sensing data were successfully used to characterise the state and proper-ties of vegetation. The information on vegetation cover and status is useful for a range of environmental modellingstudies. Recent works devoted to the understanding of the fate of Persistent Organic Pollutants (POPs) in the envi-ronment showed that forests and vegetation in general act as a «sponge» for chemicals present in air and the inten-sity of this «capture» effect depends on some vegetation parameters such as surface area, leaf composition, turnoveretc. In the framework of the DARFEM experiment conducted in late June 2001, different airborne hyperspectralimages were acquired and analysed to derive some vegetation parameters of relevance for multimedia models, suchas the spatial distribution of plant species and their relative foliage biomass. The study area, south west of Milan,encompasses a range of land cover types typical of Northern Italy, including intensive poplar plantations and natu-ral broad-leaf forest. An intensive field campaign was accomplished during the aerial survey to collect vegetationparameters and radiometric measurements. Results obtained from the analysis of hyperspectral images, map of veg-etation species, Leaf Area Index (LAI) and foliage biomass are presented and discussed.

Mailing address: Dr. Mirco Boschetti, Istituto per ilRilevamento Elettromagnetico dell’Ambiente (IREA),CNR, Via Bassini 15, 20133 Milano (Italy); e-mail: bo-schetti.m@irea.cnr.it

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detected in air, water, vegetation and fauna of themost remote pristine regions reached by a long-range transport process. These compounds are se-mi-volatile and therefore able to volatilize fromsoils and move through the atmosphere towardsareas which can be considerably far away fromthe sources. PCBs (polychlorobiphenyls), DDTs,HCHs (hexachlorocyclohexanes), PCDDs/Fs (di-oxins and furans) are in fact characterized by alow water solubility and low degradability in theenvironment. Their low solubility results in high-er affinity for the organic phases such as organ-isms or organic carbon rich phases such as vege-tation.

Recently much attention has focused on theunderstanding of the global cycle of these xeno-biotics using plant foliage as sampling tools of aircontamination (Calamari et al., 1991). Vegetablebiomass plays an important role in the global cy-cling of lipophilic persistent organic pollutantsbecause of its large amount of organic matter ca-pable of binding and accumulating pollutants(Wania and McLachlan, 2001). Atmospheric up-take of POPs is considered the main accumula-tion pathway (McLachlan and Horstmann, 1998).

Multimedia environmental models were de-veloped in an attempt to describe the environ-mental fate of POPs between different compart-ments at regional scale. Recently, the importanceof vegetation compartments, particularly in thecase of coniferous and deciduous forests (Cala-mari et al., 1991; McLachlan, 1999; Wania andMcLachlan, 2001), in multimedia mass balancemodels was shown to be necessary to accuratelypredict the fate of chemicals in the air-soil trans-fer. Spatially distributed multimedia models,which can calculate the fate of POPs in entire re-gions, are strongly limited by the availability ofdata representing the distribution, structure andfunction of different vegetation species, in termsof biomass and its spatial and temporal variabili-ty. A better description of the characteristics ofthe vegetal biomass on a territory could accurate-ly predict the fate of POPs.

Radiometric measurements collected fromEarth observation sensors onboard of aerial andsatellite platforms offer the opportunity to de-scribe the terrestrial biosphere at different spatialscales. Remote sensing techniques were success-fully used to characterize the state and properties

of vegetation types. Satellite remote sensingnowadays furnishes basic data for forest invento-ries providing information about structure andtypology of forest over wide areas. Aerial sur-veys with hyperspectral images can be used toproduce cover mapping that gives a detailed spa-tial distribution of natural forest species in com-plex and heterogeneous areas (Martin et al.,1998; Thomas et al., 2002). Moreover a quanti-tative estimation of vegetation biophysical prop-erties, such as the Leaf Area Index (LAI) andFractional cover (Fc), can be retrieved from re-mote sensing data by statistical relationshipswith ground measurements (Chen et al., 1997;Turner et al., 1999; Boschetti et al., 2002). LAIis one of the most important bio-physical param-eters because it quantifies the amount of foliagearea per unit of ground surface area. Being ameasure of the surface that interacts with light,LAI is therefore related to bio-physical vegeta-tion activities, such as photosynthesis, respira-tion, transpiration. Foliage biomass, due to thelipid content of leaves, governs assimilationprocesses and represents a necessary input to themultimedia models able to describe the fate ofPOPs (Cousins and Mackay, 2001).

This work, conducted in the frame of theDARFEM (DAIS and ROSIS for Forest Eco-system Monitoring) experiment (Brivio et al.,2001), aims at a better understanding of the ca-pabilities of hyperspectral airborne data in thedetermination of the vegetation parametersused in POPs multimedia models. In particularhyperspectral images were used to map the nat-ural species distribution and to retrieve leaf areaindex and foliage biomass.

2. Study area

The study area belongs to Ticino River re-gional park and is located South West of Milan,Italy (fig. 1). In a few kilometres the site encom-passes a range of land cover types typical ofNorthern Italy, including intensive poplar plan-tations, natural broad-leaved forest, water cours-es, rice fields, grassland and transitional wood-land/shrub representing forest regeneration.Two sub-sites in this landscape received the fo-cus of the investigation, the natural forest and

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the poplar plantation, to evaluate the role onPOPs fate of a natural area and of an arborealagro-ecosystem. The broadleaf forest in this re-gion is in fact a unique ecosystem, almost com-pletely undisturbed, and it represents the naturalecological climax for the Po River plain. Thepoplar plantation (Kyoto forest or fast growingforest) is an intensive crop which is a permanentinvestigation site of CARBOEUROFLUX network,managed by the Institute for Environment andSustainability of the Joint Research Centre (Is-pra, Italy).

3. Materials

3.1. Remote sensing data

DARFEM experiment, developed in theframework of EU-funded HySens project coor-dinated by DLR (German Aerospace Agency),was specifically designed to provide a better

understanding of the capability of airborne hy-perspectral observations in the retrieval ofphysiologically relevant vegetation parameters.

Aerial surveys were operated from 10.30 to11 am on 20th June 2001 using two spectrome-ters of DLR: DAIS 7915 (Digital AirborneImaging Spectrometer) and ROSIS (ReflectiveOptics System Imaging Spectrometer). Thenext day the area was also surveyed by MIVIS(Multispectral Infrared and Visible ImagingSpectrometer) of CNR LARA project, simulta-neously with Landsat 7 ETM+ passage.

DAIS 7915 is a whisk-broom scanner oper-ating in 79 spectral channels in the range fromvisible to thermal infrared wavelengths with512 image pixels per scan line.

ROSIS is a push-broom imaging spectrom-eter whose spectral range is limited to the visi-ble and near infrared wavelengths (430-850nm). Table I lists the main specifications of thetwo hyperspectral systems compared with theMIVIS sensor.

Fig. 1. Location of study site within the Ticino River regional park; Landsat-7 ETM+ image of the target areawith the Ticino water course acquired 21/06/2003.

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Since the reflectance distribution of vegeta-tion is strongly anisotropic, the flight lines weredesigned with the aim of maximizing the numberof DAIS directional observations in the principalsolar plane of the area (Brivio et al., 2001). Mul-ti-angular observations were used, in the frame-work of DARFEM project, for the inversion ofradiative transfer models (Meroni et al., 2002).

For the scope of this work, i.e. species distri-bution from classification and LAI estimationfrom semi-empirical modeling, only data ac-quired in the solar plane, where the Bi-direction-al Reflectance Distribution Function (BRDF)effects are minimized, were utilized.

During the aerial survey synchronous radio-metric ground measurements were acquired fordifferent purposes. FieldSpec FR spectrora-diometer, operating in a wavelength range from350 to 2500 nm, was used to collect spectra fromdifferent natural surfaces and over two differentartificial targets to evaluate the subsequent pro-cedure of radiometric correction. The aerosol op-tical thickness of the atmosphere was estimatedby means of the Langley method using data col-lected by the MS-120 Eko Sun-photometer(spectral peak at 368 nm, 500 nm, 678 nm and778 nm). Aerosol content at 550 nm is in fact thenecessary input to the radiative transfer codes(i.e. 6S, Lowtran, ATCOR4) for the atmosphericcorrection of airborne hyperspectral data.

The atmospheric correction was made byDLR using ATCOR4 considering the effects ofangular dependence of the atmospheric radiance

and transmittance, and the aerosol contributionsfrom values estimated through ground photomet-ric measurements collected during the overpass.

3.2. Field campaign

The field campaign of this study was ad-dressed to forestry species identification and LAImeasurements. The species distribution can bemapped through automatic classifications, butthose methods need to be trained with spectralexamples of the species present in the images.LAI can be spatially estimated using semi-empir-ical regression model between ground measure-ments and spectral radiometric observations(Chen and Chilar, 1996; Eklundh et al., 2001).

Beside the poplar plantation, characterizedby hybrid poplar (Populus Euroamericano), themain species identified in the natural forest areainclude black poplar (Populus nigra), Englishoak (Quercus robur), black locust (Robiniapseudoacacia) and white willow (Salix alba).Several sample areas with homogeneous pres-ence of natural species were identified on thefield with the aid of forest specialists and geo-coded using a GPS Garmin III+ in order to rec-ognize those areas on the images.

LAI measurements were obtained with theLAI-2000 Plant Canopy Analyser (PCA) in thesame week as the aerial survey for the poplarplantation, and in the following weeks for thenatural forest areas. The LAI-2000 PCA instru-

Table I. Spectral and geometric characteristics of different hyperspectral sensors. Flight height was 1800 m de-termining a pixel size for DAIS and MIVIS of about 2.5 m and for ROSIS of 1 m.

Sensors Scanner type FoV IFoV [mrad] Spectrometers Channels # Spectral range [nm]

DLR DAIS Whisk broom ±26° 3.3 1 32 400-10002 8 1500-18003 32 2000-2500

1 3000-50004 6 8000-12600

DLR ROSIS Push broom ±8° 0.56 1 115 430-860

CNR MIVIS Whisk broom ±35° 2 1 20 430-8302 8 1150-15503 64 2000-25004 10 8200-12700

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ment correlates the total leaf area with the prob-ability of light penetration, canopy gap fraction,through the canopy (LI-COR, Inc., Lincoln, Ne-brasca, U.S.A.). LAI-2000 PCA measures lightintensities, in spectral range of blue, in five con-centric fields of views at zenith angle of 7°, 23°,38°, 53° and 68° corresponding to 5 differentsensors (Welles and Norman, 1991).

The measurements acquired by the LAI-2000 instruments represent an effective LeafArea Index (LAIe) rather than the LAI of thegreen part (Chen and Chilar, 1996). Thesemeasurements in fact do not take into consider-ation the clumping effects present in vegetation.In natural conditions this effect can be neglect-ed due to the complexity of the forest and therandom distribution of leaves. Moreover, theLAIe has to be considered a Plant Area Index(PAI) because all the non-leaf tissues (stems,branches etc.) that intercept light contribute tothe gap fraction measurements. This effect wasnot measured because it can be considered neg-ligible in our conditions. The measurementswere conducted to minimise errors due to dif-ferent illumination conditions, boundary effectsand the non random distribution of the poplarplantation (Colombo et al., 2002).

4. Data analysis

Measurements collected at every field site in-clude spectral signatures of the surface, oversto-ry LAI measurements, digital photography of the

understory (nadir taken) and overstory (zenithtaken), species typology and in some cases alsoleaf chlorophyll and water content. Each site wasgeo-located using a GPS system and all data col-lected were recorded and managed in a dedicat-ed GIS, able to integrate these data with ancillarytopographic and thematic maps of the study area.These data were then overlapped to the remotesensing images in order to study and select thespectral responses of the relative pixels.

4.1. Classification

The classification was conducted on theDAIS data due to their spectral range coveringshort-wave infrared (SWIR) region. In fact,SWIR bands are considered very important forvegetation analysis (Martin et al., 1998; Fuenteset al., 2000). An analysis of the spectral qualityand importance of the DAIS channels was con-ducted for the features selection before the classi-fication. Spectral channels between #41 to #43and #61 to #68 were rejected because of the lowS/N ratio.

4.2. Training selection and mapping algorithm

The spectral examples for the different class-es were chosen on the images overlapping theGPS position of the field survey. The pixels oftraining were selected by visual interpretation ofdifferent RGB composition and by the analysis

Fig. 2. Examples of spectral endmembers selected by correlation analysis conducted on 20 DAIS spectra. Im-age location was defined with the help of GPS measurements and visual interpretation of false colour imagery.

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of NDVI (Normalized Different Vegetation In-dex) maps in order to identify the brightest pixelrepresenting dense crowns: these pixels guaran-tee that shadows and canopy opening or otherspecies influence do not affect the radiometricmeasurement. As a result of a correlation analy-sis conducted over 20 spectra selected from theGPS positions, some classes, such as the Englishoak, present for the classification more than oneendmember spectra representing natural vari-ability due to different ecological conditions (fig.2). Spectral bands selected were processedthrough Spectral Angle Mapper (SAM) algo-rithm using a training data set including all theendmembers for the different species. SAM im-plemented in ENVI software is a physically-based spectral classification that uses an n-di-mensional angle to match pixels to referencespectra extracted from the training data set(Kruse et al., 1993). The algorithm determinesthe spectral similarity between two spectra bycalculating the angle between the spectra, treat-ing them as vectors in a space with dimensionsequal to the number of bands. SAM algorithmdistributes the image pixels in the appropriateclass depending on the reference spectrum thatpresents the minimum angle. The procedure alsorequires a specified maximum acceptable anglein radians between spectral vectors; definition ofthreshold for the not classified pixels is always acritical step. Threshold value was fixed at 0.04radians for this study.

4.3. LAI estimation

Leaf area index for an extended area can bedetermined through the definition of a semi-em-pirical regression model between ground meas-urements and spectral Vegetation Indices (VIs),such as band ratios or normalized bands differ-ence, computed from remotely sensed images(Chen and Chilar, 1996; Eklundh et al., 2001).Semi-empirical relationships can be found in dif-ferent mathematical forms depending on vegeta-tion type and forest structure (Chen et al., 1997;Turner et al., 1999). The relationships are alsostrongly affected by the understory compositionand canopy closure (Spanner et al., 1990; Brownet al., 2000) and by the accuracy of ground-

based measurements (Chen and Chilar, 1996). Inorder to find the best semi-empirical relationshipbetween VIs image derived and LAI field meas-urements, a set of 4×4 pixels windows were ex-tracted from the georeferenced DAIS image. Thewindows were selected on the basis of GPSmeasurements position and on the analysis ofNDVI image: their homogeneity was statistical-ly guaranteed by a coefficient of variation foreach band generally smaller than 5%.

The best model obtained by regressiveanalysis between values of LAI-2000 measure-ments and values of Simple Ratio (SR) is loga-rithmic type (fig. 3) showing a correlation coef-ficient of 0.57

(4.1)

Therefore the mathematical expression for de-termination of LAI is

(4.2)

The SR index is calculated as the ratio betweennear infrared (central wavelength 802 nm) andred (central wavelength 659 nm) correspondingto DAIS channels #18 and #10. The relationwas checked with Fisher test for its statisticalsignificance; the F value expresses by the rela-tion (F=49.57) largely overpass the F criticalvalue both for 95% (Fc=4.10) and 99% (Fc==7.35) level of confidence.

.LAI e .

( . )SR

3 13

5 54

=-

. ( ) .ln LAISR 3 13 5 54= + .

Fig. 3. Logarithmic regressive relationship betweenLAI and SR. Leaf area index values appear to be dis-tributed in two different populations. The mature nat-ural forest express the highest values of LAI.

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5. Analysis of the results

5.1. Map of species distribution

The map of species distribution obtainedfrom the automatic classification of DAIS hyper-

spectral imagery using SAM algorithm is pre-sented in fig. 4a. Visual inspection of this map in-dicates that these data, thanks to high spectral andspatial resolution, seem to be suitable to performa detailed identification of different forest speciesin a complex ecosystem. The large homogeneous

Fig. 4a,b. Remote sensing results: a) map of the forest species distribution obtained from automatic classifica-tion of DAIS hyperspectral images (grey tones correspond to not relevant areas, such as bare soil, water and ricefields); b) map of Leaf Area Index (LAI) for the natural forest and poplar plantation. LAI values are computedon the classes identified in the forest species map.

a b

Table II. Confusion matrix computed for the evaluation of classification accuracy. PA refers to producer accu-racy and UA to user accuracy.

Classes Natural Oak Willow False- Poplar Bare Grassland Total PA (%) UA(%)poplars acacia plantation soil

Unclassified 5 7 12 5 5 35 10 79Natural poplars 16 5 0 1 0 0 0 22 57.14 72.73English oak 7 22 0 7 0 0 1 37 55.00 59.46White willow 0 0 55 0 0 0 3 58 76.39 94.83Black locust 0 3 0 20 0 0 1 24 54.05 83.33Poplar plantation 0 2 0 1 132 0 2 137 96.35 96.35Bare soil 0 0 0 0 0 28 0 28 44.44 100.00Grassland 0 1 5 3 0 0 151 160 89.88 94.38Total 28 40 72 37 137 63 168 530

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purple areas represent the poplar plantation, thatoccupies most of the study zone. The white wil-low trees, marked in blue, are found in the ripar-ian zone, whereas the English oak, spotted yellowarea, is classified mainly in the natural area.

The agreement between reference data andclassification results was analyzed by means ofthe confusion matrix to assess the accuracy ofthe automatic procedure (table II). Although thenumber of samples for the accuracy analysis issmall, due to the difficulty of the selection oftest pixels in the mixed forest, the sample sizeused guarantees a level of confidence higherthan 85% for all classes (Van Genderen et al.,1978). The overall accuracy achieved is 78%with a Kappa coefficient of 0.73.

The confusion matrix also computes classProducer (PA) and User Accuracies (UA).These statistical indices show that some speciescan be identified and mapped with a higher ac-curacy (poplar plantation PA=UA=96%, wil-low PA=76% and UA=95%) than other speciessuch as English oak and black locust, which aremore difficult to map. In general, in a complexecosystem, characterized by a strong hetero-geneity, the identification of different specieswas more complicated. These accuracy resultsare similar and even better when compared withrecently published works (Martin et al., 1998;Thomas et al., 2002). Low accuracy values forsoil are due to the inefficiencies in endmemberscollection. This class was not involved in thisstudy and was neglected in further analysis.

5.2. Maps of LAI and foliage biomass

Statistical stability of the semi-empiricalmodel, derived by regressive analysis between

LAI and SR, was evaluated with the Leave OneOut technique. In order to estimate the goodnessin prediction of the model, the coefficient of cor-relation in prediction Q 2 and Standard DeviationError in Prediction (SDEP) of the model werecalculated for predicted LAI values, excludingthe subset of measurements used for model deri-vation. This analysis showed a value of Q 2=0.51and of SDEP=1.27 underlining the possibilityof applying the model in prediction. LAI mapwas then computed only for those pixels belong-ing to the vegetation classes previously mappedin the study area (fig. 4b). LAI values range from0.41 to 5.15, with maximum values occurring inthe natural forest for the class of natural poplar.

Multimedia models used to study the fate ofPOPs and their redistribution in different envi-ronmental compartments, from the atmosphereto soil or water via vegetation interface, requirefoliage biomass information. Foliage Biomass(FB) was estimated with the following model:

(5.1)

where FBi is the foliage biomass of vegetationclass i (kg/m2), LAIi is the leaf area index ofvegetation class i, SLAi is the specific leaf area(m2/kg) of vegetation class i, and (FW/DW)i isthe relative ratio between fresh and dry biomassweight of vegetation class i.

Table III reports the values of SLA as wellas the index (FW/DW) used in this work; theseparameters depend on the different species andwere measured during previous experiments(Marazzato, 2001; Ceriani, 2003). Consideringthe range of variability of the parameters usedin the eq. (5.1), it can be observed that LAI es-timations considerably determine the value of

FB LAISLA DW

FW1i i

i i

# #= b l

Table III. Specific Leaf Area (SLA) and ratio between fresh and dry weight (FW/DW) values.

Species Latin name SLA (m2/kg) FW/DW

Black poplar Populus nigra 13.60 3.25English oak Quercus Robur 14.25 2.65Black locust Robinia pseudacacia 11.65 3.05White willow Salix alba 11.65 3.15

Grassland Dactylis glomerata 22.00 3.83

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the FB results: in fact LAI values vary by a fac-tor of around 10 (0.5-6.5) while SLA and ratioFW/DW by a factor of 2 (11.65-22.00) and 1.5(2.65-3.83) respectively (table III).

Therefore, it can be inferred that the spatialestimation of FB is strongly affected by the LAIvalue selected, justifying a spatial determina-tion of this parameter as obtained in this work.

Three main vegetation types considered havedifferent biomass and the maximum is reachedby the natural forest (fig. 5a). Field LAI meas-urements for the grassland type were not avail-able, therefore for this class the biomass valuescorrespond to a mean value found in literature(Allen et al., 1998). The second histogram (fig.5b) shows the biomass values for the differentspecies identified in the natural forest area, themaximum value is found for natural poplar. Thediverse species have a different role in the filtereffect and consequently make a specific contri-bution to the fate of POPs. The possibility of de-termining vegetation cover, species and LAI,from remote observation allows us to attributespatial specific biomass values to the studied re-gion, considering that the usual practice is tospecify an average biomass value to the entire re-gion which can be highly uncertain given thevery subjective expert judgment needed.

6. Conclusions

In this research remote sensing techniqueswere successfully used to characterise the state

and properties of vegetation types. The auto-matic classification of aerial hyperspectral da-ta allowed us to produce species distributionmap with a good accuracy. More efforts areneeded to define efficient methods of analysis,capable of both fusing multisensor multispec-tral information and exploiting the spatial rela-tionships of neighbouring pixels in order to ex-tract highly accurate information. Neural net-work techniques can be suitable to treat thismulti-source approach. Moreover, a quantita-tive estimation of LAI has been achieved fromremote sensing data, vegetation index, by sta-tistical relationships with ground measure-ments. The semi-empirical model defined has apredictive validity but further analyses are re-quired. The definition of specific relationshipsfor the natural area and the poplar plantationshould guarantee better results. More field LAImeasurements in the natural areas are thereforeneeded. The biomass map produced by GIStechniques represents a useful input for spatial-ly distributed multimedia models that take intoaccount the role of vegetation type.

The approach of using remote sensing tech-niques to achieve a detailed analysis of typeand status of vegetation type can help to under-stand the fate of persistent organic pollutants(POPs) in the environment. These data repre-sent significant input for the application of spa-tially distributed multimedia models that isusually strongly limited by the availability ofdata representing the structure and function ofvegetation.

Fig. 5a,b. Mean and maximum biomass values a) for the main vegetation types (natural forest, poplar planta-tion and grassland) and b) for the different species identified within natural forest.

a b

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Acknowledgements

This work was supported by the EU-fundedproject HySens coordinate by DLR (Germany)and by JRC that manages and maintains the in-strumentation of the study area. We gratefullyacknowledge the Ticino River regional park stafffor the necessary local support and Dr. Ceraboli-ni and Dr. Ceriani for their comments and sug-gestions and for making SLA data available.

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