Application of Imaging Spectrometer Data inIdentifying Environmental Pollution Causedby Mining at Rodaquilar, Spain

G. Ferrier*

The Rodaquilar mining area in southern Spain has portance. Methods for assessing the spread of these per-been mined for gold for an extensive period, most re- nicious, possibly toxic, elements have traditionally beencently in the 1940s and 1950s. This activity has resulted geochemical, hydrologic, and geophysical in nature, andin waste rock and tailings being dispersed from the mine thus have required the collection of numerous samplesworkings down to a large tailings dump and then along followed by laboratory measurements. While remotea river valley eventually reaching the sea. This tailings sensing at optical wavelengths cannot directly detectdump material consists of a variety of ferruginous mate- trace metals, it can be used to map the minerals whichrials that often contain trace elements that are environ- host these metals. In recent years remote sensing has beenmentally harmful and possibly toxic. These ferruginous successfully used to aid in this process (e.g., Fenster-materials have distinctive spectral features which make maker and Miller, 1994; Swayze et al., 1996; Farrand,them amenable to detection and mapping by airborne 1997, Farrand and Harsanyi, 1997). The advent of highimaging spectrometer data. The dispersion of the tailings quality airborne imaging spectrometer data provided bymaterial was studied using two semiquantitative tech- systems such as the Airborne Visible/Infrared Imagingniques, matched filtering and linear spectral unmixing Spectrometer (AVIRIS), (Chrien et al., 1996) enablesand qualitative methods using band ratios and the varia- new quantitative and qualitative techniques to be appliedtion in strength of spectral features. The distinct ferrugi- in assessing the environmental impact of mining activities.nous mineral phases resolved by these methods were then This study demonstrates the use of imaging spec-compared to laboratory reference spectrum using qualita- trometry data in determining different ferruginous spe-tive analysis of the spectral profiles and two quantitative cies and in mapping the spread of tailings from a goldtechniques, spectral angle mapping and cross correlo- mining district. This study has utilized spectrum ex-gram spectral matching. The distribution of the ferrugi- tracted from AVIRIS data collected over this site, and,nous materials identified from the imaging spectrometer after correlating these results with field spectrum anddata supports the results of laboratory experiments on ground geochemical information, both qualitative andthe dependence of the formation of iron species on their semi-quantitative estimates of the nature and dispersal ofgeochemical and physical situation. Elsevier Science the tailings material were made.Inc., 1999

The Study AreaThis study is concerned with an area of gold mineraliza-INTRODUCTIONtion at Rodaquilar in Southern Spain (Fig. 1). The epi-

The release of trace metals into the environment by min- thermal gold mineralization at Rodaquilar is of the aciding operations is of major health and environmental im- sulphate type (Arribas et al., 1989), hosted by rhyolitic

ignimbrite deposits and domes within the caldera. There* Environmental Systems Science Center, University of Reading, are economic ores of gold-alunite and lead-zinc-silver-

Reading, United Kingdom gold veins, principally concentrated in ring and radialAddress correspondence to G. Ferrier, Department of Geogra- fractures around the western margin of the Cinto calderaphy, University of Hull, Hull, HU6 7RX, UK. E-mail: geo@hull.ac.ukReceived 2 February 1998; revised 23 October 1998. (Transaccion mine), and alunite deposits in the Los Tol-

REMOTE SENS. ENVIRON. 68:125–137 (1999)Elsevier Science Inc., 1999 0034-4257/99/$–see front matter655 Avenue of the Americas, New York, NY 10010 PII S0034-4257(98)00105-9

126 Ferrier

Figure 1. Location map of the study area, Rodaquilar (Southeastern Spain), with the mine workings and tailings dump marked.

los area at the north-eastern margin of the Rodaquilar Other spectrum are obviously complex mixtures. Anotherproblem with identification is that, with the exception ofcaldera (Heald et al., 1987). The exploitation of these de-

posits has left an area composed of unvegetated open pit scorodite, the other less common mineral species de-tected by XRD do not appear in reflectance spectral li-mine workings and tailings dumps (Figs. 1 and 2) sur-

rounded by an area of hydrothermal alteration (argillic braries such as that of Grove et al. (1992).and siliceous), in volcanics of Tertiary age.

Characteristic Materials Associated withMine TailingsField Studies

Rock and soil samples were collected from around the Trace elements can become aggregated in iron oxide andoxyhydroxide minerals (e.g., hematite, goethite, ferrihy-mining area as well as a number of field spectrum col-

lected at the time of the data acquisition. Samples were drite, etc.) and/or mineraloids either through adsorptionon the minerals surface or through direct incorporationcollected from Rodaquilar and these were analysed min-

eralogically using XRD and their reflectance spectrum into the mineral’s surface or through direct incorporationinto the mineral structure (Schwertmann and Taylor,captured in the laboratory with the GER SIRIS spectro-

radiometer. The GER SIRIS has a spectral resolution of 1977).Ferris et al. (1989) indicated that an abundance of2–6 nm, which means that ground spectrum can be re-

lated to the AVIRIS spectrum without losing any infor- poorly crystallized iron oxides are commonly produced inacid mine drainage sediments. In environments wheremation. The XRD data indicate the existence of quartz,

alunite, (natro-)jarosite, and, in smaller amounts, pyro- high levels of Fe(III) are made available by the rapid oxi-dation of Fe(II), ferrihydrite precipitation is favored. Thisphyllite, kaolinite and illite—minerals expected from

prior knowledge of the alteration mapping. Other minor poorly ordered hydrous compound is thermodynamicallyunstable and usually converts with time to more stableminerals detected with some confidence include calcite,

gypsum, albite, muscovite, braunite (CaMn14SiO4), clino- crystalline forms, such as hematite or goethite (Fischerand Schwertmann, 1975). Schwertmann and Murad (1983)clase (Cu3(AsO4)(OH)3), nacrite (Al2Si2O5(OH)4), scorodite

(FeAsO4.2H2O), variscite (AlPO4.2H2O), and brushite (Ca- showed that goethite and hematite form from ferrihy-drite by two different and competitive mechanisms, goe-PO3(OH).2H2O).

From the laboratory spectrum only alunite and jaro- thite crystals form in solution from dissolved Fe(III) ionsproduced by the dissolution of ferrihydrite, whereas he-site spectral characteristics could be identified with con-

fidence and correlated to the equivalent XRD results. matite forms through an internal dehydration and re-

Imaging Spectrometry of Mining Pollution 127

Figure 2. Location map of the study area, Rodaquilar, with the alteration zones marked.

arrangement within the ferrihydrite aggregates. Which detection and mapping in imaging spectrometer data. Asthe pH of the stream water increases, from dilution withend product predominates depends on the pH and the

temperature of the system. Foreign ions and compounds higher pH sources, the secondary minerals precipitateout as stream bed coatings. Because the heavy mineralsalso influence the nature of the reaction product and

modify its crystal habit (Cornell et al., 1987). The con- can substitute for Fe, they are also precipitated from so-lution as constituents of secondary minerals or as con-version time for ferrihydrite increases dramatically with

decreasing pH. However, even under the most adverse taminants absorbed onto the surfaces of the secondaryminerals. Subsequent pulses of low-pH water may dis-conditions 90% of the ferrihydrite had changed to goe-

thite and hematite within 1000 days and at a pH of 3 solve the secondary minerals and remobilize the heavymetals and transfer them downstream.the time of half conversion from ferrihydrite to goethite

and hematite was approximately 300 days (Schwertmannand Murad, 1983). DATA AND IMAGE PROCESSINGSwayze et al. (1996) mapped acid-generating miner-als produced from a gold, silver, lead, and zinc deposit The remote sensing data primarily used in this study was

from the AVIRIS imaging spectrometer. AVIRIS data wasat Leadville, Colorado. At Leadville the sulfide oxidationprocess is biologically driven along complex chemical acquired over Rodaquilar under clear conditions at 13:37

on 15 July 1991 as part of a swath of five contiguous scenespathways with feedback reactions that enhance the speedand magnitude of oxidation. Release of heavy metals is by an ER-2 aircraft as part of the MAC-EUROPE’91

campaign. Contemporaneous Thematic Mapper Simula-facilitated by sulfide oxidation, since many of the sulfidescontain the heavy metals (e.g., Pb, As, Cd, Ag and Zn). tor (TMS) (11-channel) data were also collected together

with infrared photography. Limited fieldwork was carriedThe oxidation-weathering process produces low pH wa-ter in which the heavy metals dissolve as aqueous phases out around the time of the overflight involving collection

of field spectrum at specific sites and the assessment ofwhich are then transported by runoff into nearby streams.Secondary minerals, such as jarosite, ferrihydrite, schwert- vegetation cover.

The AVIRIS consists of four separate spectrometersmannite, goethite, and hematite, are formed by sulfideoxidation or precipitation from metal-rich water. These and the D-spectrometer that records the SWIR pro-

duced very noisy data over Rodaquilar. Signal-to-noisesecondary minerals are Fe-rich, and, as is shown in Fig-ure 3, such minerals have unique reflectance characteris- ratios for the first three spectrometers were good, aver-

aging about 100 for bright targets and in the rangetics in the visible and near-infrared portion of the spec-trum (0.4–1.3 lm) and thus are highly amenable to 100–50 for dark targets. The signal-to-noise ratio for the

128 Ferrier

effects, only an apparent reflectance value can be re-trieved. A number of techniques for retrieving apparentsurface reflectance values for this data were used includ-ing the empirical line technique and a number of radia-tive transfer modeling methods. The application of thesetechniques is discussed fully in Ferrier and Wadge (1996)and Ferrier, (1997). When inspecting the spectra derivedfrom the AVIRIS data, Figures 6 and 9, two wavelengthregions, around 0.94 and 1.14 lm, can be shown to ex-hibit quite noisy spectral profiles. These artifacts arecaused by errors in the conversion from radiance to ap-parent surface reflectance. These wavelength regionshave very high atmospheric attenuation due to water va-por and hence are very sensitive to errors in the estima-tion of the total water path length. A major contributionto the error in estimating total water path length is thevariation in the path length due to topographic effects.In this project the limited number of field survey sitesmeant that this effect could not be fully removed. How-ever, outside these high attenuation wavelengths, the re-sultant effects on the spectral profiles are very limited.

Spectral Subsectioning and Noise WhiteningThe AVIRIS scene covering the Rodaquilar mine wassubsectioned to 400 samples by 400 lines to concentratesolely on the Rumbla del Playazo River Valley. The datawas also spectrally subsampled to the first 105 bands cov-ering the 0.4–1.32 lm waverange. This resampled sectionwas subjected to noise processing as suggested by Board-man et al. (1995) and Farrand and Harsanyi (1997). Aminimum noise fraction (MNF) transform (Green et al.,1988) was applied which produces a set of principal com-

Figure 3. Laboratory spectrum of hematite (OH-1A), ponent images ordered in terms of decreasing signalgoethite (OH-2A), and natrojarosite (SO-7A) from Grove quality. By performing an inverse MNF transform utiliz-et al. (1992). b) Laboratory spectrum of ferrihydrite

ing only the significant (i.e., signal bearing) images, a full(GDS75), hematite (GSS27), and jarosite (GDS24) fromUSGS spectral library [in ENVI Version 2.0, Research image cube can be reproduced in which the noise has aSystems, Inc. (1995)]. gaussian distribution and unit variance. By performing

the forward and inverse MNF transforms on the appar-ent surface reflectance AVIRIS data, data cubes wereD-spectrometer, however, was poor, averaging about 12produced that contained a lower noise component thanin the central part of the range (near 2.2 lm) but fallingthe original data. The spectrum from these transformedoff to 5 or less at either limit for bright targets and stayingimage cubes could be directly compared against the re-below 5 for dark targets throughout the range. The effec-flectance spectrum that were measured in the field ortive loss of the SWIR data was a major setback for thelaboratory.primary aim of the Rodaquilar project which was the study

of the alteration zones associated with the epithermalSpectral Data Processinggold deposits at Rodaquilar. However, in this study it isThe plot of the eigenvalues of the MNF transform indi-of less importance as the variations in spectral profile in

the visible and VNIR wavelengths are of greater interest. cates there are seven prominent eigenvalues (Fig. 4),which suggests there are seven or possibly eight visible–

Reduction to Apparent Surface Reflectance VNIR endmembers. The principal components deter-mined by the MNF transform were plotted in both 2 andThe reflectance values retrieved from remotely sensedn dimensions using the Environment for the Visualisa-data are affected by many factors including the physicaltion of Images (ENVI) Version 2.0 (Research Systems,state of the surface and its orientation towards the SunInc., 1995) software package. The seventh MNF imageat the time of data acquisition. Therefore, even after the

correction of the remotely sensed data for atmospheric was found to very clearly represent the tailings dump

Imaging Spectrometry of Mining Pollution 129

Figure 4. Plot of the eigenvalues of the MNF transform.

material. When the principal components from the MNFtransform are plotted against each other, the tailingsdump material is seen to form an extremely distinctspectral grouping (Fig. 5). A “pixel purity index” (PPI)was applied to the MNF image bands that contained anyinformation. This algorithm (Boardmann et al., 1995) ex-amines the n-dimensional data cloud (where n is the in-trinsic dimensionality of the data) in a series of projec-tions to find the most spectrally extreme pixels. Those

Figure 6. AVIRIS-derived spectral profiles of the four ferrige-pixels deemed “purest” were then examined using the n-nous endmembers (tailings—material 1; mine—material;

dimensional visualizer in ENVI. Vertices in the n-dimen- tailings—material 2 and the stream material).sional data cloud(s) were extracted. The surface materialson the ground covered by these pixels could then beidentified based on their visible and infrared reflectance identified, sea, shade, live vegetation, tailings dump ma-spectrum. After this analysis eight endmembers were terial1, tailings dump material2, mine material, stream

material and country rock. The mean spectral signalsfrom the four endmembers assumed to contain some fer-Figure 5. Scatterplot of the MNF transform output: MNFruginous material are shown in Figure 6.Band 5 against MNF Band 7. m indicates the distinct

cluster of the tailings dump material.

Spectral AnalysisIn order to assess the extent and degree of the dispersionof the tailings dump material, a number of spectral anal-ysis techniques have been applied ranging from purelyvisual, qualitative analysis to techniques employing vari-ous numerical algorithms. Even the analysis employingnumerical techniques cannot be classified as quantitativeanalysis of the remote sensing data as this usually relatesto concentration. This attribute is not readily obtainablefrom remote sensing data because the length, or the dis-tance through the surface material traversed by a photonis not obtainable. The techniques employed here, matchedfilter and linear unmixing, will yield different estimatesof mean percent cover and are therefore most appropri-ately classed as semiquantitative.

130 Ferrier

Playazo to within 600 m of the beach at El Playazo. Thevariations in the AVIRIS-derived spectral profile of thistailings dump material along the Rumbla del Playazo areshown in Figure 9. It can be seen that there is a reduc-tion in the height of the local reflectivity maximum fromapproximately 70% at position 1 in the tailings dump toapproximately 50% at position 8 about 600 m from thebeach. The absorption edges, shoulders, and minima inthe spectral profiles remains quite distinct up to position8. From position 8 to position 10 the absorption featuresin the spectral profiles are not really identifiable. Look-ing at the spectrum from position 1 to position 8, it isquite clear that the tailings dump material is being trans-ported at least as far as position 8. The diminution of thespectral profile is at least partly due to a decrease in theamount of tailings dump material in the bed of the Rum-

Figure 7. Field spectrum of tailings dump material bla del Playazo. From position 8 to position 10 (at the(0.4–2.5 lm). beach) the tailings dump material either disappears or is

being obscured.

Qualitative Analysis of DispersionSemi-Quantitative Analysis of Dispersion

The spectral profile of the tailings dump material (Fig.Matched Filtering7) shows an absorption edge at 0.54 lm, a reflectanceMatched filtering maximizes the response of a knownshoulder at 0.63 lm, a local reflectivity maximum at 0.74endmember and suppresses the response of the compos-lm, a band minimum at 0.85 lm, and another reflec-ite unknown background, thus “matching” the known sig-tance shoulder at approximately 1.04 lm. A qualitativenature (Harsanyi and Chang, 1994). Matched filteringimpression of the dispersion of this tailings dump mate-performs a partial unmixing finding the abundances ofrial can be obtained by dividing the AVIRIS band cen-user-defined endmembers. It provides a rapid means oftered at 0.74 lm by the AVIRIS band centered at 0.85detecting specific minerals based on matches to specificlm. The resulting image (Fig. 8) shows very clearly the

tailings dump material extending down the Rumbla del library or image endmember spectrum and does not re-

Figure 8. Band ratio image of AVIRIS bands at 0.85 lm divided by band at 0.746 lm.

Figure 9a. Map of the locations of sample points for the AVIRIS-derived spectral profiles along theRumbla del Playazo.

Figure 9b. AVIRIS-derived spectral profiles from sample Figure 9c. AVIRIS-derived spectral profiles from samplepoints 1–3 along the Rumbla del Playazo (see Fig. 9a), no points 4–10 along the Rumbla del Playazo (see Fig. 9a), withoffset along the y-axis. a 20% offset along the y-axis for each spectral profile.

132 Ferrier

The results of the unmixing analysis are generallyvery good (Fig. 11). The pixels with the poorest result(0.0) are displayed as pure white whereas the pixels withthe highest result (1.0) are displayed as pure black. Thecontour lines (see Fig. 1) are also in black and underliethe unmixing results. The first endmember (tailings dumpmaterial1) is mainly concentrated in the large tailingsdump with only a few occurrences located along theRumbla del Playazo. The second endmember (tailingsdump material2) is mainly concentrated along the Rum-bla del Playzo, showing a variation in concentration. Italso appears on the beach at La Playazo. The third end-member (mine material) has a very limited but clear out-crop located in the mine workings. The fourth endmem-Figure 10. The results of the match filtering along the Rumblaber (stream material) shows a limited, linear distributiondel Playazo.most clearly southwest of the main tailings dump. Thefifth endmember shows very clearly the distribution ofthe live vegetation. Of most interest is its patchy distribu-quire knowledge of all the endmembers within an imagetion along the Rumbla del Playazo and especially thescene. Matched filter results are presented as grey-scalelarge patches of vegetation located within 600 m of theimages with values from 0 to 1.0, which provide a meansbeach at El Playazo. While all the spectra shown in Fig-of estimating relative degree of match to the referenceure 8 show some minor influence of vegetation (a smallspectrum (where 1.0 is a perfect match).red edge near 0.7 lm) there is not an significant increaseThe mean spectrum from a region of interest of 100in the vegetation influence as the Rumbla nears the beach.pixels from the tailings dump was calculated and used as

The reduction in the contribution of the tailings ma-the endmember in the matched filtering. The scores ofterial to the overall spectral signal along the last 600 m ofthe match filter in the Rumbla del Playazo area (Fig. 10)the Rumbla could be the result of a number of factors. Ifrange from 0.2 to 0.9 and vary quite rapidly on a pixel-increased vegetation was masking the spectral signatureto-pixel basis. The poorest results (0.0) are displayed asof the tailings material, then there should be more evi-pure white while the best results (1.0) are displayed asdence of vegetation spectral features in the profiles near-pure black. The contour lines (see Fig. 1) are also in

black and underlie the match filter results. The results est the beach. It is possible that dead vegetation or plantclearly show the tailings dump, the mine workings, and litter might be more abundant closer to the beach, anda trail of tailings dump material extending down the this could be helping to mask the spectral response ofRumbla del Playao, finally stopping about 600 m from the tailings material. More probably, there is more coun-the beach at El Playazo. try rock/gravel mixing with the tailings material the fur-

ther one gets from the mine causing a diminution of theLinear Spectral Unmixingspectral response of the tailings material. The spectralLinear spectral unmixing is a means of determining theprofiles displayed in Figure 9 are the pixels displayingrelative abundances of materials depicted in multispec-the strongest contribution from the tailings material attral imagery based on the materials spectral characteris-each point along the Rumbla.tics. The reflectance at each pixel of the image is as-

sumed to be a linear combination of the reflectance ofeach material (or endmember) present within the pixel.

RESULTSThe number of endmembers must be no more thann11, where n is the number of spectral bands (Adams Discussion of Endmemberset al., 1993; Sabol et al., 1992). After identifying these

Of the eight endmembers defined above, four weretypes or endmembers from the image or spectral library,thought likely to contain some ferruginous material. Inthe appropriate proportional mixture to fit the spectrumorder to gain further insight into the processes occurring,of each pixel is computed using least squares techniquesit is necessary to try to identify these four endmembers(e.g., Settle and Drake, 1993).using qualitative and quantitative techniques. These end-Spectral unmixing results are highly dependent onmembers were initially compared to laboratory spectrumthe input endmembers. The endmembers defined afterfrom the JPL and USGS spectral libraries. Spectrum forinspecting the MNF transform images in two and morethe most probable mineral phases : hematite (OH-1A),dimensions and also the results of the pixel purity indexgoethite (OH-2A), jarosite (SO-7A), and ferrihydriteanalysis indicated eight spectrally distinct endmembers in

the visible-VNIR wavebands. (USGS) are shown in Figure 3.

Imaging Spectrometry of Mining Pollution 133

Figure 11. The results of the spectral unmixing analysis: a) tail-ings dump material1, b) tailings dump material2, c) mine mate-rial, d) stream material, and e) live vegetation.

Qualitative Analysis of Endmember Types The second material associated with the main tail-ings dump (tailings dump material2) appears spectrallyThe main tailings dump material (tailings dump mate-quite similar to the tailings dump material1 except forrial1) has an absorption edge at approximately 0.54 lm,but a general reduction in the intensity of the absorptiona shoulder at 0.63 lm, a local reflectivity maximum atfeatures and the location of the absorption shoulder at0.74 lm, a band minimum at 0.85 lm, and another re-approximately 0.59–0.6 lm. This material is locatedflectance shoulder at 1.04 lm. These features plus thearound the rim of the main tailings dump in a fewgeneral shape of the spectrum correspond very stronglypatches, at some points along the Rumbla del Playazowith the hematite (OH-1A) reference spectrum and indi-

cate that it is very hematite-rich. and on the beach at La Playazo. These features again

134 Ferrier

correspond strongly with the hematite reference spec- of the sum of the correlation at m54 and m524 dividedby 2 and subtracted from 1 astrum (OH-1A) but also suggests either some contamina-

tion or diminution in the tailings dump material.skewness512

|rm542rm524|2

The stream material endmember spectral profile isflatter than the first two endmembers with an absorption

In this case the mean spectrum for the four ferrugi-edge at 0.54 lm, two local reflectivity maximums at 0.57nous endmembers were used as inputs into the crosslm and 0.76 lm and two indistinct band minima at ap-correlogram spectral matching and compared to four ref-proximately 0.66 lm and 0.87–0.89 lm.erence laboratory spectrum, hematite (OH-1A), goethiteThe mine material endmember has an absorption(OH-2A), natrojarosite (SO-7A), and ferrihydrite (GDS75).edge at approximately 0.54 lm, a shoulder at 0.57 lm, aThe cross-correlogram provides information for discrimi-local reflectivity maximum at approximately 0.73–0.75 lm,nating between mineral species, specifically the cross-and a band minimum at approximately 0.85–0.86 lm.correlation at m50, the shape of the correlogram andThese features strongly indicate the presence of hematitethe position of the peak of the function. The results ofand suggest the presence of another ferruginous species.spectral matching for the four endmembers are shown inFigures 12 and Table 1. The closer the match of the testQuantitative Analysis of Endmember Typesspectra to the reference one will result in the cross-To assist in the determination of these endmembers twocorrelogram have a match of 1.0 at m50 and a symmet-quantitative techniques have been applied to this spectralrical curve at match positions moving away from m50.analysis. The first technique was the spectral angle map-

The cross-correlogram for tailings material1 showsper (Kruse et al., 1993) which determines spectral simi-hematite with the highest correlation at match positionlarity between the image endmembers and the laboratoryat m50 and the most asymmetric curve, with a skewnessspectrum. The lower the spectral angle between twoof 0.995. The other reference spectra have much lowerspectrum, the more similar they are. The main tailingscorrelations at m50 and much more skewed spectraldump material (tailings dump material1) was very similarmatch curves. The cross correlogram for tailings mate-to the hematite library spectrum (OH-1A) with the bestrial2 looks very similar to tailings material1 with hematitematch of 0.039 and a mean of 0.054. The second mate-having the highest correlation at match position m50.rial associated with the tailings dump (tailings dump ma-However the correlation is lower than for tailings mate-terial2) had its best fit of 0.042 with goethite (OH-2A)rial1, the goethite curve has a higher correlation valuewhereas the best fit for the mine material was 0.052 withand the skewness value for goethite and hematite arejarosite (SO-7A). Ferrihydrite, from the USGS spectralidentical. The cross-correlogram for the mine material islibrary, gave a poor match with all these endmembers.more complicated with goethite, jarosite, and hematiteThe second technique was a cross-correlogram spec-having quite similar correlation values at m50. Thetral matching approach (Van der Meer and Bakker,skewness values are also reasonably similar. The cross1997). A cross-correlogram is constructed by calculatingcorrelogram for the stream material shows goethite withthe cross-correlation coefficient between a test spectrumby far the highest correlation value at m50 and with the(a pixel spectrum) and a reference spectrum (a labora-highest skewness factor.tory or endmember pixel) at different match positions.

The results from both the qualitative and quantita-By convention, the reference spectrum is moved and re-tive approaches to endmember identification is that tail-ferred to a negative match position when shifting towardings dump material1 band minimum and shoulder neara longer wavelength. Thus match position 1 means that0.85 lm and 0.63 lm corresponds to the 6A1→4T1 andwe are calculating the cross correlation between the test 6A1→4T2 transitions, respectively (Morris et al., 1985).spectrum and the reference spectrum in which all chan-These features correspond exceptionally strongly to thenels have been shifted by one channel position numberhematite spectrum and can therefore be classified asto the lower end of the spectrum. The cross correlation, such. Tailings dump material2 is in some aspects spec-

at each match position, m, is equivalent to the linear cor- trally quite similar to tailings dump material1, which sug-relation coefficient and is defined as the ratio of the co- gests it is mineralogically quite similar but has one orvariance to the product of the sum of the standard devia- more additional features which distinguishes it from thetions. If we denote the test and reference spectrum as other tailings dump material. This could be a combina-kt and kr, respectively, and define n as the number of tion of grain-size or contamination, by jarosite or goe-overlapping positions, the cross correlation for match po- thite, causing reduction in hematitic signal or possiblysition m can be calculated as even some chemical reaction changing the hematitic ma-

terial into another ferruginous phase. The mine materialrm5

nok rk t2ok rok t

√nok2r2(ok r)2][nok2

t2(ok t)2] again has again strong hematitic spectrum features butin addition has features suggesting the presence of jaro-site. The stream material is located away from the mainThe skewness of the pixel correlogram was calculated

and rescaled between 0 and 1, taking the absolute value tailings dump mainly along streams. It has quite a dis-

Imaging Spectrometry of Mining Pollution 135

Figure 12. a) Cross-correlation plots for the tailings dump material1 endmember against hematite (hem), goethite (goeth), ferri-hydrite (ferr), and natro-jarosite (njar mod). b) Cross-correlation plots for the tailings dump material2 endmember against hematite(hem), goethite (goeth), ferrihydrite (ferr), and natro-jarosite (njar mod). c) Cross-correlation plots for the stream material end-member against hematite (end2 hem), goethite (end2 goeth), ferrihydrite (end2 ferr), and natro-jarosite (end2 njar). d) Cross-correlation plots for the mine material endmember against hematite (hem), goethite (goeth), ferrihydrite (ferr), and jarosite (jar).

tinct spectral profile which strongly suggests it is in part tailings dumps have therefore remained undisturbed foralmost 40 years. Metastable iron species such as ferrihy-made up of goethite.drite were found to be completely absent, and insteadthere was a widespread distribution of very well-formedCONCLUSIONS hematite and, to a much lesser extent, goethite material.The results of Schwertmann and Murad (1983) experi-The major phase of mining activity at Rodaquilar ended

in the late 1950s with only very minor activity at the site ments support very strongly the hypothesis that enoughtime has elapsed to allow the full conversion of the ironsince then. The unvegetated open pit mine workings and

136 Ferrier

Table 1. Skewness Values for the Cross Correlogram a MAC-Europe Principal Investigatorship to G. Wadge. I wouldSpectral Match of the Ferrigenous Endmembers against also like to thank the editor and the referees for their helpfulFerrigenous Mineral Phases Reference Spectrum (see comments.Fig. 12a–d)

Hematite Goethite Jarosite Ferrihydrite REFERENCESTailings 1 0.995 0.987 0.970 0.929Tailings 2 0.994 0.994 0.863 0.934

Adams, J. B., Smith, M. O., and Gillespie, A. R. (1993), Im-Mine 0.983 0.989 0.955 0.852aging spectroscopy: interpretation based on spectral mixtureStream 0.968 0.992 0.985 0.969analysis. In Remote Geochemical Analysis: Elemental andMineralogical Composition (C. M. Pieters and P. A. J. En-glert, Eds.), Cambridge University Press, New York, pp.

species from their metastable form (ferrihydrite) to sta- 145–166.ble forms (hematite and goethite). Arribas, A., Jr., Rytuba, J. J., Rye, R. O., et al. (1989), Prelimi-

nary study of the ore deposits and hydrothermal alterationThe results of Schwertmann and Murad (1983) ex-in the Rodaquilar Caldera Complex, southeastern Spain,periments also help explain the distribution of the ironUSGS Open-File Report 89-327, U.S. Geological Survey,species. The finding that hematite is formed from ferri-Reston, VA, 39 pp.hydrite through an internal dehydration and rearrange-

Boardmann, J. W., Kruse, F. A., and Green, R. O. (1995),ment within the ferrihydrite aggregates strongly supportsMapping target signatures via partial unmixing of AVIRISthe hypothesis that the concentration of the hematitedata. In Summaries of the Fifth Annual JPL Airborne Earthmaterial in the main tailings dump was due to this pro-Science Workshop, Washington, DC, 23–26 January, JPLcess. The distribution of the goethite along narrowPublication 95-1, Jet Propulsion Laboratory, Pasadena, CA,

stream beds also corresponds strongly to their findings Vol. 1, pp. 23–26.that goethite is produced from ferrihydrite that been dis- Cornell, R. M., Giovanoli, R., and Schindler, P. W. (1987), Ef-solved into Fe(III) ions. fect of silicate species on the transformation of ferrihydrite

The association of jarosite in the mine workings with into goethite and hematite in alkaline media. Clays Claysecondary hematite and goethite further down the dis- Min. 35:21–28.persion path is similar to that described by Swazye et al. Chrien, T. G., Green, R. O., Chovit, C. J., Eastwood, M. L.,

and Sarture, C. M. (1996), Calibration of the Airborne Visi-(1996). They found that the jarosite and jarosite-goethiteble/Infrared Imaging Spectrometer in the laboratory. Inmine-waste areas had a high acid-generating capacity andSummaries of the Sixth Annual JPL Airborne Earth Scienceare surface point sources for acid water and heavy met-Workshop, JPL Publication 96-4, Jet Propulsion Laboratory,als. Areas covered by goethite and hematite can still con-Pasadena, CA, Vol. 1, pp. 39–48.tain heavy minerals (adsorbed on or as components of

Farrand, W. H. (1997), Identification and mapping of ferric ox-the secondary minerals), but they will not be mobile inide and oxyhydroxide minerals in imaging spectrometer datathe absence of low pH water, normally found in the jaro-of Summitville, Colorado, U.S.A., and the surrounding San

site-rich areas. Juan Mountains. Int. J. Remote Sens. 18:1543–1552.In this article it has been demonstrated that the na- Farrand, W. H., and Harsanyi, J. C. (1997), Mapping the distri-

ture of the ferruginous species and consequently the dis- bution of mine tailings in the Coeur d’Alene River Valley,persion of the contaminated material can be obtained Idaho, through the use of a constrained energy minimizationfrom imaging spectrometer data. This study has shown technique. Remote Sens. Environ. 59:64–76.that the tailings material, containing pernicious, possibly Fenstermaker, L. K., and Miller, J. R. (1994), Identification of

fluvially redistributed mill tailings using high spectral resolu-toxic, trace material, has been dispersed all the way fromtion aircraft data. Photogramm. Eng. Remote Sens. 60:the mine workings, through the main tailings dump and989–995.for approximately 3.5 km along the Rumbla del Playazo

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Ferrier, G., and Wadge, G. (1996), The application of imagingand around Rodaquilar significantly affect the surroundingspectrometry data to mapping alteration zones associated

environment. Ineffective laws relating to the use of partic- with gold mineralization in southern Spain. Int. J. Remoteular chemicals and the proper handling of waste material Sens. 17:331–350.in the past has lead to significant environmental impact. Ferris, F. G., Tazaki, K., and Fyfe, W. S. (1989), Iron oxides inTherefore, the escape of this contaminated material from acid mine drainage environments and their assocaition withthe large tailings dump along the Rumbla del Playazo all bacteria. Chem. Geol. 74:321–330.the way to the beach at La Playazo is not unexpected. Fischer, W. R., and Schwertmann, U. (1975), The formation of

hematite from amorphous iron (III) hydroxide. Clays ClayMin. 23:33–37.This work was supported by Natural Environment Research

Green, A. A., Berman, M., Switzer, P., and Craig, M. D. (1988),Council Grant F60/G6/12. The MODTRAN3 code was suppliedby James Chetwynd and JPL supplied the AVIRIS data under A transformation for ordering multispectral data in terms of

Imaging Spectrometry of Mining Pollution 137

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The spectral image processing system (SIPS)–interactive vi- (1996), Mapping acid-generating minerals at the Californiasualization and analysis of imaging spectrometer data. Re- Gulch Superfund Site in Leadville, Colarado using imagingmote Sens. Environ. 44:145–163. spectroscopy. In Summaries of the Sixth Annual JPL Air-

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