SYNTHESIS REPORT

FOR PUBLICATION

CONTRACT No : BRE2-CT92-0201

PROJECT No : BE -5361

TITLE : NEW TECHNOLOGIES FOR MINERAL EXPLORATION ANDSURVEILLANCE OF ENVIRONMENTAL IMPACTS OFMINING OPERATIONS - BASED ON REMOTE SENSINGAND MULTIDATA SET ANALYSIS (NEW TECH).

PROJECTCOORDINATOR : GESELLSCHAFT FUR ANGEWANDTE FERNERKUNDUNG

DR. PETER VOLK (GAF)

PARTNERS : GESELLSCHAFT FUR ANGEWANDTE FERNERKUNDUNG(GAF), MUnchen, FRG

MINAS DE ALMADEN Y ARRAYANES SA (MAYASA),Almaden, E

INSTITUTE OF MATHEMATICAL MODELLING (lMM),(lMSOR Group), Tech. Univ. of Denmark, Lyngby, DK

DEUTSCHE FORSCI-IUNGSANSTALT FUR LUFT- UNDRAUMFAHRT (DLR), Oberpfaffenhofen, FRG

REFERENCE PERIOD FROM 01.11.1992 to 30.04.1995

STARTING DATE :01.11.1992 DURATION :30 MONTHS

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**** PROJECT FUNDED BY THE EUROPEAN** * . COMMUNITY UNDER THE BRITE/EURAM* ** * * PROGRAMME

DATE :31.07.1995

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* Gesellschaft fur Angewandte Fernerkundung (GAF),Germany (Johannes I-feymann, Peter Vo!k),

● Minas de Almaden y Arrayanes, S.A. (MAYASA),Almaden, Spain (Javier Almagro, Enrfque Ortega),

Leonrodstrasse 68, 80636 Mtinchen,

Paseo de la GasteHana 18, 28046

● Institute of Mathematical Mode{ling (1 MM, IM!XIR Image Group), Technical University ofDenmark, Bui[ding 321, 2800 Lyngby, Denmark (Knut Conradsen, Bj~rne Ersb~ll, AlIanNielsen), and

● Deutsche Forschungsanstalt fur Luft- und Raumfahrt (DLR), Oberpfaffenhofen, 82234Wef31ing, Germany (Frank Lehmann, Andreas Mtil/er).

ABSTRACT

This paper describes the research carried out for the development of ‘new technologies formineral exploration and surveillance of environmental impacts of mining operations - based onremote sensing and multidata set analysis’. Remotely sensed data from space- and airborneplatforms (Landsat-MSS, -TM, ERS-I SAR, JERS-I SARI GERIS, ATM) are analysed for theirfeasibility to be the basis for environmental impact assessment of mining operations and theirrelated industries. Data processing and analysis strategies are developed to extract and re-combine relevant information from multidata sets using statistical analysis, Artificial NeuralNetworks (ANN) and Geographic Information Systems (G IS). Dedicated processing conceptsfor the detection of ancient and recent dumps, vegetation damage, change detection, semi-quantitative assessment of air pollution (smog density) under certain conditions and newvisualization tools are introduced. Through data calibration, data integration, and the verificationwith field spectrometry, laboratory analysis of soil and plant samples, and field observations thedeveloped methodologies are verified and evaluated.These investigations are performed in three different testsites in Spain and Germany. Statistical(R- and Q-mode MUSECC} and analogue analysis methods for multiternporal data sets(Landsat MSS and TM} are presented for change detection in the Rio Tinto area (Spain) andmapping of forest losses in the Erzgebirge (Germany). Additional data {like a DTM,meteorological data etc.} are integrated in the analysis through a GIS. Geophysical,geochemical, and spaceborne microwave datasets are re-processed (2-D semivariograms,depth modelling, data fusion), integrated in the analysis, and their potential for environmentaland geologic applications discussed.

lNTRODuCTION

Four European partners have associated to execute this joint research project in the fields ofgeodata analysis, new remote sensing technologies, and its application for mining andsurveillance of environmental impacts of mining activities and their related industries.Gesellschaft fur Angewandte Fernerkundung (GAF, Company for Applied Remote Sensing) is aGerman consulting company speciaiised in remote sensing, GIS, and gee-applications. Minas

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de Almaden y Arrayanes, S.A. (MAY’ASA) is a Spanish mining company producing mercury andexecuting exploration and environmental projects. The Danish image processing group at theinstitute for Mathematical Modelling (lMSOR at iMM, The University of Denmark) is specizdisedin geostatistical and image analysis research. Deulsche ForschungssanstaH filr Luft- undRaumfahrt (DLR, German Aerospace Research Establishment) is a public-funded, Germanresearch institution which is active - among other fields of aerospace research - in theemployment of new airborne sensors and the development of dedicated processing routines forsuch data (Institute for Optoelectronics).This project was funded by the European Community under the BRITE/EURAM programme(DG X11, Contract N“ BF?E2-CT92-0201, Project N’ BE-5361 )

The project started in November 1992 and was successfully completed in time in April 1995.Research tasks were undertaken in three testsites: The central pant of the “Pyrite Belt” in theHuelva Province (Spain), the “Alcudia Anticline” South of Aknaden in the La Mancha Province(Spain), and the “Eastern Erzgebirge” in the State of Saxony (Germany), representing a typicalMediterranean environment and humid conditions of the mid latitudes respectively. (Additionallydata from SW-Greenland are included for limited research on areas with arctic conditions.)

The Pyrite Belt in the SW of the Iberian peninsula is actively explored and mined for more than4000 years. It covers more than 8000 km2 and stretches from Spain to Portugal. Even with thisextraordinary mining history there is still a high potential for exploration and several companiesare actuaJly working in this area.The deposits are large masses of massive sulphide with variable amounts of Cu, Zn, Pb, Sn,related to sporadic Mn minera[izations. Every deposit is connected space and time to a complexvolcanic activity during the lower carboniferous, before the variscan erogenic event shaped thearea. The extensive thrusting and fold!ng created a rather complicated structure, which enablesthe detection of hidden deposits by e.g. overthrusted sediments.Moreover, the large amount of spoils and dumps in the area during this long period of time,causes serious environmental problems. The climatic conditions (arid Mediterranean environment)are ideal to check the capabilities of remote sensing and G IS techniques for detecting andmonitoring contamination processes from mining activities.

The variscan structure named Alcudia Anticline is located in the central iberian zone of Spain,and has been intensively mined for Pb, Zn, and Ag starling with the Remans (2000 years b.p.).Actually no mining occurs, but with the turn of the last century more than 500 small/medium sizedmines operated simultaneously.The mineralizations are to low temperature hydrothermal vein swarms showing a structuralcontrol. They are hosted in Precambrian sediments which crop out in the cores of the anticlines.The emplacement happened in the end of the variscan orogenesis.Considering the actual situation of the metal prices, this area is mot a prime target for exploration.But it is ideal for the application of remote sensing and GIS techniques to investigatecontamination from old mining and processing in numerous small places. It is of special interest todetect reman contamination sources since they are already covered by soil and morphologicallyextremely difficult to map.

The Eastern ErzgebirgehJorth Bohemian districts comprise one of the most extensively minedareas in Europe. A large, geochemical anomalous block of uplifted Paleozoic and Precambrianrock hosts a number of important mineralization, which have been exploited for more than 1000years. Among the characteristic and potentially interesting eiements are S.n, W, U, Au, Ag, Sb, Zn.To the South the North-Bohemian domain contributes similar mineralizafions, also leading to thedevelopment of extensive mining and heavy industries.

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Both regions are characterised by a high population density, large forest and nature resources.Environmental damages partly caused by mining and related industrial processing are severe.Due to political changes relevant fieJd data are available now and make this region a perfect testarea for the feasibility of advanced remote sensing and G 1S techniques for environmentalsurveillance in a humid environment of the mid latitudes.

Starting point was the absence of “dual-use” methods and applications of new sensor/geodataanalysis for mining exploration @ environmental management in relation to mining activitiesranging from mine exploration, -development and -production to mine reclamation. Successfulpre-operational applications require complete case studies, which in case of today’s world-widemining industry should also demonstrate improved management possibilities in different climaticand environmental regimes. New sensor technology (radar and hyperspectra! sensing) has notbeen investigated using an integrated approach for its economic potential prior to the start ofthis project.Hidden deposits are of main interest, and geophysical/geochemical methods alone do notsatisfy the information needs. In this context reprocessing of geophysical/geochemical datausing multivariate spatial statistical methods, depth modelling and the extraction of tectonicdata from new sensor data [e.g. spaceborne SAR) are to be mentioned. [n Europe andelsewhere environmental concerns are playing an important role in the discussion about miningprojects. Remotely sensed data provide a unique potential to map and monitor specificenvironmental aspects. Methods originally developed for exploration can also be adapted toenvironmental problems. Mining and industry induced pollution of air, soil, and vegetation areinvestigated in Spain and Germany.

The research activities carried out to develop new technologies for the a.m. objectives can bedescribed through the following three main steps:

* Completion and establishment of data bases.

Spatial data bases of exploration- and environmental relevant information of three majortestsites are be completed or constructed. Additional digital data have been formatted to fitinto a rasterhector data base structure. This includes a campaign to acquire and calibratenew imaging spectrometer data over the Erzgebi rge testsite (DAEDALUS ATM). Also newERS-I SAR and JERS-1 SAFi data were acquired, besides data from well known systems(Landsat TM and MSS).

* Processing concepts for existing and new data.

Mostly original data cannot be analysed without pre-processing and the application ofdedicated algorithms. Processing concepts for multispectra], hypewpectral and microwavedata had to be developed for exploration and environmental applications in context withmining. Available geophysical data can be evaluated much better by the application ofcornpfiler-intensive-mode-lling tools before entering the data base.

● Integration in the data base, interactive and statistical correlation analysis.

Methods of spatial and multidimensional statistical analysis and interactive processing haveto be applied. Results are proposed standard procedures to define target areas forexploration on precious and base metals, and to assess the environmental impact of miningoperations. The updated and supplemented data bases have to be tested interactively andanalysed for their potential in environmental impact mapping and monitoring (changedetection). Procedures for the combination and interpretation of relevant data layers wereelaborated.

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TECHNICAL DESCRIPTION

The initial project activity was the com~letion of the thematic data bases am[vina a hvbrid,,, ”

rasterlvector G IS approach. For the testsite Eastern Erzgebi rge for example 85 data l&ershave been gee-referenced and stored in a spatial data base. In total 30 remote sensing datasets (Landsat MSS and TM, ERS-I SAR, JEI?S-1 SAt?, DAEDALUS ATM, GERLS) have beenacquired and processed. Further thematic data such as geology, soil geochemistry,geophysics, climatology, hydrology, topography and administration layers have been integrated.The work for the assembly of the database included data capture (digitisation from thematicmaps), data transformation, rectification of remotely sensed raster data, definition of thereference co-ordinate system, and re-projection of different geometry in order to fit to thegeometric base.As example the database for the testsite Eastern Erzgebirge (hybrid raster/vector GIS) shall belisted:

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Landsat TM 192/25 geocoded winter data, 6 bands, date: 01. Feb. 1987Landsat TM 192/25 geocoded summer’data, 6 bands, date: 09. Aug. 1992ERS-I SAR PR1 4046/2583, 5549/2583 and 6551/2583, dates: 24. April, 07. Aug. and 16,Ott. 1992JERS-I SAR D304-215 data, date: 19. Oct. 1993ATM (DAEDAUJS) data, 11 bands, date: 06. July 1991, areas: Freital and KonigssteinATM data, 11 bands, date: 03. Aug. 1993, area: Altenberg (atmospherically andgeometrically corrected)CIR aerial photographs for selected areas, different dates and qualitiesSoil geochemistry for 15 elements, raster sampling 100xI 00m, 16bit, area: AltenbergDigital Terrain Model from 1:250000 map, 250m raster, 16bitBouguer gravimetryr 500m raster, 16bitAeromagnetic data, total intensity, 250m raster, 16bitGeologica! maps at different scalesTopographic maps at different scales (1 :25000, 1 :50000, 1 :100000, 1 :200000 and1:400 000)Cadastre of major polluting industriesDiverse climatological data and reports

The historic TM and MSS data (MSS 208/25, 13.08.1972 and TM 192/25, i 0.07.1984) used forthe change detection in the Eastern Erzgebirge testsite are property of a forest research projectrunning at (3AF in the close vicinity of the testsite, The databases for the Spanish testsites arestructured in a similar way.

The research and development of new methods was guided by tasks which are described in thefollowing sections.

● Improvement of tectonic models using structural enhancement and interpretationmethods.

The techniques for merging different sensor data drew special attention for improvedinterpretation capabilities. Merging methods on the basis of Intensity, Hue, Saturation (IHS)transformations have been first introduced in geoscientific applications by HAYDN, R. et al. in

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1982. During the predecessor projects MAI Ml /0009-D(B) and MA2M-CT9CPOOI O dedicatedmethods based or? the I W theory have been developed and successfully applied,

But up to now only original and slightly enhanced data have been overlaid to other data sets ase.g. satellite images. However, the principal problem to obtain such products is the “legibility ofthe information.The first vertical derivative of the gravimetric data set is already in its raw form very good suitedfor structural analysis. But it lacks the association vith topographic and surface features in orderto be properly interpreted.

To overcome this, a merge was performed using this strongly enhanced data as colourinformation and the pre-stretched TM winter data as “albedo background”. It was the first time thatthe capabilities of multispectral winter data have been used in such a concept, eta. the shadedrelief effect on a landscape, the presentation of the most significant infrastruc~ure, ~ties, rivers. Itcombines indeed most of the specific advantages of both data sets.

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● Spatial depth analysis of geophysical data to obtain new information on potentialfertile lithologic units in a 500-1 OOOm depth range.

The two geophysical datasets (Bouguer gravimetry and Aeromagnetics) are characterised by thespecific properties of potential fields. This fact can be used to perform some tailor-made dataenhancements, which allows to increase the interpretability of the data significantly and allows tomerge the geophysical data with other related data sets in an optimum way.

Both data sets were converted by means of a Fourier transformation from the space- to thefrequency domain and a range of different filter operators applied. Filters applied are: High pass,band pass, vertical continuation, gradient and others. After successful application of one orseveral operators in sequence in the frequency domain the re-transformation into the spacedomain yields the result.By this procedure the g ravimetry and the magnetic field can be continued upwards anddownwards, allowing to compare them with other existing data, increase spatial differentiation orseparate residuals form the regional field. Specific improvements can be done through thecalculation of the first and second vedical and horizontal gradients. For the gravimetry data theapparent density can be derived under some simplifying assumptions. For the underground virtualprisms with a certain depth extent are assumed and their respective density calculated.Interpretation of magnetic anomalies is severely hampered by a strong asymmetric shape andshift of the anomaly in !OW latitudes, the only exception being the high latitude area around thepoles. A specific operator “reduction to the pole” was applied to the magnetic data and generatesa synthetic anomaly without these disturbing effects.h addition a more quantitative analysis of the same geophysical data was executed. Furthermodelling of the field and its anomalies was performed for some prominent structural andiithologic features of interest. [t consists of depth-to-source determination based on a statisticalevaluation of the frequency power spectrum.

* Delineation of geobotanical anomalies from GERIS hyperspectral data incombination with field spectrometry.

A spectral classification was carried out for the scene ‘Sotiel’ based on the ENVI version of theSpectral Angie Mapper. As input a spectral library of GERIS spectra was used. The spectral

library was created in an iterative process. [n the first iteration the input spectra were defined asaverage spectrum {5’5 pixels) of approx. 5 homogeneous ground targets. With these spectra apre-classification was carried out. In the next iteration average spectra of targets, that have notbeen classified previously were added. This process was continued until 70 0/0 of the scenewere classified. Unclassified areas remain mainly in the marginal parts of the image caused bythe roil correction and in water targets. Special attention was drawn on the discrimination ofmining materials and their spatial distribution over the test scene. In Fig. 5.4.k. it can be seenthat large areas are covered by mining materials. These materials are either dumps of theactive mine near Sotiel Coronada or remainhgs of former aclMities. Most of the contaminatedareas are located close to the riverbed of the Rio Odiel and therefore, are of potential risk forthe environment. R can be seen in the classified image that the spectral response of the waterof the Rio C)diei changes downstream of a large mining dump and later is not longer classifiedas a water largeLIRIS Mark IV field spectrometer measurements of Jara, Eucalyptus and Pine, all typical plantsfor this Mediterranean environment, were used as input for the SAM algorithm. A discriminationof different vegetation types seems to be possible. The main components contributing to thevegetation cover in the scene are jara and eucalyptus spread all over the scene. Only in a fewareas the amount of those components is lower. The comparison with the NDVl computed fromband 11 (645 nm) and 16 (800 nm) shows a good agreement in areas where bush fires tookplace. in the lower central part of the scene, the NDVI and the SAM feature Pronounceddifferences. The NDVI only sfiows a slight decrease in intensity whereas the SAM o~tput is verylow. it seems, that there is some kind of vegetation cover present which is not similar to any ofthe input spectra.It is known from the ground survey, that in this area the same kind of vegetation is growing likein other parts of the testsite. For this reason, not only density effects but also changes in thespectral response of the vegetation can be expected that might be related to the geologicalsituation.

* Feasibility of ATM and TM data for delineating ancient mining dumps.

A study of the spectral response of each type of land cover was performed for the Iestsite“Quinto del Hierro” in the Alcudia Antic!ine. For this purpose the data was sub-sampled intosections of 400 pixels each. The capability of discriminating different land cover could beevaluated for each band. Thus, an operational 13GB or II-H combination of bands could beidentified. Further investigation of the ATM data was pedormed by app[ying a PrincipalComponent Analysis (PCA) and calculating band ratios. The central conclusions of thisinvestigation which relates spectral data to soil contamination were as follows:

0 The RGB combination of ATM bands 11-10-6 is the most applicable association of bands.● The ratios of bands 11/6, 6/1, 6/2 and 10/8 provides the best discrimination.e The RGB combination of the ratios 11/6, 612 and 10/8 achieves the best results,* There is not one singie PC, which discriminates the contaminates area correctly.e The RGB combination of PC2, PC4 and PC6 is the best combination of PCs, however,

the results are less favorable than the combination of single bands.

The results obtained with the analysis of the ATM data have been directly transferred to the TMdata by relating the equivalent bands between the two sensors. Even when considering thesignificant differences in spatial resolution between the data (6 m versus 30 m), the basicproperties of discriminating the features of interest are we[l preserved in the TM data. The areaof contaminated soil could be located quite precisely with a RGB combination of bands 5-7-4.

An investigation into the applicability and discriminatory capabilities of the two sensors hasbeen carried out by an interactive analysis of the digitised and interpolated data layers(geological maps, historical mining data, geochemical data and results of the soil analysis) withthe image data.The sheer amount of types of data indicates the complexity of the procedure. Furthermore, twosimilar mineralizations in similar geological environment and Iithology and with the sameclimatic conditions (La I?eina and Quinto del Hierro) show a completely different spectralbehaviour. An explanation of this obvious discrepancy could be provided by the very differentage of mining activities at the two sites. The Quinto del Hierro mine dates back to Romanexploitation developed over 2000 years ago, while the La Reina mine activities started about 80years ago. [t is likely that the mobility of heavy metals is very low at those sites and long periodsare required for their displacement in the soil.The soil samples analysed were collected from a soil profile, where four horizons weredelimited. A total of 26 elements and the pH value were determined for each horizon. During asubsequent procedure, some soil samples were collected inside and outside the boundaries ofthe Quinto del Hierro dump, where the contamination level varies significantly within a fewmeters.

* Restoration and substitution of incomplete and inhomogeneousmulti-element data (geochemistry).

geochemical and

Traditionally experimental cross-semivariograms are presented as 1-D plots that reflect thespatial behaviour of multivariate variables, e.g. the contents of different e~ements as measuredin geochemical surveys with irregular sampling. These plots show the cross-semivariograrns asfunctions of distance pools of the displacement vector for each direction pool at a time.

If pooling in the Cartesian co-ordinates rather than pooling in the polar co-ordinates isconsidered, 2-D representations of the cross-semivariogram can be estimated as image datawith pixel size equal to the size of the chosen displacement pool.To illustrate the 2-D (cross-) var{ogram concept we study the spatial behaviour of regularlysampled variables from a geochemical .swmey in the eastern Erzgebirge in Wxony, Germany.‘The variables under study are 24,173 samples of natural logarithms of concentrations of thefifteen elements Ag, B, Ba, Be, Bi, Co, Cu, Li, Mn, Mo, Nb, Pb, Sn, Ti and V analysed byemission spectrography. These geochemical data and geophysical data, namely 144,090sampies of magnetic data, and 43,561 samples of gravimetry data some of which cover thesame geographical area as the geochemica[ data have been ana!ysed and integrated by meansof standard mukivariate statistical methods, methods developed in the previous program (GAF,MAYASA, IMSOR, & DLR, 1993) and methods developed in this program.

e New concepts for statistic! mullitemporal analysis of multispectral data.

To cope with the need to analyse multivariate and truly multitemporal data (i.e. observationsfrom more than two points in time) the theory for canonical correlation cart be extended to dealwith more than two sets of variables. Resuits from such multiset canonical correlation(MUSE(X) analysis are linear combinations that transform the original variables (bands) intonew variables that show decreasing similarity over time. The minimum similarity variables aremeasures of change in all bands simultaneously. If working with gridded data (e.g. Landsat TMdata) a map show;ng the absolute va!ue of thevariables will show the locations of pixels that

highest order canonical variates for each set ofhave minimum similarity with the other sets of

variables. The nature of the change can be observed by studying shifts in levels, differences indispersion matrices, and by looking at the correlation between the minimum similarity variatesand the originai variabies. Thus muitiset canonicai correlation analysis forms the basis of amultivariate, truly muititemporai change detection scheme that is believed to very useful in e.g.surveillance operations.

● Semi-quantitative mapping of air quaiity (smog density) using Landsat TM datafrom winter season of typical inversion situation in mining areas (North Bohemianzone).

Before starting the processing part of the investigations, an important assumption has to bemade: The density of smog below the inversion iayer is proportional to the concentration ofcontaminants as poisonous gases and dust. Even this correlation cannot be proven directly, it canbe assumed to be generafly correct from isoiated data as like the concentration of lignite-fedpower plants and chemical industry in the Southern margin of the test area. Further hints for thiscorrelation are given in REiN, E. (1968).This fieid yields some of the most remarkable results. Already in the siightly enhanced TM winterimage the location of the actively air polluting power plants and other imporfant industries can bereaiised, further the smog below the inversion layer is clearly visible. A number of processingschemes were investigated to improve the delineation and to heip in the semi-quantitative anaiysisof smog densities. The TM winter data (01. Feb. 1987) were recorded on a typical vvinteriyinversion situation.The finai processing scheme can be summarised as:

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3.4.5.6.7.8.9.10.

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Calculation of principai components of the winterscene dated 01. Febr. 1987Visual determination of PC which shows smog significantly, but is homogeneous in its DNdistributionStretching of PC#2Low pass fiitering: 201 x201 windowStretching and offsetting of fiitered image to extract air polluted regionsContouring the image in 7 intervalsCo[our coding of contour linesStretching of different images to be displayed in the backgroundCombination of stretched image and contour linesFiirn recording and enlargement for interpretation

Mapping of pollution-induced forest loss from multitemporal MSS and TM data andidentification of the relationship between severe forest damages and theoccurrence of mining and industry-induced air pollution.

After gee-referencing the MSS data (13. 08.1972) were stretched for forest differentiation (FCC,bands 4, 2 and 1 ) end eniarged to a scale of 1 : 100 000. The TM data (09.08,1992 and10.07. 1984] were processed in a similar way with different bands (4, 5 and 3) and enlarged to ascale of 1 : 50 000. The TM images were taken as reference at a iarge scaie for the differentforest types (coniferous, deciduous and mixed stands) and their interpretation was verified withaeriai photos (scaie 1 :34000, CiR). Visual interpretation ied to forest cover data sets (GIS) forthe analysis and overiay of forest loss. Furthermore graphical and statistical analyses were carriedout in the GIS.

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The change detection of forest cover in the Erzgebirge from 1972 to 1992 by visual interpretationof Landsat MSS and TM data reveals the effects of environmental impacts over years. Foreststands are clearly interpretable and changes identifiable. The analysis of forest losses in a ISIS incombination with additional data sets (height, slopes, aspect, precipitation, watershed, stateboundary). For this ana!ysis it is assumed that forest management is just able to react on forestdamages and marked prices for spruce woods. For low prices forest stands will only be cleared ifthey are severely damaged and would die if not used immediately. Therefore, clearings willcorrelate with highest forest damage due to environmental impacts (acid rains and fog, so called“ Rauchschaden”).Furthermore the analysis of the 1984 data shows clearly that most of the losses happenedbetween 1972 and 1 !384. In the period between 1972 and 1984 significant higher losses of forestwere detected (78,57.) than in the second period from 1984 to 1992 (21 ,5?4) (Table 5.1 d.). Thisis specially true for the Eastern part of the testsite whereas the losses in the western partprogressed more or less continuously and later in time. This effect maybe caused by the distanceto the emitters in the North Bohemian Basin and indicates therefore a spatial and temporalcorrelation with the main emitters of SOL. The comparison of the scenes from 1984 and 1992demonstrated furthermore that a regeneration of the damaged areas seems to become more andmore difficult.

The results of tine GIS analysis are:

● There is no correlation of forest \osses with the watershed.* The correlation of forest losses and state boundary is caused by different management

strategies of clearings, i.e. reforestation, on the Czech and German side (Table 5.1 f.).● The strongest correlation is with elevation and therefore with precipitation and height of the

free inversion layer. Most losses are between 800 and 1100 meters [Fig. 5. 1.c.).● A slight correlation exists with slopes facing Eastern and Southern directions (E, S, SE).● 7&5% of the forest losses between 1972 and 1992 was already lost in i 984.Forest losses are expanding from the easternmost parts to the western parts of the testsite since1972.

The GIS analysis also showed the necessity to interpret multidata sets graphically@ statisticallyto gain reliable results. Furthermore, it became clear through several tests, that historic MSS data(1972) with very !OW radiometric quality (banding, low range of reflectance values, some hazyparts in the image) can only be analysed visualiy.The anaiysis in the GIS

● improvement of mapping accuracy of different Iandcover classes related to mining(dumps, polluted soils, reclamated and re-contaminated mining areas, etc.) by theapplication of advanced image processing and classification techniques onmicrowave and spectral data.

Since ERS-1 SAR data are heavily disturbed by “speck\e noise” ANN should be able to classifyagricullu ral Ianduse in f [at areas. To develop a dedicated net topology for a Backpropagation Netwas therefore the aim of this task. The findings of this investigations should help to define theapplication potential of spaceborne SAR data for land applications using digital classificationtechniques.An Artificial Neural Network (ANN) consists of nodes and weighted paths that connect thesenodes. The net is a model of nerve cell structures like those apparent in the human brain. The

connection between a neuron ~ and a neuron j is weighted with a factor wj~, which is variable andadapted during the learning phase.One must distinguish between self-organizing or unsupervised and supervised trained networks.The transition between both is fluent. Unsupervised learning is comparable to classical c[usteranalysis and supervised learning to classification. For landuse classification purposes of ERS-ISAR data a supervised classification is applied using a Backpropagation Net.A Backpropagation Net consists of an input layer, one or more hidden layers and an output layer.The number of hidden layers and their dimensions depend on the complexity of the givenseparation problem. The number of neurones on the output !ayer matches the number of desiredclasses. Every layer is completely connected with one-directional weighted links to the followinglayer.A Backpropagation Net is able to approximate an arbitrary mathematical projection between theinput and the output layer. One can distinguish between three processing phases:

Learning phase During the learning phase the projection is learned by the Net, where the trainingdata sets are presented.

)?eclassh%af}on phase: This phase serves for the determination of the Net’s approximation quality.C/assificafkm phase The entire data set is classified by the trained Net.

● Verification of TM data with laboratory analysis of soil samples and of chlorophyllcontent and multi-elements of leaves.

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For the analysis of the environmental impact of mining activities and forest vegetation in the RioTinto zone, no studies have been undertaken until now. Therefore, the investigation had to setout from some basic concepts, [n order to evaluate the environmental situation of the Rio Tintozone, operations were executed in three different lines:

4 The multi-temporal analysis of multi-spectra! Landsat TM data.● A soil analysis, which covers various elements, an analysis of leaves and ashes and an

analysis of the chlorophyll level of samples collected in the test area.* A multi-layer integration of all previous data sets and other types of environmental

information that was incorporated.

Four different types of analyses were completed:

● Analysis of chlorophyll level in leaves;● element analysis in leaves;● analysis of ashes of leaves;● pH level;

All the data were collected from 40 samp!ing points, which were selected within the area ofinterest. The area was delimited by a spectral study according to the pollution level. At each ofthe sampling points between four and eight samples were collected of leaves from various pinetrees. From each tree, samples were taken from both the northern and the southern face.

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● Feasibility of hyper- and multispectral data and field spectrometry for the analysis andmonitoring of open mines.

Due to the strong morphology in the Spanish testsites as well as in the Erzgebirge region theuse of albedo independent algorithms is necessary for the analyses of the remote sensing data.One powerful tool for this purpose is the ‘Spectral Angle Mapping’ algorithm that is included inthe software packages SIPS developed at the University of Colorado and the commercial imageprocessing tool ENVI. In the SAM algorithm the spectra are treated as vectors in the “rib” -dimensional space. “rib” in this case represents the number of bands of the desired sensor. Thescalar product between a reference spectrum and each pixel of the scene is calculated. If theworking scene is atmospherically corrected the reference spectrum can either be derived fromthe scene, taken from a spectral library or resampled from ground measurements. The lengthof the vector which represents the albedo of the pixel does not affect the match. The output caneither contain one band for every input spectrum, where bright pixels indicate a higher similarityin shape to the reference spectrum than dark ones or the method can be used for classification.In this case the output is a one band classification image with as many classes as input spectrawere chosen. A user defined threshold value determines the maximum allowed spectraldifference between input and reference spectrum. if none of the reference spectra fulfils thiscriterion the corresponding pixel is not classified.

The Spectral Angle Mapping algorithm can also be use to determine the spectral similarity oflibrary spectra to spectra measured with a ground spectrometer in the laboratory or in the field.The scalar product between reference spectra derived from open domain (e.g. JPL, USGS) oruser defined spectral libraries and a various number of test spectra is calculated. As result aselectable number of library spectra spectra which fit best to the individual test spectrum andthe corresponding angle value are computed. This method is suitable as well for vegetation asfor rock and soil measurements. The length of the vector which represents the albedo of thespectrum does not affect the match. This is advantageous for ASD Spectra which often showstrong differences in the reflecticm ievel due to the design of the instrument (f[exibie fibre cableleads to changes in the illumination intensity).

e Use of satefiite radar image data for the mapping of mining features and evaluationof their potential for interpretation of geologic structures.

Both, the single signatures and context based backscatter values are important for detectingmining activities in the testsite Eastern Erzgebirge. Four different approaches have been tested:Clustering, EBIS-classification, thresholding and visual interpretation. For the visual interpretationand cluster analysis both the original and the GMAP-fi/tered images have been evaluated.The results of the unsupervised cluster analysis by using 25 clusters showed that thedifferentiation of most of the iand cover types is not possible. A cluster merge up to four classesonly expressed four different illumination or backscatter intensities but not Ianduse classes. Thelowest backscatter was found on shadowed slopes (on the descending orbit data: the west-oriented slopes) with clearings. This means a straight dependency on topography.The EBIS-classification algorithm is based on the mathematics concept of “evidential reasoning”after the DEMPSTER-SHAFER-theory. Different distribution functions can be applied onmultispectral or multitemporal data where each channel represents one individual feature space.For each class several descriptors are available. The weight of evidence that was calculated foreach descriptor can be connected, equivalent to special belief functions, and used for theclassification process.

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To visualise specific ranges of grey values, i.e. the highest or lowest backscatter values, theoriginal data are displayed applying a special lookup-table. This simple threshold method (only theuser defined range is displayed) is a helpful tool for rapid visualization of the geographicdistribution of single targets without further digital enhancements. On this way single point targetsare easier to detect and assigned if additional data can be used. Most of the high backscattersrepresent buildings, i.e. from the town of Altenberg, Geising or Zinnwald. Some interesting targetsthat gave some indication of mining activities are targets like shafts, towers and quarries. Lookingon the weak backscatter response the impressive old pit in Akenberg, the Pinge”, comes out verywell. Other smaller pits were not detectable by this way, because of ground solution limits andweak geometric accuracy in hilly regions.

A comparative structural analysis has been executed. The basis are 1:200000 processed imageproducts from ERS-I SAR, Landsat TM winter data, NC?AA, qravimetrv and aeromaanetics.fiesu!ts area rather perfect “view in two layers” to this exceptiona~structural unit: -

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The expression of surface features through eroshm, landuse etc.The depth and surface information on important physical properties.

Furthermore a comparison of ERS-1 SAF? and JERS-I SAR data was performed in the sameway as the analysis of ERS-1 data for geologic structures.

● Improvement of pre-processing techniques for airborne scanner data.

To some extent, airborne scanner data will always be geometrically distotied. This is due to theinstabilities of the moving platform and the inherent characteristics of the scanner dataacquisition. The following specified distortions can be observed in an airborne scanner image:Panoramic distortions, over- or undersampling effects, projective distortions, drift effects andperspective distortions.The most common approach for the geometric correction of Remote Sensing data is theselect!on of ground control points and the transformation of the image into a reference co-ordinate system via a polynomial least square fit (non-parametric method). The disadvantage ofthis method is primarily the large number of ground control points that have to be selectedacross the whole scene, in order to obtain a satisfactory fit. The selection of ground controlpoints is a tedious and man power intensive process, especially with images acquired in rural ormarine areas, where often only a few land marks can be identified both in the image and on themap.A clear improvement of the rectification accuracy, with a smaller number of ground controlpoints at the same time, can be obtained, if available positional data for the aircraft isincorporated in a geometric correction model, prior to the gee-coding process.New scanner systems, such as the DAIS 7915 are equipped with gyroscopes that continuallyrecord the differential angular movements around the major axes of the plane (roll, pitch, yaw).Results from the analysis of the DAEDALUS scanner data, flown in a !30228 at a mediumaltitude of 2000 m (corresponding IFOV of 5 m) exhibited shifts up to 200 m for recorded pixels,if the displacement due to the angular movements is evaluated.

For the correction of the reflective channels of an airborne spectrometer for each spectral bandthe at-sensor radiance is calculated from the digital number (DN, grey l~vel) usincalibration coefficients. This calibration coefficient is usuaHy given in mW cm s~i pm 9 ~~~

the same unit applies to the calibration coefficients.

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The at-sensor radiance is compared with a table of radiance values obtained by a computersimulation, where the radiance is calculated for different ground albedos taking into accountatmospheric data (e.g. radiosonde) and the geometry (sensor flight altitude, ground elevation,solar geometry, flight heading). A linear interpolation is then used to obtain the ground albedofrom the radiance matching the measured radiance.The method assumes that the calibration coefficients in each band are known either fromlaboratory measurementsatmospheric input data.

_..or from an inflight calibration. The method also depends on reliable

RESULTS AND CONCLUSIONS

The most remarkable results could be achieved in the field of environmental applications. Bymeans of mu [titem poral change detection techniques forest degradation couid be monitoredand related to certain environmental conditions in the testsite Eastern Erzgebirge. Thisprocedure now reached an operational level with high reliability through careful imageinterpretation and integration into a Geographical Information System (GIS). [n order to gainreliable results the G IS analysis clearly demonstrated the necessity to interpret multiple data setsgraphically @statistically, This means not only a graphical overlay of different data, but also newlogical combinations and calculation of new geometries and relations. Furthermore, throughseveral tests it became evident that historic MSS data (1972) with very low radiometric quality(banding, low range of reflectance values, some hazy parts in the image) can only be analysedvisually.A straight forward assessment of forest damages in the Eastern Erzgebirge by means of ratiotechniques applied to Landsat TM data was highly influenced by topography and standcharacteristics like density, ages, and mixture of species. Therefore it was necessary to carry outan a priori stratification of forest areas which could be derived from band ratios with a surprisinglygood quality.The development of new image processing techniques for environmental impact assessment ofair polluticn with TM winter has given remarkable insights to meteorological conditions, Thisbecomes even more interesting if processed imagery is overlaid with elevation data andstatistical and spatial data from major industrial emitters of pollution. It could demonstrated thatheavy forest damages and losses are not directly linked to strong emitters of the miningindustry. Heavy forest damages and losses, and low air quality are mainly governed bytopographical, climatological factors and the overall spatial density of Iignite-powerplants andindustry in general (North Bohemian Basin). The lack of suitable and operational analysisstrategies for satellite remote sensing data for environmental planning and monitoring tasks canbe al least for some important aspects solved.{n the field of data integration it could be shown that dedicated processing techniques willprovide new insights in spatially distributed data of different sources and scales. Especiai!y iffeatureless geophysical and geochemical data sets are integrated with data showing a highdensity of topographical features.Radar data from the new ERS-I and JERS-I are heavily disturbed by speckle noise and theirpotential for land application is very much limited, In the area of SAR image data restoration(speckle removal) both the maximum a posterior (MAP) estimation and contextual imageenhancement techniques show very promising results. For mapping of geological structures(Iineaments), except if structures are parai[el to illumination (beam direction), spaceborne SARdata are complementary to other remotetopologies for ANN and the ability of

sensing sources.backpropagation

Through the development of dedicatednets to fit an arbitrary mathematical

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separation function in the feature space on muliseasonal SAR it is possible to c[assify crops in flatareas.

The work performed in the Quinto del Hierro zone demonstrated the capability of remotesensing data to detect the contamination in soik as a consequence of mining activities. Thiswas found to be true even in cases where the contaminated area was covered by a layer of0.5m of soil, developed during the [a.st 2000 years, through an indirect assessment ofanomalies in vegetation.The results were verified by using two different sensors, Landsat TM and ATM. The integrationof this data clearly demonstrated their high operational potential in this field. However, thetransfer of the methodology developed for the Qu~nto de] Hierro zone to other testsites in theAlcudia Anticline and Pyrite Belt area was found to be less efficient in discriminatingcontaminated zones. The transfer of the procedures to specific areas wili require modificationsof the standard methodology according to site particularities, such as soil type, vegetationcover, source and age of contamination etc. (lm? important finding is that ancient Pb/Ag miningand ore processing does not harm the recent vegetation cover decisively (Central Spain).The interactive user interface and the integration of spatial with conventional data stored in adatabase facilitated the anaiysis and the interpretation of the available information,

Further a methodology for the study of the environmental impact on traditional mining zones,through the integration of remote sensing techniques and Geographical Information Systems(GIS) has been developed. This methodology leads to the systematically elaboration of thegeographical data bases on the mining zones, the mapping of contaminated zones, as well as thestudy of the problems related to the long-time relationship between the mining activity and theenvironment. All the methods support cheap, rapid and systematically work which can be used bymining companies and companies or institutions working in the environmental field.

The remote sensing techniques have been tested during the development of this project inrelation to the detection of ancient dumps covered by soil and vegetation, and in the estimation ofits environmental impact. Traditional methods require at least 500/. more of time and manpowerfacilities. Research and systematic sampling of huge areas is not necessary in this way. Theresearch and sampling process can be focused in the most anomalous zones detected with theuse of remote sensing technology. In the same way, new remote sensing processing techniqueshave been investigated and developed in order to obtain the purposes of the project, which aredifferent from other traditional remote sensing applications.

The work on environmental monitoring has aimed at developing techniques that could assesschanges based on measurements from different platforms. What one could call direct changedetection methods will more or less require measurements taken with the same type ofinstruments, Often used is a simple difference method based on satellite imagery from two timepoints but covering the same area: The changes are measured by differencing channel values.This method is sensitive to calibration problems and it cannot be used if a new platform is used.The MAD concepts developed overcome these problems. During the course of a monitoringscheme it is also needed to compare several time points, and the MU.SECC concept allows thisand is still maintaining the attractive properties of the MAD concept with respect to allowing fordifferent data modalities.It is believed that tnese methods offer analyses that may not be accomplished with existingmethods. Therefore, they will form a very useful part of the ‘tool box’ of environments!monitoring.The methods will allow the setting up of an automated system for depicting areas in e.g.,satellite imagery where substantial changes have occurred. Those areas are clustered withrespect to the different nature and magnitude of the changes. Therefore, they may be used in a

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‘warning system’. A human interface is, of course, needed when reasons for the changgs haveto be assessed.

New analytical tools such as l?- and Q-mode multiset canonical correlation analysis, 2-D cross-semivariograms and a hierarchical procedure for the simulation of geological structures(reservoirs) based on a discrete Markov random field have been implemented in computerprograms. Also, linear 2-D elliptic cone and elliptic spherical semh.wiogram models wereadopted and implemented. These models allow to estimate ranges of influence and sillsanisotropy ratios and directions of irregularly sampled spatial data. These methods hoJd animmense potential in the analysis of geoscience data as shown in applications with selecteddata from the testsites.

The basic philosophy in the work on hidden deposits has been to combine “inversion methods”with constrained simulation in order to assess the }ikeiy extension of geological units andbodies. It is believed that the constrained simulation results are ve~ relevant, indeed, and thegroup tries 10 pursue this work through additional funding. The Geological Surveys in Denmarkare very interested in the work, and presentations at international meetings have definitelyconfirmed that this is an area with a substantia~ exploitation potential on a medium time rangeterm.

Re!ated to the problem of hidden deposits is the general exploration problem. in this area veryuseful results on the structure of geochemica! samples have been obtained. Through analysesof the two dimensional semi-variogram and by utilising methods developed in a previous EU-project it was possible to derive ve~ useful information on possible mirteralizations. It has beenpossible to interest the authorities in Saxony in these analyses and some investigationsperformed jointly are published.

The operational use of high resolution airborne remote sensing data is still very muchdepending on fast and cost-effective geometric pre-processing. [t must a!low for the automaticcorrection of geometric distortions such as roH, pitch, yaw and the drift effect. Software forthese purposes was constantly improved and operationali.zed during project but still needsfurther improvements.One general prob!em in the evaluation of hyperspectra! information is the high amount of datato be treated and the lack of parametrization and classification algorithms driving theinformation extraction. The results of the digital data analysis carried out in the framework ofthe project made clear that the developed and tested algorithms (e.g. KLAS.9XIR, SAM) areuseful for the information extraction with respect to soil anomalies as well as the monitoring ofvegetation. Airborne imaging spectrometry has proven to be a valuable tool for the detectionand classification of mining dumps as vve[l in a humid climate (Erzgebirge) as in aMediterranean environment (SW- and Central Spain). Ground spectral measurement givereason to believe that the evaluation of the vitality of vegetation based on their spectralresponse is possible. Since the observed variations in vegetation are very minute it becameclear that the derivation of plant parameters from airborne spectral data requires high dataquality and an overflight in an appropriate period (late summer/earIy autumn).

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ACKNOWLEDGMENTS

The authors would like to thank the following people and institutions for their support:

Commission of the European Communities, 13GX11, Directorate C: industrial and MaterialsTechnologies, BRITE/EURAM program me for the funding of the project (Contract N“ BRE2-CT92-0201, Project No BE-5361).Arne Drud for the immediate interest he took in the non-eigenvalue type optimisation problemsin the multiset canonical correlation analysis. Arne wrote the GAMS NLP so!ver CONOPTapplied, and he also wrote the GAMS code that is the heart of the computer program thatperforms this analysis.

Dr. Klaus Kogler (scientific officer for the project at the CEC, DGXII) for the encouragingdiscussions and active interest he took in the progress of the project.

Dr. Ottomar Krentz from the Saxonian State Survey for Environment and Geology (SachsischesLandesamt fur Umwelt und Geology, SLfUG) for his engagement, participation and support.

The Saxonian State Survey for Environment and Geology (Dr. E. GeiEler, Head of Department)for the provision of the aeromagnetic, geophysical and geochemical data and the DTM (250mraster) for the use in this project.

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