Mineo_arctic_final_report

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					                                     MINEO
                                      IST–1999-10337
                 Assessing and monitoring the environmental impact of mining
                    activities in Europe using advanced Earth Observation
                                           techniques



           MINEO Arctic environment test site
Contamination/impact mapping and modelling – Final report




                     Project funded by the
                  European Community under
                    the Information Society
                    Technology Programme
                          (1998-2002)
                                       IST – 1999 - 10337                      August 2002

                                MINEO Arctic environment test site   Contamination/impact mapping and
                                                                          modelling – Final report


Primary authors of this report are:

Tamstorf, M.P. and Aastrup, P.
National Environmental Research Institute, Dept. of Arctic Environment
Frederiksborgvej 399
P.O.Box 358
DK-4000 Roskilde
Denmark

Tukiainen, T.
Geological Survey of Denmark and Greenland
Øster Voldgade 10
DK-1350 Copenhagen K
Denmark




Front cover: Contamination map of tailings (blue), polluted (yellow) and non-polluted (green)
Cassiope tetragona heath near the Blyklippen Mine. Washout of tailings through the river system is
shown by the blue arrows.
                                        IST – 1999 - 10337                       May 2003

                                 MINEO Arctic environment test site   Contamination/impact mapping and
                                                                           modelling – Final report



                                           Abstract

The use of hyperspectral data for mapping of contamination from the former lead-zinc mine in
Mestersvig, Northeast Greenland, was tested. Hyperspectral data were obtained from an airborne
HyMap scanner in august 2000.
Field work done in August 2001 included sampling of field spectra, and of river sediments, soil, beach
sand, tailings, lichen and vascular plants. Concentrations of copper, lead and zinc were determined by
chemical analysis (AAS). Concentrations of lead and zinc are highly elevated close to the mine site.
Element concentrations at locations more than 1 km from the mine site are much lower, but still higher
than baseline levels in Greenland.
Mapping was done using standard hyperspectral processing techniques like Spectral Angle Mapper
(SAM) and verifying the results with the field measurements of pollution and spectra. Tailings, and
tailings deposited in a river draining the area, were mapped. In a first step, approach tailings were
mapped together with similar materials (alluvium). This map was further refined using a continuum
removal approach to remove alluvium that was not related to high levels of sphalerite (zinc). It was
possible to map the extension of Cassiope tetragona-heath affected by tailings and the corresponding
non-affected type.
It is concluded that mapping of tailings was successful and that monitoring could be improved with a
better delineation of areas affected by tailings using hyperspectral data. Especially, monitoring costs
could be reduced by stricter focus on contaminated areas.
                                                              IST – 1999 - 10337                                         August 2002

                                                  MINEO Arctic environment test site                     Contamination/impact mapping and
                                                                                                              modelling – Final report



                                                                  Content
1.      Introduction................................................................................................................................... 1
1.1. Expected contribution of hyperspectral data and GIS modelling.................................................... 1
2. Description of the site.................................................................................................................... 2
2.1.    Geology........................................................................................................................................... 3
2.2.    Climate ............................................................................................................................................ 4
2.3.    Vegetation ....................................................................................................................................... 5
2.4.    History of exploitation .................................................................................................................... 6
2.5.    Mining operations ........................................................................................................................... 6
2.6.    Related environmental problems..................................................................................................... 9
2.7.    Sources of pollution ........................................................................................................................ 9
        2.7.1. Tailings...............................................................................................................................9
        2.7.2. Ore/concentrate ................................................................................................................10
3.      Hyperspectral data...................................................................................................................... 11
3.1. Hyperspectral data acquisition ...................................................................................................... 11
3.2. Data quality check, problems encountered ................................................................................... 14
4. Other available relevant environmental data........................................................................... 15
4.1. Earlier investigations of pollution ................................................................................................. 15
     4.1.1. Monitoring in the Kong Oscars Fjord ..............................................................................15
     4.1.2. Terrestrial studies in Mestersvig ......................................................................................15
4.2. Arctic Monitoring and Assessment Programme (AMAP) ............................................................ 15
     4.2.1. Chemical analyses............................................................................................................15
     4.2.2. Vegetation analyses..........................................................................................................18
5. Field Spectroscopy ...................................................................................................................... 20
5.1.    Sample selection, description of the material, relevance in environmental perspective ............... 20
5.2.    Brief description of the spectroradiometers used .......................................................................... 20
5.3.    Feeding Mineo Spectral Library, spectra categories included in MSL......................................... 20
5.4.    Spectra description ........................................................................................................................ 21
6.      Data pre-processing .................................................................................................................... 22
6.1. Atmospheric correction................................................................................................................. 22
6.2. Bad bands...................................................................................................................................... 22
6.3. EFFORT correction....................................................................................................................... 23
6.4. Geometric correction..................................................................................................................... 23
     6.4.1. Accuracy of IMU data......................................................................................................23
     6.4.2. Digital Terrain Model ......................................................................................................23
     6.4.3. Parametric geocoding of hyperspectral data ....................................................................24
     6.4.4. Mosaicing of hyperspectral data ......................................................................................24
6.5. Assessment of pre-processing ....................................................................................................... 25
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                                                  MINEO Arctic environment test site                   Contamination/impact mapping and
                                                                                                            modelling – Final report


7. Description of image processing procedures and algorithms used in contamination/impact
mapping ................................................................................................................................................ 27
7.1. Objective....................................................................................................................................... 27
7.2. Procedures and algorithms............................................................................................................ 27
     7.2.1. Minimum Noise Fraction (MNF)..................................................................................... 27
     7.2.2. Spectral Angle Mapper (SAM)........................................................................................ 27
     7.2.3. Mixture Tuned Matched Filtering (MTMF) .................................................................... 28
7.3. Description of the maps produced ................................................................................................ 29
     7.3.1. Area overview.................................................................................................................. 30
     7.3.2. 3D area overview ............................................................................................................. 32
     7.3.3. Minimum Noise Fraction (MNF)..................................................................................... 34
     7.3.4. Digital Terrain Model ...................................................................................................... 37
     7.3.5. Land cover map ............................................................................................................... 38
     7.3.6. Lithological map .............................................................................................................. 41
     7.3.7. Normalised Difference Vegetation Index map ................................................................ 43
     7.3.8. Tailings deposit and washout........................................................................................... 46
     7.3.9. 3D tailings deposit and washout ...................................................................................... 50
     7.3.10. Polluted vegetation map................................................................................................... 51
8. Description of the GIS database ................................................................................................ 53
8.1. Database preparation..................................................................................................................... 53
8.2. Database content ........................................................................................................................... 53
9. GIS modelling.............................................................................................................................. 54
10. Conclusions, assessment of results............................................................................................. 55
10.1. Assessment of results.................................................................................................................... 56
      10.1.1. Tailings ............................................................................................................................ 56
      10.1.2. Vegetation........................................................................................................................ 57
10.2. Results versus user demand .......................................................................................................... 57
10.3. Future plans .................................................................................................................................. 58
11. References.................................................................................................................................... 59
A1. Appendices..................................................................................................................................... 1
1.1. Vegetation analyses ........................................................................................................................ 1
1.2. Chemical analyses........................................................................................................................... 2
1.3. Spatial distribution of heavy metals in field samples ..................................................................... 9
     1.3.1. Pollution levels from 1971............................................................................................... 26
                                                              IST – 1999 - 10337                                         August 2002

                                                   MINEO Arctic environment test site                    Contamination/impact mapping and
                                                                                                              modelling – Final report



                                                           List of figures

Figure 1: Image showing Mestersvig area. Blyklippen refers to the mine and Nyhavn refers to the
     harbour. The red line indicates the road and the blue line indicates where samples have been
     taken along the coastline. ASTER colour-infrared image composition acquired 19 August 2000.3
Figure 2: Massive lead (galena) ore. Loose block in the river bed, River Lejrelv. The hammer shaft is
     80 cm long.......................................................................................................................................4
Figure 3: Cassiope tetragona heath up-valley from the mine site. No trace of tailings was found at this
     site. ..................................................................................................................................................5
Figure 4: Cassiope tetragona heath close to tailings dump. Tailings deposited by wind heavily pollutes
     this area ...........................................................................................................................................6
Figure 5 The upper open-pit mining area at Blyklippen..........................................................................7
Figure 6 Overview of the mining area seen from the lower part of the valley. The black buildings in
     the mining village are the fuel towers. ............................................................................................8
Figure 7: Oblique view over the tailings deposit below the Blyklippen mine.......................................10
Figure 8: Flight lines of the airborne hyperspectral survey (lilac colour). Frame centres (red crosses)
     and frame numbers of the concurrent aerial photography indicated along the flight lines.
     Background image: false colour mosaic of HyMap bands 26 (Red), 16 (Green) and 6 (Blue). Red
     circles: areas of poor (distorted) HS data ......................................................................................13
Figure 9: Proportion of Cassiope tetragona, liev and dead, litter, organic crust and tailings in poor
     Cassiope-heath with Salix, Silene, Luzula confusa and Vaccinium uliginosum. Right columns are
     undisturbed and left columns are affected by tailings...................................................................19
Figure 10: Vegetation analysis in an area affected by tailing. The frame was equipped with two grids
     with a vertical distance of 3 cm. This allows the observer to aim directly underneath each cross.
     .......................................................................................................................................................19
Figure 11 Laboratory spectral signatures for four endmembers present at Mestersvig. ........................21
Figure 12: Assessment of pre-processing using field measurements from Mestersvig airstrip. Thin
     lines in the graph are +/- 1 standard deviation. .............................................................................25
Figure 13: The theory of the Spectral Angle Mapper algorithm. The algorithm finds the angle between
     the pixel and the target and map according to a specified threshold angle. ..................................28
Figure 14: An overview of the area prepared as a colour composite of bands 29 (R), 17 (G) and 11 (B).
     .......................................................................................................................................................31
Figure 15: A 3D view of the colour composite of bands 29 (R), 17 (G) and 11 (B). The vertical
     exaggeration is set to twice the actual height. ...............................................................................33
Figure 16 Eigen value plot for the MNF transform on the entire Mestersvig dataset............................34
Figure 17: Colour composite of MNF bands 13 (R), 7 (G) and 5 (B). Tailings are shown at the arrow.
     .......................................................................................................................................................36
Figure 18: A shaded relief for the approximate time of overflight (GMT 6-AUG-2000 15:00:00). .....37
Figure 19: Land cover map of the Mestersvig area. Red frame refers to the Mestersvig graben area
     shown in Figure 17........................................................................................................................39
Figure 20: Average spectral signatures used for mapping the land cover types. ...................................40
Figure 21: Domkirken mountain seen from the Blyklippen mine (West). The Upper Permian
     (Profilbjerg Member (P), Domkirken Member (D), Aggersborg Member (A)) and Triassic
     (Wordie Creek Member (W)) sediments well exposed along the eastern margin of the Mestersvig
     graben. Arrows point to the areas misclassified as tailings..........................................................41
Figure 22: Major lithological units of the eastern part of Mestersvig graben as mapped from the
     HyMap data (Table 7, Figure 21). The location of the area is shown in Figure 19. .....................42
Figure 23: Normalised Difference Vegetation Index (NDVI). Water, sediments and snow are masked
     in blue, brown and white, respectively..........................................................................................45
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                                                   MINEO Arctic environment test site                    Contamination/impact mapping and
                                                                                                              modelling – Final report


Figure 24: Distribution of tailings deposit and similar materials shown in red on the B/W background.
     Tailings deposit is marked with 1 and the major deposition in the lower riverbed is marked with
     2. ................................................................................................................................................... 47
Figure 25: Selected HyMAp spectra from selected contaminated and uncontaminated localities. The
     yellow curve is from a locality approximately 1 km ESE of the Blyklippen mine (Figure 21). .. 48
Figure 26 Tailings and sphalerite alluvium (blue) in the area after filtering with the continuum
     removed band 109......................................................................................................................... 49
Figure 27: The tailings and washout have been draped on the 3D map to visualise the deposition of
     material in the lower parts of the river bed. Tailings are here shown in blue with the deposit
     numbered 1 and the deposition in the lower riverbed as 2............................................................ 50
Figure 28: Distribution of the tailings, polluted Cassiope and non-polluted Cassiope around the mine
     shown as blue, yellow and green, respectively. Luxurious vegetation is shown in red................ 51
Figure 29 Continuum removed image spectra for Cassiope tetragona heath. ....................................... 52
Figure 30: Main results from the mapping is the mapping of tailings (blue), polluted vegetation
     (yellow) and healthy vegetation of the same type (green). The washout is shown by the blue
     arrows............................................................................................................................................ 55
Figure 31: NDVI for the fraction of pixels within each type of tailings, polluted Cassiope tetragona
     and healthy Cassiope tetragona..................................................................................................... 57
Figure 32: Lead (Pb) in samples of tailings, river bed and beach sands. Concentrations range from 2.54
     to 3625.48 mg/kg. ......................................................................................................................... 11
Figure 33: Zinc (Zn) in field samples from tailings, river bed and beach sands. Concentrations range
     from 6.09 to 21.24 mg/kg. ............................................................................................................ 13
Figure 34: Copper (Cu) in field samples from tailings, river bed and beach sands. Concentrations
     range from 0.76 to 403.75 mg/kg.................................................................................................. 15
Figure 35: Lead (Pb) in lichen samples (Cetraria nivalis). Concentrations range from 2.47 to 1534,31
     mg/kg. ........................................................................................................................................... 17
Figure 36: Zinc (Zn) in lichen samples (Cetraria nivalis). Concentrations range from 12.04 to 590.79
     mg/kg. ........................................................................................................................................... 19
Figure 37: Lead (Pb) in surface soil samples. Concentrations range from 13.2 to 3043.49 g/kg.......... 21
Figure 38: Zinc (Zn) in surface soil samples. Concentrations range from 12.89 to 14914.79 mg/kg. .. 23
Figure 39: Copper (Cu) in surface soil samples. Concentrations range from 1.99 to 403.75 mg/kg. ... 25
                                                           IST – 1999 - 10337                                       August 2002

                                                MINEO Arctic environment test site                  Contamination/impact mapping and
                                                                                                         modelling – Final report



                                                        List of Tables
Table 1: Annual ore productions from Blyklippen lead-zinc mine..........................................................8
Table 2: Chemical analyses of samples of tailings, august 1979. Results shown from various fractions
     (cf. Asmund, 1979). ......................................................................................................................10
Table 3: Chemical analyses of metal content in various media in 2001. Results are given as geometric
     means, standard deviation, and minimum and maximum values. All results are found in
     Appendix 1.2. ................................................................................................................................17
Table 4: Specifications for the used field spectroradiometers ...............................................................20
Table 5: Example of level of information for a tailings spectrum in MINEO Spectral Library ............21
Table 6: Cover types with the number of points and maximum SAM angle that have been used in the
     mapping.........................................................................................................................................40
Table 7: Lithology members with number of ROI points and max. SAM angle used in the mapping..42
Table 8: The geographical coordinates for the images. .........................................................................53
Table 9: Average species composition (6 samples) of poor Cassiope-heath with Salix, Silene, Luzula
     confusa og Vaccinium uliginosum. The left column shows a heath, which was heavily affected
     by tailings. The right column shows an undisturbed heath. ............................................................1
Table 10: Chemical analyses of tailings. .................................................................................................2
Table 11: Chemical analyses of soil ........................................................................................................3
Table 12: Chemical analyses of Salix arctica ..........................................................................................4
Table 13: Chemical analyses of Luzula confusa and L. spicata ..............................................................4
Table 14: Chemical analyses of river sediments......................................................................................5
Table 15: Chemical analyses of Cetraria nivalis......................................................................................6
Table 16: Chemical analyses of Cassiope tetragona................................................................................7
Table 17: Chemical analyses of Beach sand............................................................................................8
Table 18: Pollution levels of lead, zinc and copper in the river systems around Mestersvig
     (Kunzendorf, 1977). ......................................................................................................................26
                                                           IST – 1999 - 10337                                       May 2003

                                                MINEO Arctic environment test site                 Contamination/impact mapping and
                                                                                                        modelling – Final report



                                                List of Appendices
A1. Appendices..................................................................................................................................... 1
1.1. Vegetation analyses ........................................................................................................................ 1
1.2. Chemical analyses........................................................................................................................... 2
1.3. Spatial distribution of heavy metals in field samples ................................................................... 10
1.3.1. Pollution levels from 1971…………………………………………………………………….18
       IST – 1999 - 10337                      August 2002

MINEO Arctic environment test site   Contamination/impact mapping and
                                          modelling – Final report
                                          IST – 1999 - 10337                      August 2002

                                  MINEO Arctic environment test site   Contamination/impact mapping and
                                                                            modelling – Final report




1. INTRODUCTION
Monitoring of pollution from mines is important to assess potential health risks for humans, wildlife,
and plants. Monitoring sites are usually selected to be representative for detection of elevated levels of
substances in any direction, as distance from the mine and wind directions, topography and
atmospheric conditions highly influence the distribution of pollution. Therefore, it is often necessary
to establish a relatively large number of sampling stations and to analyse a large number of samples to
assess the level of pollution and the delineation of areas affected by pollution. Monitoring involves
chemical analyses of samples of soil and organisms taken close to the mine site as well as far away.

Remote sensing techniques offer the possibility to get an overview of large areas at relatively low cost.
Analysis of hyperspectral data has given promising results for detection of surfaces with specific
characteristics such as elevated levels of for example heavy metals. It is foreseen that environmental
monitoring will be improved and costs minimised by using hyperspectral data. The use of such data
will provide a background for selecting monitoring sites at the most relevant positions and make it
possible to detect the appearance of new polluted sites at a low cost with a smaller number of
monitoring locations.

The MINEO project involves six mine sites in Portugal, Germany, Austria, Great Britain, Finland and
Greenland. The present report focuses on the Arctic test site in Northeast Greenland. The Arctic test
site is a former lead-zinc mine in production from 1956 to 1963. The Arctic test site gives the
possibility to evaluate the use of hyperspectral data in an environment characterised by a thin layer of
vegetation which is non-affected by other local sources of pollution. The mine was in operation at a
time when there was not taken much care of the environment. Therefore pollution has been extensive.
On the other hand a long time has gone since the pollution was highest, and it may have been diluted.

Field studies were initiated at the arctic test site with sampling of hyperspectral data in the summer
2000. We performed field work in 2001 at the mine site and in the surroundings of the Mestersvig
area. This work included sampling of field spectra, samples for chemical analysis and vegetation
studies at sites affected by tailings in an attempt to examine pollution as well as effects of pollution on
plants. All sampling was co-ordinated with NERI’s on-going monitoring of the marine environment.
Data processing and reporting was completed during 2001-2003.

1.1.     Expected contribution of hyperspectral data and GIS modelling

In the Mestersvig area it is expected that the hyperspectral data will provide a background for a more
accurate delineation of sources of pollution and polluted areas. Especially, it would be of interest to
identify deposits of tailings along the river Tunnelelv and along the coast on both sides of the harbour
Nyhavn. This will have at least two important perspectives: monitoring costs will decrease and
delineation of pollution will be much better defined. In addition, we hope to assess long term pollution
effects on vegetation types.




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2. DESCRIPTION OF THE SITE

Blyklippen Mine at Mestersvig is located inside the North East Greenland National Park around 72˚N,
23.5˚W in between the sea and the Stauning Alps which reaches an altitude of 2831 m.a.s. Mestersvig
airstrip and Nyhavn Harbour is located in the lowland close to the coast while Blyklippen is situated in
a valley approx. 10 km inland from the fjord (Figure 1). Distance to the nearest Greenlandic
community, Ittorqortoormiut (Scoresbysund) is approximately 270 kilometers.




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                                                                                                   N




Figure 1: Image showing Mestersvig area. Blyklippen refers to the mine and Nyhavn refers to the
harbour. The red line indicates the road and the blue line indicates where samples have been taken
along the coastline. ASTER colour-infrared image composition acquired 19 August 2000

2.1.     Geology

The Blyklippen lead-zinc deposit is situated in the north-east part of a major graben structure
measuring 4 x 12 km (‘Mestersvig graben’; Witzig 1954) dissecting the predominantly Permian
sediments. The downthrow of sediments in the graben may be up to c. 1000 metres.

The bulk of the sedimentary sequence is made up arkosic sandstone with subordinate conglomerate,
silt, shaly limestone and calcareous and bituminous shale. Mafic sills and dykes of Tertiary age intrude
the sediments.

The Blyklippen lead-zinc deposit comprises a sulphide lens within a major quartz vein zone in a fault
orientated 150°/40° - 90° E. The quartz vein zone is at least 1000 m long and it is cut at the northern
end by a fault. The quartz vein gradually disappears to the south. The thickness of the quartz vein
varies from a few metres up to 50 metres. The mined-out ore body was a sulphide lens 1 -10 m thick,
300 m long and 160 m high, which consisted of 65 % quartz, 15 % sphalerite, 10 % galena, 5-10 %
barite and trace amounts of chalcopyrite and fahlore. The copper and silver contents were 120 ppm
and 15 ppm, respectively. Galena- and sphalerite-rich sections alternate along the ore lens. In general,
sphalerite is enriched in the lower and northern part of the ore body whereas galena (Figure 2) is
enriched near the surface and in the southern part of the ore body.

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Figure 2: Massive lead (galena) ore. Loose block in the river bed, River Lejrelv. The hammer shaft is
80 cm long.

Members of the Lauge Koch expedition discovered galena bearing quartz veins in 1948. The follow-
up investigations in 1948 and 1949 led to the discovery of a number of lead-zinc occurrences.

2.2.    Climate

The climate of the Mestersvig area is arctic with a mean monthly temperature of the warmest month
below 10 degrees C. Only the months, June, July and August have mean temperatures above freezing
point. The annual mean temperature at Mestersvig is –10.5˚C.

During spring and autumn there are periodic changes between freezing and thaw enhancing the
physical erosion of the bedrock. This process is very important in the erosion of Arctic bedrock while
chemical erosion, unlike at lower latitudes, is very limited due to the low temperatures.

Precipitation at Mestersvig is above 300 mm a year with April to June being the driest months. Up to
2/3 of the precipitation at Mestersvig falls as snow and measurements from 1982-1985 showed that the
snow stays from mid/late September until mid-July. The maximum snow depth gets close to 150 cm
during March to May.

The wind patterns in Mestersvig are controlled mainly by the orientation of the fjord where winds
move in and out along the fjord. Most of the winds are light with the major parts of the stronger winds
coming from the ice cap.

These wind patterns have been recorded in Mestersvig and do not necessarily apply to the mine area.

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Due to the local topography the wind patterns at the surface vary from place to place. From the
distribution of wind transported tailings it seems that the strongest winds are from SW and W.

2.3.     Vegetation

The flora and vegetation in the Mestersvig area are comparable to that of Jameson Land 75 km to the
south. Bay and Holt (1986) mapped and classified the vegetation in the latter area in detail in 1982-85.
Dwarf shrub heaths of several types dominate in mesic and dry habitats while fens and grassland
dominate in wet areas. Figure 3 shows undisturbed Cassiope tetragona dwarf shrub heath and Figure 4
shows a similar heath influenced by tailings.




Figure 3: Cassiope tetragona heath up-valley from the mine site. No trace of tailings was found at this
site.




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Figure 4: Cassiope tetragona heath close to tailings dump. Tailings deposited by wind heavily pollutes
this area

Generally, the vegetation layer has a high cover of vascular plants and reaches a height of 20-30 cm;
mostly it is less than 10 cm. Lichens and mosses occur all over the area. Mestersvig is situated in the
transition zone between the High Arctic and the low arctic zones and species diversity is high for the
climatic regime.

Species like Vaccinium uliginosum, Betula nana, and Cassiope tetragona dominate in wet dwarf shrub
heaths, while the these species, plus Dryas octopetala, dominate in the more dry heath types. In fens
species like Eriophorum scheuchzeri, Carex saxatilis, C. rariflora and C. stans dominate together with
characteristic species like Ranunculus sulphureus and Arctagrostis latifolia.

Complete cover of lichens does not occur in the area. Important species for monitoring purposes such
as Cetraria nivalis mostly occur as single specimens.

2.4.    History of exploitation

The most important metal occurrences discovered so far are those at Blyklippen and Sortebjerg
(Eklund 1949). Detailed mapping and ore geological investigations were undertaken at Blyklippen by
the Lauge Koch expeditions in 1950 and 1951.

From 1952 to 1954 the newly established company, Nordisk Mineselskab A/S, continued the
investigations. This company ran the Mestersvig lead-zinc mine from 1956 to 1963, long before
environmental issues were taken into consideration as a part of a mining project. The mine is located
about 10 km from the coast of Kong Oscars Fjord in Northeast Greenland. A road from the harbour
facilities to the mine site was constructed and the ore was shipped out in summer. A landing strip for
aircraft was established.

2.5.    Mining operations
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The cut-and-fill mining method was selected for the Blyklippen ore body. The width of the stopes was
the same as vein width. Low grade and barren parts of the vein were used as pillars as extensively as
possible. The filling material was taken from the scree and the hanging wall on the surface. In
connection with fill caving the apex of the ore body was mined as open pit (Figure 5).




                Figure 5 The upper open-pit mining area at Blyklippen.

As the rock temperature in the ore body was below the freezing point, it was necessary to use pre-
warmed water for drilling. The broken ore in the cut-and-fill stopes was scraped by slusher hoists. The
ore was hauled on the main level at 420 m above sea level in Granby cars to an underground crushing
station.

The Mestersvig area with permafrost, annual mean temperature of - 7°C and during the period
between December and February a mean temperature of - 20°C, implies that a mine plant with
buildings of the conventional type has many disadvantages in the Arctic. At Blyklippen all the plants
(crushing, grinding, flotation, thickeners, filters, drying furnaces, diesel power plant and air
compressors) were built as underground facilities. To accommodate the various installations some
140-200 m3 of sandstone were excavated in 1955. During the exploration phase (1952-1954) an airport
and a road from the coast and airport to the mine site were constructed. At the mine site wooden
houses containing an office, hospital, workshop, crew houses, warehouses and a canteen were erected.

The crushing plant was excavated so as to join the haulage level. Two crushers were installed one jaw
crusher and one cone crusher with a screen between. The ore from the mine cars was crushed at 2.5
cm. The crushed ore was stored in an ore-pass rise, having a 24-hour mill capacity excavated between
the crushing and flotation chambers. The flotation chamber was a 60 m long room inclined at 18°.
Highest in the chamber were the two mills - one rod mill for primary grinding and one tube mill for
regrinding of middlings from flotation. For flotation, eleven double cells were used for lead flotation
and the same number for zinc flotation.

The periodic shortage of water, between December and April in particular, put some constraints on the
concentration process and special attention was paid to an effective recycling of the water.

The open landscape and the prevailing hard winds made outdoors stockpiling of concentrates very un-
economical. To avoid building of expensive warehouses or storage silos, the concentrate was stored in
sacks on pallets in the open air. The tailing resulting from the processing was dumped outside the mine
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below the mine-entrance. The water from the mine flowed through the tailing deposit and transported
this material further into the river system (Figure 6).


                                                                     Open pit
                           Mining village




                                                        Lower mine

                              Tailings




Figure 6 Overview of the mining area seen from the lower part of the valley. The black buildings in
the mining village are the fuel towers.

During the lifetime of the mine (1955-1963) 544 600 tons of ore were produced (Table 1). From an
economic point of view, the lead concentrate was the most important source of income.

Table 1: Annual ore productions from Blyklippen lead-zinc mine.
Period (1/10-30/9)       Tonnage                   Lead (%)                     Zinc (%)

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1955-1956                45000                   8.2                             10.5
1956-1957                87400                   8.5                             10.7
1957-1958                90200                   10                              8
1958-1959                92500                   12                              10
1959-1960                86200                   8                               12
1960-1961                101500                  10.7                            8.7
1961-1962                41800                   3.8                             10.3
Concentrate production:
58 500 tons Pb-concentrate (82.7 % Pb, 115 ppm Ag)
74 600 tons Zn-concentrate (63.7 % Zn)


2.6.     Related environmental problems

The first comprehensive environmental studies were initiated as late as in 1979, more than 15 years
after the mine was closed (Hansen, 1985). It was documented that there were very high levels of lead
and zinc in water and sediments from the river Tunnelelv which drains the area surrounding the former
mine. Sediments contained as much as 13% zinc and 0.7% lead, while the water contained up to
150 g/l dissolved zinc and 12 g/l dissolved lead.

It was also shown that Kong Oscars Fjord was affected by pollution from the former mine and beach
sand, seaweed and sculpins contained elevated levels of lead and zinc. The proximate sources of
pollution were the area close to the Harbour Nyhavn, the Tunnelelv delta with Noret and the delta
northeast of Nyhavn. The ultimate sources of this pollution were the tailings deposit just outside the
mine. In addition to the marine pollution, there was also found elevated levels of lead and zinc in
lichens up to 10 km from the mine site. Since 1979 there has not, however, been paid much attention
to the terrestrial pollution, the main sources of which are dust from the tailings deposit and probably
also dust spread during transportation from the mine to the harbour. The concentrate was transported
on lorries in canvas bags.

In the early 1990’s a clean-up and restoration project was carried out. Buildings and equipment were
buried in a quarry east of the tailings deposit. Chemicals, toxic substances and other material that were
considered to be environmentally dangerous were transported out of the area for storage and
destruction. The following detailed account is primarily based on Hansen (1985).

Figure 1 shows a view of the whole area with indications of potential polluted areas.

2.7.     Sources of pollution

2.7.1.      Tailings

In 1979 it was estimated that the tailings deposit covered 6.000 m2 with varying depths up to 190 cm.
The total quantity of tailings was estimated to be 33.000 m3 or about 66.000 t. The chemical
composition of tailings (Table 2) was analysed in 1983 by Nordisk Mineselskab and Hansen (1985)
found concentrations of 2.13% Zn, 0.66% Pb, 0.04% Cu, 5.2% Ba and 2 ppm Ag. This was estimated
to equal 1600 t of sphalerite, 380 t of galena, and 60 t of chalcopyrite. These figures equal 1.400 t Zn,
430 t Pb, 25 t Cu, 3.400 t Ba and 132 t Ag. In addition to this it was estimated that there was also 6.6 t
of Cd. As can be expected from the weathering behaviour of galena the finer fractions of tailings are
more enriched in galena

Nordisk Mineselskab estimated that the whole mining operation produced 411.600 t of tailings. In
1979 it was estimated that 85% of the tailings had been washed out into Tunnelelv by rain, snowmelt,

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and by small streams intersecting the tailings deposit (Figure 7). Dust has also been dispersed to the
neighbouring areas by wind.

Table 2: Chemical analyses of samples of tailings, august 1979. Results shown from various fractions
(cf. Asmund, 1979).
Fraction
              Weight, g    Weight %     Zn %       Pb %         Cu ppm    Cd ppm

> 200         111.3        38.06        (2.3)*     (0.2)*

200-100       88.5         30.27        2.36       0.48         2573      97

100-50        48.3         16.52        2.32       1.22         2672      100

<50           44.3         15.15        2.44       3.02         3470      120

Total         292.4        100          (2.34)**   (0.89)**

*estimated, **calculated




2.7.2.        Ore/concentrate

The concentrate was transported to the harbour in canvas bags on open lorries and dust may have
dispersed along the road during this operation. In the harbour the bags were transported out to the ship
on barges. At several occasions barge loads of canvas bags with concentrate were dropped into the sea
accidentally.




Figure 7: Oblique view over the tailings deposit below the Blyklippen mine.

Elevated levels of zinc, lead and cadmium have primarily been documented in the marine environment
and in the river Tunnelelv, but also in the terrestrial environment.


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3. HYPERSPECTRAL DATA
3.1.      Hyperspectral data acquisition

The airborne system for the acquisition of hyperspectral data and concurrent stereoscopic aerial
photography for the MINEO project (Tukiainen, 2000) was used for the survey of the Mestersvig area
on the 6th of August 2000. The survey was flown with the following survey specifications:

IFOV (m)                       5 metres
Overlap per line (%)           20
Approximate ground speed       150 knots (277 km/h)




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Figure 8: Flight lines of the airborne hyperspectral survey (lilac colour). Frame centres (red crosses)
and frame numbers of the concurrent aerial photography indicated along the flight lines. Background
image: false colour mosaic of HyMap bands 26 (Red), 16 (Green) and 6 (Blue). Red circles: areas of
poor (distorted) HS data

The HyMap scanner, built by Integrated Spectronics Inc of Sydney, Australia, has four spectrometers
in the interval 0.45 to 2.45 nanometers excluding the two major atmospheric water absorption
windows. The bandwidths are not constant, but vary between 15 and 18 nanometers. The scanner also
has an on-board bright source calibration system, which is used to monitor the stability of the signal.
The signal/noise ratio measured outside the aircraft with a sun angle of 30° and a 50% reflectance
standard is more than 500/1 except near the major atmospheric water absorption bands. The scanner is
mounted on a hydraulically actuated Zeiss-Jena SM 2000 stabilised platform. The platform provides
+/- 5 degrees of pitch and roll correction. The yaw can be offset by +/- 20 degrees with +/- 8 degrees
of stabilisation. The platform provides a residual error in nadir pointing of less than 1 degree and
reduces aircraft motion effects by a factor ranging from 10:1 to 30:1.

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For the HyMap instrument the IFOV of 5 metres correspond to the flight altitude of 2500 metres (8200
feet) at which the scanner’s swath width is approximately three kilometres. For a mountainous area
like Mestersvig, the flight altitude was determined from the local topographic base level of 400
metres.

The survey was started 14:26 GMT and took 50 minutes. The area was covered as requested. The
concurrent aerial photography returned 158 frames with stereoscopic coverage. The weather
conditions were stable with a minimal cloud cover. The survey layout is shown in Figure 8. The aerial
photos covering the ground at higher altitudes, where also extensive areas of snow and ice are
common, suffer to a variable degree from overexposure. This resulted in a severe loss of detail
particularly in the western part of the surveyed area. To minimise the effects of the changing
illumination, N–S orientation of the flight paths was maintained during the survey.

3.2.    Data quality check, problems encountered

The contractor delivered the HS ‘at sensor’ radiance data for each flight line as ENVI BIL (band
interleaved) file along with dark current data (ENVI BIL) and IMU (Inertial Monitoring Unit)-data
(ASCII) as separate files.

Using the Minimum Noise Fraction (MNF) transform of radiance and dark current data, the overall
quality of the HS data was checked. The dark current data were also examined by using Fourier-
transformation to detect possible periodic effects, which might be due to the electrical and technical
disturbances from the spectrometer or the aircraft.

The quality check of the radiance data did not reveal distinct errors or noticeably decreased spectral
quality of the HS data. The fact that the radiance data were delivered as mirror images caused extra
work to organise the data for parametric geocoding as described in section 6.4

 The flight line IMU data for 8 flight lines were not correctly synchronised. In most cases minor
editing could solve the problem. The IMU-data for line number 7 were offset 140 scan lines. To locate
and restore the IMU-error for this flight line was very time consuming.

Due to the stable atmospheric conditions during the survey flight, the raw data are without major
geometric distortions.

There were two minor instances of turbulence, which caused so much distortion that the IMU-system
was not able to create data for the recovery of these anomalies (Figure 8)




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4. OTHER AVAILABLE RELEVANT ENVIRONMENTAL DATA
4.1.      Earlier investigations of pollution

This section includes data that have been sampled from various surveys, which have been performed
before the start of this project.

4.1.1.        Monitoring in the Kong Oscars Fjord

NERI has conducted studies in the fjord in order to assess the risk that humans ingested contaminated
fish or seals (Agger et al. 1991, Asmund 1979, Hansen 1985, Aastrup et al. 2001). Studies have shown
elevated levels of lead in mussels and fish (Sculpins), while levels in seals were not higher than in
other areas in Greenland. It is expected that levels will continue to decrease.

4.1.2.        Terrestrial studies in Mestersvig

NERI investigated a series of terrestrial media to document levels of selected metals. These studies
showed elevated levels of metals in lichens close to the mine site and at distances up to 10 km away
(Hansen 1985, Aastrup et al. 2001). Windblown dust is the major source of pollution of the terrestrial
environment.

4.2.      Arctic Monitoring and Assessment Programme (AMAP)

General studies of levels of pollutants in the Arctic have been conducted since 1991 through the
international Arctic Monitoring and Assessment Programme (AMAP 1997, AMAP 2002). AMAP's
current objective is providing reliable and sufficient information on the status of, and threats to, the
Arctic environment, and providing scientific advice on actions to be taken in order to support Arctic
governments in their efforts to take remedial and preventive actions relating to contaminants.

AMAP has provided knowledge of anthropogenic pollution in the Arctic. None of the studies,
however, have focused on the terrestrial environment in Northeast Greenland. Generally, background
element concentrations for most metals of environmental concern are low in the terrestrial
environment, but levels can be high around geological anomalies.

In relation to metals it was concluded that:
    • Of the heavy metal contamination in the Arctic, industrial sources in Europe and North
         America account for up to one-third of the deposition, with maximum input in winter.
    • Regulatory actions in Europe and North America are reducing the sources of some Persistent
         Organic Pollutants (POP), heavy metals, sulphur and nitrogen contaminants.

The latest report from 2002 focused on mercury, lead and cadmium. It was concluded, that mercury
deposition is increasing in the Arctic and that deposition of lead has undergone a dramatic reduction
since the use of leaded gasoline has been banned. Regarding cadmium, it was concluded that the
cadmium level in some seabirds is high enough to cause kidney damage and therefore continued
monitoring was recommended.

In Greenland, baseline levels in soil samples were <12-37 g/g for copper, <12-13,8 g/g for zinc,
and <0,04-13 g/g for lead. Baseline levels for lead in lichens was <6.4-8.8 g/g (AMAP, 1998).

4.2.1.        Chemical analyses



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Samples of tailings, soil, river sediments and beach sand were taken by hand. Soil samples were taken
just beneath the vegetation layer. Plant samples were taken as whole individuals but only leaves were
analysed, with the exception of Cassiope tetragona where all above ground parts were used. All
samples were stored in polyethylene bags and put in a deep freezer until chemical analysis was
performed. Samples were analysed by the National Environmental Research Institute, Dept. of Arctic
Environment. All analyses were run under an analytical quality protocol involving the analysis of
procedural blanks and certified reference materials. Freeze dried samples were crushed and
homogenised in an agate mortar. Elements were determined by Atomic Absorption Spectroscopy
(AAS). The method of Loring and Rantala (1992) for the digestion of sediment and soil samples with
nitric and hydrofluoric acid was used, followed by the addition of boric acid to neutralise excess HF.
The dissolution was performed in 50 ml Teflon bombs.

Generally, the chemical analyses show extremely variable results for all media as shown in Table 3.
Chemical analyses can be found in Appendix 1.2. The geographical distribution of element levels can
be seen in Appendix 1.3. The geographical distribution of lead, zinc and copper is quite similar for all
three elements as seen in Figure 32 to Figure 39. Levels are much higher in the mining area at the
mine site Blyklippen than at all other locations. Zinc concentrations are weakly elevated in the river
bed close to Mestersvig.

In the lichen Cetraria nivalis both lead and zinc concentrations are highly elevated at Blyklippen
while levels are low at all other locations. In soil samples the pattern is the same. Very high
concentrations of lead, zinc and copper are found close to the mine site and low concentrations at all
other locations.

Levels are, however, lower than in earlier studies. Table 18 in Appendix 1.3.1 shows the data of
Kunzendorf (1977) collected in the river systems around Mestersvig in 1971 when a lot of the old
tailings dump was still intact.

Beach sand
In beach sand the copper content is generally low, while the content of both lead and zinc are
extremely high at some locations. Compared to the previous study in 1996, peak levels are about the
same level for zinc while the peak level of lead is about three times higher in 2001 than in 1996.

Cassiope tetragona
Cu concentrations are generally low, while a few samples have extremely high concentrations of both
lead and zinc. Peak levels are about 9 and 55 times higher than the ones found in 1979 for lead and
zinc, respectively. The high levels are found at the mine site.

Cetraria nivalis
Generally, concentrations of both lead and zinc are extremely variable with peak levels about the same
level as in 1979. The lead concentrations are quite high compared to baseline levels about 1-2 mg/kg
in Greenland (Riget et al. 2000). Zinc concentrations are also quite high compared to baseline levels.

River sediment
The concentrations of lead and zinc have extremely high values compared to the levels found along
the beach in both 1996 and 1979. The level of pollution is still very high close to the mine site. The
concentrations of copper are generally low but with a peak value of more than 300 mg/kg.

Salix arctica
The content of copper is generally low while there are very high concentrations of lead, zinc and
cadmium. Peak levels, however, are about the same level as in 1979 for zinc. Lead concentrations are
high compared to 1979.


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Soil
The concentrations of copper in the upper layer are generally at levels comparable to those found in
1979 and at the same levels as the background for Greenland. The concentrations of lead are also at
the same general level as in 1979 although there are higher peak levels in 2001. The concentrations are
higher than those found at baseline locations. In contrast, zinc levels have decreased compared to 1979
and generally zinc concentrations are at the same level as at background locations. There are, however,
still locations with extremely high levels of zinc.

Tailings
Copper concentrations are lower than in 1979 but show very high variation. The levels of lead and
zinc are about the same as in 1979.

Table 3: Chemical analyses of metal content in various media in 2001. Results are given as geometric
means, standard deviation, and minimum and maximum values. All results are found in Appendix 1.2.
                       Cu (mg/kg) Pb (mg/kg) Zn (mg/kg)
Beach sand
Geometric mean                 7,22         39,99        80,56
St.dev.                       10,72        320,02       658,85
Min                            0,76          2,54          6,09
Max                           53,45      1478,92       3870,21
Number of samples                         42

Cassiope tetragona
Geometric mean                12,36          31,37       118,18
St.dev.                      129,94       3412,63       4287,46
Min                            3,89           2,57        25,13
Max                          353,83       9155,11      11540,96
Number of samples                          7

Cetraria nivalis
Geometric mean                    >5         14,71         31,69
St.dev.                                     245,57         93,27
Min                                           2,47         12,04
Max                                       1534,31         590,79
Number of samples                          39

River sediment
Geometric mean                17,66          63,85       190,42
St.dev.                       46,33         600,78      4005,51
Min                            5,33          10,76        21,24
Max                          309,80       3625,48      25291,80
Number of samples                          41

Salix arctica
Geometric mean                 13,22          22,61       610,81
St.dev.                        19,78        333,09        996,06
Min                             8,66           5,81       307,81
Max                            53,56        754,62       2651,00
Number of samples                           5


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Soil
Geometric mean                  23,78          78,38        171,00
St.dev.                         90,96         752,89       3674,93
Min                              1,99          13,20         12,89
Max                            403,75       3043,49       14914,79
Number of samples                            45

Tailing
Geometric mean                 322,55       5000,72       22257,13
St.dev.                        148,56       3366,22       13798,11
Min                            133,86       1868,15       11094,50
Max                            621,60      12622,40       59656,64
Number of samples                            11


4.2.2.      Vegetation analyses

Most of the vegetation cover in the Mestersvig area consists of dwarf shrub heath. A subtype of this is
Cassiope tetragona heath, which is abundant in close vicinity to the Blyklippen mine. Areas of this
type close to the tailings deposit were clearly affected by thin layers of tailings (dust 2 cm thick).
Therefore a botanical analysis of affected and non-affected areas was performed.

Analysis of vegetation composition was performed using a quadrate point-intercept method as used in
the International Tundra Experiments (ITEX). The recommended standard method for ITEX plots is a
fixed, square point frame, with 100 measurements spaced equidistantly within the frame (Figure 10).
Our frame had sides of 70 cm so that distance between points was 7 cm. Libelles were placed on two
legs to allow adjustment of the frame to the horizontal. Placing the points closer than 7 cm will result
in over sampling of a very small area and repeated sampling of the same individuals in many
ecosystems.

The frame was placed on the ground with the A corner in the southwest with the legs driven gently
into the substrate to help stabilise the frame. Then the level of the frame was adjusted according to the
libelles on two legs. For each point the specimen directly below each cross was recorded. Unless
otherwise noted, the assumption was that the species recorded was live material, and that the hit was
on leaf or other green material (unless the species is a moss or lichen). It was noted whether the plant
species was dead. We recorded species composition in 6 frames randomly placed at each site and
calculated the average cover of each species.

The vegetation analyses were performed in poor Cassiope-heath with Salix, Silene, Luzula confusa
and Vaccinium uliginosum and in a similar heath, which was highly affected by tailings that had been
blown or washed into the heath. Table 9 in Appendix A1.1 shows average species composition (6
samples) of poor Cassiope-heath with Salix, Silene, Luzula confusa and Vaccinium uliginosum. The
species, Cerastium arcticum, Cetraria nivalis, Harrimanella hypnoides, Polygonum viviparum, and
Saxifraga oppositifolia, which are found at the undisturbed site, do not occur at the tailings site.
However, Dryas octopetala was the only species that was only found at the disturbed site.

Figure 9 shows the proportion of Cassiope tetragona, Cassiope tetragona (dead), litter, organic crust
and tailings. These elements constitute 82% of the cover at the tailings site and 75% of the cover at the
undisturbed site. The figure clearly illustrates that the cover of live Cassiope is significantly smaller at
the tailings site than at the undisturbed site, and that the cover of dead plant material is very large at
the tailings site.


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               0.50
               0.45
               0.40
               0.35
               0.30
               0.25
               0.20
               0.15
               0.10
               0.05
               0.00
                       Cassiope       Cassiope        Litter      Organic       Tailing
                       tetragona      tetragona                    crust
                                         dead

                                      Influenced by tailings    Undisturbed

Figure 9: Proportion of Cassiope tetragona, liev and dead, litter, organic crust and tailings in poor
Cassiope-heath with Salix, Silene, Luzula confusa and Vaccinium uliginosum. Right columns are
undisturbed and left columns are affected by tailings.


Figure 10 shows an example of the vegetation that is affected by tailing. The frame for point-quadrate
analyses is seen installed on the vegetation type.




Figure 10: Vegetation analysis in an area affected by tailing. The frame was equipped with two grids
with a vertical distance of 3 cm. This allows the observer to aim directly underneath each cross.




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5. FIELD SPECTROSCOPY
Field spectroscopy was carried out twice, briefly during the flight campaign 2000 and more thourough
one year later at the same time of year as the flight campaign. The main effort was put into the study
of deposition of minerals and tailings and it is assumed that significant transport of these materials did
not take place during the year that had passed. Assuming that wind patterns and run-off had not
changed compared to the earlier years, the field work was considered valid for inclusion in the image
analysis and validation.

The field work had three main purposes:
   • Ground truth of homogenous targets during flight campaign
   • Collecting representative spectral signatures of minerals and tailings connected to the mine
        site, riverbed and harbour area
   • Collecting representative spectral signatures of vegetation in areas close to the mine that might
        have been contaminated by tailings and dust

The ground truth spectra for the calibration purpose were collected on the 6th August 2000 during the
flight campaign on the gravel airstrip at Mestersvig. All other spectra were collected the following
year, 22 July – 1 August 2001.

5.1.    Sample selection, description of the material, relevance in environmental perspective

A total of 410 spectra were collected during the two field campaigns with 27 reference spectra on the
day of the flight campaign and 383 in 2001. We sampled 147 spectra of minerals and sediments and
236 spectra of plants and vegetation types.

5.2.    Brief description of the spectroradiometers used

Two different spectroradiometers were used during the field survey: a GER Mark V and a GER 2600.
The instruments are set up as described in Table 4.

Table 4: Specifications for the used field spectroradiometers
                                         GER 2600 configuration         GER Mark V configuration
Spectral range                      350 nm to 2500 nm                   350 nm to 3000 nm
Channels                            640                                 763
Linear Arrays                       (1) 512 Si                          (1) 512 Si
                                    (1) 128 PbS                         (1) 128 PbS
Bandwidth                           1.5 nm: 350 nm to 1050 nm           2 nm: 350 nm to 1050 nm
                                    11.5 nm: 1050nm to 2500 nm          6 nm: 1050nm to 2500 nm

5.3.    Feeding Mineo Spectral Library, spectra categories included in MSL

All spectra from the field campaign have been fed into the MINEO Spectral Library (MSL). MSL
includes the spectral signature, photo of the measured material, information from chemical or
botanical analyses and general information on the relevance of the material to contamination at the test
site. An example can be seen in Table 5.
These spectra are representative of the material analysed during the field campaign. They can be thus
further used for other environmental studies addressing the same problems and same material in Arctic
environments.




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Table 5: Example of level of information for a tailings spectrum in MINEO Spectral Library
Date and time of measurement: 23-07-01, 15:11
Geographical co-ordinates: -24.11665, 72.18827 (Mestersvig, Blyklippen, Greenland)
Contact: NERI, Denmark
Contamination: metal toxicity
Site type: tailings
Instrument: GER 2600-1005




          Filename: M0_230701_007.sig                          Filename: M0_230701_029.jpg

The actual levels of contamination from the chemical analyses have been included in the GIS database
described in chapter 8.

5.4.      Spectra description

Field measurements of spectral signatures are limited because minerals seldom occur pure in the size
measurable by the spectroradiometer. More often the measured signature will be a combination of two
or more spectra. Each mineral (or surfacetype) has specific reflectance features and it is important to
be able to find these features when trying to estimate f.ex. abundance of specific minerals within one
spectral signature.

Laboratory spectra are therefore used for identifying specific absorption features of the relevant
materials. show laboratory examples of 4 pure materials found at Mestersvig (galena, sphalerite,
chalcopyrite and Cassiope tetragona leaves).




          Figure 11 Laboratory spectral signatures for four endmembers present at Mestersvig.
              Black: galena, red: chalcopyrite, green: sphalerite, blue: Cassiope tetragona.

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6. DATA PRE-PROCESSING
The pre-processing of the HyMap imagery includes atmospheric and geometric correction. The
imagery is acquired from an altitude of 2900 metres making atmospheric influence less than when
using traditional satellite-borne sensors but with larger geometric distortion. The entire pre-processing
included:
                 Atmospheric correction using ATREM
                 ⇒ Bad bands removal
                 ⇒ EFFORT correction with field spectra
                 ⇒ Geometric correction (Parametric geocoding)
                 ⇒ Mosaic

A short description of the pre-processing is given below with emphasis put on the atmospheric and
geometric correction.

6.1.    Atmospheric correction

The airborne HS survey in 2000 consumed most of the resources reserved for field work which meant
that only a very modest field programme for the contemporaneous field spectroradiometric
measurements could be carried out. Due to lack of helicopter support in 2000, the reference
measurements were done within the area of Mestersvig airfield. The spectroradiometric data are
clearly too scarce for usable atmospheric correction based on the empirical line technique.

ATREM (ATmosphere REMoval Program,CSES (1992)) has been successfully used for the
atmospheric correction of data from various airborne imaging spectrometers in a wide range of
climatic environments.

In ATREM, the Malkmus narrow band model and a pressure scaling approximation are used in
calculating atmospheric transmittances of all gases. The atmospheric scattering is modelled using the
6S code (Vermote et al., 1997). The spatial and temporal variations of atmospheric water vapour pose
difficulties in removing water vapour absorption features in hyperspectral data. In this algorithm, the
amount of water vapour is derived on a pixel-by-pixel basis from the data using the 0.94- and the 1.14-
µm water vapour bands and a three-channel rationing technique. The derived water vapour values are
then used for modelling water vapour absorption effects in the entire 0.4-2.5-µm region. The retrieved
scaled surface reflectance can be converted to real surface reflectance if surface topography is known.

The ATREM-software was used to process the HS data from Mestersvig with the following modelling
parameters:
            • All gases
            • Continental aerosol model
            • Visibility 80 km
            • Subarctic summer model
            • Ozone 0.34 cm

The ATREM-processed data as such seem to make a satisfactory basis for the further spectral
processing steps. Output is “Scaled surface reflectance” where the effects of water vapour have been
removed on a pixel by pixel basis. The effect of other gases and aerosols has been removed based on
assumption of a flat surface. The errors will be most dominant in the spectral regions around 1.4 and
1.9 µm.



6.2.    Bad bands
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The following bands were marked as bad bands due to atmospheric distortion, mainly from the major
H2O band around 1.4 and 1.9 µm. The signal in these absorption bands was so low that atmospheric
correction was insufficient. Bands 1, 63-69, 93-99 were marked as bad and were therefore not
included in the processing and mapping.

6.3.      EFFORT correction

An EFFORT correction was run on the data in order to smooth the spectral signatures for a better fit to
the field measurements. EFFORT is the acronym for Empirical Flat Field Optimal Reflectance
Transformation (Boardman, 1998). The transformation makes minor adjustments to the reflectance
data in order to make image spectra comparable with field measured spectra. The correction is run
with the following polynomial orders:
        • Band 2-20            12
        • Band 21-62           12
        • Band 70-92           12
        • Band 100-126         12

At the present study eight field spectra were used as reality spectra in the processing. The spectra were
from: vegetation analysis 1, tailings, snow, airstrip, tidal sands, river sediment, Betula nana and
Cassiope tetragona.

6.4.      Geometric correction

Airborne imaging spectrometers, which have a wide field of view (FOV) introduce the serious problem
of geometric distortion in the data. The problem becomes increasingly important in the areas of more
variable topography with large altitude differences. The terrain elevations of the Mestersvig area that
range from 0 (sea level) to more than 1100 metres have a dramatic impact on the geometry of the
acquired HS-data.

The airborne HS-system by using the IMU-device and differential GPS (DGPS) records the ephemeris
data (latitude, longitude, altitude, pitch, yaw and roll) for the imaging spectrometer. Provided the
IMU-data have an adequate accuracy, the geometric correction of the HS-data should be possible. The
geometric correction of HS-data from the IMU-output is only possible if a digital terrain model
(DTM) of reasonable accuracy and detail is available.

6.4.1.       Accuracy of IMU data

The survey contractor was not able to give an estimate of the accuracy of IMU-data. The accuracy is
affected by the quality of pre-flight calibration/alignment of the IMU-device, and drift of the IMU-
instrument during the flight. The accuracy of the IMU-data was examined by geocoding some flight
lines to sea level (‘flat surface geocoding’). Comparison of the geocoded data with known control
points along the shoreline showed deviations varying from some tens of meters to more than 100
metres both in X and Y directions. The observed deviations and their magnitude demonstrate the need
of reliable ground control points to correct the errors and drift of the output from IMU.

6.4.2.       Digital Terrain Model

The airborne system generated 158 frames of predominantly high quality B/W aerial photos with a
good stereoscopic coverage along as well as across track. The system also produced geo-location data
(X, Y, Z) for each frame centre. The B/W negatives were scanned (1200 DPI) by using a desktop
quality scanner.

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The survey layout with good frame overlaps along with the frame centre geo-location data made it
possible to use the EnsoMosaic software developed by Stora Enso Forest Consulting Oy Ltd and the
Technical Research Centre of Finland (VTT). The system is based on the bundle block adjustment,
which is an iterative mathematical process to solve the orientation of the images and the location of
the perspective centres simultaneously for a large image block. The system is reasonably user friendly
and can handle a large number of images on a ‘normal’ Windows based Intel workstation. The entire
Mestersvig area (158 frames) was processed in one run. The geolocation accuracy of the resulting
DEM has the same order of magnitude as the location accuracy of the frame centres. The comparison
between the ortho-rectified aerial photos and the known ground control points showed that the
deviations were as a rule within 20 metres. The location accuracy of the DEM was improved by using
ground control points during the DEM-generation. Comparison of the vertical component with the
known values indicated that derived elevation figures were less accurate. This is probably due to the
use of a less accurate scanner. Here again the final product was improved by using ground control
points. The final DEM was resampled to a 5 x 5 m grid. Some areas at higher elevations (snow
covered) were over exposed during the aerial photography with a considerable loss of details, which of
course also affected the quality of DEM in these areas.

The EnsoMosaic software is considered to be an affordable solution for the (quick) generation of a
DEM from systematic sets of stereoscopic aerial photography. The process could be further automated
and speeded up by acquiring the aerial photography with a digital camera.

6.4.3.      Parametric geocoding of hyperspectral data

Parametric approach for the orthorectification of airborne scanner data has become the most important
means of removing the geometrical distortions of images from the airborne scanners. By definition,
the parametric approach relies on physically measured auxiliary data :

•   Navigation data:          longitude, latitude, altitude, and attitude data (roll, pitch & true
    heading)
•   Digital Elevation Model: Initiates the final geometry of the geocoded image
•   Image/sensor information: FOV, IFOV, scanning frequency, starting time, image dimensions

The parametric geocoding of the HS data was done using the PARGE software from ReSe
Applications Schläpher and the Remote Sensing Laboratories (RSL) of the University of Zurich,
Switzerland. The PARGE software uses the HyMap ephemeris data (IMU data) and the georeferenced
DEM as input for the geocoding process. The user may attempt to correct the IMU-errors/offsets by
using ground control points. To preserve the spectral characteristics of the data, the resampling method
was based on the nearest neighbour-technique.

The overall geolocation accuracy of the geocoded flight lines is variable and as such not adequate for
the mosaicing of the HS data in a terrain like Mestersvig. The geocoded flight data as GIS - layer in
combination with the field measurements (GPS-geolocations) often does not provide a meaningful
registration of the data sets.

The reasons for the somewhat poor geolocation accuracy of the geocoded data are obvious:
inaccuracies in the IMU data, accompanied by local minor errors in DEM resulted in considerable
deviations in the mountainous terrain.



6.4.4.      Mosaicing of hyperspectral data

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The primary objective of the HS data mosaicing in the Mestersvig area is to provide one dataset with
reasonably accurate geocoding covering the area. The variably insufficient geocoding of the flight
lines was improved by using a set of available, irregularly spaced control points for the Delaunay
triangulation warping. The ground points were corresponding points identified in the overlap between
the flight lines. Line 3 (with the mine-site) was chosen as base for this warping. This method fits
triangles to the irregularly spaced GCPs and interpolates values to the output grid. We did the
resampling by using the nearest neighbour option.

The final mosaic of the HS data is based on the 8 flight lines (from line # 2 through # 8) which were
warped by using the Delaunay triangulation.




Figure 12: Assessment of pre-processing using field measurements from Mestersvig airstrip. Thin
lines in the graph are +/- 1 standard deviation.

6.5.    Assessment of pre-processing

The purpose of the pre-processing done in this study is to facilitate the use of field spectra in the
mapping of contamination from Blyklippen. This pre-supposes that pre-processing is sufficient to
correct the data for influences that alter the spectral signal between the ground and the HyMap-sensor
resulting in different signals recorded by the airborne imaging spectrometer and the hand-held ground
spectroradiometers. As described in the previous sections, an atmospheric and geometric correction
has been carried out at Mestersvig. A further smoothing of the data with EFFORT has also been used
in order to remove some of the noisy look of the spectra.
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An assessment of the pre-processing was performed by comparing spectra from the HS imagery and
ground measurements over the same target measured during over flight.

The Mestersvig airstrip was used for this purpose. The airstrip is a homogenous gravel surface,
horizontal and easily recognisable on the imagery. Figure 12 shows the comparison of the 20 field
spectra that were measured along a transect on the gravel surface.

As seen in the graph of Figure 12 the spectra from the field (Reference spectra) and from the imagery
(Image spectra) are not strictly comparable. Differences are also seen in the continuum removed
spectra. The reason for these differences is probably due to non-corrected atmospheric influence.
BRDF effects are considered very small on this flat, homogeneous surface and sun-angle effects
would only result in an off-set difference.

The conclusion from this, however, is that the field measured spectra cannot be used directly in the
mapping process. It is therefore decided to use image spectra from the airborne HS data for mapping
and confirm the character of the image spectra by use of field spectra. This is described in more detail
in the next chapter.




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7. DESCRIPTION OF IMAGE PROCESSING PROCEDURES AND
   ALGORITHMS USED IN CONTAMINATION/IMPACT MAPPING
The procedures and algorithms described below are all included as standard tools in the ENVI 3.5
software unless other is noted. The algorithms are validated extensively in the scientific literature and
further references and in-depth descriptions of the methods can be found in Kruse et al. (1993),
Boardman et al. (1995) and the ENVI manual.

7.1.      Objective

The objective of the image processing of the hyperspectral data from Mestersvig is to map the
contamination in the area that originated from the mining process. The main processes are believed to
be dust contamination from wind-transported tailings and distribution of tailings through the river
system into the fjord.

7.2.      Procedures and algorithms

The main hyperspectral methods used in this study are Minimum Noise Fraction (MNF) used for
minimising noise and limiting the number of bands while maintaining information, Spectral Angle
Mapper (SAM) used for classification and Mixture Tuned Matched Filtering (MTMF) which was used
in an attempt to map the abundance of several materials. The methods differ from traditional image
processing methods in that they are enhanced for handling hyperspectral data with many bands. The
use of traditional methods (e.g. maximum likelihood classification) will be limited for this amount of
bands compared to the mentioned methods.

7.2.1.       Minimum Noise Fraction (MNF)

MNF was used to minimise the number of bands while maintaining as much information as possible
and segregating noise for future removal. This was done using the method developed by Boardman
and Kruse (1994). Boardman and Kruse describe MNF as: The MNF transform .... is essentially two
cascaded Principal Components transformations. The first transformation, based on an estimated
noise covariance matrix, decorrelates and rescales the noise in the data. This first step results in
transformed data in which the noise has unit variance and no band-to-band correlations. The second
step is a standard Principal Components transformation of the noise-whitened data. For the purposes
of further spectral processing, the inherent dimensionality of the data is determined by examination of
the final eigenvalues and the associated images. The data space can be divided into two parts: one
part associated with large eigenvalues and coherent eigenimages, and a complementary part with
near-unity eigenvalues and noise-dominated images. By using only the coherent portions, the noise is
separated from the data, thus improving spectral processing results. (RSI, 2002).

In this study MNF is used both for visual inspection and evaluation of the data as well as for removing
noise before further processing. MNF was used primarily on the entire dataset which will smooth out
some distinct absorption features. The aim however was to gather information from a larger area and
be able to extract possible areas of interest within the larger area. A targeted analysis using a smaller
area would preserve more of the “important” absorption bands.

7.2.2.       Spectral Angle Mapper (SAM)

SAM was the main method used for mapping contaminated surface types and materials in Mestersvig.
Mixture Tuned Match Filtering (MTMF) was also tried but it didn’t add information to the maps
extracted using SAM. Also a lot of effort was put into visual evaluation of different band


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combinations of the reflectance imagery and of different band combinations from the Minimum Noise
Fraction (MNF) transformation.

Spectral Angle Mapper uses the angles of a reference spectrum to map the given material (Kruse et al,
1993). A threshold is chosen for the angle so that only pixels with angles similar to the reference
spectra will be mapped (Figure 13).




Figure 13: The theory of the Spectral Angle Mapper algorithm. The algorithm finds the angle between
the pixel and the target and map according to a specified threshold angle.

This method is relatively insensitive to illumination and albedo effects of the target so that cross track
illumination effects need not to be corrected.

The input for the SAM algorithm is spectra of the relevant surface types to be mapped. These can be
either image or field spectra. In Mestersvig it was chosen to use image spectra for two reasons:

1. The field spectra were sampled one year later than the image. This means that the spectra do not
   reflect exactly the same surface.
2. The atmospheric and geometric correction was not sufficient to make a complete fit between the
   reference samples and the image spectra taken at the airstrip at the time of image acquisition.

Instead, the image spectra were verified by comparison with the field spectra using Spectral Analyst
with the SAM algorithm. Spectral Analyst finds the best fit of field measurements to a given image
spectra using the SAM algorithm as explained above. The fit of every field measurement is given a
score between 0 and 1 with 1 being a complete fit. Typical score values between the materials
measured in the field and the image spectra was around 0.8. This procedure ensured that known areas
actually corresponded to the types measured in the field.

7.2.3.      Mixture Tuned Matched Filtering (MTMF)

MTMF is a method that finds the abundance of given materials using partial unmixing. Partial
unmixing is based on the theory that if a pixel is made of three materials each covering 33% then the
resulting spectral signature for that pixel is the weighted sum of those three pixels. Given that all pure
materials in an area are known, the MTMF technique can estimate the abundance of those materials
within each pixel. As mentioned above the method was not successful in this area and will therefore
not be further described. Additional information on this method can be found in the ENVI manual.

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7.3.       Description of the maps produced

The following maps have been extracted from the DTM and HyMap imagery to illustrate the
contamination in the Mestersvig area. The maps were prepared to assist in the visual interpretation of
the area during the analysis and for inclusion in the overall assessment of the state of the area.

       •   Area overview
       •   3D view
       •   MNF combination
       •   Shaded relief (time of campaign)
       •   Land cover map
       •   Lithological map
       •   NDVI map
       •   Tailings washout through the river system
       •   3D view of washout
       •   Polluted vegetation in the mine area

The following pages show these maps with a short description of the methods used.




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7.3.1.     Area overview

Figure 14 was made by a combination of the atmospherically and geometric corrected bands 29, 17
and 11 displayed in red (R), green (G) and blue (B), respectively. This combination enhances the
signal from the infrared bands in the red and hence shows the vegetation in red colours. The
combination is used for both visual inspection and presentations of maps from the area.




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Figure 14: An overview of the area prepared as a colour composite of bands 29 (R), 17 (G) and 11
(B).




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7.3.2.      3D area overview

Figure 15 shows the same image as in Figure 11, draped over the digital terrain model combinations of
bands to help identifying features and materials in the imagery. Many landscape features are directly
linked to the morphology and therefore more easily interpreted when shown in 3D. This figure
illustrates among other things how the steepness of slope changes along the river bed and thereby
indicate the potential river current and resulting relative transported sediment grain size.




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                                                                      Blyklippen Mine




          Mestersvig airstrip




                                   Nyhavn Harbour




Figure 15: A 3D view of the colour composite of bands 29 (R), 17 (G) and 11 (B). The vertical
exaggeration is set to twice the actual height.




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7.3.3.     Minimum Noise Fraction (MNF)

A MNF transform was made from all the spectral bands (excluding the marked bad bands). Most
information is put in the first bands. Eigen value plots of the information in the MNF bands showed
that most the information in the 110 bands had been gathered in the first 20 bands. The rest of the
MNF bands are mostly noise (Figure 16).




         Figure 16 Eigen value plot for the MNF transform on the entire Mestersvig dataset


The MNF bands were used for visual inspection of materials and for tests of the MTMF technique.

Figure 17 shows a combination of MNF bands 13, 7 and 5 which was very useful in determining the
tailings extent. The delineation of lithological details is also clearly seen from the MNF-data.




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Figure 17: Colour composite of MNF bands 13 (R), 7 (G) and 5 (B). Tailings are shown at the arrow.




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7.3.4.      Digital Terrain Model

The digital terrain model is used primarily for the georectification and mosaic of the hyperspectral
imagery. The digital terrain model is based on the airphotos taken during the campaign on 6. August
2000. The survey was flown at 6600 feet above sea level resulting in a photo-set with high resolution.
The digital terrain model was produced with the program ENSOMosaic (a semi-automatic
autorectification tool) and positions from handheld GPS measurements were used as ground control
points. The resulting DTM has a resolution of 5 by 5 metres.

A shaded relief shows the light conditions the time of flight. This shaded relief is made for 6 August
2000 at 13.00 local time (Figure 18). The image can help to identify shadowed areas and to distinguish
between dark shadows and water surfaces, which both have very low reflectances and hence are
difficult to seperate. This relief also exemplifies the coarse resolution of the DTM in some areas at
higher altitudes where the quality of the aerial photography was deterred by the overexposure. An
example of a poorly covered area is in the mid-left part of the area.




Figure 18: A shaded relief for the approximate time of overflight (GMT 6-AUG-2000 15:00:00).




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7.3.5.      Land cover map

The land cover map is made from a SAM classification based on field-validated areas of major land
cover types (region of interest) for the purpose of a generel introduction to the area. The following
land cover types are mapped (Figure 19):
• Seawater
• Lakes, rivers and shadows
• Sediments (river bed etc.)
• Bare ground (rocks etc.)
• Tailing
• Snow
• Luxurious vegetation (Herb slopes etc.)
• Grassland
• Moist heath (Fen, luxurious dwarf shrub heath etc.)
• Dry heath (Dryas-, Cassiope-heath etc.)
• Abrasion – fell field




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Figure 19: Land cover map of the Mestersvig area. Red frame refers to the Mestersvig graben area
shown in Figure 17.

The cover type abrasion-fell field was not mapped using ROI but found during the post-classification
investigation of unclassified pixels. It turned out that this cover type with small coverage of vegetation
was not detected in the ROI’s. A comparison with visual inspection of RGB-views and the NDVI
image further supported this theory and the unclassified pixels were therefore named abrasion-fell
field.

The region of interest for each class included between 135 and 11500 points. The average spectra for
each region was calculated and fed into the SAM classification. The spectra are shown in Figure 20.




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Figure 20: Average spectral signatures used for mapping the land cover types.

The SAM classification resulted in a rule image for each cover type and these were used to manually
find the maximum angle needed to correctly map the individual cover types. The maximum angles
used are listed in Table 6.

Table 6: Cover types with the number of points and maximum SAM angle that have been used in the
mapping.
Cover type                                            Points in ROI     Maximum
                                                                        SAM angle
Seawater                                              11473             0.500
Lakes, rivers and shadows                             1106              0.250
Sediments (river bed etc.)                            619               0.075
Bare ground (rocks etc.)                              1298              0.050
Tailing                                               155               0.030
Snow                                                  1045              0.300
Luxurious vegetation (Herb slopes etc.)               189               0.075
Grassland                                             135               0.100
Moist heath (Fen, luxurious dwarf shrub heath etc.)   603               0.075
Dry heath (Dryas-, Cassiope-heath etc.)               875               0.075
Abrasion – fell field                                 -*                -*
* The unclassified pixels corresponded to a cover type with a small amount of vegetation and
therefore named abrasion-fell field




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7.3.6.     Lithological map

Lithological maps can be made from the HyMap data but they are also available from previous ground
surveys. Lithologies stand out clearly on the upper non-vegetated, well exposed slopes of the
mountains (Figure 21).




Figure 21: Domkirken mountain seen from the Blyklippen mine (West). The Upper Permian
(Profilbjerg Member (P), Domkirken Member (D), Aggersborg Member (A)) and Triassic (Wordie
Creek Member (W)) sediments well exposed along the eastern margin of the Mestersvig graben.
Arrows point to the areas misclassified as tailings




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Figure 22: Major lithological units of the eastern part of Mestersvig graben as mapped from the
HyMap data (Table 7, Figure 21). The location of the area is shown in Figure 19.

The exposed pre-Quaternary lithological units can be reliably mapped by using SAM (Figure 22). The
available HS-data often make it possible to enhance further internal compositional details within the
members (e.g. discrete calcareous, bituminous or clay-rich horizons etc.). This is, however, beyond the
scope of this report.

The points used for the mapping are reported below in Table 7. Notice the very small angle indicating
a very good fit to the spectra that were used in the SAM procedure. Reference spectra representing
field-validated lithological units were selected from the HS images.

Table 7: Lithology members with number of ROI points and max. SAM angle used in the mapping
Member & Thickness                   Chronology            Points/ROI          Angle
Mafic sills and dykes                Tertiary              55                  0.02
Wordie Creek Member (> 500 m)        Trias                 250                 0.02
Aggersborg Member (180 m)            Upper Permian         288                 0.02
Domkirken Member (200 m)             Upper Permian         180                 0.02
Profilbjerg Member (1300 – 1500 m)   Upper Permian         224                 0.02
Blyklippen Member (800 – 1000 m)      Lower Permian        345                 0.02




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7.3.7.      Normalised Difference Vegetation Index map

The NDVI map shows the level of greenness of the vegetation (Figure 23). It is calculated as the
difference in reflection between the near-infrared (NIR) and the red spectral bands. The NDVI for
HyMap image is calculated using band 26 as the NIR band and band 16 as the red band. This
corresponds to the wavelengths used in other sensors, e.g. Landsat, SPOT, Ikonos, etc. Non-vegetated
areas are found with NDVI values below approx. 0.1 and these areas are masked using the land cover
types from the land cover map. As opposed to some of the other mine sites in the MINEO project,
there were no clear changes in NDVI in the immediate surroundings of the mine site. Major changes
related to the mine is the pollution from windblown tailings but this lowering of NDVI is more due to
the physical appearance of the tailings than to changes in species composition or vegetation stress.




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Figure 23: Normalised Difference Vegetation Index (NDVI). Water, sediments and snow are masked
in blue, brown and white, respectively.




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7.3.8.      Tailings deposit and washout

The distribution of tailings in the area is mapped using image spectra from locations with known
content (Figure 24). A ROI has been drawn on the tailings deposit and the average spectra used for the
classification. The average spectra from the HS data were compared to the field spectra with Spectral
Analyst. This comparison showed that the used mean image spectra best fitted the field spectra of
tailings (fit of 0.804). A maximum angle for the SAM classification of 0.035 was used to map the
tailings.




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                                                                2




                      1




Figure 24: Distribution of tailings deposit and similar materials shown in red on the B/W background.
Tailings deposit is marked with 1 and the major deposition in the lower riverbed is marked with 2.

The mapping results based on the SAM alone indicate that the method here is subject to
misclassification. The distribution of pixels classified as ‘tailings’/affected by ‘tailings’ in Figure 24
indicates that both some Quaternary/Recent material and pre-Quaternary lithologies are classified as
tailings (see Figure 17). It is reasonable to assume that the misclassified material in most cases is made
up of a fine-grained material from alluvium reworked by wind.

The tailings from the Blyklippen mine are a mineral mixture, which is predominantly made up of
quartz and barite with a variable content of sphalerite, galena and chalcopyrite. Quartz is by far the
most important constituent which together with barite, and to a lesser extent sphalerite, will ‘dictate’
the overall spectral properties of the tailings. When looking for different minerals it is however
important to look in detail on the relevant spectral absorption bands.

Average spectra from selected localities affected by tailings and waste are shown in Figure 25. The
closer examination of spectra from known contaminated localities reveals that the absorption at 2.18-

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2.22 µm seems to increase as a function of sphalerite content. This is well seen in the continuum-
removed spectre. The feature is most conspicuous for the HyMap band 109 (2.2206 µm).


                    Spectra from contam inated m aterials (continuum rem oved)
          1.00




          0.95
  Value




          0.90



                                                                Colors of Spectra:

                                                                M isclassified as contam inated
          0.85
                                                                Tailing
                                                                Alluvium
                                                                Contam inated m aterial
                                                                Contam inated alluvium


          0.80
             2.10               2.15              2.20               2.25               2.30                 2.35
                                                         W avelength

Figure 25: Selected HyMAp spectra from selected contaminated and uncontaminated localities. The
yellow curve is from a locality approximately 1 km ESE of the Blyklippen mine (Figure 21).


This feature can be conveniently used for the final validation of the SAM-results. Here the continuum
removed spectral data of the HyMap band 109 were used to validate the SAM-results. Based on the
visual examination of the data, a threshold value of 0.95 was selected for the validation of SAM-
results shown on the next page (Figure 26). The described procedure has removed most of the obvious
misclassifications.

There are still a few localities which are ‘misclassified’ as tailings. The most striking locality of this
type is situated approximately 1 km ESE of the tailings deposit. This locality is most probably related
to a lithological feature similar to the Blyklippen deposit.




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Figure 26 Tailings and sphalerite alluvium (blue) in the area after filtering with the continuum
removed band 109.




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7.3.9.      3D tailings deposit and washout
Figure 27 shows the mapped tailings on a false colour combination draped on the DTM. This method
enhances the understanding of the processes in the transport of the tailings through the river system
and the deposition in the lower river bed. After the steep drop from the Blyklippen mine valley
through the Tunnelelv the material is redeposited when the slope flattens and smaller particles are
deposited (2).




                                                                                          1




                                                             2




Figure 27: The tailings and washout have been draped on the 3D map to visualise the deposition of
material in the lower parts of the river bed. Tailings are here shown in blue with the deposit numbered
1 and the deposition in the lower riverbed as 2.




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7.3.10.     Polluted vegetation map

Distribution of the tailings-polluted Cassiope tetragona and non-polluted Cassiope tetragona heaths
around the mine is performed with SAM. A map displaying the three types in red, green and blue has
been prepared so that coloured pixel corresponds to tailings, non-polluted Cassiope tetragona heath
and polluted Cassiope tetragona heath in R, G, B, respectively (Figure 28).




Figure 28: Distribution of the tailings, polluted Cassiope and non-polluted Cassiope around the mine
shown as blue, yellow and green, respectively. Luxurious vegetation is shown in red.

Mapping is based on image spectra from areas with known distribution of the three types. The spectre
has been verified for these types by comparison to field spectra using the SAM tool in Spectral
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Analyst. The maximum angle used for the mapping was 0.75 and 0.5 for non-polluted and polluted,
respectively. The polluted and non-polluted vegetation types have been analysed in the field and
described in chapter 4.

In Figure 29 the sphalerite absorption feature between 2.18 and 2.22 µm is shown for the polluted and
non-polluted Cassiope tetragona heath. As explained in 7.3.8 the absorption feature is more
pronounced in these bands for higher levels of sphalerite content and this feature can be found also in
the image spectra as seen in the figure.




             Figure 29 Continuum removed image spectra for Cassiope tetragona heath.
                        Blue spectra: Polluted; Red spectra: non-polluted.




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8. DESCRIPTION OF THE GIS DATABASE
The GIS database for the Arctic Test Site includes several layers describing the area around the mine,
along the river and towards the harbour area.

8.1.       Database preparation

The database has been prepared for use in ArcView 3.2 with spatial analyst. The area is limited to the
coverage of the hyperspectral data as listed in the table below.

8.2.       Database content

The database includes the following layers:

       •   Digital terrain model and derivatives (slope, aspect etc.)
       •   Hyperspectral data, atmospherically corrected
       •   Multispectral data
       •   Geological maps
       •   Land cover map
       •   Contamination maps (from HyMap data)
       •   Contamination levels at point locations

The raster data are based on the digital terrain model or the HyMap images. The mosaic of the 8 flown
strips (2-9) covers most of the region of interest excluding the Nyhavn harbour area. The mosaic has
been cut at upper left pixel coordinate (250, 200) and lower right pixel (2799, 2249) resulting in an
image of 2600*2000 pixels. All raster layers are georectified to UTM zone 27 (WGS84) with a pixel
size of 5m*5m. The corner coordinates are listed in Table 8.

Table 8: The geographical coordinates for the images.
                   UTM-X                UTM-Y                     DD-longitude         DD-latitude
Upper left         390748               8019714                   -24.211975           72.249786
Lower left         390748               8009719                   -24.196381           72.160319
Upper right        403743               8019714                   -23.830550           72.255633
Lower right        403743               8009719                   -23.816800           72.166136




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9. GIS MODELLING
GIS modelling in the traditional concept has not been performed at the Arctic Site. The maps have
been developed using image analysis tools which essentially are raster GIS software. But a comparison
and analysis between many different kinds of information layers are not performed.

Only a few other layers of information are available from this area. No meteorological layers are
available from the valley and run-off patterns, which could be used for run-off, and redeposit-
modelling of tailings could not be extracted from the DTM. No field measurements of river discharge
have been made which also would support the modelling of the redeposition of tailings through the
river system.




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10. CONCLUSIONS, ASSESSMENT OF RESULTS
The Mestersvig mine site is generally regarded as the most polluted mine site in Greenland. Therefore,
and for logistic reasons, it was selected as the Arctic test site in the MINEO project. It was expected
that pollution related to the tailings deposit and the harbour area would be suitable for testing of the
use of hyperspectral data to map pollution. Also, it was expected that there might be possibilities for
testing use of hyperspectral data for mapping polluted vegetation.




Figure 30: Main results from the mapping is the mapping of tailings (blue), polluted vegetation
(yellow) and healthy vegetation of the same type (green). The washout is shown by the blue arrows.



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10.1.     Assessment of results

At an early stage after completion of the field work it was realised that the effort should be directed
towards mapping of tailings which had been dispersed along the river course and towards mapping of
vegetation directly affected by tailings. Levels of pollution are low, and it soon became evident that
mapping of specific substances would be difficult with the spectral resolution of the HyMap data. This
resulted primarily from a field survey a year after the hyperspectral campaign and from the fact that
field measurements could not be used in the mapping process. It was, however, possible to map the
distribution of tailings and the direct effect on the vegetation in the area close to the mine (Figure 30).

The generally low content of contaminants (both in terms of chemistry and mineralogy) implied that
very subtle spectral features had to be evaluated as possible indicators of environmental impacts. It
became evident in the course of spectral processing, that standard universally used spectral mapping
procedures, such as SAM had to be supplemented by a more detailed analysis and evaluation of the
spectral characteristics of the materials. The examination of continuum removed spectra turned out to
be an important means of evaluating the SAM-results.

The sun angle during the HS data acquisition (the location of the Mestersvig area at the latitude of 720
N and the period of data acquisition) is distinctly lower than for the other MINEO test sites, which
without doubt resulted in the decreased signal/noise characteristics of the HS data.

10.1.1.      Tailings

During the lifespan of the mine the tailings were deposited just outside the mine, close to a river
(Aastrup et al 2001). The river carried most of the tailings away from the tailings deposit and re-
deposited them along the river and at the river mouth close to Kong Oscars fjord. Chemical analyses
of sediment samples collected along the coast have shown that the tailings are a source of pollution,
especially zinc. We expected to find tailings mixed with river sediments along the river and along the
coast at the river outlet in Noret. The analytical results of the alluvial material close to ‘Noret’ show
distinctly elevated concentrations of Zn and Pb but it has not been possible to detect this material by
using the HyMap data. The increasing water content of the material when approaching the sea is likely
to decrease the SWIR1 and SWIR2 signal levels resulting in poor sensitivity to detect for instance
sphalerite.

Several attempts were made to map tailings based on surface characteristics of zinc and lead bearing
minerals (galena, sphalerite, and chalcopyrite) and on the spectra sampled in the field at the tailings
deposit. As described in chapter 6.5 it was not possible to correct the imagery to true reflectance and
the quality of applied ATREM correction was questioned. Mapping based on the field spectra directly
has therefore not been possible. Instead the mapping of tailings was done using image spectra from the
known tailings deposit.

The tailings deposit is clearly visible in Figure 27 (Point 1) at the mine site and also tailings seem to be
detected near the river outlet and in the flat area (Point 2).

Chemical analyses of sediment samples from Point 2 confirm that levels of lead and zinc here are
higher than background values. The values are however lower on a factor 10 or more than values
found in the tailings and detection must therefore also be connected to other reasons. The detection of
some of these areas may be caused by the presence of minerals or sediments with surface
characteristics comparable to those of tailings.

In one case the tailings signature of the HyMap data most probably is related to purely lithological
features similar to the host rock types of the Blyklippen ore deposit.


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The available maps, however, would be an important tool to identify areas, where further studies
should be done including sampling for chemical analysis. The project has been successful in regard to
identification of the most important areas. It is concluded, that it is of extreme importance to have field
spectra and field analyses available from the polluted area. Thorough knowledge of the site is
important and it would not have been possible to map pollution based on available spectral libraries.

10.1.2.      Vegetation

A botanical survey showed that focus should be close to the tailings deposit, where there was a direct
effect from tailings in a Cassiope tetragona dwarf-shrub heath. It was not possible to identify other
suitable vegetated areas, which could be regarded as polluted compared to other sites with comparable
vegetation type. The area with affected vegetation had a thin layer of tailings and it was noticed that
there were many dead Cassiope tetragona plants. This was confirmed by detailed botanical analyses
of affected and non-affected vegetation in the field and by NDVI from the images. The NDVI
occurrences within the mapped classes of polluted and non-polluted Cassiope tetragona heath showed
a significant difference (Figure 31).

                                                0.25
                                                                Tailing
                                                0.20
                           fraction of pixels




                                                                                                  Healthy Cassiope
                                                0.15
                                                                        Polluted Cassiope

                                                0.10


                                                0.05


                                                0.00
                                                       0.0        0.1          0.2          0.3     0.4       0.5    0.6
                                                                                        NDVI


Figure 31: NDVI for the fraction of pixels within each type of tailings, polluted Cassiope tetragona
and healthy Cassiope tetragona.

The polluted and non-polluted Cassiope tetragona heath have clearly different levels of NDVI with
polluted having a very low mean value of 0.15 and the healthy heath a mean value of 0.34. In
particular the amount of tailings and the high levels of dead material bring down the NDVI values
towards a level of non-vegetated areas. The spectral absorption feature in sphalerite at 2.18-2.22 µm
was also clearly visible in the spectral signatures from the polluted vegetation sites.

10.2.     Results versus user demand

From a user’s point of view the ideal situation would be to have a tool, which could produce maps,
showing extension of possible contamination plumes. At the Arctic test site this could be partially
achieved as shown by the results from the mapping of tailings and areas affected by them.

We consider that the main reason for difficulties to produce mining impact assessment maps for the
Mestersvig area is, that the pollution level is low and the resolution of data simply is not sufficient to
detect more subtle types of environmental impact such as slightly increased stress on vegetation. The
results clearly show that the vegetation affected by the tailings, as confirmed by the botanical survey,
can be also detected by using the HS data. The lowest levels of vegetation stress detectable from the
HS data remain to be established by future studies.


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The ability to map pollution levels and possible impacts may be pursued in two directions – the
mapping of abundance of minerals containing the pollutant and the mapping of impact on vegetation.
The result is in both cases dependent on the ability to distinguish anomalies in both the spectral and
the spatial domain. It has been possible to map areas with enriched levels of sphalerite, the level of
detection is however not well known.

We have, however, produced maps that would have been valuable if they had been available when
planning the first studies of pollution from the mine.

At the present stage and with the available data from Mestersvig, it is possible to produce maps that
show which areas to consider when planning monitoring programs. It is important to realise that level
of pollution still should be found from chemical analyses of samples of relevant media in the
environment.

Polluted areas can be delineated and hence monitoring costs can be reduced. Further studies are
needed to validate the use of the method in areas with low content of pollutants, as we have not
succeeded in defining a lower level of pollution that can be detected by the HyMap data.

10.3.   Future plans

At the Arctic test site future work should be concentrated on linking the field samples and chemical
analyses to the hyperspectral mapping. More research should be carried out for the development of an
adequate routine method for the atmospheric correction of the HS data acquired in Arctic conditions.
More detailed studies of areas with known pollution should be done with weight put in the MNF
analysis and use of laboratory spectra of endmembers. Geostatistical analyses will be used to
extrapolate the chemical analyses for aerial estimates of the pollution. Future work will also be put
into a more detailed DTM so that run-off modelling can be extracted. Such studies will add more
knowledge on the area and pollution and also help verify or explain the misinterpreted tailings areas
and help to identify lower levels of detectable pollutants.




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11. REFERENCES
Aastrup P., Tamstorf M. & Tukiainen T., 2001. Blyklippen lead-zinc mine, Mestersvig. Existing
knowledge. Mineo Site Report. Danmarks og Grønlands Geologiske Undersøgelse Rapport 2001/115.
38 pp.

Agger C. T., Asmund G., Dietz R. & Johansen P., 1991 (in Danish). Miljøundersøgelser ved
Mestersvig 1991. Teknisk rapport. Grønlands miljøundersøgelser. 23 pp.

AMAP, 1998. AMAP Assessment Report: Arctic Pollution Issues. Arctic Monioring and Assessment
Programme (AMAP), Oslo, Norway. xii-859 pp.

AMAP, 1997. Arctic Pollution Issues: A State of the Arctic Environment Report. Oslo. Norway

AMAP, 1998. AMAP Assessment Report: Arctic Pollution Issues. Arctic Monioring and Assessment
Programme (AMAP), Oslo, Norway. xii-859 pp.

AMAP, 2002. Arctic Pollution 2002. Arctic Monitoring and Asessment Program 2002. Oslo

Asmund G., 1979 (in Danish). Oversigt over miljøundersøgelser ved Mestersvig 1979 (in Danish).
Grønlands Geologiske Undersøgelse. 8 pp.

Asmund G., Riget F. & Johansen P., 1997 (in Danish). Miljøundersøgelser ved Mestersvig 1996.
Danmarks Miljøundersøgelser. 31 s. - Faglig rapport fra DMU, nr. 202.

Bay C. & Holt S., 1986 (in Danish). Vegetationskortlægning af Jameson Land 1982-86. 40 pp.
Grønlands Fiskeri- og Miljøundersøgelser og Grønlands Botaniske Undersøgelser.

Boardman, J. W., 1998. Post-ATREM polishing of AVIRIS apparent reflectance data using EFFORT:
a lesson in accuracy versus precision, in Summaries of the Seventh JPL Airborne Earth Science
Workshop, v. 1, p. 53.

Boardman, J. W., Kruse, F. A., & Green, R. O. 1995: Mapping target signatures via partial unmixing
of AVIRIS data in Summaries, Fifth JPL Airborne Earth Science Workshop, JPL Publication 95-1, v.
1, pp. 23-26.

Boardman, J. W., and Kruse, F. A., 1994, Automated spectral analysis: a geological example using
AVIRIS data, north Grapevine Mountains, Nevada: in Proceedings, ERIM Tenth Thematic
Conference on Geologic Remote Sensing, Environmental Research Institute of Michigan, Ann Arbor,
MI, pp. I-407 - I-418.

CSES 1992. CSES (Center for the Study of Earth from Space), ATREM (Atmosphere Removal
Program) Users Guide, University of Colourado, 1992

Eklund, J., 1949 (in Danish). Blyforekomsterne ved Mestersvig, Østgrønland – Internal NM-report
1/49: 34 pp.

Hansen M. M., 1985 (in Danish). Redegørelse for miljøsituationen i forbindelse med Tailing-depotet
ved Mestersvig. Foreløbig Rapport, januar 1985. Grønlands Fiskeri- og miljøundersøgelser. 63 pp.

Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J., and
Goetz, A. F. H., 1993. The Spectral Image Processing System (SIPS) - Interactive Visualization and
Analysis of Imaging spectrometer Data: Remote Sensing of Environment, v. 44, p. 145 - 163.
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Kunzendorf, H., 1977. Geochemical exploration in the Mestersvig area, central east Greenland, 1971.
Unpublished report, RISØ, N-29-7: 60 pp.

Loring D. H., Rantala R .T. T.¸ 1992. Manual for geochemical analyses of marine sediments and for
suspended particulate matter. Earth-Sciences Reviews 1992; 32: 235-283.

Riget F., Asmund G. & Aastrup P., 2000. The use of lichen (Cetraria nivalis) and moss (Rhacomitrium
lanuginosum) as monitors for atmospheric deposition in Greenland. The Science of the Total
Environment 245: 137-148.

RSI (2002). ENVI manual, version 3.6. Research Systems Inc., Boulder, USA.

Tukiainen, T., 2000. Assessing and monitoring the environmental impact of mining activities in
Europe using advanced Earth Observation techniques - Airborne Hyperspectral Survey
Danmarks og Grønlands Geologiske Undersøgelse Rapport 2000/104, 24pp.

Vermote, E., Tanré, D., Deuzé, J.L., Herman, M., Morcrette, J.J. (1997). Second Simulation of the
Satellite Signal in the Solar Spectrum (6S). User Guide. Dep. of Geography, Univ. of Maryland,
NASA-Goddard Space Flight Center-code 923, Greenbelt, MD, USA. 218 pp.

Witzig, E. 1954. Stratigraphische und tektonische Beobachtungen in der Mesters Vig-Regin (Scoresby
Land, Nordost-grönland). –Meddr Grønland 72, 5: 26 pp.




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A1. APPENDICES
1.1.     Vegetation analyses

Table 9: Average species composition (6 samples) of poor Cassiope-heath with Salix, Silene, Luzula
confusa og Vaccinium uliginosum. The left column shows a heath, which was heavily affected by
tailings. The right column shows an undisturbed heath.
                                              Affected by tailings            Undisturbed
      Species                           Average         St. dev.        Average    St. dev.
      Bar jord                                   0.0200          0.0303     0.0017          0.0041
      Carex scirpoidea                           0.0050          0.0122     0.0200          0.0200
      Carex scirpoidea, dead                     0.0033          0.0082     0.0100          0.0089
      Cassiope tetragona                         0.0217          0.0098     0.2083          0.0534
      Cassiope tetragona, dead                   0.3283          0.1098     0.1650          0.0579
      Cerastium arcticum                         0.0000          0.0000     0.0017          0.0041
      Cetraria nivalis                           0.0000          0.0000     0.0017          0.0041
      Dryas octopetala                           0.0017          0.0041     0.0000          0.0000
      Dryas octopetala, dead                     0.0050          0.0122     0.0000          0.0000
      Harrimanella hypnoides                     0.0000          0.0000     0.0017          0.0041
      Lichen                                     0.0017          0.0041     0.0650          0.0281
      Litter                                     0.0917          0.0588     0.1883          0.0585
      Luzula confusa                             0.0400          0.0341     0.0017          0.0041
      Luzula confusa, dead                       0.0117          0.0098     0.0000          0.0000
      Mos                                        0.0167          0.0186     0.0300          0.0276
      Organic crust                              0.1233          0.0628     0.1900          0.0696
      Polygonum viviparum                        0.0000          0.0000     0.0033          0.0052
      Polygonum viviparum, dead                  0.0000          0.0000     0.0033          0.0052
      Salix arctica                              0.0167          0.0186     0.0500          0.0268
      Salix arctica, dead                        0.0067          0.0121     0.0100          0.0155
      Saxifraga oppositifolia                    0.0017          0.0041     0.0167          0.0207
      Saxifraga oppositifolia, dead              0.0000          0.0000     0.0017          0.0041
      Silene acaulis                             0.0050          0.0122     0.0133          0.0082
      Silene acaulis, dead                       0.0000          0.0000     0.0050          0.0055
      Sten                                       0.0367          0.0320     0.0117          0.0098
      Tailing                                    0.2633          0.1461     0.0000          0.0000




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1.2.   Chemical analyses

Table 10: Chemical analyses of tailings.
NERI ID Cu (mg/kg) Pb (mg/kg) Zn (mg/kg) Longitude Latitude
 24004             459          4088     59657 -24,10523 72,19033
 24005             134          1868     11094 -24,10651 72,18998
 24006             311          5465     21499 -24,10987 72,18975
 24007             207          3699     18040 -24,11119 72,18949
 24008             180          4165     18794 -24,11230 72,18927
 24009             308          3721     39593 -24,11667 72,18828
 24010             369          3045     17348 -24,11844 72,18775
 24011             439          9425     19900 -24,11906 72,18747
 24012             622         12622     15002 -24,11998 72,18733
 24013             344          5747     23660 -24,12119 72,18699
 24014             516          9816     28368 -24,12230 72,18666




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Table 11: Chemical analyses of soil
NERI ID Cu (mg/kg) Pb (mg/kg) Zn (mg/kg) Longitude Latitude
 23902              34              66   176 -24,12857 72,18939
 23905              22            262    355 -24,13879 72,18940
 23908               7              18    42 -24,14714 72,18846
 23911              11              14    19 -24,14993 72,18896
 23914              11              50    35 -24,15685 72,18911
 23917              23            237     53 -24,16734 72,18839
 23920              22              24    54 -24,15924 72,18702
 23923              16              23   115 -24,14357 72,18375
 23926              17              51    96 -24,13245 72,18295
 23929              20            124    226 -24,11352 72,18491
 23937              18              99   118 -24,09461 72,21066
 23941              24              97   101 -24,01536 72,22481
 24003              31            264   1213 -24,12979 72,18625
 24015             224          2325    8424 -24,11248 72,18974
 24016             180          1786    8713 -24,11248 72,18974
 24017             404            330  14915 -24,11248 72,18974
 24018             185          2305    8590 -24,11248 72,18974
 24019             322            316  13094 -24,11248 72,18974
 24022               9              83    57 -24,07325 72,16458
 24025              10              58    56 -24,09619 72,17563
 24028              19              92   193 -24,13230 72,18893
 24031               6              13    32 -24,07444 72,18500
 24034              15              58    92 -24,14607 72,17772
 24037              18              51    70 -24,16506 72,17262
 24044              12              50    80 -24,11488 72,18286
 24047             138          2518    5090 -24,11678 72,18625
 24048             275          3043    8199 -24,11678 72,18625
 24050              10              51    43 -24,11355 72,18491
 24053              20              26    44 -24,06399 72,21599
 24056              21              39    52 -24,03959 72,21872
 24059              10              16    66 -24,04798 72,22693
 24062              33              74   106 -24,02904 72,23056
 24065              11              59    52 -23,99925 72,23119
 24068              28              63   121 -23,94150 72,25646
 24091              12              20    71 -23,90567 72,22527
 24093              19              60   115 -23,90843 72,21920
 24101               2              17    13 -24,14904 72,18866
 24103              12              14    30 -24,15346 72,18578
 24105              25              76   323 -24,03152 72,09249
 24109              35            138    500 -24,02626 72,09375
 24113              18              25   195 -24,01891 72,09298
 24117              18              18    42 -23,96731 72,24952
 24119               8              21    28 -23,96617 72,24412
 24122              28              18    79 -23,93183 72,27030
 24128              24              48    75 -23,92795 72,26381




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Table 12: Chemical analyses of Salix arctica
NERI ID Cu (mg/kg) Pb (mg/kg) Zn (mg/kg) Cd (mg/kg) Longitude Latitude
 23931              54           755         2651 11,64 -24,11243 72,18793
 24040               9               9        308  5,13 -24,10866 72,17753
 24106              10               6        559  7,63 -24,03152 72,09249
 24111               9             13         511 13,30 -24,02626 72,09375
 24114               9             11         364  7,57 -24,01891 72,09298



Table 13: Chemical analyses of Luzula confusa and L. spicata
NERI ID Cu (mg/kg) Pb (mg/kg) Zn (mg/kg) Cd (mg/kg) Longitude Latitude
 23932              42           497         2827            7,40 -24,11243 72,18793
 24041               6              5          155           0,26 -24,10866 72,17753




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Table 14: Chemical analyses of river sediments
NERI ID GEUS ID Cu (mg/kg) Pb (mg/kg) Zn (mg/kg) Ba (mg/kg) Longitude Latitude
 24094         .                10            11    23 .      -23,90420 72,21742
 24095         .                15            18    36 .      -23,90644 72,21594
 24096         .                  8           19    21 .      -23,90650 72,21592
 24102         .                  5           16    28 .      -24,20938 72,17313
 23885         1                  8           28    47    493 -24,10525 72,18231
 23882         2                10            28    52    269 -24,10514 72,18094
 24193         3                15            20    82    264 -24,10453 72,18564
 23998         4                15            23    78    381 -24,11358 72,18442
 23993         5                15            18    80    309 -24,11944 72,18256
 23886         6                16            23    78    338 -24,12244 72,18086
 23889         7                36            18    58    228 -24,13475 72,17833
 23896        18                35          694   1684   1019 -24,08836 72,21100
 23995        19                18            99   687    559 -23,91136 72,24958
 23897        20                15          147    836    797 -24,10483 72,20044
 23893        21                25            96   302    633 -24,10483 72,19547
 23997        22                10            23    58    221 -24,10275 72,18975
 23994        23                22            28   100    452 -24,10431 72,18647
 23888        29                21         1456   6290    663 -24,14542 72,19069
 23894        30                28            52    43    582 -24,15056 72,19246
 24195        31                16            20    35    367 -24,14709 72,19180
 23895        32                17            96   921    310 -24,14306 72,19149
 23890        33                22            55   208    370 -24,14098 72,19169
 23883        34                12            45    82    353 -24,13597 72,19189
 24199        35                12            37    97    505 -24,13080 72,19142
 23999        36                17            38   136    391 -24,12863 72,19046
 24198        37                20            86   132    423 -24,12865 72,19047
 23996        38               310         3625  25292    138 -24,11795 72,18788
 23880        39                29          153    870    710 -23,94584 72,22762
 24196        40                11            35   282    246 -23,94550 72,22785
 24194        41                24          107    749    686 -23,94578 72,22838
 23892        42                16            54   409    204 -23,94200 72,22938
 24200        89                49          123    180    287 -24,09286 72,21072
 23877        90                  8         195    178    113 -24,06428 72,21619
 23884        91                21          134    699    757 -23,98361 72,23569
 23881        92                21          506    926    815 -23,90836 72,25500
 23879        93                  7         349    355     92 -23,91142 72,25461
 23891        94                26          115    555    479 -23,91286 72,22353
 23887        95                23            97   418    645 -23,91044 72,22286
 24000        96                21          120    331    607 -23,89914 72,22506
 24197        97                19            25    76    171 -23,91100 72,21983
 23878        98                16            14    39    207 -23,90350 72,21711




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Table 15: Chemical analyses of Cetraria nivalis
NERI ID Cu (mg/kg) Pb (mg/kg) Zn (mg/kg) Longitude Latitude
 23901              <5              7            37 -24,12857 72,18939
 23904              <5           130             83 -24,13879 72,18940
 23907              <5              3            29 -24,14714 72,18846
 23910              <5             17            12 -24,14993 72,18896
 23913              <5              3            14 -24,15685 72,18911
 23916              <5             14            21 -24,16734 72,18839
 23919              <5              3            18 -24,15924 72,18702
 23922              <5              8            24 -24,14357 72,18375
 23925              <5             13            41 -24,13245 72,18295
 23928              <5          1534            591 -24,11352 72,18491
 23936              <5             28            28 -24,09461 72,21066
 23939              <5             39            24 -24,01536 72,22481
 24001              <5           256             75 -24,13990 72,18327
 24002              <5             36           144 -24,12979 72,18625
 24020              <5             79            61 -24,10859 72,17757
 24021              <5             10            26 -24,07325 72,16458
 24024              <5             62            61 -24,09619 72,17563
 24027              <5             13            50 -24,13230 72,18893
 24030              <5              3            15 -24,07444 72,18500
 24033              <5             12            20 -24,14607 72,17772
 24036              <5              3            16 -24,16506 72,17262
 24045              <5           118             95 -24,11488 72,18286
 24049              <5             32            34 -24,11355 72,18491
 24052              <5             57            36 -24,06399 72,21599
 24055              <5             40            28 -24,03959 72,21872
 24058              <5              3            19 -24,04798 72,22693
 24061              <5              4            21 -24,02904 72,23056
 24064              <5             35            35 -23,99925 72,23119
 24067              <5             15            28 -23,94150 72,25646
 24090              <5             16            29 -23,90567 72,22527
 24092              <5             16            51 -23,90843 72,21920
 24104              <5             30           103 -24,03152 72,09249
 24108              <5              3            13 -24,02626 72,09375
 24112              <5              3            23 -24,01891 72,09298
 24116              <5              2            14 -23,96731 72,24952
 24118              <5              9            24 -23,96617 72,24412
 24121              <5              6            16 -23,93183 72,27030
 24127              <5              3            15 -23,92795 72,26381
 24188              <5              3            18 -24,00562 72,26782




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Table 16: Chemical analyses of Cassiope tetragona
NERI ID Cu (mg/kg) Pb (mg/kg) Zn (mg/kg) Cd (mg/kg) Longitude Latitude
 23933              73          1075          1896  8,65 -24,11243 72,18793
 23935             354          9155         11541 60,47 -24,11243 72,18793
 24042               6            12            35  0,07 -24,10866 72,17753
 24043               8            29            64  0,32 -24,10866 72,17753
 24107               4              4           25  0,03 -24,03152 72,09249
 24110               4              3           31  0,02 -24,02626 72,09375
 24115               4              3           29  0,01 -24,01891 72,09298




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Table 17: Chemical analyses of Beach sand
NERI ID Cu (mg/kg) Pb (mg/kg) Zn (mg/kg) Longitude Latitude
 23942               3            10        17 -23,73393 72,15818
 23943              11            13        36 -23,70782 72,17583
 23945               4             9        23 -23,72161 72,18621
 23947               1             8         6 -23,73955 72,22107
 23949              17            12        25 -23,80305 72,23029
 23951               3             7        11 -23,81561 72,24647
 23953              21            10        31 -23,82806 72,25304
 23955               5            10        12 -23,85058 72,25360
 23957               3            14        13 -23,86564 72,25284
 24070              21          1479      1346 -23,92676 72,25909
 24071               4           419       314 -23,92680 72,25922
 24072               4           653       615 -23,92680 72,25922
 24073               4           405       277 -23,92681 72,25929
 24074               5           286       284 -23,92693 72,25939
 24075              12            26        51 -23,92695 72,25940
 24076               5            63        55 -23,90887 72,25516
 24077               5            79        72 -23,90833 72,25514
 24078               5           133        83 -23,90785 72,25495
 24079              24           756      1243 -23,90798 72,25490
 24080              16           465       672 -23,90785 72,25470
 24081               6           132       244 -23,91197 72,25474
 24082              12           147       501 -23,91254 72,22393
 24083              23           163      1173 -23,91154 72,22381
 24084              53          1191      3870 -23,90959 72,22367
 24085              23            95       499 -23,90827 72,22355
 24086              10            49       303 -23,90632 72,22371
 24087               8            47       362 -23,90537 72,22371
 24088               2            12        12 -23,90281 72,22439
 24089              13           104       528 -23,89797 72,22472
 24120              17            10        42 -23,93225 72,27135
 24123              10             9        40 -23,93266 72,27132
 24124               6            10        14 -23,92996 72,26727
 24125              12             9        16 -23,92969 72,26753
 24126               4             9        14 -23,92991 72,26735
 24138              38             3       100 -24,29354 72,34732
 24140              16            95       523 -24,31610 72,32845
 24143              24             9        30 -24,25848 72,32410
 24144               3            13        15 -24,12618 72,27867
 24183               1            14        20 -24,09762 72,27046
 24185               2            13        21 -24,06125 72,26857
 24187               4            17        18 -24,00573 72,26772
 24189               4             9        18 -23,98381 72,27148




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1.3.     Spatial distribution of heavy metals in field samples

The following pages show the spatial distribution of lead, zinc and copper in the field samples from
2001. The field samples have been grouped into sediments (tailings, river bed and beach sands),
lichens (Cetraria nivalis) and soils (surface samples from the locations of the lichen samples).




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Figure 32: Lead (Pb) in samples of tailings, river bed and beach sands. Concentrations range from
2.54 to 3625.48 mg/kg.




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Figure 33: Zinc (Zn) in field samples from tailings, river bed and beach sands. Concentrations range
from 6.09 to 21.24 mg/kg.




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Figure 34: Copper (Cu) in field samples from tailings, river bed and beach sands. Concentrations
range from 0.76 to 403.75 mg/kg.




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Figure 35: Lead (Pb) in lichen samples (Cetraria nivalis). Concentrations range from 2.47 to 1534,31
mg/kg.




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Figure 36: Zinc (Zn) in lichen samples (Cetraria nivalis). Concentrations range from 12.04 to 590.79
mg/kg.




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Figure 37: Lead (Pb) in surface soil samples. Concentrations range from 13.2 to 3043.49 g/kg.




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Figure 38: Zinc (Zn) in surface soil samples. Concentrations range from 12.89 to 14914.79 mg/kg.




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Figure 39: Copper (Cu) in surface soil samples. Concentrations range from 1.99 to 403.75 mg/kg.




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1.3.1.     Pollution levels from 1971
The following Table 18 list the results from a survey done in the river systems at Mestersvig in 1971
(Kunzendorf, 1977). Levels were higher at that time and especially the distribution has changed.

Table 18: Pollution levels of lead, zinc and copper in the river systems around Mestersvig
(Kunzendorf, 1977).
   GGU          LATITUDE         LONGITUDE        Lead (Pb)      Zinc (Zn) Copper (Cu)
    ID           degrees            degrees         mg/kg         mg/kg         mg/kg
   100257           72.16791           -24.14914          29.0          62.0         10.0
   100254           72.16173           -24.15359          30.0          72.0         10.0
   100256           72.16735           -24.15159          34.0          65.0         11.0
   100257           72.16480           -24.15263          29.0          62.0         10.0
   100258           72.17079           -24.14772          35.0          66.0          7.0
   100259           72.17276           -24.14275          32.0          57.0          5.0
   100260           72.17544           -24.13323          32.0          50.0         11.0
   100284           72.18786           -24.09558          34.0          55.0         13.0
   100285           72.18498           -24.08740          32.0          48.0          5.0
   100286           72.18591           -24.06924          29.0          55.0         24.0
   148286           72.22365           -23.90080        320.0         2765.0         35.0
   148287           72.22366           -23.90612        307.0         5260.0         83.0
   148288           72.22695           -23.92526        316.0         2369.0         34.0
   148289           72.23554           -23.96975        436.0         4086.0         63.0
   148290           72.23256           -23.97884        306.0         2210.0         34.0
   100288           72.24099           -23.98021        671.0         7657.0        118.0
   100289           72.24250           -23.98765        315.0         1430.0         27.0
   148291           72.22820           -23.98771        874.0         9502.0        119.0
   148292           72.22581           -24.00089       1027.0         8447.0        109.0
   148293           72.21485           -24.03738        408.0         4262.0         54.0
   148294           72.21404           -24.05772        731.0        11108.0        120.0
   148295           72.20923           -24.08509       2531.0        57025.0        451.0
   100261           72.19215           -24.09656       4133.0        73758.0        706.0
   100262           72.19530           -24.09763       3002.0        65654.0        533.0
   100263           72.20257           -24.09487        564.0         4802.0         43.0
   100275           72.20494           -24.02671          30.0          48.0         22.0
   100276           72.20280           -24.02795          25.0          67.0         38.0
   100277           72.20027           -24.02886          29.0          67.0         37.0
   100278           72.19847           -24.02990          29.0          76.0         61.0
   100279           72.19461           -24.03245          30.0          91.0        141.0
   100274           72.20526           -24.02118          30.0          49.0         12.0
   100280           72.19399           -24.01960          30.0          63.0         26.0
   100271           72.20106           -24.00907          33.0          77.0         62.0
   100272           72.19958           -24.01573          30.0          72.0         57.0
   100281           72.20050           -24.00845          38.0          74.0         15.0
   148297           72.19391           -23.98825        239.0          281.0         32.0
   148296           72.19231           -23.98168          37.0          63.0         14.0
   100264           72.20360           -24.00763          42.0          68.0         16.0
   148300           72.20780           -24.01158          49.0          57.0         27.0
   100283           72.17979           -24.06079          32.0          50.0          6.0
   100282           72.18037           -24.08052          34.0          69.0          9.0
   100287           72.17614           -24.08114          32.0          92.0          8.0
   148298           72.19805           -23.99902          50.0          64.0         21.0
   100269           72.19575           -23.99492          41.0          46.0         12.0
   100266           72.18781           -23.97729          95.0         145.0         17.0
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  GGU      LATITUDE       LONGITUDE           Lead (Pb)        Zinc (Zn) Copper (Cu)
   ID       degrees         degrees             mg/kg           mg/kg      mg/kg
  100267       72.18689       -23.98136              33.0             50.0       11.0
  100270       72.18623       -23.97703              28.0             34.0        4.0
  100265       72.18795       -23.97285              47.0             69.0       22.0
  100268       72.18639       -23.97142              36.0             47.0       12.0
  100273       72.21164       -24.00822              43.0             70.0       23.0
  100213       72.17120       -24.21888              42.0             93.0       20.0
  100214       72.17186       -24.20987              40.0             67.0       12.0
  100220       72.17777       -24.12981              38.0             75.0       14.0
  100219       72.17627       -24.14250              43.0             85.0       19.0
  100218       72.17576       -24.16215              39.0             64.0       13.0
  100215       72.17290       -24.20241              35.0             79.0       10.0
  100216       72.17304       -24.19755              56.0            113.0       20.0
  100217       72.17391       -24.18836              46.0             71.0       35.0
  100245       72.14509       -24.15329              38.0             74.0        7.0
  100246       72.14538       -24.15143              45.0             72.0       17.0
  100247       72.14905       -24.14931              33.0             56.0       16.0
  100248       72.15077       -24.15151              32.0             52.0       13.0
  100251       72.15500       -24.15117              41.0             70.0       12.0
  100253       72.16116       -24.15244              38.0             75.0       19.0
  100252       72.15758       -24.15246              42.0             64.0       19.0
  100249       72.15121       -24.14840              41.0             81.0       13.0
  100250       72.15277       -24.15249              44.0             65.0       14.0
  100221       72.17929       -24.12412              34.0             70.0       11.0
  100222       72.18403       -24.10412              36.0             69.0       14.0
  100223       72.19261       -24.10854              80.0            188.0       19.0
  100224       72.19869       -24.10595              38.0             72.0       32.0
  100212       72.23002       -23.98003              51.0             64.0       25.0
  100211       72.21570       -24.00323              40.0             66.0       28.0
  100243       72.18048       -24.09460              30.0             58.0        7.0
  100241       72.16852       -24.07960              32.0             50.0       10.0
  100229       72.15898       -24.05097              37.0             61.0       24.0
  100228       72.15938       -24.05607              36.0             67.0       11.0
  100230       72.15899       -24.04461              31.0             46.0       15.0
  100237       72.16004       -24.05035              37.0             57.0       14.0
  100240       72.16643       -24.07289              34.0             54.0        9.0
  100239       72.16388       -24.06477              36.0             57.0       22.0
  100238       72.16066       -24.05761              38.0             55.0       14.0
  100244       72.17381       -24.09481              33.0             56.0        7.0
  100225       72.16826       -24.08592              33.0             61.0        4.0
  100226       72.16652       -24.08271              34.0             53.0       10.0
  100227       72.16176       -24.06521              32.0             49.0       11.0




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