TAILING MODELLED AND MEASURED SPECTRUM FOR MINE TAILING MAPPING IN TUNISIAN SEMI-ARID CONTEXT

TAILING MODELLED AND MEASURED SPECTRUM FOR

MINE TAILING MAPPING INTUNISIAN SEMI-ARID CONTEXT

N. Mezned1,2, S. Abdeljaouad1 , M. R. Boussema3

12011 IEEE Internaional Geoscience and Remote

Sensing Symposium- 29 julyN. Mezned

1 RME/FST, (Tunis, Tunisia)

3 LTSIRS/ENIT, (Tunis, Tunisia)

2 Isepbg (Tunis, Tunisia)

RME

Context

22011 IEEE Internaional Geoscience and

Remote Sensing Symposium- 29 julyN. Mezned

Mine tailing impact

Water qualitySoils Vegetation Ecologic Systems

Ba/Fl Hammam Zriba mine site Tunisia

Pb/Zn Jebel Hallouf-Bouaouane mine site Tunisia

Pb/Zn Jebel Ressas mine site Tunisia Pb/Zn Jebel Ressas mine site Tunisia

Human life

Context

Mejerda river watershed: precious source of water

3

North of Tunisia: several types of mine (Pb, Zn, Fl, …, etc.)

Environment risksa

aa

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 julyN. Mezned

Context

4

Necessity of mine tailing mapping

Advantages: low costs spatial coverage

Remote sensing: satellite data2011 IEEE Internaional Geoscience and

Remote Sensing Symposium- 29 julyN. Mezned

1) Context

2) Study area and problematic

3) The used data

4) The proposed approach

5) Experimental results

6) Conclusion and perspectives

OUTLINES

52011 IEEE Internaional Geoscience and

Remote Sensing Symposium- 29 julyN. Mezned

2) Study area and problematic

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Mine tailings

Kassab Wady

Mejerda River

Jebel Hallouf-Bouaouane Mine

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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2) Study area and problematic

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188 mille tonnes of metal (84 Pb et 64 Zn) in 1952 Abandoned since 1986

Jebel Hallouf-Bouaouane

Environment impactMine activity

Important quantity of tailing

Terrain subsidence 2011 IEEE Internaional Geoscience and

Remote Sensing Symposium- 29 julyN. Mezned

1) Context

2) Study area and problematic

3) Work positioning

4) The used data

5) The proposed approach

6) Experimental results

7) Conclusion and perspectives

OUTLINES

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3) Work positioning

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Passive Remote sensed data

Multispectral data (landsat TM, ETM+, ASTER, etc.)

Mineral mapping using Landsat ETM+ data and field spectra measured with ASD spectroradiometer , (Liu et al., 2003)

Mineral mapping using Landsat TM data, (Zhang et al., 2007)

Mine site mapping using HyMap (Taylor and Vukovic, 2001) and Probe data (Staenz et al., 2003)

Hyperspectral data (Hyperion,HyMap, etc.)

+field measured spectra

or spectra from publicly library

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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3) Work positioning

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Passive Remote sensed data

Problems :• Mine tailing risks on environment and human health

Objective: Mine tailing mapping using multispectral data Tailing modelled spectra with respect to the field truth

SMA overcome the luck of spectroradimeter

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

N. Mezned

1) Context

2) Study area and problematic

3) Work positioning

4) The used data

5) The proposed approach

6) Experimental results

7) Conclusion and perspectives

OUTLINES

112011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

N. Mezned

3) The used data

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Multispectral data: Landsat ETM+

• 6 bands,• 30 m,

Field campaign data: Mineral identification and abundance estimation

• 18 samples/dyke = 54 tailing measurements,

Landsat ETM+(05/03/2000)

Publically library: JPL spectral data

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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1) Context

2) Study area: Soil salinity, the problematic

3) The used data

4) Work positioning

5) The proposed approach

6) Experimental results

7) Conclusion and perspectives

OUTLINES

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5) The proposed approach

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Tailing modeling spectrum for ETM+ classification: spectral unmixing

ETM+ image pre-

processing

endmember select

ion

Classification

Validation

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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5) The proposed approach

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Tailing modeling spectrum for ETM+ classification: spectral unmixing

ETM+ image pre-

processing

endmember select

ion

Classification

Validation

Tailing component spectrum?

Direct mean: Measured by spectroradiometer

Indirect mean: Modelled with respect of field truth

Vegetation Soils

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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5) The proposed approach

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Tailing modeling spectrum for ETM+ classification: spectral unmixing

ETM+ image pre-

processing

endmember select

ion

Classification

Validation

Linear spectral unmixing

1. Vegetation2. Soils3. Mine tailings

3 fraction maps

Measured spectrum

Modelled spectrum

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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5) The proposed approach

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Tailing modeling spectrum for ETM+ classification: spectral unmixing

ETM+ image pre-

processing

endmember select

ion

Classification

Validation

Comparison RMS errors

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

N. Mezned

1) Context

2) Study area: Soil salinity, the problematic

3) The used data

4) Work positioning

5) The proposed approach

6) Experimental results

7) Conclusion and perspectives

OUTLINES

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5) Experimental results1. Tailing modelled spectrum: SMA

Re sampled spectra to Landsat ETM+ band passes

linear combination

JPL library

Pyrite

Quartz

Calcite

Galena

Hematite

Goethite

Kaolinite

Sphalerite

Tailing Modelled spectrum

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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5) Experimental results1. Tailing mlodelled spectrum: SMA

Sampling18 samples for each dyke = 54 samples

- X Ray Diffraction XRD

- Counting on polished sections

- Calcimetry Identification and

% of minerals

Laboratory analysis

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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5) Experimental results2. ETM+ Linear spectral unmixing

• We used both ASD measured and SMA modelled spectra in the classification processes,

Mine tailing fraction maps generated from the ETM+ linear spectral unmixing using: (a) the measured spectrum with ASD spectroradiometer and (b) the modelled tailing spectrum and (c)

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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5) Experimental results2. Classification validation

< 2.6 10-5

< 3.3 10-5

< 1.9 10-5

99.6 % of pixels have an RMS errors:

using the modelled spectrum,

using the measured spectrum.

using derived ETM+ spectrum

tailing map

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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5) Experimental results2. Classification validation

< 2.6 10-5

< 3.3 10-5

< 1.9 10-5

using the modelled spectrum,

using the measured spectrum.

using derived ETM+ spectrum

tailing map

99.6 % of pixels have an RMS errors:

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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5) Experimental results2. Classification validation

< 2.6 10-5

< 3.3 10-5

< 1.9 10-5

using the modelled spectrum,

using the measured spectrum.

using derived ETM+ spectrum

tailing map

99.6 % of pixels have an RMS errors:

2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

N. Mezned

1) Context

2) Study area: Soil salinity, the problematic

3) The used data

4) Work positioning

5) The proposed approach

6) Experimental results

7) Conclusion and perspectives

OUTLINES

252011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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6) Conclusion and perspectives

The results comparison indicate that the modelled spectrum can even better characterize the tailings in the case of semi-arid context,

The SMA approach can be an optimal solution to replace the lack of the spectroradiometer and can be applied successfully to multispectral data analysis, particularly those acquired during previous periods.

Conclusion

Perspectives

We plan for more campaign, We propose to test the SMA approach for different mining sites.

262011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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Thanks for your attention

272011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july

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