How to map oil spills from Space?

A big challenge to overcome using hyperspectral technology

Enrol to learn how to use Earth Observation to preserve the marine ecosystem from such threats

This textbook course will guide you to learn how to use hyperspectral and multispectral remote sensing images to:

  1. Map the extent of an oil spill event
  2. Identify the type of oil spilled
  3. Estimate the thickness of the oil spill in order to measure spilled oil volume


To accomplish these, two methodologies are presented presented based on satellite earth observation images. Also experiments using ground spectral measurements are used in order to build a Spectral Library and develop a model for thickness estimation.

This textbook course is ideal for students and professionals either in the EO&GIS domain or in the environmental monitoring domain.


The power of Spectral Unmixing

In oil spill mapping! While Synthetic Aperture RADAR (SAR) are used operationally to map oil spills, optical remote sensing and especially hyperspectral remote sensing can not only map, but also identify the oil spill type and estimate the spill thickness over it's entire extent!
Enroll now and advance your skills
The power of Spectral Unmixing

Course curriculum

  • 01
    Introduction
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  • 02
    Oil Spill Mapping Methodology
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  • 03
    Datasets to evaluate the Oil Spill Mapping Methodology
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    • Lesson 6: Datasets used to evaluate the methodology
  • 04
    Application of the Oil Spill Mapping Workflow
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    • Lesson 7: On ASTER Imagery (Part 1)
    • Lesson 8: On ASTER Imagery (Part 2)
    • Lesson 9: On MODIS Imagery (Part 1)
    • Lesson 10: On MODIS Imagery (Part 2)
    • Lesson 11: SpecTIR Imagery
  • 05
    Concluding Remarks for Oil Spill Mapping Workflow
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    • Lesson 12: Drafting conclusions on the application results
    • Further Reading
  • 06
    Integrated oil spill mapping methodology
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    • Lesson 13: Integrated oil spill mapping workflow
    • Lesson 14: Integrated oil spill mapping workflow graph
  • 07
    Relative Radiometric Normalization of Hyperspectral Images
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    • Lesson 15: Why to examine the relative radiometric normalization concept? FREE TRIAL
    • Lesson 16: Introducing the Relative Radiometric Concept (Part 1)
    • Lesson 17: Introducing the Relative Radiometric Concept (Part 2)
    • Lesson 18: Spectral Indices that are used to compare spectrums
    • Lesson 19: The Algorithm: Normalized Proximity and Similarity Methodology (NPSM) [Part 1]
    • Lesson 20: The Algorithm: Normalized Proximity and Similarity Methodology (NPSM) [Part 2]
    • Lesson 21: Data & Experiments to evaluate NPSM
    • Lesson 22: Evaluation Results of NPSM
    • Lesson 23: Evaluation Results of NPSM: Multispectral Images
    • Lesson 24: Evaluation Results of NPSM: Hyperspectral Images
    • Lesson 25: Concluding Remarks for the Relative Radiometric Normalization Process
    • References and Further Reading
  • 08
    Application of the integrated
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    • Lesson 26: Application of the Integrated oil spill mapping workflow
    • Lesson 27: Results of the Integrated oil spill mapping workflow (Multispectral)
    • Lesson 28: Resutls of the Integrated oil spill mapping workflow (Hyperspectral)
  • 09
    Conclusions of the Integrated oil spill mapping workflow
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    • Lesson 29: Drafting concluding remarks
  • 10
    Oil Spill thinkness Estimation
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    • Lesson 30: Introduction to the concept FREE TRIAL
    • Lesson 31: Methodological Approaches [Part 1]
    • Lesson 32: Methodological Approaches [Part 2]
    • Lesson 33: What data we need to approach the problem?
    • Lesson 34: Unmixing methods to estimate the oil spill thickness
    • Lesson 35: Oil Spill thickness estimation [Part 1]
    • Lesson 36: Oil Spill thickness estimation [Part 2]
    • Lesson 37: Concluding Remarks for Oil spill thickness estimation
    • References and Further Reading

What will you learn?

  • Hyperspectral oil spill mapping

  • Relative Radiometric Normalization

  • Oil spill type identification with hyperspectral data

  • Spectral Unmixing

  • Oil spill thickness estimation

  • Spectral Libraries building and updating

Any Prerequisites?

  • Practically none.

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Student Profile?

  • Under/post graduate students

  • Pr