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Remote Sensing of Water Pollution

What's the Master's Geo-information Science and Earth Observation about?

Water contamination weakens and destroys natural systems that support human health, food production, and biodiversity.

Managing water quality is a great challenge simply because many factors are contributing; the way we use water, practices that threaten water quality, the different pollutants and their sources and the forcing hydrology. Knowing the processes of these factors affecting water quality is not sufficient. It is essential to monitor these factors to understand how they combine and interact for different regions. 

Current monitoring practices by water managers depend largely on labour-intensive measurements, which provide only a limited insight into the spatial and temporal distributions. On the other hand, Earth Observation (EO) technology offers a significant benefit due to its synoptic coverage and repeated surveillance. However, the added value of EO data is determined, primarily, by the ability to convert these data into accurate information on water quality variables. 

This course will increase your knowledge and technical skills in using EO data to derive water quality indicators and assess their accuracy.

For whom is this course relevant?

Tertiary: at graduate and post-graduate levels.

  • MSc
  • PhD candidate

What is the course content?

The course builds upon the M-GEO- core and the WREM 2.0 courses and contains four interwoven learning modules. The first three learning modules form the centre of this course, whereas the last module comprises a closure to this course:

  1. Basic principles of remote sensing of water quality.
  2. EO data access and handling.
  3. EO-based water quality mapping with 2SeaColor.
  4. Challenge is a case study whereby the participants apply the newly gained knowledge and integrate different EO data to produce water quality maps of areas of interest.

What is the programme structure?

The teaching method is based on learning-by-doing and practice. Sets of exercises, therefore, support each lecture. There are two types of activity in this course:

  1. Learning-by-doing exercises: these exercises are paper-based, whereby the participants can enhance the learning experience of the physical concepts, e.g., solve simple problems or derive a value by applying sets of equations. In this respect, these exercises are embedded in the lecture.
  2. Skill development exercises: these exercises used python codes in the JNB environment. They focus on providing the participant with the competency to address a real-world problem, for example, using Sentinel 2 images to derive water quality variables in lakes and coastal water.

What will be achieved?

After completing this course your will be able to: 

  • - Recall pollution sources and pollutants in the marine environment 
  • - Summarise concepts of remote sensing of water quality
    • Recall the attenuation processes in the water
    • Summarise concepts of light interaction with water suspended and dissolved matters 
    • Apply parameterisations for the attenuation processes
    • Recall the concepts of radiometric quantities and their measurements 
    • Summarise concepts of deriving water quality variables from radiometric quantities
  • - Derive water quality variables from earth observation data

Why this course?

Do you want to develop the skills necessary to use satellite data to detect water pollution and monitor water quality? Then, this Online Course is what you need.

The high-level video lectures, self-study, quizzes and online discussions allow for an enjoyable online social experience. Live-streamed talks and visualised tutorials using state-of-the-art analysis tools will give you the skills to complete your learning experience on real-life study cases.

Career perspectives

Acquire the skills to use EO data to monitor water quality and detect pollution in coastal and inland waters.

Online learning, what is it like?

The course offers: 

  1. High-level video lectures, self-study, quizzes and online discussions
  2. Live-streamed talks and visualised tutorials using state-of-the-art analysis tools.

In this respect, the course is mainly based on self-study and attending the discussion sessions, with an expected study load of about 4 to 6 hours a week.

About your Diploma

Upon successful completion of this course you will receive a Certificate which will include the name of the course.

Along with your Certificate you will receive a Course Record providing all the subjects studied as part of the course. It states: the course code, subject, ECTS credits, exam date, location and the mark awarded.

If you decide to follow a full Postgraduate or Master's course at ITC, and after approval of the Examination Board, you will be exempted from the course(s) you followed successfully as an online course.

Admission requirements

  • A STEM diploma at a BSc level
  • Affinity with statistical and mathematical analysis
  • Affinity with python coding is a plus

Academic level and background

Applicants for an online course should have a Bachelor degree or equivalent from a recognised university in a discipline related to the course, preferably combined with working experience in a relevant field.

Documentation

The faculty accepts transcripts, degrees and diplomas in the following languages: Dutch, English, German, French and Spanish. It is at the discretion of the faculty to require additional English translations of all documents in other languages as well.

English language

As all courses are given in English, proficiency in the English language is a prerequisite.

If you are a national of one of the countries in this list (PDF), you are exempted from an English language test.

Please note: the requirements when applying for fellowships may vary according to the regulations of the fellowship provider.

English language tests: minimum requirements

Only internationally recognized test results are accepted.

TOEFL Paper-based Test (PBT)

550

TOEFL Internet-based Test

79-80

British Council / IELTS

6.0

Cambridge

C2 Proficiency / C1 Advanced

Computer skills

To follow online education you must have basic computer experience, regular access to internet, and e-mail. For some courses, additional computer skills are required (see description of specific course).

Technical requirements online education and assessment

For online education, we formulated guidelines to guarantee optimal performance. For online oral exams and proctoring during online assessments, the webcam and headset requirements need to be met.

GIS and remote sensing

Most online courses, except for the introductory course, require knowledge of, and skills in, working with GIS and/or digital image processing of remotely sensed data.

Candidates are asked to provide proof of identity during the registration process.

Key information

Certification
none
CROHO code
Duration
11 weeks
Application deadline
EU/EEA
24 October 2022
non EU/EEA
24 October 2022
Dutch
24 October 2022
Starting date
21 November 2022
End date
6 February 2023
ECTS
2
Tuition fees
Full period 2022 / 2023
part-time, institutional
€ 550
part-time, ODA-rate
€ 413