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Study information

Environmental Remote Sensing

Module titleEnvironmental Remote Sensing
Module codeGEOM180
Academic year2024/5
Module staff

Dr Steven Palmer (Convenor)

Duration: Term123
Duration: Weeks


Number students taking module (anticipated)


Module description

In this module, you will learn about how different remote sensing approaches are used to quantify environmental changes, and how these observations can be used to help address global challenges such as the UN Sustainable Development Goals. Through lectures you will develop your understanding of the physical principles behind a range of remote sensing approaches, as well as the strengths and weaknesses of these approaches for a range of applications in environmental science. You will then put this understanding into practice during computer-based coding and data analysis tasks. Learning will be consolidated and extended during regular discussion seminars.

You will use GIS and coding skills gained during GEOM181 Coding for Spatial Analysis, which is a co-requisite module. You will have the opportunity to use knowledge and skills acquired during this module in GEOM183 Spatial Data Science.

Module aims - intentions of the module

The overall aims of this module are to:

  • Provide you with knowledge of a range of remote sensing technologies and approaches
  • Enhance your understanding of the practical considerations of working with large remote sensing datasets
  • Develop your quantitative analysis skills during hands-on computing tasks
  • Increase your awareness of the opportunities and limitations of using remote sensing observations to address selected global challenges.

The module is designed to enhance your employability by providing you with up-to-date knowledge and practical experience of working with several cutting-edge remote sensing datasets such as that provided by ESA’s Copernicus programme.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

  • 1. Summarise the underlying physics which underpin different remote sensing approaches
  • 2. Recognise the different data requirements, applications and limitations of remote sensing data
  • 3. Demonstrate your ability to manipulate large remote sensing datasets to identify key observations
  • 4. Perform quantitative analyses of remote sensing observations relating to environmental change

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

  • 5. Extract quantitative information from large datasets with an appreciation of uncertainty and limitations
  • 6. Make informed decisions about what approaches are most suitable for quantifying different aspects of environmental change

ILO: Personal and key skills

On successfully completing the module you will be able to...

  • 7. Clearly present analytical results both graphically and in the form of written text, and to justify choices made in how information is presented
  • 8. Critically interpret quantitative observations and discuss them with respect to previously published observations.

Syllabus plan

The first half of the module will be delivered primarily via lectures and discussion seminars. These will cover a range of both passive and active remote sensing approaches and will give you the knowledge and conceptual understanding required to critically evaluate remote sensing analyses. Key published literature will be discussed during approximately fortnightly seminars. The second half of the module will be focused on developing practical skills and knowledge of working with large remote sensing datasets. In the final part of the module, you will analyse a remote sensing dataset of your choice and explore how it can be used to address a specific global challenge of your choosing. Analysis will include exposure to different tools and programs including for example QGIS and Google Earth Engine.

Topics covered in the module are expected to include:

  • Remote sensing technology: platforms and sensors
  • The physics of remote sensing: passive and active systems
  • ESA’s Copernicus programme
  • Using Google Earth Engine to handle Remote Sensing data
  • Remote sensing image analysis
  • Visualising remote sensing analyses

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and teaching 10Lectures
Scheduled Learning and teaching 2Videos
Scheduled Learning and teaching 12Practical computing tasks
Scheduled Learning and teaching 8Discussion seminars
Guided Independent study 70Computer-based tasks to support learning and assessments
Guided Independent study 48Reading relevant literature, online research to support learning and assessments

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Short seminar presentations 2 x 5-min oral presentations1, 2, 5-8 Peer-to-peer and in-class feedback from staff

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
In-class computer-based exam on ELE 301 hour 1, 2, 6, 8 Individual written feedback for each question and oral cohort feedback
Remote sensing dataset technical report 70Equivalent to 2500 words 3-8 Individual written feedback

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
In-class computer-based exam on ELEComputer-based exam1, 2, 6, 8Referral/deferral period
Remote sensing dataset technical reportTechnical report3-8Referral/deferral period

Re-assessment notes

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e., a final overall module mark of less than 50%) you will be required to submit a further assessment. The mark given for a re-assessment taken as a result of referral will count for 100% of the final mark and will be capped at 50%.

Indicative learning resources - Basic reading

Basic reading: 

  • Richards, J.A. (2022) Remote Sensing Digital Image Analysis (vol. 5) New York: Springer. E-book available via the University Library.

Indicative learning resources - Web based and electronic resources

  • ELE

Indicative learning resources - Other resources

  • Relevant journal articles will be provided in advance of the discussion seminars. 

Key words search

Remote sensing, GIS, Python coding 

Credit value15
Module ECTS


Module pre-requisites


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