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2024/25 Taught Postgraduate Module Catalogue

GEOG5710M Digital Image Processing for Environmental Remote Sensing

15 creditsClass Size: 100

Module manager: Duncan Quincey

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2024/25

Pre-requisite qualifications

Students should have completed the first semester of the MSc programme before attempting this module, or be able to evidence equivalent prior learning through other educational programmes or work experience.

This module is not approved as an Elective

Module summary

On completion of this module, you will:- Be able to identify and discuss concepts, theories and methods of earth observation from aircraft and earth orbital satellites for environmental research and for input into GIS;- Be able to use appropriate software to read, display, restore, enhance, classify and extract information about the land surface from diverse types of remote sensing images;- Be able to explain and perform the standard workflow used to turn remote sensing data (i.e. images) into information (i.e. thematic outputs) in a range of applications.


This module seeks to:
- Introduce students to the fundamental principles behind satellite image acquisition, processing and interpretation.
- Enhance student ability in how to display, restore and manipulate raw image data and undertake some simple image arithmetic.
- Increase understanding and implementation of image classification, and standard protocols for collecting training data and evaluating classification accuracy.
- Enable students to source and download their own imagery, undertake basic pre-processing, and classify their images using both supervised and unsupervised approaches, ultimately leading to assessments and interpretation of environmental change.

Learning outcomes
On successful completion of the module, you will be able to:
1. Identify and explain of the concepts, theories, methods and use of digital image processing of earth observation images for environmental research;
2. Evaluate and pre-process appropriate data before analysis and be able to identify objective image classification techniques and determine the appropriate scenarios in which to apply them.
3. Discuss and appraise selected scientific literature on earth observation, image processing of earth observation images and use of the resulting processed data in environmental research, physical geography and geographical information systems.

Skills Learning Outcomes

On successful completion of the module, you will be able to:
4. Use a market leading software in digital image processing to read, display, restore, enhance, classify and extract information about the land surface from diverse types of remote sensing images.
5. Identify and appraise the sources of spatial environmental data, and how they should be processed before analysis.
6. Select the relevant remote sensing classification methodology that can be applied to a range of societal and global environmental and planning scenarios.

These outcomes align very closely with Academic, Work Ready, Digital and Technical domains of the Leeds Skills matrix.


Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module

Teaching methods

Delivery typeNumberLength hoursStudent hours
Independent online learning hours48.00
Private study hours78.00
Total Contact hours24.00
Total hours (100hr per 10 credits)150.00

Opportunities for Formative Feedback

Formative feedback will be provided during practical sessions and lectures. The module leader will also be on hand to provide support (email / Teams etc) during the teaching weeks, in advance of assessment. Whilst not directly formative assessment, this will ensure that the students receive feedback / support on matters of need.

Methods of assessment

Assessment typeNotes% of formal assessment
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

There is no reading list for this module

Last updated: 29/04/2024 16:14:37


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