MSc Data Science (Earth and Environmental Analytics)

Year of entry: 2024

Course unit details:
Environmental Remote Sensing

Course unit fact file
Unit code GEOG60941
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Available as a free choice unit? Yes

Overview

Remote sensing provides a unique means of capturing vast quantities of spatially referenced data with complete coverage, synoptically and at a range of spatial and temporal scales. Thus, remote sensing is a fundamental tool for use in environmental modelling and in decision-support for environmental management.

 

The aim of this unit is to provide students with the knowledge and skills to enable them to use digital data for reliable thematic and quantitative information extraction. The unit places emphasis on the use of some of the more advanced computer-based techniques used for information extraction from remotely sensed data to support environmental applications. Specifically, we will be using the relatively new online Google Earth Engine Code Editor platform to code remote sensing processing operations in JavaScript.

Aims

To provide an understanding of the different ways in which remote sensing data can be used to monitor the Earth's surface. The unit provides a firm foundation in the principles and practice of remote sensing across a range of scales and perspectives

Teaching and learning methods

Lectures, computer labs , exercises, project work, directed reading, personal reading, web-based tutorials. All course materials are available via Blackboard.

There are 21 hours timetabled for this course.

- Lectures: 7 hours;

 

- Computer labs: 6 hours of directed practical’s ;

 

- Discussion and computer lab workshops: 11 hours

Knowledge and understanding

Students should:

• Understand the key principles in Earth observation (EO), including: spectral signatures of vegetation, soil and water, vegetation indices; the colour additive principle of false colour composites; image spatial, temporal, spectral and radiometric resolution.

 

• Develop an awareness of a wide range of remote sensing systems and understand how and why they suit different environmental applications.

Intellectual skills

Students should:

• Be able to handle and apply technical concepts in EO and critically evaluate the results.

• Be able to critically assess and evaluate the suitability of EO satellite derived products for particular applications.

• Develop research skills including reading, critically judging and evaluating scientific evidence; and abstracting and synthesising ideas.

Practical skills

Students should:

 

• Be able to handle remote sensing data from a range of systems using computers and appropriate software

• Be able to apply key algorithms to interpret remotely sensed imagery

• Be able to manage raster data and other spatial data files

• Be able to source appropriate EO images from online image archives

Transferable skills and personal qualities

Students should:

 

• Be able to communicate and express geospatial ideas and results in written, oral and visual form (images and graphs).

 

• Develop skills of time management, bibliographic research

Assessment methods

Answers to questions on the potential of remote sensing for long term monitoring of the Earth’s surface (1,200 words 30%).

A 2,500 word research report related to the use of multi-temporal data to understand environmental change in a location of your choosing (70%)

Feedback methods

Formative assessment and feedback (from both teaching staff and peers) is provided during the computing practical sessions. Summative feedback is provided through comments on coursework. In addition, informal discussions are welcomed throughout the module, notably through the use of discussion forums in Blackboard

Recommended reading

Core texts:

· Campbell, J. B. and Wynne R. H. (2011). Introduction to Remote Sensing, Fifth edition, Taylor and Francis, London.

 

· Lillesand, T.M., Kiefer, R.W. and Chipman, J. W. (2004) Remote Sensing and Image Interpretation, Fifth edition, Wiley, New York.

 

Other learning materials:

· Mather, P.M and Koch, M. (2011) Computer Processing of Remotely-Sensed Images, Fourth Edition, Wiley, Chichester.

 

· Jensen, J.R. (1996) Introductory Digital Image Processing, Second edition, Prentice-Hall, New Jersey

 

· Richards, J. A and Jia, X (2006) Remote Sensing Digital Image Analysis An Introduction. Berlin, Heidelberg :Springer Berlin Heidelberg, 4th Edition

Study hours

Scheduled activity hours
Lectures 7
Practical classes & workshops 17
Independent study hours
Independent study 126

Teaching staff

Staff member Role
Angela Harris Unit coordinator

Additional notes

Assessment methods

Answers to questions on the potential of remote sensing for long term monitoring of the Earth’s surface (1,200 words 30%).

A 2,500 word research report related to the use of multi-temporal data to understand environmental change in a location of your choosing (70%)

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