MSc Data Science (Earth and Environmental Analytics)

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.

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

By the end of the unit students will have an appreciation for the range of remote sensing systems available to address environmental issues and will be able to make decisions about the best techniques and technology to utilise for different environmental applications. These skills will be especially for those seeking careers in the environmental or data science commercial sectors and for those seeking to go into research careers.

The unit uses structured exercises and computer-based labs to reinforce concepts introduced in the lectures and to provide hands-on experience of image processing and image interpretation. Students are also expected to read and cite relevant peer-reviewed literature for all assessed work.

The unit uses the open access online platform Google Earth Engine (GEE) as the main processing software. The unit requires students to code image processing operations, although no previous coding experience is assumed.

Examples of topics covered include:

Fundamentals of remote sensing, Electromagnetic spectrum, Spectral signatures, Platforms, sensors and their characteristics used in remote sensing, Data quality control, Monitoring land surfaces, Time series analysis from space.

In person:

Lectures - Highlighting key points of required knowledge and explaining key concepts and theories (8 hours).
Tutorial/Discussion sessions - Checking required understanding of the main concepts (3 hours).
Computer workshops are used for students to gain hands-on experience and learning (6 hours).
Assessment workshops – Answering questions related to the assessment (6 hours).
Guided independent study (127 hours) – Additional reading, research and preparation for unit assessments.

E-learning approaches:

A virtual learning environment - Delivery of  all materials related to the unit.

• Understand of key principles in Earth observation (EO), including: spectral signatures, vegetation indices; image classification; image spatial, temporal, spectral and radiometric resolution.
• Develop an awareness of the wide range of remote sensing systems and understand how and why they suit different environmental applications.

• Handle and apply technical concepts in EO and critically evaluate the results.
• Critically assess and evaluate the suitability of EO satellite derived products for particular applications.
• Develop research skills including reading, critically judging and evaluating scientific.

• Handle remote sensing data from a range of systems using computers and appropriate software.
• Apply key algorithms to interpret remotely sensed imagery.
• Manage raster data and other spatial data files.
• Source appropriate EO images from online image archives.

• Communicate and express geospatial ideas and results in written, oral and visual form (e.g. images and graphs).
• Develop skills of time management and bibliographic research.

A poster on the potential of EO for long term environmental monitoring (500 words 20%).

A 2,500 word research paper demonstrating the use of one or more time-series data processing approaches to address an environmental research question/application of the students own choosing (80%).

Formative Assessment Tasks
Quizzes complete online and in class
Asynchronous - Written feedback provided immediately.
Synchronous - Verbal feedback in class, immediately after each question is answered.
Computer practical exercises
Written answers are provided 5-7 days after the session.

Assessment Tasks
Poster - Written feedback within 3 working weeks of submission.
Research paper - Written feedback within 3 working weeks of submission.

Basic reading

Campbell, J.B. (2007), Introduction to Remote Sensing.

Lillesand, T., Kieffer, R.W., and Chipman, J. (2008) Remote Sensing and Image Interpretation

Online reading list

https://www.readinglists.manchester.ac.uk/leganto/readinglist/searchlists/338988755210001631?auth=CAS