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Remote Sensing for Environmental Management

Module description

Spatial data acquired by satellites, and other flying platforms are increasingly used in decision-making processes about the natural environment. These ‘remotely sensed’ data are fundamentally measurements describing the way that electromagnetic radiation interacts with materials and substances on the Earth’s surface and scientists can use these to create dynamic maps and build models describing Earth system processes through space and time. In this module you will learn about the different ways that we can use remote sensing to monitor the Earth, starting from the ground and working upwards into space where Earth observation satellites are in orbit. In this module we cover the theory and applications of remote sensing drawing on examples from terrestrial and marine applications.

Module aims - intentions of the module

This module explores the wide range of methods used by scientists for ‘remote sensing’ the Earth’s many complex processes. The module is designed so that you can explore remote sensing from a range of scales and perspectives. We begin on the ground, learning the fundamentals of electromagnetic radiation and the physics of light. This allows you to grasp the background physical principles underlying the remote sensing approach. From here, we begin to scale up – firstly to drones – lightweight aircraft operated from the ground that are emerging as the latest, and most opportunistic remote sensing tools for environmental managers to use. Then we move to piloted aircraft systems, where sensors such as LiDAR are widely used. Finally, we move into space where you will learn about the plethora of Earth observation systems in operation. Throughout the focus is on considering the applications of Earth observation data, particularly within environmental science subject areas.

You will gain hands-on experience in working with data from a range of remote sensing as we move through the module, learning cutting-edge approaches and practicing the manipulation of data through open-source software (e.g. QGIS) and cloud-based geospatial processing workflows (e.g. Google Earth Engine). We will use a variety of case studies to teach you how to use algorithms for converting remote sensing data into spatial information products that are useful to the environmental sector. By the end of the module, you will have an appreciation for the range of remote sensing systems available to environmental managers, and will be able to make decisions about the best techniques and technology to utilise for different environmental applications in your future career. These skills will be especially useful if you are seeking jobs in the environmental sector, particularly within consultancy or management companies, who are frequently turning to remote sensing to assist in environmental decision-making.

The teaching contributions on this module are drawn from practical research experience gained by Karen Anderson and Bob Brewin, principally in areas of research including field spectroscopy, laser scanning, landscape modelling, drone-based sensing and satellite data analysis within terrestrial and marine systems.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

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

• 1. Recall the fundamental physical basis of remote sensing, and to describe key elements of the electromagnetic spectrum
• 2. Describe the main differences between different remote sensing systems and evaluate their relative merits effectively
• 3. Handle remote sensing data from a range of systems using computers and appropriate software
• 4. Arrive at appropriate decisions about the best remote sensing platforms and sensors for particular environmental management applications
• 5. Apply key algorithms for the extraction of spatial information from remote sensing data
• 6. Evaluate uncertainties in different remote sensing data sets

ILO: Discipline-specific skills

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

• 7. Describe in some detail essential facts and theory across a sub-discipline of the environmental sciences
• 8. Identify critical questions from the literature and synthesise research-informed examples from the literature into written work
• 9. Identify and implement, with guidance, appropriate methodologies and theories for addressing specific research problems in environmental sciences
• 10. With some guidance, deploy established techniques of analysis, practical investigation, and enquiry within environmental sciences
• 11. Describe and evaluate approaches to our understanding of the environmental sciences with reference to primary literature, reviews and research articles

ILO: Personal and key skills

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

• 12. Develop, with some guidance, a logical and reasoned argument with valid conclusions
• 13. Communicate ideas, principles and theories fluently using a variety of formats in a manner appropriate to the intended audience
• 14. Collect and interpret appropriate data and complete research-like tasks, drawing on a range of sources, with limited guidance
• 15. Evaluate own strengths and weaknesses in relation to professional and practical skills, and apply own evaluation criteria
• 16. Reflect effectively on learning experiences and summarise personal achievements
• 17. Work in a small team and deal proficiently with the issues that teamwork requires (i.e. communication, motivation, decision-making, awareness, responsibility, and management skills, including setting and working to deadlines)

Syllabus plan

The programme will consist of a series of lectures and wider-reading focused workshops each week. During some weeks there will be a hands-on practical exercise for you to undertake, or a demonstration, or a seminar. All practical classes are designed to run on open source software or over cloud-based internet platforms such as QGIS, Sentinel Hub and Google Earth Engine. All datasets are provided and shared via the ELE page.

Topics covered include:

• Fundamentals and history of remote sensing
• Electromagnetic spectrum
• Issues of scale and resolution – spatial, spectral, temporal
• Calibration of data and quality control
• Atmospheric impacts on remote sensing data
• Platforms and sensors in remote sensing
• Monitoring land surfaces
• Monitoring ocean surfaces
• Field spectroscopy
• Drones and applications
• Airborne LiDAR and applications
• Satellite remote sensing of the Earth system
• Landsat and time-series monitoring from space
• Global remote sensing products and processing data on the cloud
• New horizons for remote sensing

Practicals will cover the following broad themes:

• Spectroscopy
• Sentinel Hub
• LiDAR analysis
• Ecological zones using Google Earth Engine
• Image classification using Google Earth Engine
• Satellite-based time series analysis using Google Earth Engine
• Drone surveying

The module is assessed through three means:

1. A poster on a topic chosen from a list where innovative uses of remote sensing can be explored
2. A practical write up describing the individual findings from a series of classes where students analyse and extract information from lidar datasets
3. A technical element which will require the students to present a workflow and/or code to explain how a remote sensing experiment could be carried out

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

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

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

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 40%) 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 40%.

Indicative learning resources - Basic reading

• Campbell, J.B. (2007), Introduction to Remote Sensing
• Lillesand, T., Kieffer, R.W., and Chipman, J. (2008) Remote Sensing and Image Interpretation

Indicative learning resources - Web based and electronic resources

• ELE page: http://vle.exeter.ac.uk/course/view.php?id=4625

Indicative learning resources - Other resources

None

Key words search

Spatial Data, Natural Environment, Remote Sensing, Satellites, Spectroradiometers, UAVs, Environmental Management, Information Technology.