Postgraduate Course: Passive Earth Observation: New Platforms, Sensors, and Analytical Methods (PGGE11241)
|School||School of Geosciences
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|Summary||This course aims to provide advanced theoretical knowledge and practical skills in natural and built environment monitoring and spatial sampling. A range of the latest and next generation of EO platforms (near-ground, airborne and space-based), passive Earth observation sensors (optical (RGB), thermal and hyperspectral) and advanced processing analytical methods (camera calibration, 3D modelling and photogrammetry, and multi-sensor object-based image analysis) will be introduced.
This course will provide an introduction to the latest and the next generation of environmental monitoring and spatial sampling platforms (ground based systems, small fixed- and rotary-wing unmanned aerial vehicles (sUAVs); airborne (Airborne GeoSciences aircraft) and space-based platforms (e.g. ESA Sentinel 2 and 3 and Fluorescence Explorer missions) and the latest instruments (RGB, multispectral, spectrometers, hyperspectral fluorescence sensors). Analytical methods for scientific applications will be introduced and practical skills gained.
By the end of this course, students should have achieved and demonstrated, or be able to achieve and demonstrate, an understanding of the following:
The advantages and disadvantages of the full range of EO methods and platforms now available (field-based, UAV, airborne and space-based)
The use of small UAV platforms and their flight control systems: inertial management units (IMU); GPS, and flight aspect and position logging systems. The H&S and regulatory framework for operation of UAVs
Multispectral observations from the range of platforms, and the additional challenges of using miniature imaging system and radiometers on UAVs and the post processing and analysis of high spatial resolution (sub decimetre) imagery acquired using these sensors.
The principles of field spectroscopy and hyperspectral EO and techniques for the collection and analysis of hyperspectral data
The course will consist of 11 four-hour sessions combining lecture / tutorial and practical sessions. The typical format will be a 1-1.5 hour lecture followed by practical sessions, followed by student led discussion / reading. This is not a rigid structure and will vary slightly from week to week.
Course Delivery Information
|Academic year 2022/23, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Lecture Hours 10,
Seminar/Tutorial Hours 15,
Supervised Practical/Workshop/Studio Hours 15,
Feedback/Feedforward Hours 5,
Summative Assessment Hours 100,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
Practical Image Analysis Assessment (40%) - due Thursday, week 5.
Seminar Assessment (20%)
- Students required to submit two candidate papers by Tuesday, week 2, for selection by Course Organiser.
- Presentation outline to be submitted to Course Organiser by Monday, week 3 for feedback.
- Seminar Day: Monday, week 5.
Practical Assessment: Practical Project write-up (40%) - due Thursday, week 11.
||Practical Assignment 1 to assess the utility of platforms introduced during the first lecture. To be submitted for assessment and informative feedback (for submission end week 2 after all platforms and sampling methods have been introduced)
Practical Assignment 2 on sensors to be submitted for assessment and informative feedback (for submission end week 7 after all sensor systems have been introduced)
Final Practical assignment to be submitted for assessment (To include improved sections from 1st 2 assignments and use of practicals to illustrate and inform report)
After each assessment, in addition to each student receiving specific informative feedback, there will be a general group discussion on generic aspects for improvement.
|No Exam Information
On completion of this course, the student will be able to:
- Have an advanced understanding of passive EO approaches and the advantages and disadvantages of each
- Have an advanced understanding of the range of EO platforms (from ground- to space-based) and passive sampling approaches now available and be able to review these critically
- Have knowledge and practical skills in a range of advanced analytical EO techniques and understand the advantages and disadvantages of each of these.
- Locate, read and summarise relevant literature, from both traditional and electronic media, to extend your understanding of the topic. Develop reasoned arguments, firmly grounded in the available literature.
- Take responsibility for their own learning through reading and the preparation of assignments, and reflect upon your learning experience. Plan and write assignments, within the specified parameters and to a professional standard
|Anderson, K. and Gaston, K. (2013) Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment 11: 138- 146.|
Agisoft PhotoScan User Manual Professional Edition, Version 1.1 « www.agisoft.com/pdf/photoscan-pro_1_1_en.pdf »
Berni, J. Zarco-Tejada, P., Suárez, L. and Fereres, E. (2009) Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle. IEEE Transaction on Geosciences and Remote Sensing. 47, 3, 722 738.
Colomina, I and Molina, P. (2014) Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 92, 79-97.
Hardin. P. and Jensen, R. (2011). Unmanned Aerial Vehicles in Environmental Remote Sensing: Challenges and Opportunities, GIScience & Remote Sensing, 48:1, 99-111.
James. M. and Robson, S. (2014) Mitigating systematic error in topographic models derived from UAV and ground-based image networks. Earth Surf. Process. Landforms 39, 1413-1420
Kuenzer, C. and Dech, S. (eds) (2012) Thermal Infrared remote sensing: sensors, methods and applications. Springer DOI 10.1007/978-94-007-6639-6. Particularly Chapters 1 and 4.
Lillesand, T.M., Kiefer, R.W. and Chipman, J.W. (2004). Remote sensing and image interpretation. Wiley, New York, 4th edition or later in particular Chapters 2 (section on digital images), 3, 4 and 8.
Milton, E.J., 1987. Principles of field spectroscopy. International Journal of Remote Sensing, 8, 1807-1827.
Milton, E.J., Schaepman, M., Anderson, K., Kneubuhler, M. and Fox, N. (2009). Progress in field spectroscopy. Remote Sensing of Environment, 113, S92-S109.
Schaepman-Strub, G., Schaepman, M.E., Painter, T.H., Dangel, S. and Martonchik, J.V. (2006). Reflectance quantities in optical remote sensing-definitions and case studies. Remote Sensing of Environment, 103, 27-42.
Suggested additional resource
Locate online tutorials on Report Writing e.g http://library.bcu.ac.uk/learner/writingguides/1.02%20Reports.htm
|Graduate Attributes and Skills
||Knowledge of a wide range of theoretical ideas and practical techniques in passive EO.
Ability to consider and evaluate the advantages and disadvantages of EO platform types and the constraints these place on data acquisition, quality and fitness for purpose.
Planning skills for data acquisition and sampling strategies and understand how these influence the utility of data acquired for different purposes.
Understanding of high, medium and course spectral and spatial resolution EO data and how it can be quality assessed and analysed.
Ability to write detailed and professional technical and use critical thinking
|Course organiser||Dr Caroline Nichol
Tel: (0131 6)50 7729
|Course secretary||Mrs Lauren Blackman
Tel: (0131 6)50 2624