SPATIAL 3010 - Earth Observation III
North Terrace Campus - Semester 2 - 2020
General Course Information
Course Code SPATIAL 3010 Course Earth Observation III Coordinating Unit School of Biological Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 5 hours per week Available for Study Abroad and Exchange Y Incompatible SOIL&WAT 3010 Assumed Knowledge SPATIAL 2501 Course Description Earth observation interprets image-based information gathered by space and airborne platforms using a wide range of sensors. This course examines the principles and application of remote sensing to a range of disciplines. Principles include the interaction of electromagnetic radiation with the Earth's atmosphere and surface, spectral characteristics of earth surface materials, and the nature of imagery collected by a variety of earth-observation sensors. We discuss the use of spectral data to identify and characterise objects (minerals, soils, vegetation, water), produce thematic maps and monitor changes over time. The nature and application of specialised forms of remote sensing including gamma radiometry, hyperspectral, radar and thermal imagery are also considered. These data are relevant to a wide range of applications including geology, environmental and agricultural science. Information is extracted using digital image processing: correction, enhancement and classification of the digital data and its integration with geographic information systems and field data. Practicals use specialist image analysis software to give hands-on experience with the basics of digital image processing and application to specific projects.
Course Coordinator: Dr Ken Clarke
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
1 Explain the physical principles and concepts underlying common forms of remote sensing 2 Describe the sources, nature and characteristics of common forms of remote sensing data 3 Be able to locate sources of technical information about satellites, sensors and applications 4 Be aware of new developments and trends in earth observation 5 Perform a range of key digital image analyses using specialist software 6 Interpret the information provided by digital imagery for a range of applications and prepare reports that incorporate outputs from digital image analysis software 7 Describe how remote sensing is being used for a range of disciplines and applications 8 Choose appropriate forms of remote sensing and recommend analyses for particular applications
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2,3,4,7,8 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
5,6,7,8 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
3,4,5,6,7,8 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1,2,3,4,5,6,7,8 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- Able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
7,8 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Required ResourcesThe course lecture notes and practical manual are the key resources for this course. Materials will provided on the MyUni website (http://myuni.adelaide.edu.au). Other teaching materials including lecture recordings, additional exercises and practical notes, course documentation and past examination papers will also be posted on this site.
Recommended ResourcesRecommended (free) online textbook
The textbook "Earth Observation" is an excellent resource, from which this course will borrow lecture and recommended readings. The first two volumes ("Data" and "Processing", which are split in to six parts) are available to download free (it'll all be free, but volume 3 isn't finished yet, so... "watch this space!"TM).
If you only read one article this year it should be this one!!! (Cracknell (1998)) ;-)
It will give you strong foundation for understanding some of the most important core principals of remote sensing.
Cracknell, A.P. (1998). Synergy in remote sensing - what's in a pixel? International Journal of Remote Sensing, 19, 2025-2047
Text and reference books
* Campbell, J.B. (2006). Introduction to Remote Sensing. 4th edn. Guilford Press.
Cracknell, A. (2007). Introduction to Remote Sensing 2nd. edn. Taylor and Francis.
Drury, S.A. (2001). Image Interpretation in Geology 3rd edn. Blackwell Science.
* Gibson, P.J. and Power, C. H. (2000). Introductory Remote Sensing Principles and Concepts. London, Routledge.
* Jensen, J.R. (1986). Introductory Digital Image Processing 2nd edn. Prentice Hall.
Jensen, J.R. (2007). Remote Sensing of the Environment: An Earth Resource Perspective 2nd edn. Prentice Hall.
* Lillesand, T.M. and Kiefer, R.W. (2000). Remote Sensing and Image Interpretation. 4th edn. John Wiley & Sons, New York.
McCloy, K. (2006). Resource Management Information Systems: Remote Sensing, GIS and Modelling. 2nd edn. Taylor and Francis.
Richards, J.A. and Xiuping, J. (1999). Remote Sensing Digital Image Analysis: An Introduction 3rd edn. Springer.
*Text is available in the reserve collection or short-term loan from the Barr Smith Library.
Numerous remote sensing journals are available online through the library. Key journals include
Canadian Journal of Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing
International Journal of Remote Sensing
Journal of Spatial Science
Remote Sensing Remote Sensing of Environment
A wealth of on-line remote sensing resources and learning materials are available. Details of some are provided via MyUni for the course.
Learning & Teaching Activities
Learning & Teaching ModesThe course consists of:
2 x 1 hour lectures per week
1 x 3 hour practical session per week
The program is organised so that modules provide background concepts, theory and applications of remote sensing, and are closely followed by practical sessions that implement these methods using image analysis software. I'm using the term 'modules' instead of 'lectures', because I'm hoping to make them more interactive, and want to avoid the sleep-inducing-term 'lecture'. I'll work in as much interaction and as many practical demonstrations of principles as I have time.
It is ESSENTIAL to attend the modules or listen to the recorded materials prior to the corresponding practical session. The practical exercises will be difficult without this background, and neither I nor your demonstrators will bring you up to speed on the content you should have covered prior to practical.
The assignments draw on knowledge and skills covered in several lectures and practicals, with additional interpretation, synthesis and presentation. If you attend and complete the exercises during practical sessions, you will have achieved much of the work required for the assignments.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.A student enrolled in a 3 unit course, such as this, should expect to spend, on average 12 hours per week on the studies required. This includes both the formal contact time required to the course (e.g., lectures and practicals), as well as non-contact time (e.g., reading and revision).
Learning Activities Summary
Lecture Practical This program is indicative only. Specific details and schedule of weekly topics will be provided through the specific MyUni/Canvas modules. Week 1 Introduction to the fundamentals of remote sensing Chapter 1: Mastering the basics (of ENVI) Week 2 Sources and characteristics of remotely sensed data Chapter 2: Spatial resolution and spectral profiles Week 3 Image analysis (Novice level): Image enhancement, ratios and indices Chapter 3: Image enhancement and visualisation Week 4 Image analysis (Apprentice level): Principal components and georegistration Chapter 4: Principal components analysis Week 5 Image analysis (Journeyman level): Classification Chapter 5: Image classification Week 6 Evaluating the accuracy of remotely sensed products Chapter 6: Accuracy assessment Week 7 An introduction to a remote sensing specialisation: hyperspectral Chapter 7: Hyperspectral analysis Week 8 Spatial enhancement ("Zoom! Enhance!"), and abstractions Chapter 8: Image registration and mosaicing Week 9 An introduction to a remote sensing application: change detection and monitoring Chapter 9: Change detection Week 10 Guest lecturer from Adelaide University Drone Group Chapter 10: Drone mission planning and data analysis Week 11 Guest lecturers in related spatial disciplines Chapter 11: TBA Week 12 New content and exam prep Free time for group research project
Group Research Project
Later in this course students will form small groups (3-4 studentds) and conduct a research project based on methods covered in practicals. Projects will involve solving a real-world problem with imagery analysis techniques acquired in this course. Each group member will be asked to provide an assessment of their own and colleague's contribution towards completion of the data processing and report writing.
Small Group Discovery ExperienceLater in this course students will form small groups (3-4 studentds) and conduct a research project based on methods covered in practicals. Projects will involve solving a real-world problem with imagery analysis techniques acquired in this course. Each group member will be asked to provide an assessment of their own and colleague's contribution towards completion of the data processing and report writing.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Task Task Type Percentage of total assessment for grading purposes Hurdle
Outcomes being assessed/achieved Due date Up to 4 assignments based on practical exercises Summative 40% No 1,2,3,4,5,6,7,8 Advised in course handbook Research project based on small group discovery Summative 20% No 1,2,3,4,5,6,7,8 Advised in course handbook Final Exam Summative 40% No 1,2,4,7,8
Assessment DetailAll assessments with the exception of Assignment 3 are based on individual student submissions. While students may work together during practicals, individual assessments must be individual and original work.
Assignment 3 is based on work conducted in small groups; one assignment is submitted for the group, with individual contributions clearly identified.
Individual Assessment 1: Log Book (10 % of total): A log book containing outputs and notes from the practicals. A practical log book is to be maintained during each practical as a single document. Selected sections of the submitted log book will be assessed.
Individual Assessment 2: Quizzes (10 % of total): 2 short online quizzes based on theory and practicals
Individual Assessment 3: Assignment 1 (10 % of total): Written assignment on image classification and accuracy assessments. Illustrated written report based on practical exercises and lectures in weeks 5 and 6 (approx 1,500 words).
Individual Assessment 3: Assignment 2 (10 % of total): Written assignment on hyperspectral analysis. Illustrated written report of short answers based on practical exercises and lectures in week 7 (approx 1,000 words).
Group Research Project: Assignment 3 (20 % of total): Group remote sensing research project. A small group (3-4 students) research project based on methods covered in practicals. Groups will choose from the offered research projects and prepare a report in the form of a scientific paper. Projects will involve solving a real-world problem with imagery analysis techniques acquired in this course. Each group member will be asked to provide an assessment of their own and colleague's contribution towards completion of the data processing and report writing.
Exam (40% of total): A 2 hour written exam drawing on lecture material, exercises and practicals. Comprises short answers, questions requiring calculations and diagrams and longer written explanations. Examples of previous exam papers are provided in MyUni/Canvas.
SubmissionAssignments must be submitted electronically via MyUni. Ensure that you are familiar with procedures for doing this: if in doubt seek assistance in practical classes.
Do NOT email assignments to the lecturing or demonstrating staff – assignments are not accepted this way.
Extensions for Assessments
Extensions of deadlines for assessment tasks may be allowed for reasonable causes. Such situations include compassionate and medical grounds of the severity that would justify the awarding of a replacement examination. Evidence for the grounds must be provided when an extension is requested.
Students are required to apply for an extension to the course co-ordinator before the assessment task is due. Extensions will not be provided on the grounds of poor prioritising of time.
Penalties for Late Submission
If an extension is not applied for, or not granted then a penalty for late submission will apply. A penalty of 10% of the value of the assignment for each calendar day that is late (i.e. weekends count as 2 days), up to a maximum of 50% of the available marks will be applied. This means that an assignment that is 5 days or more late without an approved extension can only receive a maximum of 50% of the mark.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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