SOIL&WAT 3010 - Remote Sensing III
North Terrace Campus - Semester 2 - 2017
General Course Information
Course Code SOIL&WAT 3010 Course Remote Sensing 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 Assumed Knowledge SOIL&WAT 2501 Course Description Remote sensing 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: Professor Megan Lewis
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 ResourcesText 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 EnvironmentOn-line resources
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
It is important to attend the lectures or listen to the recorded lecture materials prior to the corresponding practical session. The practical exercises will be difficult to understand without this background.
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
Schedule Lecture Practical This program is indicative only. Specific details and schedule of weekly topics will be provided in the course handbook. Week 1 Introduction to Remote sensing Introduction to ERDAS Imagine image analysis software Week 2 Sources and characteristics of remotely sensed data Internet resources for remote sensing. Image interpretation, comparison of remote sensing data sources Week 3 Image display, enhancement and interpretation Image enhancement: image enhancement, multispectral transformations Week 4 Digital analysis of images Image analysis: principal components analysis Week 5 Geometric distortion of airborne and satellite imagery Geometric correction and geo-registration of images
Digital image classification
Week 6 Classification of multispectral digital imagery Digital image classification Week 7 Field data for remote sensing Advanced image classification; accuracy assessment Week 8 Hyperspectral remote sensing Hyperspectral analysis Week 9 Monitoring environmental change with remote sensing Monitoring change with multitemporal imagery Week 10 Gamma radiometric sensing Gamma radiometric data interpretation Week 11 Integrating remote sensing and GIS Integrating remote sensing and GIS for display, analysis and intrpretation Week 12 Answering the big science questions with remote sensing Completion of practical assignments
Small Group Discovery ExperienceOne major assignment conmprises group collaboration on a research project involving application of remote sensing information and analyses to an environmental or geoscience problem. Tasks involve review of relevant background research, selection and sourcing of appropriate data, choice and conduct of analyses, interpretation of results and presentation in the form of a journal article.
Students will be asked to provide peer-assessment of individual contributions to the final report, which may be used to moderate marks awarded.
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 DetailAssessments 1, 2, 3 and 4 are based on individual student submission of assignments. While students may work together during practicals, assignments must be individual and original work.
Assignment 5 is based on work conducted in small groups; one assignment is submitted for the group, with individual contributions clearly identified.
Assignment 1: Image Sources and Characteristics. (5% of total)
Written assignment of short answers, based on lectures and practicals in weeks 1 and 2 (numeric answers and approx 1,000 words).
Assignment 2: Image enhancement and interpretation (10% of total)
Illustrated written report based on practical exercises and lectures in weeks 3-5 (approx 1,000 words).
Assignment 3: Image classification (15% of total)
Illustrated written report based on practical exercises and lectures in weeks 6 and 7 (approx 1,500 words).
Assignment 4: Hyperspectral analysis (10% of total)
Illustrated written report of short answers based on practical exercises and lectures in week 8 (approx 1,000 words).
Assignment 5: Applied remote sensing project (20% of total)
Illustrated written report based on practical exercises and lectures in weeks 10 and 11 and reading of journal articles (approx. 2,500 words).
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.
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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