SOIL&WAT 3010 - Remote Sensing III

North Terrace Campus - Semester 2 - 2014

Remote sensing interprets image-based information gathered by space and airborne platforms using various scanning systems. This course examines the principles and applications 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 will discuss the use of spectral data to identify and characterise objects (rocks, soils, vegetation, water), produce thematic maps and monitor changes over time. The nature and application of specialised forms of remote sensing including radiometric data, 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. Practicals are used to give 'hands-on' experience with the basics of digital image interpretation and processing and application to specific projects.

  • General Course Information
    Course Details
    Course Code SOIL&WAT 3010
    Course Remote Sensing III
    Coordinating Unit School of Earth and Environmental Sciences
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 5 hours per week
    Incompatible GEOLOGY 3010, SOIL&WAT 3008WT & GEOG 3008
    Course Description Remote sensing interprets image-based information gathered by space and airborne platforms using various scanning systems. This course examines the principles and applications 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 will discuss the use of spectral data to identify and characterise objects (rocks, soils, vegetation, water), produce thematic maps and monitor changes over time. The nature and application of specialised forms of remote sensing including radiometric data, 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. Practicals are used to give 'hands-on' experience with the basics of digital image interpretation and processing and application to specific projects.
    Course Staff

    Course Coordinator: Professor Megan Lewis

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    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)
    Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1-8
    The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 2,3,4,6,7
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 6,7,8
    Skills of a high order in interpersonal understanding, teamwork and communication. 6,7,8
    A proficiency in the appropriate use of contemporary technologies. 1-8
    A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 1-8
  • Learning Resources
    Required Resources
    The 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 Resources
    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.

    Reference journals
    Numerous remote sensing journals are available online through the library. Key journals include
    Canadian Journal of Remote Sensing
    Geocarto International
    IEEE Transactions on Geoscience and Remote Sensing
    International Journal of Remote Sensing
    Journal of Spatial Science
    Photogrammetric Engineering 
    Remote Sensing Remote Sensing of Environment

    On-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 Modes
    The course consists of:
    • 2 X 1-hour lectures per week 
    • 1 X 3 -hour practical session per week
    The program is organised so that lectures provide background concepts, theory and applications of remote sensing, and are closely followed by practical sessions that implement these methods using image analysis software. Some scheduled lecture sessions will be used for interactive student exercises.

    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.
    Workload

    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
    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
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Task Type Percentage of total assessment for grading purposes Hurdle
    Yes/No
    Outcomes being assessed/achieved  Due date
    Assignment 1  Summative

    5%

    No 1,2,3 4 August
    Assignment 2  Summative 10% No 1,2,5 25 August
    Assignment 3 Summative 15% No 1,2,5,6 15 September
    Assignment 4 Summative 15% No 1,4,5,6 13 October
    Assignment 5 Summative 15% No 1,2,5,6,7 3 November
    Final Exam Summative 40% No 1,2,4,7,8
    Assessment Detail
    Assessments 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.

    Assignment 2: Image enhancement and interpretation (10% of total)
    Illustrated written report based on practical exercises and lectures in weeks 3-5.

    Assignment 3: Image classification (15% of total)
    Illustrated written report based on practical exercises and lectures in weeks 6 and 7.

    Assignment 4: Hyperspectral analysis (10% of total)
    Illustrated written report of short answers based on practical exercises and lectures in week 8.

    Assignment 5: Applied remote sensing assignment (20% of total)
    Illustrated written report based on practical exercises and lectures in weeks 10 and 11 and reading of journal articles.

    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.
    Submission
    Assignments 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.
    Course Grading

    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.

  • Student Feedback

    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.

  • Student Support
  • Policies & Guidelines
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