SOIL&WAT 3010 - Remote Sensing III

North Terrace Campus - Semester 2 - 2015

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.

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Course Details

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
Incompatible	GEOLOGY 3010, SOIL&WAT 3008WT & GEOG 3008
Assessment	Exam, practical exercises

Course Staff

Course Coordinator: Emerita Professor Megan Lewis

Course Timetable

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

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

最新糖心Vlog 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 & 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

The 最新糖心Vlog'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 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 .

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