SPATIAL 7008 - Earth Observation

North Terrace Campus - Semester 2 - 2021

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.

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Course Details

Course Code	SPATIAL 7008
Course	Earth Observation
Coordinating Unit	School of Biological Sciences
Term	Semester 2
Level	Postgraduate Coursework
Location/s	North Terrace Campus
Units	3
Contact	Up to 5 hours per week
Available for Study Abroad and Exchange	Y
Incompatible	SPATIAL 3010
Assessment	Practical exercises, written assignments and exam

Course Staff

Course Coordinator: Dr Ken Clarke

Course Timetable

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

Course Learning Outcomes

A successful student in this course should be able to:

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.

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 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.

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

	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 最新糖心Vlog 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 Experience

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.

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	Weighting	Hurdle	Learning Outcome	Due
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	Exam period

Assessment Detail

All 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.

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 .

Student Feedback

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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 最新糖心Vlog 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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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
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Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.

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Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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