Course coordinator
Dr William Woodgate
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- The Environment School
Prior to 2022, this course was titled: Earth Observation: Image Processing and Modelling.
Remote sensing or earth observation, is an important tool for monitoring and modelling the condition and dynamics of terrestrial, aquatic and atmospheric environments. The information extracted from images may be used in many ways, as image or thematic maps, directly in decision making, as estimates of biophysical variables or integrated with other spatial information systems for further analysis and display. This information can be collected from local to global scales for examining changes in the habitat of endangered fauna or monitoring continental scale deforestation and global scale oceanic and atmospheric conditions.
This course is a logical progression from the remote sensing concepts and skills introduced in GEOM2000/7000. GEOM3001/7001 emphasises digital image processing for analysis of remotely sensed imagery, including airborne and satellite multi-spectral, hyper-spectral and synthetic aperture radar data. Practical sessions will involve a progression of practicals in the computer laboratory using the ENVI (Environment for Visualising Images) software package. Concepts and skills acquired in these sessions will be applied in the individual student remote sensing project that can be designed to suit your area of interest.
Remote sensing or earth observation (EO) Science is an important tool for monitoring and modelling the condition and dynamics of terrestrial, aquatic and atmospheric environments. The information extracted from images may be used in many ways, as image or thematic maps, directly in decision making, as estimates of biophysical variables or integrated with other spatial information systems for further analysis and display. This information can be collected from local to global scales for countless applications, including examining changes in the habitat of endangered fauna or monitoring continental scale deforestation and global scale oceanic and atmospheric conditions.
How this course differs from the introductory GEOM 2000/7000 course:
This course is a logical progression from the remote sensing or Earth Observation Science concepts and skills introduced in GEOM2000/7000. This course emphasises digital image processing for analysis of remotely sensed imagery, including airborne and satellite multi-spectral, hyperspectral, LiDAR and synthetic aperture radar (SAR) data. Practical sessions will involve a progression of practicals in the computer laboratory, using a combination of open-source and proprietary software. Concepts and skills acquired in these sessions will be applied in the individual student remote sensing project that can be designed to suit your area and application of interest under the guidance of the teaching team.
This course builds upon the fundamentals taught in GEOM2000/7000 and will develop more advanced EO analytic skills and knowledge. What you’ll learn:
1. Advanced analytical techniques for processing and interpreting EO data.
2. Hands-on experience with big data EO platforms like Open Data Cube.
3. Techniques to calculate biophysical attributes (e.g., biomass) by combining remotely sensed data with ground data and/or modelling algorithms.
4. Advanced theoretical and practical skills for analysing new sources of remote sensing data (e.g., spaceborne LiDAR).
5. How to design, plan and execute a remote sensing project with detailed guidance.
Course requirements
Assumed background
A highly desirable pre-requisite for this course is to have introductory knowledge of remote sensing from GEOM2000 (or equivalent). Prior to attending image processing practicals students are expected to be familiar with the basics of the Windows operating system (file naming conventions and management).
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If you have any concerns or special needs in relation to this course please see the course coordinator.
ﾠ
Support for students with a disability
Any student with a disability who may require alternative academic arrangements in the course/program is encouraged to seek advice at the commencement of the semester from a Disability Learning Adviser at Student Services.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
GEOM2000
Incompatible
You can't enrol in this course if you've already completed the following:
GEOM7001
Jointly taught details
This course is jointly-taught with:
- GEOM7001
This course is co-taught between GEOM3001 and GEOM7001. Assessments are individual.
Course contact
Course staff
Lecturer
Dr William Woodgate
Demonstrator
Ms Gillian Rowan
Mr Raja Ram Aryal
Mr Timothy Devereux
Mr Avik Nandy
Ms Francesca Devoto
Mr Muhammad Hafizt
Mrs Joanna Smart
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
This course builds upon the fundamentals taught in GEOM2000 and will develop more advanced EO analytic skills and knowledge. What you’ll learn :
1. Advanced analytical techniques for processing and interpreting EO data.
2. Hands-on experience with big data EO platforms like Open Data Cube.
3. Techniques to calculate biophysical attributes (e.g., biomass) by combining remotely sensed data with ground data and/or modelling algorithms.
4. Advanced theoretical and practical skills for analysing new sources of remote sensing data (e.g., spaceborne LiDAR).
5. How to design, plan and execute a remote sensing project with detailed guidance.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Apply the fundamental concepts of remote sensing, including spatial, spectral, radiometric, and temporal resolutions, and their applications in various geographical and professional environments.
LO2.
Demonstrate proficiency in fundamental image processing operations for extracting information from remotely sensed data, including pre-processing, corrections, enhancements, classification, biophysical modelling, and accuracy assessment.
LO3.
Design, plan, and execute a complete remote sensing project, from data acquisition to analysis and presentation, integrating field data and non-image data within GIS or image processing environments.
LO4.
Evaluate the appropriateness of various remotely sensed data sets and ancillary data for specific applications.
LO5.
Apply big data Earth Observation analytic platforms for modern remote sensing applications.
LO6.
Analyse advanced remote sensing data types, including hyperspectral, LiDAR, and RADAR images, as well as conduct multi-temporal analyses.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Project |
Remote Sensing Project Proposal
|
28% |
5/09/2025 2:00 pm
Draft Project Proposal (optional) due 25/08/2025 2.00 pm |
| Quiz |
In-semester practical quiz (online)
|
16% |
23/09/2025 8:00 am |
| Project |
Remote Sensing Project Report
|
56% |
31/10/2025 2:00 pm
Draft Report (optional) due 13/10/2025 2.00 pm. |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Remote Sensing Project Proposal
- Mode
- Written
- Category
- Project
- Weight
- 28%
- Due date
5/09/2025 2:00 pm
Draft Project Proposal (optional) due 25/08/2025 2.00 pm
- Other conditions
- Student specific.
Task description
Aim:
To design an image processing project based on the image processing operations covered in weeks 1-5 of the course. This includes discussing and implementing the image processing sequence, including: geometric and radiometric corrections, spectral enhancements, spatial enhancements, image classification, and accuracy assessment. Note that some corrections may already be applied. You may choose an aspect of personal interest (land-use, agriculture, vegetation, geomorphology, soils, geology, urban form and condition, or another topic). If you do not have an interest in a particular topic, a land-cover classification project may be suitable.
Project Type: Individual Remote Sensing Project
You will work individually to apply an image processing sequence to remote sensing data for an area of your choice. The area extent is suggested to be up to 50km2 (with a large degree of freedom) in order to provide an informed analysis. You will need to select a suitable mapping or modelling application to apply. Please speak with the Course Coordinator for recommendations.
Overview:
To ensure your project is feasible you are required to submit an approx. 8-12 page proposal containing the following:
- a statement of the aim(s) of your image processing;
- a short description of the problem (preferably with relevant references),
- the used Remote sensing image data sets used,
- study site map,
- an outline or flowchart of the image processing operations you intend to apply;
- information extraction process, accuracy assessment,
- details of your intended output product(s), and,
- strengths and limitations of the proposed workflow.
The remote sensing project proposal should include scientific journal references to support the proposal project.
Examples of suitable topics are: land-cover classification for a region; quantifying mine rehabilitation, quantifying snow melt characteristics of an alpine region, evaluating seasonal vegetation patterns etc.
Please note: coastline movement is not an acceptable topic. This is the only topic not suitable for assessment.
The proposal contributes 28% to the course grade for GEOM3001/7001 and is essential for good preparation of your project and can provide a basis for the final project report.
See the supplied Proposal Template and follow this for the submission.
Submission guidelines
Online submission by Turnitin only by the due date and time. No hard copy or assignment cover sheets are required. Submission via email is not accepted.
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item, then 10% of the maximum possible mark for the assessment item (the assessment ‘marked from’ value) will be deducted as a late penalty for every day (or part day) late after the due date.
For example, if you submit your assignment 1 hour late, you will be penalised 10%; if your assignment is 24.5 hours late, you will be penalised 20% (because it is late by one 24-hour period plus part of another 24-hour period).
In-semester practical quiz (online)
- Identity Verified
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 16%
- Due date
23/09/2025 8:00 am
Task description
This quiz will be on material covered in the lecture and practical content taught till week 9. It will mostly be hands-on questions using functionality taught in the practicals.
The quiz will we conducted during the practical time in Week 9. There will be one attempt only. 90 minute duration.
Submission guidelines
It will be a Blackboard enabled Quiz.
Deferral or extension
You may be able to defer this exam.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item, then 10% of the maximum possible mark for the assessment item (the assessment ‘marked from’ value) will be deducted as a late penalty for every day (or part day) late after the due date.
For example, if you submit your assignment 1 hour late, you will be penalised 10%; if your assignment is 24.5 hours late, you will be penalised 20% (because it is late by one 24-hour period plus part of another 24-hour period).
Quiz is closed after the due date.
Remote Sensing Project Report
- Hurdle
- Mode
- Written
- Category
- Project
- Weight
- 56%
- Due date
31/10/2025 2:00 pm
Draft Report (optional) due 13/10/2025 2.00 pm.
Task description
Aim:
To complete the project described in the Project Proposal (Assessment #1).
Note: the project report can vary from that described in the proposal - please consult the Course Coordinator and/or the demonstration team for guidance.
Overview:
Upon completion of your image processing and analysis of results you are to compile a report that includes: a brief literature review on the application of remote sensing in your selected topic area; a description of the processing stages and techniques applied; a summary of the results obtained (output maps, summary statistics); comments on the value of processing techniques; and suggestions on how the results may be improved. The remote sensing project report should approx. be 4000-6000 words (excluding tables and bibliography) and should include a background review and synthesis of (at least 10 peer reviewed articles GEOM 3001, or 15 articles for GEOM 7001) refereed scientific journals describing remote sensing applications in your selected area of interest studied in your project.
Note:
Most of the allocated practical times will be used to introduce image analysis tasks.
Coastline movement is not an acceptable topic. This is the only topic not suitable for assessment.
Suggested Structure for Remote Sensing Report Write-up:
- Title
- Contents
- Abstract
- Introduction and Objectives
- Data and Methods
- Results and Discussion
- Conclusions and Future Work
- References
- Appendix with Processing Log
Your assignment will be marked out of 100 possible points and then re-scaled to a mark out of 56%. Marks are assigned using the assessment criteria.
Please use the Project Report template and also refer to the Project Proposal and Project Report guidance document for more information (posted in Blackboard).
Hurdle requirements
See ADDITIONAL COURSE GRADING INFORMATION for the hurdle relating to this assessment item.Submission guidelines
Online submission by Turnitin only by the due date and time. No hard copy or assignment cover sheets are required. Submission via email is not accepted.
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item, then 10% of the maximum possible mark for the assessment item (the assessment ‘marked from’ value) will be deducted as a late penalty for every day (or part day) late after the due date.
For example, if you submit your assignment 1 hour late, you will be penalised 10%; if your assignment is 24.5 hours late, you will be penalised 20% (because it is late by one 24-hour period plus part of another 24-hour period).
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 0% |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 30% |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: The minimum percentage required for this grade is: 45% |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 50% |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 65% |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 75% |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 85% |
Additional course grading information
Assessment Hurdle
In order to pass this course, you must meet the following requirements (if you do not meet these requirements, the maximum grade you will receive will be a 3):
- You must obtain at least 40% of the marks on the Project Report.
Supplementary assessment
Supplementary assessment is available for this course.
Should you fail a course with a grade of 3, you may be eligible for supplementary assessment. Refer to the UQ website for information on supplementary assessment and how to apply.
Supplementary assessment provides an additional opportunity to demonstrate you have achieved all the required learning outcomes for a course.
If you apply and are granted supplementary assessment, the type of supplementary assessment set will consider which learning outcome(s) have not been met.
Supplementary assessment can take any form (such as a written report, oral presentation, examination or other appropriate assessment) and may test specific learning outcomes tailored to the individual student, or all learning outcomes.
To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment.
Additional assessment information
Applications for Extensions to Assessment Due Dates
Read the information contained in the following links carefully before submitting an application for extension to assessment due date.
For guidance on applying for an extension, information is available here: https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension
For the policy relating to extensions, information is available here (Part D): https://policies.uq.edu.au/document/view-current.php?id=184
Please note the University's requirements for medical certificates here: https://my.uq.edu.au/information-and-services/manage-my-program/uq-policies-and-rules/requirements-medical-certificates
Artificial Intelligence (AI)
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing these assessment tasks. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
Recommended reading:
- CRCSI (2016a) Earth Observation: Data, Processing and Applications. (Eds. Harrison, B.A., Jupp, D.L.B., Lewis, M.M., Forster, B., Mueller, N., Smith, C., Phinn, S., Hudson, D., Grant, I., Coppa, I.) CRCSI, Melbourne.Available from:ﾠ http://www.crcsi.com.au/earth-observation-series
- Schowengerdt, R.A. 1997. Remote sensing: Models and methods for image processing, 2nd Edition, Academic Press: San Diego.
- Schott, J.R. (1997) Remote sensing: the image chain approach. Oxford University Press.
- Lillesand, T.M. and Kiefer, R.W. 2000. Remote Sensing and Image Interpretation. 4thﾠEdition, John Wiley and Sons.
Note: All readings refer to chapters Jensen (2007 2nd edition), additional information may be found in Chapter 7 in Lillesand and Kiefer (2000) and selected sections in Schott (1997) and Schowengerdt (1997).
Other useful reference books for remote sensing are:
- Purkis, S. and C. Roelfsema (2015). 11 Remote Sensing of Submerged Aquatic Vegetation and Coral Reefs. Remote Sensing of Wetlands: Applications and Advances.pp: 223.
- Goodman, J., Purkis, S. and Phinn, S.R. (2013) Coral Reef Remote Sensing: A Guide for Multi-level Sensing Mapping and Assessment. Goodman, J., Purkis, S. and Phinn, S.R. Springer Publishing pp3-25 ISBN 978-90-481-9291-5
- American Society of Photogrammetry (1983) Manual of Remote sensing, 2nd Edition, ASP, Falls Church.
- Barrett, E.C.ﾠand Curtis L.F. (1992) Introduction to environmental remote sensing. 3rd ed., Melbourne, Vic. : Chapman & Hall.
- Campbell, J. B. (1996) Introduction to remote sensing. 2nd Ed. The Guilford Press, New York.
- Cracknell A. and Hayes L. (1991) Introduction to remote sensing. London: Taylor & Francis
- Curran, P.J. (1985) Principles of remote sensing. Longman.
- Harrison, B.A.ﾠandﾠJupp , D.L.B. (1989) Introduction to remotely sensed data. Canberra : CSIRO, Division of Water Resources.
- Harrison, B.A.ﾠandﾠJupp , D.L.B. (1990) Introduction to image processing. Canberra : CSIRO, Division of Water Resources, 1990
- Richards, J.A. (1993) Remote sensing digital image analysis: an introduction. 2nd Ed. Springer Verlag.
- Robert A. Ryerson Ed. (1997) Manual of remote sensing. 3rd Edition, published in cooperation with the American Society for Photogrammetry and Remote Sensing.ﾠJ. Wiley, New YorkSSH Library - G70.4 .M36 1997 v.1, V.2, V.3.Volume. 1. Earth observing platforms and sensors, Stanley A. Morain and Amelia M. Budge (CD-ROM)
- Volume 2: Principles and applications of imaging radar, Floyd M. Henderson and Anthony J. Lewis
- Volume 3: Remote sensing for the earth sciences, Andrew Rencz.
JOURNALS:
There are a number of remote sensing texts in the main, undergraduate, and Physical Sciences and Engineering (PSE) Libraries which can be used for the course.
The bold journals are available on-line through the University Library’s web page (www.library.uq.edu.au)
The main journals which will be useful for the course include:
- Canadian Journal of Remote Sensing
- Geocarto International
- International Journal of Remote Sensing
- IEEE Transactions on Geosciences and Remote Sensing
- Photogrammetric Engineering and Remote Sensing
- Remote Sensing of Environment
- International Journal of Photogrammetry and Remote Sensing
- The Remote Sensing Journal
- Journal of Applied Remote Sensing
- Remote Sensing (Open Source)
On-line remote sensing toolkitﾠ https://www.rsrc.org.au/rstoolkit
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (28 Jul - 03 Aug) |
Lecture |
Lecture 1: Course introduction and overview Course introduction and overview |
Practical |
Practical 1: code based computing for geospatial data. Part 1: basic python and Sandbox/Planetary PC. intro to data cubes (Digital Earth Aus P1) code based computing for geospatial data: intro to data cubes (Digital Earth Aus P1) |
|
Week 2 (04 Aug - 10 Aug) |
Lecture |
Lecture 2: Digital earths & multi-temporal data analysis Digital earths & multi-temporal data analysis |
Practical |
Practical 2: code based computing for geospatial data: DEA Part 2 - multitemporal data code based computing for geospatial data: DEA P2 - multitemporal |
|
Week 3 (11 Aug - 17 Aug) |
Lecture |
Lecture 3: Open and Online (big) Data Science for Earth Observation (Part 1), Drone processing workflows (Part 2) Open and Online (big) Data Science for Earth Observation (Part 1), Drone processing workflows (Part 2) |
Practical |
Practical 3: code based computing for geospatial data: DEA Part 3 code based computing for geospatial data: DEA P3 - reading in external data |
|
Week 4 (18 Aug - 24 Aug) |
Lecture |
Lecture 4: Image data pre-processing (Geometric, Radiometric, atmospheric corrections) Pre-processing (Geometric, Radiometric, atmospheric corrections) |
Practical |
Practical 4: Introduction to ENVI with new image types |
|
Week 5 (25 Aug - 31 Aug) |
Lecture |
Lecture 5: Thematic information extraction (across platforms) Thematic information extraction (across platforms) |
Practical |
Practical 5: Pre-processing Operations (ENVI) |
|
Week 6 (01 Sep - 07 Sep) |
Lecture |
Lecture 6: Biophysical information extraction & error and accuracy assessment Biophysical information extraction & error and accuracy assessment |
Practical |
Practical 6: Supervised and unsupervised image classifications (ENVI) |
|
Week 7 (08 Sep - 14 Sep) |
Lecture |
Lecture 7: LiDAR image processing & radiative transfer model applications LiDAR image processing & radiative transfer model applications |
Practical |
Practical 7: Accuracy assessment with reference thematic map and data points (ENVI) |
|
Week 8 (15 Sep - 21 Sep) |
Lecture |
Lecture 8: Aquatic remote sensing Aquatic remote sensing |
Practical |
Practical 8: code based computing for geospatial data - other data and tools (Part 1) |
|
Week 9 (22 Sep - 28 Sep) |
Lecture |
Lecture 9: Project review discussion Project review discussion |
Practical |
In semester quiz |
|
Week 10 (06 Oct - 12 Oct) |
Practical |
Practical 9: code based computing for geospatial data & project support |
Week 11 (13 Oct - 19 Oct) |
Lecture |
Lecture 10: Drones for Earth Observation |
Practical |
Practical 10: project support |
|
Week 12 (20 Oct - 26 Oct) |
Lecture |
Lecture 11: Radar image processing and applications Radar image processing and applications |
Practical |
Practical 11: project support |
|
Week 13 (27 Oct - 02 Nov) |
Lecture |
Lecture 12: Class discussion: job ready skills and the future trajectory of space and spatial Class discussion: job ready skills and the future trajectory of space and spatial |
Practical |
Practical 12: project support |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.