Unit Outline

This unit provides an engaging, hands-on introduction to the dynamic field of remote sensing. This unit delves into the latest advancements in satellite and aerial imagery, empowering students to utilise these technologies to understand and address diverse environmental and societal challenges. In today's rapidly changing world, remote sensing is a powerful tool with applications spanning climate change studies, vegetation studies, forestry, environmental management, glaciology, oceanography, and urban studies. Starting with the fundamental physics of light and its interaction with the Earth's atmosphere and surface, this unit covers the technical and practical aspects of a range of satellite and airborne sensors. Through weekly computer practicals using ENVI software, students gain the necessary skills to display, analyse, and extract valuable information from remotely sensed imagery. This unit equips students pursuing careers in geospatial science, geography, environmental science, earth sciences, agricultural science, plant science, computing and information systems, and marine and Antarctic studies with vital scientific and professional skills. As the demand for professionals with expertise in geospatial data analysis grows, this unit prepares students to enter the job market with confidence.

Intended Learning Outcomes

As per the Assessment and Results Policy 1.3, your results will reflect your achievement against specified learning outcomes.

On completion of this unit, you will be able to:


1	Analyse and interpret remote sensing datasets by applying knowledge of electromagnetic energy and its interactions with the Earth's atmosphere and surface.
2	Address environmental and social challenges using remote sensing data and analysis techniques.
3	Apply image analysis software to display remote sensing data and produce standard spatial products.
4	Communicate remote sensing and geospatial science concepts, results, and perspectives.

Alterations as a result of student feedback

After several years of online lectures, we are brining back in-person live seminars (that can also be attended online). In addition, we are replacing the two 1-hour lectures with shorter pre-recorded videos and a more interactive 1-hour seminar focusing on live data analysis demos.

Teaching arrangements


ATTENDANCE MODE	TEACHING TYPE	LEARNING ACTIVITY	CONTACT HOURS	FREQUENCY
On Campus	Seminar	On campus seminar (software demonstrations and Q&A)	1	Weekly
	Practical	3-hour practical on campus (independent computer work in lab with tutoring support)	3	Weekly
	Independent Learning	In addition to the contact hours (1 hr/week seminar + 3hr/week computer pracs) you are expected to dedicate at least 4 hours per week on independent learning. This includes weekly pre-recorded videos that you will need to review before each seminar. You are expected to dedicate additional time to assessment tasks.	4	Weekly
Online	Seminar	Online seminar (software demonstrations and Q&A)	1	Weekly
	Tutorial (Online)	3-hour computer practical (independent computer work with online tutoring support)	3	Weekly
	Independent Learning	In addition to the contact hours (1 hr/week seminar + 3hr/week computer pracs) you are expected to dedicate at least 4 hours per week on independent learning. This includes weekly pre-recorded videos that you will need to review before each seminar. You are expected to dedicate additional time to assessment tasks.	4	Weekly

Attendance / engagement expectations

If your unit is offered On campus, it is expected that you will attend all on-campus and onsite learning activities. This is to support your own learning and the development of a learning community within the unit. If you are unable to attend regularly, please discuss the situation with your course coordinator and/or our UConnect support team.

If your unit is offered Online or includes online activities, it is expected you will engage in all those activities as indicated in the Unit Outline or MyLO, including any self-directed learning.

If you miss a learning activity for a legitimate reason (e.g., illness, carer responsibilities) teaching staff will attempt to provide alternative activities (e.g., make up readings) where it is possible.

How will I be Assessed?

For more detailed assessment information please see MyLO.

Assessment schedule


ASSESSMENT TASK #	ASSESSMENT TASK NAME	DATE DUE	WEIGHT	LINKS TO INTENDED LEARNING OUTCOMES
Assessment Task 1:	Assignment 1 Interpretation of false colour imagery	Week 5	20 %	LO1, LO2, LO3, LO4
Assessment Task 2:	Assignment 2 Applied Remote Sensing Techniques: Weekly Tasks	Week 9	40 %	LO1, LO2, LO3, LO4
Assessment Task 3:	Assignment 3: remote sensing case study	Week 14	40 %	LO1, LO2, LO3, LO4

Assessment details

Assessment Task 1: Assignment 1 Interpretation of false colour imagery

Task Description:

This assignment focuses on the visual interpretation of spectral reflectance values in false colour images, alongside an application of your understanding of the principles of electromagnetic energy, atmospheric interactions, and reflectance properties.

The use of generative artificial intelligence (AI) as a learning tool in the completion of this assessment task must follow the specific instructions of this task and must be in alignment with the UTAS guidelines on academic integrity.

Task Length:

Part 1 is a set of written answers based on practical exercise two (delivered in week two), and lecture material from weeks two and three. Part 2 is a quiz (with multiple-choice, short-answer, and calculation questions) based on lecture, reading, and practical content from weeks one to four.

Due Date:

Week 5

Weight:

20 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Analyse the interactions between electromagnetic energy and matter, demonstrating an understanding of the principles of electromagnetic energy, atmospheric interactions, and reflectance properties	LO1
2	Apply theoretical concepts of spectral reflectance and wavelengths to a practical case study, interpreting spectral signatures and image colours in different band combinations	LO2
3	Solve problems related to spectral analysis and reflectance interpretation by performing calculations and answering multiple-choice questions based on lecture, reading, and practical content from weeks one to four	LO2, LO3
4	Communicate the acquired knowledge and understanding of spectral analysis and reflectance interpretation in remote sensing through clear and concise written responses	LO4

Assessment Task 2: Assignment 2 Applied Remote Sensing Techniques: Weekly Tasks

Task Description:

This assignment comprises four practical exercises covering contrast stretching, resolutions, regions of interest, and spectral signatures. You will be assessed on your ability to apply theoretical principles and interpret remote sensing images through multiple-choice and short-answer questions.

The use of generative artificial intelligence (AI) as a learning tool in the completion of this assessment task must follow the specific instructions of this task and must be in alignment with the UTAS guidelines on academic integrity.

Task Length:

Weekly practical assessment task associated with the topic and exercise in the computer practicals.

Due Date:

Week 9

Weight:

40 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Apply theoretical principles from lecture material to practical exercise tasks, demonstrating an understanding of key remote sensing techniques, including contrast stretching, resolutions, regions of interest, and spectral signatures.	LO2
2	Interpret remote sensing images by drawing conclusions about objects and features using spectral signatures and reflectance characteristics, showcasing the ability to analyse remote sensing data	LO1, LO3
3	Solve problems related to the application of remote sensing techniques, answering multiple-choice and short-answer questions based on practical exercises and lecture material	LO2, LO3
4	Communicate understanding and application of remote sensing techniques through concise, accurate, and well-reasoned responses in short-answer questions.	LO4

Assessment Task 3: Assignment 3: remote sensing case study

Task Description:

In this capstone assignment, you will conduct an independent analysis of satellite imagery applying classification and change detection techniques in the context of a practical real-world application. This assignment will bring together your skills in remote sensing data analysis and knowledge of foundational concepts.

The use of generative artificial intelligence (AI) as a learning tool in the completion of this assessment task must follow the specific instructions of this task and must be in alignment with the UTAS guidelines on academic integrity.

Task Length:

Written report on remote sensing data analysis task (~3000 words)

Due Date:

Week 14

Weight:

40 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Select and apply appropriate image processing techniques, such as spectral indices and classification methods, to identify fire-affected pixels in the Sentinel-2 satellite imagery.	LO1, LO2
2	Analyse the impact of the bushfire event on the Tasmanian landscape by mapping the areas affected, drawing on the understanding of remote sensing concepts and principles.	LO1, LO2
3	Use image analysis software to process satellite imagery and produce accurate maps of burnt areas	LO3
4	Communicate the results of the analysis, including the identified fire-affected areas, the applied processing techniques, and the overall impact of the bushfire event, through clear visual representations and concise written explanations.	LO4

How your final result is determined

To pass this unit, you need to demonstrate your attainment of each of the Intended Learning Outcomes, achieve a final unit grade of 50% or greater, and pass any hurdle tasks.

Academic progress review

The results for this unit may be included in a review of your academic progress. For information about progress reviews and what they mean for all students, see Academic Progress Review in the Student Portal.

Submission of assignments

Where practicable, assignments should be submitted to an assignment submission folder in MYLO. You must submit assignments by the due date or receive a penalty (unless an extension of time has been approved by the Unit Coordinator). Students submitting any assignment in hard copy, or because of a practicum finalisation, must attach a student cover sheet and signed declaration for the submission to be accepted for marking.

Academic integrity

Academic integrity is about acting responsibly, honestly, ethically, and collegially when using, producing, and communicating information with other students and staff members.

In written work, you must correctly reference the work of others to maintain academic integrity. To find out the referencing style for this unit, see the assessment information in the MyLO site, or contact your teaching staff. For more detail about Academic Integrity, see Important Guidelines & Support.

Requests for extensions

If you are unable to submit an assessment task by the due date, you should apply for an extension.

A request for an extension should first be discussed with your Unit Coordinator or teaching support team where possible. A request for an extension must be submitted by the assessment due date, except where you can provide evidence it was not possible to do so. Typically, an application for an extension will be supported by documentary evidence: however, where it is not possible for you to provide evidence please contact your Unit Coordinator.

The Unit Coordinator must notify you of the outcome of an extension request within 3 working days of receiving the request.

Late penalties

Assignments submitted after the deadline will receive a late penalty of 5% of the original available mark for each calendar day (or part day) that the assignment is late. Late submissions will not be accepted more than 10 calendar days after the due date, or after assignments have been returned to other students on a scheduled date, whichever occurs first. Further information on Late Penalties can be found on the Assessments and Results Procedure.