Outline

Overview

This unit introduces methods for mapping and monitoring environmental change in coastal environments using a variety of remote sensing image analysis techniques. Students will learn, theoretically and practically, how environmental data is collected using a range of different remote sensing methodologies, and application of this data to understanding landscape processes and quantifying environmental change. Students will acquire skills in applying environmental mapping techniques to interpreting key Earth surface processes and understanding the substantial impacts that humans can have on these, in terms of both disturbance and remediation.

Unit details and rules

Academic unit	Geosciences Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	This unit assumes a sound understanding of scientific principles, HSC level mathematics and understanding of basic statistics
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Kevin Davies, kevin.davies@sydney.edu.au

Assessment

The census date for this unit availability is 2 October 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Data analysis	Remote Sensing Logbook A Laboratory exercises from modules 1 and 2	20%	Week 02 Due date: 27 Sep 2026 at 23:59	Modules 1 and 2	AI allowed
Outcomes assessed:
Written test	Remote Sensing Basics Quiz Multiple choice, true/false, short answer quiz.	20%	Week 03 Due date: 09 Oct 2026 at 11:00	50 minutes	AI prohibited
Outcomes assessed:
Data analysis	Remote Sensing Logbook B Laboratory exercises from Modules 3 and 4	20%	Week 04 Due date: 18 Oct 2026 at 23:59	Modules 3 and 4	AI allowed
Outcomes assessed:
Written test	Environmental Monitoring and Remote Sensing Quiz Multiple choice, true/false, short answer quiz.	20%	Week 05 Due date: 23 Oct 2026 at 11:00	50 minutes	AI prohibited
Outcomes assessed:
Data analysis	Remote Sensing Logbook C Laboratory exercises from modules 5 and 6	20%	Week 06 Due date: 01 Nov 2026 at 23:59	Modules 5 and 6	AI allowed
Outcomes assessed:

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	A HD grade demonstrates an exceptional understanding of the subject area / standard of work.
Distinction	75 - 84	A DI grade demonstrates outstanding understanding of the subject area / standard of work.
Credit	65 - 74	A CR grade demonstrates a good understanding of the subject area / standard of work.
Pass	50 - 64	A PS grade demonstrates a satisfactory understanding of the subject area / standard of work.
Fail	0 - 49	A FA grade demonstartes an unsatisfactory or inadequate level of understanding of the subject area / inability to complete tasks to the minimum standard expcted.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to earth observation remote sensing for environmental mapping and monitoring. Understanding the basic principles of spectroscopy, optical remote sensing by satellite, and confounding factors such as solar illumination and atmosphere.	Lecture (1 hr)
Week 01	Introduction to working with satellite images including calibration, false-colour composites, spectral signatures, cloud masking and thermal bands.	Tutorial (4 hr)
Week 02	Remote sensing image classification for landcover mapping.	Lecture (1 hr)
Week 02	Landcover mapping for a coastal environment using unsupervised and supervised classification, and accuracy assessment using the error matrix and descriptive statistics.	Tutorial (4 hr)
Week 03	Spectral indices for land applications.	Lecture (1 hr)
Week 03	Calculating spectral indices for land applications, binary mapping using thresholding techniques, and calibrating spectral indices for mapping biophysical properties.	Tutorial (4 hr)
Week 04	Detecting and mapping change using remote sensing.	Lecture (1 hr)
Week 04	Mapping environmental change using spectral indices, change vector analysis, and post-classification techniques.	Tutorial (5 hr)
Week 05	Monitoring environmental change using landscape phenology derived from remote sensing.	Lecture (1 hr)
Week 05	Deriving phenological curves from a time-series of satellite images, and extracting metrics to characterise landscape phenology.	Tutorial (4 hr)
Week 06	Spectral indices for aquatic applications.	Lecture (1 hr)
Week 06	Calculating spectral indices for aquatic applications and mapping water column and ecosystem properties.	Tutorial (4 hr)

Attendance and class requirements

Unless otherwise indicated, students are expected to attend a minimum of 80% of timetabled activities for a unit of study, unless granted exemption by the associate dean. For some units of study, the minimum attendance requirement, as specified in the relevant table of units or the unit of study outline, may be greater than 80%. The associate dean may determine that a student has failed a unit of study because of inadequate attendance.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

See Canvas.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Understand the role of environmental monitoring for quantifying system response to environmental change, and critically evaluate the effectiveness of management practices.
LO2. Understand the sources of error that are encountered in the collection and analysis of environmental data and how they can be minimised.
LO3. Apply the basic skills of spatial data integration and analysis for environmental change detection.
LO4. Apply the principles, theory and practice of monitoring and management of coastal environments.
LO5. Understand environmental data collection considerations, and techniques for processing and analysing environmental data.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Based on 2025 student feedback, the logbook assessment instructions will provide a framework for the submission. Students will also be given more resources on basic QGIS functionality to complement their learning.

Additional information

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

GEOG5004: Environmental Mapping and Monitoring

Semester 2b, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links