Outline

Overview

The critical role of geospatial science in major disturbance events, such as bushfires and changing land managment, has placed spatial science at the forefront of policy agendas, information sharing and community engagement. The disruptive nature of this field is clear and demand for expertise in GIS, Earth observation, spatial data analytics and location intelligence has grown. However, this expertise needs to be backed by an understanding of the science, conceptual principles and ethics that underpin these enabling technologies. We will incorporate the transformative potential of spatial science driven technologies demonstrated by disturbance events. This unit content will expose you to a breadth of analytical capabilities within GIS, various applications to complex environmental issues in using and disseminating geographical information and knowledge. The fundamentals of GIS, spatial modelling and Earth observation will be introduced in the context of environmental management. You will build on these foundational concepts through problem-based learning in which GIS and remote sensing methods will be applied to address issues relating to fire and biodiversity, acid sulphate soils and water security.

Unit details and rules

Academic unit	Life and Environmental Sciences Academic Operations
Credit points	6
Prerequisites ?	ENVI1003 or AGEN1002 or GEOS1XXX or BIOL1XXX or GEOS2X11
Corequisites ?	None
Prohibitions ?	GEOS3X14
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Aaron Greenville, aaron.greenville@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam Written examination	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Research analysis	Module 1 Lab work Lab skills assessment based on Module 1	10%	Week 03 Due date: 13 Mar 2026 at 23:59	Variable	AI allowed
Outcomes assessed:
Research analysis	Module 2 Lab work Lab skills assessment based on Module 2.	10%	Week 06 Due date: 02 Apr 2026 at 23:59	Variable	AI allowed
Outcomes assessed:
Research analysis	Module 3 Lab work Lab skills based on Module 3	10%	Week 09 Due date: 01 May 2026 at 23:59	Variable	AI allowed
Outcomes assessed:
Presentation	Research report and presentation Report and presentation. Presentation given in week 13 prac class.	30%	Week 13 Due date: 27 May 2026 at 23:59	3500 words, 5 minute presentation	AI allowed
Outcomes assessed:
Contribution	Academic honesty Attendance	0%	Weekly	13 weeks	AI allowed
Outcomes assessed:
Contribution	Practical and tutorial participation Minimum 80% attendance	0%	Weekly	13 weeks	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Practical reports: Practical exercises for each module using GIS software.
Research report and presentation: A report and presentation based on the GIS Project addressing a coastal, terrestrial or social science research question. Students will work in groups.
Final exam: The final written exam will test students’ knowledge gained from all lectures and practicals. This assessment is compulsory and failure to attend, attempt, or submit will result in the award of an AF grade. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator. The final exam is a hurdle task and a results < 40% will be a fail grade.

Detailed information for each assessment can be found on Canvas.

Students may use Generative AI to skill up with ArcGIS or Google Earth Engine but not to write their reports.

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	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.
Distinction	75 - 84	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.
Credit	65 - 74	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.
Pass	50 - 64	o be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty
Fail	0 - 49	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty.

For more information see sydney.edu.au/students/guide-to-grades.

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	GIS: Introduction to GIS	Lecture (2 hr)
Week 01	Introduction to ArcGIS	Practical (3 hr)
Week 02	GIS: Vector and raster data	Lecture (2 hr)
Week 02	Impact of the megafires on species: working with vectors and rasters	Practical (3 hr)
Week 03	GIS: Co-ordinate systems and map projections	Lecture (2 hr)
Week 03	Mapping citizen science data	Practical (3 hr)
Week 04	Spatial modelling: multi-criteria decision analysis in GIS	Lecture (2 hr)
Week 04	Application of raster principles	Practical (3 hr)
Week 05	Spatial modelling concepts	Lecture (2 hr)
Week 05	Spatial modelling and prediction	Practical (3 hr)
Week 06	Spatial data uncertainty: is it the achilles heal of GIS?	Lecture (2 hr)
Week 06	The role of field based validation	Practical (3 hr)
Week 07	Remote sensing: principles	Lecture (2 hr)
Week 07	Remote sensing: introduction	Practical (3 hr)
Week 08	Remote sensing: indices	Lecture (2 hr)
Week 08	Remote sensing: indices	Practical (3 hr)
Week 09	Remote sensing: classification	Lecture (2 hr)
Week 09	Remote sensing: classification	Practical (3 hr)
Week 10	Applications	Lecture (2 hr)
Week 10	GIS project	Practical (3 hr)
Week 11	Applications	Lecture (2 hr)
Week 11	GIS projects	Practical (3 hr)
Week 12	Applications	Lecture (2 hr)
Week 12	GIS projects	Practical (3 hr)
Week 13	Applications	Lecture (2 hr)
Week 13	GIS project presentations	Practical (3 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. Timetabled activities means both lectures and practical classes contribute to the minimum of 80% in-person 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.

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. define the basic terms associated with geographic information science, including technologies, systems and studies
LO2. differentiate between spatial data and spatial information
LO3. source geospatial data from government and private agencies
LO4. decipher various air-borne and space-borne sensors and their carriers
LO5. apply geo-image analytical techniques for land resources assessment and management
LO6. build spatial databases of geographical entities for data querying and retrieval
LO7. apply conceptual models of spatial phenomena for practical decision-making in, for example, land management and land use planning, etc
LO8. apply critical analysis of situations to apply the concepts of spatial phenomena to solve environmental and land resource problems
LO9. communicate results of scientific investigations effectively through various means such as oral, written and essay formats, use major GIS and remote sensing software packages, e.g. ArcGIS™.

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.

New content developed to replace content when we co-taught with GEOS3x14

Additional information

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General laboratory safety rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory

As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

ENVX3001: Environmental GIS

Semester 1, 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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links