GEOG40890 Remote Sensing

Academic Year 2022/2023

Remote Sensing is a core focus of contemporary GIS application, both in research and professional / business contexts. The purpose of this course is to provide adequate knowledge pertaining to concepts, principles and utility of Remote Sensing technology and to prepare students to apply this technology to their discipline of interest. It will also provide a sound understanding of principles and applications of remotely sensed digital image processing. The specification and use of digital imagery for investigating Earth resources and environmental applications will be discussed. Digital image processing of aerial/space borne sensors including radiometric and geometric correction, image enhancement and interpretation, mosaicking, segmentation a swell as classification techniques and its integration with GIS will be covered.

Show/hide contentOpenClose All

Curricular information is subject to change

Learning Outcomes:

On completion of the module you will have gained the following skills:

- Understanding of theoretical remote sensing considerations and technical information pertaining to a range of sensor platforms.
- Ability to use the complete range of remote sensing tools for a broad range of operational and application tasks.
- Ability to efficiently and accurately correct and interpret remotely sensed digital imagery.
- Understanding on the use of statistics pertainning to radiometric/geometric correction and classification as well as segmentation techniques.
- Knowledge and application of image enhancement techniques.
- Knowledge to use the electromagnetic spectrum to generate a variety of image products.
- Ability to discuss the interaction of remotely sensed data in a GIS and vice versa at a philosophical and practical level.

Student Effort Hours: 
Student Effort Type Hours
Lectures

12
Practical

12
Specified Learning Activities

36
Autonomous Student Learning

100
Total

160
Approaches to Teaching and Learning:
The module is largely delivered in a computer lab through hands-on demonstrative and problem-solving exercises. Each session will be supported with a lecture to deliver the theory and introduce concepts, terminology and applications.

Students will be guided through the practicals but will self-direct their learning in the individual project assignment by applying Remote Sensing to an area/topic relevant to their discipline, research or area of interest. 
Requirements, Exclusions and Recommendations
Learning Requirements:

None required

Learning Recommendations:

Geographic Information Systems
Computer skills for software installation and file management


Module Requisites and Incompatibles
Incompatibles:
BSEN40780 - Remote Sensing and GIS


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Lab-based remote sensing applications Throughout the Trimester n/a Graded No

30
Class Test: Lab-based test/exam 2 hour End of Trimester Exam n/a Graded No

30
Project: Remote sensing project Unspecified n/a Graded No

40

Carry forward of passed components
Yes
 
Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Individual feedback will be provided on the project and the exam assignments via Brightspace post-assessment. This will be complemented with general in-class group feedback.

Lillesand, T., Ralph W. Kiefer, R.W., Chipman, J. 2015, Remote Sensing and Image Interpretation, 7th Edition, . Publication Wiley & Sons. ISBN: 978-1-118-91947-7
Jensen, R.J. R. 2016. Introductory Digital Image Processing: A Remote Sensing Perspective. 4th Edition. ISBN-13: 978-0134058160
Name Role
Assoc Professor Ainhoa Gonzalez Del Campo Lecturer / Co-Lecturer
Dr Tobi Morakinyo Lecturer / Co-Lecturer
Dr Payam Sajadi Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
      
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Mon 10:00 - 11:50