Remote Sensing GEOG4094
- Academic Session: 2019-20
- School: School of Geographical and Earth Sciences
- Credits: 10
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 2 (Alternate Years)
- Available to Visiting Students: Yes
- Available to Erasmus Students: Yes
This course focuses on the grounding principles and practice of optical remote sensing and digital image processing in environmental remote sensing.
Course runs for 5 weeks; 2 hour lecture and 1 hour practical weekly
Requirements of Entry
Mandatory Entry Requirements
Fulfilment of entry requirements to Level 3 Geography or Earth Sciences
Capacity to analyse data and to think critically will be assessed using a problem-based learning exercises (40%) and a written exam (60%)
Main Assessment In: April/May
Are reassessment opportunities available for all summative assessments? No
Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.
This course aims to provide students with an understanding of the grounding principles and practice of optical remote sensing and digital image processing in environmental remote sensing.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
Subject specific learning objectives
• explain the scientific basis of optical remote sensing
• carry out the most commonly used Digital Image manipulation, Image Filtering and image enhancement approaches, and explain their uses and applications
• explain examples of the use of remote sensing data to detect and quantify environmental change
• manipulate spectral data to prepare imagery for thematic classification
• explain image clustering approaches, and to be able to apply them to semi-automatically classify spectral data in order to generate your own thematic maps
Transferable skill-learning objectives
• use Erdas Imagine software to carry out commonly-used image processing tasks
• complete tasks requiring spatial and numerical data analysis skills
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.