Gwybodaeth Modiwlau
Module Identifier
EAM5120
Module Title
Advanced Skills in Remote Sensing
Academic Year
2022/2023
Co-ordinator
Semester
Semester 2
Pre-Requisite
Other Staff
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Written Report (Written report based on the analysis of remotely sensed data.) | 50% |
| Semester Exam | 3 Hours (Practical/knowledge-based examination) | 50% |
| Supplementary Assessment | Written Report (Written report based on the analysis of remotely sensed data.) | 50% |
| Supplementary Exam | 3 Hours (Practical/knowledge-based examination) | 50% |
Learning Outcomes
On successful completion of this module students should be able to:
1. Identify, for particular applications, the most appropriate remote sensing datasets.
2. Independently identify appropriate methods for analyzing optical, lidar and radar remotely-sensed data.
3. Independently use appropriate software for the processing of optical, lidar and radar remotely sensed data.
4. Implement appropriate approaches for the derivation of products from remotely sensed data, such as DEMs and biophysical quantities.
Brief description
The module is intended to provide students with a background in advanced processing and analysis of a range of remote sensing data for a range of applications.
Content
Image analysis, classification and change detection.
Production of optical analysis ready data (ARD)
Processing and analysis of laser scanning data.
Processing and analysis of synthetic aperture radar (SAR) data
Processing and analysis of hyperspectral data
Photogrammetry.
Scripting and automating solutions for data analysis.
Production of optical analysis ready data (ARD)
Processing and analysis of laser scanning data.
Processing and analysis of synthetic aperture radar (SAR) data
Processing and analysis of hyperspectral data
Photogrammetry.
Scripting and automating solutions for data analysis.
Module Skills
| Skills Type | Skills details |
|---|---|
| Application of Number | This is a discipline which is reliant on the manipulation of digital numeric data requiring the application of mathematical knowledge. |
| Communication | Report writing and supporting each other within the development and learning of the method of data analysis. |
| Improving own Learning and Performance | A key skill beyond the MSc as this is a fast moving field. Students are encouraged to make use of online materials and further learning outside of class to advance skills. |
| Information Technology | This is Inherently a discipline which is heavily focused on computing and manipulation of digital data. |
| Personal Development and Career planning | Awareness of scientific literature, functionality of software and programming. Provision of advice and information for careers. |
| Problem solving | Remotely sensed data analysis, including classification and interpretation. Focus on environmental problems and how these data can be applied. |
| Research skills | Strategies relating to remote sensing data sources and acquisition strategies, data integration and processing with effective interpretation. Engagement with relevant literature. |
| Subject Specific Skills | Knowledge of and experience with remote sensing software and data. |
| Team work | Aiding and supporting each other with the materials being taught and technical aspects of the course. |
Notes
This module is at CQFW Level 7