Department of Geography
University College London
[Educational Aims and Objectives of the Course] [Course workload and assessment] [Timetable 2018-19] [[Reading List](#Reading List)] [How to run the practicals elsewhere]
To enable the students to:
- Understand the nature of remote sensing data and how they are acquired
- Understand different types of remote sensing instruments and their missions
- Understand basic image representation and processing
- Understand how Earth Observation data can be combined with other sources of data and data techniques (e.g. GIS)
- Understand how EO data can be used in environmental science (particularly via classification and monitoring)
- Develop practical skills in these areas, which may be useful in planning of dissertations
- Develop links with the second year course on Geographic Information Systems Science and with othet courses as appropriate (e.g. hydrology, environmental systems)
| Component | Hours |
|---|---|
| Lectures | 8 |
| Private Reading | 80 |
| Supervised Laboratory Work (Computing) | 24 |
| Independent Laboratory Work (Computing) | 20 |
| Required Written Work | 10 |
| TOTAL | 142 |
Usual range 100-150 for 1/2 course unit
- 100% Assessed Practical (3500 words) - submission date standard 2nd year submission date i.e. Fri 22th March 2019 (12 noon).
N.B.
- Penalties for late submission and over length WILL be applied
- Different arrangements for JYA/Socrates (make sure you inform the lecturers if this affects you)
| Thursday 09:00-10:00 | Thursday 11:00-12:00 | Friday 16:00-17:00 | |
|---|---|---|---|
| Week 1 | 11/1/19 LECTURE 1 Introduction to course; Environmental Remote Sensing | ||
| Week 2 | 17/1/19 COMPUTING Image Display | 17/1/19 COMPUTING Image Display | 18/1/19 LECTURE 2 Image Display and Enhancement |
| Week 3 | 24/1/19 DOWNLOAD Data download | 24/1/19 COMPUTING Image Display | 25/1/19 LECTURE 3 Spatial Information |
| Week 4 | 31/1/19 COMPUTING 2 Spatial Filtering | 31/1/19 COMPUTING 2 Spatial Filtering | 01/2/19 LECTURE 4 Image Classification |
| Week 5 | 04/2/16 COMPUTING 3 Classification | 04/2/16 COMPUTING 3 Classification | 05/2/19 LECTURE 5 Spectral Information |
| Week 6 | READING WEEK | READING WEEK | READING WEEK |
| Week 7 | 21/2/19 COMPUTING 3 Classification | 21/2/19 COMPUTING 3 Classification | 22/2/19 LECTURE 6 Environmental Modelling: I |
| Week 8 | 28/2/19 COMPUTING 4 Project | 28/2/19 COMPUTING 4 Project | 1/3/19 LECTURE 6 Environmental Modelling: II |
| Week 9 | 07/3/19 COMPUTING 4 Project | 07/3/19 COMPUTING 4 Project | 08/3/19 COMPUTING 4 Project |
| Week 10 | 14/3/19 COMPUTING 4 Project | 14/3/19 COMPUTING 4 Project | 15/3/19 Project Discussion |
| Week 11 | 21/3/19 COMPUTING 4 Project | 21/3/19 COMPUTING 4 Project | No lecture |
Lectures in Pearson G07
Computing in Pearson Building, UNIX Computer lab, Pearson 110a
- Jensen, John R. (2006) Remote Sensing of the Environment: an Earth Resources Perspective, Hall and Prentice, New Jersey, 2nd ed.
- Jensen, John R. (1995, 2004) Introductory Digital Image Processing: A Remote Sensing Perspective (Prentice Hall Series in Geographic Information Science)
- Jones, H. G and Vaughan, R. A. (2010) Remote Sensing of Vegetation, OUP, Oxford.
- Lillesand, T., Kiefer, R. and Chipman, J. (2004) Remote Sensing and Image Interpretation. John Wiley and Sons, NY, 5th ed.
- Mather, P. (2004) Computer processing of remotely sensed images: an introduction
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent