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UCL Geography Level 2 course: Environmental Remote Sensing (2019-2020)

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GEOG0027 Environmental Remote Sensing

Course Tutors 2020/21

Prof. M. Disney

Dr Qingling Wu

Prof. P. Lewis

Department of Geography

University College London

[Educational Aims and Objectives of the Course] [Course workload and assessment] [Timetable 2019-20] [[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)

Expected Course Load
Component Hours
Lectures 10
Private Reading 80
Supervised Laboratory Work (Computing) 20
Independent Laboratory Work (Computing) 20
Required Written Work 10
TOTAL 140

Usual range 100-150 for 1/2 course unit


Assessment

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)

Pre-recorded lectures Monday Live sessions 09:00-10:00
Week 1 LECTURE 1 Introduction to course 11/1/2021 COMPUTING 1 Image Display (recording)
Week 2 LECTURE 2 Image Display and Enhancement 18/1/2021 DOWNLOAD Data download (recording)
Week 3 LECTURE 3 Spatial Information 25/1/2021 COMPUTING 2 Spatial Filtering (recording)
Week 4 LECTURE 4 Image Classification 1/2/2021 COMPUTING 3 Classification (recording)
Week 5 LECTURE 5 Spectral Information 8/2/2021 COMPUTING 3 Classification Q&A (recording)
Week 6 READING WEEK READING WEEK
Week 7 LECTURE 6 Environmental Modelling: I 22/2/2021 COMPUTING 4 Project (recording: Coursework & GEE classification)
Week 8 LECTURE 7 Environmental Modelling: II 01/3/2021 COMPUTING 4 Project (recording: Coursework & R modelling)
Week 9 NO LECTURE 08/3/2021 COMPUTING 4 Project (recording: Coursework & ENVI IDL classification)
Week 10 NO LECTURE 15/3/2021 COMPUTING 4 Project (recording: Coursework Q&As)
Week 11 NO LECTURE 22/3/2021 Coursework Q&As (recording: Coursework Q&A cont.)

Lectures for this module are pre-recorded and all computing sessions are available through MS Teams on Monday 9:00-10:00. You will also need to access a Virtual UCL PC during the live sessions through UCL Desktop Anywhere (see help info at https://www.ucl.ac.uk/isd/how-to/how-to-log-to-virtual-teaching-pc). There will be optional drop-in help sessions run by PGTA every Monday and Thursday 10:00-11:00 through the same MS Teams channel.

ENVI Software

ENVI 5.5.3 will be available to registered students through Virtual UCL PCs during the live sessions via UCL Desktop Anywhere (see help info at https://www.ucl.ac.uk/isd/how-to/how-to-log-to-virtual-teaching-pc).

For ad-hoc use of ENVI software outside of the live hours, it can be accessed from UCL Desktop Anywhere. We will use the ENVI 5.5.3 (not ENVI Classic 5.5.3) version for the guided practicals before half term, and then ENVI 5.5.3 with IDL for the assessed coursework project. Additionally, you can install ENVI on your personal computer with a UCL license (http://swdb.ucl.ac.uk/package/view/id/142?filter=envi). However, support might be limited from the teaching staff. Thus, we recommend using Desktop@UCL during the term time for best support and accessibility.


  • 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

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