1. Introduction

1.1. Course notes

The material in this document is the main lecturing and course material for GEOG0027.

1.1.1. Educational Aims and Objectives of the Course

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)

  • Lay the foundations for the third year course on Earth Observation

1.1.2. Course workload and assessment

Expected Course Load

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

Timetable 2018-19

Week/Day

Thursday 11:00-12:00

Thursday 12:00-13:00

Friday 16:00-17:00

Week 1

.

.

11/1/19 LECTURE 1

.

.

.

Introduction to course

Week 2

17/1/19

17/1/19

18/1/19 LECTURE 2

.

COMPUTING 1: Image Display

COMPUTING 1: Image Display

Image Display and Enhancement

Week 3

24/1/19

24/1/19

25/1/19 LECTURE 3

.

DOWNLOAD

COMPUTING 1: Image Display

Spatial Information

Week 4

31/1/19

31/1/19

01/2/19 LECTURE 4

.

COMPUTING 2: Spatial Filtering

COMPUTING 2: Spatial Filtering

Image Classification

Week 5

04/2/16

04/2/16

05/2/19 LECTURE 5

.

COMPUTING 3a: Classification Intro

COMPUTING 3: Classification

Spectral Information

Week 6

READING WEEK

READING WEEK

READING WEEK

.

.

.

.

Week 7

21/2/19

21/2/19

22/2/19 LECTURE 6

.

Classification

COMPUTING 3: Classification

Environmental Modelling I

Week 8

28/2/19

28/2/19

1/3/19 LECTURE 6

.

COMPUTING 4: Project

COMPUTING 4: Project

Environmental Modelling II

Week 9

07/3/19

07/3/19

08/3/19

.

COMPUTING 4: Project

COMPUTING 4: Project

COMPUTING 4: Project

Week 10

14/3/19

14/3/19

15/3/19 Project

.

COMPUTING 4: Project

COMPUTING 4: Project

Discussion

Week 11

21/3/19

21/3/19

No lecture

.

COMPUTING 4: Project

COMPUTING 4: Project

.

Lectures in Pearson G07_

Computing in Pearson Building, UNIX Computer lab, Pearson 110a_

1.1.3. Assessment

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)

1.2. Reading List

  • 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

1.3. Use of these notes

1.3.1. Use online notes

The simplest way to use these notes is to read the documents online.

1.3.2. Use github notes

The main store for these notes is on gitbub and you shpuld always be able to access (or download) the notes from there.

1.3.3. Use binder

Click on the binder link Binder

Thius lets you run the notebooks, rather than just viewing them.

1.3.4. Use Python locally

To use the notes as a notebook (assuming you have `git <http://git-scm.com>`__ and python on your computer):

  1. Copy all of the notes to your local computer (if for the first time)

    mkdir -p ~/DATA/working
cd ~/DATA/working

git clone https://github.com/profLewis/geog2021.git

cd geog2021

  2. Copy all of the notes to your local computer (if for an update)

    cd ~/DATA/working/geog2021

git reset --hard HEAD

git pull

  3. Run the notebook

    ipython notebook SpatialFiltering.ipynb