Space systems for the 
Arctic monitoring

Course name

Space systems for the Arctic monitoring

Course code

М2.В4

Type of course

Compulsory

Level of course

Master studies

Year of study

First

Semester when the course is delivered

Autumn

Workload

5 ECTS

Name of lecturer

Mr. Roman Aleshko

Learning outcomes

Upon successful completion of the course the students should be able to:

• demonstrate basic knowledge of remote sensing working methodology;
• demonstrate basic knowledge of relevant physical properties of the atmosphere and the spectral properties of various objects;
• describe how various types of remote sensing data are structured, manufactured, and stored;
• describe how to use various methods of digital image processing in order to extract and visualize the information contained in the image;
• demonstrate the ability to work independently with raster data based on remote sensing in GIS.

Mode of delivery

face-to-face

Prerequisites and co-requisites

basic computer skills, basic knowledge in mathematics and physics

Course contents

1. Introduction to Space Systems and Remote Sensing
2. Satellites and Sensors
3. Elements of Visual Interpretation
4. Multispectral Remote Sensing
5. Microwave Remote Sensing
6. Remote Sensing of Sea Ice and Coastal Monitoring
7. Remote Sensing of Vegetation
8. Remote Sensing of Urban Landscapes

Recommended and required reading

Required reading:

1. John R. Jensen “Remote Sensing of the Environment: An Earth Resource Perspective”, Pearson Education, Inc.
2. Tutorial: Fundamentals of Remote Sensing, Canada Centre for Remote Sensing, http://ccrs.nrcan.gc.ca/index_e.php
3. Smith R. B. (2006) Introduction to Remote Sensing of Environment, MicroImages, 32 p.

Recommended reading:

1. Lillesand, T.M., Kiefer, R.W & Chipman, J.W. Remote Sensing and Image Interpretation. Last edition. New York: John Wiley & Sons, 2003.
2. James B. Cambell “Introduction to Remote Sensing”, Taylor & Francis
3. P.A. Burrough “Principles of Geographical Information System for Land Resource Assessment”, Oxford University Press

Planned learning activities and teaching methods

The course is divided into two parts:

• lectures, where students will have basic knowledge on space sensors and remote sensing theory;
• laboratory work, where students will get hands-on experience with specialized software intended for remote sensing data processing.

Assessment methods and criteria

One 3-hour university-based written exam at the end of the term, evaluated on a scale of A-E=pass and F=fail

Language of instruction

English

Work placement

Possible practice at the Center of Space Monitoring of the Arctic and the Forest Research Center