autumn 2017

FYS-3023 Environmental monitoring from satellite - 10 stp

Sist endret: 26.02.2018

The course is administrated by

Faculty of Science and Technology

Studiested

Tromsø |

Application deadline

Applicants from Nordic countries: 1 June for the autumn semester and 1 December for the spring semester.

Applicants from outside the Nordic countries: 1 October for the spring semester and 15 April for the autumn semester.

Type of course

The course is available as a singular course. The course is also available to exchange students and Fulbright students.

The course will only be taught if there are sufficiently many students. Please contact the student adviser as soon as possible if you are interested in following the course.

Admission requirements

Admission requirements are a Bachelor's degree in physics or similar education, including specialization in physics worth the equivalent of not less than 80 ECTS credits.https://fsweb.no/soknadsweb/login.jsf?inst=UiT

Local admission, application code 9371 - singular courses at Master's level.

Course contents

The course will give a practical introduction to methods and algorithms used for analyzing and interpreting Earth observation data. Focus will be on how Earth observation data can be applied to study and monitor the climate and the environment. The course will contain elements of image processing, pattern recognition and texture analysis. It has a significant practical component, in which various applications will be discussed.

Recommended prerequisites

FYS-2006 Signal processing, FYS-2007 Statistical signal theory, FYS-2010 Digital image processing, FYS-3001 Earth observation from satellites, FYS-3012 Pattern recognition

Objective of the course

Knowledge - The student can

  • describe fundamental principles of remote sensing by synthetic aperture radar sensors and multi-spectral optical sensors
  • describe a number of satellite remote sensing application areas within (but not limited to) the marine, cryosphere, and forest domains
  • discuss and select appropriate remote sensing sensors and modes applicable to a given application
  • discuss and select basic signal processing, image processing, and pattern recognition techniques applicable in the analysis of remote sensed imagery

 

Skills - The student can

  • explain the application domains of synthetic aperture radar and multi-spectral optical sensors with respect to environmental monitoring
  • analyze remote sensed data by applying various segmentation methods, multi-channel feature extracting method, and classification methods

 

General expertise - The student can

  • give a basic interpretation of synthetic aperture radar and multi-spectral optical Earth observation data
  • implement or apply methods and algorithms for analysis of remote sensed data in Matlab and ENVI

Language of instruction

The language of instruction is English and all of the syllabus material is in English. Examination questions will be given in English, but may be answered either in English or a Scandinavian language.

Teaching methods

Lectures: 30 hours Exercises: 30 hours

Assessment

Portfolio assessment of project assignments counting about 40 % and a final oral examination counting about 60 %. All modules in the portfolio are assessed as a whole and one combined grade is given. Assessment scale: Letter grades A-F.

Re-sit examination (section 22): There is no access to a re-sit examination in this course.

Postponed examination (sections 17 and 21): Students with valid grounds for absence will be offered a postponed examination. Both postponed project assignments and postponed oral examination are arranged during the semester if possible, otherwise early in the following semester.

See indicated sections in Regulations for examinations at the UiT The arctic university of Norway for more information.

Coursework requirements: Access to the final examination requires submission of project assignments.

Schedule

Recommended reading/syllabus

Textbook and detailed syllabus/reading list will be announced prior to course start.