Application deadline

Type of course

Admission requirements

Local admission , application code 9371 - - Master`s level singular course.

Admission requires a Bachelor`s degree (180 ECTS) or equivalent qualification, with a major in biology of minimum 80 ECTS.

Recommended prerequisites: Students should have some familiarity with geographic information systems (GIS), basic statistics and general knowledge of practical use of computers.

Course content

Objectives of the course

Knowledge

• The meaning and importance of datum and projections in spatial data
• The physical background for remote sensing emphasizing the properties of electromagnetic radiation
• Principles of global navigation satellite systems (GNSS), its importance in gathering spatial reference data and integration in GIS
• Different remote sensing platforms (Drones/UAV and space borne satellites), active (radar and LIDAR) and passive (visible, infrared, thermal) sensors and application of multi- and hyperspectral data
• Understand the nature of raster data and basis of image processing (restoration, enhancement, transformation) 
• Background of image analyzing techniques (time serial and change detection), classification (supervised, unsupervised) and spatial interpolation and regression

Skills/ Learning outcome

• How to find, download, store and implement remote sensing data in GIS
• Create informative and visual attractive maps by combining basis topographical data and thematic data layers from open access web based services 
• Extract and analyze information by selective query in tabular data and visualize it in maps 
• Determine land cover or other earth features by means of different classification methods  
• Application of different image processing techniques to improve the visibility and information content of raster data 
• Create new data layers by Boolean query in raster data and combine them in multi-criteria suitability mapping 
• Application of different image processing techniques to improve the visibility and information content of raster data
• Create informative and visual attractive maps by combining basis topographical data and thematic data layers
• Determine land cover or other earth features by means of different classification methods 
• Extract and analyze information by selective query in tabular data and visualize it in maps
• Create new data layers by Boolean query in raster data and combine them in multi-criteria suitability mapping
• Application of spatial multivariate analysis such as principle component analysis (PCA) for noise removal, data redundancy and change detection
• Apply both linear and non-linear regression techniques to analyze spatial relationships and heterogeneity
• Do simple Python scripting in order to understand how to be more efficient and productive within a GIS

General competence

• Understand the importance and usefulness of GIS and remote sensing as tools for solving practical environmental problems, presenting thematic data and as a management system for spatial data
• Independent complete a practical GIS project using remote sensing data, image processing and analyzing techniques and present the results and conclusions in a written report

Language of instruction and examination

Teaching methods

Assessment

Home examination report grades as Passed/Failed.

Obligatory required coursework:

All PC-lab assignments and multiple-choice test associated with the lectures must be approved.

Re-sit exam:

There will be a re-sit examination for students that does not pass the previous ordinary home examination.