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Høst 2022
BIO-3111 Geographical Information Systems (GIS) and Earth Observation - 10 stp
The course is administrated by
Institutt for arktisk og marin biologi
Type of course
Master course for biology students - principally aimed at MSc-students specializing in Northern Populations and Ecosystems. In addition to the Campus based teaching, BIO-3111 offer an alternative web based, distance eLearning solution.
The course is available as a singular course.
Course overlap
BIO-8102 Geographical information systems (GIS) and remote sensing data 10 stp
Course contents
An important tool when performing ecological and environmental analysis is GIS and satellites as providers of earth observation data. BIO-3111 focus on the application and integration of remote sensing data in GIS, including collecting, storing, analyzing and visualization of spatial data. The lectures focus on the basic of map projections, datum and the data´s spatial information, the fundamentals of electromagnetic radiation, reflection and absorption, satellite and sensor technology and digital image analysis. The course also present students to the theoretical background of global navigation satellite systems (GNSS) and its integration with GIS. The backbone of course activities are practical PC-exercises. Mainly open access data will be applied, along with the QGIS (ver. 3.16 'Hannover') geographic information system (GIS). QGIS is an open source GIS that can be freely installed on any PC operative systems (MS Windows OS, Mac OS, Linux OS and Android OS).
Application deadline
Concerns only admission to singular courses: Applicants from Nordic countries: 1 June for the autumn semester and 1 December for the spring semester. Exchange students and Fulbright students: 15 October for the spring semester and 15 April for the autumn semester.
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.
Objective 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 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 in a written report
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
12 hours in class lectures/video lecture, 10 hours self-learning PC-lab (both on-Campus and external students), 40 hours instructor lead PC-labs or web based eLearning PC-labs, 40 hours home exam. Total 102 hours.
Date for examination
Off campus exam hand in date 24.11.2022
The date for the exam can be changed. The final date will be announced at your faculty early in May and early in November.