BIO-3111 GIS and remote sensing - 10 stp

The course is administrated by

Faculty of Biosciences, Fisheries and Economics

Type of course

Theoretical. The course may be taken as a singular course.

Course contents

An important tool when performing ecological and environmental analysis is GIS and satellites as providers of spatial data. The theory and application of satellite remote sensing will be covered in the course. The lectures focus on the fundamentals of electromagnetic radiation, reflection and absorption, satellite and sensor technology, map projections, and digital image analysis. The main features and principles of raster GIS will be covered. In addition the theoretical background of geographical positioning systems (GPS) will be given, along with practical use of GPS and its integration with GIS. The course emphasizes practical PC-exercises using the GIS software like IDRISI and ArcGIS Desktop with emphasize on application of the Spatial Analyst module extension. Exercises focus on basic GIS-operation, image processing and the use of remote sensing data. During the course students will collect field data and complete their own GIS-project.

Recommended prerequisites

BIO-2204 Quantitative methods in environmental science and management, STA-0001 Applied statistics 1

Objective of the course

Give students the necessary theoretical background and practical skill to be able to work independently with GIS, GPS technology and remote sensing data. Make the students understand the analytic potential that GIS and remote sensed data represent in ecological an environmental studies. Prerequisite: General ecology, some familiarity with geographic information systems (GIS), basic statistics and general knowledge of practical use of PC and MS-Windows operative system desirable.

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

10 hours lectures (intensive), 24 hours self learning PC-lab, 30 hours instructor lead PC-lab, 10 hours field work, 30 hours and project work. Total 104 hours.