ENVS1125 Geoinformatics, spatial statistics and remote sensing (3 cr)

Study level:
Advanced studies
Grading scale:
English, Finnish
Responsible organisation:
Department of Biological and Environmental Science
Curriculum periods:
2020-2021, 2021-2022, 2022-2023

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An advanced course related to spatial analyses and geoinformatics.


An advanced course related to geographic information analyses, spatial statistics, and interpretation of remote sensing data. Main topics include estimation of erosion and runoff to waterways, characterization of point patterns, spatial autocorrelation, spatial modeling, spatial interpolation, and analyses and interpretation of remote sensing data sets. Programs to be used in practice are ArcGIS and R. At least ArcGIS should be familiar to students before attending the course.

Learning outcomes

After completing the course the student should be able to

  • Process and convert several types of geographic data sets with GIS and R programs into suitable formats needed for spatial analysis methods.
  • Transfer data sets between different programs (at least ArcGIS and R).
  • Convert airborne laser scanning data into different digital elevation models.
  • Perform hydrological modeling, and estimate erosion and nutrient runoff risks.
  • Visualize and interpret the contents of aerial or satellite images with pixel-based and segment-based classification methods.
  • Study the change of land use with a time series of satellite images.
  • Characterize the nature of observed point patterns.
  • Estimate the home range of moving animals with simple approaches.
  • Estimate both local and global spatial autocorrelation.
  • Fit simple spatial models to spatially autocorrelated data.
  • Use several spatial interpolation methods, including cokriging.
  • Use ArcGIS or R for solving the tasks of the exercise work.

Description of prerequisites

BENA4033 Basics of Geoinformatics, or similar prior knowledge of GIS data and programs.

Study materials

Lectures and exercises will be stored in Moodle and/or in a course-related network folder.


  • Holopainen M. et al. 2015. Geoinformatiikka luonnonvarojen hallinnassa. Helsingin yliopiston metsätieteiden laitoksen julkaisuja 7. (in Finnish, selected parts); ISBN: ISSN: 1799-313X
  • Cressie N. 2015. Statistics for Spatial Data. Revised Edition. Wiley. (Chapters 1-3); ISBN: 978-1-119-11461-1
  • Longley P., Goodchild M., Maquire D. & Rhind D. 2011. Geographic Information Systems and Science, 3rd Edition. Wiley. (selected parts); ISBN: 978-0-470-72144-5
  • Illian J., Penttinen A., Stoyan H. & Stoyan D. 2008. Statistical Analysis and Modelling of Spatial Point Patterns. Wiley. (selected parts); ISBN: 978-0-470-01491-2
  • Bivand R., Pebesma E. & Gomez-Rubio V. 2013. Applied Spatial Data Analysis with R, Second Edition. Springer. (Chapters 1-5, 7-10); ISBN: 978-1-4614-7617-7
  • Jensen J.R. 2015. Introductory Digital Image Processing: A Remote Sensing Perspective, 4th Edition. Prentice Hall. (selected parts); ISBN: 978-0-1340-5816-0

Completion methods

Method 1

Both a seminar presentation during the course and an exercise work after the course need to be passed. They will be evaluated with scale 0-5.
Evaluation criteria:
Exercise work report 50%, seminar presentation 40%, activity in exercises 10%.
Select all marked parts
Parts of the completion methods

Teaching (3 cr)

Participation in teaching
Grading scale:
English, Finnish
Study methods:

Lectures 8 x 2 h, computer exercises 8 x 4 h, seminar, exercise work.