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

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.

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.

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