Students in natural resources programs commonly take courses in geospatial technologies. An awareness of the uncertainty of spatial data and algorithms can be an important outcome of such courses. This article describes a laboratory exercise in a graduate geographic information system (GIS) class that involves collection of data for the assessment of spatial uncertainty. Students delineate a forest clearing using digital aerial photographs and global positioning system (GPS) receivers. They also measure terrain attributes such as slope, elevation, and aspect at nine selected points in the field and extract similar measures for those locations from a GIS elevation dataset. Collating data from students and groups yields a rich dataset of multiple observations. This dataset is then analyzed to develop estimates of uncertainty such as standard deviation and root mean square error (RMSE). Results from a recent lab exercise indicate that area of a forest clearing had coefficients of variation of 11.5% for delineations from aerial photographs and 7.6% from GPS delineations. The RMSE for GPS "X" coordinate, GPS "Y" coordinate, and elevation at nine terrain measurement points were 5.3, 7.1, and 3.4 m, respectively. The RMSE for slope percent was 4%, and the GIS-based slope estimate was within the range of field estimates at only seven of nine points. The RMSE for field-measured aspect was nearly 17 degrees. An online assessment of the lab exercise indicated that most students found the exercise was worth the class time devoted to it, and many students gained valuable insights about spatial uncertainty. (Contains 3 tables and 7 figures.)