NEON Data in the Classroom: An Open Education Resource for Quantifying Spatial Patterns of Raster Images Using Semivariograms and Fractals

Quantifying spatial patterns is the first step to understanding reciprocal interactions of spatial patterns and underlying ecological processes, main focus of landscape ecology. This open education resource was developed in partnership with the National Ecological Observatory Network's (NEON) Faculty Mentoring Network (FMN) to introduce students to the concept of landscape patterns using raster images from the Harvard Forest and the San Joaquin Experimental Range sites. The specific learning objectives are to: 1) design spatial sampling grid and extract data from raster images, (2) quantify spatial autocorrelation of the raster images using semi-variograms, and (3) quantify the spatial complexity of the raster images using fractals calculated by semi-variograms.

This educational resource was implemented in an upper level undergraduate course titled "Introduction to Landscape Ecology" at the University of Arkansas. Following discussion of spatial patterns and diverse tools to quantify them, students were given the data, R code, and specific questions related to the data, method, and ecological inference of the spatial patterns. Students were able to follow and reproduce the output from the R code, but struggled to grasp the concept of fractals and spatial complexity in relation to the raster images. As a result, a discussion of applications of fractals in ecology before and after implementing this exercise is strongly recommended. In conclusion, using open data products generated by NEON and other networks has great potential for enriching students' learning when integrated with foundational ecological concepts.