PS 48-140
A scalable method for plot allocation and validation in terrestrial ecology studies

Wednesday, August 7, 2013
Exhibit Hall B, Minneapolis Convention Center
David J. Gudex-Cross, National Ecological Observatory Network (NEON), Boulder, CO
Elena I. Azuaje, National Ecological Observatory Network (NEON), Boulder, CO
Rachel E. Krauss, National Ecological Observatory Network (NEON), Boulder, CO
David T. Barnett, National Ecological Observatory Network (NEON), Boulder, CO

As the study of terrestrial ecology moves toward discerning patterns at larger spatial and temporal scales, there is an increasing need for efficient and cost-effective ways to allocate sampling locations given multiple, often remote and geographically disparate, research sites. Remote sensing, combined with a stratified and spatially-balanced sampling design, provides one avenue through which this can be achieved. The National Ecological Observatory Network (NEON) comprised of sixty sites across the continent, is a unique case in point highlighting the utility of such an approach. Here we present a novel, standardized method for plot allocation and validation. It is comprised of a workflow which employs the Reversed Randomized Quadrant-Recursive Raster (RRQRR) algorithm, stratification and allocation by the National Land Cover Database (NLCD), and remote sensing of high-resolution aerial imagery (30cm-1m) to quantify stratification accuracy levels, all within a GIS environment. 


We have employed this method for multiple taxa at 29 NEON sites to date. To test overall NLCD stratification accuracy rates, we evaluated 100 RRQRR-generated points per dominant vegetation layer(s) at each site. Every point was assessed as a 20m radius circle, representing the potential plot scale and design in which many of NEON’s terrestrial measurements will be made. Accuracies ranged from 45-88%, lowest being Mixed Forest and highest Barren Land. An advantage of the RRQRR design is that a potential sampling location can be excluded due to logistical or science-related constraints (i.e. incorrect vegetation), and an alternate, predetermined sampling location is available without compromising the integrity of the sample design. As a whole, this approach has several advantages in that it is highly cost-effective, can be scaled depending on the scope of the project, and allows more data comparability through standardization regardless of geographic site location or taxa being studied. High-resolution aerial imagery is publicly available through various seamless servers and imagery providers; though it should be noted field reconnaissance may still be necessary in some cases prior to plot establishment to ensure desired accuracy levels.