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Using maximum entropy modeling for optimal selection of sampling sites for monitoring networks

May 26, 2011

Environmental monitoring programs must efficiently describe state shifts. We propose using maximum entropy modeling to select dissimilar sampling sites to capture environmental variability at low cost, and demonstrate a specific application: sample site selection for the Central Plains domain (453,490 km2) of the National Ecological Observatory Network (NEON). We relied on four environmental factors: mean annual temperature and precipitation, elevation, and vegetation type. A “sample site” was defined as a 20 km × 20 km area (equal to NEON’s airborne observation platform [AOP] footprint), within which each 1 km2 cell was evaluated for each environmental factor. After each model run, the most environmentally dissimilar site was selected from all potential sample sites. The iterative selection of eight sites captured approximately 80% of the environmental envelope of the domain, an improvement over stratified random sampling and simple random designs for sample site selection. This approach can be widely used for cost-efficient selection of survey and monitoring sites.

Citation Information

Publication Year 2011
Title Using maximum entropy modeling for optimal selection of sampling sites for monitoring networks
DOI 10.3390/d3020252
Authors Thomas J. Stohlgren, Sunil Kumar, David T. Barnett, Paul H. Evangelista
Publication Type Article
Publication Subtype Journal Article
Series Title Diversity
Series Number
Index ID 70118622
Record Source USGS Publications Warehouse
USGS Organization

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