Environmental Management

, Volume 40, Issue 1, pp 134–146 | Cite as

Using GIS to Generate Spatially Balanced Random Survey Designs for Natural Resource Applications

  • David M. Theobald
  • Don L. StevensJr.
  • Denis White
  • N. Scott Urquhart
  • Anthony R. Olsen
  • John B. Norman


Sampling of a population is frequently required to understand trends and patterns in natural resource management because financial and time constraints preclude a complete census. A rigorous probability-based survey design specifies where to sample so that inferences from the sample apply to the entire population. Probability survey designs should be used in natural resource and environmental management situations because they provide the mathematical foundation for statistical inference. Development of long-term monitoring designs demand survey designs that achieve statistical rigor and are efficient but remain flexible to inevitable logistical or practical constraints during field data collection. Here we describe an approach to probability-based survey design, called the Reversed Randomized Quadrant-Recursive Raster, based on the concept of spatially balanced sampling and implemented in a geographic information system. This provides environmental managers a practical tool to generate flexible and efficient survey designs for natural resource applications. Factors commonly used to modify sampling intensity, such as categories, gradients, or accessibility, can be readily incorporated into the spatially balanced sample design.


Monitoring Spatial sampling Probability-based survey GIS Accessibility 



We thank N. Peterson for field assistance with this research and M. Farnsworth, J. Gross, B. Noon, and E. Peterson for helpful comments on previous drafts. This research was supported by funding from the STAR Research Assistance Agreements CR-829095 and CR-829096 awarded by the US Environmental Protection Agency. This article was subjected to Agency review and approved for publication. The conclusions and opinions are solely those of the authors and are not necessarily the views of the Agency. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • David M. Theobald
    • 1
  • Don L. StevensJr.
    • 2
  • Denis White
    • 3
  • N. Scott Urquhart
    • 4
  • Anthony R. Olsen
    • 5
  • John B. Norman
    • 6
  1. 1.Natural Resource Ecology Lab, and Department of Natural Resource Recreation and TourismColorado State UniversityFort CollinsUSA
  2. 2.Department of StatisticsOregon State UniversityCorvallisUSA
  3. 3.Western Ecology DivisionUS Environmental Protection AgencyCorvallisUSA
  4. 4.Department of StatisticsColorado State UniversityFort CollinsUSA
  5. 5.Western Ecology DivisionUS Environmental Protection AgencyCorvallisUSA
  6. 6.Natural Resource Ecology LabColorado State UniversityFort CollinsUSA

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