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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
ENVIRONMENTAL ASSESSMENT

Abstract

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.

Keywords

Monitoring Spatial sampling Probability-based survey GIS Accessibility 

Notes

Acknowledgments

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.

References

  1. Baker WL, Cai Y (1992) The r.le programs for multiscale analysis of landscape structure using the GRASS geographical information system. Landscape Ecology 7:291–302CrossRefGoogle Scholar
  2. Cochran WG (1977) Sampling techniques. 3rd ed. Wiley, New YorkGoogle Scholar
  3. Courbois PJY, Urquhart NS (2004) Comparison of survey estimates of the finite population variance. Journal of Agricultural, Biological, and Environmental Statistics 9(2):236–251CrossRefGoogle Scholar
  4. Di Zio S, Fontanella L, Ippoliti L (2004) Optimal spatial sampling schemes for environmental surveys. Environmental and Ecological Statistics 11(4):397–411CrossRefGoogle Scholar
  5. Flores LA, Martinez LI, Ferrer CM (2003) Systematic sample design for estimation of spatial means. Environmetrics 14:45–61CrossRefGoogle Scholar
  6. Gilbert RO (1987) Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold, New YorkGoogle Scholar
  7. Goodchild MF, Grandfield AW (1983) Optimizing raster storage: an examination of four alternatives. Proceedings of the AutoCarto 6, Ottawa, Canada, pp. 400–407Google Scholar
  8. Griffith D (2005) Effective geographic sample size in the presence of spatial autocorrelation. Annals of the Association of American Geographers 95(4):740–760CrossRefGoogle Scholar
  9. Hall RK, Olsen A, Stevens D, Rosenbaum B, Husby P, Wolinsky GA, Heggem DT (2000) EMAP design and river reach file 3 (RF3) as a sample frame in the Central Valley, California. Environmental Monitoring and Assessment 64:69–80CrossRefGoogle Scholar
  10. Hansen MH, Madow WG, Tepping BJ (1983) An evaluation of model-dependent and probability sampling inferences in sample surveys. Journal of the American Statistical Association 78:776–760CrossRefGoogle Scholar
  11. Herlihy AT, Larsen DP, Paulsen SG, Urquhart NS, Rosenbaum BJ (2000) Designing a spatially balanced, randomized site selection process for regional stream surveys: the EMAP mid-Atlantic pilot study. Environmental Monitoring and Assessment 63:92–113CrossRefGoogle Scholar
  12. Huber B (2000) Sample: Designing random sampling programs with ArcView 3.2. Quantitative Decisions, Inc. Available from http://www.quantdec.com/sample/index.htm (accessed 3 March 2004)
  13. Jenness J (2001) Random point generator, v1.1. Jenness Enterprises. Flagstaff, AZGoogle Scholar
  14. Lesser VM (2001) Applying survey research methods to account for denied access to research sites on private property. Wetlands 21(4):639–647CrossRefGoogle Scholar
  15. Mark DM (1990) Neighbor-based properties of some orderings of two-dimensional space. Geographical Analysis 2:145–157Google Scholar
  16. Oakley KL, Thomas LP, Fancy SG (2003) Guidelines for long-term monitoring protocols. Wildlife Society Bulletin 31(4):1000–1003Google Scholar
  17. Olsen AR (2006) Software for R: psurvey.analysis (2.9). Available from http://www.epa.gov/nheerl/arm Google Scholar
  18. Overton WS (1993) Probability sampling and population inference in monitoring programs. In: Goodchild MF, Parks BO, Stayert LT (eds) Environmental modeling with GIS. Oxford University Press, New York, pp 470–480Google Scholar
  19. Pebesma EJ, Wesseling CG (1998) Gstat, a program for geostatistical modeling, prediction and simulation. Computers and Geosciences 24(1):17–31CrossRefGoogle Scholar
  20. Peterson SA, Urquhart NS, Welch EB (1999) Sample representativeness: A must for reliable regional lake condition estimates. Environmental Science and Technology 33:1559–1565CrossRefGoogle Scholar
  21. Saalfeld A (1998) Sorting spatial data for sampling and other geographic applications. GeoInformatica 2:37–57CrossRefGoogle Scholar
  22. Särndal C (1978) Design-based and model-based inference for survey sampling. Scandinavian Journal of Statistics 5:27–52Google Scholar
  23. Seaber PR, Kapinos FP, Knapp GL (1987) Hydrologic unit maps. US Geological Survey, Denver, Colorado. Water Supply Paper 2294Google Scholar
  24. Smith TH (1976) The foundations of survey sampling: a review. Journal of the Royal Statistics Society A 139:183–204CrossRefGoogle Scholar
  25. Stehman SV (1999) Basic probability sampling designs for thematic map accuracy assessments. International Journal of Remote Sensing 20(12):2423–2441CrossRefGoogle Scholar
  26. Stehman SV, (2001) Statistical rigor and practical utility in thematic map accuracy assessment. Photogrammetric Engineering and Remote Sensing 67(6):727–734Google Scholar
  27. Stehman SV, Overton WS (1996) Spatial sampling. In: Arlinghaus S (ed) Practical handbook of spatial statistics. CRC Press, Boca Raton, FL, pp. 31–63Google Scholar
  28. Stevens DL Jr. (1997) Variable density grid-based sampling designs for continuous spatial populations. Environmetrics 8:164–195CrossRefGoogle Scholar
  29. Stevens DL Jr. (2002) Sample design and statistical analysis methods for the integrated biological and physical monitoring of Oregon streams. Oregon Department of Fish and Wildlife, Report Number OPSW-ODFW-2002-07Google Scholar
  30. Stevens DL Jr, Olsen AR (2000) Spatially restricted random sampling designs for design-based and model-based estimation. In: Accuracy 2000: Proceedings of the 4th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. Eds: GBM Heuveliuk and MJP Lemmens Delft University Press, Delft pp 609–616Google Scholar
  31. Stevens DL Jr., Olsen AR (2003) Variance estimation for spatially balanced samples of environmental resources. Environmetrics 14:593–610CrossRefGoogle Scholar
  32. Stevens DL Jr., Olsen AR (2004) Spatially balanced sampling of natural resources. Journal of the American Statistical Association 99(465):262–278CrossRefGoogle Scholar
  33. Theobald DM (2003) GIS concepts and ArcGIS methods. Conservation Planning Technologies, Fort Collins, COGoogle Scholar
  34. Theobald DM, Norman JB (2006) Spatially-balanced sampling using the Reversed Randomized Quadrant-Recursive Raster algorithm: A user’s guide for the RRQRR ArcGIS v9 tool. Available from http://www.nrel.colostate.edu/projects/starmap
  35. Tobler W (1970) A computer model of simulating urban growth in the Detroit region. Economic Geography 46:234–240CrossRefGoogle Scholar
  36. Thompson SK (2002) Sampling, 2nd ed. Wiley, New YorkGoogle Scholar
  37. Thompson WL (ed) (2004) Sampling rare or elusive species. Island Press, Washington, DCGoogle Scholar

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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