Environmental and Ecological Statistics

, Volume 4, Issue 3, pp 181–204 | Cite as

The National Resources Inventory: a long-term multi-resource monitoring programme

  • S. M. NUSSER
  • J. J. GOEBEL

Abstract

Interest in natural resources and the environment has led to the development of new federal monitoring efforts, the expansion of existing federal inventory programmes, and discussions of inter-agency collaboration for natural resource assessment data collection. As federal programmes evolve, knowledge gained from existing long-term survey programmes can provide valuable contributions to statistical and operational aspects of survey efforts. This paper describes the National Resources Inventory (NRI), which has been conducted by the US Department of Agriculture's Natural Resources Conservation Service in cooperation with the Iowa State University Statistical Laboratory for several decades. The current NRI is a longitudinal survey of soil, water, and related environmental resources designed to assess conditions and trends every five years on non-federal US lands. An historical overview is provided highlighting the development of the survey programme. Sample design, data collection, and estimation procedures used in the 1992 NRI are described, and statistical issues related to long-term monitoring are discussed.

environmental statistics imputation natural resource surveys survey sampling two-phase stimation 

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References

  1. Baker, H.D., Fuller, W.A. and Goebel, J.J. (1992) Sample design and estimation procedures for the 1987 National Resources Inventory. Statistical Laboratory, Iowa State University.Google Scholar
  2. Birdsey, R.A., Hahn, J.T., MacLean, C.D., Arner, S.L., Bechtold, W.A., Williams, M.S., Schreuder, H.T., Scott, C.T., Moisen, G.G., Stage, A.R. and Born, J.D. (1995) Techniques for forest surveys when cluster plots straddle two or more conditions. Forest Science Monograph 31.Google Scholar
  3. Breidt, F.J. (1995) Markov chain designs for one-per-stratum sampling. Survey Methodology 21, 63-70.Google Scholar
  4. Breidt, F.J., McVey, A.M. and Fuller, W.A. (1996) Two-phase estimation by imputation. Journal of the Indian Society of Agricultural Statistics (Golden Jubilee number) 49, 79-90.Google Scholar
  5. Cassell, D.L. (1993) Inclusion probabilities for environmental monitoring in FHM and EMAP. 1992 Proceedings of the Section on Statistics and Environment, American Statistical Association, 82-5.Google Scholar
  6. Francisco, C.A. (1986) A quality evaluation study of the 1982 National Resources Inventory. Statistical Laboratory, Iowa State University.Google Scholar
  7. Fuller, W.A. (1990) Analysis of repeated surveys. Survey Methodology 16, 167-80.Google Scholar
  8. Fuller, W.A. (1996) Estimation for the 1992 NRI. Unpublished manuscript.Google Scholar
  9. Fuller, W.A. and Goebel, J.J. (1993) Sample design for the Alaska NRI. Unpublished manuscriptGoogle Scholar
  10. Fuller, W.A., Breidt, F.J. and Huang, H.C. (1995) The 1995 Erosion Study sample. Unpublished manuscriptGoogle Scholar
  11. Fuller, W.A., Kennedy, W.J., Schnell, D., Sullivan, G. and Park, H.J. (1986) PC CARP. Statistical Laboratory, Iowa State UniversitGoogle Scholar
  12. Goebel, J.J. and Baker, H.D. (1987) The 1982 National Resources Inventory sample design and estimation procedures. Statistical Laboratory, Iowa State University.Google Scholar
  13. Goebel, J.J. and George, T.A. (1996) Establishing a Consistent National Base for Assessing Natural Resource Issues. In Evaluating Natural Resource Use in Agriculture: Analytical Tools Now and into the Future, edited by T. Robertson, B.C. English and R.R. Alexander, University of Tennessee Institute of Agriculture, 319-4Google Scholar
  14. Goebel, J.J. and Schmude, K.O. (1982) Quality control and evaluation for the SCS National Resources Inventories. In In-Place Resource Inventories: Principles and Practice. Society of American Foresters, Publication No. 82-02, 871-6.Google Scholar
  15. Goebel, J.J., Reiser, M. and Hickman, R.D. (1985) Sampling and estimation in the 1982 National Resources Inventory. Paper presented at the 145th annual meeting of the American Statistical Association, Las Vegas, Nevada.Google Scholar
  16. Goebel, J.J., Schreuder, H.T., Geissler, P.H., House, C.C., Olsen, A.R. and Williams, W. (1996) Reinventing the Nation's Environmental Efforts: A Proposal to Merge Federal Surveys. Paper presented at American Statistical Association Workshop on Statistical Issues for Ecological and Natural Resource Monitoring Programs in the US, Washington, DC.Google Scholar
  17. Helsel, D.R. (1995) Design of a relational water-quality assessment program. 1995 Proceedings of the Biometrics Section, American Statistical Association, 60-7.Google Scholar
  18. Kasprzyk, D., Duncan, D., Kalton, G. and Singh, M.P. (1989) Panel Surveys. Wiley, New York. Kellogg, R.L., TeSelle, G.W. and Goebel, J.J. (1994) Highlights from the 1992 National Resources Inventory. Journal of Soil and Water Conservation, 49, 521-7.Google Scholar
  19. Little, R.J. and Rubin, D.B. (1987) Statistical analysis with missing data. Wiley, New York. Max, T.A., Schreuder, H.T., Hazard, J.W., Oswald, D.D., Teply, J. and Alegria, J. (1996) The Pacific Northwest Region Vegetation and Inventory Monitoring System. Research Paper PNW-RP-493, US Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, OR.Google Scholar
  20. Messer, J.J., Linthurst, R.A. and Overton, W.S. (1991) An EPA program for monitoring ecological status and trends. Environmental Monitoring and Assessment 17, 67-78.Google Scholar
  21. McCormack, R.M. (1988) Statistical challenges in the environmental sciences: a personal view. Journal of the Royal Statistical Society, Series A 151, 201-10.Google Scholar
  22. McVey, A.M., Breidt, F.J. and Fuller, W.A. (1994) Two-phase estimation through imputation. 1994 Proceedings of the Section on Survey Research Methods, American Statistical Association, 1053-8.Google Scholar
  23. Nusser, S.M. (1995) Sampling issues in regional monitoring programs. 1995 Proceedings of the Biometrics Section, American Statistical Association, 68-70.Google Scholar
  24. Nusser, S.M., Breidt, F.J. and Fuller, W.A. Design and estimation for investigating the dynamics of natural resources. Submitted to the Journal of Ecological Applications.Google Scholar
  25. Nusser, S.M., Thompson, D.M. and DeLozier, G.S. (1996) Using personal digital assistants to collect survey data. 1996 Proceedings of the Section on Survey Research Methods, American Statistical Association (forthcoming).Google Scholar
  26. Overton, W.S. and Stehman, S.V. (1993) Properties of designs for sampling continuous spatial resources from a triangular grid. Communications in Statistics-Theory and Methods 22, 2641-60.Google Scholar
  27. Overton, W.S. and Stehman, S.V. (1994) Variance estimation in the EMAP strategy for sampling discrete ecological resources. Environmental and Ecological Statistics 1, 133-52.Google Scholar
  28. Overton, W.S. and Stehman, S.V. (1995) Desirable design characteristics for long-term monitoring of ecological variables. 1995 Proceedings of the Biometrics Section, American Statistical Association, 50-9.Google Scholar
  29. Peck, S.L., Rawlings, J.O. and Finkner, A.L. (1992) A comparison of sampling design options for EMAP-Agro ecosystems. 1991 Proceedings of the Section on Survey Research Methods, American Statistical Association, 191-5.Google Scholar
  30. Platek, R., Rao, J.N.K., Särndal, C.E. and Singh, M.P. (1987) Small Area Statistics. Wiley, New York.Google Scholar
  31. Powell, D.S., McWilliams, W.H. and Birdsey, R.A. (1994) History, change and the US forest inventory. Journal of Forestry 92, 6-11.Google Scholar
  32. Saeboe, H.V. (1983) Land use and environmental statistics obtained by point sampling. Proceedings of the Session of the International Statistical Institute 50, 1317-42.Google Scholar
  33. Schreuder, H.T. and Czaplewski, R.L. (1993) Long-term strategy for the statistical design of a forest health monitoring system. Environmental Monitoring and Assessment 27, 82-94.Google Scholar
  34. Schreuder, H.T., Gregoire, T.G. and Wood, G.B. (1993) Sampling Methods for Multi-resource Forest Inventory. Wiley, New York.Google Scholar
  35. Stehman, S.V. and Overton, W.S. (1992) Properties of designs for sampling continuous spatial resources. 1991 Proceedings of the Section on Statistics and the Environment, American Statistical Association, 182-7.Google Scholar
  36. Stehman, S.V. and Overton, W.S. (1994a) Environmental Sampling and Monitoring. In Handbook of Statistics, Vol. 12, G.P. Patil and C.R. Rao (eds) Elsevier Science Publishers, Amsterdam, 263-30Google Scholar
  37. Stehman, S.V. and Overton, W.S. (1994b) Comparison of variance estimators of the Horvitz-Thompson estimator for randomized variable probability systematic sampling. Journal of the American Statistical Association 89, 30-43.Google Scholar
  38. Stevens, D.L. and Olsen, A.R. (1992) Statistical issues in environmental monitoring and assessment. 1991 Proceedings of the Section on Statistics and the Environment, American Statistical Association, 76-85.Google Scholar
  39. Tollefson, M.H. (1992) Variance estimation under random imputation. Ph.D. Thesis. Iowa State University.Google Scholar
  40. Tollefson, M.H. (1994) Imputation programs for the 1992 NRI. Unpublished manuscript.Google Scholar
  41. US Department of Agriculture (1945) Soil and water conservation needs estimates for the United States by states. Soil Conservation Service, Washington, DCGoogle Scholar
  42. US Department of Agriculture (1962) Basic statistics of the National Inventory of Soil and Water Conservation Needs. Statistical Bulletin No. 317, Washington, DCGoogle Scholar
  43. US Department of Agriculture (1981) Land resource regions and major land resource areas of the United States. Agricultural Handbook 296, Soil Conservation Service, Washington, DC.Google Scholar
  44. US Department of Agriculture (1991) Instructions for collecting 1992 National Resources Inventory sample data. Soil Conservation Service, Washington, DC.Google Scholar
  45. US Department of Agriculture (1994a) Summary Report: 1992 National Resources Inventory. Soil Conservation Service, Washington, DC., and Statistical Laboratory, Iowa State University.Google Scholar
  46. US Department of Agriculture (1994b) National Resources Inventory Data Analysis Software: User's Guide, Version 1. Soil Conservation Service, Washington, DC.Google Scholar
  47. US Geological Survey (1982) A US Geological Survey standard: Codes for the identification of hydrologic units in the United States and the Caribbean outlying areas. Geological Survey Circular 878-A.Google Scholar

Copyright information

© Chapman and Hall 1997

Authors and Affiliations

  • S. M. NUSSER
    • 1
  • J. J. GOEBEL
    • 2
  1. 1.Department of StatisticsIowa State UniversityAmesUSA
  2. 2.US Department of AgricultureNatural Resources Conservation ServiceWashingtonUSA

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