Skip to main content
Log in

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

  • Published:
Environmental Management Aims and scope Submit manuscript

Abstract

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

Literature Cited

  • Avery T.E., H.E. Burkhard. 1994 Forest measurements. McGraw-Hill, New York

    Google Scholar 

  • Blight B.J.N., A.J. Scott. 1973 A stochastic model for repeated surveys. Journal of the Royal Statistics Society B 35:61–66

    Google Scholar 

  • Brus D. J., J.J. de Gruijter. 1997 Random sampling or geostatistical modeling: Choosing between design-based and model-based sampling strategies for soil (with discussion). Geoderma 80:1–44

    Article  Google Scholar 

  • Canadian Forest Service. 2001. Canada’s National Forest Inventory. Available from http://www.nfi.cfs. nrcan.gc.ca/overview_e.html

  • Canadian Forest Service. 2005. Canada's National Forest Inventory. Canadian Forest Service website: http://www.nfi.cfs.nrcan.gc.ca/cfic/index_e.html

  • Cressie N., C.A. Gotway, M.O. Grondona. 1990. Spatial prediction from networks, Chemical Intelligence Laboratory Systems 7:251–271

    Google Scholar 

  • De Gruijter J.J., C.J.F. TerBraak. 1990. Model-See estimation from spatial samples: A reappraisal of classical sampling theory. Mathematical Geology 22:407–415

    Article  Google Scholar 

  • Diersing V.E., R.B. Shaw, D.J. Tazik 1992. US Army Land Condition-Trend Analysis (LCTA) Program. Environment Management 16:405–414

    Google Scholar 

  • Goovaerts P. 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press, New York

    Google Scholar 

  • Goovaerts P., Ph. Sonnet 1992. Study of spatial and temporal variations of hydrogeochemical variables using tactorial kriging analysis. Pages 745–756 in A. Soares (eds). Geostatistics Tróia, Kluwer Academic Publishers, Dordrecht, The Netherlands, vol. 2, pp. 745–756

    Google Scholar 

  • Gurney, M., and I. F. Daly. 1965. A multivariate approach to estimation in periodic sample surveys. Proceedings of the Society of Statistics Section American Statistics Association pp 242–257

  • Guttorp P., P.D. Sampson, K. Newman 1992. Non-parametric estimation of spatial covariance with application to monitoring network evaluation. In: A.T. Walden, P. Guttorp (eds). Statistics in environmental and earth sciences: Edward Arnold, London, pp 39–51

  • Jones R.G. 1980 Best linear unbiased estimators for repeated surveys. Journal of the Royal Statistics Society B 42:221–226

    CAS  Google Scholar 

  • Journel A. G., C.J. Huijbregts. 1978 Mining geostatistics. Academic, London

  • Krige D.G. 1966. Two-dimensional weighted moving average trend surfaces for ore-evaluation. Journal of the South African Institute of Mining and Metallurgy 66:13–38

    Google Scholar 

  • Kyriakidis P. C., A.G. Journel 1999 Geostatistical space-time models: A review. Mathematical Geology 31(6):651–684

    Article  Google Scholar 

  • Lillesand T. M., R.W. Kiefer 2000 Remote sensing and image interpretation. John Wiley & Sons, New York

    Google Scholar 

  • Matheron G. 1971 The theory of regionalized variables and its applications. Ecole des Mines de Paris, Fontainebleau

    Google Scholar 

  • McBratney A. B., R. Webster, T.M. Burgess 1981a The design of optimal sampling schemes for local estimation and mapping of regionalized variables—I. Theory and method. Computers & Geosciences 7(4):331–334

    Google Scholar 

  • McBratney A. B., R. Webster 1981b The design of optimal sampling schemes for local estimation and mapping of regionalized variables—II. Program and examples. Computers & Geosciences 7(4):335–365

    Google Scholar 

  • McBratney A. B., R. Webster. 1983 How many observations are needed for regional estimation of soil properties? Soil Science 135(3):177–183

    Google Scholar 

  • Patterson H.D. 1950 Sampling in successive occasions with partial replacement of units. Journal of the Royal Statistics Society B 12:241–255

    Google Scholar 

  • Poso, S., M. Karlsson, T. Pekkonen, and P. Härmä. 1990. A system for combining data from remote sensing, maps and field measurements for forest planning purposes. University of Helsinki, Helsinki Research Notes No. 23

  • Ranneby B., E. Rovainen 1995 On the determination of time intervals between remeasurements of permanent plots. Forest Ecology and Management 71:195–202

    Article  Google Scholar 

  • Renard, K. G., C.R. Foster, G.A. Weesies, D.K. McCool, and D.C. Yoder. 1997. Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). US Department of Agriculture, Agriculture Handbook Number 703. US Government Printing Office, Washington, DC

  • Rodriguez-Iturbe I., J.M. Mejia 1974 The design of rainfall networks in time and space. Water Resources Research 10(4):713–728

    Google Scholar 

  • Rouhani S., H. Wackernagel 1990 Multivariate geostatistical approach to space—time data analysis. Water Resources Research 26:585–591

    Article  Google Scholar 

  • Segebaden G. V. 1992. The Swedish national forest inventory—A review of aims and methods. In: A. Nyyssönen, S. Poso, and J. Rautala (eds). Proceedings of Ilvessalo symposium on national forest inventories, 17–21 August 1992, Helsinki, Finland, Research Papers of Finnish Forest Research Institute 444

  • Stein A., C.G. Kocks, J.C. Zadoks, H.D. Frinking, M.A. Ruissen, D.E. Myers 1994 A geostatistical analysis of the spatio-temporal development of Downy Mildew Epidemics in cabbage. American Phytopathological Society 84(10):1227–1239

    Google Scholar 

  • Stellingwerf D. A., Y.A. Hussin 1997 Measurements and estimations of forest stand parameters using remote sensing. VSP BV, Utrecht, The Netherlands.

    Google Scholar 

  • Switzer P. 1979 Statistical considerations in network design. Water Resources Research 15(6):1712–1716

    Article  Google Scholar 

  • Tazik, D. J., S. D. Warren, V. E. Diersing, R. B. Shaw, R. J. Brozka, C. F. Bagley, and W.R. Whitworth. 1992. US Army Land Condition Trend Analysis (LCTA) plot inventory field methods. USACERL, Technical Report N-92/03. Department of the Army, Construction Engineering Research Laboratories, Champaign, IL

  • Tomppo, E. 1987. In: Stand delineation and estimation of stand variates by means of satellite images. Research Notes No. 19. University of Helsinki, Helsinki, pp 60–76

  • Tomppo, E. 1996. Multi-source national forest inventory of Finland. In: R. Paivinen, J. Vanclay, and S. Miina, (eds). New thrusts in forest inventory, Proceedings of the Subject Group S4.02-00 “Forest Resource Inventory and Monitoring” and Subject Group S4.12-00 “Remote Sensing Technology” Volume I, IUFRO XX World Congress, 6–12 August 1995, Tampere, Finland. EFI Proceedings No. 7. European Forest Institute Research Paper, Joensuu, Finland, pp 27–42

  • U.S. Forest Service. 2005. Forest Inventory and Analysis. USDA Forest Service website:http://www.fia.fs.fed.us/about. htm

  • Wang G., G.Z. Gertner, X. Xiao, S. Wente, A.B. Anderson 2001 Appropriate plot size and spatial resolution for mapping multiple vegetation cover types. Photogrammetric Engineering & Remote Sensing 67(5):575–584.

    Google Scholar 

  • Wang G., G.Z. Gertner, A.B. Anderson 2004 Sampling design and uncertainty based on spatial variability of spectral reflectance for mapping vegetation cover. International Journal of Remote Sensing. 25(22):4961–4980

    Google Scholar 

  • Wischmeier, W. H., and D. D. Smith. 1978. In: Predicting rainfall-erosion losses from cropland east of the Rocky Mountains: Guide for selection of practices for soil and water conservation. USDA, Agriculture Handbook, No. 282. US Government Printing Office, Washington, DC, pp 1–58

  • Xiao, X., G. Z. Gertner, G. Wang, and A. B. Anderson. 2005. Optimal sampling scheme for estimation and landscape mapping of vegetation cover. Landscape Ecology, 73

Download references

Acknowledgment

We are grateful to the US Army Corps of Engineers, Construction Engineering Research Laboratory (USA-CERL) for providing support and datasets for this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to George Gertner.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gertner, G., Wang, G. & Anderson, A.B. Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling. Environmental Management 37, 84–97 (2006). https://doi.org/10.1007/s00267-004-0152-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00267-004-0152-4

Keywords

Navigation