Environmental Management

, Volume 42, Issue 6, pp 1091–1100 | Cite as

Ground-Cover Measurements: Assessing Correlation Among Aerial and Ground-Based Methods

  • D. Terrance Booth
  • Samuel E. Cox
  • Tim Meikle
  • Hans R. Zuuring


Wyoming’s Green Mountain Common Allotment is public land providing livestock forage, wildlife habitat, and unfenced solitude, amid other ecological services. It is also the center of ongoing debate over USDI Bureau of Land Management’s (BLM) adjudication of land uses. Monitoring resource use is a BLM responsibility, but conventional monitoring is inadequate for the vast areas encompassed in this and other public-land units. New monitoring methods are needed that will reduce monitoring costs. An understanding of data-set relationships among old and new methods is also needed. This study compared two conventional methods with two remote sensing methods using images captured from two meters and 100 meters above ground level from a camera stand (a ground, image-based method) and a light airplane (an aerial, image-based method). Image analysis used SamplePoint or VegMeasure software. Aerial methods allowed for increased sampling intensity at low cost relative to the time and travel required by ground methods. Costs to acquire the aerial imagery and measure ground cover on 162 aerial samples representing 9000 ha were less than $3000. The four highest correlations among data sets for bare ground—the ground-cover characteristic yielding the highest correlations (r)—ranged from 0.76 to 0.85 and included ground with ground, ground with aerial, and aerial with aerial data-set associations. We conclude that our aerial surveys are a cost-effective monitoring method, that ground with aerial data-set correlations can be equal to, or greater than those among ground-based data sets, and that bare ground should continue to be investigated and tested for use as a key indicator of rangeland health.


Image analysis Line intercept Laser point frame Point intercept SamplePoint Vegetation measurement VegMeasure 



This research was funded in part by a grant from the Wyoming State Office of the Bureau of Land Management to D.T. Booth, and in part by a grant from the USDA Small Business Innovation Research (SBIR) program, grant number 2004-33610-14343, to T. W. Meikle. We thank C. Fitzgerald and John Likins and his associates at the Lander Field Office, USDI-Bureau of Land Management, and Joe Nance, Cloudstreet Airsports, Ft Collins, CO, for their efforts in data collection. We thank A. S. Laliberte, D. A. Pyke, and A. Rango for reviewing this article.


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • D. Terrance Booth
    • 1
  • Samuel E. Cox
    • 1
  • Tim Meikle
    • 2
  • Hans R. Zuuring
    • 3
  1. 1.USDA-Agricultural Research Service, High Plains Grasslands Research StationCheyenneUSA
  2. 2.Great Bear Restoration Technologies, Hamilton Carriage HouseHamiltonUSA
  3. 3.Department of Forestry ManagementUniversity of MontanaMissoulaUSA

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