Measuring plant cover in sagebrush steppe rangelands: a comparison of methods.
Methods that are more cost-effective and objective are needed to detect important vegetation change within acceptable error rates. The objective of this research was to compare visual estimation to three new methods for determining vegetation cover in the sagebrush steppe. Fourteen management units at the US Sheep Experiment Station were identified for study. In each unit, 20 data collection points were selected for measuring plant cover using visual estimation, laser-point frame (LPF), 2 m above-ground-level (AGL) digital imagery, and 100-m AGL digital imagery. In 11 of 14 management units, determinations of vegetation cover differed (P < 0.05). However, when combined, overall determinations of vegetation cover did not differ. Standard deviation, corrected sums of squares, coefficient of variation, and standard error for the 100 m AGL method were half as large as for the LPF and less than the 2-m AGL and visual estimate. For the purpose of measuring plant cover, all three new methods are as good as or better than visual estimation for speed, standard deviation, and cost. The acquisition of a permanent image of a location is an important advantage of the 2 and 100 m AGL methods because vegetation can be reanalyzed using improved software or to answer different questions, and changes in vegetation over time can be more accurately determined. The reduction in cost per sample, the increased speed of sampling, and the smaller standard deviation associated with the 100-m AGL digital imagery are compelling arguments for adopting this vegetation sampling method.
Agricultural Research Service, United States Sheep Experiment Station, United States Department of Agriculture, Dubois, Idaho 83423, USA. sseefeldt@pw.ars.usda.gov
Steven S
SS
Seefeldt
D Terrance
DT
Booth
D000383:Agriculture; D004784:Environmental Monitoring / Q000379:methods*; D040362:Geographic Information Systems; D005843:Geography; D010945:Plants, Edible / Q000254:growth & development*; D011157:Population Dynamics; D012621:Seasons; D013997:Time Factors