Seefeldt Steven S, Booth D Terrance
Agricultural Research Service, United States Sheep Experiment Station, United States Department of Agriculture, Dubois, Idaho 83423, USA.
Environ Manage. 2006 May;37(5):703-11. doi: 10.1007/s00267-005-0016-6.
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.
需要更具成本效益且客观的方法,以便在可接受的误差率范围内检测重要的植被变化。本研究的目的是将目视估计法与三种测定蒿属植物草原植被覆盖度的新方法进行比较。在美国绵羊实验站确定了14个管理单元进行研究。在每个单元中,选择20个数据收集点,使用目视估计法、激光点框(LPF)、离地2米(AGL)的数字图像以及离地100米(AGL)的数字图像来测量植被覆盖度。在14个管理单元中的11个单元中,植被覆盖度的测定结果存在差异(P < 0.05)。然而,综合来看,植被覆盖度的总体测定结果并无差异。100米AGL方法的标准差、校正平方和、变异系数以及标准误差仅为LPF方法的一半,且小于2米AGL方法和目视估计法。就测量植被覆盖度而言,在速度、标准差和成本方面,所有这三种新方法与目视估计法一样好或更优。获取某一位置的永久图像是2米和100米AGL方法的一个重要优势,因为可以使用改进的软件重新分析植被情况或回答不同问题,并且能够更准确地确定植被随时间的变化。与100米AGL数字图像相关的每个样本成本降低、采样速度提高以及标准差更小,这些都是采用这种植被采样方法的有力理由。