A Method for Landscape-Scale Vegetation Assessment: Application to Great Basin Rangeland Ecosystems

Abstract

The growth of landscape-scale land management necessitates the development of methods for large-scale vegetation assessment. Field data collection and analysis methods used to assess ecological condition for the 47 165-h North Spring Valley watershed are presented. Vegetation cover data were collected in a stratified random design within 6 Great Basin vegetation types, and the probability of detecting change in native herbaceous cover was calculated using power analyses. Methods for using these quantitative assessment data are presented to calculate a departure index based on reference condition information from LANDFIRE (an interagency effort to map and model fire regimes and other biophysical characteristics at a mid-scale for the entire United States) Biophysical Setting models for the mountain big sagebrush (Artemisia tridentata Nutt. subsp. vaseyana [Rydb.] Beetle) vegetation type. For mountain big sagebrush in the North Spring Valley landscape, we found that the earliest successional classes were underrepresented and that mountain big sagebrush moderately invaded by conifers was more abundant than predicted by the LANDFIRE reference based on the historic range of variability. Classes that were most similar to the reference were mountain big sagebrush with the highest conifer cover and late development mountain big sagebrush with perennial grasses. Overall, results suggested that restoration or approximation of the historic fire regime is needed. This method provides a cost-effective procedure to assess important indicators, including native herbaceous cover, extent of woody encroachment, and ground cover. However, the method lacks the spatial information that would allow managers to comprehensively assess spatial patterns of vegetation condition across the mosaics that occur within each major vegetation type. The development of a method that integrates field measurements of key indicators with remotely sensed data is the next critical need for landscape-scale assessment.