Comparison of multiple historical monitoring methods across five rangeland biomes

Lemmon, South Dakota, Nunn, Colorado, Elko, Nevada, Burns, Oregon, Portola, California

Collected data on historical rangeland monitoring transects using loop frequency, line point intercept, Daubenmire frames, nested density/frequency, and biomass methods.  Methods will be compared to assess biases for each method and for methods across biomes.

May 2012 – present                      

Diversity of opinions relating to the practical use of state and transition models

Grand River National Grasslands, Lemmon, South Dakota

In recent years, there has been a national push to use ecological site descriptions (ESDs) and state and transition models (STMs) in the federal land management agencies.  In this small study, we asked five experts from one major land resource area to place 23 plots into the best fit ecological state and phase within the state and transition model.  All five experts agreed on ecological state for 11 plots and for ecological phase for five plots.  We feel that it may be necessary to rethink trying to classify land into ecological phase, and that many of the ESDs and STMs could benefit from an expert survey such as the one presented here.

January 2012 – present

Retrospective remote sensing to verify historical land management changes

Malheur National Forest, Burns, Oregon

The use of remote sensing in rangeland management has historically been limited to scientists even though it can be a powerful tool in documenting change on a landscape over time.  If a permittee on a federal land allotment makes a management change which results in changes on the landscape and ground data are unavailable, there are very few tools to help verify the permittee’s statement.  In this case study on a Forest Service allotment in Oregon, we used remote sensing in a novel way that can be replicated by land managers to help assess trends on the landscape over time.

2011 – 2012

Comparison of very large scale aerial (VLSA) imagery to ground-based rangeland monitoring data

Grand River National Grasslands, Lemmon, South Dakota

Used very high resolution imagery (< 1mm) to detect dominant graminoid species in the mixed-grass prairie and incorporated the findings into landscape classification using a state and transition model and ecological site descriptions.

2007 – 2012

Use of Kendall’s coefficient of concordance to assess agreement of observers of very high resolution imagery

Grand River National Grasslands, Lemmon, South Dakota

Ground-based vegetation monitoring methods are expensive, time-consuming and limited in sample size. Aerial imagery is appealing to managers because of the reduced time and expense and the increase in sample size. One challenge of aerial imagery is detecting differences among observers of the same imagery. Six observers analysed a set of 1-mm ground sample distance aerial imagery for graminoid species composition and important ground-cover characteristics. Kendall’s coefficient of concordance (W) was used to measure agreement among observers. The group of six observers was concordant when assessed as a group. When each of the observers was assessed independently against the other five, lack of agreement was found for those graminoid species that were difficult to identify in the aerial images.

Project website