Erosion and weed dominance often limit the recovery of burned piñon-juniper woodlands. Soil water repellency (SWR) is one factor that may contribute to this by increasing overland flow and impeding seedling establishment. In spite of these effects, the extent of SWR within piñon-juniper woodlands is unknown. In this study, the extent, severity and thickness of SWR were sampled across 41 1,000 m2 plots within three 2009 Utah wildfires. Predictive models of SWR were built from ecological site characteristic data collected at each site. Across the study, SWR was found at 37% of the points sampled. SWR extent was strongly related to piñon-juniper canopy cover (r2 = 0.60) and was found to be significantly higher in tree/shrub mound zones (71%) as compared to interspaces (16%). Endorsed predictive models of SWR extent and severity had R2 adj values of 0.63 and 0.61; both models included piñon-juniper canopy cover and relative humidity the month before the fire as coefficient terms. These results suggest that as piñon-juniper canopy cover increases due to infilling processes in the coming years, post-fire SWR extent and severity will increase. As the effects of a changing climate in the Intermountain West link additively with infilling processes to increase the frequency and intensity of wildfires, the net effect will be stronger SWR over a greater spatial extent. To cope with these changes, land managers can apply the predictive models developed in this study to prioritize fuel control and post-fire restoration treatments with respect to SWR.

This study tested the performance of classification, regression, and ordination techniques to evaluate the spatiotemporal dynamics of threats to big sagebrush ecological sites. The research was focused on invasion by annual exotic grasses and encroachment by woodlands. We sought to identify those areas that have had a persistent coverage of cheatgrass (Bromus tectorum) in big sagebrush ecological sites. We took advantage of the contrast in greenness between multi-temporal (within one year) remotely sensed vegetation indices captured in the spring and summer to find a distinct phenological signature that allowed mapping cheatgrass. We utilized support vector machines (SVM) to classify three temporal scenarios for which field data sets were available. SVM performed very well with accuracies of 70% (producer's) and 95% (user's) for the class of interest (presence of cheatgrass). This was the focus of chapter 2. In chapter 3 we report the development of vegetation continuous fields (VCF) for three years of interest 1996, 2001, and 2007 in order to detect active woodland encroachment. We prepared VCF for shrubs, trees, herbaceous vegetation, and bare ground using a suite of remotely sensed spectral reflectance, vegetation indices, and transformations. We compared the performance of multivariate regression trees (MRT) and random forests (RF) to develop the VCF multi-temporal series. RF outperformed MRT in both accuracy and ability to appropriately map the continuum of percent cover across large landscapes. We estimate that 17,570 hectares of big sagebrush lands showed encroachment by woodlands. Our goal in chapter 4 was to develop a similarity index for large rangeland landscapes. Trend assessments field sites and a long-term annual series (1984 - 2008) of remotely sensed imagery were used in conjunction with multidimensional scaling (MDS) to measure ecological distance to undesired states such as invasion by exotic annuals and encroachment by woodlands. In this chapter our units of analysis were soil-mapping units, which were predominantly composed of one ecological site (>60%). Our MDS results show that different ecological sites can be identified in the reduced MDS statistical space. The observed transitions and trajectories of mountain, Wyoming, and basin big sagebrush sites correlated well with the ecological expectation in semiarid lands. We anticipate that managers can use our protocols to update ecological site descriptions and state and transition models from a remotely sensed perspective.