Located just east of Missoula, Montana, Milltown Dam stood from 1908 to 2008 immediately downstream of the Clark Fork River’s confluence with the Blackfoot River. After the discovery of arsenic-contaminated groundwater in the nearby community of Milltown, as well as extensive deposits of contaminated sediment in the dam’s upstream reservoir, in 1981, the area was designated a Superfund site – along with much of the Upper Clark Fork Watershed. This motivated the eventual decision to remove the dam, perform environmental remediation, and reconstruct approximately five kilometers of the Clark Fork River and its floodplain. This study is part conceptual and part empirical. It describes a state-and-transition framework equipped to investigate channel evolution as well as the adjustment trajectories of other socio-biophysical landscapes. This framework is then applied to understand the post-restoration channel evolution of the Clark Fork River’s mainstem, secondary channels, and floodplain. Adopting a state-and-transition framework to conceptualize landscape evolution lets environmental managers more effectively anticipate river response under multiple disturbence scenarios and therefore use more improvisational and adaptive management techniques that do not attempt to guide the landscape toward a single and permanent end state. State-and-transition models can also be used to highlight the spatially explicit patterns of complex biophysical response. The state-and-transition models developed for the Clark Fork River demonstrate the possibility of multiple evolutionary trajectories. Neither the secondary channels nor the main channel have responded in a linear, monotonic fashion, and future responses will be contingent upon hydrogeomorphic and climatic variability and chance disturbances. The biogeomorphic adjustments observed so far suggest divergent evolutionary trajectories and that in some instances the long-term fates of the mainstem, floodplain, and secondary channels are inescapably enmeshed with one another.

Monitoring and identifying the state of rangelands on a landscape scale can be a time consuming process. In this thesis, remote sensing imagery has been used to show how the process of classifying different ecological sites and states can be done on a per pixel basis for a large landscape. Twenty-seven years' worth of remotely sensed imagery was collected, atmospherically corrected, and radiometrically normalized. Several vegetation indices were extracted from the imagery along with derivatives from a digital elevation model. Dominant vegetation components from five major ecological sites in Rich County, Utah, were chosen for study. The vegetation components were Aspen, Douglas-fir, Utah juniper, mountain big sagebrush, and Wyoming big sagebrush. Training sites were extracted from within map units with a majority of one of the five ecological sites. A Random Forests decision tree model was developed using an attribute table populated with spectral biophysical variables derived from the training sites. The overall out-of-bag accuracy for the Random Forests model was 97.2%. The model was then applied to the predictor spectral and biophysical variables to spatially map the five major vegetation components for all of Rich County. Each vegetation class had greater than 90% accuracies except for Utah juniper at 81%. This process is further explained in chapter 2. As a follow-on effort, we attempted to classify vegetation ecological states within a single ecological site (Wyoming big sagebrush). This was done using field data collected by previous studies as training data for all five ecological states documented for our chosen ecological site. A Maximum Likelihood classifier was applied to four years of Landsat 5 Thematic Mapper imagery to map each ecological state to pixels coincident to the map units correlated to the Wyoming big sagebrush ecological site. We used the Mahalanobis distance metric as an indicator of pixel membership to the Wyoming big sagebrush ecological site. Overall classification accuracy for the different ecological states was 64.7% for pixels with low Mahalanobis distance and less than 25% for higher distances.