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Item Spatiotemporal mapping of mountain pine beetle infestation severity and probability of new infestation in the central U.S. Rocky Mountains(Montana State University - Bozeman, College of Agriculture, 2018) Bode, Emma Taylor; Chairperson, Graduate Committee: Rick L. LawrenceSynchronous, widespread, and severe mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks impacted forests of western North America at unprecedented levels in recent decades. Severe MPB epidemics can degrade ecosystem services and socio-economic assets. Mapping outbreak progression informs mitigation efforts and enables analysis of MPB attack processes on a landscape scale. Existing time-series methods for mapping disturbance focus on extent rather than severity. Infestation severity, expressed as within-pixel mortality percentage, is more robust for answering a variety of ecological questions. Our objectives were to: (1) map infestation severity from 2005-2015 using a time-series regression; and (2) analyze MPB attack processes by modeling new infestation probability using spatial and environmental variables in the central U.S. Rocky Mountains. We used spectral data from all available Landsat images, topographic data, and data from U.S. Forest Service aerial detection survey (ADS) polygons to model infestation severity. We collected reference data by interpreting National Agricultural Imagery Program images. We then employed logistic regression model-based recursive partitioning (MOB) to determine: (a) to what degree nearby infestation severity increased probability of new infestation; (b) the degree of variation in probability across space and time with respect to other spatial and environmental risk factors; and (c) the extent to which these effects were directional relative to prevailing winds. Validation of our infestation severity model against a randomly selected subset of the data resulted in no statistical difference between predicted and observed severity. Our raster maps allowed us to identify lower severity infestation not recorded by the ADS. The final MOB model obtained 72.1% accuracy in predicting new infestation. Nearby infestation severity strongly influenced the probability of new infestations. This effect varied with elevation, aspect, temperature, phase of the outbreak, and spatial location. Variation in probability of infestation was highest when surrounding infestation severity was low. Use of wind-informed directional effects did not improve the model. This analysis establishes the efficacy of mapping an infestation severity time series and demonstrates that severity maps facilitate novel analyses of MPB attack processes. The processes developed here can support management decisions with timely maps of MPB infestation severity and probability of new infestation.Item An analysis of droughts in the Northeast District of Montana : their features, impact, monitoring and prediction(Montana State University - Bozeman, College of Agriculture, 1992) Hershenhorn, RayaItem Modeling the temporal and spatial variability of solar radiation(Montana State University - Bozeman, College of Agriculture, 2012) Mullen, Randall Scott; Chairperson, Graduate Committee: Lucy Marshall; Brian L. McGlynn (co-chair); Brian L. McGlynn and Lucy A. Marshall were co-authors of the article, 'Use of intensity- duration- frequency curves and exceedance- frequency curves for quantifying solar radiation variability' in the journal 'Renewable energy' which is contained within this thesis.; Brian L. McGlynn and Lucy A. Marshall were co-authors of the article, 'A beta regression model to obtain interpretable parameters and estimates of error for improved solar radiation predictions' in the journal 'Journal of applied meteorology and climatology' which is contained within this thesis.; Brian L. McGlynn and Lucy A. Marshall were co-authors of the article, 'Modeling solar radiation using the spatial auto-correlation of the daily fraction of clear sky transmissivity' in the journal 'Theoretical and applied climatology' which is contained within this thesis.; Brian L. McGlynn and Lucy A. Marshall were co-authors of the article, 'Evaluating a beta regression approach for estimating fraction of clear sky transmissivity in mountainous terrain' in the journal 'Hydrology and earth system sciences' which is contained within this thesis.Solar radiation is fundamental to ecological processes and energy production. Despite growing networks of meteorological stations, the spatial and temporal variability of solar radiation remains poorly characterized. Many solar radiation models have been proposed to enhance predictions in areas without measurement instrumentation. However, these models do not fully take advantage of the increasing number of data collection sites, nor are they expandable to incorporate additional metrological information when available. In this dissertation we: 1) developed a method of statistical analysis to summarize and communicate solar radiation reliability, 2) applied a beta regression model to leverage auxiliary meteorological information for enhanced solar radiation prediction, 3) refined the beta regression model and considered spatial auto-correlation to better predict solar radiation across space, 4) extended and evaluated these methods in a mountainous region. These advancements in the characterization and prediction of solar radiation are detailed in the following chapters of this dissertation.