http://hdl.handle.net/1957/1723 2013-05-20T14:03:45Z http://hdl.handle.net/1957/38575 Post-fire vegetation response to snow in the western United States Blauvelt, Katie The western United States is experiencing significant changes in wildfire and snow regimes as a result of warming temperatures. An amplification of wildfire activity and reduction in snow water equivalent, snow covered area, and earlier spring snowmelt are documented trends that are projected to continue into the future. With an increase in wildfire activity, it is important to understand how a reduction in snow will impact regenerating vegetation in the western United States. The first objective of this study was to assess summer vegetation biomass response to antecedent winter snow on a local scale by determining the physiographic characteristics that influence the relationship between snow and vegetation in the case of the 2002 Biscuit Fire. The second objective was to assess the broad scale regional patterns of regenerating vegetation response to snow, by comparing the correlation between summer vegetation biomass and antecedent winter snow before and after large wildfires across the western United States. Remote sensing data and spatial-temporal statistics were used to analyze the relationship between snow and vegetation. In the local scale analysis, the 2002 Biscuit Fire was analyzed, which burned over 2,000 km² in southwest Oregon and northern California. Nonparametric Multiplicative Regression (NPMR) was used to explore the complex relationships between multiple predictor variables (winter snow frequency, elevation, slope, aspect, and burn severity) and the summer vegetation response variable (enhanced vegetation index, or EVI), before and after the Biscuit Fire burned. The burned area was subset by soil type to determine how soil texture influenced the snow and vegetation relationship. In the regional scale analysis, the Pearson's Correlation Coefficient was calculated to analyze the relationship between winter snow frequency and summer EVI before and after 23 wildfires across the western United States. In the case of the Biscuit Fire, summer EVI responded negatively to snow before the fire, and responded positively to snow after the fire. EVI in coarse-textured skeletal soils exhibited the clearest shift to a positive response to snow after the fire burned, while EVI in fine-textured clay soils did not exhibit this type of shift. The regional analysis proved that wildfire disturbances affect the relationship between snow and vegetation differently across the western United States. Seven fires clustered near the Biscuit Fire in northern California and southwestern Oregon behaved similar to the Biscuit Fire, shifting from a negative pre-fire snow and EVI correlation to a less negative or positive post-fire snow and EVI correlation. The majority of these fires had relatively low average elevations (430 to 1708 m) with greater than 80% forest land cover. Ten fire areas exhibited a significant positive pre and post-fire snow and EVI correlation. The majority of these fires had relatively high average elevations (1612 to 2291 m) and consisted of greater than 50% shrub, scrub, and grass land cover. The local scale analysis suggests that the condition of the vegetation (undisturbed vs. regenerating) and the soil texture in which it grows affects its response to winter snow. The low water holding capacity of coarse-textured soils and the short root-lengths of regenerating vegetation may result in greater dependence on snow as a water resource. Regionally, vegetation type and elevation may affect the vegetation's response to snow; short-rooted shrubs at higher elevations above the transient snow zone may be more dependent on snow as a water resource. These results suggest that the relationship between snow and vegetation is not constant, depending on the condition of the vegetation. Increases in wildfire activity and a reduction of snow in the future may impact successional trajectories in certain regions where vegetation may have historically relied on snowmelt to regenerate. Graduation date: 2013 2013-05-02T00:00:00Z http://hdl.handle.net/1957/37366 A Methodology to directly input data from an uncontrolled aerial photograph into a vector based geographic information system Sneed, Jacquelin M. Historically the U.S. Forest Service has used uncorrected aerial photographs to delineate proposed and past management activities on the land base it manages. Transferring a boundary from an image not planimetrically correct to a planimetrically corrected image introduces errors. Positional accuracy of boundaries affects the number of acres the Forest is accountable for managing, and the annual sale quantity (ASQ) or annual board feet targets. The purpose of this study was to develop a methodology that eliminated the need to transfer the boundary from an uncorrected to a corrected image. Raster and vector warping methods were evaluated with reference to positional accuracy and efficiency. Due to the rugged topography of the Siuslaw National Forest, selection of ground control points (GCPs) was an important function in the accurate transformation of images. A Vector warping method, Rubber Sheeting the ARC/INFO projective transformation for all digital GCPs, to all of the Global Position System (GPS) ground control points, provided the most accurate rectification of vector boundaries that had been digitized or scanned from an uncontrolled low elevation photograph. Graduation date: 1992 1991-06-06T00:00:00Z http://hdl.handle.net/1957/37239 A global scale analysis of the spatiotemporal distribution of foliar biomass for 1988 Pross, Derek D. Many ecological systems follow a seasonal cycle affecting primary production, carbon flux, and vegetative gas emissions. The seasonal variation of ecological systems are both affected by and have effects upon climatic factors. A quantitative estimate of the seasonal variation of vegetation is required to characterize ecological systems and their interaction with climate. Monitoring the spaliotemporal variation of foliar biomass density (FBD) over one year will provide a quantitative estimate of the annual cycle and regional variation of photosynthetic activity. FBD is a quantitative measure of leafy material per unit of area produ\:ed by photosynthetically active vegetation. This seasonal variation in FBD is an important parameter for global and other large scale investigations of ecological, hydrological, and biogeochemical systems which require data and expertise from a variety of sources and disciplines. Therefore, FBD is potentially of great utility for ecologists, hydrologists, climatologists, and atmospheric scientists. Recent regional scale investigations of ecological systems concluded that the repetitive coverage and synoptic view of remotely sensed measurements provide data to monitor the seasonal variation of biomass. A method to estimate the seasonal variation of FBD at global scales has not been developed. The objective of this research is to develop a methodology that could be used to estimate the seasonal variation of FBD for the entire terrestrial biosphere. By coupling global satellite data, measured field data, and a vegetation classification, a model was developed to estimate the global spatiotemporal variation of FBD. Comparisons between literature estimates of FBD and estimated FBD from this model were made as a means of validation. A more specific comparison was conducted between grasslands based on work conducted in the Senegalese Sahel region in Africa. Finally, a sensitivity analysis was performed to characterize the potential propagation of error associated with the literature FBD estimates used to drive this model. Graduation date: 1992 1991-05-24T00:00:00Z http://hdl.handle.net/1957/35789 Automated web-based analysis and visualization of spatiotemporal data Keon, Dylan B. Most data are associated with a place, and many are also associated with a moment in time, a time interval, or another linked temporal component. Spatiotemporal data (i.e., data with elements of both space and time) can be used to assess movement or change over time in a particular location, an approach that is useful across many disciplines. However, spatiotemporal data structures can be quite complex, and the datasets very large. Although GIS software programs are capable of processing and analyzing spatial information, most contain no (or minimal) features for handling temporal information and have limited capability to deal with large, complex multidimensional spatiotemporal data. A related problem is how to best represent spatiotemporal data to support efficient processing, analysis, and visualization. In the era of "big data," efficient methods for analyzing and visualizing large quantities of spatiotemporal data have become increasingly necessary. Automated processing approaches, when made scalable and generalizable, can result in much greater efficiency in spatiotemporal data analysis. The growing popularity of web services and server-side processing methods can be leveraged to create systems for processing spatiotemporal data on the server, with delivery of output products to the client. In many cases, the client can be a standard web browser, providing a common platform from which users can interact with complex server-side processing systems to produce specific output data and visualizations. The rise of complex JavaScript libraries for creating interactive client-side tools has enabled the development of rich internet applications (RIA) that provide interactive data exploration capabilities and an enhanced user experience within the web browser. Three projects involving time-series tsunami simulation data, potential human response in a tsunami evacuation scenario, and large sets of modeled time-series climate grids were conducted to explore automated web-based analysis, processing, and visualization of spatiotemporal data. Methods were developed for efficient handling of spatiotemporal data on the server side, as well as for interactive animation and visualization tools on the client side. The common web browser, particularly when combined with specialized server side code and client side RIA libraries, was found to be an effective platform for analysis and visualization tools that quickly interact with complex spatiotemporal data. Although specialized methods were developed to for each project, in most cases those methods can be generalized to other disciplines or computational domains where similar problem sets exist. Graduation date: 2013 2012-11-16T00:00:00Z