<p> Although savanna ecosystems cover approximately 20 % of the terrestrial land surface and can have productivity equal to some closed forests, their role in the global carbon cycle is poorly understood. This study explored the applicability of a past spaceborne Lidar mission and the potential of future missions to estimate canopy height and carbon storage in these biomes. </p><p> The research used data from two Oak savannas in California, USA: the Tejon Ranch Conservancy in Kern County and the Tonzi Ranch in Santa Clara County. In the first paper we used non-parametric regression techniques to estimate canopy height from waveform parameters derived from the Ice Cloud and land Elevation Satellite&rsquo;s Geoscience Laser Altimeter System (ICESat-GLAS) data. Merely adopting the methods derived for forests did not produce adequate results but the modeling was significantly improved by incorporating canopy cover information and interaction terms to address the high structural heterogeneity inherent to savannas. Paper 2 explored the relationship between canopy height and aboveground biomass. To accomplish this we developed generalized models using the classical least squares regression modeling approach to relate canopy height to above ground woody biomass and then employed Hierarchical Bayesian Analysis (HBA) to explore the implications of using generalized instead of species composition-specific models. Models that incorporated canopy cover proxies performed better than those that did not. Although the model parameters indicated interspecific variability, the distribution of the posterior densities of the differences between composition level and global level parameter values showed a high support for the use of global parameters, suggesting that these canopy height-biomass models are universally (large scale) applicable. </p><p> As the spatial coverage of spaceborne lidar will remain limited for the immediate future, our objective in paper 3 was to explore the best means of extrapolating plot level biomass into wall-to-wall maps that provide more ecological information. We evaluated the utility of three spatial modeling approaches to address this problem: deterministic methods, geostatistical methods and an image segmentation approach. Overall, the mean pixel biomass estimated by the 3 approaches did not differ significantly but the output maps showed marked differences in the estimation precision and ability of each model to mimic the primary variable&rsquo;s trend across the landscape. The results emphasized the need for future satellite lidar missions to consider increasing the sampling intensity across track so that biomass observations are made and characterized at the scale at which they vary. </p><p> We used data from the Multiple Altimeter Beam Experimental Lidar (MABEL), an airborne photon counting lidar sensor developed by NASA Goddard to simulate ICESat-2 data. We segmented each transect into different block sizes and calculated canopy top and mean ground elevation based on the structure of the histogram of the block&rsquo;s aggregated photons. Our algorithm was able to compute canopy height and generate visually meaningful vegetation profiles at MABEL&rsquo;s signal and noise levels but a simulation of the expected performance of ICESat-2 by adjusting MABEL data's detected number of signal and noise photons to that predicted using ATLAS instrument model design cases indicated that signal photons will be substantially lower. The lower data resolution reduces canopy height estimation precision especially in areas of low density vegetation cover. </p><p> Given the clear difficulties in processing simulated ATLAS data, it appears unlikely that it will provide the kind of data required for mapping of the biophysical properties of savanna vegetation. Rather, resources are better concentrated on preparing for the Global Ecosystem Dynamics Investigation (GEDI) mission, a waveform lidar mission scheduled to launch by the end of this decade. In addition to the full waveform technique, GEDI will collect data from 25 m diameter contiguous footprints with a high across track density, a requirement that we identified as critically necessary in paper 3. (Abstract shortened by UMI.)</p>

<p> Since the late eighties, an ever increasing number of American craft breweries have opened up across the United States. Although the industry has a relatively high success rate, there are still a number of craft breweries that fail. As craft brewing is an inherently location based business, the sites selected for new brewery locations are important to that business&rsquo;s success. This research aims to examine how geodemographic information plays a role in strategic location decisions for craft breweries. By building a consumer profile for Phoenix craft brewery customers and comparing the results to the actual demographics of trade areas surrounding craft breweries, we begin to get a picture of geodemographics role in the site selection process. The research looks specifically at two locations; Bad Water Brewing Company and a site they are interested in acquiring. An analysis is performed to compare the geodemographic and behaviors of craft beer consumers in Phoenix, Arizona and those of the Bad Water Brewing location and their potential new site. This analysis ultimately results in intelligent business information related to location. The information compiled in this study can be used to make informed site selection decisions.</p>

<p> Light Detection And Ranging (LiDAR) data can be used to accurately model three- dimensional surfaces for quantifying fluvial erosion and deposition. Terrestrial LiDAR is typically used for monitoring banks, but can be used for monitoring planar forms such as point bars. Point bars are topographic features that form on the convex bank of a meander. While point bars are considered to be formed by depositional processes, they display features such as chute channels and scour holes that suggest that erosion, due to high flow events, may significantly influence point bar evolution. Through the use of Terrestrial Laser Scanning (TLS), we observed how a point bar on the White Clay Creek near Newark, Delaware, responded to a flood event with a return period of 6.1 years, and to multiple small events over a 1 year period with return periods between 1.00 and 1.25 years. Scans of the point bar were completed on April 11, 2014, May 8, 2014, and April 16, 2015. Scans were referenced to a common coordinate system, scan data representing vegetation points were removed, and three 0.1 m x 0.1 m gridded Digital Elevation Models (DEMs) were created from the remaining data. DEMs of Difference (DoDs) were calculated by subtracting the cell values in subsequent DEMs and by thresholding out positional and surface roughness errors. The 6.1 year flood that occurred between the April 11, 2014 scan and the May 8, 2014 scan resulted in 88.53 m³ of erosion and 39.12 m³ of deposition. The net volumetric change was -49.40 m³ over an area of 631.72 m². The smaller events that occurred between the May 8, 2014 scan and the April 16, 2015 scan resulted in 13.33 m³ of erosion and 53.46 m³ of deposition. The net volumetric change was x i 40.13 m³ over an area of 620.74 m². Our results suggest that 1) sediment deposited on point bars is eroded frequently by flood events; and 2) TLS can provide useful estimates of erosion and deposition. Although our results are for a short period, longer datasets can be used to calculate sediment residence times for point bar deposits. Additionally, we can gain a better understanding of how point bar deposits are preserved in the geologic record. This information is useful for creating accurate sediment budgets, remediating contamination issues, and interpreting geologic history.</p>

<p> This survey study examines cell phone usage data and focuses on the application of the data to disaster response management. Through the course of this study, the structure of cell phone usage data and its characteristics will be reviewed. Cell phone usage data provides us with valuable information about human movements and their activities. The uniqueness of the data is that it contains both spatial and temporal information and this information is free of fixed routes such as roads or any preset data capturing timing. In short, it is a very fluid kind of data which reflects our activities as humans with freedom of movement. Depending on data extraction methods, the data server can provide additional information such as application activities, battery level and charge activities. However, cell phone usage data contains shortcomings including data inconsistency and sparseness. Both the richness and the shortcomings of the data expose the hurdles required in data processing and force us to devise new ways to analyze this kind of data. Once the data has been properly analyzed, the findings can be applied to our real life problems including disaster response. By understanding human movement patterns using cell phone usage data, we will be able to allocate limited emergency resources more adequately. Even more, when disaster victims lose their cell phone functionality during a disaster, we might be able to identify or predict the locations of victims or evacuees and supply them with necessary assistance. The results of this study provide some insights to cell phone usage data and human movement patterns including the concentration of cell phone activities in specific zones and rather universal cell phone charging patterns. The potential of the data as a movement analysis resource and the application to disaster response is apparent. As a base to leverage the study to the next level, a possible conceptual model of human movement factors and data processing methods will be presented.</p>

Digital Library for Earth Science Education, DLESE / Digital libraries offer a unique and unprecedented resource through which teachers can facilitate student inquiry. In the recent National Research Council publication quoted above, National Science Education Standards, emphasis on inquiry is pervasive. Yet, when it comes to textbooks and curricula as they exist today, the clear emphasis is on learning science content disconnected from experience. Although digital libraries can't change pedagogy or textbooks, they can make it possible for students to have access to scientific information and data which interests them, a fundamental requirement for authentic inquiry. Digital libraries can provide teachers with a feasible way to let students pursue their own interests within the bounds of the curriculum and without creating an enormous amount of extra work in providing students with materials to support their investigations. This article will explore the ways in which digital libraries can support inquiry learning. We are looking at the benefits of digital libraries in high schools and middle schools through our experiences with implementation of University of Michigan's Digital Library (UMDL). In particular, we will focus here on students asking their own questions, and learning through sustained inquiry. This article will address the following questions: Why is it important for students to ask their own questions and how does it contribute to inquiry based learning? How do digital libraries help make inquiry learning possible? How is UMDL supporting sustained inquiry? What is our research telling us about tools and techniques needed to make it happen?

Digital Library for Earth Science Education, DLESE / This thesis looks at a computer-mediated communication (CMC) and publishing system used to facilitate collaborative peer review of multimedia educational objects. The occurrence of electronic scholarly publishing has increased dramatically in recent years due in part to the immediacy and overall reach of the Internet and it’s ability to transmit diverse forms of electronic media. Previous studies indicate, however, that there is a perceived lack of prestige and legitimacy associated with electronic journals as well as electronically enabled peer review. This is due in part to a perceived lack of permanence associated with electronic media, a lack of familiarity with electronic media and a lack of fully developed conventions of citation. New forms of electronically based peer review have been explored that enable a collaborative review process among reviewers and authors, breaking from traditional models where communication channels are mediated through an editor. The ability of CMC to enable collaboration within geographically dispersed communities offers strong motivation for its use. This thesis develops a framework for collaborative peer review based on social capital that suggests an overall benefit for scholarly communities that incorporate collaborative forms of review. An examination is performed of collaborative peer review used in a new journal that features multimedia-rich geoscience educational objects: the Journal of Earth System Science in Education (JESSE). Technical issues surrounding the preparation of these objects for the CMC review environment are discussed and a process model for publishing is developed. A redesign of the toolkit used to prepare objects for the review environment is implemented and task-centered usability assessments are performed. The outcome of these steps suggested a potential for increased legitimacy and prestige of electronic publishing could develop out of a well-designed CMC environment and collaborative review model. It was found that scholars who participated in the peer review perceived a benefit from the collaborative process and that the process was seen as providing a separate service from traditional peer review. On the publishing end, the redesigned toolkit implementation was seen as providing greater accessibility to non-technically oriented users.

<p> Remotely sensed data has been discussed as a possible alternative to the standard precision agriculture systems of combine-mounted yield monitors because of the burden, cost, end of season use, and inherent errors that are associated with these systems. Due to the potential quantitative use of remote sensing in precision agriculture, the primary focus of this study was to test the relationship between multitemporal/multispectral digital aerial imagery with corn (<i>Zea mays</i> L.) and soybean (<i>Glycine max </i> L.) yield. Digital aerial imagery was gathered on nine different dates throughout the 2015 growing season from two fields (one corn and one soybean) located on a farm in Story County, Iowa. To begin assessing this relationship, the digital aerial imagery was radiometrically calibrated. The radiometric calibration process used calibration tarps with known reflectance values (3, 6, 12, 22, 44, and 56 percent). The calibrated imagery was then used to calculate and output 12 different vegetation indices (VIs) and three calibrated wavebands (red, green, and near-infrared). </p><p> Next, the calibrated VIs and wavebands from the 2015 growing season were used to examine their relationship with the corn and soybean yield data collected from a combine yield monitor system. This relationship between multitemporal/multispectral digital aerial imagery with corn and soybean yield was investigated with principal component analysis and spatial modeling techniques. The results from spatial modeling of corn revealed that VIs utilizing the green waveband performed strongly. VIs such as, chlorophyll index-green, chlorophyll vegetation index, and green normalized difference vegetation index accounted for 81.6, 83.0, and 82.4 percent of the yield variability, respectively. Strong modeling relationships were also found in soybean using just the near-infrared waveband or VIs that utilized the near-infrared waveband. The near-infrared waveband captured 89.1 percent of the yield variation, while VIs such as, difference vegetation index, triangular vegetation index, soil adjusted vegetation index, and optimized soil adjusted vegetation index accounted for 87.3, 87.3, 83.9, and 83.8 percent of soybean yield variability, respectively. The temporal assessment of the remotely sensed data also identified certain VIs and wavebands that captured pivotal growth stages for detecting potential yield limiting factors. These specific growth stages varied for different VIs and wavebands for both corn and soybean. Overall, the results from this study identified that mid-to-late vegetative growth stages (prior to tasseling) and late-season reproductive stages were important parameters that provided unique information in the modeling of corn yield variability, while the later reproductive stages (just prior to senescence) were essential to capturing soybean yield variability. </p><p> Lastly, this research produced corn and soybean yield maps from the digital aerial imagery. The digital aerial imagery yield maps were then compared with maps that used kriging interpolation of the combine yield monitor data gathered from the same corn and soybean fields. The results indicated that both corn and soybean yield maps produced with multitemporal/multispectral digital aerial imagery were comparable with a standard method of kriging interpolation from yield monitor data.</p><p>

<p> Behavior is perhaps the most challenging component of an extinct organism to reconstruct and understand. Often in paleoanthropology, researchers primarily have fossils and paleoecological data; however, combining these into models of hominin behavior is difficult in practice. Yet for years archaeologists and wildlife biologists have been using Geographic Information Systems (GIS) to model the mobility behavior of humans and other animals. This research seeks to integrate the methodology of cost-distance modeling in GIS into paleoanthropology to understand hominin mobility, specifically investigating if the potential mobility pattern of <i>Australopithecus afarensis</i> can be modeled to understand how they got across Eastern Africa to their known sites. The models created for <i>Au. afarensis</i>, humans, and chimpanzees brought together walking time as a cost factor and modern slope as an impediment to movement. These values were input into the Cost Distance tool in ArcGIS with Laetoli as the source and tested on two study areas, Laetoli and Eastern Africa. Known <i>Au. afarensis</i> sites matched areas of least cost for each potential mobility pattern, which indicated that 1) none of the models could be ruled as the best potential mobility pattern for <i> Au. afarensis</i>, 2) <i>Au. afarensis</i> likely avoided steeper gradients, and 3) modern gradient data were not incompatible with the models. Despite limitations to this study, these models provide a foundation for research into hominin mobility patterns using GIS.</p><p>

<p> Digital elevation models (DEMs) are critical components of coastal flood models. Both present-day storm surge models and future flood risk models require these representations of the Earth&rsquo;s elevation surface to delineate potentially flooded areas. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) develops DEMs for United States&rsquo; coastal communities by seamlessly integrating bathymetric and topographic data sets of disparate age, quality, and measurement density. A current limitation of the NOAA NCEI DEMs is the accompanying non-spatial metadata, which only provide estimates of the measurement uncertainty of each data set utilized in the development of the DEM.</p><p> Vertical errors in coastal DEMs are deviations in elevation values from the actual seabed or land surface, and originate from numerous sources, including the elevation measurements, as well as the datum transformation that converts measurements to a common vertical reference system, spatial resolution of the DEM, and interpolative gridding technique that estimates elevations in areas unconstrained by measurements. The magnitude and spatial distribution of vertical errors are typically unknown, and estimations of DEM uncertainty are a statistical assessment of the likely magnitude of these errors. Estimating DEM uncertainty is important because the uncertainty decreases the reliability of coastal flood models utilized in risk assessments.</p><p> I develop methods to estimate the DEM cell-level uncertainty that originates from these numerous sources, most notably, the DEM spatial resolution, to advance the current practice of non-spatial metadata with NOAA NCEI DEMs. I then incorporate the estimated DEM cell-level uncertainty, as well as the uncertainty of storm surge models and future sea-level rise projections, in a future flood risk assessment for the Tottenville neighborhood of New York City to demonstrate the importance of considering DEM uncertainty in coastal flood models. I generate statistical products from a 500-member Monte Carlo ensemble that incorporates these main sources of uncertainty to more reliably assess the future flood risk. The future flood risk assessment can, in turn, aid mitigation efforts to reduce the vulnerability of coastal populations, property, and infrastructure to future coastal flooding.</p><p>

abstract: Decades ago in the U.S., clear lines delineated which neighborhoods were acceptable for certain people and which were not. Techniques such as steering and biased mortgage practices continue to perpetuate a segregated outcome for many residents. In contrast, ethnic enclaves and age restricted communities are viewed as voluntary segregation based on cultural and social amenities. This diversity surrounding the causes of segregation are not just region-wide characteristics, but can vary within a region. Local segregation analysis aims to uncover this local variation, and hence open the door to policy solutions not visible at the global scale. The centralization index, originally introduced as a global measure of segregation focused on spatial concentration of two population groups relative a region's urban center, has lost relevancy in recent decades as regions have become polycentric, and the index's magnitude is sensitive to the particular point chosen as the center. These attributes, which make it a poor global measure, are leveraged here to repurpose the index as a local measure. The index's ability to differentiate minority from majority segregation, and its focus on a particular location within a region make it an ideal local segregation index. Based on the local centralization index for two groups, a local multigroup variation is defined, and a local space-time redistribution index is presented capturing change in concentration of a single population group over two time periods. Permutation based inference approaches are used to test the statistical significance of measured index values. Applications to the Phoenix, Arizona metropolitan area show persistent cores of black and white segregation over the years 1990, 2000 and 2010, and a trend of white segregated neighborhoods increasing at a faster rate than black. An analysis of the Phoenix area's recently opened light rail system shows that its 28 stations are located in areas of significant white, black and Hispanic segregation, and there is a clear concentration of renters over owners around most stations. There is little indication of statistically significant change in segregation or population concentration around the stations, indicating a lack of near term impact of light rail on the region's overall demographics. / Dissertation/Thesis / Ph.D. Geography 2012