Typescript. / Thesis (Ph. D.)--University of Hawaii, 1967. / Bibliography: leaves [91]-103. / ix, 103 l illus., maps, tables

Thesis (PhDGeoinformatics)--University of South Australia, 2001.

Ground deformation due to volcanic magma intrusion is recognised as an important precursor of eruptive activity at a volcano. The Global Positioning System (GPS) is ideally suited for this application by being able to measure three-dimensional coordinate changes of the monitoring points over time. Due to the highly disturbed ionosphere in equatorial regions, particularly during times of maximum solar activity, a deformation monitoring network consisting entirely of single-frequency GPS receivers cannot deliver baseline solutions at the desired accuracy level. In this thesis, a mixed-mode GPS network approach is proposed in order to optimise the existing continuous single-frequency deformation monitoring system on the Papandayan volcano in West Java, Indonesia. A sparse network of dual-frequency GPS receivers surrounding the deformation zone is used to generate empirical 'correction terms' in order to model the regional ionosphere. These corrections are then applied to the single-frequency data of the inner network to improve the accuracy of the results by modelling the residual atmospheric biases that would otherwise be neglected. This thesis reviews the characteristics of existing continuously operating GPS deformation monitoring networks. The UNSW-designed mixed-mode GPS-based volcano deformation monitoring system and the adopted data processing strategy are described, and details of the system's deployment in an inhospitable volcanic environment are given. A method to optimise the number of observations for deformation monitoring networks where the deforming body itself blocks out part of the sky, and thereby significantly reduces the number of GPS satellites being tracked, is presented. The ionosphere and its effects on GPS signals, with special consideration for the situation in equatorial regions, are characterised. The nature of the empirically-derived 'correction terms' is investigated by using several data sets collected over different baseline lengths, at various geographical locations, and under different ionospheric conditions. Data from a range of GPS networks of various sizes, located at different geomagnetic latitudes, including data collected on Gunung Papandayan, were processed to test the feasibility of the proposed mixed-mode deformation monitoring network approach. It was found that GPS baseline results can be improved by up to 50% in the mid-latitude region when the 'correction terms' are applied, although the performance of the system degrades in close proximity to the geomagnetic equator during a solar maximum.

Thesis (M.S.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references.

Thesis (Ph. D.)--Kings College, Cambridge, 1966. / Includes bibliographical references.

Thesis (doctoral)--Uppsala universitet, 1990. / Added page with thesis statement inserted. Abstract and summary in English. Includes bibliographical references (p. [413]-443) and index.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 226-232).

<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>