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It is argued that for many regions of the earth, ground based remote sensing techniques for rainfall measurement are impractical due to their cost and limited range of coverage. Global rainfall estimates must therefore be provided largely by satellites, particularly those exploiting atmospheric windows in the visible/IR and microwave regimes. / A 2-D pattern matching technique using visible and IR data is proposed and its accuracy investigated. Because these wavelengths respond primarily to cloud and not rain sized drops, significant accuracy may be achieved in rain areas, but not directly in amounts. However, when long term average rain rates for raining areas are used, errors of (TURN) (+OR-) 49% for 10('5) km('2) are achieved. This compares favorably with other techniques based on cloud life histories. / Microwave radiometer-based techniques are also investigated and are found to be fundamentally limited in accuracy ((TURN) (+OR-) 70%) by the large variations in the (unknown) effective rain layer height. However, useful accuracy ((TURN) (+OR-) 20%) may be obtained over large areas ((TURN) 10('5) km('2)), provided that the long term average effective rain layer height is known. It is argued that a once or twice daily satellite rain estimate is insufficient for either accurate estimates of daily accumulation or for samples of climatological rain rates. A hybrid microwave satellite-vis/IR technique is therefore proposed in order to solve the temporal resolution problem without the necessity of orbiting many microwave radiometers. This technique would take advantage of the high ( 1/2 hour) temporal resolution of the vis/IR data and could achieve accuracies of (TURN) (+OR-) 20% for 12 hour accumulations over 10('5) km('2) provided that the mean effective rain layer height is known. The practical implementation of such a technique will ultimately depend on the relative difficulty of measuring the mean effective rain layer height, and the mean rain rate for raining areas as well as the availability of appropriate sensor platforms.

<p> The main objective of this research was to study the flux of dissolved organic carbon (DOC) from a terrestrial urbanized watershed to an estuarine system using a process-based regional hydro-ecological model and remotely sensed data.</p><p> While DOC is an important component of the global carbon cycle, the link of the variations in terrestrial carbon storage is still poorly understood. Soil moisture is a key factor that influences the amount of available water for vegetation growth and the decomposition rate of organic matter in the soil and thus contributes to the amount of DOC in the soil at the land-water boundary. The Regional Hydro-Ecological Simulation System (RHESSys) was used to model the biogeochemical cycle in the Neponset Watershed, Boston MA from 2006 to 2011. Remotely sensed indices and field measurements of soil moisture, locally measured watershed DOC values, and streamflow gauge amounts were used to evaluate the modeled results. </p><p> The fully parameterized high resolution RHESSys model was used to simulate soil moisture in the highly urbanized and fragmented Neponset watershed and displayed good correlation with the measured soil moisture values. Another two measures of soil moisture conditions (the topographic moisture index (TMI) and the remotely sensed temperature vegetation dryness index (TVDI)) were also estimated and compared with field measured data. Two nested study areas, the Neponset River Watershed and the Greater Boston Area, were utilized to correspond with two spatial resolutions. The DOC concentration data sampled in the Neponset River Watershed were analyzed and the sensitivity of the DOC simulation in RHESSys was evaluated. The simulated DOC was compared with estuarine results and a good correlation was found to exist between the measured and simulated DOC concentrations and fluxes. </p><p> This effort represents the first successful application of RHESSys model to an urbanized New England watershed and not only provided an accurate way to estimate both soil moisture and DOC flux but also provided a framework to test further hypotheses and future scenarios to benefit global carbon cycle research.</p>

<p> Permanent Global Positioning System (GPS) networks have been established around the globe for a variety of uses, most notably to monitor the activity of fault lines and tectonic plate motion. A model for utilizing GPS as a tool for hydrologic monitoring is also developed. </p><p> First, observations of the recent movement of the land surface throughout California by the Scripps Orbit and Permanent Array Center (SOPAC) GPS network are explored. Significant seasonal cycles and long term trends are related to historical observations of land subsidence. The pattern of deformation throughout the state appears to be caused by the occurrence of poroelastic deformation of the aquifer in the Central Valley, and elastic crustal loading by surface water and the winter snowpack in the Sierra Nevada Mountains. The result is a sort of teeter-totter motion between the Valley and the mountains where the Valley sinks in the dry season while the mountains lift, and the mountains sink in the wet season while the Valley lifts. </p><p> Next, the elastic crustal deformation caused by the winter snowpack is explored more thoroughly at 6 high elevations throughout the Western United States. Expected annual deformation as a result of thermoelastic and snow water equivalent are calculated using SNOTEL observations and an elastic half-space model. The results demonstrate the dominance of snow loading on the seasonal vertical land surface deformation at all 6 GPS stations. The model is then reversed and applied to the GPS vertical site-position observations in order to predict snow water equivalent. The results are compared to SNOTEL observations of snow water equivalent and soil moisture. The study concludes that GPS site-position observations are able to predict variations in snow water equivalent and soil moisture with good accuracy. </p><p> Then a model which incorporates both elastic crustal loading and poroelastic deformation was used to predict groundwater storage variations at 54 GPS stations throughout the Central Valley, CA. The results are compared to USGS water table observations from 43 wells. The predictions and observations show a similar magnitude and spatial pattern of groundwater depletion on both a seasonal and long term timescales. Depletion is focused on the southernmost part of the Valley where GPS reveals seasonal fluctuation of the water table around 2 m and 8 m/yr of water table decline during the study period. GPS also appears to respond to deformation from peat soils and changing reservoir storage in the northern parts of the Valley. </p><p> Finally, preliminary work exploring the potential for using GPS as a tool for monitoring snowmelt runoff and infiltration is explored at one station in Eastern Idaho. Taking the difference between the change in GPS water storage estimates with time and the change in SNOTEL observed snow water equivalent with time produces a time series of infiltration, or the amount of water added to storage in the geologic profile. Then subtracting the estimated infiltration and snow water equivalent from the total precipitation observed by SNOTEL produces a time series of runoff. The estimated runoff at the GPS site was compared to observations from a nearby stream gauge and the foundation for a more extensive comparison is laid out. </p><p> The overall impact of this work is to introduce the unique hydrologic information and monitoring capabilities which can be accessed through monitoring of the land surface position using GPS. As GPS networks grow and expand worldwide, the available data should be harnessed by the hydrologic community for the benefit of local water management as well as improvements to data assimilated models. The work presented here represents only a small fraction of the wealth of knowledge that could result from a budding field of GPS hydrologic remote sensing. (Abstract shortened by UMI.)</p>

The objective of this thesis is to design numerical algorithms for estimation of linear discrete-time dynamic system models, which can be written in the block-angular form. These models arise in some applications, such as navigation or communication. The estimation problem for the standard linear discrete-time dynamic systems usually can be solved by using the Kalman filter. However, we realize that for our specific models applying the conventional Kalman filter algorithms is not efficient or may cause numerical instability. In this thesis, we present an approach of using the recursive least squares technique to compute the estimates of the system states. Our approach is computationally efficient because we take full account of the structures of the models and is numerically reliable because we use orthogonal transformations in the computation. / The general approach is then extended to positioning problem. We mainly consider the short-baseline relative positioning with combined code and carrier-phase measurements in global positioning system. Because of much more special structures existing in the mathematical models for positioning; we modify the general approach to further utilize these structures for efficiency. Fixing the integer ambiguity vector and handling its dimension change are also discussed. Finally, the real data tests are given to demonstrate the effectiveness of our algorithm.

Thesis (PhD) -- University of South Australia, 1999

This thesis focuses on uncertainties in remotely sensed image segmentation and their visualisation. The first part describes a visualisation tool, allowing interaction with the parameters of a fuzzy classification algorithm by visually adjusting fuzzy membership functions of classes in a 3D feature space plot. Its purpose is to improve insight into fuzzy classification of remotely sensed imagery and related uncertainty. Additionally, alpha-shapes are used to visualise irregular shaped class clusters. The second part of the thesis describes segmentation techniques for identification of objects and quantification of their uncertainties. The Local Binary Pattern (LBP) operator is used to model texture. A multivariate extension of the standard univariate LBP operator is proposed to describe texture in multiple bands. Texture-based image segmentation, provides good results yielding valuable information about object uncertainty at transition zones. Visualisation methods described in the first part and segmentation techniques described in the second part are combined and extended to visualise object uncertainty. An object is visualised in 3D feature space and in geographic space based on a user-defined uncertainty threshold. Isosurfaces provide a visualisation technique for fast interaction facilitating visualisation of the relation between uncertainty in the spatial extent of objects and their thematic uncertainty.

Thesis (M.S.)--Rochester Institute of Technology, 1989. / Includes bibliographical references (leaves 172-178).

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.