Interferometry has gained importance as a remote sensing technique to study topography, topographic change and volume and surface scattering properties of various natural targets. Interferometric radars rely on the ability to accurately measure amplitude and phase between signals received on two spatially separated antennas. The accuracy required for interferometric measurements place tight constraints on the performance of the radar hardware. This thesis details the development, construction and testing of a two-stage, two-channel Ku band downconverter ( also referred to as Dual Channel Downconverter or DDC)- which forms the core of the interferometer - to meet the requirements to make highly accurate interferometric measurements.

Flipper-tagging, aerial surveys, and satellite telemetry was used to investigate the occurrence, migratory routes, distances traveled, swimming speeds, diving behavior, and the relation of water temperature to movements and timing of migration of wild loggerhead (Caretta caretta) and Kemp's ridley (Lepidochelys kempii) sea turtles. The behavior and movements of head-started loggerhead turtles was investigated with satellite telemetry and compared to wild turtles. Flipper-tagged loggerhead and Kemp's ridley turtles inhabit Chesapeake Bay during the warm months and many return in subsequent seasons. Aerial surveys showed that loggerhead turtles migrate from south of Cape Hatteras to northern waters during May and June, and return to the south of Cape Hatteras in the autumn, usually during October or November. Satellite telemetry supported aerial survey data, and showed that loggerhead and Kemp's ridley turtles migrate nearshore to the south of Cape Hatteras in the autumn, although one loggerhead became pelagic in the North Atlantic. Kemp's ridleys and some loggerheads migrate as far south as Florida for the winter months, while some loggerheads overwinter in the Gulf Stream off North Carolina. Loggerheads which returned to Chesapeake Bay used similar migratory routes during the northerly and southerly migrations. Loggerhead and Kemp's ridley turtles spent up to 94% of 12 h periods submerged (ridley mean = 81%, loggerhead mean = 88%), and mean dive durations ranged from 13 to 124 min (ridley mean = 66 min, loggerhead mean = 74 min), making 13 to 38 dives over a 12 h period (ridley mean = 25, loggerhead mean = 25). Temperatures measured by satellite transmitters attached to Kemp's ridley turtles ranged from 13-23 C (mean = 17 C), while loggerhead temperatures ranged from 6-33 C (mean = 20 C). Movements of turtles appear to be mitigated by temperatures about 15 C. Movements and diving behavior of head-started loggerheads were different than wild turtles. Some head-started turtles entered the Gulf Stream and traveled eastward across the Atlantic, while others wandered in various directions. Head-started loggerheads made more (mean = 69) and shorter dives (mean = 21 min) over a 12 hr period than wild turtles, and spent significantly less time submerged (mean = 54%) than wild turtles.

Coastal zone landscape characterization and empirical model development were evaluated using multi-spectral airborne imagery. Collectively, four projects are described that address monitoring and classification issues common to the resource management community. Chapter 1 discusses opportunities for remote sensing. Chapter 2 examines spectral and spatial image resolution requirements, as well as training sample selection methods required for accurate landscape classification. Classification accuracy derived from 25nm imagery with 4m pixel sizes outperformed 70nm imagery with 1m pixel sizes. Eight natural and five cultural landscape features were tested for classification accuracy. Chapter 3 investigated the ability to characterize 1m multispectral imagery into rank-ordered categorical biomass index classes of Phragmites australis. Statistical clustering and sample membership was based upon normalized field-measurements. The red imagery channel showed highly significant correlation with field measurements (p = 0.00) and explained much of its variability (r2 = 0.79). Addition of near-infra red, green, and blue image channels in a forward stepwise regression improved the coefficient of determination (r2 = 0.98). In Chapter 4, a landscape cover map was revised by incorporating expert knowledge into a simple spatial model. Examples are provided for a barrier island environment to illustrate this post-classification methodology. A prototype selection of expert rules was sufficient to change more than 20 per cent of the originally classified landscape pixels. Chapter 5 discusses the development of an empirical model that uses vegetation community classes to estimate: (a) soil type, (b) soil compaction rate, and (c) elevation. Vegetation class proved itself a reliable surrogate for estimating these variables based upon field-based statistical measures of association and significance tests. Vegetation was highly associated with four soil types (Cramer's V = 0.98) and soil compaction rates values at depths of 30 and 46cm (Cramer's V > 0.85), and was able to accurately estimate three decimeter-level elevation zones (r2 = 0.86, p = 0.00). A preliminary model to estimate transverse dune crest heights and locations under forest canopy was presented. Lastly, Chapter 6 offers a summary and concluding statements advocating continued use of remote sensing as an application tool for resource management needs.

In this study Geographic Information System (GIS) technology was integrated with remote sensing techniques in order to determine the potential for nonpoint source pollution in the Lake Palestine and Cedar Creek Reservoir watersheds of North Central Texas. The Universal Soil Loss Equation was used to determine soil erosion potential from the watersheds, and export coefficients were used to estimate nutrient loadings into the reservoirs.

The Provo River watershed has experienced land cover change over the past several decades. Land cover influences water quality inasmuch as land cover determines the type and quantity of non-point source (NPS) pollutants that may enter the water. This study examines the historical impacts of land cover changes on water quality in the Provo River using remote sensing and statistical analysis. Statistical correlations and linear regressions were used to study the relationship between various land cover types and water quality variables for six years between 1975 and 2002. This thesis supports research finding myriad impacts of urban land cover on water quality. The study also revealed that increasing pH, alkalinity, and bicarbonate levels in the Provo River are likely related to increasing urbanization of the watershed.

The Antarctic ice sheet is a geophysically - and in an age of growing concern about global warming, geopolitically - important portion of Earth. The composition and dynamics of the Antarctic ice sheet influence global climate patterns, global sea level and the planet's radiation budget. Recent evidence also suggests that the long term stability of portions of the ice sheet may be in jeopardy. In this thesis I use data from three Ku-band space-borne scatterometers to monitor changes in the backscatter signature of the Antarctic ice sheet from 1978 through 2007. Significant changes in backscatter, which result from geophysical changes in the ice sheet itself, are found over much of the Antarctic continent, especially in West Antarctica and along much of the coasts. Less drastic changes, including regular seasonal variations, are observed over much of the ice shelf. Possible scattering mechanisms are proposed and discussed. A secondary result is the demonstration of the stability of NASA's QuikSCAT scatterometer, data from which is used extensively in this thesis and in many other publications. It is shown that QuikSCAT's observation geometry and backscatter instrumentation have remained consistent to great precision throughout its nearly nine-year long mission.

No description available.

Vertical pointing Doppler radar profilers are used to explore the vertical structure of precipitation cloud systems and to provide validation information for use in weather research. In this thesis, a theoretical radar rain-backscatter model was developed to simulate profiler Doppler spectra as a function of assumed rain parameters, of which the raindrop size distribution (DSD) is the fundamental quantity used to describe the characteristics of rain. Also, profiler observations during stratiform rain are analyzed to retrieve the corresponding rain DSD’s. In particular, a gamma distribution model is introduced, which uses Rayleigh scattering portion of the Doppler velocity spectrum to estimate the raindrop size distribution. This theoretical scattering model was validated by simulating atmospheric profiles of precipitation Doppler spectra and three moments (reflectivity, mean Doppler velocity and spectral width) and then comparing these with the corresponding measurements from an S-band radar profiler during a NASA conducted Tropical Rainfall Measuring Mission (TRMM) field experiment in Central Florida in 1998. Also, the results of my analysis yielding precipitation retrievals are validated with an independent, simultaneous Joss-Waldvogel Disdrometer rain DSD observations that were collocated with the radar profiler.

The Microwave Radiometer (MWR) on the Aquarius/SAC-D mission measures microwave radiation from earth and intervening atmosphere in terms of brightness temperature (Tb). It takes measurements in a push-broom fashion at K (23.8GHz) and Ka (36.5 GHz) band frequencies using two separate antenna systems, each producing eight antenna beams. Pre-launch knowledge of the alignment of these beams with respect to the space-craft is used to geolocate the antenna footprints on ground. As a part of MWR’s on-orbit engineering check-out, the verification of MWR’s pointing accuracy is discussed here. The technique used to assess MWR’s pointing involved comparing the radiometer image of land with high-resolution maps. When the beam’s instantaneous field of view (IFOV) passes over a land water boundary, the brightness temperature changes from a radiometrically hot land scene to a radiometrically cold ocean scene. This "step-function" change in brightness temperature provides a very sensitive way to characterize the mispointing error of the MWR sensor antenna footprints. This thesis describes the algorithm used for the MWR geolocation calibration. MWR sensor observed boundaries are determined by the absolute maximum Tb slope location. A system of linear equations is produced for each sensor observed land/water crossing to determine the true intersection of the MWR track with the coastline. The observed and expected boundary locations are compared by means of an error distance. Results, presented for all eight beams of the three MWR channels, show that the mispointing error (standard deviations) are overall less than 15 km from the true coastline.

In order to produce a more current inventory of forest estimates along with change estimates, the Forest Inventory Analysis (FIA) program has moved to an annual system in which 20% of the permanent plots in a state are surveyed. The previous system sampled permanent plots in 10-year intervals by sampling states sequentially in a cycle (Wayman 2001, USDA FIA). The move to an annual assessment has introduced the use satellite technology to produce forest estimates. Wayman et al (2001) researched the effectiveness of satellite technology in relation to aerial photo-interpretation, finding the satellite method to do an adequate job, but reporting over-estimations of forest area. This research extends the satellite method a step further, introducing the use of ancillary data in post-classification. The US Forest Service has well-defined definitions of forest and nonforest land-use in its (FIA) program. Using these definitions as parameters, post-classification techniques were developed to improve forest area estimates from the initial spectral classification. A goal of the study was to determine the accuracy of using readily available ancillary data. US Census data, TIGER street files, and local tax parcel data were used. An Urban Mask was created based on population density to mask out Forested pixels in a classified image. Logistic Regression was used to see if population density, street density, and land value were good predictors of forest/nonforest pixels. Research was also conducted on accuracy when using contiguity filters. The current filter used by the Virginia Department of Forestry (VDoF) was compared to functions available in ERDAS Imagine. These filters were applied as part of the post-classification techniques. Results show there was no significant difference in map accuracies at the 95% confidence interval using the ancillary data with filters in a post-classification sort. However, the use of ancillary data had liabilities depending on the resolution of the data and its application in overlay. / Master of Science