This volume describes the application of quantitative image analysis as a means of investigating the geologic history of the Valles Marineris region of Mars. Three approaches are employed for extracting geological information from Viking Orbiter images. First, multispectral composites are studied for color variations among geologic units and to map their spatial distribution in Coprates Quadrangle. Multitemporal images are next examined for variable features of the martian surface and atmosphere. Finally, multiple phase-angle images are analyzed for variations in the photometric phase function of the surface. The results suggest that volcanism, tectonism, magmatic intrusion and eolian redistribution have each played a part in shaping the martian canyons. Several distinct spectrophotometric units are recognized in Coprates Quadrangle on the basis of the imaging data. The dark red Lunae plains are spectrally and photometrically related to the brighter dust-mantled plains, canyon wall rock and interior layered sediments, indicating a physical and not compositional difference between these materials. Spectrally exceptional sedimentary deposits are evidence for compositional diversity among the bright materials and suggest multiple origins for the layered sediments. Exposed competent bedrock surfaces include lowlying Hesperian volcanic plains and a series of cliffs interpreted as a possible sill. Dark gray, friable and incompetent materials include an in-situ Noachian wall rock layer and locally generated dune-forming sands. These materials are found to be markedly forward-scattering, casting doubt on notions that these dune deposits are composed of lithic fragments or dust grain aggregates, two of the proposed sources for the puzzling martian sands. Interpretation of this unit as a voluminous regional malic tuff deposit may have important implications for the style of past volcanism on Mars. Bright gray materials include young, possibly recent volcanics with a derived distribution consistent with the interpretation of extrusion along faults near the margins of the canyon floors. This result supports the hypothesis that the Valles originated through tectonic extension, which along with volcanism, generation of dark sands, and eolian deposition and erosion of bright dust from global storms, are processes which may continue in the Valles Marineris today.

This dissertation describes the design, construction, and calibration of a portable short wave infrared (SWIR) spectroradiometer. The main use for the instrument is the collection of ground reflectance and radiance data for the radiometric calibration of operational and proposed high spectral resolution remote-sensing systems, such as the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the High Resolution Imaging Spectrometer (HIRIS), and the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER). The instrument will also be used for cross calibrating Earth Observing System (EOS) calibration facilities and for a variety of high spectral resolution studies in earth science. The instrument is designed to be carried as a backpack unit, on a vehicle, or in a helicopter or airplane. The spectroradiometer covers the range from 1.05 to 2.45 μm. The spectral sampling interval is 1.37 nm and the spectral resolution is variable from about 5 nm to more than 100 nm. A single spectrum can be acquired in as little as 1 s. The signal-to-noise ratio (SNR) for a single 1-s scan is about 90 at a wavelength of 2.2 μm for a lambertian surface of 100% reflectance illuminated by the sun at normal incidence with 14-nm spectral resolution, a 25° background temperature, and no atmospheric attenuation. The SNR can be improved by averaging multiple scans. Field-of-view defining optics are coupled by a flexible fiber optics bundle to the spectroradiometer, which consists of a non-scanning concave holographic diffraction grating with flat focal field imaged onto a 1024-element liquid-nitrogen-cooled PtSi linear-array detector. The combination of concave grating and linear-array detector was chosen in preference to Fourier transform, Hadamard transform, and scanned grating monochromator systems on the basis of simplicity, high SNR, and greatest radiometric accuracy.

The overall topic of the research described 10 this dissertation was the partitioning of available energy at the Earth's surface into sensible and latent heat flux, with an emphasis on the development of techniques which utilize remotely sensed data. One of the major objectives was; to investigate the modification of existing techniques, developed over agricultural surfaces, to "natural" ecosystems (i.e., non-agricultural vegetation types with variable and incomplete canopy cover). Ground-based measurements of surface fluxes, vegetation cover, and surface and root-zone soil moisture from the First ISLSCP (International Land Surface Climatology Program) Field Experiment (FIFE) were used to examine the factors controlling the partitioning of energy at ground stations with contrasting surface characteristics. Utilizing helicopter-based and satellite-based data acquired directly over ground-based flux stations at the FIFE experimental area, relatively simple algorithms were developed for estimating the soil heat flux and sensible heat flux from remotely sensed data. The root mean square error (RMSE) between the sensible heat flux computed with the remotely sensed data and the sensible heat flux measured at the ground stations was 33 Wm⁻². These algorithms were then applied on a pixel-by-pixel basis to data from a Landsat-TM (Thematic Mapper) scene acquired over the FIFE site on August 15, 1987 to produce spatially distributed surface energy-balance components for the FIFE site. A methodology for quantifying the effect of spatial scaling on parameters derived from remotely sensed data was presented. As an example of the utility of this approach, NDVI values for the FIFE experimental area were computed with input data of variable spatial resolution. The differences in the values of NDVI computed at different spatial resolutions were accurately predicted by an equation which quantified those differences in terms of variability in input observations.

In order to reduce the problems of clouds, atmospheric variations, view angle effects, and the soil background variations in the high temporal frequency AVHRR data, a compositing technique is usually employed. Current compositing techniques use a single pixel selection criterion of outputting the input pixel of maximum value NDVI. Problems, however, exist due to the use of the NDVI classifier and to the imperfection of the pixel selection criteria of the algorithm itself. The NDVI was found not to have the maximum value under an ideal observation condition, while the single pixel selection criterion favors the large off-nadir sensor view angles. Consequently, the composited data still consist of substantial noise. To further reduce the noise, several data sets were obtained to study these external factor effects on the NDVI classifier and other vegetation indices. On the basis of the studies of these external factors, a new classifier was developed to further reduce the soil noise. Then, a new set of pixel selection criteria was proposed for compositing. The new compositing algorithm with the new classifier was used to composite two AVHRR data sets. The alternative approach showed that the high frequency noises were greatly reduced, while more valuable data were retained. The proposed alternative compositing algorithm not only further reduced the external factor related noises, but also retained more valuable data. In this dissertation, studies of external factor effects on remote sensing data and derived vegetation indices are presented in the first four chapters. Then the development of the new classifier and the alternative compositing algorithm were described. Perspectives and limitations of the proposed algorithms are also discussed.

No description available.

This thesis describes a multi-channel millimeter wave radiometer control system that will be used for atmospheric water vapor and temperature profile retrievals. The system consists of four subsystems which provide a total of nine frequency channels, a steerable reflector to permit slant-path measurements at different zenith angles, and a host computer for analyzing the data. The nine channels span the water vapor and oxygen absorption lines in the 20 to 60 GHz range. A distributed processing architecture is implemented to control the system. Each of the subsystems employs a signal processor and a microcontroller, which are configurable from the host. The signal processor filters the receiver's output, while the microcontroller oversees the radiometer, accepts data from the signal processor and communicates with the host. The host executes a custom shell that allows it to concurrently accept data from the subsystems, position the reflector and execute user analysis programs.

This thesis describes the first calibration of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-11 satellite. Two methods were used to perform the calibration. Both methods relied on calibrated data from a high resolution sensor that passed over the target site at the White Sands Missile Range, New Mexico, on November 21, 1988. The first approach required ground reflectance and atmospheric optical depth data taken during overpass time. The second method relied on historical ground reflectance data and utilized standard atmospheric models. The calibration gains varied widely depending on which set of calibration offsets were used, but the agreement between the gains computed by the two methods was very close: approximately 1% in channel 1 and 2% in channel 2. This close agreement indicates that Method 2, which does not require the complexity and expense of field work, could be a viable option for future calibration efforts.

An algorithm for estimating daily surface rain volumes from hourly GOES infrared images has been developed using data obtained during the Southwest Area Monsoon Project(SWAMP). Daily surface rain volumes will be estimated using derived positive linear relationships between digital infrared counts and cloud radar reflectivities. These relations provide estimates of radar reflectivities corresponding to hourly infrared images, which in term, using an assumed reflectivity-rainrate(ZR) relation(Z = 55R1.6), will are to generate hourly precipitation fields from which daily rain volumes are computed. The linear relations employed are determined through a regression analysis on digital IR counts of GOES imagery and airborne internal radar reflectivity samples. This study also explores the existence of an average linear relation between infrared pixel values and radar reflectivities.

No description available.

No description available.