Vegetation mapping in and regions facilitates ecological studies, land management, and provides a record to which future land changes can be compared. Accurate and representative mapping of desert vegetation requires a sound field sampling program and a methodology to transform the data collected into a representative classification system. Time and cost constraints require that a remote sensing approach be used if such a classification system is to be applied on a regional scale. However, desert vegetation may be sparse and thus difficult to sense at typical satellite resolutions, especially given the problem of soil reflectance. This study was designed to address these concerns by conducting vegetation mapping research using field and satellite data from the US Army Yuma Proving Ground (USYPG) in Southwest Arizona. Line and belt transect data from the Army's Land Condition Trend Analysis (LCTA) Program were transformed into relative cover and relative density classification schemes using cluster analysis. Ordination analysis of the same data produced two and three-dimensional graphs on which the homogeneity of each vegetation class could be examined. It was found that the use of correspondence analysis (CA), detrended correspondence analysis (DCA), and non-metric multidimensional scaling (NMS) ordination methods was superior to the use of any single ordination method for helping to clarify between-class and within-class relationships in vegetation composition. Analysis of these between-class and within-class relationships were of key importance in examining how well relative cover and relative density schemes characterize the USYPG vegetation. Using these two classification schemes as reference data, maximum likelihood and artificial neural net classifications were then performed on a coregistered dataset consisting of a summer Landsat Thematic Mapper (TM) image, one spring and one summer ERS-1 microwave image, and elevation, slope, and aspect layers. Classifications using a combination of ERS-1 imagery and elevation, slope, and aspect data were superior to classifications carried out using Landsat TM data alone. In all classification iterations it was consistently found that the highest classification accuracy was obtained by using a combination of Landsat TM, ERS-1, and elevation, slope, and aspect data. Maximum likelihood classification accuracy was found to be higher than artificial neural net classification in all cases.

This dissertation presents a geographical analysis of air pollution in the Tucson region. Image processing, geographic information system (GIS), climatological, and statistical tools are used to develop and analyze air pollution-related databases. These databases are then used in conjunction with a limited number of spatial measurements of ozone concentrations to create accurate and theoretically sound ground-level ozone maps. High spatial resolution, gridded, multi-temporal, atmospheric emissions inventories (EIs) of ozone precursor chemical (i.e. volatile organic compounds (VOCs) and nitrogen oxides (NOₓ)) emissions are initially developed. GIS-driven "top-down" and "bottom-up" methods are employed to create anthropogenic VOC and NOx emissions inventories while satellite imagery and field surveys are employed to create biogenic VOC (BVOC) emissions inventories. Accounting for approximately 50% of the anthropogenic emissions, on-road vehicles are the dominant anthropogenic source. The forest and desert lands emit nearly all of the BVOCs within the entire Tucson region while exotic trees such as eucalyptus, pine, and palm emit most of the BVOCs within the City of Tucson. Relationships between VOC and NOₓ emissions, atmospheric conditions, and ambient ozone levels are determined by examining spatio-temporal variations in ozone levels, temporal variations in VOC and NOₓ emissions and atmospheric conditions, atmospheric conditions which are conducive to elevated ozone levels. In addition, the likelihood of ozone transport from Phoenix to Tucson is assessed. The highest ozone levels occur at "rural," downwind monitors, occur in August, and occur during the early afternoon hours. Atmospheric conditions conducive to elevated concentrations differ between the months while inter-city ozone transport is most likely to occur in June. Pooled, cross-sectional, times series, regression models are developed with the aid of cluster analysis and principal components analysis to spatially predict daily maximum 1-hr and 8-hr average ozone concentrations. Gridded, multi-temporal estimates of VOCs and NOₓ emissions are the primary predictor variables in the regression models. The pooled models are reasonably accurate with overall R² values from 0.90 to 0.92, 6 to 7% error, and predicted concentrations that are typically within 0.003 to 0.004 ppm of the observed concentrations. The predicted highest ozone concentrations occur in a monitorless area on the eastern edge of the City of Tucson.

This dissertation studies the feasibility of enhancing the spatial resolution of multi-look remotely-sensed imagery using an iterative resolution enhancement algorithm known as Projection Onto Convex Sets (POCS). A multi-angle satellite image modeling tool is implemented, and simulated multi-look imagery is formed to test the resolution enhancement algorithm. Experiments are done to determine the optimal configuration and number of multi-angle low-resolution images needed for a quantitative improvement in the spatial resolution of the high-resolution estimate. The important topic of aliasing is examined in the context of the POCS resolution enhancement algorithm performance. In addition, the extension of the method to multispectral sensor images is discussed and an example is shown using multispectral confocal fluorescence imaging microscope data. Finally, the remote sensing issues of atmospheric path radiance and directional reflectance variations are explored to determine their effect on the resolution enhancement performance.

Optical glint patterns convey information about the roughness of the surface on which they are formed. This dissertation describes two new optical instruments that relate the variations of specular laser reflections (laser glints) from the sea surface in angular, temporal, and wavenumber space to the surface roughness. Measurements from these instruments are interpreted with the objective of improving the capabilities of remote-sensing instruments that view the ocean surface. Particular attention is paid to cm waves, which are resonant structures for microwave sensors and the most significant component of optical roughness. The scanning-laser glint meter counts laser glints in 1° angular bins over a ± 75° nadir-angle range. The video laser-glint imager is a CCD video camera that images glints from an array of diode lasers. Both instruments were deployed on the research platform FLIP in the Pacific Ocean near the Oregon coast for three weeks during September 1995. Normalized histograms of angular glint counts are interpreted as the probability density function (PDF) of sea-surface slope, a Gram-Charlier expansion of which facilitates studying the variation with wind speed and atmospheric stability of moments through order four. The PDF appears approximately Gaussian, but is skewed toward downwind slopes in the along-wind axis due to asymmetric wind waves. No skewness exists in the cross-wind axis. Slope PDFs also have positive peakedness, increasing the probability of very small and large slopes relative to a Gaussian. Surface roughness is shown to depend strongly on atmospheric stability, which is proportional to the air-water temperature difference. Both the mean-square slope and the peakedness increase with negative stability (water warmer than air) relative to the neutral-stability case (water and air temperatures equal). Increased surface roughness, due to increases in wind speed or negative stability, causes glint-count fractal dimensions to increase, glint-image power spectra to flatten, and glint-image autocorrelations to appear more wrinkled. Glint-image spectra are dominated by glint-size effects, which are related to surface curvature. New ways of modeling the interaction of electromagnetic waves with the ocean surface are suggested by the new fractal and spectral characterizations of surface roughness that are introduced here.

Airborne multispectral video was evaluated as a tool for obtaining urban land cover information for hydrological simulations. Land cover data was obtained for a small urban watershed in Tucson, Arizona using four methods: multispectral aerial video (2 meter and 4 meter pixel resolution), National High Altitude Photography (NHAP), multispectral satellite imagery from Systeme Pour l'Observation de la Terre (SPOT), and by conventional survey. A semi-automated land cover classification produced four classes: vegetation, buildings, pavement, and bare soil. The land cover data from each classification was used as input to a runoff simulation model. Runoff values generate by each simulation were compared to observed runoff. A chi-square goodness-of-fit test indicated that SPOT produced landcover data most similar to the conventional classification. In the curve number model, the SPOT data produced simulated runoff values most similar to observed runoff.

Hyper-spectral (HS) sensors are the instruments of choice for remote sensing applications involving environmental monitoring, littoral survey, and military assessment. Accurate band-to-band sensor radiometric calibration is critical for successful data mining of such HS spectral sets. Current calibration is often performed with methods not necessarily developed for HS applications. This work describes two advances which facilitate laboratory source calibrations. First, an analytical solution to the attenuation of flux within an integrating sphere, the best laboratory source of non-directional radiance for numerous radiometric applications, is given. Relative component attenuations due to integrating sphere coating, exit port escape, and atmospheric absorption are derived employing a geometrical PDF of summed probabilities. Equations providing the attenuation ratios and mean number of reflections for the three outcomes are obtained, yielding the three partial mean pathlengths and variances of all quantities. This work then describes an approach allowing accurate radiometric calibration of HS sensor bands using well-characterized and stable multi-spectral transfer radiometers. The resulting high-quality calibration enables the best representation of the truth spectral signature of the imaged scene. In order to obtain the best calibration with the least instrument complexity and expense, it is critical that the radiometer samples the source with the fewest samples at those optimal wavelengths which predict that source with the highest accuracy. The optimal source-specific bands are determined efficiently by application of the Direct Search methodology described here. Using the minimal selection of multi-spectral radiometer measurements obtained from the optimized transfer radiometer bands, one can obtain a complete and accurate calibration set for the continuum of calibration coefficients required for a robust HS application. Degradation of the prediction is documented for several typical error sources encountered with calibration, thereby defining limitations on the usefulness of the optimization approach.

My dissertation details the work I have done related to remote sensing of thermal activityon Io and thermal remote sensing used in the search for oases on Mars. At Io, I studiedtwo volcanoes, Tvashtar and Prometheus and their thermal activity. At Mars, I investigatedsuggestions of a possible oasis in one of the youngest volcanic regions, Cerberus Fossaeand nearby areas of SE Elysium.Tvashtar was the site of the first high-spatial-resolution observation of an extraterrestriallava curtain. The Tvashtar complex was also the site of a large, confined eruption a fewmonths after the fissure eruption. I discuss the work involved in estimating the brightnesstemperatures and power output of both eruptions as seen by the Galileo SSI. I also discusscooling and eruption-style models and their application to Tvashtar. In every geometricallycorrect observation of Prometheus, we have seen a 100 km tall SO2 gas and dust plumeabove its flow field. This plume and field migrated ~80 km between the Voyager and Galileo eras. I describe the work I performed in modeling the plume's creation as lava-volatileinteractions at the flow fronts.My Mars research entailed the search for thermal systems and constraints on nearsurfacewater ice in an equatorial region that contains some of the youngest lava flows onMars. This region, SE Elysium, also shows evidence of contemporaneous water and lava. Life as we know it requires a source of energy and liquid water, so a geologically youngregion containing both water and energy is an obvious place to study. I show, however, thatthe recent suggestions of extant near-surface water ice and possible endogenic energy escapeare not necessary, and that the thermal imaging of the region requires rock rather thanwater ice near the surface. I also show that the current instruments at Mars are insufficientfor the remote discovery of thermal reservoirs and then discuss some possible remedies.

This document proposes an objective technique to estimate the intensity and predict the formation of tropical cyclones using infrared satellite imagery. As the tropical cyclone develops from an unstructured cloud cluster and intensifies the cloud structures become more axisymmetric around an identified reference point or center. This methodology processes the image gradient to measure the level of symmetry of cloud structures, which characterizes the degree of cloud organization of the tropical cyclone.The center of a cloud system is calculated by projecting and accumulating parallel lines to the gradient vectors. The point where the highest number of line intersections is located pinpoints a common point where the corresponding gradients are directed. This location is used as the center of the system. Next, a procedure that characterizes the departure of the weather system structure from axisymmetry is implemented. The deviation angle of each gradient vector relative to a radial line projected from the center is calculated. The variance of the set of deviation angles enclosed by a circular area around the center describes the axisymmetry of the system, and its behavior through time depicts its dynamics. Results are presented that show the time series of the deviation angle variances is well correlated with the National Hurricane Center best-track estimates.The formation of tropical cyclones is detected by extending the deviation-angle variance technique, it is calculated using every pixel in the scene as the center of the cloud system. Low angle variances indicate structures with high levels of axisimmetry, and these values are compared to a set of thresholds to determine whether a cloud structure can be considered as a vortex. The first detection in a sequence indicates a nascent storm. It was found that 86% of the tropical cyclones during 2004 and 2005 were detected 27 h on average before the National Hurricane Center classified them as tropical storms (33kt).Finally, two procedures to locate the center of a tropical cyclone are compared to the National Hurricane Center best-track center database. Results show that both techniques provide similar accuracy, which increases as the tropical cyclone evolves.

Salinity has been shown to reduce vegetative growth, crop quality, and yield in agricultural crops. Remote sensing is capable of providing data about large areas. This project was designed to induce salinity stress in a crop, pak choi, and thereafter monitor the response of the crop as expressed by its spectral reflectances. The project was conducted in the National Taiwan University Phytotron, and spectral data was collected using a GER 2600. Yield and soil salinity (ECe) were also measured. After three seasons of data were collected, wavelengths sensitive to salinity were selected. These wavelengths, which are within the spectral response of biochemicals produced by plants as a response to soil salinity, were used to create two indices, the Salinity Stress Index (SSI) and the Normalized Salinity Stress Index (NSSI). After creating the indices tests were conducted to determine the efficacy of these indices in detecting salinity and drought stresses as compared to existing indices (SRVI and NDVI). This project induced salinity and drought stress in a crop, pak choi, and thereafter monitored the response of the crop as expressed by its spectral reflectances. The SSI and NSSI correlated well to both ECe and marketable yield. Additionally the SSI and NSSI were found to provide statistical differences between salinity stressed treatments and the control treatment. Drought stress was not detected well by any of the indices reviewed although the SSI and NSSI indices tended to increase with drought stress and decrease with salinity stress. As a final test, specific ion toxicities of sodium and chloride were tested against the developed indices (SSI and NSSI) and existing indices (NDVI, SRVI, and NDWI). There were no differences in SSI and NSSI responses to specific ion concentration in the high salinity treatments. These results indicated that the SSI and NSSI are not sensitive to the specific ion concentration in irrigation water. However, the SSI and NSSI were higher for the sodium water than the choride water in the low salinity treatments. It is likely that this difference was caused by the fact that the high SAR water decreased infiltration and caused water stress.

This study aimed to address the current status of the United Arab Emirates date palms. The first chapter focused on the development of the date palm sector in the UAE. A huge increase in the date palm number was achieved in the past few decades. In the same time, there are critical issues facing this development, such as water demand, salinity, and Red Palm Weevil. The second chapter is a greenhouse experiment to test the growth of twelve date palm seeds at four NaCl levels, control, 3000, 6000, and 12000 ppm. Optimal growth found at control and 3000 ppm of NaCl. Relative growth rate (RGR), biomass, and NL decreased significantly by increasing salinity; however, no significant differences were observed in the average SGR for any cultivars. Increased NaCl leads to significant decreases in K+, Mg2+, and Ca2+ contents of plants. Na:K ratios were lower in shoots than in roots. Lulu, Fard, Khnaizi, Nabtat Safi, and Razez cultivars showed higher RGR and biomasses whereas Khnaizi, Mesally, and Safri had higher Na:K ratios than other cultivars in the control indicating higher Na+ discriminations from plant parts. The third chapter studied the vegetation change in the eastern region of the UAE. Due to shortage of fresh water resources, the vegetation of the eastern region of the UAE has experienced a series of declines resulting from salinization of groundwater. To assess these changes, field measurements combined with Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) based Soil Adjusted Vegetation Index (SAVI) were analyzed. Images from two dates, 1987 and 2000 were acquired to enable the computation of the greenness anomalies for three sites in the eastern region, Fujairah, Kalba, and Hatta. The results show an overall increase in the agricultural area, associated with a severe decrease in vegetation greenness and health conditions, particularly in the Kalba study area. The SAVI values decreased with increased soil salinity, permitting the identification of salt-affected areas. Potential areas of further research range from studying the effects of tree spacing and understory crops as immediate and potential solutions to maintain productivity and mitigate the salinity problem.