<p> In California, natural vegetation is experiencing an increasing amount of stress due to prolonged droughts, wildfires, insect infestation, and disease. Remote sensing technologies provide a means for monitoring plant species presence and function temporally across landscapes. In this his dissertation, I used hyperspectral visible shortwave infrared (VSWIR), hyperspectral thermal (TIR), and hyperspectral VSWIR + broadband TIR imagery to derive key observations of plant species across a gradient of environmental conditions and time frames. In Chapter 2, I classified plant species using hyperspectral VSWIR imagery from 2013&ndash;2015 spring, summer, and fall. Plant species maps had the highest classification accuracy using spectra from a single date (mean kappa 0.80&ndash;0.86). The inclusion of spectra from other dates decreased accuracy (mean kappa 0.78&ndash;0.83). Leave-one-out analysis emphasized the need to have spectra from the image date in the classification training, otherwise classification accuracy dropped significantly (mean kappa 0.31&ndash;0.73). In Chapter 3, I used hyperspectral TIR imagery to determine the extent that high precision spectral emissivity and canopy temperature can be exploited for vegetation research at the canopy level. I found that plant species show distinct spectral separation at the leaf level, but separability among species is lost at the canopy level. However, species&rsquo; canopy temperatures exhibited different distributions among dates and species. Variability in canopy temperatures was largely explained by LiDAR derived canopy structural attributes (e.g. canopy density) and the surrounding environment (e.g. presence of pavement). In Chapter 4, I used combined hyperspectral VSWIR and broadband TIR imagery to monitor plant stress during California&rsquo;s 2013&ndash;2015 severe drought. The temperature condition index (TCI) was calculated to measure plant stress by using plant species&rsquo; surface minus air temperature distributions across dates. Plant stress was not evenly distributed across the landscape or time with lower elevation open shrub/meadows, showing the largest amount of stress in June 2014, and August 2015 imagery. Plant stress spatial variability across the study area was related to a slope&rsquo;s aspect with highly stressed plants located on south or south-southwest facing slopes. Overall, this dissertation quantifies the ability to temporally study plant species using hyperspectral VSWIR, hyperspectral TIR, and combined VSWIR+TIR imagery. This analysis supports a range of current and planned missions including Surface Biology and Geology (SBG), Environmental Mapping and Analysis Program (EnMAP), National Ecological Observatory Network (NEON), Hyperspectral Thermal Emission Spectrometer (HyTES), and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). </p><p>

Landsat images are analyzed to investigate the causes of turbidity variations in lower Chesapeake Bay surface water. Visual analysis and image enhancement are used in association with optical film density data obtained along selected Bay transects. The optical density data of all images, inversely related to surface turbidity, are used to produce residual turbidity profiles showing turbidity above and below average conditions. meteorological conditions have Images with similar tidal or their residual optical density data averaged to identify probable causes of above average turbidity levels. Freshwater discharge does not directly contribute suspended sediment to Chesapeake Bay, except from the Potomac River during times of high freshwater flow. Much of the detected surface turbidity is associated with resuspension by tidal currents. Flood currents cause higher surface turbidity along the Eastern Shore frorn the Bay mouth to off the Rappahannock River mouth. High ebb-related turbidity occurs north of the Rappahannock River and in the western half of Chesapeake Bay south of Wolf Trap Shoals. Currents during spring tide produce higher surface turbidity south of the Rappahannock River than currents during other portions of the lunar cycle. Strong wind causes greater surface turbidity than low wind except when wind direction opposes tidal currents. A large fetch (20 km) parallel to wind direction results in higher surface turbidity downwind. A correlation exists between surface turbidity and water depth. Surface turbidity is lower in deeper water due to the weaker effect of tidal and wind resuspension. Resuspension of bottom sediment affects surface in waters as deep as 40 feet.

Elevated water levels and large waves during storms cause beach erosion, overwash, and coastal flooding, particularly along barrier island coastlines. While predictions of storm tracks have greatly improved over the last decade, predictions of maximum water levels and variations in the extent of damage along a coastline need improvement. In particular, physics based models still cannot explain why some regions along a relatively straight coastline may experience significant erosion and overwash during a storm, while nearby locations remain seemingly unchanged. Correct predictions of both the timing of erosion and variations in the magnitude of erosion along the coast will be useful to both emergency managers and homeowners preparing for an approaching storm. Unfortunately, research on the impact of a storm to the beach has mainly been derived from "pre" and "post" storm surveys of beach topography and nearshore bathymetry during calm conditions. This has created a lack of data during storms from which to ground-truth model predictions and test hypotheses that explain variations in erosion along a coastline. We have developed Coastal Lidar and Radar Imaging System (CLARIS), a mobile system that combines a terrestrial scanning laser and an X-band marine radar system using precise motion and location information. CLARIS can operate during storms, measuring beach topography, nearshore bathymetry (from radar-derived wave speed measurements), surf-zone wave parameters, and maximum water levels remotely. In this dissertation, we present details on the development, design, and testing of CLARIS and then use CLARIS to observe a 10 km section of coastline in Kitty Hawk and Kill Devil Hills on the Outer Banks of North Carolina every 12 hours during a Nor'Easter (peak wave height in 8 m of water depth = 3.4 m). High decadal rates of shoreline change as well as heightened erosion during storms have previously been documented to occur within the field site. In addition, complex bathymetric features that traverse the surf-zone into the nearshore are present along the southern six kilometers of the field site. In addition to the CLARIS observations, we model wave propagation over the complex nearshore bathymetry for the same storm event. Data reveal that the complex nearshore bathymetry is mirrored by kilometer scale undulations in the shoreline, and that both morphologies persist during storms, contrary to common observations of shoreline and surf-zone linearization by large storm waves. We hypothesize that wave refraction over the complex nearshore bathymetry forces flow patterns which may enhance or stabilize the shoreline and surf-zone morphology during storms. In addition, our semi-daily surveys of the beach indicate that spatial and temporal patterns of erosion are strongly correlated to the steepness of the waves. Along more than half the study site, fifty percent or more of the erosion that occurred during the first 12 hours of the storm was recovered within 24 hours of the peak of the storm as waves remained large (>2.5 m), but transitioned to long period swell. In addition, spatial variations in the amount of beach volume change during the building portion of the storm were strongly correlated with observed wave dissipation within the inner surf zone, as opposed to predicted inundation elevations or alongshore variations in wave height.

Salinity represents the major environmental threat to arable land in Western Australia and many other parts of the world. This study was designed to establish criteria for a practical remote sensing system using the visible, reflected and shortwave infrared for the early detection and mapping of salinity. The results are principally from a group of study sites on the CSIROs Yalanbee Experiment Station, and from other significant sites during the agricultural cycles of 1985-7.Analysis of imagery from the Geoscan Multispectral Airborne Scanner showed that best discrimination between study sites affected by salinity, and those not affected, was provided by bands 3 (650-700 nm), 4 (830-870nm) and band 6 (1980-2080nm). The maximum discrimination occurred in a September 1986 flight (spring-flush). Although excellent discrimination was also evident in August and November in 1985, this could not be reproduced in November 1986. The visible and reflected infrared bands 3 and 4 featured prominently, but the significance of the short wave infrared bands was evident especially when vegetative ground cover became a less dominant factor.Field spectra collected over the same period with the Geoscan Portable Field Spectroradiometer (PFS) supported the aircraft data to a certain extent. Detailed analysis of the fine non-correlated structure of narrow constructed bands, from PFS data, indicated that improved discrimination between sites could be provided over a wider time window extending into the summer and autumn. This is when weather-related conditions, i.e. cloud, soil moisture and sun angle, are more conducive to extensive surveys.The importance of at least one narrow band centred near 1985 nm was determined. Laboratory spectra of bare soil from sites measured on an Hitachi Spectrophotometer also provided the importance of the shortwave region adjacent to the 1900 nm water ++ / absorption.The study evaluated the spatial and spectral characteristics of existing satellite systems such as Thematic Mapper and the Multispectral Scanner on the Landsat series and determined that a spatial resolution of about 20-30 metres was most appropriate for detection of salinity at a scale whereby management could be implemented.Ground electromagnetic techniques were evaluated during the study and the EM-38 Ground Conductivity Unit proved valuable for characterizing salinity status of the sites. The Lowtran Computer Code was used to model atmospheric attenuation and results indicated that the positioning of a narrow shortwave infrared waveband, centred at 1985 nm, is possible.

Ice clouds occur quite frequently, yet so much about these clouds is unknown. In recent years, numerous investigations and field campaigns have been focused on the study of ice clouds, all with the ultimate goal of gaining a better understanding of microphysical and optical properties, as well as determining the radiative impact. Perhaps one of the most recognized instruments used for such research is the Moderate-resolution Imaging Spectroradiometer (MODIS), carried aboard the NASA EOS satellites Terra and Aqua. The present research aims to support ongoing efforts in the field of ice cloud research by use of observations obtained from Terra and Aqua MODIS. First, a technique is developed to infer ice cloud optical depth from the MODIS cirrus reflectance parameter. This technique is based on a previous method developed by Meyer et al. (2004). The applicability of the algorithm is demonstrated with retrievals from level-2 and -3 MODIS data. The technique is also evaluated with the operational MODIS cloud retrieval product and a method based on airborne ice cloud observations. From this technique, an archive of daily optical depth retrievals is constructed. Using simple statistics, the global spatial and temporal distributions of ice clouds are determined. Research has found that Aqua MODIS observes more frequent ice clouds and larger optical depths and ice water paths than does Terra MODIS. Finally, an analysis of the time series of daily optical depth values revealed that ice clouds at high latitudes, which are most likely associated with synoptic scale weather sytems, persist long enough to move with the upper level winds. Tropical ice clouds, however, dissipate more rapidly, and are in all likelihood associated with deep convective cells.

Ice clouds occur quite frequently, yet so much about these clouds is unknown. In recent years, numerous investigations and field campaigns have been focused on the study of ice clouds, all with the ultimate goal of gaining a better understanding of microphysical and optical properties, as well as determining the radiative impact. Perhaps one of the most recognized instruments used for such research is the Moderate-resolution Imaging Spectroradiometer (MODIS), carried aboard the NASA EOS satellites Terra and Aqua. The present research aims to support ongoing efforts in the field of ice cloud research by use of observations obtained from Terra and Aqua MODIS. First, a technique is developed to infer ice cloud optical depth from the MODIS cirrus reflectance parameter. This technique is based on a previous method developed by Meyer et al. (2004). The applicability of the algorithm is demonstrated with retrievals from level-2 and -3 MODIS data. The technique is also evaluated with the operational MODIS cloud retrieval product and a method based on airborne ice cloud observations. From this technique, an archive of daily optical depth retrievals is constructed. Using simple statistics, the global spatial and temporal distributions of ice clouds are determined. Research has found that Aqua MODIS observes more frequent ice clouds and larger optical depths and ice water paths than does Terra MODIS. Finally, an analysis of the time series of daily optical depth values revealed that ice clouds at high latitudes, which are most likely associated with synoptic scale weather sytems, persist long enough to move with the upper level winds. Tropical ice clouds, however, dissipate more rapidly, and are in all likelihood associated with deep convective cells.

The Gulf of California and its surrounding land areas provide a classic example of recently rifted continental lithosphere. The recent tectonic history of eastern Baja California has been dominated by oblique rifting that began at ~12 Ma. Thus, extensional tectonics, bedrock lithology, long-term climatic changes, and evolving surface processes have controlled the tectono-geomorphological evolution of the eastern part of the peninsula since that time. In this study, digital elevation data from the Shuttle Radar Topography Mission (SRTM) from Baja California were corrected and enhanced by replacing artifacts with real values that were derived using a series of geostatistical techniques. The next step was to generate accurate thematic geologic maps with high resolution (15-m) for the entire eastern coast of Baja California. The main approach that we used to clearly represent all the lithological units in the investigated area was objectoriented classification based on fuzzy logic theory. The area of study was divided into twenty-two blocks; each was classified independently on the basis of its own defined membership function. Overall accuracies were 89.6 %, indicating that this approach was highly recommended over the most conventional classification techniques. The third step of this study was to assess the factors that affected the geomorphologic development along the eastern side of Baja California, where thirty-four drainage basins were extracted from a 15-m-resolution absolute digital elevation model (DEM). Thirty morphometric parameters were extracted; these parameters were then reduced using principal component analysis (PCA). Cluster analysis classification defined four major groups of basins. We extracted stream length-gradient indices, which highlight the differential rock uplift that has occurred along fault escarpments bounding the basins. Also, steepness and concavity indices were extracted for bedrock channels within the thirty-four drainage basins. The results were highly correlated with stream length-gradient indices for each basin. Nine basins, exhibiting steepness index values greater than 0.07, indicated a strong tectonic signature and possible higher uplift rates in these basins. Further, our results indicated that drainage basins in the eastern rift province of Baja California could be classified according to the dominant geomorphologic controlling factors (i.e., faultcontrolled, lithology-controlled, or hybrid basins).

Soil moisture in the shallow subsurface is a primary hydrologic state governing land-atmosphere interaction at various scales. The primary objectives of this study are to model soil moisture in the root zone in a distributed manner and determine scaling properties of surface soil moisture using passive microwave remote sensing. The study was divided into two parts. For the first study, a root zone soil moisture assessment tool (SMAT) was developed in the ArcGIS platform by fully integrating a one-dimensional vadose zone hydrology model (HYDRUS-ET) with an ensemble Kalman filter (EnKF) data assimilation capability. The tool was tested with dataset from the Southern Great Plain 1997 (SGP97) hydrology remote sensing experiment. Results demonstrated that SMAT displayed a reasonable capability to generate soil moisture distribution at the desired resolution at various depths of the root zone in Little Washita watershed during the SGP97 hydrology remote sensing experiment. To improve the model performance, several outstanding issues need to be addressed in the future by: including "effective" hydraulic parameters across spatial scales; implementing subsurface soil properties data bases using direct and indirect methods; incorporating appropriate hydrologic processes across spatial scales; accounting uncertainties in forcing data; and preserving interactions for spatially correlated pixels. The second study focused on spatial scaling properties of the Polarimetric Scanning Radiometer (PSR)-based remotely sensed surface soil moisture fields in a region with high row crop agriculture. A wavelet based multi-resolution technique was used to decompose the soil moisture fields into larger-scale average soil moisture fields and fluctuations in horizontal, diagonal and vertical directions at various resolutions. The specific objective was to relate soil moisture variability at the scale of the PSR footprint (800 m X 800 m) to larger scale average soil moisture field variability. We also investigated the scaling characteristics of fluctuation fields among various resolutions. The spatial structure of soil moisture exhibited linearity in the log-log dependency of the variance versus scale-factor, up to a scale factor of -2.6 (6100 m X 6100 m) irrespective of wet and dry conditions, whereas dry fields reflect nonlinear (multi-scaling) behavior at larger scale-factors.

This dissertation consists of three studies dealing with particulate organic carbon (POC). The first study describes the temporal and spatial variability of particulate matter (PM) and POC, and physical processes that affect the distribution of PM and POC with synchronous remote sensing data. The purpose of the second study is to develop POC algorithms in the Gulf of Mexico based on satellite data using numerical methods and to compare POC estimates with spectral radiance. The purpose of the third study is to investigate climatological variations from the temporal and spatial POC estimates based on SeaWiFS spectral radiance and physical processes, and to determine the physical mechanisms that affect the distribution of POC in the Gulf of Mexico. For the first and second studies, hydrographic data from the Northeastern Gulf of Mexico (NEGOM) study were collected on each of 9 cruises from November 1997 to August 2000 across 11 lines. Remotely sensed data sets were obtained from NASA and NOAA using algorithms that have been developed for interpretation of ocean color data from various satellite sensors. For the third study, we use the time-series of POC estimates, sea surface temperature (SST), sea surface height anomaly (SSHA), sea surface wind (SSW), and precipitation rate (PR) that might cause climatological variability and physical processes. The distribution of surface PM and POC concentrations were affected by one or more factors such as river discharge, wind stress, stratification, and the Loop Current/Eddies. To estimate POC concentration, empirical and model-based approaches were used using regression and principal component analysis (PCA) methods. We tested simulated data for reasonable and suitable algorithms in Case 1 and Case 2 waters. Monthly mean values of POC concentrations calculated with PCA algorithms. The spatial and temporal variations of POC and physical forcing data were analyzed with the empirical orthogonal function (EOF) method. The results showed variations in the Gulf of Mexico on both annual and inter-annual time scales.

Thesis (M.S.)--Kent State University, 2008. / Title from PDF t.p. (viewed Sept. 28, 2009). Advisor: Donna Witter. Keywords: chlorophyll; Lake Erie; remote sensing; algorithm; atmospheric correction. Includes bibliographical references (p. 58-66).