Gustavious P. Williams

Detection of Amorphously Shaped Objects Using Spatial Information Detection Enhancement (SIDE)

Pattern recognition of amorphously shaped objects such as gas plumes, oil spills, or epidemiological spread is difficult because there is no definite shape to match. We consider detection of such amorphously shaped objects using a neighborhood model which operates on a concept of loose spatial contiguity: there is a significant probability that a pixel surrounded by the object of interest itself contains that object of interest, and boundaries tend to be smooth. These assumptions are distilled into a single-parameter prior probability model to use in a maximum a posteriori hypothesis test. The method is evaluated against synthetic data generated from hyperspectral imagery and DIRSIG simulation results. These tests indicate significant improvement on the ROC curves.

Spatiotemporal Variability of Lake Water Quality in the Context of Remote Sensing Models

This study demonstrates a number of methods for using field sampling and observed lake characteristics and patterns to improve techniques for development of algae remote sensing models and applications. As satellite and airborne sensors improve and their data are more readily available, applications of models to estimate water quality via remote sensing are becoming more practical for local water quality monitoring, particularly of surface algal conditions. Despite the increasing number of applications, there are significant concerns associated with remote sensing model development and application, several of which are addressed in this study. These concerns include: (1) selecting sensors which are suitable for the spatial and temporal variability in the water body; (2) determining appropriate uses of near-coincident data in empirical model calibration; and (3) recognizing potential limitations of remote sensing measurements which are biased toward surface and near-surface conditions. We address these issues in three lakes in the Great Salt Lake surface water system (namely the Great Salt Lake, Farmington Bay, and Utah Lake) through sampling at scales that are representative of commonly used sensors, repeated sampling, and sampling at both near-surface depths and throughout the water column. The variability across distances representative of the spatial resolutions of Landsat, SENTINEL-2 and MODIS sensors suggests that these sensors are appropriate for this lake system. We also use observed temporal variability in the system to evaluate sensors. These relationships proved to be complex, and observed temporal variability indicates the revisit time of Landsat may be problematic for detecting short events in some lakes, while it may be sufficient for other areas of the system with lower short-term variability. Temporal variability patterns in these lakes are also used to assess near-coincident data in empirical model development. Finally, relationships between the surface and water column conditions illustrate potential issues with near-surface remote sensing, particularly when there are events that cause mixing in the water column.

Reservoir water quality monitoring using remote sensing with seasonal models: case study of five central-Utah reservoirs

Abstract Remote sensing models estimate chlorophyll concentrations by correlating spectral reflectance and reservoir chlorophyll. Different algal populations have different spectral signatures and thus different correlation models, an issue typically addressed by developing and applying a model using the same satellite image. Here we exploit these population differences by developing seasonal models that can be applied to other images from that season. We rely on algal succession and assume the phytoplankton population is relatively constant over a season, dividing the growth season into 3 parts as substitutes for population measurements. We present seasonal models developed using data from 2 Utah reservoirs, Deer Creek and Jordanelle, which have comprehensive long-term field datasets large enough to provide adequate near-coincident data for model development. We then apply the chlorophyll-estimation models to 5 reservoirs in north-central Utah and present the trends in the average, maximum, and variance of the chlorophyll concentration for each reservoir over a nearly 40-year period. We present examples of chlorophyll distribution maps that show spatial patterns and discuss implications for field sampling design and analysis. We found that season-specific models perform well for satellite images from the same season but do not perform well against images from other seasons. We suggest that these models can be used with confidence in the season for which they were developed, allowing analysis of historical data and providing current information on reservoir conditions without accompanying field samples.

The Use of Digital Elevation Models (DEMs) for Bathymetry Development in Large Tropical Reservoirs

Bathymetry is the study of underwater depth of lake or ocean floors. In other words, bathymetry is the underwater equivalent to hypsometry (Miller et al., 2010), and this can be used also to describe the shape and volume of water reservoirs (Obregon et al., 2011). The bathymetry is generally obtained by recording water depths throughout a water body and connecting the recorded points of equal water depth. Hence, a bathymetric map is estimated from the water depth between two points of a known depth. There may be discrepancies in any given map depending on the number of depth measurements taken: the more depth measurements recorded, the more accurate the map is.

An Assessment of the Disposal of Petroleum Industry NORM in Nonhazardous Landfills

In this study, the disposal of radium-bearing NORM wastes in nonhazardous landfills in accordance with the MDEQ guidelines was modeled to evaluate potential radiological doses and resultant health risks to workers and the general public. In addition, the study included an evaluation of the potential doses and health risks associated with disposing of a separate NORM waste stream generated by the petroleum industry--wastes containing lead-210 (Pb-210) and its progeny. Both NORM waste streams are characterized in Section 3 of this report. The study also included reviews of (1) the regulatory constraints applicable to the disposal of NORM in nonhazardous landfills in several major oil and gas producing states (Section 2) and (2) the typical costs associated with disposing of NORM, covering disposal options currently permitted by most state regulations as well as the nonhazardous landfill option (Section 4).

Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches

This paper considers an experimental approach for assessing algorithms used to exploit remotely sensed data. The approach employs synthetic images that are generated using physical models to make them more realistic while still providing ground truth data for quantitative evaluation. This approach complements the common approach of using real data and/or simple model-generated data. To demonstrate the value of such an approach, the behavior of the FastICA algorithm as a hyperspectral unmixing technique is evaluated using such data. This exploration leads to a number of useful insights such as: (1) the need to retain more dimensions than indicated by eigenvalue analysis to obtain near-optimal results; (2) conditions in which orthogonalization of unmixing vectors is detrimental to the exploitation results; and (3) a means for improving FastICA unmixing results by recognizing and compensating for materials that have been split into multiple abundance maps.

A neighborhood model for detection in hyperspectral images

The neighborhood model provides a moderate complexity method of introducing the concept of smoothness into a detection problem. As tested here, the smoothness is reduced to a simple scalar quantity whose probability is easily computed. The concept is fairly general, moving from vector matched filter processing as originally formulated to any scalar image. The result is a nonlinear filter which is edge preserving and classifier-enhancing, resulting in improvements in the ROC curve in all classifiers tested, the neighborhood modeling.

Hindcasting Water Quality In An Optically Complex System

As is the case with many large lakes, field sampling records (and the understanding of historical water quality) in the Great Salt Lake natural surface water system are heavily limited due to time and cost constraints, as well as a number of independent organizations collecting and managing data. To address these deficiencies, remote sensing of surface water quality is used to hind-cast historical conditions of algal blooms in the GSL surface water system (GSLSWS). This system is unique because its lakes are closely connected, yet have widely varying characteristics and conditions. An approach for development of lake-specific models is demonstrated, using historical Landsat and field-sampled data. This study builds on previous studies of historical water quality which have used broad-spectral remote sensing data and near-coincident field samples by evaluating the ability of models to accurately estimate water quality under optically complex conditions (such as high turbidity and shallow conditions). We also examine the spatiotemporal variability of the field-sampled data and address the issue of near-coincidence between a historic dataset of fieldsamples and remote sensing images. Existing field sampling campaigns for this area do not provide sufficient information about adverse conditions or long-term spatiotemporal patterns. Results of the remote sensing model application however may provide useful metrics for algal bloom dynamics, including timing of blooms, duration and spatial extent of algal blooms, and how these dynamics vary over time and within the surface water system. Products of the remote sensing models broaden the foundation of understanding of water quality conditions, which can be used to move forward with better monitoring and management practices.

Correlated maximum likelihood temperature/emissivity separation of hyperspectral images

We consider a model for temperature/emissivity separation in hyperspectral image processing. The emissivity is modulated by both the black body function and the atmospheric down- welling. This dual modulation has made it difficult to extract both temperature and emissivity, since offsets in one variable can be compensated by the other. Previous work with only a single wavelength is extended to the multiple wavelength (vector) case. Downwelling radiance is modeled as a Gaussian random vector. As before, the emissivity contributes to both the mean and variance of the observations. The covariance of the downwelling is assumed to have correlated elements, providing additional information beyond what is seen in the scalar case. Gradient ascent is used as an approach to find the maximum likelihood solution, demonstrating that a single maximum exists.

Temperature emissivity separation: Estimation with a parameter affecting both the mean and variance of the observation

We consider a model for temperature-emissivity separation (TES) in hyperspectral image processing. The emissivity is modulated by both the black body function and the atmospheric downwelling. The interaction has made it difficult to extract both temperature and emissivity, since offsets in one can be compensated by the other. Working with only a single wavelength component, we propose here a model in which the downwelling is considered as a random variable (or vector). The emissivity thus contributes to both the variance and mean of the observations. This leads to a maximum likelihood estimator for the emissivity. We compute an expression for the bias of this estimator, and show how it can be used to produce an unbiased estimator. An estimator for the temperature is also given. These two estimators can be used iteratively, providing separation of the temperature and emissivity components.