Oil spills are a frequent occurrence in the Gulf of Mexico (GOM). They occur by two principle processes: natural oil seepage and accidental spills during petroleum extraction, transportation, and consumption. Marine oil spill can be highly dangerous because wind, waves, and currents can scatter an oil spill over a wide area in a few hours. Accurate detection and predicting the fate of oil not only from large spills, but also chronic small-scale emissions lead to a better investigation of the effect of oil on the environment. Remote sensing plays an important role in oil spill response and monitoring by providing the oil slick location and its spatial and temporal distribution. The aim of this dissertation was to use Synthetic Aperture Radar (SAR) images in the GoM as a means to, map the location of the anthropogenic oil slicks reported by National Response Center (NRCen) and quantify their volume, identify chronic oil spill locations, analyze oil slick extent and drift by wind and surface currents, study the fate of Deepwater Horizon (DWH) oil spill. This dissertation consists of studies that are compiled into three manuscripts that are published, accepted for publication or ready for submission. One of the objectives in this research was to examine the feasibility of SAR images in oil slick detection. We used SAR images to obtain more precise estimates of the magnitude of the hydrocarbon discharges reported by NRCen in the GoM. These reports depend largely on unverified reporting from responsible parties and third parties, have not been validated by an independent assessment in terms of location and magnitude, and associated with three categories of source: 1) pipelines, platforms, or other energy industry sources, 2) the former location of the Taylor offshore platform, and 3) undetermined sources. A total of 67 reports were visible in 66 archived SAR images from 2004 to 2012 describing transient events. Of those, oil slicks observed at the Taylor site were generally much larger than those corresponding to other NRCen reports, and indicated a chronic source at this location. These long wind-driven layers of floating oil released from the Taylor site were verified by field sampling, aerial photography, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) 30-m resolution data, and Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua medium resolution (250-500 m) data. A Texture Classifying Neural Network Algorithm (TCNNA) delineated oil slicks area from SAR images. Comparison of SAR-extracted and NRCen-reported oil slicks areas showed a consistent under reporting by NRCen. Our second objective was to estimate the surface residence-time of the oil slicks and to determine the importance of wind and surface currents on the trajectory and fate of the released oil. Oil slicks released from natural hydrocarbon seeps located in Green Canyon 600 (GC600) lease block in the GoM were analyzed in 41 SAR images. A relatively simple surface oil drift model deriving with wind and surface currents was used to obtain the closest resemblance between the simulated oil pathways and the length and shape of the oil slicks observed in SAR images. The average surface residence-time predicted from the hindcast modeling was 6.4 hr (± 5.7 hr). Respectively, the effect of winds and surface currents on disappearance and stretching of the oil slicks from sea surface were indicated. Results from the numerical experimentation were supported by in situ observations conducted by a wind-powered autonomous surface vehicle (SailDrone). Finally, our third objective was to discuss the fate of remaining oil after the DWH oil spill. The hypothesis of what happened to the surface discharge of DWH oil was tested by surface oil advection model, weathering, and fate data. The inputs of surface oil advection model derived from: 1) The volume distribution of floating oil during the DWH discharge quantified by 166 SAR images, 2) Modeled wind time series from the North American Mesoscale Forecast System (NAM), 3) Ocean currents from the HYbrid Coordinate Ocean Model (HYCOM). Evaporation of volatiles from surface oil was simulated by Oil Spill Contingency And Response (OSCAR) model. Daily magnitude and spatial distribution of aerial dispersant application and burning operations were obtained from publicly available databases. At each time step these weathering and fate data were subtracted from the modeled distribution of oil volume on the water surface. Results were compared to SAR images of DHW oil spill in order to verify the amount of oil which was 1) suspended below the surface or buried through sedimentation, 2) washed ashore, and 4) resurfaced through time. / A Dissertation submitted to the Department of Earth, Ocean, and Atmospheric Science in partial fulfillment of the Doctor of Philosophy. / Spring Semester 2017. / February 1, 2017. / Includes bibliographical references. / Ian R. MacDonald, Professor Directing Dissertation; Tarek Abichou, University Representative; Mark Bourassa, Committee Member; William Dewar, Committee Member; Dmitry Dukhovskoy, Committee Member.

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Small UAVs (drones) are increasingly useful for field data acquisition in the geosciences. Drone images and videos can be processed via digital photogrammetry to produce a 3D digital outcrop model (DOM). DOMs provide opportunities to “return” to an outcrop after fieldwork is complete, collect data from outcrops that are inaccessible, or may even provide opportunities to radically increase data volume of geometric characterizations of geological structures. Our study focuses on understanding the limitations of digital measurements and interpretations used to create stratigraphic columns by comparing 2D and 3D results to traditional stratigraphic descriptions and measurements from fieldwork. In this study, a drone collected photos and videos of a well-exposed section of the Palm Spring Formation in the Mecca Hills, California, which is divided into lower and upper units by an angular unconformity and a change in overall texture. In the field, 100 meters of section were measured in 10-cm increments. Markers were placed on the beds throughout the section and surveyed by GPS; these markers were captured by subsequent high-resolution aerial imagery. A low-resolution DOM, a high-resolution DOM, and a high-resolution with video DOM were created in Pix4DMapper via Structure from Motion (SfM) photogrammetry. The resulting dense point clouds and 3D textured meshes were used to measure projected 3D lengths for each DOM and to create stratigraphic columns from each DOM. Additionally, a stratigraphic section from a simple photomosaic of the UAV photos was created. The comparisons between the five methods yielded inconsistent bed thickness measurements and lithologic facies. Overall, the discrepancies suggest that differences between a digitally produced stratigraphic log and a stratigraphic log produced using traditional field techniques are not systematic nor due to distortion of digital models, and simple scaling will not produce a completely accurate representation of the section. DOM-based measurements provide more accurate strike and dip measurements of stratigraphic layers, leading to more accurate bed thickness measurements than 2-dimensional photomosaic measurements or field measurements. However, all of the digital stratigraphic sections misrepresent lithology due to image distortion and smearing at the grain scale when producing the digital model, so that clear identification of lithology is difficult even when major lithofacies are known based on prior fieldwork. The sensitivity of errors in bed thickness is due to the number and the types of images and video data collected and utilized in the point cloud as well as the processing template chosen. While DOMs can provide access across large outcrops and potentially generate large data sets, understanding sources of error is critical to assess uncertainty in these results and thus the potential for misinterpretation. This assessment requires initial fieldwork with traditional methods to calibrate the DOM’s analysis followed by fieldwork to validate DOM’s results. Thus, if used in conjunction with fieldwork, the digital techniques may be able to substantially improve data collection, serve as a long-term record for continued research, and provide a critical platform for integrating new data sets and research collaborations. / Geology

In previous research, longleaf pine was shown to be spectrally separable from loblolly pine when using high-resolution multispectral data from the WorldView-2 imaging satellite. However, analysis of such high-resolution datasets would be computationally inefficient over a large landscape such as the southeastern United States. Therefore, the objective of this thesis was to approximate the minimum spatial resolution required to separate these two southern pine species. A pan-sharpened, spectrally subset (NIR bands only) WorldView-2 dataset was spatially resampled from 0.46m to 0.5m, 1.0m, 2.0m, 4.0m, 8.0m, and 16.0m. Supervised classification was performed on each of these resampled resolutions. The results of the overall accuracies of these classifications showed that 2.0m is the approximate minimum spatial resolution required to accurately separate these species. Classification accuracy drops between 2.0m and 4.0m as pixel sizes more closely approximate tree crown sizes and spectral variance increases.

Satellite precipitation estimation at high spatial and temporal resolutions is beneficial for research and applications in the areas of weather, flood forecasting, hydrology, and agriculture. In this research, image processing and pattern recognition tools are incorporated into the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) methodology to enhance satellite precipitation and rainfall estimation. The enhanced algorithm incorporates five main steps to derive precipitation estimates: 1) segmenting the satellite infrared cloud images into patches; 2) extracting features from the segmented cloud patches; 3) feature selection or dimensionality reduction; 4) categorizing the cloud patches into separate groups; and 5) obtaining a relationship between the brightness temperature of cloud patches and the rain- rate (T-R) for every cluster. In this study, in addition to the features utilized for cloud patch classification, wavelet and lightning features are also extracted. The lightning feature is defined as the number of flashes occurring within 15 minutes of the nominal IR image scan. Both feature selection and dimensionality reduction techniques are examined to reduce the dimensionality as well as diminish the effects of the redundant and irrelevant features. The feature selection technique includes a Feature Similarity Selection (FSS) method and a Filter-Based Feature Selection using Genetic Algorithm (FFSGA). The Entropy Index (EI) fitness function is used to evaluate the feature subsets. Furthermore, Independent Component Analysis (ICA) was examined and compared to other linear and nonlinear unsupervised dimensionality reduction techniques to reduce the dimensionality and increase the estimation performance. In addition to a Self Organizing Map (SOM) neural network, the link-based cluster ensemble method is also examined in this research. In the final step, the Median Merging (MM) and Selected Curve Fitting (SCF) techniques are incorporated. After applying a Probability Matching Method (PMM) to each single patch and obtaining the T-R for each patch, a Median Merging technique which computes the median rain-rate for a given temperature is applied. A Selected Curve Fitting (SCF) procedure is also used to obtain the T-R for each cluster. The results show that the enhanced algorithm incorporating the above techniques improves precipitation estimation.

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