Geology topics

Victoria G. Stengel

Publications and source records attributed to Victoria G. Stengel.

11 recordsLinked to original sources

USGS2026

Aquatic reflectance derived from Sentinel-2 Multispectral Imager data for inland waters in the conterminous United States

Satellite-based earth observation is a robust tool for tracking change in ecosystems. While terrestrially focused applications of remote sensing have empowered wide adoption for research and management, remote sensing of inland aquatic ecosystems remains comparably nascent. This divergence, in part, stems from the lack of standardized, accessible, and near real-time remotely sensed surface reflectance, atmospherically corrected for aquatic environments. To date, surface reflectance products at national scales and with minimal latency are typically designed exclusively for terrestrial environments. Rectifying this situation can be accomplished by applying aquatic-focused atmospheric correction algorithms independent of those used for terrestrial ecosystems. As a first step to filling this data gap, we present the first national scale, dynamically updated, analysis-ready, aquatic reflectance dataset for inland water derived from Sentinel-2 for the conterminous United States.

conterminous United States↗

USGS2025

Gulf Coast Basin CORE-CM initiative final report

The Bureau of Economic Geology at the University of Texas at Austin (UT-BEG) is leading the Gulf Coast Carbon Ore, Rare Earth, and Critical Minerals (CORE-CM) Initiative to assess the potential to produce critical minerals (CMs), including rare earth elements (REEs) from coal, coal ash, and produced water related to oil and gas production, and related materials (alumina processing waste [red mud], heavy mineral sands, graphite, and zeolite) within the Gulf Coast Basin. This project represents the first phase in a long-term program and provides reconnaissance data that will be foundational for future work by assessing resources and suggesting plans to be conducted in future work and expanding stakeholder engagement. The project includes several tasks designed to identify, characterize, and assess several necessary aspects for development of CMs and REEs in the Gulf Coast Basin.

Gulf Coast basin↗

USGS2024

Hyperspectral (VNIR-SWIR) analysis of roll front uranium host rocks and industrial minerals from Karnes and Live Oak Counties, Texas Coastal Plain

VNIR-SWIR (400–2500 nm) reflectance measurements were made on the surfaces of various cores, cuttings and sample splits of sedimentary rocks from the Tertiary Jackson Group, and Catahoula, Oakville and Goliad Formations. These rocks vary in composition and texture from mudstone and claystone to sandstone and are known host rocks for roll front uranium occurrences in Karnes and Live Oak Counties, Texas. Spectral reflectance profiles, 569 in total, were reduced to 125 representative spectral signatures, which were analyzed using the U.S. Geological Survey's (USGS) Material Identification and Characterization Algorithm (MICA). MICA uses an automated continuum-removal procedure together with a least-squares linear regression to determine the fit of observed sample spectral absorption features to those of reference mineral standards in a spectral library. The reference minerals include various clay, mica, carbonate, ferric and ferrous iron minerals and their mixtures. In addition, absorption feature band-depth analysis was done to identify rock surfaces exhibiting absorption features related to uranium and zeolite minerals, which were not included in the command files used to execute MICA. Rocks from each of the four geologic units produced broadly similar spectral signatures as a result of comparable mineral compositions, but there were some notable differences. For example, Ca- and Na-montmorillonite was matched most frequently to the spectral absorption features in 2-μm (∼2000–2500 nm) wavelengths, while goethite occurred often at 1-μm (∼400–1000 nm) wavelengths. The latter is related to limonitic iron-staining in and around oxidized zones of the uranium roll front as described in previous papers. Rocks of the Jackson Group differed from those of the Catahoula, Oakville and Goliad units in that the former exhibited spectral features we interpret as being due to the presence of lignite-bearing mudstone layers. Goliad rocks exhibit spectral features related to dolomite, gypsum, anhydrite, and an unidentified green clay mineral that is possibly glauconite . Jackson Group rocks also exhibit weak but well-resolved absorption features at 964 and 1157 nm related to either or both zeolite minerals clinoptilolite and heulandite. These zeolite minerals and a few spectra exhibiting hydrous silica absorption features are indicative of alteration of volcanic glass in tuffaceous mudstone and claystone layers. A few sample spectra exhibited strong absorption features at around 1135 nm related to the uranium mineral coffinite. Both the 1135 nm coffinite and 1157 nm zeolite absorption features overlap somewhat, potentially making them difficult to distinguish without additional hyperspectral field, laboratory or remote sensing data. The results of this study were compared to mixtures of minerals described for ore, gangue and alteration minerals in deposit models for sandstone-hosted uranium, sedimentary bentonite and sedimentary zeolite. Use of these spectra can help facilitate mapping of both waste materials from the legacy mining of the above commodities, as well as future exploration and resource assessment activities.

Texas↗

USGS2023

Mapping abandoned uranium mine features using Worldview-3 imagery in portions of Karnes, Atascosa and Live Oak Counties, Texas

Worldview-3 (WV3) 16-band multispectral data were used to map exposed bedrock and mine waste piles associated with legacy open-pit mining of sandstone-hosted roll-front uranium deposits along the South Texas Coastal Plain. We used the “spectral hourglass” approach to extract spectral endmembers representative of these features from the image. This approach first requires calibrating the imagery to reflectance, then masking for vegetation, followed by spatial and spectral data reduction using a principal component analysis-based procedure that reduces noise and identifies homogeneous targets which are “pure” enough to be considered spectral endmembers. In this case, we used a single WV3 image which covered an ~11.5 km by ~19.5 km area of Karnes, Atascosa and Live Oak Counties, underlain by mined rocks from the Jackson Group and Catahoula Formation. Up to 58 spectral endmembers were identified using a further multi-dimensional class segregation method and were used as inputs for spectral angle mapper (SAM) classification. SAM classification resulted in the identification of at least 117 mine- and mine waste-related features, most of which were previously unknown. Class similarity was further evaluated, and the dominant minerals in each class were identified by comparison to spectral libraries and measured samples of actual Jackson Group uranium host rocks. Redundant classes were eliminated, and SAM was run a second time using a reduced set of 23 endmembers, which were found to map these same features as effectively as using the full 58 set of endmembers, but with significantly reduced noise and spectral outliers. Our classification results were validated by evaluating detailed scale mapping of three known mine sites (Esse-Spoonamore, Wright-McCrady and Garbysch-Thane) with published ground truth information about the vegetation cover, extent of erosion and exposure of waste pile materials and/or geologic information about host lithology and mineralization. Despite successful demonstration of the utility of WV3 data for inventorying mine features, additional landscape features such as bare agricultural fields and oil and gas drill pads were also identified. The elimination of such features will require combining the spectral classification maps presented in this study with high-quality topographic data. Also, the spectral endmembers identified during the course of this study could be useful for larger-scale mapping efforts using additional well-calibrated WV3 images beyond the coverage of our initial study area.

Texas↗

USGS2022

A novel method for conducting a geoenvironmental assessment of undiscovered ISR-amenable uranium Resources: Proof-of-concept in the Texas Coastal Plain

A geoenvironmental assessment methodology was developed to estimate waste quantities and disturbances that could be associated with the extraction of undiscovered uranium resources and identify areas on the landscape where uranium and other constituents of potential concern (COPCs) that may co-occur with uranium deposits in this region are likely to persist, if introduced into the environment. Prior to this work, a method was lacking to quantitively assess the environmental aspects associated with potential development of undiscovered uranium resources at a scale of a uranium resource assessment. The mining method of in situ recovery (ISR) was historically used to extract uranium from deposits in the Goliad Sand of the Texas Coastal Plain. For this reason, the study’s methodology projected the following types of wastes and disturbances commonly associated with ISR based on historical ISR mining records: the mine area, affected aquifer volume, mine pore volume, water pumped and disposed during uranium extraction and restoration, and radon emissions. Within the tract permissive for the occurrence of undiscovered uranium resources, maps and statistics of factors were derived that indicate the potential contaminant pathways. The percentage of days meeting the criteria for air stagnation indicate the potential for radon accumulation; the geochemical mobility of COPCs in groundwater in combination with effective recharge indicates the potential for infiltration of surface-derived COPCs; the geochemical mobility of COPCs in groundwater combined with hydraulic conductivity indicates the propensity for transmitting fluids away from contaminated or mined aquifers; and finally, geochemical mobility of COPCs in surface water combined with the factor for climatic erosivity (R factor) indicates the potential for COPCs to persist in surface waters due to runoff. This work resulted in a new methodology that can be applied to any undiscovered mineral resource to better understand possible wastes and disturbances associated with extraction and identify areas on the landscape where COPCs are likely to persist.

Texas↗

USGS2022

A methodology to assess the historical environmental footprint of in-situ recovery (ISR) of uranium: A demonstration in the Goliad Sand in the Texas Coastal Plain, USA

In-situ recovery (ISR) has been the only technique used to extract uranium from sandstone-hosted uranium deposits in the Pliocene Goliad Sand in the Texas Coastal Plain. Water plays a crucial role throughout the ISR lifecycle of production and groundwater restoration yet neither the water use nor other environmental footprints have been well documented. The goal of this study is to examine historical records for all six ISR operations completed in the Goliad Sand to identify and quantify parameters that indicate the surface and aquifer disturbances, water use, and radon emissions. Overall, the average mine area was 0.00023 ± 0.00006 acres per pound (ac/lb) U 3 O 8 . The average mine pore volume was 48.9 ± 50 gal/lb U 3 O 8 with a minimum affected aquifer volume of 0.51 ± 0.08 cubic feet per pound (cu ft/lb) U 3 O 8. An average of 258 ± 40 gallons (gal) of fluid were disposed per pound (lb) U 3 O 8 , with an average of 169 ± 26 gal/lb U 3 O 8 attributed to restoration and 89 ± 36 gal/lb U 3 O 8 attributed to the uranium production phase. The average radon emitted was 1.06 × 10 −3 ± 7.4 × 10 −4 curies per pound (Ci/lb) U 3 O 8 . Goodness-of-fit ( R 2 ) values are ≥0.79 for linear regressions of the amount of uranium produced versus mine area, mine pore volumes, mine aquifer volumes, water pumped, and total water disposed. The R 2 value for radon emitted was 0.68. However, the water disposed only during the uranium production phase is more strongly correlated to the number of production days ( R 2 = 0.96) than to uranium production ( R 2 = 0.84), whereas the volume of water disposed during restoration is more strongly correlated to the “pore volume” ( R 2 = 0.97) than to uranium production ( R 2 = 0.90). Pore volume is an industry term used to describe the amount of fluid circulated through the aquifer during the uranium production period and stipulated in bond agreements in order to satisfy groundwater restoration requirements. Models constructed in this study can be used to estimate probable water use and the extent of surface and aquifer disturbances associated with ISR-amenable undiscovered uranium resources in the Goliad Sand. The historical perspective offered by the data compiled and correlations may prove useful to both industry and regulators.

Texas↗

USGS2022

Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data

Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll- a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon , and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella , a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.

New York, Oregon, Texas↗

USGS2022

Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data

Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end users. First, numerous approaches to estimate aquatic chlorophyll-a are available and can produce inconsistent results. Second, lack of quantitative in situ observations limits opportunities to train models for specific waterbodies, such that models developed for other systems must be used instead. We (1) implement univariate and multivariate logistic regression models to estimate the probability that aquatic chlorophyll-a concentrations exceed an accepted threshold beyond which harmful effects become likely and (2) evaluate the use of visually classified bloom/no-bloom satellite imagery to augment in situ training data. Using a binary classification of aquatic chlorophyll-a exceeding 10 μg / L, we found that (1) logistic regression models were ∼80 % accurate, (2) univariate models trained with visually classified data produce nearly the same accuracy (79%) as models trained with in situ observations (80%), and (3) augmenting in situ chlorophyll-a observations with visual classifications outperformed (82% accuracy) models trained on in situ observations alone (80% accuracy). These results provide a framework for evaluating multiple spectral indices in retrieving algal bloom presence or absence and illustrate that training data derived directly from satellite imagery can be useful in augmenting in situ observations.

Idaho, Oregon↗

USGS2018

Characterization of peak streamflows and flood inundation of selected areas in southeastern Texas and southwestern Louisiana from the August and September 2017 flood resulting from Hurricane Harvey

Hurricane Harvey made landfall near Rockport, Texas, on August 25, 2017, as a Category 4 hurricane with wind gusts exceeding 150 miles per hour. As Harvey moved inland, the forward motion of the storm slowed down and produced tremendous rainfall amounts over southeastern Texas, with 8-day rainfall amounts exceeding 60 inches in some locations, which is about 15 inches more than average annual amounts of rainfall for eastern Texas and the Texas coast. Historic flooding occurred in Texas as a result of the widespread, heavy rainfall; wind and flood damages were estimated to be $125 billion, and the storm resulted in at least 68 direct fatalities. In the immediate aftermath of the Harvey-related flood event, the U.S. Geological Survey (USGS) and the Federal Emergency Management Agency initiated a cooperative study to evaluate the magnitude of the flood, determine the probability of occurrence, and map the extent of the flood in Texas. Seventy-four USGS streamflow-gaging stations in Texas with at least 15 years of record and no large data gaps in the period of record had a 2017 annual peak streamflow related to Harvey ranking in the top five of all annual peaks for each given station. New peaks of record streamflow were recorded at 40 of the 74 USGS streamflow-gaging stations. The number of years of peak streamflow record for the 74 analyzed streamflow-gaging stations ranged from 18 to 105, with a mean number of 55 years. The annual exceedance probability estimates for the analyzed streamflow-gaging stations ranged from less than 0.2 to 14.0 percent. USGS field crews surveyed 2,123 high-water marks to obtain water-surface elevations, in feet above the North American Vertical Datum of 1988. In some locations, several water-surface elevations were averaged to obtain 1 water-surface elevation, resulting in 1,258 water-surface elevations. Some of these high-water marks were used, along with peak-stage data from USGS streamflow-gaging stations, to create 19 inundation maps to document the areal extent of the maximum depth of the flooding. Digital datasets of the inundation area, modeling boundary, water-depth rasters, and final map products are available from the USGS data release associated with this report ( https://doi.org/10.5066/F7VH5N3N ).

Arkansas, Louisiana, Texas↗

USGS2017

The importance of parameterization when simulating the hydrologic response of vegetative land-cover change

Computer models of hydrologic systems are frequently used to investigate the hydrologic response of land-cover change. If the modeling results are used to inform resource-management decisions, then providing robust estimates of uncertainty in the simulated response is an important consideration. Here we examine the importance of parameterization, a necessarily subjective process, on uncertainty estimates of the simulated hydrologic response of land-cover change. Specifically, we applied the soil water assessment tool (SWAT) model to a 1.4 km 2 watershed in southern Texas to investigate the simulated hydrologic response of brush management (the mechanical removal of woody plants), a discrete land-cover change. The watershed was instrumented before and after brush-management activities were undertaken, and estimates of precipitation, streamflow, and evapotranspiration (ET) are available; these data were used to condition and verify the model. The role of parameterization in brush-management simulation was evaluated by constructing two models, one with 12 adjustable parameters (reduced parameterization) and one with 1305 adjustable parameters (full parameterization). Both models were subjected to global sensitivity analysis as well as Monte Carlo and generalized likelihood uncertainty estimation (GLUE) conditioning to identify important model inputs and to estimate uncertainty in several quantities of interest related to brush management. Many realizations from both parameterizations were identified as behavioral in that they reproduce daily mean streamflow acceptably well according to Nash–Sutcliffe model efficiency coefficient, percent bias, and coefficient of determination. However, the total volumetric ET difference resulting from simulated brush management remains highly uncertain after conditioning to daily mean streamflow, indicating that streamflow data alone are not sufficient to inform the model inputs that influence the simulated outcomes of brush management the most. Additionally, the reduced-parameterization model grossly underestimates uncertainty in the total volumetric ET difference compared to the full-parameterization model; total volumetric ET difference is a primary metric for evaluating the outcomes of brush management. The failure of the reduced-parameterization model to provide robust uncertainty estimates demonstrates the importance of parameterization when attempting to quantify uncertainty in land-cover change simulations.

Hydrology and Earth System Sciences↗

USGS2016

Simulation of streamflow and the effects of brush management on water yields in the Double Mountain Fork Brazos River watershed, western Texas 1994–2013

The U.S. Geological Survey, in cooperation with the City of Lubbock and the Texas State Soil and Water Conservation Board, developed and calibrated a Soil and Water Assessment Tool watershed model of the Double Mountain Fork Brazos River watershed in western Texas to simulate monthly mean streamflow and to evaluate the effects of brush management on water yields in the watershed, particularly to Lake Alan Henry, for calendar years 1994–2013. Model simulations were done to quantify the possible change in water yield of individual subbasins in the Double Mountain Fork Brazos River watershed as a result of the replacement of shrubland (brush) with grassland. The simulation results will serve as a tool for resource managers to guide brush-management efforts. The model was calibrated from 1994 through 2008 and validated from 2009 through 2013 with streamflow data collected at the U.S. Geological Survey streamflow-gaging station 08079600 Double Mountain Fork Brazos River at Justiceburg, Texas (hereinafter referred to as the “Justiceburg gage”). Simulated monthly mean streamflow showed agreement with measured monthly mean streamflow for the 1994–2013 study period: the percentage bias was +6, the coefficient of determination was 0.73, and the Nash–Sutcliffe coefficient of model efficiency was 0.71. The calibrated watershed model was used to perform brush-management simulations. The National Land Cover Database 2006, which was the land-cover data used to develop the watershed model, was modified to simulate shrubland replacement with grassland in each of the 35 model subbasins. After replacement of shrubland with grassland in areas with land slope less than 20 percent and excluding riparian areas, the modeled 20-year (1994 through 2013) water yields to Lake Alan Henry increased by 114,000 acre-feet or about 5,700 acre-feet per year. In terms of the increase in water yield per acre of shrubland replaced with grassland, the average annual increase in water yield was 17,300 gallons per acre. Within the modeled subbasins, the increase in average annual water yield ranged from 5,850 to 34,400 gallons per acre of shrubland replaced with grassland. Subbasins downstream from the Justiceburg gage had a higher average annual increase in water yield (21,700 gallons per acre) than subbasins upstream from the streamflow-gaging station (16,800 gallons per acre).

Texas↗