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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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119 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SU-E-J-238: Monitoring Lymph Node Volumes During Radiotherapy Using Semi-Automatic Segmentation of MRI Images

Purpose: Identification and image-based monitoring of lymph nodes growing due to disease, could be an attractive alternative to prophylactic head and neck irradiation. We evaluated the accuracy of the user-interactive Grow Cut algorithm for volumetric segmentation of radiotherapy relevant lymph nodes from MRI taken weekly during radiotherapy. Method: The algorithm employs user drawn strokes in the image to volumetrically segment multiple structures of interest. We used a 3D T2-wturbo spin echo images with an isotropic resolution of 1 mm3 and FOV of 492×492×300 mm3 of head and neck cancer patients who underwent weekly MR imaging during the course of radiotherapy. Various lymph node (LN) levels (N2, N3, N4'5) were individually contoured on the weekly MR images by an expert physician and used as ground truth in our study. The segmentation results were compared with the physician drawn lymph nodes based on DICE similarity score. Results: Three head and neck patients with 6 weekly MR images were evaluated. Two patients had level 2 LN drawn and one patient had level N2, N3 and N4'5 drawn on each MR image. The algorithm took an average of a minute to segment the entire volume (512×512×300 mm3). The algorithm achieved an overall DICE similarity score of 0.78. The time taken for initializing and obtaining the volumetric mask was about 5 mins for cases with only N2 LN and about 15 mins for the case with N2,N3 and N4'5 level nodes. The longer initialization time for the latter case was due to the need for accurate user inputs to separate overlapping portions of the different LN. The standard deviation in segmentation accuracy at different time points was utmost 0.05. Conclusions: Our initial evaluation of the grow cut segmentation shows reasonably accurate and consistent volumetric segmentations of LN with minimal user effort and time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-C-207A-01: A Novel Maximum Likelihood Method for High-Resolution Proton Radiography/proton CT

Purpose: Multiple Coulomb scattering is the largest contributor to blurring in proton imaging. Here we tested a maximum likelihood least squares estimator (MLLSE) to improve the spatial resolution of proton radiography (pRad) and proton computed tomography (pCT). Methods: The object is discretized into voxels and the average relative stopping power through voxel columns defined from the source to the detector pixels is optimized such that it maximizes the likelihood of the proton energy loss. The length spent by individual protons in each column is calculated through an optimized cubic spline estimate. pRad images were first produced using Geant4 simulations. An anthropomorphic head phantom and the Catphan line-pair module for 3-D spatial resolution were studied and resulting images were analyzed. Both parallel and conical beam have been investigated for simulated pRad acquisition. Then, experimental data of a pediatric head phantom (CIRS) were acquired using a recently completed experimental pCT scanner. Specific filters were applied on proton angle and energy loss data to remove proton histories that underwent nuclear interactions. The MTF10% (lp/mm) was used to evaluate and compare spatial resolution. Results: Numerical simulations showed improvement in the pRad spatial resolution for the parallel (2.75 to 6.71 lp/cm) and conical beam (3.08 to 5.83 lp/cm) reconstructed with the MLLSE compared to averaging detector pixel signals. For full tomographic reconstruction, the improved pRad were used as input into a simultaneous algebraic reconstruction algorithm. The Catphan pCT reconstruction based on the MLLSE-enhanced projection showed spatial resolution improvement for the parallel (2.83 to 5.86 lp/cm) and conical beam (3.03 to 5.15 lp/cm). The anthropomorphic head pCT displayed important contrast gains in high-gradient regions. Experimental results also demonstrated significant improvement in spatial resolution of the pediatric head radiography. Conclusion: The proposed MLLSE shows promising potential to increase the spatial resolution (up to 244%) in proton imaging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Optimizing a Knowledge-driven Prospectivity Model for Gold Deposits Within Peräpohja Belt, Northern Finland

This paper combines knowledge- and data-driven prospectivity mapping approaches by using the receiver operating characteristics (ROC) spatial statistical technique to optimize the process of rescaling input datasets and the process of data integration when using a fuzzy logic prospectivity mapping method. The methodology is tested in an active mineral exploration terrain within the Paleoproterozoic Peräpohja Belt (PB) in the Northern Fennoscandian Shield, Finland. The PB comprises a greenschist to amphibolite facies, complexly deformed supracrustal sequence of variable quartzites, mafic volcanic rocks and volcaniclastic rocks, carbonate rocks, black shales, mica schists and graywackes. These formations were deposited on Archean basement and 2.44 Ga layered intrusions, during the multiple rifting of the Archean basement (2.44–1.92 Ga). Younger intrusive units in the PB comprise 2.20–2.13 Ga gabbroic sills or dikes and 1.98 Ga A-type granites. Metamorphism and complex deformation of the PB took place during the Svecofennian orogeny (1.9–1.8 Ga) and were followed by intrusions of post-orogenic granitoids (1.81–1.77 Ga). The recent mineral exploration activities have indicated several gold-bearing mineral occurrences within the PB. The Rompas Au-U mineralization is hosted within deformed and metamorphosed calc-silicate veins enclosed within mafic volcanic rocks and contains uranium-bearing zones without gold and very high-grade (>10,000 g/t Au) gold pockets with uraninite and uraninite-pyrobitumen nodules. In the vicinity of the Rompas, a magnesium skarn hosted disseminated-stockwork gold mineralization was also recognized at the Palokas-Rajapalot prospect. The exploration criteria translated into a fuzzy logic prospectivity model included data derived from regional till geochemistry (Fe, Cu, Co, Ni, Au, Te, K), high-resolution airborne geophysics (magnetic field total intensity, electromagnetic, gamma radiation), ground gravity and regional bedrock map (structures). The current exploration licenses and exploration drilling sites for gold were used to validate the knowledge-driven mineral prospectivity model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Characterizing natural fractures and sub-seismic faults for well completion of Marcellus shale in the MSEEL Consortium project, West Virginia, USA

The Middle Devonian Marcellus shale play has emerged as a major world-class hydrocarbon accumulation and represents one of the largest and most prolific shale plays in the world. According to many outcrop studies in the region, natural fractures are well developed in the Marcellus Shale. However, evaluating fractures in the subsurface is often a significant challenge due to a lack of sufficient data. Therefore, in the Marcellus Shale Energy and Environment Laboratory (MSEEL) consortium project, significant efforts have been made to acquire high-quality image logs in the Marcellus laterals. The project provided tremendous opportunities to characterize the natural fractures and sub-seismic faults and to evaluate their impact on well stimulation. In this study, about 70,000 ft of acquired high-resolution logging while drilling (LWD) acoustic images from five long laterals located in Monongalia County, West Virginia, were processed and interpreted. In addition, the study used high-quality micro-resistivity images from a pilot well, allowing the evaluation of natural fractures in the entire Marcellus vertical sequence. Based on the available acoustic images, the natural fractures were classified into three basic categories: high-amplitude fractures, low-amplitude fractures, and faults. Further, larger open fractures can also be determined when a low-amplitude fracture is evident on caliper images. The fractures in the Marcellus usually have a medium to high angle dip; however, multiple fracture sets in terms of strike orientation were clearly observed in all the laterals. The fracture set with a strike at NE-SW (or 60-240 deg) seems to be the predominant one in all the wells. A few other sets, including those with N-S, NWW-SEE, and E-W strikes, were also observed. Several sub-seismic faults, with mostly a low dip angle and a NE-SW strike, have also been seen in two of the laterals. The fracture density is variable across all the laterals, ranging from very low (or none) to very high (up to 5 fractures per ft). The average fracture density for all the laterals is about 1 fracture per 10 ft. In the vertical sequence, the natural fracture development showed a clear preference for shale or shaly facies over carbonate-rich or thin limestone layers. The interpreted fracture and fault data were used as input data for the stimulation design with the purpose of better understanding the fractures’ impact on well stimulation. Production data from the laterals were also used to evaluate the natural fractures’ influence on well performance. The quality image database and the consistent interpretation results for the entire project enabled a systematic approach to characterizing fractures and, more importantly, to evaluating the impact of fractures on well stimulation and production.

03 NATURAL GAS↗
DOE OSTI2026

Forest aboveground biomass estimation through integration of sentinel-2 and PALSAR-2 time series: assessing models trained on GEDI and field inventory benchmarks

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluating the potential for bird‐habitat models to support biodiversity‐friendly urban planning

Abstract Urban expansion poses a major threat to wildlife populations. Biodiversity‐friendly urban landscapes could deliver benefits for both wildlife and people, by incorporating conservation and ecosystem services objectives. Well‐designed urban developments could also soften the ecological impacts of urbanization. However, delivering urban landscapes that integrate biodiversity requirements effectively remains challenging. Ecological models, designed to predict wildlife population responses to alternative urban designs, could prove effective in supporting the creation of biodiversity‐friendly urban landscapes. Here, we combine national‐scale bird abundance data with high resolution, spatially explicit habitat data to characterize relationships between bird densities and urban landscape form in Britain. From these analyses and cross validation, we evaluate the potential for well‐parameterized, species‐specific models to be used to predict bird densities in novel or modified urban areas. Our analyses indicate that responses of bird abundance to urban habitat are species‐specific and complex, with few variables consistently affecting a large proportion of species. However, contiguous areas of greenspace within urban sites are preferential for accommodating breeding birds, compared to a more fragmented arrangement of multiple, small greenspace patches. In combination, the bird‐habitat relationships identified could successfully predict observed variation in abundance for most bird species considered. Further evaluation of habitat descriptor variables, spatial scales of species' habitat use and analytical modelling approaches may be needed to improve the predictive ability of bird‐habitat models for certain species, particularly waterbirds and those observed less frequently in urban areas. Synthesis and applications . We modelled breeding bird abundance in built‐up areas with respect to the characteristics and contexts of urban environments. While most variables were important for multiple species, responses overall were species‐specific, so simple assemblage metrics, like diversity, will not describe the variation in bird communities well. However, the results illustrate the potential of an evidence‐based, spatially explicit evaluation of urban development impacts on biodiversity, by predicting the consequences for bird numbers. Subject to verification of predictive ability, practitioners can apply the models to compare, for example, land‐sparing and sharing within developments, or to quantify the biodiversity requirements for effective offsetting. This would be facilitated by incorporation into an online tool allowing user‐determined input scenarios.

Plummer, Kate E.↗
DOE OSTI2014

Utilization of the St. Peter Sandstone in the Illinois Basin for CO2 Sequestration

This project is part of a larger project co-funded by the United States Department of Energy (US DOE) under cooperative agreement DE-FE0002068 from 12/08/2009 through 9/31/2014. The study is to evaluate the potential of formations within the Cambro-Ordovician strata above the Mt. Simon Sandstone as potential targets for carbon dioxide (CO2) sequestration in the Illinois and Michigan Basins. This report evaluates the potential injectivity of the Ordovician St. Peter Sandstone. The evaluation of this formation was accomplished using wireline data, core data, pressure data, and seismic data acquired through funding in this project as well as existing data from two additional, separately funded projects: the US DOE funded Illinois Basin – Decatur Project (IBDP) being conducted by the Midwest Geological Sequestration Consortium (MGSC) in Macon County, Illinois, and the Illinois Industrial Carbon Capture and Sequestration (ICCS) Project funded through the American Recovery and Reinvestment Act (ARRA), which received a phase two award from DOE. This study addresses the question of whether or not the St. Peter Sandstone may serve as a suitable target for CO2 sequestration at locations within the Illinois Basin where it lies at greater depths (below the underground source of drinking water (USDW)) than at the IBDP site. The work performed included numerous improvements to the existing St. Peter reservoir model created in 2010. Model size and spatial resolution were increased resulting in a 3 fold increase in the number of model cells. Seismic data was utilized to inform spatial porosity distribution and an extensive core database was used to develop porosity-permeability relationships. The analysis involved a Base Model representative of the St. Peter at “in-situ” conditions, followed by the creation of two hypothetical models at in-situ + 1,000 feet (ft.) (300 m) and in-situ + 2,000 ft. (600 m) depths through systematic depthdependent adjustment of the Base Model parameters. Properties for the depth shifted models were based on porosity versus depth relationship extracted from the core database followed by application of the porosity-permeability relationship. Each of the three resulting models were used as input to dynamic simulations with the single well injection target of 3.2 million tons per annum (MTPA) for 30 years using an appropriate fracture gradient based bottom hole pressure limit for each injection level. Modeling results are presented in terms of well bottomhole pressure (BHP), injection rate profiles, and three-dimensional (3D) saturation and differential pressure volumes at selected simulation times. Results suggest that the target CO2 injection rate of 3.2 MTPA may be achieved in the St. Peter Sandstone at in-situ conditions and at the in-situ +1,000 ft. (300 m) depth using a single injector well. In the latter case the target injection rate is achieved after a ramp up period which is caused by multi-phase flow effects and thus subject to increased modeling uncertainty. Results confirm that the target rate may not be achieved at the in-situ +2,000 ft. (600 m) level even with multiple wells. These new modeling results for the in-situ case are more optimistic than previous modeling results. This difference is attributed to the difference in methods and data used to develop model permeability distributions. Recommendations for further work include restriction of modeling activity to the in-situ +1,000 ft. (300 m) and shallower depth interval, sensitivity and uncertainty analysis, and refinement of porosity and permeability estimates through depth and area selective querying of the available core database. It is also suggested that further modeling efforts include scope for evaluating project performance in terms of metrics directly related to the Environmental Protection Agency (EPA) Class VI permit requirements for the area of review (AoR) definition and post injection site closure monitoring.

Will, Robert↗
DOE OSTI2017

High-Resolution Characterization of Comingled Contaminants in the Deep Vadose Zone, Hanford Site, Washington - 17387

The contaminated deep vadose zone in the Central Plateau of the Hanford Site in Washington state presents a unique challenge to remediation. The Comprehensive Environmental Response, Compensation, and Liability Act, commonly known as the CERCLA project responsible for addressing these deep contaminants is designated as the 200-Deep Vadose Zone Operable Unit (DV-1 project). The vadose zone is 55-82 meters (180-270 feet) thick and is contaminated with both radiological and chemical contaminants from multiple historical waste streams. The mobile contaminants from this waste are still present in the deep vadose zone and pose a potential threat to the groundwater. Characterizing the nature and extent of the comingled mobile contaminants is an important step before assessing possible remedial alternatives. One factor controlling the migration and distribution of subsurface contaminants is the lithologic heterogeneity of the vadose zone sediments. A sampling plan for the DV-1 project includes continuous sediment core collection and analysis to identify key lithologic factors (i.e., small-scale bedding and structural features) that act as barriers, preferential pathways or conduits for mobile contaminants migrating through the vadose zone. Over six hundred meters (2,000 feet) of intact sediment core from 26 boreholes were collected in 1.5 meters (5 feet) long, 10 centimeter (4-inch) diameter segments. These intact sediment samples provide significant improvement in the level of detail and understanding of mechanisms and features controlling contaminant distribution that is not possible with traditional homogenized drill cuttings or conventional split-spoon samples. Detailed geological descriptions and photographs of the intact core are correlated to high-resolution geophysical logs which aid in the development of a comprehensive contaminant distribution profile of the borehole. These lithologic interpretations are used to define specific depth intervals for sampling based on changes in moisture, lithology, and elevated gamma radiation. The selected samples are analyzed for contaminants of concern, geochemical parameters, and hydraulic properties. Significant lithologic features observed in the intact cores include sharp contact boundaries to graded bedding, iron-oxide and carbonate cementation, silty laminations, clastic dike intrusions, and soft sediment deformation. Preliminary geologic logs integrated with the sample analysis indicate features that correlate to the variable distribution of contaminants both laterally and vertically. Integration of measured hydraulic parameters to lithologic units in individual borehole profiles provide key inputs for more precise geo-framework and vadose zone flux models supporting the DV-1 project. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Active Interrogation using Photofission Technique for Nuclear Materials Control and Accountability

Innovative systems with increased sensitivity and resolution are in great demand to detect diversion and to prevent misuse in support of nuclear materials management for the U.S. fuel cycle. Nuclear fission is the most important multiplicative process involved in non-destructive active interrogation. This process produces the most easily recognizable signature for nuclear materials. In addition to thermal or high-energy neutrons, high-energy gamma rays can also excite a nucleus and cause fission through a process known as photofission. Electron linear accelerators (linacs) are widely used as the interrogating photon sources for inspection methods involving photofission technique. After photofission reactions, prompt signals are much stronger than the delayed signals, but it is difficult to quantify them in practical measurements. Delayed signals are easily distinguishable from the interrogating radiation. Linac-based, advanced inspection techniques utilizing the delayed signals after photofission have been extensively studied for homeland security applications. Previous research also showed that a unique delayed gamma ray energy spectrum exists for each fissionable isotope. In this work, high-energy delayed γ-rays were demonstrated to be signatures for detection, identification, and quantification of special nuclear materials. Such γ-rays were measured in between linac pulses using independent data acquisition systems. A list-mode system was developed to measure low-energy delayed γ-rays after irradiation. Photofission product yields of 238 U and 239 Pu were determined based on the measured delayed γ-ray spectra. The differential yields of delayed γ-rays were also proven to be able to discriminate nuclear from non-nuclear materials. The measurement outcomes were compared with Monte Carlo simulation results. It was demonstrated that the current available codes have capabilities and limitations in the simulation of photofission process. A two-fold approach was used to address the high-rate challenge in used nuclear fuel assay based on photofission technique. First, a standard HPGe preamplifier was modified to improve its capabilities in high-rate pulsed photofission environment. Second, advanced pulse processing algorithms were shown to greatly improve throughput rate without large sacrifice in energy resolution at ultra-high input count rate. Two customized gamma spectroscopy systems were also developed in real-time on FPGAs. They were shown to have promising performance matching available commercial units.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500

Anthropogenic increases in atmospheric greenhouse gas concentrations are the main driver of current and future climate change. The integrated assessment community has quantified anthropogenic emissions for the shared socio-economic pathway (SSP) scenarios, each of which represents a different future socio-economic projection and political environment. Here, we provide the greenhouse gas concentrations for these SSP scenarios using the reduced-complexity climate–carbon-cycle model MAGICC7.0. We extend historical, observationally based concentration data with SSP concentration projections from 2015 to 2500 for 43 greenhouse gases with monthly and latitudinal resolution. CO 2 concentrations by 2100 range from 393 to 1135 ppm for the lowest (SSP1-1.9) and highest (SSP5-8.5) emission scenarios, respectively. We also provide the concentration extensions beyond 2100 based on assumptions regarding the trajectories of fossil fuels and land use change emissions, net negative emissions, and the fraction of non-CO 2 emissions. By 2150, CO 2 concentrations in the lowest emission scenario are approximately 350 ppm and approximately plateau at that level until 2500, whereas the highest fossil-fuel-driven scenario projects CO 2 concentrations of 1737 ppm and reaches concentrations beyond 2000 ppm by 2250. We estimate that the share of CO 2 in the total radiative forcing contribution of all considered 43 long-lived greenhouse gases increases from 66% for the present day to roughly 68% to 85% by the time of maximum forcing in the 21st century. For this estimation, we updated simple radiative forcing parameterizations that reflect the Oslo Line-By-Line model results. In comparison to the representative concentration pathways (RCPs), the five main SSPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are more evenly spaced and extend to lower 2100 radiative forcing and temperatures. Performing two pairs of six-member historical ensembles with CESM1.2.2, we estimate the effect on surface air temperatures of applying latitudinally and seasonally resolved GHG concentrations. We find that the ensemble differences in the March–April–May (MAM) season provide a regional warming in higher northern latitudes of up to 0.4 K over the historical period, latitudinally averaged of about 0.1 K, which we estimate to be comparable to the upper bound (~5 % level) of natural variability. In comparison to the comparatively straight line of the last 2000 years, the greenhouse gas concentrations since the onset of the industrial period and this studies' projections over the next 100 to 500 years unequivocally depict a “hockey-stick” upwards shape. The SSP concentration time series derived in this study provide a harmonized set of input assumptions for long-term climate science analysis; they also provide an indication of the wide set of futures that societal developments and policy implementations can lead to – ranging from multiple degrees of future warming on the one side to approximately 1.5°C warming on the other.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Quantifying Rates of Erosion Using Historical Aerial Photos - 19478

Erosion can play an important role in the fate and transport of waste at sites where disposal of long-lived waste is anticipated. The statistical characterization of key processes related to erosion is essential to the understanding of site stability through time. In this work multi-temporal historical aerial images are analyzed in conjunction with orthophotography and Lidar data to develop probability distributions for variables representing important erosion processes. Scans of photographic negatives were obtained for multiple photographs and several negatives were scanned with a calibrated photogrammetric scanner by Geomni (formerly Blue Skies Consulting) in Belen, New Mexico. Other negatives were provided by USDA or scanned on a high-resolution, large format media scanner in the United States National Archives Cartographic Research Room, College Park, Maryland (1939). An additional set of aerial photographs was obtained from the USGS EROS Data Center web site. For other years, photographic prints were available for aerial photography. Several prints were scanned with a large-format media scanner at 1200 dpi. Ground control points for selected digital images used in the stereoscopic analyses were identified in ArcMap. Ground control points were collected by matching features which could be identified in the aerial photography as well as the orthophotography or shaded relief imagery from the Lidar dataset. Such features included fence line intersections, road intersections, railroad bridges, and structures, depending on the age of the photography. Many old fence lines and abandoned roads remained visible in the shaded relief imagery while they were completely obscured by tree canopy in modern photography. Ground control elevations were sampled from the Lidar-based DEM or directly from point clouds. The image coordinates and matching three-dimensional projected coordinates of the ground control points were exported to IDL where parameters for a three-dimensional direct linear transformation (DLT) were obtained. Built-in singular value decomposition routines were used to solve an overdetermined set of equations for the eleven parameters of the DLT for a particular historical photograph. A solution to the DLT parameters was obtained for each historical aerial photograph in which features of interest were identified using the stereoscope. Ultimately, this process resulted in the identification of locations for features like gully heads using both the Lidar dataset and projection of the estimated location from the historical aerial photo under consideration back to the Lidar dataset. The distance between the estimate of the location from the historical image and that of the Lidar dataset was the estimated distance the feature has moved. This distance was then divided by the number of years between the Lidar dataset and the year of the historical aerial image of interest to get a rate of movement through time. This was done for several dozen points on each historical aerial image. There was inevitable uncertainty associated with the estimation of points from the historical aerial imagery. This includes estimation of changes in locations of features like gully heads through time. In this work a method to estimate the uncertainty associated with measurements of locations of features using the stereoscopic analysis is developed and applied. For features with locations in space that do not change (e.g., road intersections, etc.) the difference in measurements of locations from the Lidar dataset and that of the estimate from the historical aerial image provides an observation of the measurement error. These points used to estimate the measurement error are referred to as control points. For each of the historical aerial images, a different measurement error distribution is estimated using control points. These measurement errors are combined with the estimated distances from the stereoscopic analyses to produce distributions of gully retreat rates and hillslope erosion rates used along with output from Erosion Working Group (EWG) Landscape Evolution Models (LEMs) to provide statistical probability distributions for input into the PPA Model. Specifically, gully head retreat rate and gully widening rate were characterized using statistical probability distributions. The central limit theorem was then applied to develop rates that represent long-term averages that are used to inform a PPA Model built in GoldSim. The importance of these rates with respect to characterization of future risk can then assessed using sensitivity analyses. This provides an approach to determine if subsequent reduction of the uncertainty associated with the probability distributions of erosion rates will correspond to meaningful reduction in the estimation of future risk from the site. Ultimately this is a general method that utilizes historical aerial imagery to estimate rates of erosion with uncertainty. This information can be used to inform PPA models to project future risks from a given site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗