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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Temporal multispectral and 3D analysis of Cerro de Pasco, Peru

Mining operations across the world often lead to contamination of land, water resources, ecosystems and in some cases, entire communities.Results of recent health and ground sampling studies revealed extensive lead contamination within the populace and around the City of Cerro de Pasco, Peru. Tailings excavated from a large open pit zinc mine in the center of the city have been aggregated in four large stockpiles within close proximity to neighborhoods, schools, and hospitals. Visual comparison of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery from 2001 and Sentinel-2 imagery from 2018 suggests a size increase in one tailing stockpile in particular near the neighborhood of Paragsha. Due to ongoing mining efforts, the hypothesis motivating the work presented here is that Pb-bearing minerals would be detectable through multispectral analysis, an increase in Pb mineral percent abundance would be observed and tailing stockpile volume would be detectable between 2001 and 2016. This hypothesis is tested using Spectral Angle Mapper (SAM), Adaptive Coherence Estimator (ACE), and Jeffries-Matusita distance calculation on ASTER (2001) and Sentinel-2 (2018) VNIR and SWIR bands. Volume and area estimate of tailing stockpiles were calculated using a photogrammetrically derived point cloud. SAM detected the presence of five Pb-bearing minerals around Cerro de Pasco and Paragsha. The results of the temporal SAM analysis displayed an increase of approximately 17% of Pb-bearing minerals around the greater Cerro de Pasco city area and approximately 11% for the neighborhood of Paragsha. Jeffries-Matusita distance results suggest clear correlation between contamination sources and affected locations. Total tailing stockpile volume was measured to be approximately 200,300,000 m 3 . Volume for Pile 4 was estimated to have increased by approximately 46,000,000 m 3 between 2001 and 2018. Lastly, these presented results will hopefully inspire and guide future remote sensing campaigns, perhaps involving a UAV or aircraft-based hyperspectral instrument.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

High-Precision Characterization of Seismicity from the 2022 Hunga Tonga-Hunga Ha'apai Volcanic Eruption

The earthquake swarm accompanying the January 2022 Hunga Tonga-Hunga Ha'apai (HTHH) volcanic eruption includes a large number of posteruptive moderate-magnitude seismic events and presents a unique opportunity to use remote monitoring methods to characterize and compare seismic activity with other historical caldera-forming eruptions. We compute improved epicentroid locations, magnitudes, and regional moment tensors of seismic events from this earthquake swarm using regional to teleseismic surface-wave cross correlation and waveform modeling. Precise relative locations of 91 seismic events derived from 59,047 intermediate-period Rayleigh- and Love-wave cross-correlation measurements collapse into a small area surrounding the volcano and exhibit a southeastern time-dependent migration. Regional moment tensors and observed waveforms indicate that these events have a similar mechanism and exhibit a strong positive compensated linear vector dipole component. Precise relative magnitudes agree with regional moment tensor moment magnitude ($M_w$) estimates while also showing that event sizes and frequency increase during the days after the eruption followed by a period of several weeks of less frequent seismicity of a similar size. The combined information from visual observation and early geologic models indicate that the observed seismicity may be the result of a complex series of events that occurred after the explosive eruption on 15 January, possibly involving rapid resupply of the magma chamber shortly after the eruption and additional faulting and instability in the following weeks. In addition, we identify and characterize an $M_w$ 4.5 event five days before the paroxysmal explosion on 15 January, indicating that additional seismic events preceding the main eruption could have been identified with improved local monitoring. As a result, our analysis of the HTHH eruption sequence demonstrates the value of potentially utilizing teleseismic surface-wave cross correlation and waveform modeling methods to assist in the detailed analysis of remote volcanic eruption sequences.

58 GEOSCIENCES↗
DOE OSTI2022

A Lagrangian analysis of pockets of open cells over the southeastern Pacific

Abstract. Pockets of open cells (POCs) have been shown to develop within closed-cell stratocumulus (StCu), and a large body of evidence suggests that the development of POCs result from changes in small-scale processes internal to the boundary layer rather than large-scale forcings. Precipitation is widely viewed as a key process important to POC development and maintenance. In this study, GOES-16 satellite observations are used in conjunction with MERRA-2 winds to track and compare the microphysical and environmental evolution of two populations of closed-cell StCu selected by visual inspection over the southeastern Pacific Ocean: one group that transitions to POCs and another comparison group (CLOSED) that does not. The high spatiotemporal resolution of the new GOES-16 data allows for a detailed examination of the temporal evolution of POCs in this region. We find that POCs tend to develop near the coast, last tens of hours, are larger than 104 km2, and often (88 % of cases) do not re-close before they exit the StCu deck. Most POCs are observed to form at night and tend to exit the StCu during the day when the StCu is contracting in area. Relative to the CLOSED trajectories, POCs have systematically larger effective radii, lower cloud drop number concentrations, a comparable conditional in-cloud liquid water path, and a higher frequency of more intense precipitation. Meanwhile, no systematic environmental differences other than boundary layer height are observed between POC and CLOSED trajectories. Interestingly, there are no differences in reanalysis aerosol optical depth between both sets of trajectories, which may lead one to the interpretation that differences in aerosol concentrations are not influencing POC development or resulting in a large number that re-close. However, this largely depends on the reanalysis treatment of aerosol–cloud interactions, and the product used in this study has no explicit handling of these important processes. These results support the consensus view regarding the importance of precipitation on the formation and maintenance of POCs and demonstrate the utility of modern geostationary remote sensing data in evaluating the POC life cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

CROCUS Sodar Measurements of Lower Atmospheric Wind Profiles at Argonne Testbed for Multiscale Observational Science (ATMOS) Site

The Scintec MFAS Sodar (Multiple-Frequency Acoustic Sounder) is an autonomous, ground-based acoustic remote sensing system designed to measure vertical profiles of horizontal wind speed, wind direction, and vertical velocity in the lower atmosphere. The instrument transmits sequences of acoustic pulses and detects the Doppler-shifted sound waves backscattered by small-scale temperature and velocity fluctuations caused by atmospheric turbulence. From these Doppler shifts, the system derives three-dimensional wind vectors by combining radial velocities from multiple beam orientations.The MFAS Sodar operates with a first usable range gate beginning at approximately 30 m above ground level and a configurable vertical resolution of 10 m. Under favorable acoustic conditions, the system provides wind profiles extending up to 600 m above ground level. Measurements are processed into 15-minute averaged profiles containing wind speed, direction, vertical velocity, and diagnostic quantities such as signal-to-noise ratio and echo strength.This dataset was collected at the Argonne Testbed for Multiscale Observational Science (ATMOS) facility in Lemont, Illinois, as part of DOE's CROCUS Urban Integrated Field Laboratory (UIFL) initiative. The purpose of these observations is to characterize the vertical wind structure and boundary-layer evolution across the urban–suburban gradient of the greater Chicago region. In particular, these data are intended to improve understanding of how local meteorology, such as lake-breeze penetration, nocturnal jets, and daytime mixing, varies between the densely built urban core and the suburban periphery. The MFAS observations provide critical context for evaluating high-resolution model simulations and for integrating with complementary lidar, radar, and in-situ meteorological measurements within the broader CROCUS UIFL network.All data are archived in NetCDF (Network Common Data Form) format and include wind and diagnostic parameters. The files can be accessed and analyzed using standard software that supports NetCDF, such as Python (e.g., xarray, netCDF4), MATLAB, R (e.g., ncdf4, raster), or Panoply (NASA’s NetCDF visualization application).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

R and DE Robotic Sensor Intern Team: Remote 3D Mapping Sensor Suite

The primary goal of the sensor suite is to provide an internal 3D map of the HCAEX tunnel. When looking for a sensor that would provide XYZ point cloud data for mapping the decision was between a stereo camera, a 3D Lidar, or a 2D Lidar that would need to be spun around. The issues with the stereo camera were that it does not have a large field-of-view and that its depth information was not nearly as accurate as even the baseline Lidars. The selection between Lidars is detailed. The Hokuyo UTM-30LX-EW shown provides a 270 deg. FOV with high accuracy and resolution while the largest FOV on a 3D Lidar is 40 deg.. This was the reason a 2D Lidar was chosen over the 3D Lidar. Even though the 2D Lidar requires additional components and software to generate a 3D map, its FOV advantage is too great given that the HCAEX tunnel is a relatively confined space. The rotating planar scan from the 2D Lidar can be stitched into a 3D scan as shown in the graphic below. Along with XYZ data, the Lidar provides return intensity data in color which can be used to distinguish between different materials within the tunnel. To provide visual feedback a camera system needed to be identified. The common solution for this is a high resolution camera with a small FOV mounted on a pan-tilt system. But, given the critical nature of the visual feedback we looked into solutions without the need for moving parts. Two panospheric cameras give a FOV large enough to get a full 360 deg. view.. Camera color data (RGB) can also be combined with the 3D mapping data from the Lidar and overlaid on the map to give the user the ability to see inside the entire length of the tunnel as if they were inside. Situational awareness is required for the crawler in the tunnel in order for it to be able to register the 3D scans to create a final map. For this we procured a Microstrain 3DM-GX5-25 Inertial Measurement Unit (IMU). To perform the Lidar rotation we have two servos driving the pan and tilt. The servo controls and most data processing will be performed via an onboard computer. The sensor components are to be packaged together using 3D printed mounts into a standalone unit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Evaluating the state-of-the-art in remote volcanic eruption characterization Part II: Ulawun volcano, Papua New Guinea

Retrospective eruption characterization is valuable for advancing our understanding of volcanic systems and evaluating our observational capabilities, especially with remote technologies (defined here as a space-borne system or non-local, ground-based instrumentation which include regional and remote infrasound sensors). In June 2019, the open-system Ulawun volcano, Papua New Guinea, produced a VEI 4 eruption. We combined data from satellites (including Sentinel-2, TROPOMI, MODIS, Himawari-8), the International Monitoring System infrasound network, and GLD360 globally detected lightning with information from the local authorities and social media to characterize the pre-, syn- and post-eruptive behaviour. The Rabaul Volcano Observatory recorded ~24 h of seismicity and detected SO 2 emissions ~16 h before the visually-documented start of the Plinian phase on 26 June at 04:20 UTC. Infrasound and SO2 detections suggest the eruption started during the night on 24 June 2019 at 10:39 UTC ~38 h before ash detections with a gas-dominated jetting phase. Local reports and infrasound detections show that the second phase of the eruption started on 25 June 19:28 UTC with ~6 h of jetting. The first detected lightning occurred on 26 June 00:14 UTC, and ash emissions were first detected by Himawari-8 at 01:00 UTC. Post-eruptive satellite imagery indicates new flow deposits to the south and north of the edifice and ash fall to the west and southwest. In particular, regional infrasound data provided novel insight into eruption onset and syn-eruptive changes in intensity. We conclude that, while remote observations are sufficient for detection and tracking of syn-eruptive changes, key challenges in data latency, acquisition, and synthesis must be addressed to improve future near-real-time characterization of eruptions at minimally-monitored or unmonitored volcanoes.

58 GEOSCIENCES↗
DOE OSTI2025

Ionospheric Disturbances in GNSS TEC Data: SpaceX Falcon 9 Deorbit Maneuvers Over CONUS in April–May 2024

Traveling ionospheric disturbances (TIDs) driven by a large number of internal and external sources are detectable with dense networks of ground‐based Global Navigation Satellite System (GNSS) receivers' measurements of total electron content (TEC). We present the newly developed System for Rapid Analysis of Ionospheric Dynamics (S‐RAID), providing data and visual GNSS TEC products of TIDs of periods ~2–120 min and horizontal resolution up to tens of kilometers for years 2017–2024. The S‐RAID data reveal myriad natural and anthropogenic TIDs from meteorology and space weather, and from human spaceflight activities. In this report, we focus on new prominent disturbances found during SpaceX second stage deorbit maneuvers in April–May 2024. Signatures include waves emanating from the Falcon's trajectory above California and depletions following its deorbit and passage over Arizona. These findings suggest further opportunities to detect and quantify small‐scale events in the ionosphere as well as to understand the responses of the atmosphere‐ionosphere system to known inputs.

58 GEOSCIENCES↗
DOE OSTI2020

Double-Shell Tank Primary Bottom Visual Inspection Results - 20025

A pressing need for the Double-Shell Tank Integrity Program at Hanford is of the ability to inspect the bottom of the tanks. Though the DSTs' annulus spaces (the space between the internal and external tank walls) have been inspected visually since 1992 and with Ultrasonic Testing (UT) since 1996, inspections of the tank bottoms have been able to be conducted due to limitations of available technology, omitting about 40 percent of the tank surface below the maximum fill level from routine inspection. Visual inspections of the tank annulus have been conducted up to five times in some DSTs and portions of the annulus space for all of the DSTs has been inspected twice with UT. This region includes the walls of the primary tank and secondary liner along with accessible portions of the secondary liner bottom. Due to this lack of technology, the degradation of the primary tank bottom in Tank AY-102 was undetected until the tank leaked into its annulus space, confirmed in 2012. Since that time, WRPS has been working with the nondestructive examination community and the Pacific Northwest National Laboratory, to evaluate potential technologies to conduct these challenging remote inspections. Recent work has included the development, testing, and deployment of several remote visual inspection technologies to observe the condition of the primary tank bottom of the DSTs by leveraging air distribution slots in the refractory pad on which it rests. Robotic solutions to conduct this inspection were developed with a detailed understanding of the physical configuration and operational logistics. Understanding and subsequent communication of these details to vendors within the robotics and nondestructive examination community was a critical component to the successful production of these new tools. Throughout the FY16-FY18 years, vendors from nondestructive examination community were solicited for ideas to meet the tank bottom inspection challenge. Two tank bottom visual inspection solutions were then pursued to meet that challenge. Both tools were successfully deployed at Tank AP-107, and provided valuable visuals, never before obtained from the under tank environment of a double-shell tank. While there are minor opportunities for improvement identified with each system, general outcomes were overwhelmingly positive. Both systems were successfully able to navigate the refractory air slot pattern from the outer edge to the tank center and record video footage. The micro-crawler provided high quality and very stable footage through the air slot pattern, but took longer to deploy into the slots and traverse through them than the alternative system. The push-pull tethered camera was able to navigate all seven of its target air slots in a matter of a few hours, which included deployment, movement between slot entrances, and complete removal of the system from the annulus environment. Through these air slots, new information was gained about the tank bottom and refractory pad condition of tank AP-107, AP-106, AP-108, and AN-102. While some debris was noted in the air slots, including sand, chipped refractory, and construction materials, the refractory was found to be structurally stable and adequately supporting the primary tank bottom through the full diameter of the tank. Primary tank bottom steel exterior was found to be in good condition, free of ongoing or aggressive corrosion conditions. Looking toward the future, robotic access to and visual inspection of refractory air slots and the primary tank bottom steel has now proven viable. Incorporating periodic air slot inspections as a regular practice within the Double-Shell Tank Integrity Program is the next step, allowing trending of any changes in condition. As ultrasonic testing infrastructure was leveraged to complete the initial system deployments described in this report, it makes sense to adopt that same strategy for ongoing periodic examinations and deploy these primary tank bottom inspection tools alongside ultrasonic testing operations. Development of visual inspection tools to evaluate the primary tank bottom region of the double-shell tanks was planned as the first stage of inspection. Continued development of volumetric sensors and incorporation of these inspection tools into Hanford's ongoing Double-Shell Tank Integrity Program are the next steps, seeking to continuously expand understanding of asset integrity and remaining useful life. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2018

Engineering Scale Testing of Robotic Inspection Tools for Double Shell Tanks at Hanford - 18349

Waste found in the annulus of the AY-102 double-shell tank (DST) at Hanford has prompted the need for the development of inspection tools that are able to access confined spaces around and underneath the tanks. During the last year, Florida International University (FIU) has been developing inspection tools that are capable of accessing the tank floors through the air supply line, the air refractory channels and the drain lines. This effort has led to the development of a pneumatic pipe crawler and a miniature rover. In this paper, updates to the inspection tool designs and the full scale testing of both systems are presented. The tools provide visual feedback for the operator, carrying with them onboard cameras and light sources. The images are streamed back live via a tether, which also provides power to the devices. To increase the sensing capabilities of both tools, temperature, humidity and radiation sensor modules are being integrated onto the developed systems, and can be interchanged depending on the inspection objectives. The miniature rover measures 7 cm in length and 3.8 cm in width, and travels upside down on the bottom of the tank to avoid debris accumulated in the channels. The video is also being processed real-time to provide control of the unit semi-autonomously. The pipe crawler measures approximately 76 cm in length and 6.4 cm in diameter, and travels using pneumatic actuators that emulate the peristaltic movement contractions typical of worm type robots. A full-scale sectional mock-up of the DST foundation, refractory system and annulus is currently being developed as a test-bed for the testing of the inspection tools. The intention of the test-bed is to demonstrate the viability of using both inspection tools in configurations similar to the actual tank. The test-bed includes a 2.43 m wide section of both tank liners, refractory channels and concrete foundation with drain slots. The test-bed is designed to be modular so that various plate thicknesses can be easily utilized and different refractory configurations can be incorporated. Currently, the refractory channels are of the tree type structure with turns that are approximately 30 degrees. Both inspection tools were demonstrated in the mock-up facility. The miniature rover ran successfully through the drain slots from the annulus to central plenum and had no issues making the 90-degree turns. The peristaltic crawler was able to navigate through the 7.62 cm air supply line from the annulus to the central plenum. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Autonomous 3D and Radiation Mapping in Tunnel Environments Using Aerial Robots - 18156

In this paper we present the algorithms and systems developed to enable autonomous 3D and radiation mapping, as well as radiological source localization in tunnel environments using aerial robots. The developed technologies aim to address the need of autonomous robotic inspection of nuclear sites such as facilities in the process of being decommissioned. As a motivational example, the PUREX tunnels at the Hanford Site are considered and the robot is designed to be able to conduct multi-modal 3D/radiation characterization of the tunnel environment without any prior knowledge or map provided beforehand. In order to enable the necessary GPS-denied simultaneous localization and mapping in dark, visually-degraded tunnel environments, a multi-modal mapping unit was developed which fuses the data from stereo vision cameras synchronized with flashing LEDs, inertial sensors, and three time-of-flight 3D depth sensors. The robot's pose and surrounding map are estimated at 20 Hz through a depth-enhanced visual- inertial localization and mapping framework. Provided this ability to estimate the robot's pose and develop a dense map in real-time, a receding horizon sampling-based autonomous exploration path planning algorithm was utilized and equips the robot with the ability to explore its environment given no prior map. The aforementioned key robotic functionalities are further extended through the integration of miniaturized fully solid-state scintillation detectors, utilizing advanced scintillators and Silicon Photomultipliers. The derived sensor data is employed for radiation mapping, which when fused with the 3D maps and the spectroscopic source localization methods, allow the aerial robotic platform to localize the radiation source and identify the specific source isotope. The source location is estimated through a geometric algorithm which can localize a source given only three measurements and short dwell times. The developed systems were tested using two small aerial robot platforms of the F450 and F550 frame classes with weights of 2.6 kg and 2.9 kg respectively. All localization and planning as well as high-level control algorithms are executed onboard. The scintillation detector is integrated on the robot and its data is collected through a dedicated microcontroller. A set of field experiments were conducted inside the dark and GPS-denied environment of a 4.25 m wide, 7.5 m tall, and 137 m long railroad tunnel. A Cs-137 source was placed within the tunnel, the associated 3D/radiation maps were derived, and the source location was estimated. Through these efforts, the goal is to develop the robotic technology that would enable the autonomous systematic inspection or emergency response in nuclear waste decommissioning facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Model, data, and code for paper "Modeling of streamflow in a 30-kilometer-long reach spanning 5 years using OpenFOAM 5.x"

The data package includes data, model, and code that support the analyses and conclusions in the paper titled “modeling of streamflow in a 30-kilometer-long reach spanning 5 years using OpenFOAM 5.x”. The primary goal of this paper is to demonstrate that key streamflow properties such as water depth, flow velocity, and dynamic pressure in a natural river at 30-kilometer scale over 5 years can be reliably and efficiently modeled using the computational framework presented in this paper. To support the paper, various data types from remote sensing, field observations, and computational models are used. Specific details are described as follows. Firstly, the river bathymetry data was obtained from a Light Detection and Ranging (LiDAR) survey. This data is then converted to a triangulated surface format, STL, for mesh generation in OpenFOAM. The STL data can be found in Model_Setups/BaseCase_2013To2015/constant/triSurface. The OpenFOAM mesh generated using this STL file can be found in constant/polyMesh. Other model setups, boundary and initial conditions can be found in /system and /0.org under folder BaseCase_2013To2015. A similar data structure can also be found in BaseCase_2018To2019 for the simulations during 2018 and 2019. Secondly, the OpenFOAM simulations need the upstream discharge and water depth information at the upstream boundary to drive the model. These data are generated from a one-dimensional hydraulic model and the data can be found under the folder Model_Setups /1D model Mass1 data. The mass1_65.csv and mass1_191.csv files include the results of the 1D model at the model inlet and outlet, respectively. The Matlab source code Mass1ToOFBC20182019.m is used to convert these data into OpenFOAM boundary condition setups.With the above OpenFOAM model, it can generate data for water surface elevation, flow velocity, and dynamic pressure. In this paper, the water surface elevation was measured at 7 locations during different periods between 2011 and 2019. The exact survey locations (see Fig1_SurveyLocations.txt) can be found in folder Fig_1. The variation of water stage over time at the 7 locations can be found in folder /Observation_WSE. The data type include .txt, .csv, .xlsx, and .mat. The .mat data can be loaded by Matlab.We also measured the flow velocities at 12 cross-sections along the river. At each cross-section, we recorded the x, y locations, depth, three velocity components u,v,w. These data are saved to a Matlab format which can be found under folder /Observation_Velocity and /Fig_1. The relative locations of velocity survey locations to the river bathymetry can be found in Figure 1c.The water stage data at the 7 locations from OpenFOAM, 1D, and 2D hydraulic models are also provided to evaluate the long-term performance of 3D models vs 1D/2D models. The water stage data for the 7 locations from OpenFOAM have been saved to .mat format and can be found in /OpenFOAM_WSE. The water stage data from the 1D model are saved in .csv format and can be found in /Mass1_WSE. The water stage from the 2D model is saved as .mat format and can be found in / Mass2_WSEIn addition, the OpenFOAM model outputs the information of hydrostatic and hydrodynamic pressure. They are saved as .mat format under folder /Fig_11/2013_1. As the files are too large, we only uploaded the data for January 2013. The area of different ratio of dynamic pressure to static pressure for all simulation range, i.e., 2013-2015, are saved to .mat format. They can be found in /Fig_11/PA. Further, the data of wall clock time versus the solution time of the OpenFOAM modeling are also saved to .mat format under folder /Fig_13/LogsMat. In summary, the data package contains seven data types, including .txt, .csv, .xlsx, .dat, .stl, .m, and .mat. The former 4 types can be directly open using a text editor or Microsoft Office. The .mat format needs to be read by Matlab. The Matlab source code .m files need to be run with Matlab. The OpenFOAM setups can be visualized in ParaView. The .stl file can be opened in ParaView or Blender. The data in subfolders Fig_1 to Fig_10 and Fig_12 are copied from the aforementioned data folders to generate specific figures for the paper. A readME.txt file is included in each subfolder to further describe how the data in each folder are generated and used to support the paper.Please use the data package's DOI to cite the data package. Please contact yunxiang.chen@pnnl.gov if you need more data related to the paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Machine learning to classify animal species in camera trap images: Applications in ecology

Motion-activated cameras (“camera traps”) are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analysed, typically by visually observing each image, in order to extract data that can be used in ecological analyses. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or “out-of-distribution” in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an r package (Machine Learning for Wildlife Image Classification) that allows the users to (a) use the trained model presented here and (b) train their own model using classified images of wildlife from their studies. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analysing images. Our r package makes these methods accessible to ecologists.

54 ENVIRONMENTAL SCIENCES↗