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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Post survey report for AOP Assignable Asset collection of Crested Butte, CO

This report contains details of the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) assignable asset (AA) flight over Crested Butte, Colorado (CO). The report is being prepared for Lawrence Berkeley National Lab (LBNL), the primary clients for the flights who contracted the NEON AOP for flights during June 2018. The report includes a summary of the acquired flight lines over the planned area, results of calibration surveys, and quality assurance (QA) results of the acquired data. Detailed reports on the acquired data and processing results were also delivered with the data. LBNL contracted the AA NEON AOP flights to observe watersheds of interest at Crested Butte with remotely sensed data including high resolution LiDAR, imaging spectroscopy, and high-resolution camera imagery. The remote sensing imagery acquired by the AOP intendeds to support LBNL on an interdisciplinary project on hydrology, biogeochemistry, and ecosystem functioning in the area surrounding the Rocky Mountain Biological Laboratory (RMBL), one of the most prominent ecological study sites in the United States.

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRS1999

Lidar Altimeter Measurements of Canopy Structure: Methods and Validation for Closed Canopy, Broadleaf Forests

Lidar altimeter observations of vegetated landscapes provide a time-resolved measure of laser pulse backscatter energy from canopy surfaces and the underlying ground. Airborne lidar altimeter data was acquired using the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) for a successional sequence of four, closed-canopy, deciduous forest stands in eastern Maryland. The four stands were selected so as to include a range of canopy structures of importance to forest ecosystem function, including variation in the height and roughness of the outer-most canopy surface and the vertical organization of canopy stories and gaps. The character of the SLICER backscatter signal is described and a method is developed that accounts for occlusion of the laser energy by canopy surfaces, transforming the backscatter signal to a canopy height profile (CHP) that quantitatively represents the relative vertical distribution of canopy surface area. The transformation applies an increased weighting to the backscatter amplitude as a function of closure through the canopy and assumes a horizontally random distribution of the canopy components. SLICER CHPs, averaged over areas of overlap where lidar ground tracks intersect, are shown to be highly reproducible. CHP transects across the four stands reveal spatial variations in vegetation, at the scale of the individual 10 m diameter laser footprints, within and between stands. Averaged SLICER CHPs are compared to analogous height profile results derived from ground-based sightings to plant intercepts measured on plots within the four stands. Tbe plots were located on the segments of the lidar ground tracks from which averaged SLICER CHPs were derived, and the ground observations were acquired within two weeks of the SLICER data acquisition to minimize temporal change. The differences in canopy structure between the four stands is similarly described by the SLICER and ground-based CHP results, however a Chi-square test of similarity documents differences that are statistically significant. The differences are discussed in terms of measurement properties that define the smoothness of the resulting CHPs and Lidar Altimeter Measurements of Canopy Structure - Harding et al. canopy properties that may vertically bias the CHP representations of canopy structure. The statistical differences are most likely due to the more noisy character of the ground-based CHPs, especially high in the canopy where ground-based sightings are rare resulting in an underestimate of canopy surface area and height, and to departures from the assumption of horizontal randomness which bias the CHPs toward the observer (upward for SLICER and downward for ground-based CHPs). The results demonstrate that the SLICER observations reliably provide a measure of canopy structure that reveals ecologically interesting structural variations such as those characterizing a successional sequence of closed-canopy, broadleaf forest stands.

Harding, D. J.↗
DOE OSTI2026

Raw Lidar and Camera Data Synchronized with Precipitation and Present Weather Data

As part of the sensor characterization task of the SMART 2.0 project, this dataset includes raw data from three spinning lidars ([Ouster OS2-128](https://ouster.com/products/scanning-lidar/os2-sensor/), [Velodyne Puck (VLP-16)](https://velodynelidar.com/products/puck/), and [Velodyne Ultra Puck (VLP-32)](https://velodynelidar.com/products/ultra-puck/)), one camera ([Mako G-319](https://www.alliedvision.com/en/camera-selector/detail/mako/g-319/)), and one present weather sensor ([Vaisala FD-70](https://www.vaisala.com/en/products/weather-environmental-sensors/forward-scatter-fd70)). All data were synchronized, with the log start time indicated in the file name (HHMMSS). The data can be filtered by date, log time (HHMMSS), sensor, frame ID, and weather classification. These data were gathered statically at the Argonne Testbed for Multiscale Observational Science (ATMOS). Two target stop signs were placed in view of the sensors to contribute a target for comparing sensor data under different conditions. The weather data for each day are stored in netCDF “.nc” files. The lidar data contain the X, Y, Z, intensity, reflectivity, and ring from Ouster OS2-128 rev6, Velodyne VLP-16, and Velodyne VLP-32 lidars. ![raw lidar image](LiDAR_pointcloud_ATMOS.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1986

Analysis of cirrus from airborne lidar and radiometer observations

Lidar and passive radiance sensing data on cirrus clouds gathered in instrumented high altitude aircraft flights are analyzed, with emphasis on cirrus emittance characteristics. The data were collected with a U-2 aircraft flying at about 19 km altitude. Lidar data defined the vertical structure of the cloud which, when combined with independent atmospheric temperature data, permitted radiance to be derived from thermal radiance data. Sample return lidar images are provided of cirrus and cirrostratus clouds, along with the calculated emittance data.

Spinhirne, J. D.↗
NASA NTRS1997

Atmospheric Layers Measured from the NASA DC-8 During PEM-West B and Comparison with PEM-West A

The Pacific Exploratory Mission-West B (PEM-West B) explored atmospheric layer structure using measurements of O3, H2O, CO, and CH4 from the NASA DC-8 fast-response instruments. The mission took place in February-March 1994 over the western Pacific, mainly in the northern hemisphere. Results were compared with similar measurements made during the Pacific Exploratory Mission-West A (PEM-West A) in September-October 1991. PEM-West B sampled 94 vertical profiles, with an average atmospheric depth per profile of 6.4 km, and this sampling yielded 254 discrete layers. PEM-West A sampled 105 profiles, also with a 6.4 km average depth, yielding 538 layers. Both missions revealed that layers containing high ozone and low water vapor were the most abundant, and low ozone and high water vapor layers were the next most abundant. Lidar images and potential vorticity cross sections showed the former associated with subsidence from the stratosphere in middle latitudes, spreading into the tropics. The latter was associated with convection from the boundary layer. The partition among different types of layers only changed slightly in the two missions, although PEM-West B had half as many layers. Compared to PEM-West A, PEM-West B showed only one-third the number of layers associated with polluted continental air. However, PEM-West B revealed significantly more layers showing characteristics of descended clean marine air. In some cases these layers originated from the southern hemisphere. For ozone- and water-vapor-rich layers, the ozone deviation amplitude was higher in low latitudes and lower in high latitudes in PEM-West B. The mean thickness of layers increased from about 450 m in PEM-West A to 680 in in PEM-West B. Layers also existed in the Intertropical Convergence Zone. CO2 measurements had sufficient vertical resolution to show layer structure as well.

Wu, Zhongxiang↗
NASA NTRS2004

Lidar Data Products and Applications Enabled by Conical Scanning

Several new data products and applications for elastic backscatter lidar are achieved using simple conical scanning. Atmospheric boundary layer spatial and temporal structure is revealed with resolution not possible with static pointing lidars. Cloud fractional coverage as a function of altitude is possible with high temporal resolution. Wind profiles are retrieved from the cloud and aerosol structure motions revealed by scanning. New holographic technology will soon allow quasi-conical scanning and push-broom lidar imaging without mechanical scanning, high resolution, on the order of seconds.

Schwemmer, Geary K.↗
NASA NTRS2011

Lidar Luminance Quantizer

This innovation addresses challenges in lidar imaging, particularly with the detection scheme and the shapes of the detected signals. Ideally, the echoed pulse widths should be extremely narrow to resolve fine detail at high event rates. However, narrow pulses require wideband detection circuitry with increased power dissipation to minimize thermal noise. Filtering is also required to shape each received signal into a form suitable for processing by a constant fraction discriminator (CFD) followed by a time-to-digital converter (TDC). As the intervals between the echoes decrease, the finite bandwidth of the shaping circuits blends the pulses into an analog signal (luminance) with multiple modes, reducing the ability of the CFD to discriminate individual events

Quilligan, Gerard↗
NASA NTRS2011

Lidar-Based Navigation Algorithm for Safe Lunar Landing

The purpose of Hazard Relative Navigation (HRN) is to provide measurements to the Navigation Filter so that it can limit errors on the position estimate after hazards have been detected. The hazards are detected by processing a hazard digital elevation map (HDEM). The HRN process takes lidar images as the spacecraft descends to the surface and matches these to the HDEM to compute relative position measurements. Since the HDEM has the hazards embedded in it, the position measurements are relative to the hazards, hence the name Hazard Relative Navigation.

Myers, David M.↗
NASA NTRS2015

FINESSE: Field Investigations to Enable Solar System Science and Exploration

The FINESSE (Field Investigations to Enable Solar System Science and Exploration) team is focused on a science and exploration field-based research program aimed at generating strategic knowledge in preparation for the human and robotic exploration of the Moon, near-Earth asteroids (NEAs) and Phobos and Deimos. We follow the philosophy that "science enables exploration and exploration enables science." 1) FINESSE Science: Understand the effects of volcanism and impacts as dominant planetary processes on the Moon, NEAs, and Phobos & Deimos. 2) FINESSE Exploration: Understand which exploration concepts of operations (ConOps) and capabilities enable and enhance scientific return. To accomplish these objectives, we are conducting an integrated research program focused on scientifically-driven field exploration at Craters of the Moon National Monument and Preserve in Idaho and at the West Clearwater Lake Impact Structure in northern Canada. Field deployments aimed at reconnaissance geology and data acquisition were conducted in 2014 at Craters of the Moon National Monument and Preserve. Targets for data acquisition included selected sites at Kings Bowl eruptive fissure, lava field and blowout crater, Inferno Chasm vent and outflow channel, North Crater lava flow and Highway lava flow. Field investigation included (1) differential GPS (dGPS) measurements of lava flows, channels (and ejecta block at Kings Bowl); (2) LiDAR imaging of lava flow margins, surfaces and other selected features; (3) digital photographic documentation; (4) sampling for geochemical and petrographic analysis; (5) UAV aerial imagery of Kings Bowl and Inferno Chasm features; and (6) geologic assessment of targets and potential new targets. Over the course of the 5-week field FINESSE campaign to the West Clearwater Impact Structure (WCIS) in 2014, the team focused on several WCIS research topics, including impactites, central uplift formation, the impact-generated hydrothermal system, multichronometer dating of impact products, and using WCIS as an analog test site for crew studies of sampling protocols. The FINESSE team visited and mapped all of the major islands within West Clearwater Lake. Excellent cliff exposures around the coasts of many of the islands allowed a general stratigraphy of impactites to be defined. Notable differences to previous work includes the discovery of a monomict lithic breccia and a medium to coarse grained impact melt rock. In addition, ample rock samples were returned from West Clearwater for geochronology study. Geochronology work centers around laboratory analyses of these samples (and samples collected in the future or obtained from archives housed at the Canadian Geological Survey). Samples returned from the FINESSE field season have been evaluated for suitability for geochronologic analysis, and selected samples have been crushed for mineral separation and/or sawed for the preparation of polished petrologic thin sections. Heavy minerals (e.g., zircon, titanite, and apatite) will be separated from the crushed material for (U-Th)/He geochronology. The sections will be used for laser ablation 40Ar/39Ar research after neutron irradiation. This presentation will highlight the exciting science and exploration work conducted by FINESSE, as well as future plans for continued research.

Phobos & Deimos↗
NASA NTRS2022

Drought Monitoring with Drones: A Hundred Fields at a Time

Drought frequency and severity are likely to increase due to global warming. Droughts already have a substantial negative influence on agriculture and the economy and finding ways to reduce their effects could have a monumental impact. Although NASA already has satellites deployed to collect drought data, these satellites are more for global drought indexing than local. To alleviate droughts on a local level, this paper proposes the use of drones to map soil moisture, plant health, and other drought indicators. The proposed drone design is a fixed-wing UAV equipped with a hyperspectral camera, a LiDAR sensor, and an array of weather sensors. These tools will permit it to reliably capture the necessary data to enhance suggestions on improving drought management practices. The collected data from the drones will be deployable in many ways, including for agricultural and non-agricultural applications. The hyperspectral camera has applications in monitoring the health of crops within a field to direct relief measures to the crops most in need. Thermal and LiDAR imaging can be deployed for locating leaks, predicting shortages of water bodies, and determining a field’s water needs. To implement a drought-monitoring drone, the recommended steps include building a prototype drone design that is equipped with the outlined instruments. The prototype drone could then be deployed to collect training data to guide a neural network that would provide interpretations and predictions from the data for users. After the prototype design is iterated upon, it will be ready for deployment and inform water management methods to serve the world in our battle against drought.

climate change↗
DOE OSTI2024

Semi-automatic image annotation using 3D LiDAR projections and depth camera data

Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS2010

Super Resolution Image Enhancement for a Flash Lidar: Back Projection Method

In this paper a new image processing technique for flash LIDAR data is presented as a potential tool to enable safe and precise spacecraft landings in future robotic or crewed lunar and planetary missions. Flash LIDARs can generate, in real-time, range data that can be interpreted as a 3-dimensional (3-D) image and transformed into a corresponding digital elevation map (DEM). The NASA Autonomous Landing and Hazard Avoidance (ALHAT) project is capitalizing on this new technology by developing, testing and analyzing flash LIDARs to detect hazardous terrain features such as craters, rocks, and slopes during the descent phase of spacecraft landings. Using a flash LIDAR for this application looks very promising, however through theoretical and simulation analysis the ALHAT team has determined that a single frame, or mosaic, of flash LIDAR data may not be sufficient to build a landing site DEM with acceptable spatial resolution, precision, size, or for a mosaic, in time, to meet current system requirements. One way to overcome this potential limitation is by enhancing the flash LIDAR output images. We propose a new super-resolution algorithm applicable to flash LIDAR range data that will create a DEM with sufficient accuracy, precision and size to meet current ALHAT requirements. The performance of our super-resolution algorithm is analyzed by processing data generated during a series of simulation runs by a high fidelity model of a flash LIDAR imaging a high resolution synthetic lunar elevation map. The flash LIDAR model is attached to a simulated spacecraft by a gimbal that points the LIDAR to a target landing site. For each simulation run, a sequence of flash LIDAR frames is recorded and processed as the spacecraft descends toward the landing site. Each run has a different trajectory profile with varying LIDAR look angles of the terrain. We process the output LIDAR frames using our SR algorithm and the results show that the achieved level of accuracy and precision of the SR generated landing site DEM is more than adequate for detecting hazardous terrain features and identifying safe areas.

Bulyshev, Alexander↗
NASA NTRS1990

Summary of results and conclusions based on analysis of volume imaging and high spectral resolution lidar data acquired during FIRE phase 1, part 1

The collection of long term global statistics on cloud cover may be most easily accomplished with satellite based observations; however, measurements derived from passive satellite retrieval methods must be calibrated and verified by in situ or ground based remote sensor observations. Verification is not straight forward, however, because the highly variable nature of cloud altitude, morphology, and optical characteristics complicates the scaling of point measurements to satellite footprint sized areas. This is particularly evident for cirrus clouds which may be organized on horizontal scales of 10's of meters to 8 km or more, and have optical depths ranging from less than .003 to greater than 3. Cirrus clouds can strongly influence earths' radiative balance, but, because they are often transmissive, cirrus clouds are difficult to detect and characterize from satellite measurements. Because of its precise ranging capabilities, spatial resolution and sensitivity, lidar observations have played an important role in the detection, depiction, and characterization of cirrus clouds. Some of the characteristics of cirrus clouds are summarized which observed the High Spectral Resolution and Volume Imaging Lidars during the phase 1 IFO and ETO periods.

Grund, Christian J.↗
NASA NTRSDate not supplied

High Resolution Terrain Sensing Lidar for Precision Navigation and Safe Landing of Space and Aerial Vehicles

A 3-D imaging flash lidar sensor employing a resolution enhancement algorithm is being developed at NASA Langley Research Center for providing Terrain Relative Navigation and Hazard Avoidance capabilities onboard spacecraft landing on the Moon, Mars, and other planetary bodies. This lidar sensor, we refer to as Terrain Sensing Lidar (TSL), is a solution for future missions that require landing at pre-designated sites near high value resources or at areas of high scientific value, while avoiding hazardous terrain features, such as escarpments, craters, slopes, and rocks, or pre-deployed assets. TSL can also benefit terrestrial applications such as autonomous aerial vehicles without reliance on signals from Global Positioning System (GPS). The feasibility of the TSL concept has been shown through a series of drone, fixed-wing aircraft, and helicopter flight tests. A prototype version of the TSL has been recently assembled for conducting another set of flight tests to demonstrate its readiness for upcoming landing missions. This paper describes the TSL, provides its performance parameters, and explains its operational concepts for landing missions.

Precision Navigation↗
NASA NTRS2011

Lidar Systems for Precision Navigation and Safe Landing on Planetary Bodies

The ability of lidar technology to provide three-dimensional elevation maps of the terrain, high precision distance to the ground, and approach velocity can enable safe landing of robotic and manned vehicles with a high degree of precision. Currently, NASA is developing novel lidar sensors aimed at needs of future planetary landing missions. These lidar sensors are a 3-Dimensional Imaging Flash Lidar, a Doppler Lidar, and a Laser Altimeter. The Flash Lidar is capable of generating elevation maps of the terrain that indicate hazardous features such as rocks, craters, and steep slopes. The elevation maps collected during the approach phase of a landing vehicle, at about 1 km above the ground, can be used to determine the most suitable safe landing site. The Doppler Lidar provides highly accurate ground relative velocity and distance data allowing for precision navigation to the landing site. Our Doppler lidar utilizes three laser beams pointed to different directions to measure line of sight velocities and ranges to the ground from altitudes of over 2 km. Throughout the landing trajectory starting at altitudes of about 20 km, the Laser Altimeter can provide very accurate ground relative altitude measurements that are used to improve the vehicle position knowledge obtained from the vehicle navigation system. At altitudes from approximately 15 km to 10 km, either the Laser Altimeter or the Flash Lidar can be used to generate contour maps of the terrain, identifying known surface features such as craters, to perform Terrain relative Navigation thus further reducing the vehicle s relative position error. This paper describes the operational capabilities of each lidar sensor and provides a status of their development. Keywords: Laser Remote Sensing, Laser Radar, Doppler Lidar, Flash Lidar, 3-D Imaging, Laser Altimeter, Precession Landing, Hazard Detection

Amzajerdian, Farzin↗
NASA NTRS2013

Lidar Sensors for Autonomous Landing and Hazard Avoidance

Lidar technology will play an important role in enabling highly ambitious missions being envisioned for exploration of solar system bodies. Currently, NASA is developing a set of advanced lidar sensors, under the Autonomous Landing and Hazard Avoidance (ALHAT) project, aimed at safe landing of robotic and manned vehicles at designated sites with a high degree of precision. These lidar sensors are an Imaging Flash Lidar capable of generating high resolution three-dimensional elevation maps of the terrain, a Doppler Lidar for providing precision vehicle velocity and altitude, and a Laser Altimeter for measuring distance to the ground and ground contours from high altitudes. The capabilities of these lidar sensors have been demonstrated through four helicopter and one fixed-wing aircraft flight test campaigns conducted from 2008 through 2012 during different phases of their development. Recently, prototype versions of these landing lidars have been completed for integration into a rocket-powered terrestrial free-flyer vehicle (Morpheus) being built by NASA Johnson Space Center. Operating in closed-loop with other ALHAT avionics, the viability of the lidars for future landing missions will be demonstrated. This paper describes the ALHAT lidar sensors and assesses their capabilities and impacts on future landing missions.

Amzajerdian, Farzin↗