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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Light Detection and Ranging (LIDAR) From Space - Laser Altimeters

Light detection and ranging, or lidar, is like radar but atoptical wavelengths. The principle of operation and theirapplications in remote sensing are similar. Lidars havemany advantages over radars in instrument designs andapplications because of the much shorter laser wavelengthsand narrower beams. The lidar transmitters and receiveroptics are much smaller than radar antenna dishes. Thespatial resolution of lidar measurement is much finer thanthat of radar because of the much smaller footprint size onground. Lidar measurements usually give a better temporalresolution because the laser pulses can be much narrowerthan radio frequency (RF) signals. The major limitation oflidar is the ability to penetrate clouds and ground surfaces.

Sun, Xiaoli↗
NASA NTRS2013

Relative Navigation Light Detection and Ranging (LIDAR) Sensor Development Test Objective (DTO) Performance Verification

The NASA Engineering and Safety Center (NESC) received a request from the NASA Associate Administrator (AA) for Human Exploration and Operations Mission Directorate (HEOMD), to quantitatively evaluate the individual performance of three light detection and ranging (LIDAR) rendezvous sensors flown as orbiter's development test objective on Space Transportation System (STS)-127, STS-133, STS-134, and STS-135. This document contains the outcome of the NESC assessment.

Dennehy, Cornelius J.↗
NASA NTRS1995

Analog Processing Assembly for the Wake Vortex Lidar Experiment

The Federal Aviation Administration (FAA) and NASA have initiated a joint study in the development of reliable means of tracking, detecting, measuring, and predicting trailing wake-vortices of commercial aircraft. Being sought is an accurate model of the wake-vortex hazard, sufficient to increase airport capacity by reducing minimum safe spacings between planes. Several means of measurement are being evaluated for application to wake-vortex detection and tracking, including Doppler RADAR (Radio Detection and Ranging) systems, 2-micron Doppler LIDAR (Light Detection And Ranging) systems, and SODAR (Sound Detection And Ranging) systems. Of specific interest there is the lidar system, which has demonstrated numerous valuable capabilities as a vortex sensor Aerosols entrained in the vortex flow make the wake velocity signature visible to the lidar, (the observable lidar signal is essentially a measurement of the line-of-sight velocity of the aerosols). Measurement of the occurrence of a wake vortex requires effective reception and monitoring of the beat signal which results from the frequency-offset between the transmitted pulse and the backscattered radiation. This paper discusses the mounting, analysis, troubleshooting, and possible use of an analog processing assembly designed for such an application.

Stowe, Edwood G.↗
NASA NTRSDate not supplied

3D Scanning Laboratory

The 3D Scanning Lab uses a number of technologies to capture 3D surface data. Those technologies are structured light scanning, discrete point measurement photogrammetry and Light Detecting and Ranging(LIDAR). Structured light is a non-contact optical technique used to capture as built surface geometries quickly and accurately. LIDAR is a laser scanning technique used to capture 3D surfaces. And photogrammetry uses a certified DSLR camera to measure discrete registered and non-registered points in 3D space. MSFC’s 3D Scanning Team uses 3D scanning to provide hardware inspections, reverse engineered CAD models, manufacturing/process development and digital assemblies. The inspection process compares the captured data to CAD, providing a color plot detailing the deviations of the scan data to the CAD model. Dimensional measurements and GD&T can also be interrogated. Inspection can provide scan-to-scan data comparisons which is ideal for assessing tested hardware, e.g. comparing data captured before and after hardware testing. Reverse engineering is used to develop CAD models from scan data. This is useful for heritage hardware studies, developing building or structural models and inputs for simulation studies and design efforts. The lab uses the 3D surface scan data to drive manufacturing/machining processes, termed match machining. This technique has been used to modify nozzles, injectors, additive manufactured parts, composite barrel sections, etc. Parts of an assembly can be scanned and assembled in a virtual environment providing an accurate 3D representation of the assembled hardware in its as-scanned state. This can provide detailed information of internal components that would otherwise not be accessible.

Advanced Manufacturing↗
NASA NTRS2013

A Survey of LIDAR Technology and Its Use in Spacecraft Relative Navigation

This paper provides a survey of modern LIght Detection And Ranging (LIDAR) sensors from a perspective of how they can be used for spacecraft relative navigation. In addition to LIDAR technology commonly used in space applications today (e.g. scanning, flash), this paper reviews emerging LIDAR technologies gaining traction in other non-aerospace fields. The discussion will include an overview of sensor operating principles and specific pros/cons for each type of LIDAR. This paper provides a comprehensive review of LIDAR technology as applied specifically to spacecraft relative navigation. HE problem of orbital rendezvous and docking has been a consistent challenge for complex space missions since before the Gemini 8 spacecraft performed the first successful on-orbit docking of two spacecraft in 1966. Over the years, a great deal of effort has been devoted to advancing technology associated with all aspects of the rendezvous, proximity operations, and docking (RPOD) flight phase. After years of perfecting the art of crewed rendezvous with the Gemini, Apollo, and Space Shuttle programs, NASA began investigating the problem of autonomous rendezvous and docking (AR&D) to support a host of different mission applications. Some of these applications include autonomous resupply of the International Space Station (ISS), robotic servicing/refueling of existing orbital assets, and on-orbit assembly.1 The push towards a robust AR&D capability has led to an intensified interest in a number of different sensors capable of providing insight into the relative state of two spacecraft. The present work focuses on exploring the state-of-the-art in one of these sensors - LIght Detection And Ranging (LIDAR) sensors. It should be noted that the military community frequently uses the acronym LADAR (LAser Detection And Ranging) to refer to what this paper calls LIDARs. A LIDAR is an active remote sensing device that is typically used in space applications to obtain the range to one or more points on a target spacecraft. As the name suggests, LIDAR sensors use light (typically a laser) to illuminate the target and measure the time it takes for the emitted signal to return to the sensor. Because the light must travel from the source, to

Christian, John A.↗
NASA NTRS2020

Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests

Light detection and ranging (LiDAR) data can provide direct measurements of vegetation structures but are limited by the sparse spatial coverage. Polarimetric synthetic aperture radar (SAR) can perform large-scale high-resolution mapping without weather constraints but the information about vegetation and ground subsurface are mixed in the backscatter data. In this paper, we adopted the Random Forests algorithm to train an upscaling function using tree canopy cover (TCC) and canopy height model (CHM) derived from Goddard’s LiDAR, Hyperspectral and Thermal Imager (G-LiHT) data. The regression model is then applied to the L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign to map the TCC and CHM over the Delta Junction area in interior Alaska.

Moghaddam, Mahta↗
NASA NTRS2014

A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach.

Light Detection & Ranging (LIDAR)↗
NASA NTRS2021

Spectral attenuation coefficients from measurements of light transmission in bare ice on the Greenland Ice Sheet

Light transmission into bare glacial ice affects surface energy balance, biophotochemistry, and light detection and ranging (lidar) laser elevation measurements but has not previously been reported for the Greenland Ice Sheet. We present measurements of spectral transmittance at 350–900 nm in bare glacial ice collected at a field site in the western Greenland ablation zone (67.15∘ N, 50.02∘ W). Empirical irradiance attenuation coefficients at 350–750 nm are ∼ 0.9–8.0 m−1 for ice at 12–124 cm depth. The absorption minimum is at ∼ 390–397 nm, in agreement with snow transmission measurements in Antarctica and optical mapping of deep ice at the South Pole. From 350–530 nm, our empirical attenuation coefficients are nearly 1 order of magnitude larger than theoretical values for optically pure ice. The estimated absorption coefficient at 400 nm suggests the ice volume contained a light-absorbing particle concentration equivalent to ∼ 1–2 parts per billion (ppb) of black carbon, which is similar to pre-industrial values found in remote polar snow. The equivalent mineral dust concentration is ∼ 300–600 ppb, which is similar to values for Northern Hemisphere warm periods with low aeolian activity inferred from ice cores. For a layer of quasi-granular white ice (weathering crust) extending from the surface to ∼ 10 cm depth, attenuation coefficients are 1.5 to 4 times larger than for deeper bubbly ice. Owing to higher attenuation in this layer of near-surface granular ice, optical penetration depth at 532 nm is 14 cm (20 %) lower than asymptotic attenuation lengths for optically pure bubbly ice. In addition to the traditional concept of light scattering on air bubbles, our results imply that the granular near-surface ice microstructure of weathering crust is an important control on radiative transfer in bare ice on the Greenland Ice Sheet ablation zone, and we provide new values of flux attenuation, absorption, and scattering coefficients to support model development and validation.

IceSat-2↗
NASA NTRSDate not supplied

The Kinematic Navigation and Cartography Knapsack (KNaCK): Demonstrating SLAM (Simultaneous Localization and Mapping) LiDAR as a Tool for Exploration and Mapping of Lunar Pits and Caves.

Renewed interest in deep space exploration has made establishing a persistent human presence on the Moon and eventually Mars a priority. Sustained habitation of the Moon will require in-situ resource utilization (ISRU) and protection from radiation at the lunar surface. Lunar caves are of interest to the space community because they could provide shelter from radiation, access to water deposits, and a space for habitation, as well as hold information about the geology, volcanology, and evolution of the Moon. Developing the tools to explore, map, and characterize these voids is key to understanding the Moon as a planetary body and to establishing a permanent human presence there. Our team is currently developing tools that enable ultra-high resolution terrain mapping and navigation using mobile light detection and ranging (LIDAR) technology and simultaneous localization and mapping (SLAM) algorithms in fully GPS-denied and no-light environments. 3D mapping with LiDAR and SLAM is relatively under-used, particularly in the context of planetary exploration and planetary analog environments. The backpack mounted LiDAR instrument currently under development by the KNaCK (Kinematic Navigation and Cartography Knapsack) team has demonstrated the potential of mobile SLAM LiDAR for lunar, planetary, and terrestrial cave exploration, study, and utilization. A description of the instrument and associated SLAM algorithms used for mapping in GPS-denied environments are referenced here.

LiDAR↗
NASA NTRS2016

Simulation of Satellite, Airborne and Terrestrial LiDAR with DART (I):Waveform Simulation with Quasi-Monte Carlo Ray Tracing

Light Detection And Ranging (LiDAR) provides unique data on the 3-D structure of atmosphere constituents and the Earth's surface. Simulating LiDAR returns for different laser technologies and Earth scenes is fundamental for evaluating and interpreting signal and noise in LiDAR data. Different types of models are capable of simulating LiDAR waveforms of Earth surfaces. Semi-empirical and geometric models can be imprecise because they rely on simplified simulations of Earth surfaces and light interaction mechanisms. On the other hand, Monte Carlo ray tracing (MCRT) models are potentially accurate but require long computational time. Here, we present a new LiDAR waveform simulation tool that is based on the introduction of a quasi-Monte Carlo ray tracing approach in the Discrete Anisotropic Radiative Transfer (DART) model. Two new approaches, the so-called "box method" and "Ray Carlo method", are implemented to provide robust and accurate simulations of LiDAR waveforms for any landscape, atmosphere and LiDAR sensor configuration (view direction, footprint size, pulse characteristics, etc.). The box method accelerates the selection of the scattering direction of a photon in the presence of scatterers with non-invertible phase function. The Ray Carlo method brings traditional ray-tracking into MCRT simulation, which makes computational time independent of LiDAR field of view (FOV) and reception solid angle. Both methods are fast enough for simulating multi-pulse acquisition. Sensitivity studies with various landscapes and atmosphere constituents are presented, and the simulated LiDAR signals compare favorably with their associated reflectance images and Laser Vegetation Imaging Sensor (LVIS) waveforms. The LiDAR module is fully integrated into DART, enabling more detailed simulations of LiDAR sensitivity to specific scene elements (e.g., atmospheric aerosols, leaf area, branches, or topography) and sensor configuration for airborne or satellite LiDAR sensors.

Gastellu-Etchegorry, Jean-Philippe↗
NASA NTRS2011

Hazard Detection Software for Lunar Landing

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project is developing a system for safe and precise manned lunar landing that involves novel sensors, but also specific algorithms. ALHAT has selected imaging LIDAR (light detection and ranging) as the sensing modality for onboard hazard detection because imaging LIDARs can rapidly generate direct measurements of the lunar surface elevation from high altitude. Then, starting with the LIDAR-based Hazard Detection and Avoidance (HDA) algorithm developed for Mars Landing, JPL has developed a mature set of HDA software for the manned lunar landing problem. Landing hazards exist everywhere on the Moon, and many of the more desirable landing sites are near the most hazardous terrain, so HDA is needed to autonomously and safely land payloads over much of the lunar surface. The HDA requirements used in the ALHAT project are to detect hazards that are 0.3 m tall or higher and slopes that are 5 or greater. Steep slopes, rocks, cliffs, and gullies are all hazards for landing and, by computing the local slope and roughness in an elevation map, all of these hazards can be detected. The algorithm in this innovation is used to measure slope and roughness hazards. In addition to detecting these hazards, the HDA capability also is able to find a safe landing site free of these hazards for a lunar lander with diameter .15 m over most of the lunar surface. This software includes an implementation of the HDA algorithm, software for generating simulated lunar terrain maps for testing, hazard detection performance analysis tools, and associated documentation. The HDA software has been deployed to Langley Research Center and integrated into the POST II Monte Carlo simulation environment. The high-fidelity Monte Carlo simulations determine the required ground spacing between LIDAR samples (ground sample distances) and the noise on the LIDAR range measurement. This simulation has also been used to determine the effect of viewing on hazard detection performance. The software has also been deployed to Johnson Space Center and integrated into the ALHAT real-time Hardware-in-the-Loop testbed.

Huertas, Andres↗
NASA NTRSDate not supplied

The Kinematic Navigation and Cartography Knapsack (KNaCK) LiDAR System: Overview and Applications.

Improved terrain characterization and navigation sensors and methods are needed to enhance crew safety, ISRU return, and scientific understanding of future landing sites. Specific to the Artemis Program and sustained exploration at the lunar South Pole, extreme low-angle solar illumination conditions pose significant challenges to existing photogrammetry-based robotic navigation. Additionally, a major challenge for navigation on the Moon and other planetary surfaces is the lack of Global Positioning and Navigation Systems (GPS or GNSS). Thus, there is a need for an alternative to image-based navigation that allow for precise and accurate mapping in GPS-denied environments on any planetary body. Here, we describe the Kinematic Navigation and Cartography Knapsack (KNaCK) LiDAR system; a backpack-mounted, mobile navigation and terrain mapping system that uses a velocity-sensing coherent light detection and ranging (LiDAR) system based on a frequency modulated continuous wave (FMCW) technique, contains minimal moving parts, and employs sophisticated positioning algorithms. During a traverse, this instrument emits light pulses to continually scan a scene to build a three-dimensional point cloud representation of topography. A measure of the Doppler-velocity at each of millions of range points sampled per second allows for a 6 degree of freedom (6- DoF) estimate of the sensor’s position and the development of novel position-from-velocity mapping and positioning algorithms for loop-closure in GPS denied environments. Included with paper is the video presentation for the Figure 2: FMCW-LiDAR sensor on Kinematic Navigation and Cartography Knapsack (KNaCK) (Aeva Aeries 1)

M. Zanetti↗
NASA NTRS2012

Lidar Measurements for Desert Dust Characterization: An Overview

We provide an overview of light detection and ranging (lidar) capability for describing and characterizing desert dust. This paper summarizes lidar techniques, observations, and fallouts of desert dust lidar measurements. The main objective is to provide the scientific community, including non-practitioners of lidar observations with a reference paper on dust lidar measurements. In particular, it will fill the current gap of communication between research-oriented lidar community and potential desert dust data users, such as air quality monitoring agencies and aviation advisory centers. The current capability of the different lidar techniques for the characterization of aerosol in general and desert dust in particular is presented. Technical aspects and required assumptions of these techniques are discussed, providing readers with the pros and cons of each technique. Information about desert dust collected up to date using lidar techniques is reviewed. Lidar techniques for aerosol characterization have a maturity level appropriate for addressing air quality and transportation issues, as demonstrated by some first results reported in this paper

Mona, L.↗
NASA NTRS1982

Airborne lidar measurements of the Soufriere eruption of 17 April 1979

At the time of the Soufriere, St. Vincent, volcanic eruption of April 17, 1979, a NASA P-3 aircraft with an uplooking lidar (light detection and ranging) system onboard was airborne 130 kilometers east of the island. Lidar measurements of the fresh volcanic ash were made approximately 2 hours after the eruption, 120 kilometers to the northeast and east. On the evening of April 18, the airborne lidar, on a southerly flight track, detected significant amounts of stratospheric material in layers at 16, 17, 18, and 19.5 kilometers. These data, and measurements to the north on April 19, indicate that the volcanic plume penetrated the stratosphere to an altitude of about 20 kilometers and moved south during the first 48 hours after the eruption.

Fuller, W. H., Jr.↗
NASA NTRS2000

Airborne Coherent Lidar for Advanced In-Flight Measurements (ACLAIM) Flight Testing of the Lidar Sensor

The purpose of the ACLAIM program is ultimately to establish the viability of light detection and ranging (lidar) as a forward-looking sensor for turbulence. The goals of this flight test are to: 1) demonstrate that the ACLAIM lidar system operates reliably in a flight test environment, 2) measure the performance of the lidar as a function of the aerosol backscatter coefficient (beta), 3) use the lidar system to measure atmospheric turbulence and compare these measurements to onboard gust measurements, and 4) make measurements of the aerosol backscatter coefficient, its probability distribution and spatial distribution. The scope of this paper is to briefly describe the ACLAIM system and present examples of ACLAIM operation in flight, including comparisons with independent measurements of wind gusts, gust-induced normal acceleration, and the derived eddy dissipation rate.

Soreide, David C.↗
NASA NTRS2021

Quantifying the Observational Requirements of a Space-borne LiDAR Snow Mission

This study quantifies the level of observational accuracy required from a spaceborne light detection and ranging (LiDAR) snow depth retrieval mission for enabling beneficial impacts for snow estimation. The study is conducted over a region in Western Colorado using a suite of observing system simulation experiments(OSSEs).The Joint UK Land Environment Simulator, version 5.0 (JULES v5.0) is employed to simulate a suite of idealized LiDAR observations, considering a range of LiDAR snow depth retrieval errors, different hypothetical sensor swath widths, and the impact of cloud cover on observability. These simulated observations are then assimilated into the Noah land surface modelwith multi-parameterization options, version 3.6 (Noah-MP v3.6) model. This data assimilation setup is used to systematically evaluate the potential utility of LiDAR observations for improving modeled snow water equivalent (SWE)estimates and water budget variables such as runoff. Results from the OSSE runsshow that, in general, assimilation of synthetic LiDAR observations provide beneficial impacts when theLiDAR snow depth retrieval error standard deviation (σerror) is below 60 cm.Based on comparisons between the realistic (i.e., swath-limited and cloud-attenuated) case and the idealized (i.e., infinite swath width in the absence of cloud cover) case,this study concludes that observations with a conservative error standard deviation threshold of 40 cm (i.e., upper limit of the snow depth retrieval error that adds value to the SWE estimates viaassimilation) are needed for improving modeled snow estimates. More than a 33% reduction in SWEroot mean square errors and more than a 15% increase in correlation coefficientsare achieved when σ error ≤ 40 cmusing a 170-km sensor swath width in the presence of cloud attenuation effects. Further, the integrated hydrologic response, as represented by total (surface and subsurface) runoff estimates during the snow ablation season, are also enhanced when assimilating synthetic LiDAR snow depth retrievals with errors below this level.

Yonghwan Kwon↗
NASA NTRS2016

Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and Lidar Data

Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of AGB and estimate AGB growth using LiDAR data. The proposed model accommodates temporal misalignment between field measurements and remotely sensed data-a problem pervasive in such settings-by including multiple time-indexed measurements at plot locations to estimate AGB growth. We pursue a Bayesian modeling framework that allows for appropriately complex parameter associations and uncertainty propagation through to prediction. Specifically, we identify a space-varying coefficients model to predict and map AGB and its associated growth simultaneously. The proposed model is assessed using LiDAR data acquired from NASA Goddard's LiDAR, Hyper-spectral & Thermal imager and field inventory data from the Penobscot Experimental Forest in Bradley, Maine. The proposed model outperformed the time-invariant counterpart models in predictive performance as indicated by a substantial reduction in root mean squared error. The proposed model adequately accounts for temporal misalignment through the estimation of forest AGB growth and accommodates residual spatial dependence. Results from this analysis suggest that future AGB models informed using remotely sensed data, such as LiDAR, may be improved by adapting traditional modeling frameworks to account for temporal misalignment and spatial dependence using random effects.

Babcock, Chad↗
NASA NTRS2016

Beyond 3-D: The New Spectrum of Lidar Applications for Earth and Ecological Sciences

Capturing and quantifying the world in three dimensions (x,y,z) using light detection and ranging (lidar) technology drives fundamental advances in the Earth and Ecological Sciences (EES). However, additional lidar dimensions offer the possibility to transcend basic 3-D mapping capabilities, including i) the physical time (t) dimension from repeat lidar acquisition and ii) laser return intensity (LRIλ) data dimension based on the brightness of single- or multi-wavelength (λ) laser returns. The additional dimensions thus add to the x,y, and z dimensions to constitute the five dimensions of lidar (x,y,z, t, LRIλ1... λn). This broader spectrum of lidar dimensionality has already revealed new insights across multiple EES topics, and will enable a wide range of new research and applications. Here, we review recent advances based on repeat lidar collections and analysis of LRI data to highlight novel applications of lidar remote sensing beyond 3-D. Our review outlines the potential and current challenges of time and LRI information from lidar sensors to expand the scope of research applications and insights across the full range of EES applications.

Eitel, Jan U. H.↗