Search NASA⌕ Search

SEARCH · Search NASA

Results for “Light Detection & Ranging (LIDAR)”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
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.↗
DOE OSTI2025

Unmanned Aircraft Systems (UAS) and Light Detection and Ranging (LiDAR)/Camera Technologies to Detect Avian Events and Other Environmental Measures at Utility- Scale Power Plants (Final Report)

The goal of this project was to develop and validate two complementary, cost-effective remote sensing technologies to monitor avian fatalities at utility-scale solar facilities: fixed platform (Animal Activity Monitoring-AAM) and aerial-based (Uncrewed Aircraft Systems-UAS). This project used these features with machine learning to automate the detection of avian carcasses and nests at solar facilities.

14 SOLAR ENERGY↗
DOE OSTI2026

CHESS 2025: Field-collected vegetation attributes and site photos

This dataset represents field observations of vegetation samples collected as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Samples were collected in the field using tablet computers and digital forms, with target data differing by sample type (individual trees, individual shrubs, or 1-meter square plots of meadow and subshrub vegetation). Field samples were collected within 72 hours of airborne data collection using the National Ecological Observatory Network’s Aerial Observation Platform (NEON AOP). The NEON AOP collected waveform LiDAR (Light Detection and Ranging) and imaging spectrometer data in 426 spectral bands from the visible to shortwave infrared. Remote sensing data for the project is available on ESS-DIVE (DOI and citation to be added upon publication). Field data collected included canopy height and per-species horizontal proportional cover for meadow plots, species identity and height information for shrubs, as well as species identity, height, diameter at breast height, and health assessment information for trees. Photos of the focal site and surrounding landscape were taken for all sampling sites and are included in this archive. Green leaves or needles were collected for plant trait and foliar chemistry analysis. This data is archived separately (DOI and citation to be added upon publication). High-precision geospatial data for each sample (crown perimeter polygons for trees and shrubs, plot boundaries for meadow plots) is available here (Henderson et al., 2026). Field and remote sensing protocols largely followed those of a previous field and airborne imaging campaign performed in 2018 (described in Chadwick et al. 2020). Field data from the 2018 campaign can be found here (Chadwick et al., 2020 doi:10.15485/1618130). Because different field measurements were taken for meadow, shrub, and tree sites, data from these three sample types are archived as separate tables (chess_meadow_site_cleaned.csv, chess_shrub_site_cleaned.csv, chess_tree_site_cleaned.csv). Meadow proportional cover data is stored in a separate table (chess_meadow_cover_cleaned.csv). Taxonomy was treated identically between sample types, and the dataset shares a common set of voucher specimens (chess_voucher_IDs_cleaned.csv), as well as a single species list (chess_species_list_cleaned.csv). All taxonomic determinations were performed to the species level, and adhere to the Global Biodiversity Information Facility (GBIF) backbone taxonomy as of January 10th, 2026 (GBIF Secretariat 2023). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also 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.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2026

CHESS 2025: Leaf Area Index (LAI) for meadow, shrub, tree, and understory vegetation

This dataset contains Leaf Area Index (LAI) measurements made as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Data were collected in the Upper Gunnison Basin, Colorado, across three study domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). Field observations of LAI were collected within 72 hours of airborne data collection by the National Ecological Observatory Network’s Aerial Observation Platform (NEON AOP). The NEON AOP collected waveform LiDAR (Light Detection and Ranging) and imaging spectrometer data in 426 spectral bands from the visible to shortwave infrared. LAI measurements were collected using the LICOR LAI-2200C Plant Canopy Analyzer following protocols outlined in the instrument manual (LI-COR 2019). Sampling targeted four distinct vegetation types: meadows, shrubs, trees, and aspen forest understory. We have archived data separately by site type because different field methods were used for each. At meadow sites, measurements were made at the four corners of 1m x 1m plots, with the instrument moving inward toward the center of the plot. At shrub sites, we measured the canopies of individual shrubs. At tree sites, we made measurements within a 10m x 10m subplot centered around a focal tree, with 30 observations taken on a regular grid. At aspen understory sites, we measured overstory trees following the tree protocol and understory herbaceous vegetation following the meadow protocol. All measurements included above-canopy (A) and below-canopy (B) readings, with specific protocols for scattering correction measurements in direct-sun conditions. Data were processed using the R package `rlai` (Worsham 2025). This package includes functions to calculate LAI, gap fraction, apparent clumping factor (Ω), scattering correction, and other canopy metrics. Package contents: Full file descriptions appear in ‘flmd.csv’. Files named according to the convention ‘lai_*_summary_data_cleaned.csv’ contain summary values of LAI, apparent clumping factor (Ωapp), and scattering correction factors for each site. These are the analysis-ready products that most data users will work with. Files named ‘lai_*_metadata_cleaned.csv’ contain additional site-level observations made during field collection. We have also archived intermediate and supplementary data for users who wish to check our processing approach or apply alternative methods. ‘raw_lai_2200C.zip’ contains the raw files as read from the LI-COR instrument, with no processing applied, in TXT format. The zip archive contains subdirectories by site type, which are further subdivided by sampling area. Filenames correspond to the sampling site number. ‘intermediate_results.zip’ contains detailed output from the processing routines, in JSON format. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘scattering_correction_logs.zip’ contains logfiles from the implementation of Kobayashi et al.'s (2013) scattering correction algorithm. The logfiles report values of several parameters at each iteration of the algorithm, as the model converges toward a stable solution. They are intended for users who want to verify scattering correction performance. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘spot_checks.csv’ reports LAI and other values for a small number of files processed with LI-COR FV2200 software (LI-COR 2013) using the same control parameters as in our R-based approach. Additional metadata are provided in a data dictionary describing column names and definitions (dd.csv), and in a file-level metadata file (flmd.csv). All zip files can be expanded with common archive utilities. TXT, CSV, and JSON files can be ingested into R or Python computing environments or read in common text editor utilities. Geospatial information: Geospatial data for mapping measurement site locations are in the files CHESS_polygons_lai_UTM.geojson, CHESS_polygons_shrub_UTM.geojson, and CHESS_polygons_meadow_UTM.geojson in the companion geospatial package for the 2025 CHESS campaign, ‘CHESS 2025: Location data for field observations and sampling’ (Henderson et al., 2026). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also 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. * Todorov and Worsham are co–first authors.

2018 NEON and 2025 CHESS Campaigns↗
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↗
DOE OSTI2026

A primer on forest structure measurement with lidar for ecologists

Light detection and ranging (lidar) technology has fundamentally advanced the way we measure forest structure, facilitating new insights into ecological processes. Lidar for forest ecology applications is deployed on multiple types of platforms that operate from the ground, air, or space, and each has associated strengths and limitations. Ideally, the choice of what kind of lidar to use in a particular study should be guided by the ecological question of interest; however, practical considerations of cost, data availability, and processing tools can be equally important. This synthesis is a practical introduction to how different lidar platforms characterize forest structure (e.g., tree size/location, wood volume, branching structure, aboveground biomass, leaf properties), designed for a general audience of ecologists (not remote sensing scientists) seeking an accessible introduction to the use of lidar. We also provide examples of novel ecological insights from recent lidar research and describe current limitations and areas of expected improvement. Last, we include an appendix of data collected from terrestrial, mobile, unoccupied aerial system, airplane, and satellite lidar platforms within a common temperate forest area, with associated code to allow new lidar users to visualize and manipulate data in R.

Cushman, KC [ORNL] (ORCID:0000000234641151)↗
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.↗
DOE OSTI2026

CHESS 2025: Waveform LiDAR data from NEON AOP surveys

This dataset provides Level 1 (L1) full-waveform light detection and ranging (LiDAR) data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. Waveform LiDAR data can provide more detailed information about objects on the ground than discrete point clouds typically do, and they are often used for granular target segmentation and characterization of subcanopy vegetation. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary waveform LiDAR data delivered by NEON and are provided per flightline in compressed Pulsewaves format, an open-source binary file standard. A Pulsewaves object comprises a two files: a pulse (.pls) file, which stores the geographic origin, outgoing vector, and metadata for every laser pulse emitted by the scanner, and a wave file (.wvs), which stores the sequential amplitude samples of the outgoing pulse and the returning signals. The files are published here in their compressed forms (.plz, .wvz). All waveform data were processed following the theoretical workflow described in the NEON L0-to-L1 Waveform LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022a); however, the Pulsewaves output format differs from a legacy format described in that document. Waveform amplitude samples are recorded at 1 nanosecond intervals. All coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Waveform data for the UPTA survey area were collected without incident and the published records are complete. However, both the ALMO and CRBU collections experienced issues that resulted in incomplete data for those areas. On collection day 2018-06-16 a hardware failure caused the waveform digitizer to lose data from the eastern edge of the ALMO site (Figure 22). The waveform data for flightlines 2–20 could not be extracted from the digitizer, and the data proved unrecoverable. As a result, a portion of the site does not have coverage with waveform data. Although no hardware failure was observed during collection over the CRBU area, final waveform files generated by vendor software contained only ~25% of the expected number of return pulses. After discovery, NEON initiated troubleshooting with the vendor. The root cause of the data ablation had not been identified at the time of publication. Additional data will be published in an update to this package if further recovery proves successful. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also 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.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2026

CHESS 2025: Discrete-return LiDAR point clouds from NEON AOP surveys

This dataset provides Level 1 (L1) discrete-return light detection and ranging (LiDAR) point cloud data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary unclassified discrete-return LiDAR data delivered by NEON and are provided per flightline as LASzip (LAZ) 1.4 Format 6 files. Data were processed following the workflow described in the NEON L0-to-L1 Discrete Return LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022). Each record in the unclassified point clouds represents a geolocated laser target/return recorded by the LiDAR system, with values for X, Y, Z position and return intensity. All point coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Flight metadata describing flightline boundaries and positional uncertainty by point are also included. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also 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.

2018 NEON and 2025 CHESS Campaigns↗
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↗
DOE OSTI2024

Extended Application of State LiDAR Datasets in Locating Orphaned Wells in Appalachian Region

Location inaccuracies in historical and state oil and gas well databases present a major challenge in locating these orphaned wells. To address this, modern scientific methods such as Light Detection and Ranging (LiDAR), aerial magnetic remote sensing, and digital GIS products have been employed. LiDAR technology uses light to detect surface area changes, providing detailed surface views. This is a workflow to process LiDAR data for use in locating orphaned wells.

Gorantla, Vijaya [NETL Site Support Contractor, Na↗
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)↗
DOE OSTI2024

Fast, Nondestructive and Precise Biomass Measurements Are Possible Using Lidar-Based Convex Hull and Voxelization Algorithms

Light detection and ranging (lidar) scanning tools are available that can make rapid digital estimations of biomass. Voxelization and convex hull are two algorithms used to calculate the volume of the scanned plant canopy, which is correlated with biomass, often the primary trait of interest. Voxelization splits the scans into regular-sized cubes, or voxels, whereas the convex hull algorithm creates a polygon mesh around the outermost points of the point cloud and calculates the volume within that mesh. In this study, digital estimates of biomass were correlated against hand-harvested biomass for field-grown corn, broom corn, and energy sorghum. Voxelization (r = 0.92) and convex hull (r = 0.95) both correlated well with plant dry biomass. Lidar data were also collected in a large breeding trial with nearly 900 genotypes of energy sorghum. In contrast to the manual harvest studies, digital biomass estimations correlated poorly with yield collected from a forage harvester for both voxel count (r = 0.32) and convex hull volume (r = 0.39). However, further analysis showed that the coefficient of variation (CV, a measure of variability) for harvester-based estimates of biomass was greater than the CV of the voxel and convex-hull-based biomass estimates, indicating that poor correlation was due to harvester imprecision, not digital estimations. Overall, results indicate that the lidar-based digital biomass estimates presented here are comparable or more precise than current approaches.

Environmental Sciences & Ecology↗
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↗
DOE OSTI2026

CHESS 2025: Orthorectified airborne RGB imagery from NEON AOP surveys

This dataset provides Level 1 (L1) and Level 3 (L3) orthorectified Red-Green-Blue (RGB) imagery collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). This high-resolution imagery is a photographic record of red, green, and blue visible light from sunlight reflected off of the Earth’s surface. The data comprise full-color images of the ground surface and are primarily intended to provide context to imaging spectroscopy and light detection and ranging (LiDAR) data. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. RGB images were acquired using the PhaseOne IXM-RS150F high-resolution digital camera onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). The package data include both an L1 product comprising one camera frame per file and an L3 mosaic aligned to the Universal Transverse Mercator (UTM) Zone 13N grid and the World Geodetic System (WGS) 84 projection. Both products are provided in geotif (.tif) format at 0.1 m ground resolution. The bulk of the imagery was collected during the main CHESS field campaign from June 13 to July 15, 2025. Additional images of a portion of the Upper Taylor (UPTA) domain were collected on September 18, 2025, to fill gaps in imagery identified after the main campaign was complete. RGB camera imagery is not radiometrically calibrated, and therefore pixel values should not be exploited for scientific analysis. Pixel values have undergone a manual adjustment to enhance feature identification. The imagery is rigorously geolocated which does allow for reliable geometric information to be retrieved. To generate the orthorectified imagery, the NEON AOP camera captured visible spectrum in red, green, and blue bands. The raw images were then processed using NEON’s camera orthorectification workflow. A boresight calibration flight was made to build a complete camera, distortion, and alignment model. Color balance/white balance and exposure correction were applied to the raw RGB images. The corrected images were orthorectified by ray-tracing image pixels to a lidar-derived digital surface model (DSM) mesh using the refined camera model, outputting orthorectified raster pixels on a regular grid. Flightline-level data were mosaicked by selecting per-pixel contributions from overlapping orthorectified images using line-of-sight (LOS) zenith angle minimization to reduce edge distortions. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also 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.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2025

Realistic Noise Generation to Enhance Realism of Virtual Lidar Scans

Many real-world phenomena corrupt light detection and ranging (lidar) measurements, such as laser energy attenuation, variations in aerosol concentration and composition with height, and hard target returns. Accurate studies of lidar scans using virtual lidar methods should include some realistic model of these corrupting effects to generate more realistic simulations of lidar scans. We present a simple model that characterizes noise caused by energy attenuation and aerosol stratification. The model requires limited inputs and is developed for a Halo Photonics Streamline XR lidar but is readily generalizable for other lidar systems. A critical component of this model is a model of the standard deviation of measured wind speed as a function of the backscattered signal’s signal-to-noise ratio. We derive a general model for this behavior that can be adapted to different scan settings.

17 WIND ENERGY↗
DOE OSTI2024

System and method for adaptable lidar imaging

The present disclosure relates to an adaptive light detection and ranging (lidar) system. In one implementation the system may have a controller and lidar control software in communication with the controller. A focusing control subsystem may be included which is configured to control focusing and detection of a laser beam. An emitter/detector subsystem may be included which is responsive to commands from the focusing control subsystem to generate at least one laser beam which is used to implement a plurality of different focusing modes for imaging a scene.

Panas, Robert Matthew↗