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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2021

Development of an Innovative Inspection Tool for Superheater Tubes in Fossil Fuel Power Plants

Fossil fuel power plants are complex systems containing multiple components that require periodic health monitoring. Failures in these systems can lead to increased downtime for the plant, reduction of power, and significant cost for repairs. Inspections of the plant’s superheater tubes are typically manual, laborious, and extremely time-consuming. This is due to their small diameter size (between 1.3 and 7.6 cm) and the coiled structure of the tubing. In addition, the tubes are often stacked close to each other, limiting access for external inspection. This paper presents the development and testing of an electrically powered pipe crawler that can navigate inside 5 cm diameter tubes and provide an assessment of their health. The crawler utilizes peristaltic motion within the tubes via interconnected modules for gripping and extending. The modular nature of the system allows it to traverse through straight sections and multiple 90° and 180° bends. Additional modules in the system include an ultrasonic sensor for tube thickness measurements, as well as environmental sensors, a light detecting and ranging (LiDAR) sensor, and camera. Furthermore, these modules utilize a gear system that allows for 360° rotation and provides a means to inspect the entire internal circumference of the tubes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗
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↗
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↗
DOE OSTI2023

Stand validation of lidar forest inventory modeling for a managed southern pine forest

We evaluated area-based approaches (ABAs) to light detection and ranging (lidar) predictions of plot- and stand-level forest attributes (tree count, height, basal area, volume, aboveground biomass, broadleaf/conifer, and diameter at breast height — “diameter”). ABA methods included post-stratification (PS), ordinary least squares (OLSs) regression, k nearest neighbors ( kNN), and random forest (RF). This study was conducted on the Savannah River Site in South Carolina, USA. Plot- and stand-level predictions were validated against fixed-radius 0.04 ha (0.1 acre) plots in 49 ≈2.0 ha (5 acre) stands. Our findings demonstrate that lidar can be incorporated operationally into forest inventory systems to provide stand-level inferences for a wide range of forest attributes. Volume predictions for specific diameter classes, however, often fared poorly (root mean squared error (RMSE) > 100%) for the methods we explored, especially for larger (less common) diameter trees. Stand-level results were consistently better than pixel-level results (10–200+ percentage points). kNN and RF performed similarly and better than OLS and PS, but RF was the most robust to model configurations, while kNN has practical advantages such as simultaneous predictions of many attributes.

Forestry↗