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NASA NTRS1998

The Laser Vegetation Imaging Sensor (LVIS): An Airborne Laser Altimeter for Mapping Vegetation and Topography

The Laser Vegetation Imaging Sensor (LVIS) is an airborne laser altimeter designed to quickly and extensively map surface topography as well as the relative heights of other reflecting surfaces within the laser footprint. Since 1997, this instrument has primarily been used as the airborne simulator for the Vegetation Canopy Lidar (VCL) mission, a spaceborne mission designed to measure tree height, vertical structure and ground topography (including sub-canopy topography). LVIS is capable of operating from 500 m to 10 km above ground level with footprint sizes from 1 to 60 m. Laser footprints can be randomly spaced within the 7 degree telescope field-of-view, constrained only by the operating frequency of the ND:YAG Q-switched laser (500 Hz). A significant innovation of the LVIS altimeter is that all ranging, waveform recording, and range gating are performed using a single digitizer, clock base, and detector. A portion of the outgoing laser pulse is fiber-optically fed into the detector used to collect the return signal and this entire time history of the outgoing and return pulses is digitized at 500 Msamp/sec. The ground return is then located using software digital signal processing, even in the presence of visibly opaque clouds. The surface height distribution of all reflecting surfaces within the laser footprint can be determined, for example, tree height and ground elevation. To date, the LVIS system has been used to monitor topographic change at Long Valley caldera, CA, as part of NASA's Topography and Surface Change program, and to map tree structure and sub-canopy topography at the La Selva Biological Research Station in Costa Rica, as part of the pre-launch calibration activities for the VCL mission. We present results that show the laser altimeter consistently and accurately maps surface topography, including sub-canopy topography, and vegetation height and structure. These results confirm the measurement concept of VCL and highlight the benefits of airborne prototypes of spaceborne instruments.

Bryan, J.↗
NASA NTRS1999

The Laser Vegetation Imaging Sensor (LVIS): A Medium-Altitude, Digitization-Only, Airborne Laser Altimeter for Mapping Vegetation and Topography

The Laser Vegetation Imaging Sensor (LVIS) is an airborne, scanning laser altimeter designed and developed at NASA's Goddard Space Flight Center. LVIS operates at altitudes up to 10 km above ground, and is capable of producing a data swath up to 1000 m wide nominally with 25 m wide footprints. The entire time history of the outgoing and return pulses is digitized, allowing unambiguous determination of range and return pulse structure. Combined with aircraft position and attitude knowledge, this instrument produces topographic maps with decimeter accuracy and vertical height and structure measurements of vegetation. The laser transmitter is a diode-pumped Nd:YAG oscillator producing 1064 nm, 10 nsec, 5 mJ pulses at repetition rates up to 500 Hz. LVIS has recently demonstrated its ability to determine topography (including sub-canopy) and vegetation height and structure on flight missions to various forested regions in the U.S. and Central America. The LVIS system is the airborne simulator for the Vegetation Canopy Lidar (VCL) mission (a NASA Earth remote sensing satellite due for launch in 2000), providing simulated data sets and a platform for instrument proof-of-concept studies. The topography maps and return waveforms produced by LVIS provide Earth scientists with a unique data set allowing studies of topography, hydrology, and vegetation with unmatched accuracy and coverage.

Blair, J. Bryan↗
NASA NTRS2011

3D Vegetation Mapping Using UAVSAR, LVIS, and LIDAR Data Acquisition Methods

The overarching objective of this ongoing project is to assess the role of vegetation within climate change. Forests capture carbon, a green house gas, from the atmosphere. Thus, any change, whether, natural (e.g. growth, fire, death) or due to anthropogenic activity (e.g. logging, burning, urbanization) may have a significant impact on the Earth's carbon cycle. Through the use of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and NASA's Laser Vegetation Imaging Sensor (LVIS), which are airborne Light Detection and Ranging (LIDAR) remote sensing technologies, we gather data to estimate the amount of carbon contained in forests and how the content changes over time. UAVSAR and LVIS sensors were sent all over the world with the objective of mapping out terrain to gather tree canopy height and biomass data; This data is in turn used to correlate vegetation with the global carbon cycle around the world.

Uninhabited Aerial Vehicle Synthetic Aperture Rada↗
NASA NTRS2000

Future Applications Using Return-Pulse Correlation from Imaging Laser Altimeters

The Laser Vegetation Imaging Sensor (LVIS) is an airborne, wide-swath, digitization-only laser altimeter capable of collecting full return waveforms (i.e. echoes) from laser footprints ranging in diameter from 1 to 80 m across up to a 1 km wide data swath. The return waveform can be used to enhance the accuracy of laser ranging and to provide information about the vertical structure of vegetation and topography within each laser footprint. Although extremely small laser footprints (< 1 0 cm diameter) generally return simple, impulse responses to their target surface, larger footprints typically exhibit complex returns representing the diverse vertical distributions of surfaces contained in each footprint. Only a handful of airborne and spaceborne laser altimeters record the return echo or return pulse that is reflected from the Earth's surface (i.e. NASA's LVIS, SLA, VCL, and GLAS laser altimeters). Waveforms are currently interpreted to extract a timing or ranging point or points to represent the mean ground elevation or the vertical height of vegetation. But, recent progress using pulse shape correlation techniques shows promise for a variety of science applications involving change detection of surface topography and vegetation as well as potential for improving data processing by correlating images or crossovers to solve systematic biases. We show example correlation images from LVIS and discuss instrument design implications and potential science applications.

Blair, J. Bryan↗
NASA NTRS2003

Full-Waveform, Wide-Swath Lidar Imaging of Forested and Urban Areas in Leaf-On Conditions: Development, Results and Future Direction

Full-Waveform lidar measurements provide unprecedented views of the vertical and horizontal structure of vegetation and the topography of the Earth s surface. Utilizing a high signal-to-noise ratio lidar system, larger than typical laser footprints (10-20 m), and the recorded time history of interaction between a short-duration (approx. 10 ns) pulse of laser light and the surface of the Earth, full-waveform lidar is able to simultaneously image sub-canopy topography as well as the vertical structure of any overlying vegetation. These data reveal the true 3-D vegetation structure in leaf-on conditions enabling important biophysical parameters such as above-ground biomass to be estimated with unprecedented accuracy. An airborne lidar mission was conducted July-August 2003 in support of the North America Carbon Program. NASA s Laser Vegetation Imaging Sensor (LVIS) was used to image approximately 2,000 km$^2$ in Maine, New Hampshire, Massachusetts and Maryland. Areas with available ground and other data were included (e.g., experimental forests, FLUXNET sites) in order to facilitate as many bio- and geophysical investigations as possible. Data collected included ground elevation and canopy height measurements for each laser footprint, as well as the vertical distribution of intercepted surfaces. Data will be publicly distributed within 6- 12 months of collection. Further details of the mission, including the lidar system technology, the locations of the mapped areas, and examples of the numerous data products that can be derived from the return waveform data products will be presented. Future applications including detection of ground and vegetation canopy changes and a spaceborne implementation of wide-swath, full-waveform imaging lidar will also be discussed.

Blair, James B.↗
NASA NTRS2004

Full-waveform, Laser Altimeter Measurements of Vegetation Vertical Structure and Sub-canopy Topography in Support of the North American Carbon Program

Full-waveform, scanning laser altimeters (i.e. lidar) provide a unique and precise view of the vertical and horizontal structure of vegetation across wide swaths. These unique laser altimeters systems are able to simultaneously image sub-canopy topography and the vertical structure of any overlying vegetation. These data reveal the true 3-D distribution of vegetation in leaf-on conditions enabling important biophysical parameters such as canopy height and aboveground biomass to be estimated with unprecedented accuracy. An airborne lidar mission was conducted in the summer of 2003 in support of preliminary studies for the North America Carbon Program. NASA's Laser Vegetation Imaging Sensor (LVIS) was used to image approximately 2,000 sq km in Maine, New Hampshire, Massachusetts and Maryland. Areas with available ground and other data were included (e.g., experimental forests, FLUXNET sites) in order to facilitate numerous bio- and geophysical investigations. Data collected included ground elevation and canopy height measurements for each laser footprint, as well as the vertical distribution of intercepted surfaces (i.e. the return waveform). Data are currently available at the LVIS website (http://lvis.gsfc.nasa.gov/). Further details of the mission, including the lidar system technology, the locations of the mapped areas, and examples of the numerous data products that can be derived from the return waveform data products are available on the website and will be presented. Future applications including potential fusion with other remote sensing data sets and a spaceborne implementation of wide-swath, full-waveform imaging lidar will also be discussed.

Blair, B.↗
NASA NTRS2002

Imaging Laser Altimetry in the Amazon: Mapping Large Areas of Topography, Vegetation Height and Structure, and Biomass

A flight mission of NASA GSFC's Laser Vegetation Imaging Sensor (LVIS) is planned for June-August 2003 in the Amazon region of Brazil. The goal of this flight mission is to map the vegetation height and structure and ground topography of a large area of the Amazon. This data will be used to produce maps of true ground topography, vegetation height, and estimated above-ground biomass and for comparison with and potential calibration of Synthetic Aperture Radar (SAR) data. Approximately 15,000 sq. km covering various regions of the Amazon will be mapped. The LVIS sensor has the unique ability to accurately sense the ground topography beneath even the densest of forest canopies. This is achieved by using a high signal-to-noise laser altimeter to detect the very weak reflection from the ground that is available only through small gaps in between leaves and between tree canopies. Often the amount of ground signal is 1% or less of the total returned echo. Once the ground elevation is identified, that is used as the reference surface from which we measure the vertical height and structure of the vegetation. Test data over tropical forests have shown excellent correlation between LVIS measurements and biomass, basal area, stem density, ground topography, and canopy height. Examples of laser altimetry data over forests and the relationships to biophysical parameters will be shown. Also, recent advances in the LVIS instrument will be discussed.

Blair, J. Bryan↗
NASA NTRS1999

Gaussian Decomposition of Laser Altimeter Waveforms

We develop a method to decompose a laser altimeter return waveform into its Gaussian components assuming that the position of each Gaussian within the waveform can be used to calculate the mean elevation of a specific reflecting surface within the laser footprint. We estimate the number of Gaussian components from the number of inflection points of a smoothed copy of the laser waveform, and obtain initial estimates of the Gaussian half-widths and positions from the positions of its consecutive inflection points. Initial amplitude estimates are obtained using a non-negative least-squares method. To reduce the likelihood of fitting the background noise within the waveform and to minimize the number of Gaussians needed in the approximation, we rank the "importance" of each Gaussian in the decomposition using its initial half-width and amplitude estimates. The initial parameter estimates of all Gaussians ranked "important" are optimized using the Levenburg-Marquardt method. If the sum of the Gaussians does not approximate the return waveform to a prescribed accuracy, then additional Gaussians are included in the optimization procedure. The Gaussian decomposition method is demonstrated on data collected by the airborne Laser Vegetation Imaging Sensor (LVIS) in October 1997 over the Sequoia National Forest, California.

Hofton, Michelle A.↗
NASA NTRS2000

Primary and Secondary Controls on Measurements of Forest Height Using Large-Footprint Lidar at the Hubbard Brook LTER

On September 26, 1999, we mapped canopy structure over 90% of the Hubbard Brook Experimental Forest in White Mountain National Forest, New Hampshire, using the Laser Vegetation Imaging Sensor (LVIS). This airborne instrument was configured to emulate data expected from the Vegetation Canopy Lidar (VCL) space mission. We compared above ground heights of the tallest surfaces detected by lidar with average forest canopy heights estimated from tree-based measurements in or near 346 0.05 ha plots (made in autumn of 1997 and 1998). Vegetation heights had by far the predominant influence on lidar top heights, but with this large data set we were able to measure two significant secondary effects: those of steepness or slope of the underlying terrain and of tree crown form. The size of the slope effect was intermediate between that expected from models of homogeneous canopy layers and for solitary tree crowns. The first detected surfaces were also proportionately taller for plots with more basal area in broad leaved northern hardwoods than for mostly coniferous plots. We expected this because of the contrast between the shapes of cumulative distributions of surface area for elliptical or hemi-elliptical tree crowns and those for conical crowns. Correcting for these secondary effects, when appropriate data are available for calibration, may improve vegetation structure estimates in regional studies using VCL or similar lidar data sources.

Knox, Robert G.↗
NASA NTRS2007

Simulation Studies of Forest Structure using 3D Lidar and Radar Models

The use of lidars and radars to measure forest structure attributes such as height and biomass are being considered for future Earth Observation missions. Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield information about the vertical profile of the canopy. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and is useful for estimating biomass. Interferometric SAR (InSAR) has been shown to yield forest canopy height information. For example, the height of scattering phase retrieved from InSAR data is considered to be correlated with the three height and the spatial structure of the forest stand. There is much interest in exploiting these technologies separately and together to get important information for carbon cycle and ecosystem science. More detailed information of the electromagnetic radiation interactions within forest canopies is needed. And backscattering models can be of much utility here. As part of a NASA funded project to explore data fusion, a three-dimensional (3D) coherent radar backscattering model and a 3D lidar backscatter models were used to investigate the use of large footprint lidar, SAR and InSAR for characterizing realistic forest scenes. For this paper, we use stem maps and other forest measurements to develop a realistic spatial structure of a spruce-hemlock forest canopy found in Maine, USA. The radar and lidar models used measurements of the 3D forest scene as input and simulated the coherent radar backscattering signature and 1064nm energy backscatter, respectively. The relationships of backscatter derived forest structure were compared with field measurements. In addition, we also had detailed airborne lidar (Laser Imaging Vegetation Sensor, LVIS) data available over the stem map sites that was used to study the accuracies of tree height derived from modeled SAR backscatter and the scattering phase center retrieved from the simulated InSAR data will be compared with the height indices, or other structure parameters derived from the lidar data. These results will address the possible synergies between lidar and radar in data in terms of forest structural information.

Sun, Guoqing↗
NASA NTRS2007

Physical Interpretation of the Correlation Between Multi-Angle Spectral Data and Canopy Height

Recent empirical studies have shown that multi-angle spectral data can be useful for predicting canopy height, but the physical reason for this correlation was not understood. We follow the concept of canopy spectral invariants, specifically escape probability, to gain insight into the observed correlation. Airborne Multi-Angle Imaging Spectrometer (AirMISR) and airborne Laser Vegetation Imaging Sensor (LVIS) data acquired during a NASA Terrestrial Ecology Program aircraft campaign underlie our analysis. Two multivariate linear regression models were developed to estimate LVIS height measures from 28 AirMISR multi-angle spectral reflectances and from the spectrally invariant escape probability at 7 AirMISR view angles. Both models achieved nearly the same accuracy, suggesting that canopy spectral invariant theory can explain the observed correlation. We hypothesize that the escape probability is sensitive to the aspect ratio (crown diameter to crown height). The multi-angle spectral data alone therefore may not provide enough information to retrieve canopy height globally

Schull, M. A.↗
NASA NTRS2011

Forest Biomass Mapping From Lidar and Radar Synergies

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed. In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R(exp 2) of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI Mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R2 of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar- SAR synergy 63 was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.

Sun, Guoqing↗
NASA NTRS2012

Retrieval of Vertical LAI Profiles Over Tropical Rain Forests using Waveform Lidar at La Selva, Costa Rica

This study explores the potential of waveform lidar in mapping the vertical and spatial distributions of leaf area index (LAI) over the tropical rain forest of La Selva Biological Station in Costa Rica. Vertical profiles of LAI were derived at 0.3 m height intervals from the Laser Vegetation Imaging Sensor (LVIS) data using the Geometric Optical and Radiative Transfer (GORT) model. Cumulative LAI profiles obtained from LVIS were validated with data from 55 ground to canopy vertical transects using a modular field tower to destructively sample all vegetation. Our results showed moderate agreement between lidar and field derived LAI (r2=0.42, RMSE=1.91, bias=−0.32), which further improved when differences between lidar and tower footprint scales (r2=0.50, RMSE=1.79, bias=0.27) and distance of field tower from lidar footprint center (r2=0.63, RMSE=1.36, bias=0.0) were accounted for. Next, we mapped the spatial distribution of total LAI across the landscape and analyzed LAI variations over different land cover types. Mean values of total LAI were 1.74, 5.20, 5.41 and 5.62 over open pasture, secondary forests, regeneration forests after selective-logging and old-growth forests respectively. Lastly, we evaluated the sensitivities of our LAI retrieval model to variations in canopy/ground reflectance ratio and to waveform noise such as induced by topographic slopes. We found for both, that the effects were not significant for moderate LAI values (about 4). However model derivations of LAI might be inaccurate in areas of high-slope and high LAI (about 8) if ground return energies are low. This research suggests that large footprint waveform lidar can provide accurate vertical LAI profile estimates that do not saturate even at the high LAI levels in tropical rain forests and may be a useful tool for understanding the light transmittance within these canopies.

Tang, Hao↗
NASA NTRS2012

Forest Canopy Cover and Height from MISR in Topographically Complex Southwestern US Landscape Assessed with High Quality Reference Data

This study addresses the retrieval of spatially contiguous canopy cover and height estimates in southwestern USforests via inversion of a geometric-optical (GO) model against surface bidirectional reflectance factor (BRF) estimates from the Multi-angle Imaging SpectroRadiometer (MISR). Model inversion can provide such maps if good estimates of the background bidirectional reflectance distribution function (BRDF) are available. The study area is in the Sierra National Forest in the Sierra Nevada of California. Tree number density, mean crown radius, and fractional cover reference estimates were obtained via analysis of QuickBird 0.6 m spatial resolution panchromatic imagery usingthe CANopy Analysis with Panchromatic Imagery (CANAPI) algorithm, while RH50, RH75 and RH100 (50, 75, and 100 energy return) height data were obtained from the NASA Laser Vegetation Imaging Sensor (LVIS), a full waveform light detection and ranging (lidar) instrument. These canopy parameters were used to drive a modified version of the simple GO model (SGM), accurately reproducing patterns ofMISR 672 nm band surface reflectance (mean RMSE 0.011, mean R2 0.82, N 1048). Cover and height maps were obtained through model inversion against MISR 672 nm reflectance estimates on a 250 m grid.The free parameters were tree number density and mean crown radius. RMSE values with respect to reference data for the cover and height retrievals were 0.05 and 6.65 m, respectively, with of 0.54 and 0.49. MISR can thus provide maps of forest cover and height in areas of topographic variation although refinements are required to improve retrieval precision.

Chopping, Mark↗
NASA NTRS2013

Characterization of Canopy Layering in Forested Ecosystems Using Full Waveform Lidar

Canopy structure, the vertical distribution of canopy material, is an important element of forest ecosystem dynamics and habitat preference. Although vertical stratification, or "canopy layering," is a basic characterization of canopy structure for research and forest management, it is difficult to quantify at landscape scales. In this paper we describe canopy structure and develop methodologies to map forest vertical stratification in a mixed temperate forest using full-waveform lidar. Two definitions-one categorical and one continuous-are used to map canopy layering over Hubbard Brook Experimental Forest, New Hampshire with lidar data collected in 2009 by NASA's Laser Vegetation Imaging Sensor (LVIS). The two resulting canopy layering datasets describe variation of canopy layering throughout the forest and show that layering varies with terrain elevation and canopy height. This information should provide increased understanding of vertical structure variability and aid habitat characterization and other forest management activities.

lidar↗
NASA NTRS2012

Achieving Accuracy Requirements for Forest Biomass Mapping: A Spaceborne Data Fusion Method for Estimating Forest Biomass and Lidar Sampling Error

The synergistic use of active and passive remote sensing (i.e., data fusion) demonstrates the ability of spaceborne light detection and ranging (LiDAR), synthetic aperture radar (SAR) and multispectral imagery for achieving the accuracy requirements of a global forest biomass mapping mission (+/-20 Mg/ha or 20%, the greater of the two, for at least 80% of grid cells). A data fusion approach also provides a means to extend 3D information from discrete spaceborne LiDAR measurements of forest structure across scales much larger than that of the LiDAR footprint. For estimating biomass, these measurements mix a number of errors including those associated with LiDAR footprint sampling over regional-global extents. A general framework for mapping above ground live forest biomass density (AGB) with a data fusion approach is presented and verified using data from NASA field campaigns near Howland, ME, USA, to assess AGB and LiDAR sampling errors across a regionally representative landscape. We combined SAR and Landsat-derived optical (passive optical) image data to identify contiguous areas (>0.5 ha) that are relatively homogenous in remote sensing metrics (forest patches). We used this image-derived data with simulated spaceborne LiDAR derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (LVIS) to compute AGB and estimate LiDAR sampling error for forest patches and 100 m, 250 m, 500 m, and 1 km grid cells. At both the patch and grid scales, we evaluated differences in AGB estimation and sampling error from the combined use of LiDAR with both SAR and passive optical and with either SAR or passive optical alone. First, this data fusion approach demonstrates that incorporating forest patches into the AGB mapping framework can provide sub-grid forest information for coarser grid-level AGB reporting. Second, a data fusion approach for estimating AGB using simulated spaceborne LiDAR with SAR and passive optical image combinations reduced forest AGB sampling errors 12%-38% from those where LiDAR is used with SAR or passive optical alone. In absolute terms, sampling errors were reduced from 14-40 Mg/ha to 11-28 Mg/ha across all grid scales and prediction methods, where minimum sampling errors were 11, 15, 18, and 22 Mg/ha for 1 km, 500 m, 250 m, and 100 m grid scales, respectively. Third, spaceborne global scale accuracy requirements were achieved whereby at least 80% of the grid cells at 100 m, 250 m, 500 m, and 1 km grid levels met AGB accuracy requirements using a combination of passive optical and SAR along with machine learning methods to predict vegetation structure metrics for forested areas without LiDAR samples. Finally, using either passive optical or SAR, accuracy requirements were met at the 500 m and 250 m grid level, respectively..

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↗