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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Polarimetric, Two-Color, Photon-Counting Laser Altimeter Measurements of Forest Canopy Structure

Laser altimeter measurements of forest stands with distinct structures and compositions have been acquired at 532 nm (green) and 1064 nm (near-infrared) wavelengths and parallel and perpendicular polarization states using the Slope Imaging Multi-polarization Photon Counting Lidar (SIMPL). The micropulse, single photon ranging measurement approach employed by SIMPL provides canopy structure measurements with high vertical and spatial resolution. Using a height distribution analysis method adapted from conventional, 1064 nm, full-waveform lidar remote sensing, the sensitivity of two parameters commonly used for above-ground biomass estimation are compared as a function of wavelength. The results for the height of median energy (HOME) and canopy cover are for the most part very similar, indicating biomass estimations using lidars operating at green and near-infrared wavelengths will yield comparable estimates. The expected detection of increasing depolarization with depth into the canopies due to volume multiple-scattering was not observed, possibly due to the small laser footprint and the small detector field of view used in the SIMPL instrument. The results of this work provide pathfinder information for NASA's ICESat-2 mission that will employ a 532 nm, micropulse, photon counting laser altimeter.

Harding, David J.↗
NASA NTRS2006

Draft Plan for Characterizing Commercial Data Products in Support of Earth Science Research

This presentation introduces a draft plan for characterizing commercial data products for Earth science research. The general approach to the commercial product verification and validation includes focused selection of a readily available commercial remote sensing products that support Earth science research. Ongoing product verification and characterization will question whether the product meets specifications and will examine its fundamental properties, potential and limitations. Validation will encourage product evaluation for specific science research and applications. Specific commercial products included in the characterization plan include high-spatial-resolution multispectral (HSMS) imagery and LIDAR data products. Future efforts in this process will include briefing NASA headquarters and modifying plans based on feedback, increased engagement with the science community and refinement of details, coordination with commercial vendors and The Joint Agency Commercial Imagery Evaluation (JACIE) for HSMS satellite acquisitions, acquiring waveform LIDAR data and performing verification and validation.

Ryan, Robert E.↗
NASA NTRS1999

Landscape-Scale Canopy Complexity in and Near Braulio Carillo National Park, Costa Rica

Using medium-large footprint lidar sampling of approximately 500 square km of Costa Rica, we assessed the vertical and horizontal complexity of a forest-dominated tropical landscape. As expected, vertical extents of structure and canopy heights estimated from lidar waveforms were smaller in high elevation forests than in forests at lower elevations. In areas of the park and long-protected areas of La Selva Biological Station, forests typically had more consistent ratios of median height to total height than areas with other types of recent land use. Areas outside the park exhibited both stronger and weaker spatial correlations in canopy properties than most areas within the park. We also simulated the effects of these differences on data products gridded from lidar transects, like those produced by the Vegetation Canopy Lidar (VCL) Mission.

Knox, Robert G.↗
NASA NTRS2011

Modeling the Height of Young Forests Regenerating from Recent Disturbances in Mississippi using Landsat and ICESat data

Many forestry and earth science applications require spatially detailed forest height data sets. Among the various remote sensing technologies, lidar offers the most potential for obtaining reliable height measurement. However, existing and planned spaceborne lidar systems do not have the capability to produce spatially contiguous, fine resolution forest height maps over large areas. This paper describes a Landsat-lidar fusion approach for modeling the height of young forests by integrating historical Landsat observations with lidar data acquired by the Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud, and land Elevation (ICESat) satellite. In this approach, "young" forests refer to forests reestablished following recent disturbances mapped using Landsat time-series stacks (LTSS) and a vegetation change tracker (VCT) algorithm. The GLAS lidar data is used to retrieve forest height at sample locations represented by the footprints of the lidar data. These samples are used to establish relationships between lidar-based forest height measurements and LTSS-VCT disturbance products. The height of "young" forest is then mapped based on the derived relationships and the LTSS-VCT disturbance products. This approach was developed and tested over the state of Mississippi. Of the various models evaluated, a regression tree model predicting forest height from age since disturbance and three cumulative indices produced by the LTSS-VCT method yielded the lowest cross validation error. The R(exp 2) and root mean square difference (RMSD) between predicted and GLAS-based height measurements were 0.91 and 1.97 m, respectively. Predictions of this model had much higher errors than indicated by cross validation analysis when evaluated using field plot data collected through the Forest Inventory and Analysis Program of USDA Forest Service. Much of these errors were due to a lack of separation between stand clearing and non-stand clearing disturbances in current LTSS-VCT products and difficulty in deriving reliable forest height measurements using GLAS samples when terrain relief was present within their footprints. In addition, a systematic underestimation of about 5 m by the developed model was also observed, half of which could be explained by forest growth that occurred between field measurement year and model target year. The remaining difference suggests that tree height measurements derived using waveform lidar data could be significantly underestimated, especially for young pine forests. Options for improving the height modeling approach developed in this study were discussed.

Li, Ainong↗
NASA NTRS2011

Radar and Lidar Radar DEM

Using radar and lidar data, the aim is to improve 3D rendering of terrain, including digital elevation models (DEM) and estimates of vegetation height and biomass in a variety of forest types and terrains. The 3D mapping of vegetation structure and the analysis are useful to determine the role of forest in climate change (carbon cycle), in providing habitat and as a provider of socio-economic services. This in turn will lead to potential for development of more effective land-use management. The first part of the project was to characterize the Shuttle Radar Topography Mission DEM error with respect to ICESat/GLAS point estimates of elevation. We investigated potential trends with latitude, canopy height, signal to noise ratio (SNR), number of LiDAR waveform peaks, and maximum peak width. Scatter plots were produced for each variable and were fitted with 1st and 2nd degree polynomials. Higher order trends were visually inspected through filtering with a mean and median filter. We also assessed trends in the DEM error variance. Finally, a map showing how DEM error was geographically distributed globally was created.

remote sensing↗
NASA NTRS2011

Measuring Effective Leaf Area Index, Foliage Profile, and Stand Height in New England Forest Stands Using a Full-Waveform Ground-Based Lidar

Effective leaf area index (LAI) retrievals from a scanning, ground-based, near-infrared (1064 nm) lidar that digitizes the full return waveform, the Echidna Validation Instrument (EVI), are in good agreement with those obtained from both hemispherical photography and the Li-Cor LAI-2000 Plant Canopy Analyzer. We conducted trials at 28 plots within six stands of hardwoods and conifers of varying height and stocking densities at Harvard Forest, Massachusetts, Bartlett Experimental Forest, New Hampshire, and Howland Experimental Forest, Maine, in July 2007. Effective LAI values retrieved by four methods, which ranged from 3.42 to 5.25 depending on the site and method, were not significantly different ( b0.1 among four methods). The LAI values also matched published values well. Foliage profiles (leaf area with height) retrieved from the lidar scans, although not independently validated, were consistent with stand structure as observed and as measured by conventional methods. Canopy mean top height, as determined from the foliage profiles, deviated from mean RH100 values obtained from the Lidar Vegetation Imaging Sensor (LVIS) airborne large-footprint lidar system at 27 plots by .0.91 m with RMSE=2.04 m, documenting the ability of the EVI to retrieve stand height. The Echidna Validation Instrument is the first realization of the Echidna lidar concept, devised by Australia's Commonwealth Scientific and Industrial Research Organization (CSIRO), for measuring forest structure using full-waveform, ground-based, scanning lidar.

Zhao, Feng↗
NASA NTRS2012

Measuring Gap Fraction, Element Clumping Index and LAI in Sierra Forest Stands Using a Full-Waveform Ground-Based Lidar

The Echidna Validation Instrument (EVI), a ground-based, near-infrared (1064 nm) scanning lidar, provides gap fraction measurements, element clumping index measurements, effective leaf area index (LAIe) and leaf area index (LAI) measurements that are statistically similar to those from hemispherical photos. In this research, a new method integrating the range dimension is presented for retrieving element clumping index using a unique series of images of gap probability (Pgap) with range from EVI. From these images, we identified connected gap components and found the approximate physical, rather than angular, size of connected gap component. We conducted trials at 30 plots within six conifer stands of varying height and stocking densities in the Sierra National Forest, CA, in August 2008. The element clumping index measurements retrieved from EVI Pgap image series for the hinge angle region are highly consistent (R2=0.866) with those of hemispherical photos. Furthermore, the information contained in connected gap component size profiles does account for the difference between our method and gap-size distribution theory based method, suggesting a new perspective to measure element clumping index with EVI Pgap image series and also a potential advantage of three dimensional Lidar data for element clumping index retrieval. Therefore further exploration is required for better characterization of clumped condition from EVI Pgap image series.

Zhao, Feng↗
NASA NTRS2014

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

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

Light Detection & Ranging (LIDAR)↗
NASA NTRS2020

Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels

We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborneALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-daybaseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low(≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e.,<90 Mg/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is<60% (or<100 Mg/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg/ha to 47.4 Mg/h with UAVSAR and from 57.1 Mg/ha to 46 Mg/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions.

NISAR↗
NASA NTRS2019

The Global Ecosystem Dynamics Investigation (GEDI) Lidar Laser Transmitter

The Global Ecosystems Dynamics Investigation (GEDI) Lidar, is an Earth Science remote sensing instrument aboard the International Space Station (ISS) and the Japanese Experiment Module (JEM). Its core mission is to measure the global carbon balance of Earth's forests by using a set of three solid state laser transmitters in a multibeam waveform capture lidar technique. GEDI's laser transmitters and precision optical system transmits over 3.4 million laser pulses to the Earth every hour, each pulse producing an individual 3-D biomass column measurement. To enable a successful two-year mission, the lasers had to be reliable, highly repeatable in performance with each measurement power cycle, and designed with minimal part count for reduced manufacture complexity and cost. These transmitters are in-house products; developed, constructed, qualified, and fully integrated into the GEDI instrument at NASA's Goddard Space Flight Center. We will present the lasers' path from initial design to flight operation, with emphasis on the major milestones, critical issues, and lessons learned. Full credit goes to the excellent team effort that led to the successful commissioning and initiation of full-time science operations in March 2019.

Coyle, D. Barry↗
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 NTRS2000

Characterization of a 16-Bit Digitizer for Lidar Data Acquisition

A 6-MHz 16-bit waveform digitizer was evaluated for use in atmospheric differential absorption lidar (DIAL) measurements of ozone. The digitizer noise characteristics were evaluated, and actual ozone DIAL atmospheric returns were digitized. This digitizer could replace computer-automated measurement and control (CAMAC)-based commercial digitizers and improve voltage accuracy.

Williamson, Cynthia K.↗
NASA NTRS1998

Characterization of a 16-bit Digitizer System for Lidar Data Acquisition

A new 16-bit 6-MHz compact, lightweight waveform digitizer module has been tested using actual 300-nm lidar atmospheric returns. The noise level of this digitizer was tested and found to be substantially below the ozone number density to be measured. The digitizer is inexpensive and compact enough to be deployed in UAV aircraft and spacecraft environments. With 16-bit digitizers a resolution of 0.046 mV/step can be achieved, substantially improving resolution over 12-bit systems. This digitizer will find widespread use in future DIAL receiver systems.

Williamson, Cynthia K.↗
NASA NTRS1995

Comparison of Retracking Algorithms Using Airborne Radar and Laser Altimeter Measurements of the Greenland Ice Sheet

This paper compares four continental ice sheet radar altimeter retracking algorithms using airborne radar and laser altimeter data taken over the Greenland ice sheet in 1991. The refurbished Advanced Application Flight Experiment (AAFE) airborne radar altimeter has a large range window and stores the entire return waveform during flight. Once the return waveforms are retracked, or post-processed to obtain the most accurate altitude measurement possible, they are compared with the high-precision Airborne Oceanographic Lidar (AOL) altimeter measurements. The AAFE waveforms show evidence of varying degrees of both surface and volume scattering from different regions of the Greenland ice sheet. The AOL laser altimeter, however, obtains a return only from the surface of the ice sheet. Retracking altimeter waveforms with a surface scattering model results in a good correlation with the laser measurements in the wet and dry-snow zones, but in the percolation region of the ice sheet, the deviation between the two data sets is large due to the effects of subsurface and volume scattering. The Martin et al model results in a lower bias than the surface scattering model, but still shows an increase in the noise level in the percolation zone. Using an Offset Center of Gravity algorithm to retrack altimeter waveforms results in measurements that are only slightly affected by subsurface and volume scattering and, despite a higher bias, this algorithm works well in all regions of the ice sheet. A cubic spline provides retracked altitudes that agree with AOL measurements over all regions of Greenland. This method is not sensitive to changes in the scattering mechanisms of the ice sheet and it has the lowest noise level and bias of all the retracking methods presented.

Ferraro, Ellen J.↗
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 NTRS2012

Radiative Transfer Through Clouds and Its Applications in Support of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) Mission

The Greenland and Antarctic ice sheets, which contain enough ice to raise sea level by about 7 and 60 m, respectively, are losing mass at an increasing rate. To acquire continuous information of the cryosphere, after the Ice, Cloud, and land Elevation Satellite (ICESat) (2003-2010), NASA is actively planning for the ICESat-2 mission. Both ICESat and ICESat-2 are space-borne lidar altimetry systems. The systems measure the time of flight of the arriving photons that are reflected by the surface to deduce the elevation of the underlying terrain. As one of NASA's top priority missions, ICESat-2 is scheduled to launch in 2016. One of the major science goals of ICESat-2 is to quantify the ice sheet mass balance to determine its contributions to the sea level change and its impacts on ocean circulation (Abdalati et al. 2010). Compared to ICESat, which operates at 40 Hz and records the reflected laser energy as a waveform, the significantly improved ICESat-2 lidar employs a 532 nm micro-pulse photon counting system that operates at a high frequency of 10kHz with single photon detectability (Yang et al. 2012). To achieve its science goals, ICESat-2 requires the ability of detecting the elevation change with an accuracy of 0.2 cm/year over the entire ice sheet. Since every photon emitted by the lidar system will travel through the atmosphere, clouds can certainly affect the flight time of the arriving photons. Forward scattering by cloud particles increases the photon path length, thus resulting in biases in ice sheet elevation measurements known as atmospheric path delay (Duta et al. 2001, Yang et al. 2010, 2011). To ensure the accuracy of ICESat-2 surface elevation measurements, it is critical to understand how clouds would affect the travel time of arriving photons. In this talk, we will first present a framework that simulates the behavior of a space-borne 532 mn micro-pulse photon counting lidar in cloudy and clear atmospheres. To investigate the process of laser propagation through clouds, a 3-D Monte Carlo radiative transfer model is used to simulate the photon path distribution and the Poisson distribution is adopted for the number of photon returns. Since the photon counting system only registers the time of the first arriving photon within the detector "dead time", the retrieved average surface elevation tends to bias towards higher values. This is known as the first photon bias. With the scenarios simulated here, the first photon bias for clear sky is about 6.5 cm. Clouds affect surface altimetry in two ways: (1) cloud attenuation lowers the average number of arriving photons and hence reduces the first photon bias; (2) cloud forward scattering increases the photon path length and makes the surface appear further away from the satellite. Compared to clear sky, the average surface elevation detected by the photon counting system for cloudy sky with optical depth 1.0 is 4.0 to 6.0 cm lower for the simulations conducted. The effect of surface roughness on the accuracy of elevation retrievals will also discussed.

Yang, Yuekui↗