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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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NASA NTRS2000

BOREAS TE-5 CO2 Concentration and Stable Isotope Composition

The BOREAS TE-5 team collected measurements in the NSA and SSA on gas exchange, gas composition, and tree growth. This data set contains measurements of the concentration and stable carbon (C-13/C-12 and oxygen (O-18/O-16) isotope ratios of atmospheric CO2 in air samples collected at different heights within forest canopies. The data were collected to determine the influence of photosynthesis and respiration by the forest ecosystems on the concentration and stable isotope ratio of atmospheric CO2 These measurements were collected at the SSA during each 1994 IFC at OJP, OBS, and OA sites. Measurements were also collected at the NSA during each 1994 IFC at the OJP, T6R5S TE UBS, and T2Q6A TE OA sites. The stable isotope ratios are expressed using standard delta notation and in units of per mil. The isotope ratios are expressed relative to the international standard, PDB, for both carbon and oxygen samples. The data are stored in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

Hall, Forrest G.↗
NASA NTRS2005

NASA Tech Briefs, October 2005

Topics covered include: Insect-Inspired Optical-Flow Navigation Sensors; Chemical Sensors Based on Optical Ring Resonators; A Broad-Band Phase-Contrast Wave-Front Sensor; Progress in Insect-Inspired Optical Navigation Sensors; Portable Airborne Laser System Measures Forest-Canopy Height; Deployable Wide-Aperture Array Antennas; Faster Evolution of More Multifunctional Logic Circuits; Video-Camera-Based Position-Measuring System; N-Type delta Doping of High-Purity Silicon Imaging Arrays; Avionics System Architecture Tool; Updated Chemical Kinetics and Sensitivity Analysis Code; Predicting Flutter and Forced Response in Turbomachinery; Upgrades of Two Computer Codes for Analysis of Turbomachinery; Program Facilitates CMMI Appraisals; Grid Visualization Tool; Program Computes Sound Pressures at Rocket Launches; Solar-System Ephemeris Toolbox; Data-Acquisition Software for PSP/TSP Wind-Tunnel Cameras; Corrosion-Prevention Capabilities of a Water-Borne, Silicone-Based, Primerless Coating; Sol-Gel Process for Making Pt-Ru Fuel-Cell Catalysts; Making Activated Carbon for Storing Gas; System Regulates the Water Contents of Fuel-Cell Streams; Five-Axis, Three-Magnetic-Bearing Dynamic Spin Rig; Modifications of Fabrication of Vibratory Microgyroscopes; Chamber for Growing and Observing Fungi; Electroporation System for Sterilizing Water; Thermoelectric Air/Soil Energy-Harvesting Device; Flexible Metal-Fabric Radiators; Actuated Hybrid Mirror Telescope; Optical Design of an Optical Communications Terminal; Algorithm for Identifying Erroneous Rain-Gauge Readings; Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads; Lightweight Thermal Insulation for a Liquid-Oxygen Tank; Stellar Gyroscope for Determining Attitude of a Spacecraft; and Lifting Mechanism for the Mars Explorer Rover.

Source record↗
NASA NTRS2019

Global Ecosystem Dynamics Investigation (GEDI) Instrument Alignment and Test

NASA's Global Ecosystem Dynamics Investigation (GEDI) instrument was launched Dec. 5, 2018, and installed on the International Space Station 419 km from Earth. The GEDI is a Light Detection and Ranging (LIDAR) instrument; measuring the time of flight of transmitted laser beams to the Earth and back to determine altitude for geospatial mapping of forest canopy heights. The need for very dense cross track sampling for slope measurements of canopy height is accomplished by using three individual laser transmitter systems, where each beam is split into two beams by a birefringent crystal. Furthermore, one transmitter is equipped with a diffractive optical element splitting the two beams into four, for a total of 8 beams. The beams are reflected off of the features and imaged by an 800 mm diameter Receiver Telescope Assembly, composed of a Ritchey-Chretien telescope, a refractive aft optics assembly and focal plane array which collects and focuses the light from the 8 probe beams into the 8 science fibers, each with a field of view on the Earth subtending 300 mu rad. The dense cross-track sampling mandated a custom designed dual-fiber interface. The science fibers had to be aligned to the nominal, projected laser spots. The alignment was highly dependent on optimization and co-positioning of the fibers pair-wise due to mechanical constraints. This paper presents the end-to-end alignment and metrology of the full optical system from transmitter elements through receiver telescope, aft-optics, focal plane and receiver fibers performed at NASA Goddard Space Flight Center.[GRAPHICS]

Bente Eegholm↗
NASA NTRS2017

Large-scale fine-resolution products of forest disturbance using new approaches from spaceborne SAT interferometry

Spaceborne SAR interferometry (InSAR) has the potential ofdetecting forest change on a global scale with fine (meter-level)spatial resolution as well as on a monthly/weekly basis under allweather conditions. This is significant to characterize the land usechange and its impact on climate change. In this paper, bothsingle-pass and repeat-pass SAR interferometry from spacebornesensors are combined in order to detect and quantify (withNormalized RMSE 30%) forest disturbance at a large scale(dozens of kilometers) however with a fine spatial resolution (<1 hectare) based on two newly developed approaches. The singlepassInSAR approach is not only able to detect forest disturbancebut also capable of characterizing meter (or even sub-meter)level change of forest phase-center (mean) height due to forestgrowth and/or degradation. The methodology described in thispaper can be considered as complimentary tools and thus can becombined with the existing PolInSAR technique (that has beenwidely used for retrieving forest height from single-pass SARinterferometry). These methods are extensively validated with thepast and current spaceborne single-pass and repeat-pass InSARmissions (i.e. JAXA’s ALOS-1, ALOS-2 and DLR’s TanDEM-X)over subtropical forests in Australia as well as tropical forestsin Brazil. Such techniques also serve as observing prototypes forthe fusion of the future spaceborne InSAR missions (such asNASA-ISRO’s NISAR and DLR’s TanDEM-L).

Schmidt, Michael↗
NASA NTRS2020

Forest Potential Productivity Mapping by Linking Remote-Sensing-Derived Metrics to Site Variables

A fine-resolution region-wide map of forest site productivity is an essential need for effective large-scale forestry planning and management. In this study, we incorporated Sentinel-2 satellite data into an increment-based measure of forest productivity (biomass growth index (BGI)) derived from climate, lithology, soils, and topographic metrics to map improved BGI (iBGI) in parts of North American Acadian regions. Initially, several Sentinel-2 variables including nine single spectral bands and 12 spectral vegetation indices (SVIs) were used in combination with forest management variables to predict tree volume/ha and height using Random Forest. The results showed a 10–12 % increase in out of bag (OOB) r2when Sentinel-2 variables were included in the prediction of both volume and height together with BGI. Later, selected Sentinel-2 variables were used for biomass growth prediction in Maine, USA and New Brunswick, Canada using data from 7738 provincial permanent sample plots. The Sentinel-2 red-edge position (S2REP) index was identified as the most important variable over others to have known influence on site productivity. While a slight improvement in the iBGI accuracy occurred compared to the base BGI model (~2%), substantial changes to coefficients of other variables were evident and some site variables became less important when S2REP was included.

site productivity↗
NASA NTRS2021

Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

Elias Ayrey↗
NASA NTRS2007

Simulation Studies of the Effect of Forest Spatial Structure on InSAR Signature

The height of scattering phase retrieved from InSAR data is considered being correlated with the tree height and the spatial structure of the forest stand. Though some researchers have used simple backscattering models to estimate tree height from the height of scattering center, the effect of forest spatial structure on InSAR data is not well understood yet. A three-dimensional coherent radar backscattering model for forest canopies based on realistic three-dimensional scene was used to investigate the effect in this paper. The realistic spatial structure of forest canopies was established either by field measurements (stem map) or through use of forest growth model. Field measurements or a forest growth model parameterized using local environmental parameters provides information of forest species composition and tree sizes in certain growth phases. A fractal tree model (L-system) was used to simulate individual 3- D tree structure of different ages or heights. Trees were positioned in a stand in certain patterns resulting in a 3-D medium of discrete scatterers. The radar coherent backscatter model took the 3-D forest scene as input and simulates the coherent radar backscattering signature. Interferometric SAR images of 3D scenes were simulated and heights of scattering phase centers were estimated from the simulated InSAR data. The effects of tree height, crown cover, crown depth, and the spatial distribution patterns of trees on the scattering phase center were analyzed. The results will be presented in the paper.

Sun, Guoqing↗
NASA NTRSDate not supplied

Vegetation Heights for Landsat Land Cover Classes in the Interior Boreal Forest Region of Alaska

Accurate determination and mapping of vegetation height is a prerequisite for regional estimations of above ground biomass, carbon pool dynamics, and improved modeling of future climate-vegetation-soil interactions. This study combined land cover class maps (at 30-m pixel resolution) derived from Landsat image bands with canopy vegetation heights detected at 1-m resolution from NASA G-LiHT Lidar flights, both from the year 2014 for the Middle Tanana Valley near Fairbanks, Alaska. The statistical distributions and structural attributes from Lidar plant heights were derived and refined for all Landsat vegetation classes. Results showed that the tallest cover classes in this region were deciduous forests (dominated by birch and aspen), followed by evergreen spruce and wooded wetlands. Open shrubland and herbaceous cover areas were determined to be 2 - 5 m shorter, on average, than these boreal forest stands. Tall shrubland cover areas were slightly shorter than most spruce-dominated evergreen forest areas. Nearly 60% of all low shrubland and herbaceous wetland pixels were detected with at least one tree crown greater than 10 m tall. This was in contrast to evergreen and deciduous forest classes that showed 88% of stands with at least one tree crown greater than 10 m tall.

Vegetation↗
NASA NTRS2021

Tracking the Rates and Mechanisms of Canopy Damage and Recovery Following Hurricane Maria Using Multitemporal Lidar Data

Hurricane Maria, a Category 4 storm, snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne Lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100–800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥ 1 m in canopy height (mean = −7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13.5% of the study area had canopy heights ≤ 2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss > 1 m (2017–2018) followed by rapid height gain > 1 m (2018–2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no height changes larger than ± 1 m in either period. Canopy layers < 10 m tall accounted for most increases in canopy height and fractional cover between 2018 and 2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage and highlight the diversity of ecosystem impacts from heterogeneous spatial patterns and vertical stratification of forest regrowth following a major disturbance event.

forest structure↗
NASA NTRS2021

Tracking the Rates and Mechanisms of Canopy Damage and Recovery Following Hurricane Maria Using Multitemporal Lidar Data

Hurricane Maria, a Category 4 storm, snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne Lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100–800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥ 1 m in canopy height (mean = −7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13.5% of the study area had canopy heights ≤ 2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss > 1 m (2017–2018) followed by rapid height gain > 1 m (2018–2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no height changes larger than ± 1 m in either period. Canopy layers < 10 m tall accounted for most increases in canopy height and fractional cover between 2018 and 2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage and highlight the diversity of ecosystem impacts from heterogeneous spatial patterns and vertical stratification of forest regrowth following a major disturbance event.

forest structure↗
NASA NTRS1996

Dual-Frequency Interferometric SAR Observations of a Tropical Rain-Forest

Repeat-pass, interferometric, radar observations of tropical rain-forest collected by the Shuttle Imaging Radar C (SIR-C) in the state of Rondonia, Brazil, reveal signal coherence is destroyed at C-band (5.6-cm) in the forest, whereas L-band (24-cm) radar signals remain strongly coherent over the entire landscape. At L-band, the rms difference in inferred topographic height between the forest and adjacent clearings is 5 m, equivalent to the height noise. Atmospheric delays are large, however, forming kilometer-sized anomalies with a 1.2-cm rms one way. Radar interferometric studies of the humid tropics must therefore be conducted at long radar wavelengths, with kilometric baselines or with two antennas operating simultaneously.

Rignot, E.↗
NASA NTRS1996

Dual-Frequency Interferometric SAR Observations of a Tropical Rain-Forest

Repeat-pass, interferometric, radar observations of tropical rain-forest collected by the Shuttle Imaging Radar C (SIR-C) in the state of Rondonia, Brazil, reveal signal coherence is destroyed at C-band (5.6-cm) in the forest, whereas L-band (24-cm) radar signals remain strongly coherent over the entire landscape. At L-band, the rms difference in inferred topographic height between the forest and adjacent clearings is 5 m, equivalent to the height noise. Atmospheric delays are large, however, forming kilometer-sized anomalies with a 1.2-cm rms one way. Radar interferometric studies of the humid tropics must therefore be conducted at long radar wavelengths, with kilometric base-lines or with two antennas operating simultaneously.

Rigot, E.↗
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 NTRS2023

Two Decades of Land Cover Change and Forest Fragmentation in Liberia: Consequences for the Contribution of Nature to People

The Guinean forests of West Africa have been identified as a global biodiversity hotspot due to its exceptional concentrations of endemic species and exceptional loss of habitats. The majority of what remains of the Guinean forests lies within Liberia, a country whose share of total wealth is nearly equally distributed into human and natural capital. The Liberian government seeks a more inclusive development agenda that forges a path for improved human capital while sustainably managing its natural capital wealth, which requires consistent data on land cover change and forest disturbance over time. To address this need, Landsat data were used to map and quantify land cover change and forest fragmentation in Liberia between 2000 and 2018. In addition, LiDAR data from the Global Ecosystem Dynamics Investigation (GEDI) mission were applied to assess the integrity of forest remnants. Between 2000 and 2018, only 1% of all forest cover classes (i.e., dense/primary, open/secondary and sparse/degraded) were converted into non-forest classes, with the most observed change being between these three forest classes. During the study period, 27% of the dense/primary forest class transitioned to either the open/secondary or sparse/degraded canopy classes through consistent fragmentation along the edges of the last large remaining blocks of dense/primary forest located in the north-west and south-east of Liberia and more than 14% of dense/primary forest areas identified in previous studies as “essential natural capital” for either biodiversity, forest carbon storage or provision of freshwater ecosystem services were degraded. The 2018 GEDI-based measurements show that the overall average height of dense/primary forest decreases by 24% and 48%, and canopy closure decreases by 33% and 59%, when transitioned to the open/secondary and sparse/degraded classes, respectively. The information derived from this analysis will be critical for informing the development of new policies and actions, leading to more sustainable forest management in Liberia.

Celio De Sousa↗
NASA NTRS2014

The Uncertainty of Biomass Estimates from Modeled ICESat-2 Returns Across a Boreal Forest Gradient

The Forest Light (FLIGHT) radiative transfer model was used to examine the uncertainty of vegetation structure measurements from NASA's planned ICESat-2 photon counting light detection and ranging (LiDAR) instrument across a synthetic Larix forest gradient in the taiga-tundra ecotone. The simulations demonstrate how measurements from the planned spaceborne mission, which differ from those of previous LiDAR systems, may perform across a boreal forest to non-forest structure gradient in globally important ecological region of northern Siberia. We used a modified version of FLIGHT to simulate the acquisition parameters of ICESat-2. Modeled returns were analyzed from collections of sequential footprints along LiDAR tracks (link-scales) of lengths ranging from 20 m-90 m. These link-scales traversed synthetic forest stands that were initialized with parameters drawn from field surveys in Siberian Larix forests. LiDAR returns from vegetation were compiled for 100 simulated LiDAR collections for each 10 Mg · ha(exp -1) interval in the 0-100 Mg · ha(exp -1) above-ground biomass density (AGB) forest gradient. Canopy height metrics were computed and AGB was inferred from empirical models. The root mean square error (RMSE) and RMSE uncertainty associated with the distribution of inferred AGB within each AGB interval across the gradient was examined. Simulation results of the bright daylight and low vegetation reflectivity conditions for collecting photon counting LiDAR with no topographic relief show that 1-2 photons are returned for 79%-88% of LiDAR shots. Signal photons account for approximately 67% of all LiDAR returns, while approximately 50% of shots result in 1 signal photon returned. The proportion of these signal photon returns do not differ significantly (p greater than 0.05) for AGB intervals greater than 20 Mg · ha(exp -1). The 50m link-scale approximates the finest horizontal resolution (length) at which photon counting LiDAR collection provides strong model fits and minimizes forest structure uncertainty in the synthetic Larix stands. At this link-scale AGB greater than 20 Mg · ha(exp -1) has AGB error from 20-50% at the 95% confidence level. These results suggest that the theoretical sensitivity of ICESat-2 photon counting LiDAR measurements alone lack the ability to consistently discern differences in inferred AGB at 10 Mg · ha(exp -1) intervals in sparse forests characteristic of the taiga-tundra ecotone.

LiDAR↗
NASA NTRS2017

Climate Limits Across Space and Time on European Forest Structure

The impact climate has on forests has been extensively studied. However, the large scale effect climate has on forest structures, such as average diameters, heights and basal area are understudied in a spatially explicit manner. The limits, tipping points and thresholds that climate places on forest structures dictate the services a forest may provide, the vulnerability of a forest to mortality and the potential value of the timber there within. The majority of current research either investigates climate impacts on forest pools and fluxes, on a tree physiological scale or on case studies that are used to extrapolate results and potential impacts. A spatially explicit study on how climate affects forest structure over a large region would give valuable information to stakeholders who are more concerned with ecosystem services that cannot be described by pools and fluxes but require spatially explicit information - such as biodiversity, habitat suitability, and market values. In this study, we quantified the limits that climate (maximum, minimum temperature and precipitation) places on 3 forest structures, diameter at breast height, height, and basal area throughout Europe. Our results show clear climatic zones of high and low upper limits for each forest structure variable studied. We also spatially analyzed how climate restricts the potential bio-physical upper limits and creates tipping points of each forest structure variable and which climate factors are most limiting. Further, we demonstrated how the climate change has affected 8 individual forests across Europe and then the continent as a whole. We find that diameter, height and basal area are limited by climate in different ways and that areas may have high upper limits in one structure and low upper limits in another limited by different climate variables. We also found that even though individual forests may have increased their potential upper limit forest structure values, European forests as a whole have lost, on average, 5.0%, 1.7% and 6.5% in potential mean forest diameter, height and basal area, respectively.

Moreno, Adam La Salle↗
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↗