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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

An Imaging System for Automated Characteristic Length Measurement of Debrisat Fragments

The debris fragments generated by DebriSat's hypervelocity impact test are currently being processed and characterized through an effort of NASA and USAF. The debris characteristics will be used to update satellite breakup models. In particular, the physical dimensions of the debris fragments must be measured to provide characteristic lengths for use in these models. Calipers and commercial 3D scanners were considered as measurement options, but an automated imaging system was ultimately developed to measure debris fragments. By automating the entire process, the measurement results are made repeatable and the human factor associated with calipers and 3D scanning is eliminated. Unlike using calipers to measure, the imaging system obtains non-contact measurements to avoid damaging delicate fragments. Furthermore, this fully automated measurement system minimizes fragment handling, which reduces the potential for fragment damage during the characterization process. In addition, the imaging system reduces the time required to determine the characteristic length of the debris fragment. In this way, the imaging system can measure the tens of thousands of DebriSat fragments at a rate of about six minutes per fragment, compared to hours per fragment in NASA's current 3D scanning measurement approach. The imaging system utilizes a space carving algorithm to generate a 3D point cloud of the article being measured and a custom developed algorithm then extracts the characteristic length from the point cloud. This paper describes the measurement process, results, challenges, and future work of the imaging system used for automated characteristic length measurement of DebriSat fragments.

Moraguez, Mathew↗
NASA NTRS2014

An Efficient, Multi-Layered Crown Delineation Algorithm for Mapping Individual Tree Structure Across Multiple Ecosystems

Deriving individual tree information from discrete return, small footprint LiDAR data may improve forest above ground biomass estimates, and provide tree-level information that is important in many ecological studies. Several crown delineation algorithms have been developed to extract individual tree information from LiDAR point clouds or rasterized canopy height models (CHM), but many of these algorithms have difficulty discriminating between overlapping crowns, and also may fail to detect understory trees. Our approach uses a watershed based delineation of a CHM, which is subsequently refined using the LiDAR point cloud. Individual tree detection was validated with stem mapped field data from the Smithsonian Environmental Research Center (SERC), Maryland, and on a plot and stand level through comparisons of stem density and basal area to delineated metrics at both SERC and a study area in the Sierra Nevada, California. For individual tree detection, the algorithm correctly identified 70% of dominant trees, 58% of co-dominant trees, 35% of intermediate trees and 21% of suppressed trees at SERC. The algorithm had difficulty distinguishing between crowns of small, dense understory trees of approximately the same height. Delineated crown volume alone explained 53% and 84% of the variability in basal area at the SERC and Sierra Nevada sites, respectively. The algorithm produced crown area distributions comparable to diameter at breast height (DBH) size class distributions observed in the field in both study sites. The algorithm detected understory crowns better in the conifer-dominated Sierra Nevada site than in the closed-canopy deciduous site in Maryland. The ability for the algorithm to reproduce both accurate tree size distributions and individual crown geometries in two dissimilar and complex forests suggests great promise for applicability to a wide range of forest systems.

LiDAR↗
NASA NTRS2018

Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion

The vast extent and inaccessibility of boreal forest ecosystems are barriers to routine monitoring of forest structure and composition. In this research, we bridge the scale gap between intensive but sparse plot measurements and extensive remote sensing studies by collecting forest inventory variables at the plot scale using an unmanned aerial vehicle (UAV) and a structure from motion (SfM) approach. At 20 Forest Inventory and Analysis (FIA) subplots in interior Alaska, we acquired overlapping imagery and generated dense, 3D, RGB (red, green, blue) point clouds. We used these data to model forest type at the individual crown scale as well as subplot-scale tree density (TD), basal area (BA), and aboveground biomass (AGB). We achieved 85% cross-validation accuracy for five species at the crown level. Classification accuracy was maximized using three variables representing crown height, form, and color. Consistent with previous UAV-based studies, SfM point cloud data generated robust models of TD (r(sup 2) = 0.91), BA (r(sup 2) = 0.79), and AGB (r(sup 2) = 0.92), using a mix of plot- and crown-scale information. Precise estimation of TD required either segment counts or species information to differentiate black spruce from mixed white spruce plots. The accuracy of species-specific estimates of TD, BA, and AGB at the plot scale was somewhat variable, ranging from accurate estimates of black spruce TD (+/−1%) and aspen BA (−2%) to misallocation of aspen AGB (+118%) and white spruce AGB (−50%). These results convey the potential utility of SfM data for forest type discrimination in FIA plots and the remaining challenges to develop classification approaches for species-specific estimates at the plot scale that are more robust to segmentation error.

aboveground biomass↗
NASA NTRS2020

Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar

Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R^2 = 0.81, RMSE = 1.09 kg/sq. m) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R^2 = 0.74, RMSE = 1.26 kg/sq. m), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts./sq. m) to the G-LiHT SfM point cloud (500–2000 pts./sq. m) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.

Michael Alonzo↗
NASA NTRS2018

Fusion of Multiple Low-Resolution NASA Airborne Snow Observatory (ASO) Lidar Data for Forest Vegetation Structure Characterization

Airborne lidar provides timely updated maps for monitoring forest change at high resolution but it has been little used for that purpose due to the scarcity of long-term time-series over a common area. The NASA Jet Propulsion Laboratory Airborne Snow Observatory (ASO) is a landscape-level monitoring system that provides ongoing multi-year remote sensing measurements over mountainous ecosystems to primary quantify snow volume and dynamics. It collects low-resolution lidar data (~1.5 pt/m2) with a nominal weekly frequency up to 12 times a year with measurements that span 2013-2017 over 12 mountain watersheds across the western US that currently face ecological threads. In this work, we present a method to automatically register ASO weekly low-resolution lidar point clouds in order to calculate spatially consistent datasets (~12 pt/m2) adapted to fine scale forestry studies. We test the method using 12 lidar datasets acquired over the Tuolumne River Basin (Sierra Nevada, California) in the spring and summer of 2014. On average, the ASO lidar system provides accurate measurements in terms of geolocation (0.38m and 0.12m for the horizontal and vertical dimension, respectively) but some datasets are biased up to 1.38m and 0.53m, respectively. Our registration method successfully corrected for systematic bias improving the 3D geometry of forest point clouds.

Painter, Thomas H.↗
NASA NTRS2021

A Semi-Empirical Scheme for Bathymetric Mapping in Shallow Water by ICESat-2 and Sentinel-2: A Case Study in the South China Sea

To derive shallow water bathymetry for coastal areas, a common approach is to deploy a scanning airborne bathymetric light detection and ranging (LiDAR) system or a shipborne echosounder for ground surveys. However, recent advancements in satellite remote sensing, including the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) offer new tools for generating satellite derived bathymetry (SDB). The key payload onboard ICESat-2 is the Advanced Topographic Laser Altimeter System (ATLAS), a micro-pulse, photon-counting LiDAR system, simultaneously emitting six separate 532 nm beams at 10 kHz pulse rate. However, despite its high resolution, the major limitation for bathymetry is that ICESat-2 only provides along-track height profiles, leaving observation gaps between the parallel ground tracks. Merging ICESat-2 observations with optical multispectral imagery, as demonstrated herein, provides an effective solution for deriving a full scene of water depth in light of the spectral attenuation behavior. This study aims to combine ICESat-2 and Sentinel-2 optical data to derive shallow water bathymetry (depth <20 m) at six islands and reefs in the South China Sea. ICESat-2 ATL03 point clouds of georeferenced photons are first filtered to determine the seafloor elevation along the ground track. Results indicate a root-mean-square error (RMSE) of 0.26–0.61 m as compared with independent observations from an airborne LiDAR campaign. Next, three semi-empirical functions, namely the Modified Linear/Polynomial/Exponential Ratio Models with its kernel formed by the log ratio between Sentinel-2′s green and blue bands, are used to fit the spectral data with ICESat-2 height profiles. After water depth mapping using the trained model, independent ICESat-2 point clouds are used to validate the Sentinel-2 derived bathymetry. The RMSE values of the three models using the weighted average of multiple images for these six islands are within 0.50–0.90 m in 0–15 m deep. We also demonstrate that a synthesis of satellite laser altimetry and optical remote sensing can produce SDB results that potentially meet the requirement of category C in Zones of Confidence (ZOC) of the Electronic Navigational Chart (ENC) in 0–8 m deep. It is foreseen that ICESat-2 will be a helpful tool for mapping coastal and shallow waters around the world especially where bathymetric data are unavailable.

Coastal Bathymetry↗
NASA NTRS2015

Preliminary Findings of Inflight Icing Field Test to Support Icing Remote Sensing Technology Assessment

NASA and the National Center for Atmospheric Research have developed an icing remote sensing technology that has demonstrated skill at detecting and classifying icing hazards in a vertical column above an instrumented ground station. This technology has recently been extended to provide volumetric coverage surrounding an airport. Building on the existing vertical pointing system, the new method for providing volumetric coverage will utilize a vertical pointing cloud radar, a multifrequency microwave radiometer with azimuth and elevation pointing, and a NEXRAD radar. The new terminal area icing remote sensing system processes the data streams from these instruments to derive temperature, liquid water content, and cloud droplet size for each examined point in space. These data are then combined to ultimately provide icing hazard classification along defined approach paths into an airport.

remote sensors↗
NASA NTRS2016

Analysis of 2015 Winter In-Flight Icing Case Studies with Ground-Based Remote Sensing Systems Compared to In-Situ SLW Sondes

National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) have developed an icing remote sensing technology that has demonstrated skill at detecting and classifying icing hazards in a vertical column above an instrumented ground station. This technology has recently been extended to provide volumetric coverage surrounding an airport. Building on the existing vertical pointing system, the new method for providing volumetric coverage utilizes a vertical pointing cloud radar, a multi-frequency microwave radiometer with azimuth and elevation pointing, and a NEXRAD radar. The new terminal area icing remote sensing system processes the data streams from these instruments to derive temperature, liquid water content, and cloud droplet size for each examined point in space. These data are then combined to ultimately provide icing hazard classification along defined approach paths into an airport. To date, statistical comparisons of the vertical profiling technology have been made to Pilot Reports and Icing Forecast Products. With the extension into relatively large area coverage and the output of microphysical properties in addition to icing severity, the use of these comparators is not appropriate and a more rigorous assessment is required. NASA conducted a field campaign during the early months of 2015 to develop a database to enable the assessment of the new terminal area icing remote sensing system and further refinement of terminal area icing weather information technologies in general. In addition to the ground-based remote sensors listed earlier, in-situ icing environment measurements by weather balloons were performed to produce a comprehensive comparison database. Balloon data gathered consisted of temperature, humidity, pressure, super-cooled liquid water content, and 3-D position with time. Comparison data plots of weather balloon and remote measurements, weather balloon flight paths, bulk comparisons of integrated liquid water content and icing cloud extent agreement, and terminal-area hazard displays are presented. Discussions of agreement quality and paths for future development are also included.

aviation meteorology↗
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 NTRSDate not supplied

Establishing an Urban Heat Exposure Severity Index for Infrastructure Prioritization in Tempe, Arizona, Using NASA Earth Observations and LiDAR

Located on the banks of the Salt River in the Sonoran Desert, Tempe, Arizona, features a semi-arid climate with summer daily maximum temperatures regularly exceeding 37.8°C. Tempe is also subject to the southwestern monsoon season from July-September and the humidity exacerbates the high temperatures. Furthermore, the rapid urbanization experienced in Tempe has resulted in an intensification of the urban heat island. The summer of 2020 shattered the previous record of days exceeding 43.4°C, leading to higher energy and water costs, lower comfort, and increased risk of heat stroke for residents. Recognizing the impacts of extreme heat, the City of Tempe partnered with the Healthy Urban Environments initiative and NASA DEVELOP to identify census tracts that experience a higher mean land surface temperature than the city average. The NASA DEVELOP team used remotely sensed land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and albedo data calculated from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) instruments from 2015 to 2020 to create heat hazard and exposure maps. LiDAR point cloud data, provided by the United States Geological Survey through Arizona State University’s Map and Geospatial Hub, were used to derive 3D buildings, building footprints, and tree point data for a shading analysis of walking paths, roads, and buildings at the census tract level. In situ meteorological measurements including air temperature and humidity were used to compare the macro-scale temperature measurements. The team worked with the City of Tempe to develop a methodology to process available data and identify areas of highest concern for urban heat effects within the city. With these insights, Tempe, Arizona can better address these issues with data-driven information to make decisions regarding heat mitigation and adaptation efforts.

John Dialesandro↗
NASA NTRSDate not supplied

Ice Accretion Roughness Variations on a Hybrid CRM65-Midspan Wing Model

Ice accretion roughness measurements were performed in the Icing Research Tunnel (IRT) at NASA Glenn Research Center for the Hybrid CRM65-Midspan model in a range of icing conditions. The Hybrid CRM65-Midspan model was chosen for this investigation because 1) the model exhibits high sweep relative to models previously explored in the roughness investigations, 2) the model has leading edge characteristics similar to wing shapes currently used in mid-size commercial airliners, and 3) the sweep and thickness ratios relate better to hybrid lifting body designs for N+2 and N+3 vehicles than other models available. The investigation consisted of multiple sets of tests which focused on 1) 0-angle of attack cases replicating the conditions employed by Anderson et al. (1998) using both Appendix C and SLD cloud conditions, 2) cases based on the “Max Scallop” case by Broeren et al. (2016) and a “High Temperature” case with cloud properties similar to the “Max Scallop” case. Additional tests were performed 1) based on the “Max Scallop” case with variations in freestream static temperature and 2) using test section speeds near 10,000-hold flight conditions. The point clouds were characterized using the approach of McClain and Kreeger (2013) for the ice roughness variations and using the approach of McClain (2016) for the mean ice thickness variations. The resulting roughness and mean thickness variations generally follow the temporal scaling previously identified using on airfoil models without sweep, but the collapse of the time progression profiles is not as tight as found for past measurements on models without sweep. LEWICE and modified panel-method predictions were used explore spatial roughness variations and to compare to the roughness correlations developed by McClain et al. (2021) for the “Max Scallop” cases.

Icing↗
NASA NTRS2005

Thermodynamic and cloud parameter retrieval using infrared spectral data

High-resolution infrared radiance spectra obtained from near nadir observations provide atmospheric, surface, and cloud property information. A fast radiative transfer model, including cloud effects, is used for atmospheric profile and cloud parameter retrieval. The retrieval algorithm is presented along with its application to recent field experiment data from the NPOESS Airborne Sounding Testbed - Interferometer (NAST-I). The retrieval accuracy dependence on cloud properties is discussed. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud top level are obtained. For both optically thin and thick cloud situations, the cloud top height can be retrieved with an accuracy of approximately 1.0 km. Preliminary NAST-I retrieval results from the recent Atlantic-THORPEX Regional Campaign (ATReC) are presented and compared with coincident observations obtained from dropsondes and the nadir-pointing Cloud Physics Lidar (CPL).

Zhou, Daniel K.↗
NASA NTRS2007

Physically-Retrieving Cloud and Thermodynamic Parameters from Ultraspectral IR Measurements

A physical inversion scheme has been developed, dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders, to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1-d) variational multi-variable inversion solution is used to improve an iterative background state defined by an eigenvector-regression-retrieval. The solution is iterated in order to account for non-linearity in the 1-d variational solution. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud top level are obtained. For both optically thin and thick cloud situations, the cloud top height can be retrieved with relatively high accuracy (i.e., error < 1 km). NPOESS Airborne Sounder Testbed Interferometer (NAST-I) retrievals from the Atlantic-THORPEX Regional Campaign are compared with coincident observations obtained from dropsondes and the nadir-pointing Cloud Physics Lidar (CPL). This work was motivated by the need to obtain solutions for atmospheric soundings from infrared radiances observed for every individual field of view, regardless of cloud cover, from future ultraspectral geostationary satellite sounding instruments, such as the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and the Hyperspectral Environmental Suite (HES). However, this retrieval approach can also be applied to the ultraspectral sounding instruments to fly on Polar satellites, such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) on the NPOESS Preparatory Project and the following NPOESS series of satellites.

Zhou, Daniel K.↗
NASA NTRS1977

Meteor streams in the making

The well-known associations of meteor streams with periodic comets and the probable cometary origin of the zodiacal dust cloud point to the importance, in the cometary debris, of particles with masses exceeding roughly 10 to the -6th gram. It is shown that these large particles dominate in the sunward-oriented anomalous tails of comets. Their study is essential for meaningful estimates of the mass of meteor streams and of the injection rate of the cometary debris that contributes to the zodiacal cloud. Favorable conditions for the detection of anomalous tails can be recognized in advance, as demonstrated by the successful predictions for comets Kohoutek (1973 XII) and Bradfield (1975p). To answer the question as to whether short-period comets can support the zodiacal cloud, a study of anomalous tails at future returns of these comets is considered indispensable.

Sekanina, Z.↗
NASA NTRS2014

Ice Roughness in Short Duration SLD Icing Events

Ice accretion codes depend on models of roughness parameters to account for the enhanced heat transfer during the ice accretion process. While mitigating supercooled large droplet (SLD or Appendix O) icing is a significant concern for manufacturers seeking future vehicle certification due to the pending regulation, historical ice roughness studies have been performed using Appendix C icing clouds which exhibit mean volumetric diameters (MVD) much smaller than SLD clouds. Further, the historical studies of roughness focused on extracting parametric representations of ice roughness using multiple images of roughness elements. In this study, the ice roughness developed on a 21-in. NACA 0012 at 0deg angle of attack exposed to short duration SLD icing events was measured in the Icing Research Tunnel at the NASA Glenn Research Center. The MVD's used in the study ranged from 100 micrometer to 200 micrometers, in a 67 m/s flow, with liquid water contents of either 0.6 gm/cubic meters or 0.75 gm/cubic meters. The ice surfaces were measured using a Romer Absolute Arm laser scanning system. The roughness associated with each surface point cloud was measured using the two-dimensional self-organizing map approach developed by McClain and Kreeger (2013) resulting in statistical descriptions of the ice roughness.

Icing↗
NASA NTRS2012

Photogrammetric and Global Positioning System Measurements of Active Pahoehoe Lava Lobe Emplacement on Kilauea, Hawaii

Basalt is the most common rock type on the surface of terrestrial bodies throughout the solar system and -- by total volume and areal coverage -- pahoehoe flows are the most abundant form of basaltic lava in subaerial and submarine environments on Earth. A detailed understanding of pahoehoe emplacement processes is necessary for developing accurate models of flow field development, assessing hazards associated with active lava flows, and interpreting the significance of lava flow morphology on Earth and other planetary bodies. Here, we examine the active emplacement of pahoehoe lobes along the margins of the Hook Flow from Pu'u 'O'o on Kilauea, Hawaii. Topographic data were acquired between 21 and 23 February 2006 using stereo-imaging and differential global positing system (DGPS) measurements. During this time, the average discharge rate for the Hook Flow was 0.01-0.05 cubic m/s. Using stereogrammetric point clouds and interpolated digital terrain models (DTMs), active flow fronts were digitized at 1 minute intervals. These areal spreading maps show that the lava lobe grew by a series of breakouts tha t broadly fit into two categories: narrow (0.2-0.6 m-wide) toes that grew preferentially down-slope, and broad (1.4-3.5 m-wide) breakouts that formed along the sides of the lobe, nearly perpendicular to the down-flow axis. These lobes inflated to half of their final thickness within approx 5 minutes, with a rate of inflation that generally deceased with time. Through a combination of down-slope and cross-slope breakouts, lobes developed a parabolic cross-sectional shape within tens of minutes. We also observed that while the average local discharge rate for the lobe was generally constant at 0.0064 +/- 0.0019 cubic m/s, there was a 2 to 6 fold increase in the areal coverage rate every 4.1 +/- 0.6 minutes. We attribute this periodicity to the time required for the dynamic pressurization of the liquid core of the lava lobe to exceed the cooling-induced strength of the lobe margins. Using DGPS-derived DTMs of the topography before and after pahoehoe lobe emplacement, we observed that the lava typically concentrated within existing topographic lows, with the lobe reaching a maximum thickness of approx 1.2 m above the lowest points of the initial topography and above reverse-facing slopes. Lobe margins were typically controlled by high-standing topography, with the zone directly adjacent to the final flow margin having average relief that is approx 4 cm higher than the lava-inundated region. This suggests that irregularities approx 25% of the height of the smallest breakout elements (i.e., toes) can exert a strong control on the paths of low-discharge pahoehoe lobes, with stagnated toes forming confining margins that allow interior portions of flow to topographically invert the landscape by inflation.

Hamilton, Christopher W.↗
NASA NTRS1972

Variation of cloud emissivity in the infrared.

The spectral radiative properties of solid and liquid H2O particles in the thermal infrared are examined. The study of these properties is aimed at an explanation of the spectral features associated with certain clouds pointed out by Hanel et al. (1972) in an analysis of the results of the Nimbus 4 infrared spectroscopy experiment.

Curran, R. J.↗