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NASA NTRS2019

Using Deep Learning to Automate Inference of Meteoroid Pre-Entry Properties

Properly assessing the asteroid threat depends on the knowledge of asteroid pre-entry parameters, such as size, velocity, mass, density, and strength. Although a vast number of possible bodies to study exist, such characterization of asteroid populations is currently limited by substantial costs associated with space rendezvous missions and rare meteorite findings. As asteroids fragment, ablate, and decelerate in the atmosphere, they emit light detectable by ground-based and space-borne instruments. Earth’s atmosphere, thus, becomes an accessible laboratory that enables impactor risk assessments by facilitating inference of the pre-entry parameters. These asteroid pre-entry conditions are typically deduced by modeling the entry and breakup physics that best reproduce the observed light or energy deposition curve. However, this process requires extensive manual trial-and-error of uncertain modeling parameters. Automating meteor modeling and inference would improve property distributions used in risk assessments and enable population characterization as more light curves become more readily available through the presence of space assets and ground-based camera networks. We previously developed a genetic algorithm to automate meteor modeling by using the fragment-cloud model (FCM) to search for the values of the FCM input parameters (e.g., diameter) that generate energy deposition profiles that match the observed one. Now, we apply deep learning to infer asteroid diameter, velocity, and density from observed energy deposition curves. We trained and tested our neural network models with synthetic energy deposition curves modeled using the FCM rubble pile implementation. We present an application of a 1D convolutional neural network and compare its performance to other attempted regressors and machine learning techniques, such as a fully connected neural network and Random Forest regression, to demonstrate its capabilities. We validate our model weights and approach using the Chelyabinsk, Tagish Lake, Benešov, Košice, and Lost City meteors.

Tarano, Ana Maria↗

NASA NTRS1988

Clustering and voids in the Lyman-alpha forest

In a statistical study of quasar absorption lines, no evidence is found for large voids in the Ly-alpha forest, contrary to some recent suggestions. The 28 Mpc gap found by Crotts (1987) in the spectrum of Q0420-388 is not statistically significant, and it is possible, from a sample of 18 quasar spectra, to reject at the 2 sigma level the hypothesis that comoving voids of 30 Mpc fill as much as 15 percent of the universe. However, a significant (at the 99 percent level) excess of small line intervals of velocity width 200-600 km/s is found. This provides evidence either for the gravitationally induced correlations proposed in some models or for the fragmented shells proposed in others.

Ostriker, Jeremiah P.↗

NASA NTRS2017

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Cropland mapping; cropland areas; 30-m; Landsat-8;↗

NASA NTRS2000

Ectomycorrhizal specificity patterns in a mixed Pinus contorta and Picea engelmannii forest in Yellowstone National Park

We used molecular genetic methods to test two hypotheses, (i) that host plant specificity among ectomycorrhizal fungi would be common in a closed-canopy, mixed Pinus contorta-Picea engelmannii forest in Yellowstone National Park and (ii) that specificity would be more common in the early successional tree species, P. contorta, than in the invader, P. engelmannii. We identified 28 ectomycorrhizal fungal species collected from 27 soil cores. The proportion of P. engelmannii to P. contorta ectomycorrhizae was nearly equal (52 and 48%, respectively). Of the 28 fungal species, 18 composed greater than 95% of the fungal community. No species was associated exclusively with P. contorta, but four species, each found in only one core, and one species found in two cores were associated exclusively with P. engelmannii. These fungi composed less than 5% of the total ectomycorrhizae. Thus, neither hypothesis was supported, and hypothesized benefits of ectomycorrhizal specificity to both trees and fungi probably do not exist in this system.

Trees/microbiology↗

NASA NTRS1994

Consequences of impacts of small asteroids and comets with Earth

The fragmentation of a small asteroid in the atmosphere greatly increases its cross sections for aerodynamic braking and energy dissipation. At a typical impact velocity of 22 km/s, the atmosphere absorbs more than half the kinetic energy of stony meteoroids with diameters, D(sub m), less than 220 m and iron meteoroids with D(sub m) less than 80 m. The corresponding diameter for comets with impact velocity 50 km/s is D(sub m) less than 1600 m. Most of the atmospheric energy dissipation occurs in a fraction of a scale height, so large meteors appear to 'explode' or 'flare' at the end of their visible paths. This dissipation of energy in the atmosphere protects the earth from direct impact damage (e.g., craters), but it produces a blast wave that can do considerable damage. The area of destruction around the impact point in which the over-pressure in the blast wave exceeds 4 lb/sq in = 2.8 x 10(exp 5) dynes/cu cm, which is enough to knock over trees and destroy buildings, increases rapidly from zero for chondritic meteoroids less than 56 m in diameter (15 megatons) to about 200 sq km for those 80 m in diameter (48 megatons); the probable diameter of the tunguska impactor of 1908 is about 80 m. Crater formation and earthquakes are not significant in land impacts by stony asteroids less than about 200 m in diameter because of the air protection. A tsunami is probably the most devastating type of damage for asteroids 200 m to 1 km in diameter. An impact by an asteroid this size anywhere in the Atlantic would devastate coastal areas on both sides of the ocean. An asteroid a few kilometers across would produce a tsunami that would reach the foothills of the Appalachian Mountains in the upper half of the East Coast of the United States. Most of Florida is protected from a tsunami by the gradual slope of the ocean off its coast, which causes most of the tsunami energy to be reflected back into the Atlantic. The atmosphere plume produced by asteroids with diameters exceeding about 120 m cannot be contained by the atmosphere, so this bubble of high-temperature gas forms a new layer on top of the atmosphere. The dust entrapped in this hot gas is likely to have optical depths exceeding tau = 10 for asteroids with diameters exceeding about 0.5 to 1 km. The optical flux from asteroids 60 m or more in diameter is enough to ignite pine forests. However, the blast wave from an impacting asteroid goes beyond the radius in which the fire starts. The blast wave tends to blow out the fire, so it is likely that the impact will char the forest, as at Tunguska, but the impact will not produce a sustained fire. Because comets dissipate their energy much higher in the atmosphere than asteroids, they illuminate a much larger region and their blast wave is weaker. So they are much more effective in producing large fires. This suggests that the KT impactor was a comet rather than an asteroid.

Hills, J. G.↗