scholarly journals ELEMENTAL AND TOPOGRAPHIC MAPPING OF LAVA FLOWSTRUCTURES IN MARE SERENITATIS ON THE MOON

2017 ◽  
Vol XLII-3/W1 ◽  
pp. 163-170 ◽  
Author(s):  
C. Wöhler ◽  
A. Grumpe ◽  
D. Rommel ◽  
M. Bhatt ◽  
U. Mall
Keyword(s):  
Lava Flow ◽  
Flow Structure ◽  
Hyperspectral Image ◽  
Image Data ◽  
Elemental Abundance ◽  
Lava Flows ◽  
Stereo Image ◽  
The Moon ◽  
Lunar Reconnaissance Orbiter ◽  
Mare Serenitatis

The detection of lunar lava flows based on local morphology highly depends on the available images. The thickness of lava flows, however, has been studied by many researchers and lunar lava flows are shown to be as thick as 200 m. Lunar lava flows are supposed to be concentrated on the northwestern lunar nearside. In this study we present elemental abundance maps, a petrological map and a digital terrain model (DTM) of a lava flow structure in northern Mare Serenitatis at (18.0° E, 32.4° N) and two possible volcanic vents at (11.2° E, 24.6° N) and (13.5° E, 37.5° N), respectively. Our abundance maps of the refractory elements Ca, Mg and our petrological map were obtained based on hyperspectral image data of the Moon Mineralogy Mapper (M3) instrument. Our DTM was constructed using GLD100 data in combination with a shape from shading based method to M3 and Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) image data. The obtained NAC-based DEM has a very high effective resolution of about 1–2 m which comes close to the resolution of the utilized NAC images without requiring intricate processing of NAC stereo image pairs. As revealed by our elemental maps and DEM, the examined lava flow structure occurs on a boundary between basalts consisting of low-Ca/high-Mg pyroxene and high-Ca/low-Mg pyroxene, respectively. The total thickness of the lava flow is about 100 m, which is a relatively large value, but according to our DEM the lava flow may also be composed of two or more layers.

scholarly journals Trajectory Determination of Chang’E-5 during Landing and Ascending

2021 ◽  
Vol 13 (23) ◽  
pp. 4837
Author(s):  
Peng Yang ◽  
Yong Huang ◽  
Peijia Li ◽  
Siyu Liu ◽  
Quan Shan ◽  
...  
Keyword(s):  
Image Data ◽  
Landing Site ◽  
Polynomial Functions ◽  
The Moon ◽  
B Spline ◽  
Lunar Reconnaissance Orbiter ◽  
Orbiter Image ◽  
Positioning Method ◽  
Trajectory Determination

Chang’E-5 (CE-5) is China’s first lunar sample return mission. This paper focuses on the trajectory determination of the CE-5 lander and ascender during the landing and ascending phases, and the positioning of the CE-5 lander on the Moon. Based on the kinematic statistical orbit determination method using B-spline and polynomial functions, the descent and ascent trajectories of the lander and ascender are determined by using ground-based radiometric ranging, Doppler and interferometry data. The results show that a B-spline function is suitable for a trajectory with complex maneuvers. For a smooth trajectory, B-spline and polynomial functions can reach almost the same solutions. The positioning of the CE-5 lander on the Moon is also investigated here. Using the kinematic statistical positioning method, the landing site of the lander is 43.0590°N, 51.9208°W with an elevation of −2480.26 m, which is less than 200 m different from the LRO (Lunar Reconnaissance Orbiter) image data.

LROC candidate images with solar glints off Lunar Laser Retroreflectors: A dedicated tool of NASA's Moon Trek

2020 ◽  
Author(s):  
Costanza Rossi ◽  
Natalie Gallegos ◽  
Luciana Filomena ◽  
Shan Malhotra ◽  
Emily Law ◽  
...  
Keyword(s):  
Coordinate System ◽  
Image Data ◽  
The Moon ◽  
Lunar Laser Ranging ◽  
Time Intervals ◽  
Morphological Pattern ◽  
Ground Station ◽  
Lunar Laser ◽  
Lunar Reconnaissance Orbiter ◽  
Phase Angles

<p>The Lunar Laser Ranging (LLR) investigations have provided time high-precision measurements of geodesy, dynamics and distance of the Earth-Moon system, and inferences about lunar interior and gravitational physics. LLR studies are supported by a total of five passive Laser Retro-Reflectors (LRR) placed on the Moon surface by the past missions Apollo-11, -14, -15 and Luna-17 and -21. The detection of their positions is decisive to improve the measurement accuracy and the data from alternative instrumentations contributed to their analysis. The Lunar Reconnaissance Orbiter Camera (LROC) operated by using the Standardized Lunar Coordinate System as reference system has acquired images of the Moon surface that represent data applicable to LLR planning and research. Several LROC images present nominal lighting conditions and solar glints reflected off of an LRR. Glints represent specular reflections of light that define higher-precision measurement of LRR position. In this way, their detection plays an important role in LRR analysis. The identification of candidate images with solar glints through time allows researchers to record these measurements. NASA and INFN-LNF (National Lab of Frascati) have collaboratively developed an LLR tool to support glint identification. The tool can be accessed using the Moon Trek (https://trek.nasa.gov/moon) which is one of the web based interactive visualization and analysis portals provided by the NASA’s Solar System Trek (https://trek.nasa.gov) project. The tool facilitates current ranging studies as well as planning of future missions that involve ranging activities such as future retroreflector deployments. Glint identification has been performed by using the LLR tool that allows us to investigate the image data, and to compute geometric calculations and LLR analyses. The tool with SPICE computations is provided to search for nominal conditions to catch a solar glint off of a retroreflector, to search for time intervals in which a reflector can be seen from a ground station on Earth, and to search in PDS database for images with these conditions. Moon Trek’s LLR tool allows us to find time intervals when spacecraft positioning was able to catch a solar glint reflected off of a retroreflector by setting the maximum incidence and phase angles. This analysis is accompanied by the search for LROC images available in Planetary Data System (PDS) that have solar glint off the LRR. Using the Moon Trek, it is possible to identify LROC images with solar glint off the LRR and to recognize optimal LROC candidates. This research allows us to identify good examples of LROC images that present solar glints. More than six candidate images over a period of 10 years of LROC data were recognized. In this contribution, we present the recognized LROC candidates and we show their detection in the image data, by avoiding the bias of the surface high albedo and the morphological pattern that can interfere with the analysis. The identification of solar glints off LRR will allow us to find previous observation that might be incorrect and to measure the LRR position in the Standardized Lunar Coordinate System of LROC images. These measures will be then compared with the ephemeris calculations obtained from LLR data.</p>

scholarly journals A global rockfall map of the Moon powered by AI and Big Data

2020 ◽  
Author(s):  
Valentin Bickel ◽  
Jordan Aaron ◽  
Andrea Manconi ◽  
Simon Loew ◽  
Urs Mall
Keyword(s):  
Spatial Distribution ◽  
High Resolution ◽  
Lunar Surface ◽  
Image Data ◽  
Landing Site ◽  
Planetary Science ◽  
The Moon ◽  
Lunar Orbiter ◽  
Narrow Angle ◽  
Lunar Reconnaissance Orbiter

<p>Under certain conditions, meter to house-sized boulders fall, jump, and roll from topographic highs to topographic lows, a landslide type termed rockfall. On the Moon, these features have first been observed in Lunar Orbiter photographs taken during the pre-Apollo era. Understanding the drivers of lunar rockfall can provide unique information about the seismicity and erosional state of the lunar surface, however this requires high resolution mapping of the spatial distribution and size of these features. Currently, it is believed that lunar rockfalls are driven by moonquakes, impact-induced shaking, and thermal fatigue. Since the Lunar Orbiter and Apollo programs, NASA’s Lunar Reconnaissance Orbiter Narrow Angle Camera (NAC) returned more than 2 million high-resolution (NAC) images from the lunar surface. As the manual extraction of rockfall size and location from image data is time intensive, the vast majority of NAC images have not yet been analyzed, and the distribution and number of rockfalls on the Moon remains unknown. Demonstrating the potential of AI for planetary science applications, we deployed a Convolutional Neural Network in combination with Google Cloud’s advanced computing capabilities to scan through the entire NAC image archive. We identified 136,610 rockfalls between 85°N and 85°S and created the first global, consistent rockfall map of the Moon. This map enabled us to analyze the spatial distribution and density of rockfalls across lunar terranes and geomorphic regions, as well as across the near- and farside, and the northern and southern hemisphere. The derived global rockfall map might also allow for the identification and localization of recent seismic activity on or underneath the surface of the Moon and could inform landing site selection for future geophysical surface payloads of Artemis, CLPS, or other missions. The used CNN will soon be available as a tool on NASA JPL’s Moon Trek platform that is part of NASA’s Solar System Treks (trek.nasa.gov/moon/).</p>

scholarly journals Rheological and morphological evolution of basaltic lava flows

2018 ◽  
Author(s):  
◽  
Arianna Soldati
Keyword(s):  
Lava Flow ◽  
Flow Structure ◽  
Flow System ◽  
Morphological Evolution ◽  
Human Life ◽  
Active Volcano ◽  
Flow Dynamics ◽  
Lava Flows ◽  
System Structure ◽  
Basaltic Lava

Over 500 million people live in proximity of an active volcano globally. Although lava flows rarely endanger human life, they often destroy critical infrastructure. Advancing our understanding of lava flow dynamics is therefore critical to developing accurate hazard assessment, with key socio-economic impacts for many communities. This work focuses on basaltic lava rheology, which exerts a first-order control on flow dynamics and is reflected in lava morphology. In particular, I address the following research questions: (1) How does the rheology of active flows evolve during emplacement; and (2) How can we use flow morphology to infer the rheology of inactive flows? ... At Piton de La Fournaise (La R�union, FR DOM), I addressed the longstanding question of how pre-existing topography controls lava flow system structure in volume-limited flows (Soldati et al., accepted). I concluded that a steep slope results in a single, stable channel, whereas a gentle slope results in an unstable, braided channel. The findings of this study allow us to interpret and explain the observed flow structure on the basis of pre-existing volcano topography, and to forecast future flow structure. This allowed me to determine that rheology neither affects nor is affected by flow system configuration.

scholarly journals Anatomy of a Paroxysmal Lava Fountain at Etna Volcano: The Case of the 12 March 2021, Episode

2021 ◽  
Vol 13 (15) ◽  
pp. 3052
Author(s):  
Sonia Calvari ◽  
Alessandro Bonaccorso ◽  
Gaetana Ganci
Keyword(s):  
Lava Flow ◽  
Ground Deformation ◽  
Monitoring Network ◽  
Lava Flows ◽  
Etna Volcano ◽  
Lava Fountain ◽  
Eruptive Episode ◽  
Lava Fountains ◽  
Borehole Strainmeter ◽  
Ash Plume

On 13 December 2020, Etna volcano entered a new eruptive phase, giving rise to a number of paroxysmal episodes involving increased Strombolian activity from the summit craters, lava fountains feeding several-km high eruptive columns and ash plumes, as well as lava flows. As of 2 August 2021, 57 such episodes have occurred in 2021, all of them from the New Southeast Crater (NSEC). Each paroxysmal episode lasted a few hours and was sometimes preceded (but more often followed) by lava flow output from the crater rim lasting a few hours. In this paper, we use remote sensing data from the ground and satellite, integrated with ground deformation data recorded by a high precision borehole strainmeter to characterize the 12 March 2021 eruptive episode, which was one of the most powerful (and best recorded) among that occurred since 13 December 2020. We describe the formation and growth of the lava fountains, and the way they feed the eruptive column and the ash plume, using data gathered from the INGV visible and thermal camera monitoring network, compared with satellite images. We show the growth of the lava flow field associated with the explosive phase obtained from a fixed thermal monitoring camera. We estimate the erupted volume of pyroclasts from the heights of the lava fountains measured by the cameras, and the erupted lava flow volume from the satellite-derived radiant heat flux. We compare all erupted volumes (pyroclasts plus lava flows) with the total erupted volume inferred from the volcano deflation recorded by the borehole strainmeter, obtaining a total erupted volume of ~3 × 106 m3 of magma constrained by the strainmeter. This volume comprises ~1.6 × 106 m3 of pyroclasts erupted during the lava fountain and 2.4 × 106 m3 of lava flow, with ~30% of the erupted pyroclasts being remobilized as rootless lava to feed the lava flows. The episode lasted 130 min and resulted in an eruption rate of ~385 m3 s−1 and caused the formation of an ash plume rising from the margins of the lava fountain that rose up to 12.6 km a.s.l. in ~1 h. The maximum elevation of the ash plume was well constrained by an empirical formula that can be used for prompt hazard assessment.

Weighting hyperspectral image data for improved multivariate curve resolution results

2008 ◽  
Vol 22 (9) ◽  
pp. 482-490 ◽  
Author(s):  
Howland D. T. Jones ◽  
David M. Haaland ◽  
Michael B. Sinclair ◽  
David K. Melgaard ◽  
Mark H. Van Benthem ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Image Data ◽  
Multivariate Curve Resolution ◽  
Curve Resolution ◽  
Hyperspectral Image Data

scholarly journals Efficient Lossless Compression of Multitemporal Hyperspectral Image Data

2018 ◽  
Vol 4 (12) ◽  
pp. 142 ◽  
Author(s):  
Hongda Shen ◽  
Zhuocheng Jiang ◽  
W. Pan
Keyword(s):  
Data Compression ◽  
Hyperspectral Image ◽  
Image Data ◽  
Lossless Compression ◽  
Large Data ◽  
Hyperspectral Data ◽  
Compression Performance ◽  
Temporal Correlations ◽  
Sensing Applications ◽  
Data Volume

Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.

Anomalous Dielectric Variability in the central peak of Tycho crater on the Moon: New insights from Mini-RF S-band Observations

2021 ◽  
Author(s):  
Shashwat Shukla ◽  
Gerald Wesley Patterson
Keyword(s):  
Dielectric Constant ◽  
Hyperspectral Image ◽  
Central Peak ◽  
Skin Depth ◽  
The Moon ◽  
Near Surface ◽  
Low Dielectric ◽  
Crater Morphology ◽  
High Dielectric ◽  
The Impact

<p>One of the unique candidates to explore the evolution of physical surface processes on the Moon is Tycho, a dark haloed impact crater representing well-preserved bright ray pattern and intact crater morphology. Sampling of the central peak in such complex crater formation proves significant in terms of unraveling intriguing science of the lunar interior. With the current state-of-the-art radar technology, it is possible to evaluate the response of the geologic features constrained in the near surface and subsurface regolith environments. This can be achieved by modelling the dielectric constant of media, which is a physical parameter crucial for furthering our knowledge about the distribution of materials within different stratigraphic layers at multiple depths. Here, we used the applicability of Mini-RF S-band data augmented with a deep learning based inversion model to retrieve the dielectric variations over the central peak of the Tycho crater. A striking observation is made in certain regions of the central peak, wherein we observe anomalously high dielectric constant, not at all differentiated in the hyperspectral image and first Stokes parameter image, which usually is a representation of retrieved backscatter of the target. The results are also supported by comparing the variations in the scattering mechanisms. We found those particular regions to be associated with high degree of depolarization, thereby attributing to the presence of cm- to m- scale scatterers buried within a low dielectric layer that are not big enough to produce even-bounce geometry for the radar wave. Moreover, we also observe high rock concentration in the central peak slopes from DIVINER data and NAC images, indicating the exposure of clasts ranging in size from 10 meter to 100s of meter. Furthermore, from surface temperature data, these distinctive outcrops sense warmer temperature at night than the surrounding, which suggests the existence of thermal skin depth in such vicinities. Interestingly, we are able to quantify the pessimistic dielectric constant limit of the large boulder in the middle of the central peak, observable at the Mini-RF radar wavelength, as 4.54 + j0.077. Compared to the expected dielectric constant of rocks, this value is lowered significantly. One probable reason could be the emergence of small radar shadows due to the rugged surface of the boulder on the radar illuminated portion. From our analysis, we showcase the anomalous dielectric variability of Tycho central peak, thereby providing new insights into the evolution of the impact cratering process that could be important for both science and necessary for framing human or robotic exploration strategies.  </p>

Sign in / Sign up

Export Citation Format

Share Document