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 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.

scholarly journals THE LOST INDIAN CHANDRAYAAN 2 LANDER VIKRAM AND ROVER PRAGYAAN FOUND INTACT IN SINGLE PIECE ON THE MOON

2020 ◽  
Vol 8 (12) ◽  
pp. 103-109
Author(s):  
Jag Mohan Saxena ◽  
H M Saxena ◽  
Priyanka Saxena
Keyword(s):  
Lunar Surface ◽  
Landing Site ◽  
Space Research ◽  
The Moon ◽  
Lunar Orbiter ◽  
Exact Location ◽  
Indian Space Research Organization ◽  
Single Piece ◽  
Mission Control ◽  
Lunar Lander

The Lunar Lander Vikram of the Moon Mission Chandrayaan 2 of the Indian Space Research Organization (ISRO) lost communication with the Lunar Orbiter and the mission control nearly 2.1 kms above the lunar surface during its landing on the Moon on 7th September, 2019. The exact location and the sight of the lost lander and rover are still elusive. We present here the exact location and first images of the lander Vikram and rover Pragyaan sighted on the lunar surface. It is evident from the processed images that the lander was intact and in single piece on landing away from the scheduled site and its ramp was deployed to successfully release the rover Pragyan on to the lunar surface. This contradicts earlier reports that the lander was disintegrated into small pieces and debris which were scattered far away from the proposed landing site.

scholarly journals Topographic mapping of the Mars MC quadrangles using HRSC data

2021 ◽  
Author(s):  
Elke Kersten ◽  
Klaus Gwinner ◽  
Gregory Michael ◽  
Alexander Dumke ◽  
Ralf Jaumann
Keyword(s):  
High Resolution ◽  
Image Data ◽  
Landing Site ◽  
Planetary Science ◽  
Space Science ◽  
Topographic Map ◽  
Martian Surface ◽  
Global Mapping ◽  
Science Conference ◽  
Map Series

<p>The High Resolution Stereo Camera (HRSC) of ESA’s Mars Express mission [1, 2] is still running nominally and delivering new image strips to fill remaining gaps that lead to a contiguous coverage of the Martian surface at high resolution stereo. As a push broom scanning instrument with nine CCD line detectors mounted in parallel, its unique feature is the ability to obtain along-track stereo images and four colors during a single orbital pass. Thus, panchromatic stereo and color images from single orbits of the HRSC have been used to produce digital terrain models (DTMs) and orthoimages of the Martian surface since 2004 [3].</p><p>Since 2010 new HRSC multi-orbit data products have been generated, which have been developed into a global mapping program organized into MC-30 half-tiles, since 2014 [4,5]. Based on continuous coverage of an area, regional DTMs and orthomosaics can be produced by combining image data from multiple orbits using specifically adapted techniques for block-adjustment, DTM interpolation and image equalization [6]. The resulting DTMs and color orthomosaics are the baseline for a controlled topographic map series of Mars. The extents of the regional products follow the MC-30 (Mars Chart) global mapping scheme of Greeley and Batson [7]. For the generation of the DTMs and color mosaics, the MC-30 quadrangles are further divided into East (E) and West (W). In parallel to the completion of the first half-tile DTM and color mosaic (MC-11-E) we developed a concept for a topographic map series of Mars [8,9]. To limit data volumes and map sizes, each quadrangle is subdivided into eight tiles (i.e. each half-tile into four tiles). The map scale of 1:700,000 is a compromise between the high DTM and orthomosaic resolution of 50 m/pxl and an acceptable hardcopy format of about 1 m in width to 2 m in height (≜14 pxl/mm). MC-11 was selected to be produced first because it contains the finally selected landing site, Oxia Planum, of ESA’s ExoMars mission with the Rosalind Franklin rover. After MC-11, the Global Topography and Mosaics Task Group (GTMTG) of the HRSC Science Team focussed on MC-13, which hosts the landing site of the Perseverance rover from NASA’s Mars 2020 mission, Jezero crater. The next HRSC MC quadrangles will also be equatorial ones (i.e. 19 and 20).</p><p>All maps are available for the public at the HRSC team website (http://hrscteam.dlr.de/HMC30/index.html).</p><p>[1] Neukum, G., et al., ESA Special Publication, 1240, pp. 17-36, 2004. [2] Jaumann, R., et al., Planetary and Space Science 55, pp. 928-952, 2007. [3] Gwinner, K., et al., Earth and Planetary Science Letters, 294, pp. 506-519, 2010. [4] Gwinner, K, et al., 41st Lunar and Planetary Science Conference, #2727, 2010. [5] Dumke, A., et al., Lunar and Planetary Science Conference, #1533, 2010. [6] Gwinner, K. et al., Planetary and Space Science, 126, pp. 93-138, 2016. [7] Greeley, R. and Batson, G., Planetary Mapping, Cambridge University Press, Cambridge, 1990. [8] Schulz, K., Bachelor Thesis, Beuth Hochschule für Technik Berlin, 2017. [9] Kersten, E., et al., EPSC Abstracts Vol. 12, EPSC2018-352, 2018.</p>

scholarly journals AN INTEGRATED PHOTOGRAMMETRIC AND PHOTOCLINOMETRIC APPROACH FOR PIXEL-RESOLUTION 3D MODELLING OF LUNAR SURFACE

2018 ◽  
Vol XLII-3 ◽  
pp. 1117-1122
Author(s):  
W. C. Liu ◽  
B. Wu
Keyword(s):  
High Resolution ◽  
Lunar Surface ◽  
Image Matching ◽  
Point Cloud ◽  
3D Modelling ◽  
Conjugate Points ◽  
Stereo Pair ◽  
Pixel Resolution ◽  
Dense Image ◽  
Lunar Reconnaissance Orbiter

High-resolution 3D modelling of lunar surface is important for lunar scientific research and exploration missions. Photogrammetry is known for 3D mapping and modelling from a pair of stereo images based on dense image matching. However dense matching may fail in poorly textured areas and in situations when the image pair has large illumination differences. As a result, the actual achievable spatial resolution of the 3D model from photogrammetry is limited by the performance of dense image matching. On the other hand, photoclinometry (i.e., shape from shading) is characterised by its ability to recover pixel-wise surface shapes based on image intensity and imaging conditions such as illumination and viewing directions. More robust shape reconstruction through photoclinometry can be achieved by incorporating images acquired under different illumination conditions (i.e., photometric stereo). Introducing photoclinometry into photogrammetric processing can therefore effectively increase the achievable resolution of the mapping result while maintaining its overall accuracy. This research presents an integrated photogrammetric and photoclinometric approach for pixel-resolution 3D modelling of the lunar surface. First, photoclinometry is interacted with stereo image matching to create robust and spatially well distributed dense conjugate points. Then, based on the 3D point cloud derived from photogrammetric processing of the dense conjugate points, photoclinometry is further introduced to derive the 3D positions of the unmatched points and to refine the final point cloud. The approach is able to produce one 3D point for each image pixel within the overlapping area of the stereo pair so that to obtain pixel-resolution 3D models. Experiments using the Lunar Reconnaissance Orbiter Camera - Narrow Angle Camera (LROC NAC) images show the superior performances of the approach compared with traditional photogrammetric technique. The results and findings from this research contribute to optimal exploitation of image information for high-resolution 3D modelling of the lunar surface, which is of significance for the advancement of lunar and planetary mapping.

Experiment LEND of the NASA Lunar Reconnaissance Orbiter for High-Resolution Mapping of Neutron Emission of the Moon

Astrobiology ◽  
2008 ◽  
Vol 8 (4) ◽  
pp. 793-804 ◽  
Author(s):  
I.G. Mitrofanov ◽  
A.B. Sanin ◽  
D.V. Golovin ◽  
M.L. Litvak ◽  
A.A. Konovalov ◽  
...  
Keyword(s):  
High Resolution ◽  
Neutron Emission ◽  
The Moon ◽  
High Resolution Mapping ◽  
Lunar Reconnaissance Orbiter ◽  
Resolution Mapping

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 KAGUYA observation of global emissions of indigenous carbon ions from the Moon

2020 ◽  
Vol 6 (19) ◽  
pp. eaba1050 ◽  
Author(s):  
Shoichiro Yokota ◽  
Kentaro Terada ◽  
Yoshifumi Saito ◽  
Daiba Kato ◽  
Kazushi Asamura ◽  
...  
Keyword(s):  
Solar Wind ◽  
Lunar Surface ◽  
Carbon Ions ◽  
The Moon ◽  
Considerable Influence ◽  
Lunar Orbiter ◽  
Volatile Element ◽  
Emission Fluxes ◽  
Formation And Evolution ◽  
Global Emissions

Carbon is a volatile element that has a considerable influence on the formation and evolution of planetary bodies, although it was previously believed to be depleted in the Moon. We present observations by the lunar orbiter KAGUYA of carbon ions emitted from the Moon. These emissions were distributed over almost the total lunar surface, but amounts were differed with respect to lunar geographical areas. The estimated emission fluxes to space were ~5.0 × 104 per square centimeter per second, which is greater than possible ongoing supplies from the solar wind and micrometeoroids. Our estimates demonstrate that indigenous carbon exists over the entire Moon, supporting the hypothesis of a carbon-containing Moon, where the carbon was embedded at its formation and/or was transported billions of years ago.

scholarly journals GEOPOSITIONING PRECISION ANALYSIS OF MULTIPLE IMAGE TRIANGULATION USING LRO NAC LUNAR IMAGES

2016 ◽  
Vol XLI-B4 ◽  
pp. 369-374
Author(s):  
K. Di ◽  
B. Xu ◽  
B. Liu ◽  
M. Jia ◽  
Z. Liu
Keyword(s):  
Three Dimensional ◽  
Landing Site ◽  
Vast Number ◽  
Multiple Image ◽  
Narrow Angle ◽  
Polynomial Coefficients ◽  
Rational Polynomial ◽  
Lunar Reconnaissance Orbiter ◽  
Sensor Models ◽  
Image Pairs

This paper presents an empirical analysis of the geopositioning precision of multiple image triangulation using Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images at the Chang’e-3(CE-3) landing site. Nine LROC NAC images are selected for comparative analysis of geopositioning precision. Rigorous sensor models of the images are established based on collinearity equations with interior and exterior orientation elements retrieved from the corresponding SPICE kernels. Rational polynomial coefficients (RPCs) of each image are derived by least squares fitting using vast number of virtual control points generated according to rigorous sensor models. Experiments of different combinations of images are performed for comparisons. The results demonstrate that the plane coordinates can achieve a precision of 0.54 m to 2.54 m, with a height precision of 0.71 m to 8.16 m when only two images are used for three-dimensional triangulation. There is a general trend that the geopositioning precision, especially the height precision, is improved with the convergent angle of the two images increasing from several degrees to about 50°. However, the image matching precision should also be taken into consideration when choosing image pairs for triangulation. The precisions of using all the 9 images are 0.60 m, 0.50 m, 1.23 m in along-track, cross-track, and height directions, which are better than most combinations of two or more images. However, triangulation with selected fewer images could produce better precision than that using all the images.

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