scholarly journals A New Lunar DEM Based on the Calibrated Chang’E-1 Laser Altimeter Data

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Yong Huang ◽  
Shengqi Chang ◽  
Songhe Qin ◽  
Peijia Li ◽  
Xiaogong Hu ◽  
...  
Keyword(s):  
Systematic Errors ◽  
Empirical Method ◽  
Altimeter Data ◽  
Systematic Bias ◽  
The Moon ◽  
Harmonic Model ◽  
Laser Altimeter ◽  
Lunar Topography ◽  
Dem Accuracy ◽  
Spherical Harmonic Model

To improve the lunar DEM accuracy derived from CE-1 altimeter data, CE-1 laser altimeter data are calibrated in this paper. Orbit accuracy and ranging accuracy are the two most important factors to affect the application of altimeter data in the lunar topography. An empirical method is proposed to calibrate CE-1 altimeter data, using gridded LOLA DEM to correct systematic errors of CE-1 altimeter data, and the systematic bias is about -139.52 m. A new lunar DEM grid model based on calibrated CE-1 altimeter data with the spatial resolution of 0.0625°  × 0.0625° is obtained as well as a spherical harmonic model at 1400th order. Furthermore, the DEM accuracy is assessed through the comparison with the nearside landmarks of the Moon, and the results show that the DEM accuracy is improved from 127.3 m to 48.7 m after the calibration of laser altimeter data.

A degree 180 spherical harmonic model for the lunar topography

2003 ◽  
Vol 31 (11) ◽  
pp. 2377-2382 ◽  
Author(s):  
Jinsong Ping ◽  
Kosuke Heki ◽  
Koji Matsumoto ◽  
Yoshiaka Tamura
Keyword(s):  
Spherical Harmonic ◽  
Harmonic Model ◽  
Lunar Topography ◽  
Spherical Harmonic Model

scholarly journals Alignment determination of the Hayabusa2 laser altimeter (LIDAR)

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Hirotomo Noda ◽  
Hiroki Senshu ◽  
Koji Matsumoto ◽  
Noriyuki Namiki ◽  
Takahide Mizuno ◽  
...  
Keyword(s):  
Altimeter Data ◽  
Gravity Assist ◽  
Laser Altimeter ◽  
Trajectory Error ◽  
Flight Data ◽  
Camera Image ◽  
Operation Period ◽  
The Cross ◽  
Along Track

AbstractIn this study, we determined the alignment of the laser altimeter aboard Hayabusa2 with respect to the spacecraft using in-flight data. Since the laser altimeter data were used to estimate the trajectory of the Hayabusa2 spacecraft, the pointing direction of the altimeter needed to be accurately determined. The boresight direction of the receiving telescope was estimated by comparing elevations of the laser altimeter data and camera images, and was confirmed by identifying prominent terrains of other datasets. The estimated boresight direction obtained by the laser link experiment in the winter of 2015, during the Earth’s gravity assist operation period, differed from the direction estimated in this study, which fell on another part of the candidate direction; this was not selected in a previous study. Assuming that the uncertainty of alignment determination of the laser altimeter boresight was 4.6 pixels in the camera image, the trajectory error of the spacecraft in the cross- and/or along-track directions was determined to be 0.4, 2.1, or 8.6 m for altitudes of 1, 5, or 20 km, respectively.

scholarly journals Measurement of ice-sheet topography using satellite-radar interferometry

1996 ◽  
Vol 42 (140) ◽  
pp. 10-22 ◽  
Author(s):  
Ian Joughin ◽  
Dale Winebrenner ◽  
Mark Fahnestock ◽  
Ron Kwok ◽  
William Krabill
Keyword(s):  
Ice Sheets ◽  
Ice Sheet ◽  
Surface Displacement ◽  
Horizontal Resolution ◽  
Radar Interferometry ◽  
Altimeter Data ◽  
Laser Altimeter ◽  
Digital Elevation ◽  
Satellite Radar ◽  
Ice Sheet Topography

AbstractDetailed digital elevation models (DEMs) do not exist for much of the Greenland and Antartic ice sheets. Radar altimetry is at present the primary, in many cases the only, source of topographic data over the ice sheets, but the horizontal resolution of such data is coarse. Satellite-radar interferometry uses the phase difference between pairs of synthetic aperture radar (SAR) images to measure both ice-sheet topography and surface displacement. We have applied this technique using ERS-1 SAR data to make detailed (i.e. 80 m horizontal resolution) maps of surface topography in a 100 km by 300 km strip in West Greenland, extending northward from just above Jakobshavns Isbræ. Comparison with а 76 km long line of airborne laser-altimeter data shows that We have achieved a relative accuracy of 2.5 m along the profile. These observations provide a detailed view of dynamically Supported topography near the margin of an ice sheet. In the final section We compare our estimate of topography with phase contours due to motion, and confirm our earlier analysis concerning vertical ice-sheet motion and complexity in ERS-1 SAR interferograms.

Global Mapping and Analysis of the Structural Features in the Northern Smooth Plains of Mercury

2021 ◽  
Author(s):  
marco cardinale ◽  
Gaetano Di Achille ◽  
David A.Vaz
Keyword(s):  
Imaging System ◽  
Structural Features ◽  
Planetary Science ◽  
Altimeter Data ◽  
Normal Faults ◽  
Laser Altimeter ◽  
Volcanic Material ◽  
Geological Map ◽  
Dual Imaging

<p>Orbital data from the Messenger spacecraft (1) reveal that part of the Mercury surface is covered by smooth plains, which are interpreted to be flood volcanic material across the planetary surface (2). In this work, we present a detailed geo-structural map of the northern smooth plains between<span class="Apple-converted-space">  </span>latitudes 29°N and 65°N. Our 1:100.000-scale map is obtained semi-automatically, using an algorithm to map all scarps from a DEM (3,4) followed by visual inspection and classification in ArcGIS. We created a DEM<span class="Apple-converted-space">  </span>using the raw MLA (Mercury Laser Altimeter) data (1) ,with 500 m/pix, and we used the Mercury Messenger MDIS (Mercury Dual Imaging System) (1,2) base map with 166m per pixel for the classification stage. With this approach, we mapped and characterized 51664 features on Mercury, creating a database with several morphometric attributes (e.g. length, azimuth, scarp height) which we will use to study the tectonic evolution of the smooth plains.<span class="Apple-converted-space"> </span></p> <p>In this way, we classified wrinkle ridges’s scarps, ghost craters, rim craters and central peaks. The morphometric parameters of the wrinkle ridges will<span class="Apple-converted-space">  </span>be quantitatively analyzed, in order to characterizer the possible tectonic process that could have formed them.</p> <p>This map can be considered an enhancement for the north pole of the global geological map of Mercury (1, 5).</p> <p> </p> <p>References</p> <ul> <li>Hawkins, S. E., III, et al. (2007), The Mercury Dual Imaging System on the MESSENGER spacecraft, Space Sci. Rev., 131, 247–338..<span class="Apple-converted-space"> </span></li> <li>Denevi, B. W., et al. (2013), The distribution and origin of smooth plains on Mercury, J. Geophys. Res. Planets, 118, 891–907, doi:10.1002/jgre.20075.</li> <li>Alegre Vaz, D. (2011). Analysis of a Thaumasia Planum rift through automatic mapping and strain characterization of normal faults. Planetary and Space Science, 59(11-12), 1210–1221. doi:10.1016/j.pss.2010.07.008 .</li> <li>Vaz, D. A., Spagnuolo, M. G., & Silvestro, S. (2014). Morphometric and geometric characterization of normal faults on Mars. Earth and Planetary Science Letters, 401, 83–94. doi:10.1016/j.epsl.2014.05.022.</li> <li>Kinczyk, M. J., Prockter, L., Byrne, P., Denevi, B., Buczkowski, D., Ostrach, L., & Miller, E. (2019, September). The First Global Geological Map of Mercury. In <em>EPSC-DPS Joint Meeting 2019</em> (Vol. 2019, pp. EPSC-DPS2019).</li> </ul>

Error identification in orbital laser altimeter data by machine learning

2021 ◽  
Author(s):  
Oliver Stenzel ◽  
Robin Thor ◽  
Martin Hilchenbach
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Space Science ◽  
Altimeter Data ◽  
Laser Altimetry ◽  
Error Identification ◽  
Laser Altimeter ◽  
Data Set ◽  
Baseline Design ◽  
Solar System Bodies

<p>Orbital Laser altimeters deliver a plethora of data that is used to map planetary surfaces [1] and to understand interiors of solar system bodies [2]. Accuracy and precision of laser altimetry measurements depend on the knowledge of spacecraft position and pointing and on the instrument. Both are important for the retrieval of tidal parameters. In order to assess the quality of the altimeter retrievals, we are training and implementing an artificial neural network (ANN) to identify and exclude scans from analysis which yield erroneous data. The implementation is based on the PyTorch framework [3]. We are presenting our results for the MESSENGER Mercury Laser Altimeter (MLA) data set [4], but also in view of future analysis of the BepiColombo Laser Altimeter (BELA) data, which will arrive in orbit around Mercury in 2025 on board the Mercury Planetary Orbiter [5,6]. We further explore conventional methods of error identification and compare these with the machine learning results. Short periods of large residuals or large variation of residuals are identified and used to detect erroneous measurements. Furthermore, long-period systematics, such as those caused by slow variations in instrument pointing, can be modelled by including additional parameters.<br>[1] Zuber, Maria T., David E. Smith, Roger J. Phillips, Sean C. Solomon, Gregory A. Neumann, Steven A. Hauck, Stanton J. Peale, et al. ‘Topography of the Northern Hemisphere of Mercury from MESSENGER Laser Altimetry’. Science 336, no. 6078 (13 April 2012): 217–20. https://doi.org/10.1126/science.1218805.<br>[2] Thor, Robin N., Reinald Kallenbach, Ulrich R. Christensen, Philipp Gläser, Alexander Stark, Gregor Steinbrügge, and Jürgen Oberst. ‘Determination of the Lunar Body Tide from Global Laser Altimetry Data’. Journal of Geodesy 95, no. 1 (23 December 2020): 4. https://doi.org/10.1007/s00190-020-01455-8.<br>[3] Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. ‘PyTorch: An Imperative Style, High-Performance Deep Learning Library’. Advances in Neural Information Processing Systems 32 (2019): 8026–37.<br>[4] Cavanaugh, John F., James C. Smith, Xiaoli Sun, Arlin E. Bartels, Luis Ramos-Izquierdo, Danny J. Krebs, Jan F. McGarry, et al. ‘The Mercury Laser Altimeter Instrument for the MESSENGER Mission’. Space Science Reviews 131, no. 1 (1 August 2007): 451–79. https://doi.org/10.1007/s11214-007-9273-4.<br>[5] Thomas, N., T. Spohn, J. -P. Barriot, W. Benz, G. Beutler, U. Christensen, V. Dehant, et al. ‘The BepiColombo Laser Altimeter (BELA): Concept and Baseline Design’. Planetary and Space Science 55, no. 10 (1 July 2007): 1398–1413. https://doi.org/10.1016/j.pss.2007.03.003.<br>[6] Benkhoff, Johannes, Jan van Casteren, Hajime Hayakawa, Masaki Fujimoto, Harri Laakso, Mauro Novara, Paolo Ferri, Helen R. Middleton, and Ruth Ziethe. ‘BepiColombo—Comprehensive Exploration of Mercury: Mission Overview and Science Goals’. Planetary and Space Science, Comprehensive Science Investigations of Mercury: The scientific goals of the joint ESA/JAXA mission BepiColombo, 58, no. 1 (1 January 2010): 2–20. https://doi.org/10.1016/j.pss.2009.09.020.</p>

scholarly journals Polyaxial Figures of the Moon

2011 ◽  
Vol 1 (4) ◽  
pp. 348-354 ◽  
Author(s):  
H. İz ◽  
X. Ding ◽  
C. Dai ◽  
C. Shum
Keyword(s):  
Center Of Mass ◽  
The Moon ◽  
Shape Parameters ◽  
Axially Symmetric ◽  
Laser Altimetry ◽  
Additional Information ◽  
All Solutions ◽  
Ellipsoidal Model ◽  
Lunar Topography ◽  
Topography Model

Polyaxial Figures of the MoonThis study investigates various models to represent the gross geometric shape of the Moon. Asymmetric polyaxial geometric models-namely three-, four- and six-axial lunar figure - are compared and contrasted with the axially symmetric three-axis ellipsoidal model derived from Chang'e 1 and SELENE laser altimetry data. All solutions confirm a hydrostatically stable lunar shape shifted with respect to the lunar center of mass by topography. Model solutions with increasing complexity offer additional information about the regional properties of the lunar topography. Solution statistics suggest that axially symmetric lunar figures and their center of figure parameters can be replaced by an equivalent asymmetric lunar shape centered at the center of mass of the Moon. Thus, using only three shape parameters, one can derive an "egg" shape that better accommodates the true geometry of the Moon.

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