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.

A Method of Extracting High-Accuracy Elevation Control Points from ICESat-2 Altimetry Data

2021 ◽  
Vol 87 (11) ◽  
pp. 821-830
Author(s):  
Binbin Li ◽  
Huan Xie ◽  
Shijie Liu ◽  
Xiaohua Tong ◽  
Hong Tang ◽  
...  
Keyword(s):  
Large Scale ◽  
Comprehensive Evaluation ◽  
Photon Counting ◽  
High Accuracy ◽  
Airborne Lidar ◽  
Altimeter Data ◽  
Control Points ◽  
Laser Altimeter ◽  
Terrain Elevation ◽  
Along Track

Due to its high ranging accuracy, spaceborne laser altimetry technology can improve the accuracy of satellite stereo mapping without ground control points. In the past, full-waveform ICE, CLOUD, and Land Elevation Satellite (ICESat) laser altimeter data have been used as one of the main data sources for global elevation control. As a second-generation satellite, ICESat-2 is equipped with an altimeter using photon counting mode. This can further improve the application capability for stereo mapping because of the six laser beams with high along-track repetition frequency, which can provide more detailed ground contour descriptions. Previous studies have addressed how to extract high-accuracy elevation control points from ICESat data. However, these methods cannot be directly applied to ICESat-2 data because of the different modes of the laser altimeters. Therefore, in this paper, we propose a method using comprehensive evaluation labels that can extract high-accuracy elevation control points that meet the different level elevation accuracy requirements for large scale mapping from the ICESat-2 land-vegetation along-track product. The method was verified using two airborne lidar data sets. In flat, hilly, and mountainous areas, by using our method to extract the terrain elevation, the root-mean-square error of elevation control points decrease from 1.249–2.094 m, 2.237–3.225 m, and 2.791–4.822 m to 0.262–0.429 m, 0.484–0.596 m, and 0.611–1.003 m, respectively. The results show that the extraction elevations meet the required accuracy for large scale mapping.

scholarly journals Airborne Validation of ICESat-2 ATLAS Data Over Crevassed Surfaces and Other Complex Glacial Environments: Results From Experiments of Laser Altimeter and Kinematic GPS Data Collection From a Helicopter Over a Surging Arctic Glacier (Negribreen, Svalbard)

2021 ◽  
Author(s):  
Ute Christina Herzfeld ◽  
Matthew Lawson ◽  
Thomas Trantow ◽  
Thomas Nylen
Keyword(s):  
Data Collection ◽  
Altimeter Data ◽  
Gps Data ◽  
Laser Altimeter ◽  
Kinematic Gps ◽  
Atlas Data ◽  
Height Determination ◽  
Ice Surface ◽  
Airborne Laser ◽  
Along Track

The topic of this paper is the airborne evaluation of ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) measurement capabilities and surface-height-determination over crevassed glacial terrain, with a focus on the geodetical accuracy of geophysical data collected from a helicopter. To obtain surface heights over crevassed and otherwise complex ice surface, ICESat-2 data are analyzed using the density-dimension algorithm for ice surfaces (DDA-ice), which yields surface heights at the nominal 0.7~m along-track spacing of ATLAS data. As the result of an ongoing surge, Negribreen, Svalbard, provided an ideal situation for the validation objectives in 2018 and 2019, because many different crevasse types and morphologically complex ice surfaces existed in close proximity. Airborne geophysical data, including laser altimeter data (profilometer data at 905~nm frequency), differential Global Positioning System (GPS), Inertial Measurement Unit (IMU) data, on-board-time-lapse imagery and photographs, were collected during two campaigns in summers of 2018 and 2019. Airborne experiment setup, geodetical correction and data processing steps are described here. To date, there is relatively little knowledge of the geodetical accuracy that can be obtained from kinematic data collection from a helicopter. Our study finds that (1)~Kinematic GPS data collection with correction in post-processing yields higher accuracies than Real-Time-Kinematic (RTK) data collection. (2)~Processing of only the rover data using the Natural Resources Canada Spatial Reference System Precise Point Positioning (CSRS-PPP) software is sufficiently accurate for the sub-satellite validation purpose. (3)~Distances between ICESat-2 ground tracks and airborne ground tracks were generally better than 25~m, while distance between predicted and actual ICESat-2 ground track was on the order of 9~m, which allows direct comparison of ice-surface heights and spatial statistical characteristics of crevasses from the satellite and airborne measurements. (4)~The Lasertech Universal Laser System (ULS), operated at up to 300~m above ground level, yields full return frequency (400~Hz) and 0.06-0.08~m on-ice along-track spacing of height measurements. (5)~Cross-over differences of airborne laser altimeter data are 0.1918 $\pm$ 2.385~m along straight paths over generally crevassed terrain, which implies a precision of approximately 2.4~m for ICESat-2 validation experiments. (6)~In summary, the comparatively light-weight experiment setup of a suite of small survey equipment mounted on a Eurocopter (Helicopter AS-350) and kinematic GPS data analyzed in post-processing using CSRS-PPP leads to high accuracy repeats of the ICESat-2 tracks. The technical results (1)-(6) indicate that direct comparison of ice-surface heights and crevasse depths from the ICESat-2 and airborne laser altimeter data is warranted. The final result of the validation is that ICESat-2 ATLAS data, analyzed with the DDA-ice, facilitate surface-height determination over crevassed terrain, in good agreement with airborne data, including spatial characteristics, such as surface roughness, crevasse spacing and depth, which are key informants on the deformation and dynamics of a glacier during surge.

scholarly journals MABEL photon-counting laser altimetry data in Alaska for ICESat-2 simulations and development

2016 ◽  
Author(s):  
K. M. Brunt ◽  
T. A. Neumann ◽  
J. M. Amundson ◽  
J. L. Kavanaugh ◽  
M. S. Moussavi ◽  
...  
Keyword(s):  
New Technology ◽  
Photon Counting ◽  
Sampling Strategy ◽  
Laser Altimeter ◽  
Glacier Surface ◽  
Southeast Alaska ◽  
Ice Cloud ◽  
Melt Ponds ◽  
Along Track

Abstract. Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) is scheduled to launch in 2017 and will carry the Advanced Topographic Laser Altimeter System (ATLAS), which is a photon-counting laser altimeter and represents a new approach to satellite determination of surface elevation. Given the new technology of ATLAS, an airborne instrument, the Multiple Altimeter Beam Experimental Lidar (MABEL), was deployed in July 2014 to Alaska to provide data needed for satellite-algorithm development, simulating key elements of the photon-counting sampling strategy, and assessing elements of the resulting data that may vary seasonally. Here, we compare MABEL lidar data to in situ observations in Southeast Alaska to assess instrument performance in summer conditions and in the presence of glacier surface melt ponds and a wet snowpack. Results indicate that: 1) the ATLAS 90 m beam-spacing strategy will provide a robust assessment of across-track slope that is consistent with shallow slopes (<1°) of an ice-sheet interior over 50 to 150 m length scales; 2) the dense along-track sampling strategy of photon counting systems provides crevasse detail; and 3) MABEL 532 nm wavelength light may be sampling the surface and subsurface of shallow (approximately 2 m deep) supraglacial melt ponds.

scholarly journals Airborne Validation of ICESat-2 ATLAS Data Over Crevassed Surfaces and Other Complex Glacial Environments: Results From Experiments of Laser Altimeter and Kinematic GPS Data Collection From a Helicopter Over a Surging Arctic Glacier (Negribreen, Svalbard)

2021 ◽  
Author(s):  
Ute Christina Herzfeld ◽  
Matthew Lawson ◽  
Thomas Trantow ◽  
Thomas Nylen
Keyword(s):  
Data Collection ◽  
Altimeter Data ◽  
Gps Data ◽  
Laser Altimeter ◽  
Kinematic Gps ◽  
Atlas Data ◽  
Height Determination ◽  
Ice Surface ◽  
Airborne Laser ◽  
Along Track

The topic of this paper is the airborne evaluation of ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) measurement capabilities and surface-height-determination over crevassed glacial terrain, with a focus on the geodetical accuracy of geophysical data collected from a helicopter. To obtain surface heights over crevassed and otherwise complex ice surface, ICESat-2 data are analyzed using the density-dimension algorithm for ice surfaces (DDA-ice), which yields surface heights at the nominal 0.7~m along-track spacing of ATLAS data. As the result of an ongoing surge, Negribreen, Svalbard, provided an ideal situation for the validation objectives in 2018 and 2019, because many different crevasse types and morphologically complex ice surfaces existed in close proximity. Airborne geophysical data, including laser altimeter data (profilometer data at 905~nm frequency), differential Global Positioning System (GPS), Inertial Measurement Unit (IMU) data, on-board-time-lapse imagery and photographs, were collected during two campaigns in summers of 2018 and 2019. Airborne experiment setup, geodetical correction and data processing steps are described here. To date, there is relatively little knowledge of the geodetical accuracy that can be obtained from kinematic data collection from a helicopter. Our study finds that (1)~Kinematic GPS data collection with correction in post-processing yields higher accuracies than Real-Time-Kinematic (RTK) data collection. (2)~Processing of only the rover data using the Natural Resources Canada Spatial Reference System Precise Point Positioning (CSRS-PPP) software is sufficiently accurate for the sub-satellite validation purpose. (3)~Distances between ICESat-2 ground tracks and airborne ground tracks were generally better than 25~m, while distance between predicted and actual ICESat-2 ground track was on the order of 9~m, which allows direct comparison of ice-surface heights and spatial statistical characteristics of crevasses from the satellite and airborne measurements. (4)~The Lasertech Universal Laser System (ULS), operated at up to 300~m above ground level, yields full return frequency (400~Hz) and 0.06-0.08~m on-ice along-track spacing of height measurements. (5)~Cross-over differences of airborne laser altimeter data are 0.1918 $\pm$ 2.385~m along straight paths over generally crevassed terrain, which implies a precision of approximately 2.4~m for ICESat-2 validation experiments. (6)~In summary, the comparatively light-weight experiment setup of a suite of small survey equipment mounted on a Eurocopter (Helicopter AS-350) and kinematic GPS data analyzed in post-processing using CSRS-PPP leads to high accuracy repeats of the ICESat-2 tracks. The technical results (1)-(6) indicate that direct comparison of ice-surface heights and crevasse depths from the ICESat-2 and airborne laser altimeter data is warranted. The final result of the validation is that ICESat-2 ATLAS data, analyzed with the DDA-ice, facilitate surface-height determination over crevassed terrain, in good agreement with airborne data, including spatial characteristics, such as surface roughness, crevasse spacing and depth, which are key informants on the deformation and dynamics of a glacier during surge.

Using the Along-Track Altimeter Data for a Verification of the Numerical Wave Models

2018 ◽  
pp. 20-31
Author(s):  
V. Polnikov ◽  
◽  
F. Pogarsky ◽  
N. Zilitinkevich ◽  
A. Kubryakov ◽  
...  
Keyword(s):  
Altimeter Data ◽  
Numerical Wave ◽  
Wave Models ◽  
Along Track

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

&lt;p&gt;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&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;latitudes 29&amp;#176;N and 65&amp;#176;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&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;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.&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt; &lt;p&gt;In this way, we classified wrinkle ridges&amp;#8217;s scarps, ghost craters, rim craters and central peaks. The morphometric parameters of the wrinkle ridges will&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;be quantitatively analyzed, in order to characterizer the possible tectonic process that could have formed them.&lt;/p&gt; &lt;p&gt;This map can be considered an enhancement for the north pole of the global geological map of Mercury (1, 5).&lt;/p&gt; &lt;p&gt;&amp;#160;&lt;/p&gt; &lt;p&gt;References&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Hawkins, S. E., III, et al. (2007), The Mercury Dual Imaging System on the MESSENGER spacecraft, Space Sci. Rev., 131, 247&amp;#8211;338..&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/li&gt; &lt;li&gt;Denevi, B. W., et al. (2013), The distribution and origin of smooth plains on Mercury, J. Geophys. Res. Planets, 118, 891&amp;#8211;907, doi:10.1002/jgre.20075.&lt;/li&gt; &lt;li&gt;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&amp;#8211;1221. doi:10.1016/j.pss.2010.07.008&amp;#160;.&lt;/li&gt; &lt;li&gt;Vaz, D. A., Spagnuolo, M. G., &amp; Silvestro, S. (2014). Morphometric and geometric characterization of normal faults on Mars. Earth and Planetary Science Letters, 401, 83&amp;#8211;94. doi:10.1016/j.epsl.2014.05.022.&lt;/li&gt; &lt;li&gt;Kinczyk, M. J., Prockter, L., Byrne, P., Denevi, B., Buczkowski, D., Ostrach, L., &amp; Miller, E. (2019, September). The First Global Geological Map of Mercury. In &lt;em&gt;EPSC-DPS Joint Meeting 2019&lt;/em&gt; (Vol. 2019, pp. EPSC-DPS2019).&lt;/li&gt; &lt;/ul&gt;

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

&lt;p&gt;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.&lt;br&gt;[1] Zuber, Maria T., David E. Smith, Roger J. Phillips, Sean C. Solomon, Gregory A. Neumann, Steven A. Hauck, Stanton J. Peale, et al. &amp;#8216;Topography of the Northern Hemisphere of Mercury from MESSENGER Laser Altimetry&amp;#8217;. Science 336, no. 6078 (13 April 2012): 217&amp;#8211;20. https://doi.org/10.1126/science.1218805.&lt;br&gt;[2] Thor, Robin N., Reinald Kallenbach, Ulrich R. Christensen, Philipp Gl&amp;#228;ser, Alexander Stark, Gregor Steinbr&amp;#252;gge, and J&amp;#252;rgen Oberst. &amp;#8216;Determination of the Lunar Body Tide from Global Laser Altimetry Data&amp;#8217;. Journal of Geodesy 95, no. 1 (23 December 2020): 4. https://doi.org/10.1007/s00190-020-01455-8.&lt;br&gt;[3] Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. &amp;#8216;PyTorch: An Imperative Style, High-Performance Deep Learning Library&amp;#8217;. Advances in Neural Information Processing Systems 32 (2019): 8026&amp;#8211;37.&lt;br&gt;[4] Cavanaugh, John F., James C. Smith, Xiaoli Sun, Arlin E. Bartels, Luis Ramos-Izquierdo, Danny J. Krebs, Jan F. McGarry, et al. &amp;#8216;The Mercury Laser Altimeter Instrument for the MESSENGER Mission&amp;#8217;. Space Science Reviews 131, no. 1 (1 August 2007): 451&amp;#8211;79. https://doi.org/10.1007/s11214-007-9273-4.&lt;br&gt;[5] Thomas, N., T. Spohn, J. -P. Barriot, W. Benz, G. Beutler, U. Christensen, V. Dehant, et al. &amp;#8216;The BepiColombo Laser Altimeter (BELA): Concept and Baseline Design&amp;#8217;. Planetary and Space Science 55, no. 10 (1 July 2007): 1398&amp;#8211;1413. https://doi.org/10.1016/j.pss.2007.03.003.&lt;br&gt;[6] Benkhoff, Johannes, Jan van Casteren, Hajime Hayakawa, Masaki Fujimoto, Harri Laakso, Mauro Novara, Paolo Ferri, Helen R. Middleton, and Ruth Ziethe. &amp;#8216;BepiColombo&amp;#8212;Comprehensive Exploration of Mercury: Mission Overview and Science Goals&amp;#8217;. 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&amp;#8211;20. https://doi.org/10.1016/j.pss.2009.09.020.&lt;/p&gt;

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