Mars Science Posters 1

Application of Automated Crater Detection for Mars Crater GIS Database Production J. I. Simpson *, J. R. Kim **, J-P. Muller ** * Dept. of Civil, Environmental and Geomatic Engineering, UCL, London WC1E 6BT, UK. ** MSSL, Dept. of Space and Climate Physics, UCL, Surrey, RH5 6NT, UK. GIS Files Motivation • Demand from planetary geologists for impact crater databases

Duplicates between sets resolved using distance & radius measure

Assessment Results 2 regions on Mars were chosen for their geological diversity, Elysium Planitia and Iani Vallis.

• As spatial resolution of imagery increases, volumes become too large for manual digitisation

Iani Vallis

Elysium Planitia

• Accuracy of automated algorithms must be quantified and improved • Fully automated crater detection systems not yet available

Approach

© USGS

• Best case: 157 craters detected in overlap • Worst case: 45 craters detected in overlap

• Aim is to emulate a fully automated system to demonstrate feasibility and perform a quantitative assessment of existing automated system • Process HRSC images using KimMuller** automated crater detection algorithm • Calculate the detection and quality rates for each image

Detection Percent = (100 * TP) / (TP + FN)

• Automatically merge results into single, georeferenced GIS format shapefiles • As part of the merging process, automatically resolve duplicate detections for side overlapping orbits

Performance Assessment • A software tool was developed to aid a quantitative assessment, optimised for simplicity and speed

2,543 Craters in Elysium Planitia Green = TP Red = FP Blue = FN Yellow = Duplicate

Quality Percent= (100 * TP) / (TP + FN + FP) Summary Detection % 84.45 Quality % 78.92 Branching Factor 0.09 for crater diameters >= 8 pixels when averaged across 11,400 craters by visual inspection, in 2 different geological environments.

8,857 Craters in Iani Vallis • Two regions on Mars were selected and the HRSC images were processed using the Kim-Muller algorithm: • Using the tool, craters from the automated detection were rapidly tagged as true positives (TP), false positives (FP) or false negatives (FN) • For each set of detection results, Shufelt’s metrics, originally designed for building extraction from digital imagery were calculated as follows

Green = TP Red = FP Blue = FN Yellow = Duplicate

Detection %

Image Elysium 1196 Elysium 2066 Elysium 2099 Elysium 2110 Elysium 2121 Elysium 2143 Elysium 2154 Elysium 2165 Elysium 2176 Subtotal

TP 142 168 369 105 65 366 346 143 237 1,941

17 11 8 2 10 26 9 28 30 141

21 30 33 4 32 47 47 25 54 293

Total 180 209 410 111 107 439 402 196 321 2,375

Iani 0912 Iani 0923 Iani 0934 Subtotal

2,592 2,131 3,313 8,036

159 229 305 693

163 710 785 1,658

Total

9,977

834

1,951

Detection Percent = (100 * TP) / (TP + FN) Quality Percent= (100 * TP) / (TP + FN + FP) Branching Factor = FP / TP

GIS Database Production

FP

FN

=

Quality %

100 * TP TP + FN

=

Branching Factor

100 * TP TP + FN + FP

=

FP TP

87.12 84.85 91.79 96.33 67.01 88.62 88.04 85.12 81.44 85.59

78.89 80.38 90.00 94.59 60.75 83.37 86.07 72.96 73.83 80.09

0.12 0.07 0.02 0.02 0.15 0.07 0.03 0.20 0.13 0.09

2,927 3,070 4,403 10,400

94.08 75.01 80.84 83.31

88.55 69.41 75.24 77.74

0.06 0.11 0.09 0.09

12,775

84.45

78.92

0.09

Conclusions • Image quality significantly affects detection results

• Craters from automated detection converted into polygon shapefiles

• Detections were reduced by a factor of 3 for the worst case compared with the best case

• Results merged

• It is possible to perform a quantifiable assessment of automated crater detection algorithms in the absence of existing ground truth databases

• False positives removed • False negatives added • Duplicates in overlap regions resolved by identifying closest matching pair in each set via distance measurement factor (incorporating radii and distance between centres) • Georeferenced by defining projected coordinate system in projection file (optional part of shapefile format)

• The construction of a fully automated crater detection system is achievable • It is conceivable that automated crater detection algorithms will be improved sufficiently to the point where they become a useful tool especially if DTMs are included

MSSL/DEPARTMENT OF SPACE AND CLIMATE PHYSICS

Flyby. High-resolution photos of equatorial region.

Mariner 7 (USA)

1969

Flyby. High-resolution photos of southern hemisphere.

Mariner 9 (USA)

1971

Orbiter. Year-long mapping mission, detailed photos of Phobos and Deimos.

Mars 2 (USSR)

1971

Orbiter. Dropped a capsule to the surface.

Mars 3 (USSR)

1973

Orbiter and Lander. First TV pictures from the surface of another planet.

Mars 5 (USSR)

1973

Orbiter. High-quality photos of southern hemisphere region.

Viking 1 (USA)

1975

Orbiter and Lander. First sustained surface science.

Viking 2 (USA)

1975

Orbiter and Lander. Discovered water frost on surface.

Phobos (USSR)

1988

Orbiter. Returned pictures of Phobos.

Mars Global Surveyor (USA)

1996

Orbiter. Global, multispectral mapping mission. Two-year mapping effort begins in 1998.

Mars Pathfinder (USA)

1996

Lander with Rover. First of a new generation of small, lightweight planetary craft. Paves way for following Mars missions. Sojourner rover provides technology demonstration and an imaging science package for surface studies.

Mars Surveyor ’98 Orbiter (USA)

1998

Orbiter. Completes scientific reconnaissance begun by Mars Global Surveyor. International participation.

Mars Surveyor ’98 Lander (USA)

1998

Lander. Explores high Martian latitudes where polar ices form. International participation

Planet-B (Japan)

1998

Orbiter. Will study interaction of solar wind with Martian atmosphere.

Future Mars Surveyors (USA)

2001– 2007

Orbiter/Lander Suites. Launching at 2-year intervals, with international participation. Lightweight sciences packages, with possible inclusion of rovers and sample return in 2005 with possible international partners.

g i

1969

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Mariner 6 (USA)

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Flyby. First close-up pictures of surface.

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1965

mars

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Mariner 4 (USA)

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Accomplishments

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Launch

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Spacecraft

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ings of modern spacecraft and instruments. We have learned that Mars, like Mercury, Venus, and Earth, is a small (in solar system terms), rocky planet that developed relatively close to the Sun. Mars has been subject to some of the same planetary processes—volcanism, impact events, and atmospheric effects—associated with the formation of the other “terrestrial” planets. But unlike Earth, Mars retains much of the surface record of its evolution and history. For millions of years, the Martian surface has been bare of water, and not subjected to the erosions and crustal plate movement that continually resurface Earth. So, Mars today can reveal to us the geologic history of a terrestrial planet in a way that Earth cannot. From Mars, we can learn things about our home planet that our home planet cannot teach us. Our sense of what we can learn from Mars has been both expanded and refined as we have studied the planet over the last three decades. Today, we know the Martian climate has indeed changed, and the planet’s surface has lost what liquid water it once had. Layered terrains near the Martian poles suggest that the planet’s climate changes have been periodic, perhaps caused by a regular change in the planet’s orbit. If this is so, we need to know more. The surface of Mars is intriguing. The planet is smaller that Earth, but its surface is dominated by a few features, larger than any terrestrial counterparts—a string of huge volcanoes sitting atop a bulge the size of the United Evidence of dried riverbeds, such as this fossilized dendritic drainage States, an equatorial rift valley more than 4,800 system, indicate the planet was once warmer and wetter. kilometers long, and a planet-encircling cliff separating northern plains from southern highlands. The surface of Mars tells a tale of planetary formation we are century telescopes, to be similar to human-made water canals yet to understand. Tectonism—the geological development on Earth, fueling the idea that Mars was perhaps inhabited. and alteration of a planet’s crust—has on Earth been in the Today, we know there are no canals on Mars, but there form of sliding plates that grind against each other in some are natural channels apparently carved by past water flow. area and spread apart in the seafloors. But Martian tectonism We know there are no civilizations, and it is unlikely that seems to have been mostly vertical, with hot lavas pushing there are any extant life forms, but there may be fossils of upwards through the crust to the surface. We need to know life forms from a time when there was water. These intrigumore about these processes if we are to fully understand ing possibilities are only a small part of our broad scientific what has happened—and may happen—on Earth. interest in the Red Planet—an interest fueled by the findars—the Red Planet, the Bringer of War—has inspired over the centuries wild flights of imagination, and at the same time intense scientific interest. A source of hostile invaders of Earth, the home of dying civilizations, a rough-and-tumble mining colony of the future—all are in the realm of science fiction, but they are based on seeds planted by centuries of scientific observation. Mars has shown itself to be the most Earth-like of the planets, with polar ice caps that grew and receded with the change of seasons, and markings that looked, to 19th-

the martian fleet

d

the allure of the red planet

SOLAR SYSTEM EXPLORATION DIVISION

NP-1997-02-223-HQ

missions of discovery

Many impact craters are visible in this Viking orbiter image, confirming that Mars has long been geologically inactive.

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ost of our current knowledge of Mars is the result of investigations conducted by a fleet of spacecraft beginning with the Mariners in the mid-1960s (see the table, “The Martian Fleet”). The Mariner 4, 6, and 7 flyby missions returned photos and weather data from the southern hemisphere of Mars that put to rest hopes of finding a civilization, and that gave the impression that Mars, like the Moon, has long been geologically inactive. The data from the 1971 Mariner 9 orbital mission created quite a different picture. Looking at the entire planet, Mariner 9 revealed huge volcanic mountains in the northern Tharsis region, so large that they deformed the planet’s sphericity. One of these, Olympus Mons, at more than 26 kilometers high (above Martian “sea-level”), remains the largest volcano observed in our solar system. Mariner 9 also revealed the awesome Vallis Marineris, a gigantic equatorial rift valley deeper and wider than the Grand Canyon and longer than the distance from New York to Los Angeles! Mariner 9 also gave us our first views of the Martian moons Phobos and

Deimos, two asteroid-like bodies that may in fact be asteroids captured by Martian gravity. Although Mariner 9 photos showed none of the fabled irrigation canals, the mission did disclose evidence of surface erosion and dried riverbeds, indicating the planet was once capable of sustaining liquid water. This fueled the possibility that life may be (or have been) possible on Mars. To investigate, two Viking spacecraft were dispatched to Mars in 1975. Each consisted of an orbiter and a lander. The orbiters surveyed the planet while the landers monitored surface weather conditions, took pictures, and tested the soil for signs of life. Viking 1’s photos revealed reddish desertlike landscape blanketed with rocks and dune-like drifts of dust. Some 5,000 kilometers away, Viking 2 observed a slightly more rolling, duneless landscape, where patches of frost covered the ground in the Martian winter. From the weather stations, we quickly learned that these regions of Mars are too cold, and the atmosphere too thin, for liquid water to exist. The experiments designed to test for life showed some intriguing chemistry, but no signs of life. Using the best technology available in their time, the Mariners and Vikings helped address centuries-old questions about Mars. But many new questions have arisen in the years since then. Today, we seek to understand Mars as a planetary system akin to our own Earth.

remaining questions

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ur studies of Mars to date have left us with a sense of what we can learn from the Red Planet in the years to come. Recent studies of meteorites believed to have originated on Mars suggest that there may be mineral evidence of primitive life forms in the soils and rocks of the Martian terrain. Confirmation of the ancient presence of such life forms would provide powerful keys to new understandings of the origins of life in our solar system. Understanding climate change is also a critical issue for life on Earth. We need to fully understand when the Martian climate has undergone change, why, and what happened. The past presence of water required a denser atmosphere than now exists. What happened to that atmosphere? Where did the surface water go? These questions cannot be understood in isolation from others. Has the periodic change in Martian climate been caused by a regular fluctuation in the Martian orbit? If so, what causes that fluctuation? Is there a relation to terrestrial phenomena, such as our ice ages? How have volcanism and impacts from comets and meteorites created the terrains we see today in the southern highlands and northern lowlands of Mars? What is the tectonic history of

This high-resolution scanning electron microscope image shows an unusual tube-like structural form that is less than 1/100th the width of a human hair in size found in a meteorite believed to be of Martian origin.

the next generation

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The Viking landers returned photographs of desert-like landscape. The reddish coloration is caused by the chemical weathering of iron-rich rocks.

the planet. Is Earth alone among the terrestrial planets in exhibiting plate tectonics? Why? The answers to these and other questions about the Red Planet await the next generation of Mars explorers.

n 1996, Mars Pathfinder and Mars Global Surveyor launched the next wave of Mars exploration. The Pathfinder approach demonstrates new, lightweight, low-cost lander, rover, and imaging technologies while characterizing Martian soils and rocks in the vicinity of the landing site. Mars Global Surveyor inaugurates an ambitious program of orbital science to recapture the science lost with the Mars Observer spacecraft. Martian weather, seasonal change, surface features, and composition will be studied in detail over Mars Global Surveyor’s 2-year mapping phase, providing our first comprehensive, high-resolution look at the near-surface and surface phenomena on Mars. These missions set the stage for the Mars Surveyor series, which will send similarly lightweight orbiters and landers to Mars every 2 years into the first decade of the next century. Orbiters will provide synoptic coverage of areas and phenomena of interest,

while acting as data relay stations for landers. Landers will probe the soils and test the rocks in search of clues regarding the origins and evolution of the Red Planet, and will look for tell-tale signs of life forms, past and present. We envision the Mars Surveyor program as the linchpin for NASA participation in all future international Mars exploration programs. Although to date Mars exploration missions have been conducted on a national scale by the United States and Russia, the allure of Mars is international in scope. Data exchange from previous planetary missions is already international, and other nations are now planning Mars missions for the next decade (see the table, “The Martian Fleet”), some of which will include international cooperation. Mars may have been named for the god of war, but the day is fast approaching when international expeditions will investigate the Red Planet in the name of peace.

Mars Namesake & Symbol Distance from the Sun Distance from the Earth Period of Rotation Equatorial Diameter Equatorial Inclination to Ecliptic Gravity Atmosphere Main Component Pressure at Surface Temperature Moons (2) Rings Orbital Eccentricity Orbital Inclination to Ecliptic Magnetic Field Density

Roman God of War Maximum: 249 mil km Minimum: 206 mil km Maximum: 399 mil km Minimum: 56 mil km 24.6 hrs (= 1 Martian day) 6,786 km 25.2° 0.38 of Earth’s Carbon Dioxide ~8 millibars (vs. 1,000 on Earth) –143°C to +17°C Phobos (Fear), 21 km diameter Deimos (Panic), 12 km diameter None 0.093 1.85° To be determined. Very weak, if any.

Enhanced visualization of Mars surface features from HRSC DTM Tomaž Podobnikar A B S T R A C T

Shaded reliefs, height codings, or profiles are commonly used for the visual interpretation of digital terrain models (DTM). Nevertheless, visualizations like these are not well suited to represent all details served by a DTM. The main aim of the presented results is to support the understanding of the areomorphology by means of enhanced, cartographic visualization methods. As a byproduct, the results may be used for detection of possible incorrect patterns caused by locally erroneous data or by interpolation artifacts. The proposed methods are evaluated on DTMs derived from HRSC images of Candor

1

& Peter Dorninger

Chasma and Nanedi Vallis. The DTMs were determined during the HRSC DTM test (C. Heipke et al., 2007: Evaluating planetary digital terrain models – the HRSC DTM test, PSS (Elsevier), in press) with a resolution of 50 meters per pixel. We present visualization of whole orbits (i.e. an extension of several hundreds kilometers) and of a selected area in the south-east of the Nanedi Vallis area and selected part of Candor Chasma. The presented images were derived by means of cartographic visualization techniques aiming at the enhancement of areomorphologic details as determined from DTMs. They are

based on the following methods for the landform representation: - z-coding representation of absolute or relative heights with different contrasts or colors regarding to the elevations of landform; bipolar differentiation technique as a relative z-coding of ”continuos” contour lines - analytical contextual operations: slope, aspect, curvature - edge enhancement: increasing the lighting (contrast) with respect to the natural aspect of landform; highlights ridges and valleys, peaks and sinks, calculating 360º shadows,

C A N D O R C H A S M A (140 X 840 K M )

Data characteristics: - Orthoimage: 50 m - HRSC DTM: 50 m, additionally resampled to 250 and 2000 m, and smoothed

Hill shading (hsh.)

NANEDI VALLIS (50 X 50 KM)

1,2

different filtering… - local runoff behavior: analysis of local height differences; depressions and drainage An important aspect of our research is multiscale visualization in order to represent various features like smooth or rough details. Different methods of landform abstraction at different scales are combined in single visualizations. Enhanced visualization is an important and powerful tool to assess or to represent Mars surface or landform or to support other activities. Combination of different techniques can increase understandability of landforms' shapes and thus support future decision making.

HRSC DTM, hill shading

Orhoimage, RGB channels

Bipolar diff. (int. 50 m)

Bipolar diff. (int. 100 m)

Bipolar diff. (int. 250 m)

Bipolar diff. (int. 500 m)

Curvature

Curvature (smoothed DTM)

Slope

Exposition (aspect)

Curvature (red and blue)

Shadows 360º (above, below)

Shadows 360º (above, B&W)

Bip. d. (orig. – smoothed DTM)

Curvature (smothed DTM, recl.)

Filtering (different window size)

Drainage

Depressions

Bip. d. (int. 250 m) + hsh.

Bip. d. (int. 500 m) + hsh.

Bip. d. (50 m) + hsh.

Bip. d. (orig. – sm. DTM) + hsh.

Sh. 360º (above, below) + hsh

Depressions + z-coding + hsh.

Curvature (edges) + hsh.

Sh.360º(ab.,bel.) + curv. + hsh.

Z

-

Curv. + hsh.

Drainage

Bip. d. + curv. + hsh. z-c.+bip.+curv.+hsh. Sh.360º + curv. + hsh.

C O D I N G A N A L Y T I C A L E D G E E N H A N C E M E N T R U N O F F C O M B I N A

N A N E D I V A L L I S (250 X 870 K M )

T

C A N D O R C H A S M A (115 X 75 K M )

Hill shading

I O N z-coding + curv. + hsh.

Bipolar diff. + curv. + hsh.

Sh.360º(ab.,bel.) + curv. + hsh.

S

Drainage, locally

Curvature (edges) + hsh.

Sh.360º(ab.,bel.) + curv. + hsh.

1

Vienna University of Technology Institute of Photogrammetry and Remote Sensing Gusshausstrasse 27-29, A-1040 Vienna, Austria www.ipf.tuwien.ac.at 2

Scientific Research Centre of the Slovenian Academy of Sciences and Arts Novi trg 2, SI-1000 Ljubljana, Slovenia, www.zrc-sazu.si

[email protected]

European Space Agency European Mars Science and Exploration Conference: Mars Express & ExoMars ESTEC, Noordwijk, The Netherlands, 12-16 November, 2007

MAPPING CLOUDS MICROPHYSICS WITH OMEGA/MEX. J.-B. Madeleine1,2, J.-P. Bibring1, B. Gondet1, F. Forget2, F. Montmessin3, A. Spiga2, D. Jouglet1, M. Vincendon1, Y. Langevin1, F. Poulet1, B. Schmitt4. 1IAS, Orsay, France. 2Laboratoire de Météorologie Dynamique, Institut Pierre Simon Laplace, Paris, France. 3Service d’Aéronomie, Institut Pierre Simon Laplace, Paris, France. 4Laboratoire de Planétologie de Grenoble, France. [email protected]

Summary Near-IR hyper-spectral imaging of clouds is made possible by the OMEGA (Observatoire pour la Minéralogie, l’Eau, les Glaces et l’Activité) instrument [1]. Image cubes (x,λ,y) of kilometer-scale spatial resolution with wavelengths spanning 0.35 to 5.1 µm are used to 1) detect clouds and map their microphysics using RGB compositions and 2) retrieve ice crystals size and cloud opacity.

Both methods are described in the lower panel, and meteorological applications are presented below. Visible images and false color maps are used along with a framework (fig. 1.) which gives an assessment of the particle size and cloud opacity corresponding to a given color. Local retrieval of these parameters is also achieved. Improvements of the model are underway to take into account parameter uncertainties and retrieve ice crystals size and cloud opacity over an entire orbit.

Meteorological applications Polar regions : The different response of the 1.5 µm and 3 µm absorption bands to the ice particle size [2] is used in RGB compositions to distinguish between surface frost and clouds, as illustrated on image a. Seasonal frost appears in magenta around 76°N, and on the rim of a crater. Spring clouds appear in blue at the margin of these deposits (see the spectra of fig. b.). Kilometer-scale variations in clouds microphysics are clearly seen, for exemple on image c.

Equatorial Cloud Belt : Evolution of the Aphelion Cloud Belt is characterized by an early period of cloud development (hazes and fibrous clouds) during Ls 45-130°, followed by convective cloud formation during Ls 45-130° [3,4]. Examples of these clouds are shown in figures d. e. f., and an assessment of their microphysics using the framework of fig. 1. gives intermediate particle size (type II. on fig.1.) for thick hazes (fig. d. and f.) and large crystal size (type III. on fig.1.) for convective clouds (fig. e. ; RGB composition is all covered by dark blue tones, and not shown). Inversion results (see fig. e. and f.) are consistent with TES EPF observations [6] and GCM results [12], but effective radius can reach 6 µm for the convective clouds of fig. e.

Equatorial Cloud Belt

Polar regions a.

Water-ice frost and clouds in the northern polar region.

f. I.

ORB3064_3 (75.1N,168.2W) Ls 59.4

Fine crystals re < 0.5 µm ; τc > 1

II. Intermediate crystals re ~ 3 µm ; 0.5 < τc < 2 III. Large crystals re > 4 µm ; τc > 1 Fig. 1. : Framework for reading of RGB compositions. Colors are reproduced by the radiative transfer model, with effective radius re on y-axis and cloud opacity τc on x-axis.

2.

d.

e.

1.

reff = 3 µm – τc = 3.4 Frost

Clouds

d. Clouds over Olympus Mons (upper panel) and Ascraeus Mons (lower panel). ORB3635_3 (19.0N,133.3W) - Ls 131.3 ORB3664_4 (6.1N,105.3W) - Ls 135.2 f. Thick haze near Pavonis Mons (upper panel) and model results (lower panel).

b.

ORB0563_3 (4.1S,108.0W) - Ls 53.6.

c.

reff = 6 µm – τc = 2.1 2.

1.

b. Spectra of clouds (1) and surface ice c. Cloud curtain in Vastitas Borealis. (2) measured during orbit a. 3.4 µm signature is indicated by an arrow. ORB2388_6 (44.6N,161.3W) - Ls 328.2

1) Introduction

e. Convective clouds over the Tharsis plateau (on the left) and model results (above). ORB0946_5 (21.8N,117.0W) - Ls 100.7.

3) Cloud microphysical properties

Near-IR hyper-spectral imaging of clouds is currently used on Earth as a powerful meteorological tool, and this technique is made possible on Mars by the OMEGA (Observatoire pour la Minéralogie, l’Eau, les Glaces et l’Activité, [1]) imaging spectrometer. Past analysis of clouds has been done in the visible range with Mariner 9 and Viking Orbiter [3], and more recently in the visible (MOC images [4]) and thermal infrared (TES, 6 to 50 µm [5,6]) with Mars Global Surveyor. Bridging the gap, OMEGA data are spectral image cubes (x,λ,y) of the atmosphere and the surface both in the visible and near infrared, spanning 0.35 to 5.1 µm with a spectral sampling of 0.013-0.020 µm and kilometer-scale spatial resolution. Spectral range includes water ice absorptions at 1.25, 1.5, 2 and 3 µm that can be used to detect water-ice clouds and derive their microphysical properties. Analyzing the kilometer-scale microphysics of clouds on Mars is key to understanding their formation, their role in the water cycle and radiative transfer of the planet, their interaction with the dust cycle, and can provide major insights into the fundamental physics of nucleation.

4) Ongoing improvements and analyses

2) Cloud cover mapping To first detect and map the cloud cover, 1.5 and 3 µm water ice absorption bands are visualized using RGB composition : - the red is proportional to the slope at the edge of the 3 µm H2O vibration band (3.4/3.525 µm criterion defined in [2]) ; - the green is proportional to the depth of the 1.5 µm water-ice absorption band (used in [7]) ; - the blue is held constant. Correlated increase of the 1.5 µm absorption depth and 3.4 µm slope reveals the formation of water ice clouds, and appears in blue. On the contrary, large 1.5 µm absorption depth and drop of the 3.4 µm slope are typical of surface grains larger than 10 µm which appear in magenta, allowing us to distinguish between surface ice and clouds (see image a.).

These two parameters are fitted during the inversion by calculating the radiative transfer through a single-layer atmosphere using the Spherical Harmonics Discrete Ordinate Method [8]. Scattering parameters are given by a Lorenz-Mie code which uses the recent ice optical constants at 180K of Grundy & Schmitt [9], and assumes that the ice crystals are spherical and follow a unimodal log-normal distribution (effective variance of 0.2). A spectrum of the same region, free of any clouds, must be used as a surface boundary condition of the model. This spectrum must be carefully chosen through a “man-in-the-loop” method, in order to make sure that the surface geology, the atmospheric dust opacity and the viewing geometry are similar to what is found for the analyzed cloudy pixel. The radiative transfer model can also reproduce the behaviour of the 1.5 µm absorption band and 3.4 µm slope to generate a framework given in figure 1. This framework is a first guiding tool for reading RGB compositions and assessing cloud particle size and opacity. Indeed, automatic inversion of the two microphysical parameters (re,τc) over an entire orbit is still a challenge that has to be addressed.

Fig. 2. : Schematic drawing of the water-ice crystals size re and clouds optical depth τc retrieval method.

Cloud optical depth and particle size can be retrieved at a given point using an inversion method presented on figure 2. The model minimizes the difference between a simulated reflectance and the observed cloud reflectance by using a downhill simplex method and a least-squares criterion. The free parameters are the optical depth of the cloud at 3.2 µm τc and the water ice crystal effective radius re.

Improvements of the inversion model are underway to take into account the retrieval uncertainties, the radiative effect of atmospheric dust [10] and ice nucleation on mineral dust cores, the contribution of the 3 µm hydration band of surface dust [11], and to quantitatively map the microphysical properties over an entire orbit. Final results from the inversion model will be compared to the water cycle simulated by the LMD/GCM (see [12] and poster [13]), and regional cloud structures will be interpreted with the help of the new LMD mesoscale model (poster [14]). References [1] Bibring, J.-P. et al. (2005), Science 307. [2] Langevin, Y. et al. (2006), JGR 112:E8. [3] Kahn, R. (1984), JGR 89. [4] Wang, H. and Ingersoll, A. P. (2002), JGR 107. [5] Smith, M. D. (2004), Icarus 167. [6] Clancy, R. T. et al. (2003), JGR 108. [7] Gondet, B. et al. (2006), AGU Abs. #P14A-02. [8] Evans, K. F. (1998), J. Atmos. Sci. 55. [9] Grundy, W. M. and Schmitt, B. (1998), JGR 103. [10] Vincendon, M. et al. (2007), JGR 112:E11. [11] Jouglet, D. et al. (2007), JGR 112:E11. [12] Montmessin, F. et al. (2004), JGR 109:E18. [13] Millour, E. et al., The new Mars Climate Database, poster #1118452. [14] Spiga, A. et al., A new mesoscale model for the Martian atmosphere, #1119867.

Mapping of Sulfates using HRSC color data L. Wendt¹, J.-P. Combe², T. B. McCord², G. Neukum¹ Why using HRSC color data for sulfate mapping ?

Berlin

Freie Universität

¹Institute of Geosciences, FU Berlin, 12249 Berlin, Germany, ²Bear Fight Center, Winthrop WA 98862, USA. [email protected]

Spectral Angle Mapper

Results of linear unmixing

Blue

channel

Apparently, the spectrally neutral, multi-scattering sulfates are mixed with both bright red material and dark

The mineral composition of Martian surface materials can be determined using

material. Therefore, mixtures of these two components

imaging spectrometers like OMEGA or CRISM. However, these datasets have

can perfectly mimic the spectra of the sulfates.

either lower spatial resolution or coverage than desired. The High Resolution Stereo Camera with its blue, green, nadir, red and infrared channels centered at

This can be shown with the Spectral Angle Mapper. This

444, 538, 677, 748 and 955 nm wavelength provides a dataset with both high spa-

analysis tool uses the angle of data points with the coor-

tial resolution and coverage (see [1] for details on the HRSC specifications). Using

dinate origin in the five-dimensonal parameter space as

this dataset to uniquely identify distinctive minerals on the Martian surface would

similarity measure. Its advantage is that only the shape

therefore allow mapping these minerals at higher detail.

of the spectra playes a role, and not overall brightness differences caused by (ideal) shadows.

Test area: Sulfates of Juventae Chasma

Figure 5 shows the result of the spectral angle mapper

We chose the sulfate deposits in in Juven-

with a reference spectrum taken at the western sulfate

tae Chasma identified by [2] as test area

outcrop (red arrow). Brighter shades of grey mean a

and looked for spectral characteristics in

higher similarity. A broad zone of high similarity sur-

the HRSC color dataset of orbit 243 that

rounds the chasma on the plane. The spectra in this zone

are unique to these outcrops. We subsam-

are even more similar to the western sulfate deposit than

pled the nadir image to match the lower

the eastern sulfate outcrop, which appears in a darker

resolution of the color channels of 50 m

grey.

per pixel, creating a five dimensional

Comparison with figure 2 yields that this highly similar

multispectral dataset.

Figure 1: Sulfate outcrops in Junventae Chasma identified by OMEGA. White: Interior Layered Deposits. Red: Kieserite. Green: Polyhydrated sulfates. Blue: Gypsum. From [2].

zone is located where the bright plane's material is partly covered with dark dust - just enough to mimic the spectrum of the sulfate outcrops. 25 km

a: ”Bright red rock”

Only three endmembers known In the five-dimensional parameter space created, up to five linear independent feature vectors can exist. These feature vectors represent endmembers - all possible vectors within the parameter space can be constructed by a linear combination of these endmembers. In an extensive study on various HRSC color images, [3] have identified only four endmembers, of which only three are present in orbit 243 :

25 km

b: ”Dark material”

25 km

c: ”Shade”

25 km

d: Modeling Errors

Figure 4: Coefficients of unmixing endmembers. Brighter shades of grey indicate higher values. Red circles: Sulfate deposits.

What the results mean Figure 4 a-c shows the result of the linear unmixing. Brighter shades of grey mean higher coefficients of the endmember regarded. The planes surrounding Juventae Chasma are modeled mostly by the ”bright red rock” endmember, the ”dark material” endmember appears mostly in the chasma and its direct vicinity, which is co-

”bright red rock“ = iron oxides

vered by dark dust (bluish in figure 2). Figure 4a and 4b appear flat: almost all

”dark material“ = unoxidized basalt

clues that Juventae Chasma is a depression are removed. They appear, as expected,

”ice“ = white polar ice caps (not present here)

in the ”shade” endmember coefficient image (4c), the topography is easily reco-

”shade“ = endmember for the color of shadow

gnizable (Note that ”brighter” means ”more shade”). Figure 4d displays the discrepancy between observed spectra and modeled spectra using the described three

Are sulfates a fourth endmember ?

endmembers.

If the sulfates (or any other mineral) were distinguish-

Scatterplots confirm non-uniqueness Scatterplots show similar results. Figure 6 shows the scatterplot of the red versus the blue channel of HRSC orbit 243, while figure 7 shows the corresponding disribution of the selected classes in figure 6. The yellow class comprises the brightest values in the two displayed channels. It consists mostly of the bright red material and covers most of the planes around Juventae Chasma. The darkest values of the red and blue channel have been chosen for the blue class. These values are found exclusively at the darkest spots of the dunes of dark material on the valley floor. The red data points in figure 6 lie within a region of interest on the western sulfate outcrop in figure 7. Figure

The linear spectral unmixing was successful

they could not be modeled by linear combinations of

If the input spectra are well chosen, a potential fourth endmember reveals itself by

milar spectra. This confirms that different mineral types

method developed by [4] using only the known three

tion into the ”shade” endmember coefficient image and the good correlation bet-

endmembers as input. A significantly higher residual

ween the distribution of bright and dark material (figure 2) and their respective

of the modeling result, correlated to the sulfates (or

endmember coefficient image (4a and b) indicate a correct choice of endmember

another outcrop), would reveal the additional end-

spectra and a successful modeling.

member.

HRSC observation geometry complicates interpretation

Input endmembers 0.5 0.4

The variations of the ”shade” coefficient in the lower part of figure 4c are suspi-

0.3

cious, as there is no shade observable in this area of figure 2. However, this varia-

0.2

tion can be a result of varying surface roughness: The HRSC color channels' ob-

0.1

servation angles are tilted up to 16° with respect to the nadir channel. This means

0.0 500

600 700 800 Wavelength [nm]

that for a given subpixel surface roughness and depending on the lighting condi-

900

Figure 3: Input endmembers for linear unmixing method. Red: red material. Blue: dark material. Black: shade.

Figure 6: Scatterplot of red vs blue channel. Yellow: bright planes in figure 7. Blue: dark material. Red: region of interest in figure 7.

chasma. These points have exactly the same spectra in

able from mixtures of the above three endmembers,

a significantly higher modeling error. The good separation of topographic informa-

Blue channel

7 also shows a ”fringe” of red data points around the the HRSC dataset as the points located within the region

them. Consequently, we applied the linear unmixing

Figure 5: Result of the Spectral Angle Mapper. Brighter shades of grey indicate a higher similarity (smaller spectral angle) with the reference spectrum taken at the red arrow.

Red channel

Linear Spectral Unmixing

25 km

of interest. The eastern sulfate outcrop does not show sican show exactly the same spectra, and the same mineral may have several different color representations in the HRSC color dataset.

Conclusion: No spectral index found This study has failed to reveal a spectral index in the domain of the HRSC dataset that is unique to sulfate outcrops. The three endmembers for bright red material, dark material and shade and their linear combinations are sufficient to explain all observed spectra in orbit 243.

tions, each color channel observes a different amount of shade in each pixel. Con-

In the sulfate outcrops, pure white sulfates are mixed

sequently, a varying surface roughness leads to changing color of the same materi-

with red and dark dust, which makes it impossible to di-

al.

stinguish them from mixtures of these two endmembers that do not contain any sulfates.

25 km

Figure 7: Spatial distribution of spectral classes in figure 6. Yellow: bright matiral, only on planes. Blue: darkest parts of dark material, only on valley floor. Red: Western sulfate outcrop and pixels with resembling values.

It remains an open question how mixtures of red and

terial” were taken directly from the image at the loca-

The three input endmembers are suffient no spectral index for sulfates

tions indicated in figure 2.

The linear spectral unmixing results presented in figure 4 show that the three end-

and sulfates can have exactly the same spectra and level

member spectra described by [3] corresponding to red, iron oxide rich material,

of brightness as shown in figure 7: one would expect that any mixture of red mate-

dark, unoxidized basalt, and shade, are enough to model all observed HRSC color

rial, dark material and transparent sulfates would be brighter than mixtures of red

spectra including those of the sulfates in Juventae Chasma. Although the mode-

material and dark material alone. This may be explained either by a third, transpa-

ling error in figure 4d in the western sulfate outcrop is slightly higher than in its

rent constituent on the planes, which raises their brightness, without being de-

surroundings, this holds only for one of the two prominent sulfate deposits: the

tected by OMEGA. Another possibility is that the HRSC observation geometry

bigger eastern ”gypsum mountain” is hardly recognizable. Moreover, the deviation

translates differences in surface roughness on the planes into brightness variations

between observed and modeled spectra is in the same order of magnitude as mode-

in the same magnitude as those caused by intermixed sulfates.

The input spectra for ”bright red rock” and ”dark ma-

Due to scattering in the Martian atmosphere, surface areas covered with shadow still receive indirect light. Therefore, shadowed areas have a different color than well-lit areas, instead of being not just darker. The endmember spectrum of ”shade” accounts for this effect. To estimate the shade spectrum, an area of bright material partly covered by shade was chosen (figure 2). The spectrum of shade was then calculated by applying a linear fit to the correlation between the individual color channels in that area. The correlation was then used to calculate the shade spectrum using an arbitrary reflectance value for the blue channel of 0.025.

25 km

Figure 2: false color composite of red, green and blue channel of HRSC orbit 243. Red arrow: location of bright material reference spectrum; cyan: location of dark material spectrum; Black rectangle: area used to derive spectrum of shade; Red circles: sulfate deposits.

ling errors clearly caused by image defects like coregistration errors between color channels, or compression and calibration errors, which manifest themselves by horizontal lines in figure 4c. A detection of the sulfates by the level of modeling error is not possible.

dark material and mixtures of red material, dark material

References: [1] Neukum (2004), ESA-SP 1240. [2] Gendrin, A. et al. (2006), LPSC XXXVII, Abs. #1872. [3] McCord, T. et al. (2007) JGR 112, DOI:10.1029/2006JE002769. [4] Adams and Gillespie (2006), Cambridge U. Press; Combe, J.-Ph. et al: Analysis of OMEGA / Mars Express hyperspectral data using a linear unmixing model: Methodology and first results. Submitted to Planetary and Space Sciences.

Mars – The Red Planet

Even though it is a small rocky planet, Mars has captured the imagination and scientific interest of humans for centuries. Knowledge about the red planet has increased with robotic missions. NASA sent its first successful mission to Mars in 1964. Numerous orbiters, landers, and rovers have followed and will continue over the next few decades. The Vision for Space Exploration calls for NASA to return to the moon and use increasingly longer stays to prepare for human missions to Mars. Through exploration and research, many myths such as Mars having an Earth-like atmosphere and climate supporting canals with flowing water and vegetation have been dismissed and much insight into the formation and evolution of the red planet has been gained.

Mars is not the closest planetary neighbor to Earth, but it is the most Earth-like. It is the fourth closest planet to the Sun. Mars has been subjected to some of the planetary processes associated with the formation of Mercury, Venus and Earth. These processes include volcanism, impact events, erosion, and other atmospheric effects. Another Earthlike characteristic is the growth and retreat of the Martian polar ice caps with the change of seasons as Mars orbits the Sun. The red planet and Earth differ in a number of ways. The Martian surface retains much of the record of its evolution because it had liquid water only during part of its evolution. Mars does experience surface erosion, but due to the absence of flowing water over much of its geologic history, the rate of erosion of the

red planet’s surface is much slower than that of the Earth, and the surface features have not shown the same level of dramatic changes that characterize Earth’s landscape. The geological development and alteration of Mars’ crust, called tectonics, differs from Earth’s. Martian tectonics seem to be vertical, with hot lava pushing upwards through the crust to the surface. On the other hand, Earth tectonics also involve sliding plates that grind against each other or spread apart on the seafloors and along fault lines. Exploration of the Martian surface by imaging orbiters has revealed some remarkable geological characteristics. Mars lays claim to the largest volcanic mountain in the solar system. Olympus Mons is about 17 miles high and 373 miles wide. Volcanoes in the northern Tharsis

region are so huge that they deformed the planet’s spherical shape. The Vallis Marineris, a gigantic equatorial rift valley, stretches a distance equivalent to the distance from New York to Los Angeles. Arizona’s Grand Canyon could easily fit into one of the side canyons of this great chasm. The Martian atmosphere which primarily is composed of carbon dioxide gas is currently too thin to allow liquid water to exist. Seasonally, great dust storms occur that engulf the entire planet. The storms’ effects are dramatic, including dunes, wind streaks and wind-carved features. There is no evidence of civilizations, and it is unlikely that there are any existing life forms, but there may be fossils of life-forms from a time when the climate was warmer and there was liquid water on the surface.

Mars Facts Average Distance from Sun Period of Rotation Period of Revolution around Sun Diameter Tilt of Axis Length of Year Moons Gravity Temperature Average Atmosphere www.nasa.gov

142 million miles 24 hours, 37 minutes 687 days 4,220 miles 25 degrees 687 Earth Days 2 (Phobos and Deimos) .375 that of Earth -81 degrees Fahrenheit Mostly Carbon Dioxide with some Argon, Nitrogen and water vapor

•

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80°

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40°

depths and heights [km]

n alla

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SURFACE OF MARS lambert transversal Equivalent Azimuthal Projection grid: Planetocentric latitude with East longitude Published by Eötvös loránd university Cosmic Materials Space research group, budapest, hungary http://planetologia.elte.hu dtM source: MgS MOlA Map © henrik hargitai 2008 iSbN hu 978-963-463-968-8

20 18 16 14 10 8 6 4 2 0 -2 -4 -6 -8

MARS ORBITAL ELEMENTS dirECtiON OF MArtiAN POlAr StAr, iN CygNuS




km

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Juventae Chasma

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206 mil lion km

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Direction of Earth First Point of Aries ls=97° NOrth WiNtEr SOlStiCE

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s

Ascraeus Mons

km 20 16 12 8 4 0 -4 -8

Winter on Earth

249 m illion

=

100× vertical exaggeration

330°

N: short, cold winter (–90 — –120°C) S: short, warm summer

60°

270°

t 1 ye ar = 3 6 5 d ay s ar sy ear it 120° =6 6 or b 9 so l ar s M 240° s = 687 Earth days Ls N: long, cold summer 210° 150° (–30 — –80°C) 180°

M

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Solar Distance: 206–249 million km Earth Distance: 54–401 million km Equatorial Radius: 3396.2 km Obliquity to orbit: 25°19’ (±10°) Orbital Period: 668.59 Mars days (=Sols) (=687 Earth days) Rotational Period (1 sol): 24h:37m Gravity: 0.38 g Length of Equator: 21 300 km Surface Area: 144.2 million km2 Atmosphere: 95% CO2; 2,6% N2 Pressure: 6 mbar [min: 0,7–Olympus, max: 12–hellas) 0. Longitude: Airy-0 crater Height Datum 3396 km radius Distance from Earth at Light speed : 03:02–22:19 min. Solar Distance: 589.2 W/m2 Satellites: Phobos, deimos

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small souther polar cap

PErihEliON ls=253°

S: long, cold winter

NOrth AutuMN EQuiNOX

Instead of names of months, on Mars we use ls (Solar Longitude) degree values to measure seasons. it shows the distance of the Sun from the First Point of Aries at the spring

equinox (0°) in the change of seasons of Mars – apart from obliquity to orbit – Solar distance plays an important role. because of the excentricity of current Mars orbit. it

affects the size of polar caps. thus in the south seasons are extreme: summer is during perihelion, winter is during aphelion; while in the north, seasons are more equalized.