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.

Motivation

Approach

Performance Assessment

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

Assessment Results

• Craters from automated detection converted into polygon shapefiles

• Results merged

• 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)

GIS Database Production

8,857 Craters in Iani Vallis

Green = TPRed = FPBlue = FNYellow = Duplicate

Conclusions

Image TP FP FN Total

Detection %

FNTP

TP

+= *100

Quality %

FPFNTP

TP

++= *100

Branching Factor

TP

FP=

Elysium 1196 142 17 21 180 87.12 78.89 0.12

Elysium 2066 168 11 30 209 84.85 80.38 0.07

Elysium 2099 369 8 33 410 91.79 90.00 0.02

Elysium 2110 105 2 4 111 96.33 94.59 0.02

Elysium 2121 65 10 32 107 67.01 60.75 0.15

Elysium 2143 366 26 47 439 88.62 83.37 0.07

Elysium 2154 346 9 47 402 88.04 86.07 0.03

Elysium 2165 143 28 25 196 85.12 72.96 0.20

Elysium 2176 237 30 54 321 81.44 73.83 0.13

Subtotal 1,941 141 293 2,375 85.59 80.09 0.09

Iani 0912 2,592 159 163 2,927 94.08 88.55 0.06

Iani 0923 2,131 229 710 3,070 75.01 69.41 0.11

Iani 0934 3,313 305 785 4,403 80.84 75.24 0.09

Subtotal 8,036 693 1,658 10,400 83.31 77.74 0.09

Total 9,977 834 1,951 12,775 84.45 78.92 0.09

MSSL/DEPARTMENT OF SPACE AND CLIMATE PHYSICS

GIS Files

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

2,543 Craters in Elysium Planitia

Green = TP Red = FPBlue = FNYellow = Duplicate

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

© USGS

ElysiumPlanitia

Iani Vallis

• Demand from planetary geologists for impact crater databases

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

• Accuracy of automated algorithms must be quantified and improved

• Fully automated crater detection systems not yet available

• Aim is to emulate a fully automated system to demonstrate feasibility and perform a quantitative assessment of existing automated system

• Process HRSC images using Kim-Muller** automated crater detection algorithm

• Calculate the detection and quality rates for each image

• Automatically merge results into single, georeferenced GIS format shapefiles

• As part of the merging process, automatically resolve duplicate detections for side overlapping orbits

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

• 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

• Image quality significantly affects detection results

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

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

• 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

Summary

Detection % 84.45Quality % 78.92Branching Factor 0.09

for crater diameters >= 8 pixels when averaged across 11,400 craters by visual inspection, in 2 different geological environments.

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

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

Duplicates between sets resolved using distance & radius measure

Spacecraft Launch Accomplishments

Mariner 4 1965 Flyby. First close-up pictures(USA) of surface.

Mariner 6 1969 Flyby. High-resolution photos(USA) of equatorial region.

Mariner 7 1969 Flyby. High-resolution photos(USA) of southern hemisphere.

Mariner 9 1971 Orbiter. Year-long mapping(USA) mission, detailed photos of

Phobos and Deimos.

Mars 2 1971 Orbiter. Dropped a capsule to(USSR) the surface.

Mars 3 1973 Orbiter and Lander. First TV(USSR) pictures from the surface of

another planet.

Mars 5 1973 Orbiter. High-quality photos of(USSR) southern hemisphere region.

Viking 1 1975 Orbiter and Lander. First(USA) sustained surface science.

Viking 2 1975 Orbiter and Lander. Discovered(USA) water frost on surface.

Phobos 1988 Orbiter. Returned pictures of(USSR) Phobos.

Mars Global 1996 Orbiter. Global, multispectralSurveyor mapping mission. Two-year(USA) mapping effort begins in 1998.

Mars 1996 Lander with Rover. First of aPathfinder new generation of small, light-(USA) weight planetary craft. Paves

way for following Mars missions.Sojourner rover provides tech-nology demonstration and animaging science package forsurface studies.

Mars Surveyor 1998 Orbiter. Completes scientific’98 Orbiter reconnaissance begun by Mars(USA) Global Surveyor. International

participation.

Mars Surveyor 1998 Lander. Explores high Martian’98 Lander latitudes where polar ices form.(USA) International participation

Planet-B 1998 Orbiter. Will study interaction(Japan) of solar wind with Martian

atmosphere.

Future Mars 2001– Orbiter/Lander Suites. LaunchingSurveyors 2007 at 2-year intervals, with inter-(USA) national participation. Light-

weight sciences packages, withpossible inclusion of rovers andsample return in 2005 with possible international partners.

ings of modern spacecraft and instruments. We have learnedthat Mars, like Mercury, Venus, and Earth, is a small (insolar system terms), rocky planet that developed relativelyclose to the Sun. Mars has been subject to some of the sameplanetary processes—volcanism, impact events, and atmos-pheric effects—associated with the formation of the other“terrestrial” planets. But unlike Earth, Mars retains much ofthe surface record of its evolution and history. For millionsof years, the Martian surface has been bare of water, and notsubjected to the erosions and crustal plate movement that

continually resurface Earth. So, Mars today canreveal to us the geologic history of a terrestrialplanet in a way that Earth cannot. From Mars, wecan learn things about our home planet that ourhome planet cannot teach us.

Our sense of what we can learn from Marshas been both expanded and refined as we havestudied the planet over the last three decades.Today, we know the Martian climate has indeedchanged, and the planet’s surface has lost whatliquid water it once had. Layered terrains near theMartian poles suggest that the planet’s climatechanges have been periodic, perhaps caused by aregular change in the planet’s orbit. If this is so,we need to know more. The surface of Mars isintriguing. The planet is smaller that Earth, but itssurface is dominated by a few features, larger thanany terrestrial counterparts—a string of huge vol-canoes sitting atop a bulge the size of the UnitedStates, an equatorial rift valley more than 4,800kilometers long, and a planet-encircling cliff sep-arating northern plains from southern highlands.

The surface of Mars tells a tale of planetary formation we areyet to understand. Tectonism—the geological developmentand alteration of a planet’s crust—has on Earth been in theform of sliding plates that grind against each other in somearea and spread apart in the seafloors. But Martian tectonismseems to have been mostly vertical, with hot lavas pushingupwards through the crust to the surface. We need to knowmore about these processes if we are to fully understandwhat has happened—and may happen—on Earth.

Mars—the Red Planet, the Bringer of War—hasinspired over the centuries wild flights of imagi-nation, and at the same time intense scientific

interest. A source of hostile invaders of Earth, the home ofdying civilizations, a rough-and-tumble mining colony ofthe future—all are in the realm of science fiction, but theyare based on seeds planted by centuries of scientific obser-vation. Mars has shown itself to be the most Earth-like ofthe planets, with polar ice caps that grew and receded withthe change of seasons, and markings that looked, to 19th-

century telescopes, to be similar to human-made water canalson Earth, fueling the idea that Mars was perhaps inhabited.

Today, we know there are no canals on Mars, but thereare natural channels apparently carved by past water flow.We know there are no civilizations, and it is unlikely thatthere are any extant life forms, but there may be fossils oflife forms from a time when there was water. These intrigu-ing possibilities are only a small part of our broad scientificinterest in the Red Planet—an interest fueled by the find-

the allure of the red planet the martian fleet

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SOLAR SYSTEMEXPLORATION DIVISION

NP-1997-02-223-HQ

Evidence of dried riverbeds, such as this fossilized dendritic drainagesystem, indicate the planet was once warmer and wetter.

In 1996, Mars Pathfinder and Mars Global Surveyorlaunched the next wave of Mars exploration. The Path-

finder approach demonstrates new, lightweight, low-costlander, rover, and imaging technologies while characterizingMartian soils and rocks in the vicinity of the landing site.Mars Global Surveyor inaugurates an ambitious program oforbital science to recapture the science lost with the MarsObserver spacecraft. Martian weather, seasonal change,surface features, and composition will be studied in detailover Mars Global Surveyor’s 2-year mapping phase, pro-viding our first comprehensive, high-resolution look at thenear-surface and surface phenomena on Mars. These missionsset the stage for the Mars Surveyor series, which will sendsimilarly lightweight orbiters and landers to Mars every 2 years into the first decade of the next century. Orbiters willprovide synoptic coverage of areas and phenomena of interest,

Our studies of Mars to date have left us with a sense ofwhat we can learn from the Red Planet in the years to

come. Recent studies of meteorites believed to have origi-nated on Mars suggest that there may be mineral evidenceof primitive life forms in the soils and rocks of the Martianterrain. Confirmation of the ancient presence of such lifeforms would provide powerful keys to new understandingsof the origins of life in our solar system. Understanding cli-mate change is also a critical issue for life on Earth. Weneed to fully understand when the Martian climate hasundergone change, why, and what happened. The past pres-ence of water required a denser atmosphere than now exists.What happened to that atmosphere? Where did the surfacewater go? These questions cannot be understood in isolationfrom others. Has the periodic change in Martian climatebeen caused by a regular fluctuation in the Martian orbit? Ifso, what causes that fluctuation? Is there a relation to ter-restrial phenomena, such as our ice ages? How havevolcanism and impacts from comets and meteorites createdthe terrains we see today in the southern highlands andnorthern lowlands of Mars? What is the tectonic history of

Deimos, two asteroid-like bodies that may in fact be aster-oids captured by Martian gravity.

Although Mariner 9 photos showed none of the fabledirrigation canals, the mission did disclose evidence of sur-face erosion and dried riverbeds, indicating the planet wasonce capable of sustaining liquid water. This fueled the pos-sibility that life may be (or have been) possible on Mars. Toinvestigate, two Viking spacecraft were dispatched to Marsin 1975. Each consisted of an orbiter and a lander. Theorbiters surveyed the planet while the landers monitoredsurface weather conditions, took pictures, and tested the soilfor signs of life. Viking 1’s photos revealed reddish desert-like landscape blanketed with rocks and dune-like drifts ofdust. Some 5,000 kilometers away, Viking 2 observed aslightly more rolling, duneless landscape, where patches offrost covered the ground in the Martian winter. From theweather stations, we quickly learned that these regions ofMars are too cold, and the atmosphere too thin, for liquidwater to exist. The experiments designed to test for lifeshowed some intriguing chemistry, but no signs of life.

Using the best technology available in their time, theMariners and Vikings helped address centuries-old ques-tions about Mars. But many new questions have arisen inthe years since then. Today, we seek to understand Mars asa planetary system akin to our own Earth.

Most of our current knowledge of Mars is the result ofinvestigations conducted by a fleet of spacecraft

beginning with the Mariners in the mid-1960s (see thetable, “The Martian Fleet”). The Mariner 4, 6, and 7 flybymissions returned photos and weather data from the south-ern hemisphere of Mars that put to rest hopes of finding acivilization, and that gave the impression that Mars, like theMoon, has long been geologically inactive. The data fromthe 1971 Mariner 9 orbital mission created quite a differentpicture. Looking at the entire planet, Mariner 9 revealedhuge volcanic mountains in the northern Tharsis region, solarge that they deformed the planet’s sphericity. One ofthese, Olympus Mons, at more than 26 kilometers high(above Martian “sea-level”), remains the largest volcanoobserved in our solar system. Mariner 9 also revealed theawesome Vallis Marineris, a gigantic equatorial rift valleydeeper and wider than the Grand Canyon and longer thanthe distance from New York to Los Angeles! Mariner 9 alsogave us our first views of the Martian moons Phobos and

missions of discovery remaining questions

the next generation

the planet. Is Earth alone among the terrestrial planets inexhibiting plate tectonics? Why? The answers to these andother questions about the Red Planet await the next genera-tion of Mars explorers.

while acting as data relay stations for landers. Landers willprobe the soils and test the rocks in search of clues regardingthe origins and evolution of the Red Planet, and will look fortell-tale signs of life forms, past and present. We envision theMars Surveyor program as the linchpin for NASA partici-pation in all future international Mars exploration programs.

Although to date Mars exploration missions have beenconducted on a national scale by the United States andRussia, the allure of Mars is international in scope. Dataexchange from previous planetary missions is alreadyinternational, and other nations are now planning Mars mis-sions for the next decade (see the table, “The MartianFleet”), some of which will include international coopera-tion. Mars may have been named for the god of war, but theday is fast approaching when international expeditions willinvestigate the Red Planet in the name of peace.

Many impact craters are visible in this Viking orbiterimage, confirming that Mars has long been geologi-cally inactive.

The Viking landers returned photographs of desert-likelandscape. The reddish coloration is caused by thechemical weathering of iron-rich rocks.

This high-resolution scanning electron microscopeimage shows an unusual tube-like structural form thatis less than 1/100th the width of a human hair in sizefound in a meteorite believed to be of Martian origin.

MarsNamesake & Symbol Roman God of WarDistance from the Sun Maximum: 249 mil km

Minimum: 206 mil kmDistance from the Earth Maximum: 399 mil km

Minimum: 56 mil kmPeriod of Rotation 24.6 hrs (= 1 Martian day)Equatorial Diameter 6,786 kmEquatorial Inclination to Ecliptic 25.2°Gravity 0.38 of Earth’sAtmosphere

Main Component Carbon DioxidePressure at Surface ~8 millibars (vs. 1,000 on Earth)

Temperature –143°C to +17°CMoons (2) Phobos (Fear), 21 km diameter

Deimos (Panic), 12 km diameterRings NoneOrbital Eccentricity 0.093Orbital Inclination to Ecliptic 1.85°Magnetic Field Density To be determined. Very weak, if any.

1,2 1Tomaž Podobnikar & Peter Dorninger

ABSTRACT

Z-CODINGANALYTICAL

EDGE ENHANCEMENTRUNOFF

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Shaded reliefs, height codings, or profiles are Chasma and Nanedi Vallis. The DTMs were based on the following methods for the land- different filtering…commonly used for the visual interpretation of determined during the HRSC DTM test (C. form representation: - local runoff behavior: analysis of local height digital terrain models (DTM). Nevertheless, Heipke et al., 2007: Evaluating planetary - z-coding representation of absolute or rela- differences; depressions and drainagevisualizations like these are not well suited to digital terrain models – the HRSC DTM test, PSS tive heights with different contrasts or colors An important aspect of our research is multi-represent all details served by a DTM. The main (Elsevier), in press) with a resolution of 50 regarding to the elevations of landform; scale visualization in order to represent various aim of the presented results is to support the meters per pixel. We present visualization of bipolar differentiation technique as a features like smooth or rough details. Different understanding of the areomorphology by means whole orbits (i.e. an extension of several relative z-coding of ”continuos” contour methods of landform abstraction at different of enhanced, cartographic visualization hundreds kilometers) and of a selected area in lines scales are combined in single visualizations.methods. As a byproduct, the results may be the south-east of the Nanedi Vallis area and - analytical contextual operations: slope, Enhanced visualization is an important and used for detection of possible incorrect selected part of Candor Chasma. aspect, curvature powerful tool to assess or to represent Mars patterns caused by locally erroneous data or by The presented images were derived by - edge enhancement: increasing the lighting surface or landform or to support other interpolation artifacts. means of cartographic visualization techniques (contrast) with respect to the natural aspect activities. Combination of different techniques

The proposed methods are evaluated on aiming at the enhancement of areomorphologic of landform; highlights ridges and valleys, can increase understandability of landforms' DTMs derived from HRSC images of Candor details as determined from DTMs. They are peaks and sinks, calculating 360º shadows, shapes and thus support future decision making.

Bipolar diff. (int. 50 m) Bipolar diff. (int. 100 m) Bipolar diff. (int. 250 m) Bipolar diff. (int. 500 m)

HRSC DTM, hill shading Orhoimage, RGB channels

Curvature Curvature (smoothed DTM) Slope Exposition (aspect)

Curvature (red and blue) Shadows 360 (above, below) Shadows 360º (above, B&W) Bip. d. (orig. – smoothed DTM)º

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 Sh.360º(ab.,bel.) + hsh. Depressions + z-coding + hsh. Curvature (edges) + hsh. curv. +

Drainage, locally Curvature (edges) + hsh. Sh.360º(ab.,bel.) + hsh.curv. +

Hill shading z-coding + curv. + hsh. Bipolar diff. + curv. + hsh. Sh.360º(ab.,bel.) + hsh.curv. +

Hill shading (hsh.) Curv. + hsh. Drainage Bip. d. + curv. + hsh. z-c.+bip.+curv.+hsh. Sh.360º + hsh.curv. +

European Space AgencyEuropean Mars Science and Exploration Conference:

Mars Express & ExoMarsESTEC, Noordwijk, The Netherlands, 12-16 November, 2007

1Vienna University of TechnologyInstitute of Photogrammetry and Remote Sensing

Gusshausstrasse 27-29, A-1040 Vienna, Austriawww.ipf.tuwien.ac.at

tp@ipf.tuwien.ac.at

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

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

Enhanced visualization of Mars surface features from HRSC DTM

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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.

jbmlmd@lmd.jussieu.fr

d. Clouds over Olympus Mons (upperpanel) and Ascraeus Mons (lower panel).

ORB3635_3 (19.0N,133.3W) - Ls 131.3ORB3664_4 (6.1N,105.3W) - Ls 135.2

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.

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.

I. Fine crystalsre < 0.5 µm ; τc > 1

II. Intermediate crystalsre ~ 3 µm ; 0.5 < τc < 2

III. Large crystalsre > 4 µm ; τc > 1

f.

c.

Water-ice frost and clouds in the northern

polar region.

ORB3064_3(75.1N,168.2W)

Ls 59.4

1.

2.

Clouds

Frost

f. Thick haze near Pavonis Mons (upper panel) and model results (lower panel).

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

reff = 3 µm – τc = 3.4

c. Cloud curtain in Vastitas Borealis.

ORB2388_6 (44.6N,161.3W) - Ls 328.2

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.

b. Spectra of clouds (1) and surface ice (2) measured during orbit a. 3.4 µm signature is indicated by an arrow.

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 crystalssize and cloud opacity over an entire orbit.

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.

a.

b.

1.

2.

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.

1) Introduction

2) Cloud cover mapping

3) Cloud microphysical properties

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.).

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.

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

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 readingRGB 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.

4) Ongoing improvements and analyses

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 the3 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]).

[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.

References

e.

e. Convective clouds over the Tharsis plateau (on the left) and model results (above).

ORB0946_5 (21.8N,117.0W) - Ls 100.7.

Polar regions Equatorial Cloud Belt

d.

reff = 6 µm – τc = 2.1

Mapping of Sulfates using HRSC color dataFreie Universität Berlin

L. Wendt¹, J.-P. Combe², T. B. McCord², G. Neukum¹ ¹Institute of Geosciences, FU Berlin, 12249 Berlin, Germany, ²Bear Fight Center, Winthrop WA 98862, USA. lorenz.wendt@fu-berlin.de

Why using HRSC color data for sulfate mapping ?The mineral composition of Martian surface materials can be determined using imaging spectrometers like OMEGA or CRISM. However, these datasets have either lower spatial resolution or coverage than desired. The High Resolution Stereo Camera with its blue, green, nadir, red and infrared channels centered at 444, 538, 677, 748 and 955 nm wavelength provides a dataset with both high spa-tial resolution and coverage (see [1] for details on the HRSC specifications). Using this dataset to uniquely identify distinctive minerals on the Martian surface would therefore allow mapping these minerals at higher detail.

Test area: Sulfates of Juventae ChasmaWe chose the sulfate deposits in in Juven-tae Chasma identified by [2] as test area and looked for spectral characteristics in the HRSC color dataset of orbit 243 that are unique to these outcrops. We subsam-pled the nadir image to match the lower resolution of the color channels of 50 m per pixel, creating a five dimensional multispectral dataset.

Linear Spectral UnmixingOnly three endmembers known In the five-dimensional parameter space created, up to five linear independent fea-ture 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 : ”bright red rock“ = iron oxides ”dark material“ = unoxidized basalt ”ice“ = white polar ice caps (not present here) ”shade“ = endmember for the color of shadow

Are sulfates a fourth endmember ?If the sulfates (or any other mineral) were distinguish-able from mixtures of the above three endmembers, they could not be modeled by linear combinations of them. Consequently, we applied the linear unmixing method developed by [4] using only the known three endmembers as input. A significantly higher residual of the modeling result, correlated to the sulfates (or another outcrop), would reveal the additional end-member.

The input spectra for ”bright red rock” and ”dark ma-terial” were taken directly from the image at the loca-tions indicated in figure 2.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 ma-terial 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 correlati-on was then used to calculate the shade spectrum using an arbitrary reflectance value for the blue channel of 0.025.

Results of linear unmixing

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

Input endmembers

Wavelength [nm]500 600 700 800 900

0.5

0.4

0.3

0.2

0.1

0.0

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

What the results meanFigure 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 mate-rial” endmember appears mostly in the chasma and its direct vicinity, which is co-vered by dark dust (bluish in figure 2). Figure 4a and 4b appear flat: almost all clues that Juventae Chasma is a depression are removed. They appear, as expected, in the ”shade” endmember coefficient image (4c), the topography is easily reco-gnizable (Note that ”brighter” means ”more shade”). Figure 4d displays the dis-crepancy between observed spectra and modeled spectra using the described three endmembers.

The linear spectral unmixing was successfulIf the input spectra are well chosen, a potential fourth endmember reveals itself by a significantly higher modeling error. The good separation of topographic informa-tion into the ”shade” endmember coefficient image and the good correlation bet-ween the distribution of bright and dark material (figure 2) and their respective endmember coefficient image (4a and b) indicate a correct choice of endmember spectra and a successful modeling.

HRSC observation geometry complicates interpretationThe variations of the ”shade” coefficient in the lower part of figure 4c are suspi-cious, as there is no shade observable in this area of figure 2. However, this varia-tion can be a result of varying surface roughness: The HRSC color channels' ob-servation angles are tilted up to 16° with respect to the nadir channel. This means that for a given subpixel surface roughness and depending on the lighting condi-tions, each color channel observes a different amount of shade in each pixel. Con-sequently, a varying surface roughness leads to changing color of the same materi-al.

The three input endmembers are suffient - no spectral index for sulfatesThe linear spectral unmixing results presented in figure 4 show that the three end-member spectra described by [3] corresponding to red, iron oxide rich material, dark, unoxidized basalt, and shade, are enough to model all observed HRSC color spectra including those of the sulfates in Juventae Chasma. Although the mode-ling error in figure 4d in the western sulfate outcrop is slightly higher than in its surroundings, this holds only for one of the two prominent sulfate deposits: the bigger eastern ”gypsum mountain” is hardly recognizable. Moreover, the deviation between observed and modeled spectra is in the same order of magnitude as mode-ling errors clearly caused by image defects like coregistration errors between color channels, or compression and calibration errors, which manifest themselves by ho-rizontal lines in figure 4c. A detection of the sulfates by the level of modeling error is not possible.

a: ”Bright red rock”

b: ”Dark material”

c: ”Shade”

d: Modeling Errors

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

Spectral Angle MapperApparently, the spectrally neutral, multi-scattering sul-fates are mixed with both bright red material and dark material. Therefore, mixtures of these two components can perfectly mimic the spectra of the sulfates.

This can be shown with the Spectral Angle Mapper. This analysis tool uses the angle of data points with the coor-dinate origin in the five-dimensonal parameter space as similarity measure. Its advantage is that only the shape of the spectra playes a role, and not overall brightness differences caused by (ideal) shadows.

Figure 5 shows the result of the spectral angle mapper with a reference spectrum taken at the western sulfate outcrop (red arrow). Brighter shades of grey mean a higher similarity. A broad zone of high similarity sur-rounds the chasma on the plane. The spectra in this zone are even more similar to the western sulfate deposit than the eastern sulfate outcrop, which appears in a darker grey.

Comparison with figure 2 yields that this highly similar 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.

Scatterplots confirm non-uniquenessScatterplots 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 disri-bution 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 Juve-ntae 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 in-terest on the western sulfate outcrop in figure 7. Figure 7 also shows a ”fringe” of red data points around the chasma. These points have exactly the same spectra in the HRSC dataset as the points located within the region of interest. The eastern sulfate outcrop does not show si-milar spectra. This confirms that different mineral types can show exactly the same spectra, and the same mineral may have several different color representations in the HRSC color dataset.

Conclusion: No spectral index foundThis study has failed to reveal a spectral index in the domain of the HRSC dataset that is unique to sulfate out-crops. 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.

In the sulfate outcrops, pure white sulfates are mixed with red and dark dust, which makes it impossible to di-stinguish them from mixtures of these two endmembers that do not contain any sulfates.

It remains an open question how mixtures of red and dark material and mixtures of red material, dark material and sulfates can have exactly the same spectra and level of brightness as shown in figure 7: one would expect that any mixture of red mate-rial, dark material and transparent sulfates would be brighter than mixtures of red material and dark material alone. This may be explained either by a third, transpa-rent constituent on the planes, which raises their brightness, without being de-tected by OMEGA. Another possibility is that the HRSC observation geometry translates differences in surface roughness on the planes into brightness variations in the same magnitude as those caused by intermixed sulfates.

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 hyper-spectral data using a linear unmixing model: Methodology and first results. Submitted to Planetary and Space Sciences.

Figure 5: Result of the Spec-tral Angle Mapper. Brighter shades of grey indicate a higher similarity (smaller spectral angle) with the refe-rence spectrum taken at the red arrow.

Blue channel

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

Red

chan

nel

Blue channel

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.

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 spec-trum; Black rectangle: area used to derive spectrum of shade; Red cir-cles: sulfate deposits.

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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 fi rst 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 fl owing 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 Earth-like 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 seafl oors 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 fi t 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 142 million miles

Period of Rotation 24 hours, 37 minutes

Period of Revolution around Sun 687 days

Diameter 4,220 miles

Tilt of Axis 25 degrees

Length of Year 687 Earth Days

Moons 2 (Phobos and Deimos)

Gravity .375 that of Earth

Temperature Average -81 degrees Fahrenheit

Atmosphere Mostly Carbon Dioxide with some

Argon, Nitrogen and water vaporwww.nasa.gov

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HydaspisChaos

Kipini

LyddaKaupYuty

Kin

Zuni

Yoro

BatosBok

Concord OrePylos

Kok

TileYatTrud

Calahorra

Hamelin

BorLuck

NuneZir

Taxco

YarCave

Warra

Mut

Nakusp

Swanage

Sefadu

MakhambetLismore

GrindavikNaar

BiraSabo

Rongxar Worcester

Belz

Rong

CairnsPoona

Argas

LeukOlom Dush

ArtaKoy

Jama Aktaj

Quick

CantouraTibrikot

Mandora

Canso

Aveiro

SulakNif

WaspamPompeii

Balvicar

Montevallo

Echus Chaos

Echus Montes

LongaSambeGolden

ButaEberswalde

Bigbee CalbeLar inta

TuraTiwiSangar

EadsLuki

Zongo

TurbiAzul

Wau

Albi

PorvooSauk

Kakori

Ochakov

Karpinsk

Mari

Kartabo

DessauSalaga

Alitus

Zilair

Falun

Octantis Mons

Dorsa ArgenteaElim

Eilat

Wynn-Williams

Australe Li

ngula

Hellas Chaos

Zea Dors

a

Poti

Njesk

o

Apia

Saheki

ChupaderoBeruri Ohara

YalgooDulovoHashir

Hypsas Vallis

Clanis Valles

Anio Valles

Scamander Vallis

Lonar

Gemini Scop

uli

Barabashov

LabeatisMons

Gamboa Bonestell

Ortygia Colles

Lagarto

LafCan Land

GarmGolLota

Elath

Arandas

[The Face]

CrayFaith

Hope EskEagle

Mohawk

BambergLutsk

GaanChom

Yakima

Vils

Davies

AniakVoeykov

Los

Vik

AkiTabor

Dubki Labria

Protva Valles

Dittaino VallesSepik Vallis

Sabrina V.

Tyras V.

Drilon V.

Subur V.

Vistula V.

Tyrrhena

Terra

S i r e n u m [Hap

py Face]

+[Inca City]

+ [Frozen Sea]

Dokka

BulharTsukuba

Rimac

CorbyRaubNain

Kumara

Baykonyr

OrindaRynok

Naju

TsauVivero

ChincoteagueKufra

MARS

Solar Distance: 206–249 million kmEarth Distance: 54–401 million kmEquatorial Radius: 3396.2 kmObliquity to orbit: 25°19’ (±10°)Orbital Period: 668.59 Mars days(=Sols) (=687 Earth days)Rotational Period (1 sol): 24h:37mGravity: 0.38 gLength of Equator: 21 300 kmSurface Area: 144.2 million km2

Atmosphere: 95% CO2; 2,6% N2Pressure: 6 mbar [min: 0,7–Olym pus,max: 12–hellas)0. Longitude: Airy-0 craterHeight Datum 3396 km radiusDistance from Earth at Lightspeed : 03:02–22:19 min.Solar Distance: 589.2 W/m2

Satellites: Phobos, deimos

Geochronology using crater counting(based on Tanaka 2001 and Hartmann2005, modified)Amazonian ([2-] 3 ga-today): little geo-logical activity, local lava flows at Elysiumand tharsis. ice ages. Periglacial environ-ment. Polat layered deposits. MedusaeFossa Formation.Hesperian (3.5-3 ga) volcanism (laterwrinkle ridges on the lava plains), forma-tion of valles Marineris rift system, burialof early impact structures of the Northernlowlands, chaos areas and outflow valleys(for example from valles Marineris). Noachian (4-3.5 ga) north-south di-chotomy, volcanism, valley networks(warmer climate or local impact heat),giant impact basins (Ares, hellas, Argyre,isidis, utopia, Chryse) 4 ga ago, „Pre-Noachian” (4,5-4 ga): ancient (todayburied) basins (QCds), early heavy bom-bardment, permanent magnetosphere

Geochemical chronology (based on Bibring et al. 2006)Siderikian: 3.8 ga-today: Cold, dry cli-mate. Water and volcanism has little ef-fect. rocks oxidize slowly (Fe(iii)-oxide,hematite - Fe2O3); Mars gets its red color.Theiikian: 4.2-3.8 ga: volcanic activitypumpes SO2 to the atmosphere, which re-acts with water and created acidic rains,which wheather rocks. Phyllocian: 4.5-4.2 ga: Warm, wet cli-mate, phyllosilicates (clay minerals) on thebottom of lakes or underground from hy-drothermal activity or impact of icy bod-ies.

1 : 320 000 0001 cm = 320 km

depths and heights [km]

201816141086420

-2-4-6-8

�

APhEliON ls=73°

PErihEliONls=253°

Direction of EarthFirst Point of Ariesls=97°

NOrth WiNtErSOlStiCE

dirECtiON OF MArtiAN POlAr StAr, iN CygNuS

206 million km

249 million km

NOrth SPriNg EQuiNOX

�

Earth Orbit 1 year=365 days1

Mars year=669 sols=687 Earth days

NOrth AutuMN EQuiNOX

30°

270°

60°

120°

150° 180°

0°

210°240°

300°

330°

dust storms

Winter on Earth

MARS ORBITAL ELEMENTSMARS HISTORY

N: long, cold summer (–30 — –80°C)

S: long, cold winter

large southerncap

small souther polar cap

NOrth SuMMErSOlStiCE

90°

�

N: short, cold winter(–90 — –120°C)

S: short, warm summer

Marsorbit

Ls

Ls

SURFACE OF MARSlambert transversal Equivalent

Azimuthal Projection grid: Planetocentric latitude with

East longitudePublished by Eötvös loránd university

Cosmic Materials Space research group, budapest, hungary

http://planetologia.elte.hudtM source: MgS MOlA

Map © henrik hargitai 2008iSbN hu 978-963-463-968-8

km201612840-4-8

km20 1612840

-4-8

tharsis MonsAmazonisPanitia

lunaePlanum

Xantheterra

Margaritifer terra Noachis terra

hellas Planitia

Promethei terra Australe PlanumNewcomb

Ares

val

lie

Scyl

la-fa

lCh

aryb

dis-

fal

Auro

rae

Chao

s

gang

es C

hasm

a

Echu

s Ch

asm

a

lycu

s Su

lci

Juve

ntae

Cha

sma

Catena, catenae Crater chainCavus, cavi hollow/sChaos Chaotic terrainChasma CanyonColles hillsdorsum, dorsa ridge/s Fossa, fossae trough/slabyrinthus intersecting troughsMensa, mensae table mountain/sMons, montes Mountain/sPlanitia Plains, basin Patera CalderaPlanum highland Plainrupes CliffScopulus WallSulci Furrowsterra highlandtholus Coneundae dunesvallis, valles valley/s vastitas Plains

Instead of names of months, onMars we use ls (Solar Longitude)degree values to measure seasons. itshows the distance of the Sun fromthe First Point of Aries at the spring

equinox (0°) in the change of sea-sons of Mars – apart from obliquityto orbit – Solar distance plays an im-portant role. because of the excen-tricity of current Mars orbit. it

affects the size of polar caps. thusin the south seasons are extreme:summer is during perihelion, winteris during aphelion; while in thenorth, seasons are more equalized.

MARS DATA NOMENCLATURE

T O P O G R A P H I C M A P O F

CROSS SECTIONAscraeus MonsOlympus Mons

648km←→

NAS

A/JP

l-CA

ltEC

h/C

OrN

Ell

PiA

0863

2

VIEW FROM BEAGLE CRATER (Opportunity)

EötvöS l. uNivErSityCOSMiC MAtEriAlSSPACE rESEArCh grOuPbudAPESt, huNgAry

Apoll

inaris

Patera

Olym

pus Mons

South

Polar

Cap

North

Polar

Cap

100× vertical exaggeration