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The Phobos information system
Planetary and Space Science ∎ (∎∎∎∎) ∎∎∎–∎∎∎
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The Phobos information system I.P. Karachevtseva a,n, J. Oberst b,c, A.E. Zubarev a, I.E. Nadezhdina a, A.A. Kokhanov a, A.S. Garov a, D.V. Uchaev a, Dm.V. Uchaev a, V.A. Malinnikov a, N.D. Klimkin d a Moscow State University of Geodesy and Cartography (MIIGAiK), MIIGAiK Extraterrestrial Laboratory (MExLab), Gorokhovsky pereulok 4, 105064 Moscow, Russian Federation b German Aerospace Center (DLR), Institute of Planetary Research, Rutherfordstrasse 2, 12489 Berlin, Germany c Technical University Berlin, Institute for Geodesy and Geoinformation Sciences, Strasse des 17 Juni 135, 10623 Berlin, Germany d Lomonosov Moscow State University, GSP-1, 119991, Leninskie Gory, Moscow, Russian Federation
art ic l e i nf o
a b s t r a c t
Article history: Received 21 August 2013 Received in revised form 24 December 2013 Accepted 30 December 2013
We have developed a Geo-information system (GIS) for Phobos, based on data from the Mars Express and Viking Orbiter missions, which includes orthoimages, global maps, terrain- and gravity field models, all referenced to the Phobos coordinate system. The data are conveniently stored in the ArcGIS software system, which provides an environment for mapping and which allows us to carry out joint data analysis and miscellaneous data cross-comparisons. We have compiled catalogs of Phobos craters using manual and automated techniques, which includes about 5500 and 6400 craters correspondingly. While crater numbers are biased by available image data resolution and illumination, we estimate that our catalog of manually detected craters contains all Phobos craters with diameters D 4 250 m which is a total of 1072 and catalog of automated detected craters are complete for craters D 4 400 m (360 craters). Statistical analysis of these large craters reveals a surplus of craters on the anti-Mars hemisphere, whereas differences in crater abundance between leading and trailing hemisphere cannot be confirmed. This in contrast to previous papers, where no such asymmetry was found (Schmedemann et al., 2014). But we cannot rule out remaining biases due to resolution, viewing angles or illumination effects. Using digital terrain model (DTM) derived from photogrammetry image processing we estimate depths of 25 craters larger than 2 km using geometric and dynamic heights (for discussion of Phobos crater morphometry see Kokhanov et al., 2014). We also have compiled catalogs of lineaments, and boulders. In particular, we mapped 546 individual grooves or crater chains, which extend in length from 0.3 km to 16.2 km. We identified and determined the sizes and locations of 1379 boulders near crater Stickney. Cross-comparisons of gravity field models against distribution patterns of grooves and boulders are currently under way and may shed light on their possible origins. Finally, we have developed a Geo-portal, which allows the science community to conveniently search for, analyze, and download data of interest from our system. Additionally we provide access to color electronic maps (e-maps) with support for layers based on Phobos geodatabase and ArcGIS tools. & 2014 Elsevier Ltd. All rights reserved.
Keywords: Phobos Thematic mapping GIS Global crater catalog Automated crater detection Geo-statistic analyses Gravity field Geo-portal
1. Introduction Planetary cartography projects benefit very much from support by GIS (Geographic Information Systems). These are very helpful for integration of the types of raw data from different spacecraft and instruments or various derived data generated by different teams, for example, PIGWAD (http://webgis.wr.usgs.gov/) or PILOT (http://pilot.wr.usgs.gov/). GIS effectively manages data,
n
Corresponding author. Tel./fax: þ7 499 267 35 13 E-mail address: [email protected] (I.P. Karachevtseva).
descriptive information and links spatial data and non-spatial information. GIS possibilities can be used for a variety of practical tasks, for general science applications, for thematic mapping and cartography (Nass et al., 2010), for planning of orbital imaging and for landing site selection (Schulz et al., 2009). The basis for such goals is the consistent and coherent storage of data within a geospatial context. For effective management and operation of the GIS, spatial data must be properly organized using data relationships and topology constraints in a geodatabase model. The conceptual approach and design of databases for celestial body have been proposed for the Moon (Cherepanova et al., 2005) based on ArcGIS capabilities (http://esri. com). Conceptual and logical models, including metadata templates of
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Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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Fig. 1. Scheme of the Phobos information system.
planetary data, were developed for the geological and geomorphological mapping (van Gasselt and Nass, 2011). Here we present practical realization of development of the geospatial information system for the Martian satellite Phobos. The main our goals are (Fig. 1): (1) to hold a variety of Phobosrelated raw data and derived data products, (2) to offer various tools for semi-automated mapping and data geo-analyses, (3) to provide access to the system to public users via geospatial and web-technologies. In this paper, we also present typical applications of our GIS which include the detection and statistical analysis of craters as well as data cross-comparisons, relevant to Phobos.
2. Data sets Our development of a Phobos GIS is motivated by the availability of large volumes of remote sensing data provided by Mars Express. Due to moderate size of Phobos and the uniqueness of the data set, we have developed a standalone geodatabase within the commercial software ArcGIS 10 (ESRI™) for data storage, data analyses, production of derived data and for mapping (Karachevtseva et al., 2012a). Two important data sets, derived from Mars Express (MEX) SRC, Super Resolution Channel (Jaumann and Neukum, 2007; Oberst et al., 2008) and Viking Orbiter (VO), provide base layers
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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(Table 1): a global digital terrain model (Zubarev et al., 2012), which includes special 3D shape features (rims of craters and grooves), and the global orthomosaic (Karachevtseva et al., 2012b). The data sets are referenced to a new geodetic control point network (Zubarev et al., 2012), for which point coordinates were recently re-analyzed (Oberst et al., this issues). The base layers (Fig. 2) were used for new Phobos mapping described in (Wählisch et al., 2014). Individual orthorectified SRC and VO images (Fig. 3, also see Appendix A) and the control point coordinates themselves
Table 1 List of Phobos geodatabase layers. No. Data products
Type, Format
1 2 3 4 5 6 7
Control point network DTM, 200 m/pixel (global) DTM, 20 m/pixel (local) DTM structure lines Topography contours, in 500 m Base orthomosaic (global) SRC and VO separate orthoimages (local)
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Catalog of craters (MExLab, manual), MExLab base orthomosaic Catalog of craters (MExLab, automatic), Stook mosaic Slopes, degree Roughness Shadowed relief Albedo mosaics Index (VNIR) mosaics Lineaments Boulders Dynamic heights, 200 m/pixel Dynamic heights, contours in 200 m Gravity potential, 200 m2/s2 per pixel Gravity potential, contours in 1 m2/s2 Phobos nomenclature (English and Russian)
Point shape-file ArcGIS Geotiff Geotiff Line shape-file ArcGIS Line shape-file ArcGIS Geotiff Geotiff (see Appendix for detailes) Polygon shape-file ArcGIS
9 10 11 12 13 14 15 16 17 18 19 20 21
Polygon shape-file ArcGIS GRID ArcGIS GRID ArcGIS GRID ArcGIS Geotiff Geotiff Line Shape-file ArcGIS Polygon Shape-file ArcGIS GRID ArcGIS Line Shape-file ArcGIS GRID ArcGIS Line shape-file ArcGIS Annotation ArcGIS
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were also included to geodatabase. DTM and orthoimages were produced using Photomod software (Racurs™ http://www.racurs. ru/?lng=en&page=634), for which a special release for the celestial bodies has been developed by MExLab. Gravity potential and dynamic height data sets (Uchaev et al., 2013) were also introduced to the system. These were derived from the new shape model (Zubarev et al., 2012) and include centrifugal and tidal forces, following methods developed by Thomas, (1993). In addition, various multispectral and color-ratio images, relevant for surface compositional studies, were uploaded (Fig. 4). The data are based on HRSC color-channel images and have been radiometrically calibrated, orthorectified, and coregistered (Patsyn et al., 2012). Using original basic layers as described above a number of secondary products (slope, surface roughness measurements, shaded relief images) were generated based on the standard tools of ArcGIS (Table 1). Efforts were made to develop the catalogs of Phobos craters as well as inventories of grooves and boulders to be included in the system. All data sets, integrated into ArcGIS geodatabase, were referenced to a sphere of radius R¼ 11.1 as recommended by the IAU (Archinal et al.,2011).
3. Applications 3.1. Craters 3.1.1. Global catalog With craters being dominant landforms on Phobos, efforts were made to produce crater catalogs. However, the completeness of crater inventories is challenged by available images of greatly different resolutions (4–80 m/pixel) obtained under various solar illumination conditions. Hand-picked craters and automatic detection techniques were used to compile the crater catalogs. As the quality of images will effect the outcome of automatic crater detection results, the catalogs have been produced based on
Fig. 2. Phobos map (equidistant cylindrical projection), including basic layers global orthomosaic and contours derived from new shape model (Zubarev et al., 2012).
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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Fig. 3. Relevant parts of MEX and VO orthoimages used for mosaicking. Different gray shades represent the different resolutions of the images: (1) (3–6 m/pixel), (2) (6–10 m/pixel), (3) (10–20 m/pixel), and (4) (20–40 m/pixel).
Fig. 4. Map of spectral indices computed from HRSC multispectral data, where index ¼(V/NIR ), and V ¼ (Gþ B)/2. G, B, and NIR are the green, blue, and near-infrared channels of HRSC, respectively (see Patsyn et al., 2012 for details).
different mosaics (see description below). Our current catalog version of hand-picked craters includes about 5500 craters based on new shape model (Zubarev et al., 2012) and the global base orthomosaic (Karachevtseva et al., 2012b) produced from SRC orthoimages (see Appendix A). All objects have attributes – surface coordinates and diameters (Table 2), – measured semi-automatically using CraterTool (Kneissl et al., 2011). 25 craters are sufficiently large (more 2 km), that depth measurements can be made using geometric (Table 2) and dynamic heights (Fig. 5 and Fig. 9) derived from the new global DTM. For more detailed discussion of Phobos crater morphology see (Basilevsky et al., this issues).
The automated detection benefits from a key characteristic of craters, which is the circular shape, realized by corresponding patterns of shadow and lighting. We used an automated texture-based crater detection algorithm (CDA), which consists of two main steps (Uchaev et al., 2012a, 2012b, 2012c): (1) detection of arc fragments of crater boundaries on images using specific masks; (2) reconstruction of crater boundaries from their arc fragments using the circle Hough transform. After initial crater detection an expert verification and correction of the detected craters can be carried out. The crater detection tool was implemented and tested on several HRSC images with different resolutions and lighting conditions. The
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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automated algorithm finds many small craters not listed in manual catalogs, but fails to detect some heavily degraded craters, in particular those with pronounced non-circular
Table 2 Coordinates, diameters and ratio depth to diameter of Phobos’ named craters, measured from new global base orthomosaic and geometric heights (DTM). No. Crater name
Center latitude (Deg)
Center longitude (Deg)
Diameter, D (km)
d/D
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
60.2 37.9 39.3 59.5 80.9 63.1 80.0 10.2 5.3 44.2 54.6 26.3 26.4 51.5 1.3 8.7 1.4
89.3 179.7 91.5 350.4 212.2 168.0 328.9 53.0 296.9 36.6 184.5 153.0 248.8 319.8 48.2 153.7 131.5
3.9 1.7 5.1 1.9 2.9 6.7 5.6 1.9 2.0 3.3 2.9 1.9 2.4 1.7 8.1 2.1 1.5
0.10 0.06 0.15 0.11 0.11 0.13 0.08 0.14 0.06 0.09 0.12 0.05 0.05 0.09 0.14 0.21 0.04
Clustril D’Arrest Drunlo Flimnap Grildrig Gulliver Hall Limtoc Öpik Reldresal Roche Sharpless Shklovsky Skyresh Stickney Todd Wendell
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shapes. During expert inspection we established that an accuracy of our crater detection algorithm is about 80% compared to manual detection result. After testing we made complete detection runs using the Small Bodies Maps (V1.0) produced by Stooke (http://sbn.psi.edu/pds/ asteroid/MULTI_SA_MULTI_6_STOOKEMAPS_V1_0/docu%20ment/ m1phobos/phobos_cyl_dlr_control.jpg). This mosaic (4.843 m/ pixel) had been created by combination of Viking mosaics, and highest resolution images with more uniform illumination from Mars Global Surveyor, Mars Express and Mars Reconnaissance Orbiter. This increases our confidence in the automatic crater detections and maximizes crater detection numbers. To fine-tune crater detection we used four additional work steps: (1) the use of low brightness–contrast corrections of the image mosaic; (2) down-sampling of image mosaic, in order to cover all crater sizes; (3) generation of tiles (for convenience the original mosaic is subdivided into overlapping rectangular pieces called “tiles”); and (4) assembling of results from individual tiles and removal of duplicate detections. Visual inspection is used to avoid artifacts entering the catalog. In order to cover those craters that our CDA was not able to detect during batch processing, we manually adjusted parameters of the algorithm for searching them at the end. We automatically detected 6369 craters (Fig. 6). By comparison with the manual detections, we see that some craters from one
Fig. 5. Dependence of depth/diameter ratio (d/D) based on dynamic heights on crater size (for D 42 km), including Stickney.
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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data set are missing in the other, and vice versa, as the data sets were produced from different original data by different techniques. By inspection of cumulative size-frequency distributions (CSFDs) we can see in both data sets a fall-off at crater diameters
less than approx. 150 m, due to resolution limits of the images. In our study, we tried to minimize illumination biases by using Stooke Phobos mosaic consisted of images with more uniform
Fig. 6. Stooke Phobos mosaics with craters (white circles) detected by automated CDA: (a) nearside (equidistant cylindrical projection); (b) north polar area, and (c) south polar area (equidistant azimuthal projection).
Fig. 7. Cumulative size-frequency distributions of automated detected Phobos craters and their lognormal models: (a) the nearside and farside; and (b) leading and trailing hemispheres.
Table 3 Crater area statistic based on catalog of manual crater detection. Area_name
Nearside Farside Leading side Trailing side North Polar area South Polar area
Size of craters, D (m)
Number of craters in study area
D o 50
504D 4100
1004D 4250
250 4 D4 500
5004D 41000
D 41000
150 250 124 276 10 0
685 797 742 740 78 26
1009 1138 1191 956 136 134
344 375 382 337 61 57
68 74 66 76 10 10
31 27 29 29 8 6
2287 2661 2534 2414 303 233
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Fig. 8. Map of Phobos surface gravity potential including self-gravitational potential, centrifugal potential and tidal potential (Uchaev et al., 2013), background shadowed relief produced from geometric heights.
Fig. 9. Map of lineaments and dynamic heights, background shadowed relief produced from dynamic heights.
illumination. Also, in this mosaic – which uses projected images – the proportion of mosaic fragments for which viewing angle effects have a significant value was relatively small. To minimize incident angle and viewing angle effects we also used a newly texture-based crater detection algorithm which can separate different lighting craters from their background. We estimate that our catalog of manually detected craters are complete for craters D 4250 m (1072 craters) and our catalog of automated detected craters are complete for craters D 4400 m (360 craters). We established that CSFDs for entire Phobos is close to CSFDs for lunar highlands, as suggested in (Thomas and Veverka, 1980), implying that the Phobos surface is in an equilibrium state.
3.1.2. Area statistics With Phobos being tidally locked in its orbit, the statistics of craters over the surface may reveal the sources of Phobos' meteoroid bombardment. Hence, based on both catalogs – manual
(Table 3) and automated detection, we measured crater distributions for different hemispheres. Statistical tests, e.g., Kolmogorov– Smirnov goodness-of-fit tests, were carried out to establish differences in crater abundance of sample data and model curves (Uchaev et al., 2012d, 2012e). The analysis was limited to craters for which inventory is believed to be complete. First, we measured cumulative size frequency distributions (CSFDs) based on automated crater detection catalog. We compared crater frequencies for Phobos nearside (01W–901W and 2701W–3601W) and farside (901W–2701W), confined to latitude regions below 601. We clearly identify a deficit of nearside craters (Fig. 7a) with a ratio at approximately of 1/1.2. The deficit can be explained by predicted shielding effects by nearby planet Mars (Christou et al., 2014) or differences in surface ages (Schmedemann et al., 2014), which may mimic a difference in meteoroid flux. Note that much of the Phobos nearside is dominated by crater Stickney, where recent erosion events may have erased smaller craters on its steep walls. Also, we cannot rule out completely remaining biases due to viewing angles or illumination effects.
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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Fig. 10. Map of boulder locations, determined from a Mars Global Surveyor high resolution image (background global base orthomosaic). The arrow (see also small zoomed image) points at a large boulder known as the “Phobos monument”.
Next, we compared crater statistics for the leading (01W– 1801W) and the trailing hemisphere (1801W–3601W) (Fig. 7b). Because of Phobos' synchronized rotation and revolution, the average encounter velocity of impactors should be higher on the leading hemisphere, implying that for given size the number of craters should be higher (Christou et al., 2014). However, the Kolmogorov–Smirnov test suggests that there is no statistically significant difference between the CSFDs for Phobos leading and trailing sides. This implies that the Phobos crater record may have been created at a time when Phobos was at much further distance from Mars and moved more slowly. Then, we compared the crater statistics for polar areas, i.e. regions beyond 601. The north polar area of Phobos has 8 large craters (D41 km), while the south polar area has only 6 craters with diameter of that size (4 and 3 craters, respectively, in the automated catalog based on the mosaic which has less resolution in the polar regions). A more detailed quantitative analysis (see Table 3) suggests that the crater surplus is 1.3, suggesting that this difference cannot be a chance fluctuation from an even probability of cratering. The crater area statistic using the catalog of manual crater detections confirms result from the automated crater detection. 3.2. Lineaments Using standard ArcGIS tools, we have digitized lineaments on Phobos. The origins of these lineaments is uncertain. While some of them may represent impact crater chains, impact ejecta “curtains” from Mars or rolling boulder tracks, other “grooves” may originate from tectonic faulting (for summaries see Basilevsky et al., this issue; Murray and Heggie, 2014). As for the craters, completeness of the mapping is greatly determined by illumination conditions and resolutions of the available images. To warrant uniform coverage, we used the new global Phobos SRC mosaic (Zubarev et al., 2012; Wählisch et al., 2014) as a basis for the mapping. In total, we identified 546 individual lineaments, which extend from 60 to 600 m width and from 300 to 16,000 m length. A new version of our lineament catalog is in progress.
The distribution patterns and cross-comparisons with the dynamic heights and slopes may help reveal their origins. Dynamic heights of Phobos were obtained for a new Phobos shape model (Zubarev et al., 2012) according to the formula which is the potential difference between the total effective Phobos potential V (Fig. 8) and reference potential V0 ¼ 66.8582 m2/s2 scaled by a mean gravity value g0 ¼0.0053 m/s2 (Thomas, 1993). As it is seen from Fig. 9, dynamic heights correspond to different Phobos surface depressions or lines of depressions. 3.3. Boulders Boulders are only visible on a few highest-resolution images. For our mapping of boulders we specifically uploaded a Mars Global Surveyor image SP255103 obtained in 2003 (1.5 m/pixel). The image was manually co-registered to the new global orthomosaic. Using CraterTools we mapped boulders in an area of about 15 km2 north-east of crater Stickney (Fig. 10). We identified a total of 1379 boulders and defined their coordinates and approximate sizes – from 2.3 m (min) to 28.8 m (max). The total boulder density in the study area is about 100 per km2 (Fig. 11a); the most common boulder size is about 4–8 m (Fig. 11b). While the origins of the boulders are uncertain, comparisons with the dynamic heights and slopes (i.e. whether they are located on gravity highs or within gravitational sinks) may help reveal their sources and emplacement mechanisms, as has been demonstrated previously (Thomas et al., 2000).
4. Geo-portal We have established public access to our Phobos data in the form of an online Geo-portal using modern spatial and web-based technology. We follow examples of systems such as PDS Geosciences Node (http://ode.rsl.wustl.edu/), Planetary GIS Web Server – PIGWAD (http://webgis.wr.usgs.gov/), Google Moon (http://www.google.com/ moon/), and Google Mars (http://www.google.com/mars/), which
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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Fig. 11. Quantitative results on boulder size: (a) cumulative boulder size-frequency distribution; and (b) spatial density of boulders for four size bins.
Fig. 12. Structure and operation of the server and client side of the Geo-portal.
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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Fig. 13. Map interface module to access results of Phobos data analysis.
provide access to planetary data based on screen views of maps with user-friendly interfaces. We have developed our prototype system in several steps: (1) design of database; (2) modeling and creating metadata, (3) loading information to database and providing public access to results. (1) For the design of the database, our foremost goal was to ensure maximum flexibility to enable the handling of various heterogeneous information in terms of content and format. This is achieved by bringing all input information to a single unified description format. There are three groups of input data: metadata, spatial data and binary data. Metadata are accepted as XMLdocuments (eXtensible Markup Language), spatial data (vector and raster) are introduced as Geography Markup Language extensions of XML (ISO 19136), binary data are stored in their native formats. (2) For the modeling, we must provide unified descriptions of all data, and define relationships and topological constraints. For modeling metadata we distinguish between global and local data description. Local metadata are individual feature attributes, whereas global metadata are common parameters of object sets (layers). Global and local metadata are created with the international ISO 19139 or the FGDC standard (http://www.fgdc.gov/ index.html) supported in the ArcGIS environment, which we use as our software platform. To provide the flexibility and interoperability of our information system for further development we will adopt the PDS4 standard (Crichton, 2012), which warrants data consistency across planetary missions, nodes and international partners. (3) The data obtained from various sources are uploaded to the database with specially developed XML-converter (Fig. 12). The architecture of the information system is based on the client-server principle. The client part of the system generates requests and receives responses from the server via WFS (Web Feature Service) protocol based on ISO 19142. The service converts WFS-requests to queries to the MS SQL database, which consists of the spatial and attributes data, and metadata. The File (or Blob) Service is designed for binary (including raster) data and runs together with the WFS-service. A user interface (Fig.13) is currently being developed using Flash technology with further plans to implement Silverlight and HTML5 þJavaScript technology on the client side to allow for larger numbers of simultaneous users. The main feature of designed Geo-portal is the ability of spatial queries and access to
the contents selecting from the list of available data set. Based on four different projections for layers displaying, users can obtain quantitative and qualitative characteristics of the objects in graphical and tabular forms and can download data. User authorization service is provided data security and access control.
5. Conclusions We have developed the spatial information system for the Martian satellite Phobos based on geospatial and web-technologies. Original data from different sources were uploaded into geodatabase, as well as derived data from various geo-analyses. In particular, the system includes global crater catalogs and inventories of Phobos lineaments and boulders. We are currently testing the prototype of our Geo-portal which is available in testing regime (http://cartsrv.mexlab.ru/geoportal/) and allows users to access our Phobos data and results of geoanalyses remotely. For public users we have developed easy access to cartography quality color e-maps published on ESRI ArcGIS server (http://www.arcgis.com). E-maps with support for layers offer data visualizations not included in Geo-portal, such as map diagram or special projection for 3-axial ellipsoid, for example, modified Bugaevsky projection developed for Phobos (Bugaevsky, et al., 1992). The number of maps will be expanded, and their list will be published on the MExLab web-site (http://mexlab.miigaik. ru/eng/projects/phobos_cartography/). Also maps will be printed in the new Atlas of Phobos (in Russian), which will include more than 30 original thematic maps at different scales. The technological solutions are open-source, and allow us to increase the functionality and performance of the service. Our geospatial information system could integrate further types and larger volumes of data. It is not limited to Phobos, but could be useful for geodesy and cartography support of future missions to other planets and satellites.
Acknowledgments Authors would like to acknowledge A. Basilevsky and M. Kreslavsky for their advice and support. This work has been
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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supported by the Russian Federation Ministry of education and science for “Development of a Planetary Data Geoportal to provide access to results of research on planets and satellites of Solar system”, Grant agreement no. 14.B37.21.1303, and by Russian Foundation for Basic Research (RFBR) for “Geodesy, cartography and exploration of the Martian satellites Phobos and Deimos” (Helmholtz–Russia Joint Research Group), Grant agreement no. 11-05-91323. Also research was partially supported by the Russian Federation Ministry of education and science for “Geodesy, cartography and exploration of planets and satellites”, Grant agreement no. 11.G34.31.0021 (Megagrant). We thank the International Space Science Institute in Bern for hosting the “Phobos and Deimos” workshops between 2010 and 2012.
Appendix A See Table A1.
Table A1 List of orthoimages used for production of base mosaic. N
Image name
Mission
Camera
Resolution, m/pixel
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
h0413_0003_sr2 h0715_0004_sr2 h0748_0003_sr2 h1558_0005_sr2 h1574_0005_sr2 h1607_0005_sr2 h2233_0005_sr2 h2780_0004_sr2 h2780_0006_sr2 h2813_0005_sr2 h2846_0006_sr2 h3310_0003_sr2 h3310_0004_sr2 h3310_0005_sr2 h3310_0006_sr2 h3761_0004_sr2 h3769_0004_sr2 h3769_0005_sr2 h3802_0003_sr2 h3802_0004_sr2 h3802_0005_sr2 h3835_0005_sr2 h3835_0006_sr2 h3835_0007_sr2 h3843_0005_sr2 h3843_0006_sr2 h3868_0004_sr2 h3876_0004_sr2 h3909_0005_sr2 h4233_0004_sr2 h4274_0004_sr2 h4274_0005_sr2 h4307_0003_sr2 h4307_0004_sr2 h4307_0005_sr2 h4307_0007_sr2 h4332_0004_sr2 h4340_0005_sr2 h4373_0003_sr2 h4414_0004_sr2 h4773_0004_sr2 h4847_0004_sr2 h4847_0005_sr2 h4880_0005_sr2 h4888_0005_sr2 h4888_0006_sr2 h5343_0004_sr2 h5362_0002_sr2 h5362_0003_sr2
Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars
SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC
17.4 11.2 11.5 33.1 35.1 36.8 19.5 5.6 5.6 7.6 12.2 5.5 5.5 5.5 5.6 18.8 7.2 7.2 8.1 8.1 8.1 11.5 11.5 11.5 6.1 6.1 15.3 11.4 18.1 19.3 10.0 10.0 5.3 5.3 5.3 5.3 17.1 6.4 11.8 13.6 9.4 6.1 6.1 11.9 10.0 10.0 9.9 9.7 9.7
Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express
11
Table A1 (continued ) N
Image name
Mission
Camera
Resolution, m/pixel
50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
h5362_0004_sr2 h5381_0003_sr2 h5381_0004_sr2 h5409_0006_sr2 h5428_0005_sr2 h5428_0006_sr2 h5447_0004_sr2 h5447_0005_sr2 h5870_0002_sr2 h5870_0003_sr2 h5870_0004_sr2 h5870_0005_sr2 h5870_0006_sr2 h5889_0004_sr2 h5889_0005_sr2 h6906_0005_sr2 h6906_0006_sr2 h6916_0004_sr2 h6916_0005_sr2 h6916_0006_sr2 h6916_0007_sr2 h6926_0005_sr2 h6926_0006_sr2 h7937_0002_sr2 h7937_0003_sr2 h7937_0004_sr2 h7937_0005_sr2 h7948_0004_sr2 h7948_0005_sr2 h7948_0006_sr2 h7959_0005_sr2 h7959_0006_sr2 h7982_0006_sr2 h8477_0003_sr2 h8535_0006_sr2 h8535_0007_sr2 h8570_0005_sr2 h8570_0006_sr2 f242a19 f246a07 f246a08 f246a62 f248a01 f248a05 f249a03 f252a63
Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Viking Orbiter Viking Orbiter Viking Orbiter Viking Orbiter Viking Orbiter Viking Orbiter Viking Orbiter Viking Orbiter
SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC VO A VO A VO B VO B VO A VO A VO A VO A
9.7 11.7 11.7 6.3 7.9 8.0 9.8 9.8 3.3 3.3 3.3 3.3 3.3 6.1 6.1 4.9 4.9 4.8 4.8 4.8 4.8 8.9 8.9 4.4 4.4 4.4 4.4 6.1 6.1 6.1 7.8 7.9 12.3 7.7 7.1 7.1 10.8 10.7 15.4 5.7 5.6 13.7 7.4 6.7 10.5 14.6
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analyses. Abstracts of 43th LPSC. In: Proceedings of the 43th Lunar and Planetary Science Conference, 1342. Karachevtseva, I.P., Konopikhin, A.A., Shingareva, K.B., Mukabenova, B.V., Nadezhdina, I.E., Zubarev, A.E., 2012b. GIS Mapping of Phobos Based on the Results of the Processing of Remote Sensing Data of Mars Express Satellite. Modern Problems of Remote Sensing of the Earth from Space. Space Research Institute, Moscow, pp. 304–311 (in Russian). Kokhanov, A.A., Kreslavsky, M.A., Basilevsky, A.T., Karachevtseva, I.P., Zubarev, A.E. 2014. Morphometry of large craters on Phobos and comparison with other bodies. Abstracts of 45th LPSC. In: Proceedings of 45th Lunar and Planetary Science Conference, 1084. http://www.hou.usra.edu/meetings/lpsc2014/pdf/ 1084.pdf. Kneissl, T., van Gasselt, S., Neukum, G., 2011. Map-projection-independent crater size-frequency determination in GIS environments – new software tool for ArcGIS. Planet. Space Sci. 59, 1243–1254. Murray, J.B., Heggie, D.C., 2014. Character and origin of Phobos' grooves, (this issue). Nass, A., van Gasselt, S., Jaumann, R., Asche, H., 2010. Implementation of cartographic symbols for planetary mapping in geographic information systems. Planet. Space Sci. 59 (11), 1255–1264, http://dx.doi.org/10.1016/j.pss.2010.08.022. Oberst, J., Schwarz, G., Behnke, T., Hoffmann, H., Matz, K.-D., Flohrer, J., Hirsch, H., Roatsch, T., Scholten, F., Hauber, E., Brinkmann, B., Jaumann, R., Williams, D., Kirk, R., Duxbury, T., Leu, C., Neukum, G., 2008. The imaging performance of the SRC on Mars Express. Planet. Space Sci. 56, 473–491. Oberst, J., Zubarev, A., Nadezhdina, I., Rambaux, N., 2014. Phobos control point network and rotation (this issue). Patsyn, V.S., Malinnikov, V.A., Grechishev, A.V., 2012. Research of spectrometric characteristics of the surface of Phobos on the HRSC data from the Mars Express spacecraft. Modern Problems of Remote the Earth Sensing from Space, 9. Space Research Institute, Moscow, pp. 312–318 (in Russian). Schmedemann, N., Michael, G., Ivanov, B.A., Murray, J., Neukum, G., 2014. The age of Phobos and its largest crater Stickney (this issue). Schulz, J., van Gasselt, S., Neukum, G., 2009. Implementation of a lunar information system and geodatabase model. ISPRS Working Group IV/7, Planetary Mapping and Databases. Thomas, P., Veverka, J., 1980. Crater densities on the satellites of Mars. Icarus 41 (3), 365–380. Thomas, P.C., 1993. Gravity, tides, and topography on small satellites and asteroids: application to surface features of the Martian satellites. Icarus 105 (2), 326–344. Thomas, P.C., Veverka, J., Sullivan, R., Simonelli, D.P., Malin, M.C., Caplinger, M., Hartmann, W.K., James, P.B., 2000. Phobos: Regolith and ejecta blocks investigated with Mars orbiter camera images. J. Geophys. Res. 105 (E6), 15091–15106. Uchaev, D.V., Malinnikov, V.A., Oberst, J., 2012a. Automated detection of craters on a planetary surface using their optical images. Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos“emka 6, 12–18 (In Russian). Uchaev, D.V., Uchaev, D.m.V., Malinnikov, V.A., 2012c. A texture-based algorithm for automated crater detection. In: Proceedings of the Conference European
Planetary Science Congress 2012, 23–28 September 2012, Madrid, Spain. Abstracts (CD-ROM). Uchaev, D.V., Uchaev, D.m.V., Malinnikov, V.A., Hoan, P.X., 2012b. Automated detection of lunar craters in planetary images using their texture features. In: Proceedings of the Conference 1970–2010: The golden age of Solar system exploration, 10–12 September 2012, Rome, Italy. Abstracts (CD-ROM). Uchaev, D.V., Uchaev, D.m.V., Malinnikov, V.A., Oberst, J., 2012d. Multifractal model for Phobos crater size-frequency distribution. In: Proceedings of the Conference European Planetary Science Congress 2012, 23–28 September 2012, Madrid, Spain. Abstracts (CD-ROM). Uchaev, D.m.V., Malinnikov, V.A., Oberst, J., 2012e. Multifractal approach to crater distribution modelling according to their diameters. Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos“emka 6, 3–8 (In Russian). Uchaev, D.m.V., Uchaev, D.V., Prutov, I., 2013. Multiscale representation of gravitational fields of small celestial bodies. Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos“emka 4, 3–8 (In Russian). van Gasselt, S., Nass, A., 2011. Planetary mapping – The datamodel0 s perspective and GIS framework. Planet. Space Sci. 59 (11), 1231–1242, http://dx.doi.org/ 10.1016/j.pss.2010.09.012. Wählisch, M., Stooke, P.J., Karachevtseva, I.P., Kirk, R., Oberst, J., Willner, K., Nadezhdina, I.E., Zubarev, A.E., Konopikhin, A.A., Shingareva, K.B., 2014. Phobos and Deimos cartography. Planet. Space Sci. (this issues). Zubarev, A.E., Nadezhdina, I.E., Konopikhin, A.A., 2012. Problems of processing of remote sensing data for modeling shapes of small bodies in the Solar system, Modern Problems of Remote Sensing of the Earth from Space. Space Research Institute, Moscow, pp. 277–285 (in Russian).
Web references (all accessed 15.01.2014) 〈http://webgis.wr.usgs.gov/〉. 〈http://pilot.wr.usgs.gov/〉. 〈http://esri.com/〉. 〈http://www.racurs.ru/?lng=en&page=634〉. 〈http://sbn.psi.edu/pds/asteroid/MULTI_SA_MULTI_6_STOOKEMAPS_V1_0/docu ment/m1phobos/phobos_cyl_dlr_control.jpg〉. 〈http://ode.rsl.wustl.edu/〉. 〈http://www.google.com/moon/〉. 〈http://www.google.com/mars/〉. 〈http://www.fgdc.gov/index.html〉. 〈http://cartsrv.mexlab.ru/geoportal/〉. 〈http://www.arcgis.com〉. 〈http://mexlab.miigaik.ru/eng/projects/phobos_cartography/〉.
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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The Phobos information system I.P. Karachevtseva a,n, J. Oberst b,c, A.E. Zubarev a, I.E. Nadezhdina a, A.A. Kokhanov a, A.S. Garov a, D.V. Uchaev a, Dm.V. Uchaev a, V.A. Malinnikov a, N.D. Klimkin d a Moscow State University of Geodesy and Cartography (MIIGAiK), MIIGAiK Extraterrestrial Laboratory (MExLab), Gorokhovsky pereulok 4, 105064 Moscow, Russian Federation b German Aerospace Center (DLR), Institute of Planetary Research, Rutherfordstrasse 2, 12489 Berlin, Germany c Technical University Berlin, Institute for Geodesy and Geoinformation Sciences, Strasse des 17 Juni 135, 10623 Berlin, Germany d Lomonosov Moscow State University, GSP-1, 119991, Leninskie Gory, Moscow, Russian Federation
art ic l e i nf o
a b s t r a c t
Article history: Received 21 August 2013 Received in revised form 24 December 2013 Accepted 30 December 2013
We have developed a Geo-information system (GIS) for Phobos, based on data from the Mars Express and Viking Orbiter missions, which includes orthoimages, global maps, terrain- and gravity field models, all referenced to the Phobos coordinate system. The data are conveniently stored in the ArcGIS software system, which provides an environment for mapping and which allows us to carry out joint data analysis and miscellaneous data cross-comparisons. We have compiled catalogs of Phobos craters using manual and automated techniques, which includes about 5500 and 6400 craters correspondingly. While crater numbers are biased by available image data resolution and illumination, we estimate that our catalog of manually detected craters contains all Phobos craters with diameters D 4 250 m which is a total of 1072 and catalog of automated detected craters are complete for craters D 4 400 m (360 craters). Statistical analysis of these large craters reveals a surplus of craters on the anti-Mars hemisphere, whereas differences in crater abundance between leading and trailing hemisphere cannot be confirmed. This in contrast to previous papers, where no such asymmetry was found (Schmedemann et al., 2014). But we cannot rule out remaining biases due to resolution, viewing angles or illumination effects. Using digital terrain model (DTM) derived from photogrammetry image processing we estimate depths of 25 craters larger than 2 km using geometric and dynamic heights (for discussion of Phobos crater morphometry see Kokhanov et al., 2014). We also have compiled catalogs of lineaments, and boulders. In particular, we mapped 546 individual grooves or crater chains, which extend in length from 0.3 km to 16.2 km. We identified and determined the sizes and locations of 1379 boulders near crater Stickney. Cross-comparisons of gravity field models against distribution patterns of grooves and boulders are currently under way and may shed light on their possible origins. Finally, we have developed a Geo-portal, which allows the science community to conveniently search for, analyze, and download data of interest from our system. Additionally we provide access to color electronic maps (e-maps) with support for layers based on Phobos geodatabase and ArcGIS tools. & 2014 Elsevier Ltd. All rights reserved.
Keywords: Phobos Thematic mapping GIS Global crater catalog Automated crater detection Geo-statistic analyses Gravity field Geo-portal
1. Introduction Planetary cartography projects benefit very much from support by GIS (Geographic Information Systems). These are very helpful for integration of the types of raw data from different spacecraft and instruments or various derived data generated by different teams, for example, PIGWAD (http://webgis.wr.usgs.gov/) or PILOT (http://pilot.wr.usgs.gov/). GIS effectively manages data,
n
Corresponding author. Tel./fax: þ7 499 267 35 13 E-mail address: [email protected] (I.P. Karachevtseva).
descriptive information and links spatial data and non-spatial information. GIS possibilities can be used for a variety of practical tasks, for general science applications, for thematic mapping and cartography (Nass et al., 2010), for planning of orbital imaging and for landing site selection (Schulz et al., 2009). The basis for such goals is the consistent and coherent storage of data within a geospatial context. For effective management and operation of the GIS, spatial data must be properly organized using data relationships and topology constraints in a geodatabase model. The conceptual approach and design of databases for celestial body have been proposed for the Moon (Cherepanova et al., 2005) based on ArcGIS capabilities (http://esri. com). Conceptual and logical models, including metadata templates of
0032-0633/$ - see front matter & 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pss.2013.12.015
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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Fig. 1. Scheme of the Phobos information system.
planetary data, were developed for the geological and geomorphological mapping (van Gasselt and Nass, 2011). Here we present practical realization of development of the geospatial information system for the Martian satellite Phobos. The main our goals are (Fig. 1): (1) to hold a variety of Phobosrelated raw data and derived data products, (2) to offer various tools for semi-automated mapping and data geo-analyses, (3) to provide access to the system to public users via geospatial and web-technologies. In this paper, we also present typical applications of our GIS which include the detection and statistical analysis of craters as well as data cross-comparisons, relevant to Phobos.
2. Data sets Our development of a Phobos GIS is motivated by the availability of large volumes of remote sensing data provided by Mars Express. Due to moderate size of Phobos and the uniqueness of the data set, we have developed a standalone geodatabase within the commercial software ArcGIS 10 (ESRI™) for data storage, data analyses, production of derived data and for mapping (Karachevtseva et al., 2012a). Two important data sets, derived from Mars Express (MEX) SRC, Super Resolution Channel (Jaumann and Neukum, 2007; Oberst et al., 2008) and Viking Orbiter (VO), provide base layers
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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(Table 1): a global digital terrain model (Zubarev et al., 2012), which includes special 3D shape features (rims of craters and grooves), and the global orthomosaic (Karachevtseva et al., 2012b). The data sets are referenced to a new geodetic control point network (Zubarev et al., 2012), for which point coordinates were recently re-analyzed (Oberst et al., this issues). The base layers (Fig. 2) were used for new Phobos mapping described in (Wählisch et al., 2014). Individual orthorectified SRC and VO images (Fig. 3, also see Appendix A) and the control point coordinates themselves
Table 1 List of Phobos geodatabase layers. No. Data products
Type, Format
1 2 3 4 5 6 7
Control point network DTM, 200 m/pixel (global) DTM, 20 m/pixel (local) DTM structure lines Topography contours, in 500 m Base orthomosaic (global) SRC and VO separate orthoimages (local)
8
Catalog of craters (MExLab, manual), MExLab base orthomosaic Catalog of craters (MExLab, automatic), Stook mosaic Slopes, degree Roughness Shadowed relief Albedo mosaics Index (VNIR) mosaics Lineaments Boulders Dynamic heights, 200 m/pixel Dynamic heights, contours in 200 m Gravity potential, 200 m2/s2 per pixel Gravity potential, contours in 1 m2/s2 Phobos nomenclature (English and Russian)
Point shape-file ArcGIS Geotiff Geotiff Line shape-file ArcGIS Line shape-file ArcGIS Geotiff Geotiff (see Appendix for detailes) Polygon shape-file ArcGIS
9 10 11 12 13 14 15 16 17 18 19 20 21
Polygon shape-file ArcGIS GRID ArcGIS GRID ArcGIS GRID ArcGIS Geotiff Geotiff Line Shape-file ArcGIS Polygon Shape-file ArcGIS GRID ArcGIS Line Shape-file ArcGIS GRID ArcGIS Line shape-file ArcGIS Annotation ArcGIS
3
were also included to geodatabase. DTM and orthoimages were produced using Photomod software (Racurs™ http://www.racurs. ru/?lng=en&page=634), for which a special release for the celestial bodies has been developed by MExLab. Gravity potential and dynamic height data sets (Uchaev et al., 2013) were also introduced to the system. These were derived from the new shape model (Zubarev et al., 2012) and include centrifugal and tidal forces, following methods developed by Thomas, (1993). In addition, various multispectral and color-ratio images, relevant for surface compositional studies, were uploaded (Fig. 4). The data are based on HRSC color-channel images and have been radiometrically calibrated, orthorectified, and coregistered (Patsyn et al., 2012). Using original basic layers as described above a number of secondary products (slope, surface roughness measurements, shaded relief images) were generated based on the standard tools of ArcGIS (Table 1). Efforts were made to develop the catalogs of Phobos craters as well as inventories of grooves and boulders to be included in the system. All data sets, integrated into ArcGIS geodatabase, were referenced to a sphere of radius R¼ 11.1 as recommended by the IAU (Archinal et al.,2011).
3. Applications 3.1. Craters 3.1.1. Global catalog With craters being dominant landforms on Phobos, efforts were made to produce crater catalogs. However, the completeness of crater inventories is challenged by available images of greatly different resolutions (4–80 m/pixel) obtained under various solar illumination conditions. Hand-picked craters and automatic detection techniques were used to compile the crater catalogs. As the quality of images will effect the outcome of automatic crater detection results, the catalogs have been produced based on
Fig. 2. Phobos map (equidistant cylindrical projection), including basic layers global orthomosaic and contours derived from new shape model (Zubarev et al., 2012).
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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Fig. 3. Relevant parts of MEX and VO orthoimages used for mosaicking. Different gray shades represent the different resolutions of the images: (1) (3–6 m/pixel), (2) (6–10 m/pixel), (3) (10–20 m/pixel), and (4) (20–40 m/pixel).
Fig. 4. Map of spectral indices computed from HRSC multispectral data, where index ¼(V/NIR ), and V ¼ (Gþ B)/2. G, B, and NIR are the green, blue, and near-infrared channels of HRSC, respectively (see Patsyn et al., 2012 for details).
different mosaics (see description below). Our current catalog version of hand-picked craters includes about 5500 craters based on new shape model (Zubarev et al., 2012) and the global base orthomosaic (Karachevtseva et al., 2012b) produced from SRC orthoimages (see Appendix A). All objects have attributes – surface coordinates and diameters (Table 2), – measured semi-automatically using CraterTool (Kneissl et al., 2011). 25 craters are sufficiently large (more 2 km), that depth measurements can be made using geometric (Table 2) and dynamic heights (Fig. 5 and Fig. 9) derived from the new global DTM. For more detailed discussion of Phobos crater morphology see (Basilevsky et al., this issues).
The automated detection benefits from a key characteristic of craters, which is the circular shape, realized by corresponding patterns of shadow and lighting. We used an automated texture-based crater detection algorithm (CDA), which consists of two main steps (Uchaev et al., 2012a, 2012b, 2012c): (1) detection of arc fragments of crater boundaries on images using specific masks; (2) reconstruction of crater boundaries from their arc fragments using the circle Hough transform. After initial crater detection an expert verification and correction of the detected craters can be carried out. The crater detection tool was implemented and tested on several HRSC images with different resolutions and lighting conditions. The
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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automated algorithm finds many small craters not listed in manual catalogs, but fails to detect some heavily degraded craters, in particular those with pronounced non-circular
Table 2 Coordinates, diameters and ratio depth to diameter of Phobos’ named craters, measured from new global base orthomosaic and geometric heights (DTM). No. Crater name
Center latitude (Deg)
Center longitude (Deg)
Diameter, D (km)
d/D
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
60.2 37.9 39.3 59.5 80.9 63.1 80.0 10.2 5.3 44.2 54.6 26.3 26.4 51.5 1.3 8.7 1.4
89.3 179.7 91.5 350.4 212.2 168.0 328.9 53.0 296.9 36.6 184.5 153.0 248.8 319.8 48.2 153.7 131.5
3.9 1.7 5.1 1.9 2.9 6.7 5.6 1.9 2.0 3.3 2.9 1.9 2.4 1.7 8.1 2.1 1.5
0.10 0.06 0.15 0.11 0.11 0.13 0.08 0.14 0.06 0.09 0.12 0.05 0.05 0.09 0.14 0.21 0.04
Clustril D’Arrest Drunlo Flimnap Grildrig Gulliver Hall Limtoc Öpik Reldresal Roche Sharpless Shklovsky Skyresh Stickney Todd Wendell
5
shapes. During expert inspection we established that an accuracy of our crater detection algorithm is about 80% compared to manual detection result. After testing we made complete detection runs using the Small Bodies Maps (V1.0) produced by Stooke (http://sbn.psi.edu/pds/ asteroid/MULTI_SA_MULTI_6_STOOKEMAPS_V1_0/docu%20ment/ m1phobos/phobos_cyl_dlr_control.jpg). This mosaic (4.843 m/ pixel) had been created by combination of Viking mosaics, and highest resolution images with more uniform illumination from Mars Global Surveyor, Mars Express and Mars Reconnaissance Orbiter. This increases our confidence in the automatic crater detections and maximizes crater detection numbers. To fine-tune crater detection we used four additional work steps: (1) the use of low brightness–contrast corrections of the image mosaic; (2) down-sampling of image mosaic, in order to cover all crater sizes; (3) generation of tiles (for convenience the original mosaic is subdivided into overlapping rectangular pieces called “tiles”); and (4) assembling of results from individual tiles and removal of duplicate detections. Visual inspection is used to avoid artifacts entering the catalog. In order to cover those craters that our CDA was not able to detect during batch processing, we manually adjusted parameters of the algorithm for searching them at the end. We automatically detected 6369 craters (Fig. 6). By comparison with the manual detections, we see that some craters from one
Fig. 5. Dependence of depth/diameter ratio (d/D) based on dynamic heights on crater size (for D 42 km), including Stickney.
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data set are missing in the other, and vice versa, as the data sets were produced from different original data by different techniques. By inspection of cumulative size-frequency distributions (CSFDs) we can see in both data sets a fall-off at crater diameters
less than approx. 150 m, due to resolution limits of the images. In our study, we tried to minimize illumination biases by using Stooke Phobos mosaic consisted of images with more uniform
Fig. 6. Stooke Phobos mosaics with craters (white circles) detected by automated CDA: (a) nearside (equidistant cylindrical projection); (b) north polar area, and (c) south polar area (equidistant azimuthal projection).
Fig. 7. Cumulative size-frequency distributions of automated detected Phobos craters and their lognormal models: (a) the nearside and farside; and (b) leading and trailing hemispheres.
Table 3 Crater area statistic based on catalog of manual crater detection. Area_name
Nearside Farside Leading side Trailing side North Polar area South Polar area
Size of craters, D (m)
Number of craters in study area
D o 50
504D 4100
1004D 4250
250 4 D4 500
5004D 41000
D 41000
150 250 124 276 10 0
685 797 742 740 78 26
1009 1138 1191 956 136 134
344 375 382 337 61 57
68 74 66 76 10 10
31 27 29 29 8 6
2287 2661 2534 2414 303 233
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Fig. 8. Map of Phobos surface gravity potential including self-gravitational potential, centrifugal potential and tidal potential (Uchaev et al., 2013), background shadowed relief produced from geometric heights.
Fig. 9. Map of lineaments and dynamic heights, background shadowed relief produced from dynamic heights.
illumination. Also, in this mosaic – which uses projected images – the proportion of mosaic fragments for which viewing angle effects have a significant value was relatively small. To minimize incident angle and viewing angle effects we also used a newly texture-based crater detection algorithm which can separate different lighting craters from their background. We estimate that our catalog of manually detected craters are complete for craters D 4250 m (1072 craters) and our catalog of automated detected craters are complete for craters D 4400 m (360 craters). We established that CSFDs for entire Phobos is close to CSFDs for lunar highlands, as suggested in (Thomas and Veverka, 1980), implying that the Phobos surface is in an equilibrium state.
3.1.2. Area statistics With Phobos being tidally locked in its orbit, the statistics of craters over the surface may reveal the sources of Phobos' meteoroid bombardment. Hence, based on both catalogs – manual
(Table 3) and automated detection, we measured crater distributions for different hemispheres. Statistical tests, e.g., Kolmogorov– Smirnov goodness-of-fit tests, were carried out to establish differences in crater abundance of sample data and model curves (Uchaev et al., 2012d, 2012e). The analysis was limited to craters for which inventory is believed to be complete. First, we measured cumulative size frequency distributions (CSFDs) based on automated crater detection catalog. We compared crater frequencies for Phobos nearside (01W–901W and 2701W–3601W) and farside (901W–2701W), confined to latitude regions below 601. We clearly identify a deficit of nearside craters (Fig. 7a) with a ratio at approximately of 1/1.2. The deficit can be explained by predicted shielding effects by nearby planet Mars (Christou et al., 2014) or differences in surface ages (Schmedemann et al., 2014), which may mimic a difference in meteoroid flux. Note that much of the Phobos nearside is dominated by crater Stickney, where recent erosion events may have erased smaller craters on its steep walls. Also, we cannot rule out completely remaining biases due to viewing angles or illumination effects.
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Fig. 10. Map of boulder locations, determined from a Mars Global Surveyor high resolution image (background global base orthomosaic). The arrow (see also small zoomed image) points at a large boulder known as the “Phobos monument”.
Next, we compared crater statistics for the leading (01W– 1801W) and the trailing hemisphere (1801W–3601W) (Fig. 7b). Because of Phobos' synchronized rotation and revolution, the average encounter velocity of impactors should be higher on the leading hemisphere, implying that for given size the number of craters should be higher (Christou et al., 2014). However, the Kolmogorov–Smirnov test suggests that there is no statistically significant difference between the CSFDs for Phobos leading and trailing sides. This implies that the Phobos crater record may have been created at a time when Phobos was at much further distance from Mars and moved more slowly. Then, we compared the crater statistics for polar areas, i.e. regions beyond 601. The north polar area of Phobos has 8 large craters (D41 km), while the south polar area has only 6 craters with diameter of that size (4 and 3 craters, respectively, in the automated catalog based on the mosaic which has less resolution in the polar regions). A more detailed quantitative analysis (see Table 3) suggests that the crater surplus is 1.3, suggesting that this difference cannot be a chance fluctuation from an even probability of cratering. The crater area statistic using the catalog of manual crater detections confirms result from the automated crater detection. 3.2. Lineaments Using standard ArcGIS tools, we have digitized lineaments on Phobos. The origins of these lineaments is uncertain. While some of them may represent impact crater chains, impact ejecta “curtains” from Mars or rolling boulder tracks, other “grooves” may originate from tectonic faulting (for summaries see Basilevsky et al., this issue; Murray and Heggie, 2014). As for the craters, completeness of the mapping is greatly determined by illumination conditions and resolutions of the available images. To warrant uniform coverage, we used the new global Phobos SRC mosaic (Zubarev et al., 2012; Wählisch et al., 2014) as a basis for the mapping. In total, we identified 546 individual lineaments, which extend from 60 to 600 m width and from 300 to 16,000 m length. A new version of our lineament catalog is in progress.
The distribution patterns and cross-comparisons with the dynamic heights and slopes may help reveal their origins. Dynamic heights of Phobos were obtained for a new Phobos shape model (Zubarev et al., 2012) according to the formula which is the potential difference between the total effective Phobos potential V (Fig. 8) and reference potential V0 ¼ 66.8582 m2/s2 scaled by a mean gravity value g0 ¼0.0053 m/s2 (Thomas, 1993). As it is seen from Fig. 9, dynamic heights correspond to different Phobos surface depressions or lines of depressions. 3.3. Boulders Boulders are only visible on a few highest-resolution images. For our mapping of boulders we specifically uploaded a Mars Global Surveyor image SP255103 obtained in 2003 (1.5 m/pixel). The image was manually co-registered to the new global orthomosaic. Using CraterTools we mapped boulders in an area of about 15 km2 north-east of crater Stickney (Fig. 10). We identified a total of 1379 boulders and defined their coordinates and approximate sizes – from 2.3 m (min) to 28.8 m (max). The total boulder density in the study area is about 100 per km2 (Fig. 11a); the most common boulder size is about 4–8 m (Fig. 11b). While the origins of the boulders are uncertain, comparisons with the dynamic heights and slopes (i.e. whether they are located on gravity highs or within gravitational sinks) may help reveal their sources and emplacement mechanisms, as has been demonstrated previously (Thomas et al., 2000).
4. Geo-portal We have established public access to our Phobos data in the form of an online Geo-portal using modern spatial and web-based technology. We follow examples of systems such as PDS Geosciences Node (http://ode.rsl.wustl.edu/), Planetary GIS Web Server – PIGWAD (http://webgis.wr.usgs.gov/), Google Moon (http://www.google.com/ moon/), and Google Mars (http://www.google.com/mars/), which
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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Fig. 11. Quantitative results on boulder size: (a) cumulative boulder size-frequency distribution; and (b) spatial density of boulders for four size bins.
Fig. 12. Structure and operation of the server and client side of the Geo-portal.
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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Fig. 13. Map interface module to access results of Phobos data analysis.
provide access to planetary data based on screen views of maps with user-friendly interfaces. We have developed our prototype system in several steps: (1) design of database; (2) modeling and creating metadata, (3) loading information to database and providing public access to results. (1) For the design of the database, our foremost goal was to ensure maximum flexibility to enable the handling of various heterogeneous information in terms of content and format. This is achieved by bringing all input information to a single unified description format. There are three groups of input data: metadata, spatial data and binary data. Metadata are accepted as XMLdocuments (eXtensible Markup Language), spatial data (vector and raster) are introduced as Geography Markup Language extensions of XML (ISO 19136), binary data are stored in their native formats. (2) For the modeling, we must provide unified descriptions of all data, and define relationships and topological constraints. For modeling metadata we distinguish between global and local data description. Local metadata are individual feature attributes, whereas global metadata are common parameters of object sets (layers). Global and local metadata are created with the international ISO 19139 or the FGDC standard (http://www.fgdc.gov/ index.html) supported in the ArcGIS environment, which we use as our software platform. To provide the flexibility and interoperability of our information system for further development we will adopt the PDS4 standard (Crichton, 2012), which warrants data consistency across planetary missions, nodes and international partners. (3) The data obtained from various sources are uploaded to the database with specially developed XML-converter (Fig. 12). The architecture of the information system is based on the client-server principle. The client part of the system generates requests and receives responses from the server via WFS (Web Feature Service) protocol based on ISO 19142. The service converts WFS-requests to queries to the MS SQL database, which consists of the spatial and attributes data, and metadata. The File (or Blob) Service is designed for binary (including raster) data and runs together with the WFS-service. A user interface (Fig.13) is currently being developed using Flash technology with further plans to implement Silverlight and HTML5 þJavaScript technology on the client side to allow for larger numbers of simultaneous users. The main feature of designed Geo-portal is the ability of spatial queries and access to
the contents selecting from the list of available data set. Based on four different projections for layers displaying, users can obtain quantitative and qualitative characteristics of the objects in graphical and tabular forms and can download data. User authorization service is provided data security and access control.
5. Conclusions We have developed the spatial information system for the Martian satellite Phobos based on geospatial and web-technologies. Original data from different sources were uploaded into geodatabase, as well as derived data from various geo-analyses. In particular, the system includes global crater catalogs and inventories of Phobos lineaments and boulders. We are currently testing the prototype of our Geo-portal which is available in testing regime (http://cartsrv.mexlab.ru/geoportal/) and allows users to access our Phobos data and results of geoanalyses remotely. For public users we have developed easy access to cartography quality color e-maps published on ESRI ArcGIS server (http://www.arcgis.com). E-maps with support for layers offer data visualizations not included in Geo-portal, such as map diagram or special projection for 3-axial ellipsoid, for example, modified Bugaevsky projection developed for Phobos (Bugaevsky, et al., 1992). The number of maps will be expanded, and their list will be published on the MExLab web-site (http://mexlab.miigaik. ru/eng/projects/phobos_cartography/). Also maps will be printed in the new Atlas of Phobos (in Russian), which will include more than 30 original thematic maps at different scales. The technological solutions are open-source, and allow us to increase the functionality and performance of the service. Our geospatial information system could integrate further types and larger volumes of data. It is not limited to Phobos, but could be useful for geodesy and cartography support of future missions to other planets and satellites.
Acknowledgments Authors would like to acknowledge A. Basilevsky and M. Kreslavsky for their advice and support. This work has been
Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i
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supported by the Russian Federation Ministry of education and science for “Development of a Planetary Data Geoportal to provide access to results of research on planets and satellites of Solar system”, Grant agreement no. 14.B37.21.1303, and by Russian Foundation for Basic Research (RFBR) for “Geodesy, cartography and exploration of the Martian satellites Phobos and Deimos” (Helmholtz–Russia Joint Research Group), Grant agreement no. 11-05-91323. Also research was partially supported by the Russian Federation Ministry of education and science for “Geodesy, cartography and exploration of planets and satellites”, Grant agreement no. 11.G34.31.0021 (Megagrant). We thank the International Space Science Institute in Bern for hosting the “Phobos and Deimos” workshops between 2010 and 2012.
Appendix A See Table A1.
Table A1 List of orthoimages used for production of base mosaic. N
Image name
Mission
Camera
Resolution, m/pixel
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
h0413_0003_sr2 h0715_0004_sr2 h0748_0003_sr2 h1558_0005_sr2 h1574_0005_sr2 h1607_0005_sr2 h2233_0005_sr2 h2780_0004_sr2 h2780_0006_sr2 h2813_0005_sr2 h2846_0006_sr2 h3310_0003_sr2 h3310_0004_sr2 h3310_0005_sr2 h3310_0006_sr2 h3761_0004_sr2 h3769_0004_sr2 h3769_0005_sr2 h3802_0003_sr2 h3802_0004_sr2 h3802_0005_sr2 h3835_0005_sr2 h3835_0006_sr2 h3835_0007_sr2 h3843_0005_sr2 h3843_0006_sr2 h3868_0004_sr2 h3876_0004_sr2 h3909_0005_sr2 h4233_0004_sr2 h4274_0004_sr2 h4274_0005_sr2 h4307_0003_sr2 h4307_0004_sr2 h4307_0005_sr2 h4307_0007_sr2 h4332_0004_sr2 h4340_0005_sr2 h4373_0003_sr2 h4414_0004_sr2 h4773_0004_sr2 h4847_0004_sr2 h4847_0005_sr2 h4880_0005_sr2 h4888_0005_sr2 h4888_0006_sr2 h5343_0004_sr2 h5362_0002_sr2 h5362_0003_sr2
Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars Mars
SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC
17.4 11.2 11.5 33.1 35.1 36.8 19.5 5.6 5.6 7.6 12.2 5.5 5.5 5.5 5.6 18.8 7.2 7.2 8.1 8.1 8.1 11.5 11.5 11.5 6.1 6.1 15.3 11.4 18.1 19.3 10.0 10.0 5.3 5.3 5.3 5.3 17.1 6.4 11.8 13.6 9.4 6.1 6.1 11.9 10.0 10.0 9.9 9.7 9.7
Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express Express
11
Table A1 (continued ) N
Image name
Mission
Camera
Resolution, m/pixel
50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
h5362_0004_sr2 h5381_0003_sr2 h5381_0004_sr2 h5409_0006_sr2 h5428_0005_sr2 h5428_0006_sr2 h5447_0004_sr2 h5447_0005_sr2 h5870_0002_sr2 h5870_0003_sr2 h5870_0004_sr2 h5870_0005_sr2 h5870_0006_sr2 h5889_0004_sr2 h5889_0005_sr2 h6906_0005_sr2 h6906_0006_sr2 h6916_0004_sr2 h6916_0005_sr2 h6916_0006_sr2 h6916_0007_sr2 h6926_0005_sr2 h6926_0006_sr2 h7937_0002_sr2 h7937_0003_sr2 h7937_0004_sr2 h7937_0005_sr2 h7948_0004_sr2 h7948_0005_sr2 h7948_0006_sr2 h7959_0005_sr2 h7959_0006_sr2 h7982_0006_sr2 h8477_0003_sr2 h8535_0006_sr2 h8535_0007_sr2 h8570_0005_sr2 h8570_0006_sr2 f242a19 f246a07 f246a08 f246a62 f248a01 f248a05 f249a03 f252a63
Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Mars Express Viking Orbiter Viking Orbiter Viking Orbiter Viking Orbiter Viking Orbiter Viking Orbiter Viking Orbiter Viking Orbiter
SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC SRC VO A VO A VO B VO B VO A VO A VO A VO A
9.7 11.7 11.7 6.3 7.9 8.0 9.8 9.8 3.3 3.3 3.3 3.3 3.3 6.1 6.1 4.9 4.9 4.8 4.8 4.8 4.8 8.9 8.9 4.4 4.4 4.4 4.4 6.1 6.1 6.1 7.8 7.9 12.3 7.7 7.1 7.1 10.8 10.7 15.4 5.7 5.6 13.7 7.4 6.7 10.5 14.6
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Please cite this article as: Karachevtseva, I.P., et al., The Phobos information system. Planetary and Space Science (2014), http://dx.doi. org/10.1016/j.pss.2013.12.015i