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Developing an Integrated Geographic Information System (GIS) for Analyzing Multi-Scalar Data to Understand Submarine Volcanic, Hydrothermal, and Tectonic.

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Presentation on theme: "Developing an Integrated Geographic Information System (GIS) for Analyzing Multi-Scalar Data to Understand Submarine Volcanic, Hydrothermal, and Tectonic."— Presentation transcript:

1 Developing an Integrated Geographic Information System (GIS) for Analyzing Multi-Scalar Data to Understand Submarine Volcanic, Hydrothermal, and Tectonic Processes along the East Pacific Rise Sean R. Keefe, Summer Student Fellow Advisor: Dr. Dan Fornari, Senior Scientist Geology & Geophysics Department Woods Hole Oceanographic Institution May 24 -August 8, 2003

2 Study Area  World Map Showing Northern EPR Inset: Bathymetry of the EPR. (Star locates area of interest.)

3 Specific Study Area

4 Objectives The goal of this summer fellowship research project was to help WHOI geologists visualize, analyze, and interpret the seafloor geology along the EPR by creating a GIS with integrated bathymetric data, navigation data, rock sample data, and digital imagery from recent ship and instrument surveys.

5 Survey of recent scientific interpretations of the geologic processes occurring at the EPR. Methods

6 The main steps for creating the new EPR GIS were: 1) Process the raw bathymetric and sidescan sonar grids 2) Process camera tow tables and rock sample tables 3) Process and integrate the digital images with new, interpolated camera tow tables 4) Create groups of related images which can be displayed like dissolve video from within the GIS Methods

7 To integrate the grids in one coordinate system in ArcGIS, the GMT grids were preprocessed into a common raster format and coordinate system usable in ArcGIS. GMT was used to change the spacing between grid points in decimal degrees, using the following UNIX command: grdedit –A filename.grd The grid file was then converted to an ASCII table, creating a one-column table with rows going from left to right (Figure 4): grd2xyz filename.grd –ZTLA > filename.asc I. Processing Bathymetric & Sidescan Sonar Grids Figure 4. One-column tableFigure 5. Header file

8 A header file was created for each grid utilizing an appropriate editor (in this case, vi ) according to its requisite number of columns and rows, geographic extents, and cell size (Figure 5): vi filename.head The unique header file was then concatenated to the 2-D ASCII file, and a new ASCII file was created (Figure 6): grd2xyz filename.head filename.asc > newfilename.asc Figure 6. Concatenated ASCII file

9 To ensure that all the grids came into ArcGIS in the same projection and datum, it was critical to understand the native format of each grid. ESRI’s ArcToolbox was used to import the raster to a floating point type of grid: Import to Raster>>>ASCII to grid [floating] Next, the grid was projected, using a geographic projection in decimal degrees, WGS84 datum. Projections>>>Define projection wizard (coverages, grids, tins)

10 Finally, the processed grids were added as layered features in the GIS. The multibeam bathymetry and sidescan sonar were left in grayscale, and a color ramp based on relative depth was applied to the ABE Microbathymetry grids. Warm colors (red being the warmest) indicate shallower depths, and cool colors (blue being the coolest) indicate deeper depths. 2.5 and 5-meter contours were also created using the 3D Analyst extension to ArcGIS. Figure 7. Creating contours from ABE microbathymetry

11 To help differentiate the contours, the 5-meter contour layer was symbolized in black, and the 2.5-meter contour layer was symbolized in green. Figure 8. Selecting colors for each contour layer

12 II. Processing Camera Tow Tables The camera tow tables come in a stream of tab-delimited data (Figure 9), so they were formatted into rows and columns (Figure 10) using an awk procedure in a Cygwin shell to define an array: awk –F, ‘{OFS=”,”;print $1, $2, $3, $4, $5”ZZ”, $6}’ interpolated.txt > gr1.tem Figure 9. Raw camera tow table: a stream of tab-delimited data Figure 10. Comma-delimited camtow table

13 An Excel formula was used to convert the time from hours-minutes-seconds to decimal time (Figure 11). This step is critical because decimal format is required by Matlab for the interpolation process. Figure 11. Tab-delimited camtow table with decimal time

14 III. Creating and processing JPEG tables In order to integrate the digital photographs with the geographic data, the camera tow track had to be represented in the GIS as point data. The challenge was that the camera tow table recorded the relative lat/long of the instrument at different times and intervals from the JPEGs. Moreover, there was no latitude or longitude recorded with the JPEG files, so it was necessary to interpolate lat/long positions for the JPEGS from the camera tow lat/long positions. Figure 12. Camera tow table with lat/longs Figure 13. List of JPEGs – no lat/longs

15 To combine these two data sets into a useable format, a file was created from the list of JPEGs, using a UNIX command in a Cygwin shell (Figure 14). Figure 14. Cygwin window – converting a list to a text file Once the raw file of the jpeg list was created (Figure 15), Excel and Wordpad were used to separate the columns of text with tabs (Figure 16). Figure 15. Raw jpeg file Figure 16. Tabulated jpeg file

16 The tab-delimited text file was then separated into fields of data (Figure 17), and a formula was applied in Excel to convert the time code from hour-minute-second format (hh:mm:ss) to decimal format (Figure 18). Figure 17. Tab-delimited jpeg fileFigure 18. Jpeg file with decimal timecode

17 Once the jpeg file was correctly formatted, a script was created in the Matlab program (Figure 19) that interpolated the lat/longs into a new table using the position from the camera tow table and the time from the jpeg table (Figure 20). This new table was then used to hyperlink the digital images to their respective, interpolated lat/long positions. Figure 19. Matlab lat/long interpolate script Figure 20. New, interpolated file

18 IV. Hyperlinking JPEGS An image viewing functionality was built into the GIS in ArcGIS. A data field called “Path” was added to the interpolated camtow table, and the name of the jpeg taken at that interpolated lat/long position was added to that field for each record. Figure 21. Interpolated file with jpeg hyperlinks

19 The new table was added into the GIS and converted to a shapefile. With this table, users can now select points along a camera tow transect that are hyperlinked to the “Path” field in the table and immediately “pop up” and view the image utilizing MS Imaging. Figure 22. Hyperlinked JPEG viewed with MS Imaging

20 V. Creating and Hyperlinking GIFS A dissolve video type of functionality was built into the GIS using animated GIFs. GIFs are sets of static digital images that play back in a sequence, with one image “dissolving” into the next. Groups of 20 JPEGs were placed in separate folders in time-index order (Figures 24 & 25). Then, Adobe ImageReady software was used to import the folders, one at a time. Each folder comes in as a frame of 20 images in a temporary animation. Figure 24. GIF foldersFigure 25. Grouped GIFs in time-index order

21 Reduced each image from 2048 x 1536 pixels to 640 x 480 pixels, set time delay for each image or “frame” to 3 secs (Figure 26). This process was repeated for each of the folders. Figure 26. Setting the time delay for animated GIFs

22 Using a copy of the interpolated JPEG table, hyperlinks to the animated GIFS were made. The “Path” data field, which had previously stored JPEG names, were replaced with GIF names every 20 positions. The 19 intervening records were deleted, thereby reducing the number of data points and allowing the user to access the GIFs more easily while “zoomed out” in the GIS. Figure 27. Deleting the JPEG/lat/long points between animated GIFs

23 Once the GIF table was edited, it was added to the GIS as data in ArcGIS. The point data was symbolized with an arrow and leader, illustrating the direction of the camera tow: from east to west. When the hyperlink tool is activated, the arrow symbol will have a blue dot at its center, indicating that a hyperlink is available for that data point. The GIS user can now select points along the camera tow transect using the hyperlink tool. This immediately opens Quick Time Player – a multimedia playback software – and plays the 20 grouped or images for 3 seconds each in their proper sequence along the camera tow track (Figure 28). Figure 28. GIF table and hyperlinked animation

24 VI. Processing Rock Sample Tables Since the rock sample tables have lat/long positions, adding rock sample data to the GIS was a relatively simple process. After standardizing the unique IDs, the fields of interest for this particular analysis (Sample ID, Lat, Long, Depth, Wire Out, K2O/TiO2*100, CaO/Al2O3, MgO#) were extracted in Excel and saved in a tab-delimited text file. The field names had to be edited, because ArcGIS will only accept field names with alpha-numeric characters or the underscore character. So, for example, the field called “K2O/TiO2*100” was renamed as “K2O_div_TiO2_times_100” (Figure 29). Figure 29. Tab-delimited text file of rock core samples

25 Once the field names had been revised, the new rock core sample table was added as data in ArcGIS. The rock sample data can now be accessed with the identify tool by pointing to one symbol and opening that record, or the entire rock sample attribute table can be opened via the table of contents and queried. Figure 32. Opening one rock sample record Figure 33. Opening the entire rock sample attribute table

26 Once all of the data had been integrated into the GIS, the images were then analzed and classified, one by one, into six general categories: “lobate,” “pillow/lobate,” “sheet flow,” “sheet/lobate,” “ropy sheet,” and “collapse features” (Figures 34-39). These classifications were made to another version of the interpolated camtow table and added to ArcGIS as a shapefile and symbolized with color. Figure 34. Example of lobate morphology Figure 35. Example of pillow/lobate morphology Figure 36. Example of a sheet flow Figure 37. Example of a sheet/lobate transition Figure 38. Example of a ropy sheet flow Figure 39. Example of a collapse feature

27 Results

28 I. GIS Integration All of the multi-scalar grids were successfully imported and integrated into ArcGIS as data layers with the navigation data, digital images, and rock sample data, and contours were made from the microbathymetry at 2.5 and 5-meter intervals to help correlate geologic features with photographic images. A description of each layer follows:

29 1) A layer that delineates the extent of the study area Figure 40. Rectangle keyed to lat/long points delineates extents of study area

30 2) A layer that shows the actual lat/long points from the raw camera tow table. Figure 41. Actual lat/long points from original camera tow table

31 3) A layer that links the interpolated lat/long points to individual JPEGs Figure 44. Interpolated camera tow positions with map tips showing date and time Figure 45. Interpolated camera tow positions with hyperlinks to individual jpegs

32 4) A layer that links interpolated lat/long points to streamed GIF images Figure 46. Interpolated camera tow positions with hyperlinks to streamed GIFs

33 5) Multibeam bathymetry at 10m resolution that covers the largest area in three layers that overlap and can be used individually or in combination Figure 47. Multibeam Bathymetry (10 m resolution)

34 6) Mosaicked tracks of DSL Sidescan Sonar at 2m resolution that overlap somewhat and can be used individually or in combination Figure 48. DSL-120A Sidescan Sonar Imagery (2 m resolution)

35 Figure 49. Setting the layer’s transparency to 50% Figure 50. The ABE microbathymetry shows through the transparent sidescan sonar

36 7) ABE Microbathymetry in 1m resolution with a color ramp applied to the layer Figure 51. ABE microbathymetry (1 m resolution) with color ramp applied

37 8) Rock core sample data for camera tows #9 and #10 in one layer, broken into 5 classes according to relative quantity of MgO and given a unique color for a defined range of values Figure 52. Rock Samples Classified by MgO Values

38 II. GIS Analysis An area of approximately 2+ km in size to the east of the EPR’s axial summit trough was studied along Camera Tow #9 within the GIS. Bathymetric contours and sidescan sonar were correlated with the geophysical features using the classification scheme outlined in the methods section, above. The results from this classification scheme indicate that there are 8 main groups of morphologies as the camera tow goes from east to west: 1. Generally pillow/lobate flows from 8:56:26 to 9:18:56 (Figures 53-55) Figure 53. Pillow-lobate flows start at 8:56:26 Figure 54. Pillow-lobate flows end at 9:18:56

39 Figure 55. Pillow-lobate flows along photo-transect 8:56:26 - 9:18:56

40 2. Sheet-lobate transitions from 9:19:56 to 9:20:11 (Figures 56-58) Figure 56. Sheet-lobate transitions start at 9:19:56 Figure 57. Sheet-lobate transitions end at 9:20:11

41 Figure 58. Sheet-lobate transitions from 9:19:56 to 9:20:11

42 3. Sheet flows from 9:20:26 to 9:20:56 (Figures 59-61) Figure 59. Sheet flows start at 9:20:26 Figure 60. Sheet flows end at 9:20:56

43 Figure 61. Sheet flows from 9:20:26 to 9:20:56

44 4. Ropy sheet flows from 9:21:11 to 9:21:26 (Figures 62-64) Figure 62. Ropy sheet flows start at 9:21:11 Figure 63. Ropy sheet flows end at 9:21:26

45 Figure 64. Ropy sheet flows from 9:21:11 to 9:21:26

46 5. Lobates from 9:21:41 to 9:42:26, with interspersed collapse features and interspersed pillow-lobates (Figures 65-69) Figure 65. Lobates begin at 9:21:41Figure 66. Lobates end at 9:42:26

47 Figure 67. Lobates from 9:21:41 to 9:42:26

48 Figure 68. Interspersed collapse features at 9:23:56, 9:31:56, and 9:35:41 Figure 69. Interspersed pillow-lobates at 9:27:26 and 9:27:41

49 6. Collapse features from 9:42:41 to 9:57:41 with some interspersed lobates (Figures 70-72). Figure 70. Collapse features begin at 9:42:41Figure 71. Collapse features end at 9:57:41

50 Figure 72. Collapse features from 9:42:41 to 10:00:11

51 7. Ropy sheet flows from 9:57:56 to 10:03:41 with some interspersed collapse features (Figures 73-75) Figure 73. Ropy sheet flows begin at 9:57:56Figure 74. Ropy sheet flows end at 10:03:41

52 Figure 75. Ropy sheet flows from 9:57:56 to 10:03:41

53 8. Lobates from 10:03:56 to 11:04:26, with an interspersing ropy sheet flow with collapse margins, from 10:56:11 to 10:57:56 (Figures 76-77) Figure 76. Ropy sheet flow with collapse margins inside long lobate flows

54 Figure 76. Lobates from 10:03:56 to 11:04:26, with an interspersing ropy sheet flow

55 Conclusions Scientists from many marine science disciplines have studied the East Pacific Rise and have collected an extraordinary amount of useful data. But, as Dawn Wright points out, “much of it exists either in an inert, noninteractive form (e.g., journal publications) or as unlinked and incompatible data sets, algorithms, and models…mid-ocean ridge scientists have taken the first step in data archiving, but …scientists will need a wide range of sophisticated programming support to coordinate not only the access and use of the data, but associated computational tools and numerical models across distributed networks of computers” [Wright 2003, 4]. Indeed, even the most valuable and expensive data cannot be fully comprehended unless it is integrated, both across disciplines and within scientific visualization and analysis tools like a GIS. The results of this research project illustrate that comparing rock samples, imagery, sonar, and bathymetry for geological analysis is efficient and intuitive with a GIS. Integrating the multi-scalar bathymetry, sidescan sonar, digital photographs, navigation data, and rock core samples with ArcGIS and multimedia proved to be a relatively simple, if detailed, process. Although there is in initial significant investment in time in the early stages of building such an integrated system, after the initial GIS is set up, the results justify the investment. Moreover, a marine GIS with integrated bathymetric, sonar, digital images, and geochemical data will most likely lead to greater scientific insight than if the data were left in their disparate parts. Although the data from only one photo-transect was analyzed, the procedures used to build the integrated data within a GIS have been well documented and can be used as a foundation for a larger, more comprehensive survey of multidisciplinary EPR datasets.

56 Next Steps There are several more things that could be done with this project which would be the next logical steps in expanding upon the existing GIS. One major task is to add the remaining camera tow transects to the GIS and integrate the relevant JPEGs and GIFs with interpolated camtow tracks, using the same procedures detailed above. Another task is to explore different ways to analyze the integrated data. While the ArcGIS system proved useful in integrating multi-scalar data in 2-D for scientific analysis, there are limitations with 2D environments. Importing the data into a true 3D spatial environment is one way to analyze the GIS more intuitively.

57 Data Sources The data for this research project were collected in November 2001 by research vessels and underwater vehicles, such as the R/V Atlantis and its Multibeam Sonar, the DSL-120 Sonar, and DSV Alvin (Figure 34). Digital images were acquired from WHOI’s new Towed Digital Camera System (TowCam) [Fornari 2003, 1] in November of The data were provided by Dr. Dan Fornari and Dr. Maurice Tivey of Woods Hole Oceanographic Institution’s Deep Submergence Lab.

58 Acknowledgements In addition to the excellent mentoring and support from Dr. Daniel J. Fornari, I would like to acknowledge the following key individuals for their helpful input on this fellowship research project:  Mr. Roger Goldsmith, Information Systems Specialist, CIS Department  Dr. Maurice Tivey, Associate Scientist, Geology & Geophysics Department  Dr. Hans Shouten, Senior Scientist, Geology & Geophysics Department I would also like to thank Dr. Michael R. Perfit, a geologist from the University of Florida, and Ms. Ann Devenish, an information systems specialist at WHOI, both of whom also contributed to this research project in its final stages.


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