Northeastern Section–41st Annual Meeting (20–22 March 2006)

Paper No. 7
Presentation Time: 1:00 PM-4:00 PM


SPAULDING, Aaron1, OFSEVIT, Ari2, BYARS, Heather E.3, BAMPTON, Matthew4, MOSHER, Rosemary4 and SWANSON, Mark T.5, (1)Department of Geosciences, Trinity College, San Antonio, TX 78212, (2)Department of Geography, Macalester College, Saint Paul, MN 55105, (3)Earth and Planetary sciences, University of Tennessee, Circle Drive, Knoxville, TN 37917, (4)Geography-Anthropology Department, University of Southern Maine, Gorham, ME 04038, (5)Department of Geoscience, University of Southern Maine, Gorham, ME 04038,

Computers and GIS are becoming commonplace in the geosciences; on the outcrop and in the field. Their power and versatility in dealing with large amounts of spatial information require new data management techniques. In the past, data collected in the field have been organized and maintained in field notebooks, journals and accompanying field maps. More recently, flat data structures have been used to collect and organize data, but these techniques, while still useful, can not organize nor maintain the large amounts of spatial data that can now be collected with the new digital mapping techniques. In many cases problems such as these can be solved by using and maintaining a geodatabase structure. Geodatabases are a rather new concept in GIS, and although they have been used in fields such as geography for a few years, they are still relatively rare in geology. Our geodatabase model is designed to organize several gigabytes of data with various formats and precisions. Using RTK GPS (precision: 2 cm) and electronic total stations (precision: <1 cm) we collected thousands of survey points tracing granitic intrusions, foliation lines, and fault lines, along with elevations, and the locations of structural measurements and assorted field photos. We also generated hundreds of low elevation aerial images with a 6m digital camera pole to create photomosaics of the outcrop surface georeferenced to RTK GPS control points. The database structure we designed can manage these data, import data from several previous years and could also incorporate data from future projects. We found that a feature dataset with multiple feature classes seems to be the most robust and efficient data model and is best suited for introducing topology rules (governing the spatial relationships between feature classes – for instance, granites must not overlap with traces of metamorphic foliations). By using coded fields in our attribute tables, we have made our data not only more descriptive, but also easier to manage and edit through topology constraints and access with data queries. The geodatabase model with the use of raster catalogues for digital photographic resources is proving to be a valuable resource for field geologists managing large digital data sets and supporting imagery.