Practical remote sensing data analysis for efficient geological field mapping: An example from the southwest portion of the Three Peaks 7.5ʹ quadrangle, southwest Utah


 We present the results of remote sensing analysis of U.S. Geological Survey digital elevation models, Landsat spectral data, and National Agriculture Imagery Program orthophotos to generate a preliminary geologic map that significantly aided our boots-on-the-ground geologic mapping of the southwest portion of the Three Peaks 7.5ʹ quadrangle in southwest Utah. Sedimentary rocks, intrusive rocks, and a variety of geologic contacts, including unconformities and faults, as well as unconsolidated alluvium are recognized in the study area. We constructed a series of geologic maps using remote sensing data and analysis techniques that are readily available to geoscientists. These techniques include band-ratioing, random forest analysis, and these analyses. Resolution of the resulting geologic maps generated by random forest analysis and principal component analysis were greatly improved by incorporating both the high resolution orthophoto and the 1/3 arc second digital elevation model into the principal component analysis. Our final remotely sensed geologic map integrated results from each technique. We used this remotely sensed geologic map to develop our preliminary plan for the field campaign. We preselected high priority targets (e.g., previously unrecognized units and faults) for in-person field analysis. We also identified highly accessible areas that allowed for efficient use of in-person field time needed for evaluation of large areas covered by relatively homogeneous units. The authors spent 25 days in the field over a seven-week field season, mapping the same area. Here, we compare the remote-sensed geologic maps with the final in-person field checked geologic map and discuss the utility of remote sensing data for detailed geologic field investigations. Preparing a remote sensing geologic map prior to field work has several advantages, including identification of mappable units, recognition of geologic contacts, and selection of priority target areas for direct evaluation of hypothesized field relationships, thereby promoting more efficient geologic mapping.