LITHOLOGIC MAPPING IN THE SILURIAN HILLS, CALIFORNIA, USING ADVANCED SPACEBORNE THERMAL EMISSION AND REFLECTION RADIOMETER (ASTER) DATA Ashley Shields, Carrie Bottenberg 1 Department of Geosciences, Idaho State University, Pocatello, ID, USA, Results The Maximum Likelihood classification (Figure 10) resulted in an accuracy of % and a Kappa coefficient of The average producer accuracy is 89.01% and the average user accuracy is 84.26%. The Spectral Angle Mapper (SAM) classification (Figure 11) resulted in an accuracy of % and a Kappa coefficient of The mean producer accuracy is 69.27% and the mean user accuracy is 63.85%. References Hewson, R. D., Cudahy, T. J., Drake-Brockman, J., Meyers, J., & Hashemi, a. (2006). Mapping geology associated with manganese mineralization using spectral sensing techniques at Woodie Woodie, East Pilbara. Exploration Geophysics, 37(4), 389. Kalinowski, A., & Oliver, S. (2004). ASTER Mineral Index Processing Manual. Remote Sensing Applications Geoscience Australia, (October 2004). Kupfer, D. H. (1960). Thrust faulting and chaos structure, Silurian Hills, San Bernardino County, California. Geological Society of America Bulletin, 71(2), 181–214. Rowan, L. C., & Mars, J. C. (2003). Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Remote Sensing of Environment, 84, 350–366. Acknowledgements Objective 1: Analyze the spectral characteristics of the lithologies of the Silurian Hills Objective 2: Produce a lithologic classification for the Pahrump Group, Riggs Formation, and granitoids of the Silurian Hills Discussion Overall, both classification methods were a success, but each has flaws that might be remedied with modified processing techniques. In future work, misclassifications can be corrected by improving the precision of the ROI’s extents, on which they are based using more accurate geologic maps and GPS ground truthing. The results of this classification may be especially useful for identifying contacts between the Pahrump Group and Riggs Formation. This classification might be a practical tool for targeting potential structural offset in the area. It may also be useful for identifying previously unobserved outcrops of these units and targeting areas of interest for field studies. Methods The images are processed using ASTER band combinations and the PCA output to enhance lithologic diversity (Figures 2-7). Spectral characteristics are assessed qualitatively using false color composite images (Figure 8). Lithologies are grouped by genetic similarities, resulting in six regions of interest (ROI) classes: (1) playa, (2) alluvium, (3) Riggs Formation, (4) Granitoids, (5) the Pahrump Group, and (6) Precambrian basement. The images are processed using SAM classification, with an angle of 0.5 and Maximum Likelihood classification methods to classify each lithology. In order to provide the software with training data for spectral comparisons, the geologic map of Kupfer (1960) was georeferenced and partially digitized (Figure 9). A confusion matrix is used to identify the user accuracy, producer accuracy, and to analyze where misclassification occur. Contact the Author: Figure 1: Natural color image of the Silurian Hills, located approximately 30 km north of Baker, California, east of Death Valley Road. The study area is bounded by a purple rectangle and the major highways are in red Introduction Multispectral remote sensing using Advanced Spacebourne Thermal Emission Reflection and Radiometer (ASTER) imagery is used to discriminate lithologies in arid regions. The ASTER sensor responds to electromagnetic spectra ranging from green to thermal infrared; it has 14 unique spectral bands providing information that the human eye cannot perceive. The imagery is processed using spectral band combinations described by Hewson et al. (2006), Kalinowski and Oliver (2004) and Rowan and Mars (2013) as well as Principal Component Analysis (PCA). Lithologies are classified with Spectral Angle Mapper (SAM) Classification and Maximum Likelihood Classification, assessing and delineating the spectral characteristics from the imagery. The results are used to remotely observe the characteristics of the landscape and to refine existing geological maps. Russell Shapiro, PhD Ashley Shields Figure 2: PCA1 band classifies spectrally similar features and assigns them a range of brightness values Figure 3: ASTER band combination [(band 7+band 9)/ (band 8)]. In this false color image, absorption features associated with limestone are represented in blue Figure 4: ASTER band combination [(band 6 + band 8)/(band 7)]. In this false color image, absorption features associated with dolostone are represented in green Study Area The Silurian Hills of the Mojave Province are primarily composed of the siliciclastic and carbonate lithologies of the Pahrump Group and Riggs Formation, as well as Mesozoic plutons and crystalline basement. The Silurian Hills are located in the Mojave Desert province, approximately 30 kilometers north of Baker, California (Figure 1). Figure 5: ASTER band combination [(band 1)/(band 2)]. In this False Color image, absorption features associated with iron are represented in orange Figure 6: ASTER band combination [(band 5 + band 7)/(band 6)]. In this false color image, absorption features associated with granitoids and gneisses are represented in red Figure 7: ASTER band combination [(band 6 + band 9)/(band 8)]. In this false color image, absorption features associated with amphibolite are represented in purple Figure 11: Spectral Angle Mapper (SAM) classification results Figure 10: Maximum Likelihood Classification results Figure 8: False color composite image where the red band assigned to [(band 5 + band 7)/(band 6)], the green band assigned to PCA1, and the blue band assigned to [(band 7+band 9)/(band 8)] Figure 9: Partially digitized 1:9600 scale geologic map from Kupfer (1960) and digitized playa Advanced Spaceborne Thermal Emission Reflection and Radiometer (ASTER) Multispectral satellite imagery was processed to delineate and enhance the spectral characteristics of lithologies processing techniques can effectively discriminate between the lithologies of arid regionsof the Silurian Hills region of California.. [C1] Advanced Spaceborne Thermal Emission Reflection and Radiometer (ASTER) imagery, The ASTER data set which contains bands from the visible, near infrared, shortwave infrared and thermal bands, is processed to delineate and enhance the spectral characteristics of lithologies comprising the Silurian Hills, California. that are suitable for lithologic discrimination. The These images areASTER data was processed, using band combination techniques and Principal Component Analysis (PCA). A lithologic classification is produced from the resultant images, using the Spectral Angle Mapper (SAM) classification, and Maximum Likelihood classification tools in ENVI 5.3. Each classification is compared to a geologic map and assessed for accuracy using a confusion matrix. The SAM classification resulted in an overall accuracy of % and the Maximum Likelihood classification resulted in an overall accuracy of %. This supports the effectiveness of this method for remote classification of the Pahrump Group and Riggs Formation of the Mojave province [C2]. [C1] [C2] [C1] The first two sentence should contain information about your study in particular to grab the readers attention[C1] [C2] The Pahrump and Riggs foramtions in the Mojave should have been mentioned earlier in the abstract[C2] Should also discuss the scale of the geologic map you were using for ground truthing Abstract Advanced Spaceborne Thermal Emission Reflection and Radiometer (ASTER) satellite imagery was processed to delineate and enhance the spectral characteristics of lithologies of the Silurian Hills region of California. The ASTER data set contains information from the visible, near infrared, shortwave infrared and thermal bands that are suitable for lithologic discrimination. The ASTER data was processed, using band combination techniques and Principal Component Analysis (PCA). A lithologic classification is produced from the resultant images, using the Spectral Angle Mapper (SAM) and Maximum Likelihood classification. Each classification is compared to a geologic map and assessed for accuracy using a confusion matrix. The SAM classification resulted in an overall accuracy of % and the Maximum Likelihood classification resulted in an overall accuracy of %. This supports the effectiveness of this method for remote classification of the Pahrump Group and Riggs Formation of the Mojave province.