Remote Sens. 2013, 5(10), 4857-4876; doi:10.3390/rs5104857

Single and Multi-Date Landsat Classifications of Basalt to Support Soil Survey Efforts

1 Department of Geosciences, Boise Center Aerospace Laboratory, Boise State University, 1910 University Drive, Boise, ID 83725, USA 2 City of Ammon, Ammon, ID 83401, USA
* Author to whom correspondence should be addressed.
Received: 4 August 2013; in revised form: 7 September 2013 / Accepted: 23 September 2013 / Published: 8 October 2013
(This article belongs to the Special Issue Geological Remote Sensing)
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Abstract: Basalt outcrops are significant features in the Western United States and consistently present challenges to Natural Resources Conservation Service (NRCS) soil mapping efforts. Current soil survey methods to estimate basalt outcrops involve field transects and are impractical for mapping regionally extensive areas. The purpose of this research was to investigate remote sensing methods to effectively determine the presence of basalt rock outcrops. Five Landsat 5 TM scenes (path 39, row 29) over the year 2007 growing season were processed and analyzed to detect and quantify basalt outcrops across the Clark Area Soil Survey, ID, USA (4,570 km2). The Robust Classification Method (RCM) using the Spectral Angle Mapper (SAM) method and Random Forest (RF) classifications was applied to individual scenes and to a multitemporal stack of the five images. The highest performing RCM basalt classification was obtained using the 18 July scene, which yielded an overall accuracy of 60.45%. The RF classifications applied to the same datasets yielded slightly better overall classification rates when using the multitemporal stack (72.35%) than when using the 18 July scene (71.13%) and the same rate of successfully predicting basalt (61.76%) using out-of-bag sampling. For optimal RCM and RF classifications, uncertainty tended to be lowest in irrigated areas; however, the RCM uncertainty map included more extensive areas of low uncertainty that also encompassed forested hillslopes and riparian areas. RCM uncertainty was sensitive to the influence of bright soil reflectance, while RF uncertainty was sensitive to the influence of shadows. Quantification of basalt requires continued investigation to reduce the influence of vegetation, lichen and loess on basalt detection. With further development, remote sensing tools have the potential to support soil survey mapping of lava fields covering expansive areas in the Western United States and other regions of the world with similar soilscapes.
Keywords: Landsat; multispectral; multitemporal; basalt; geology; lichen; Robust Classification Method; Random Forests; soil survey

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MDPI and ACS Style

Mitchell, J.J.; Shrestha, R.; Moore-Ellison, C.A.; Glenn, N.F. Single and Multi-Date Landsat Classifications of Basalt to Support Soil Survey Efforts. Remote Sens. 2013, 5, 4857-4876.

AMA Style

Mitchell JJ, Shrestha R, Moore-Ellison CA, Glenn NF. Single and Multi-Date Landsat Classifications of Basalt to Support Soil Survey Efforts. Remote Sensing. 2013; 5(10):4857-4876.

Chicago/Turabian Style

Mitchell, Jessica J.; Shrestha, Rupesh; Moore-Ellison, Carol A.; Glenn, Nancy F. 2013. "Single and Multi-Date Landsat Classifications of Basalt to Support Soil Survey Efforts." Remote Sens. 5, no. 10: 4857-4876.

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