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.
Highlights
Landowners and managers use soil map units in local Natural Resources Conservation Service (NRCS) soil surveys to understand the land use limitations of an area
It should be noted that separability decreased when fewer than 10 bands were considered for a given scene
The OOB estimates of error generated by the Random Forest (RF) classification, not directly comparable to the user’s and producer’s accuracy rates generated in Robust Classification Method (RCM), do suggest that RF classification did a better job overall at predicting non-basalt occurrences
Summary
Landowners and managers use soil map units in local Natural Resources Conservation Service (NRCS) soil surveys to understand the land use limitations of an area. Each type of soil map unit in the survey includes a description, in terms of percent of composition, of any feature with the potential to adversely affect land management practices (e.g., rock outcrop). The most accurate way to determine the percentage of rock outcrop in any soil map unit involves field transect data collection. A common member of the soil map unit descriptions in this survey area is exposed basalt bedrock. The amount of rock outcrop present is highly variable, because of the relief and eruption time of the lava flows and the amount of soil that has been deposited on them. The quantity of forage available for domestic animals and wildlife, as well as the placement of routes for energy infrastructure, such as water pipelines and roads, are highly dependent on the amount and location of rock outcrops
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