Significant Topographic Changes in the United States
Road construction is one of the primary activities in which humans move large quantities of earth materials. The importance of road construction as a major anthropogenic process is evidenced in the literature in its use as one of only a few factors to calculate the total human geomorphic impact on the environment. Case studies indicate that the cuts and fills required to create flat ground for road and building construction have a geomorphic footprint that is twice the size of the flat area, assuming a 20 percent natural slope. At a 30 percent slope, the area that needs to be graded is three times the size of the desired flat area. Thus, even though a roadway may use only a relatively small area, in hilly terrain the geomorphic imprint can be several times larger.
The figure below shows a major road cut through a prominent ridge in western Maryland, Sideling Hill. The cut was made to accommodate the route for Interstate 68. As measured from the SRTM – NED difference grid, the depth of the cut is 98 meters, and the volume of material removed is 3.52 x 106 cubic meters, which agrees to within 2.4 percent of the published volume of 3.44 x 106 cubic meters.
Although it was not used systematically in this study, an existing distance-to-nearest-road dataset could be helpful to characterize topographic change polygons. The 30-meter resolution raster dataset indicates the straight line distance to the nearest road for every location in the conterminous United States. Initially, it was thought that this dataset could be used to help label topographic change polygons as road cut or fill features by looking at the minimum distance-to-nearest-road value within a polygon. A minimum value of zero would indicate that a road crossed the polygon, and thus the feature may be due to new road construction. After viewing test areas of known road construction changes, it became clear that the roads dataset used as a basis for the distance calculations did not include many of the obvious new roads.
The next figure shows an example of the distance-to-nearest-road dataset for the Chino Hills area in Orange County, California. Even though the national distance-to-nearest-road dataset was not used in this study to automatically label change polygons, it could be useful for interpretation of topographic changes over local areas.