Methods
Methods
Data Processing Methods and Procedures
Given that the primary input datasets, NED, SRTM, and NLCD, each have a spatial resolution of 30 meters and the fact that the study area covers the conterminous United States, a considerable amount of data processing was required to detect and quantify areas of significant topographic change.
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Tabulation of Statistics Characterizing the Extent of Topographic Change
The final delineation of topographic change polygons included 5,263 distinct features, representing both cuts (decreased elevations) and fills (increased elevations). In addition, 364 polygons outline areas of reservoir construction or expansion, or other similar hydrologic land uses. Each of these polygons has numerous attributes that describe the specific surface modification, such as area and...
Filtering of Elevation Difference Mask to Identify True Topographic Changes
The output of the differencing and thresholding procedures described above was a set of pixels with elevation differences large enough to be judged significant. Ideally, these differences would represent the set of true topographic changes that could then be used for further analysis. In reality, this was not the case, as the selected differences included areas that clearly did not reflect...
Significant Change Thresholds
The next step in the data processing procedure was to threshold the difference grids to isolate areas of significant change. This procedure is commonly done in the image differencing method of change detection, and the threshold is often based on the standard deviation value of the differences. As implemented for this study, the thresholding approach incorporated the inherent absolute vertical...
Accuracy Assessment of Elevation Data
The thresholding approach (described below) used to distinguish significant differences from the raw difference grids requires a measure of the absolute vertical accuracy of each input elevation dataset. The NED and SRTM data were compared to an independent reference geodetic control point dataset from NGS. These points have centimeter-level accuracy in their horizontal and vertical coordinates...
SRTM NED Vertical Differencing
Image differencing has long been used as an effective change detection technique for coregistered digital remote sensing datasets. One image is simply subtracted from another image on a pixel-by-pixel basis. As applied to gridded DEMs, the result of image differencing is a differential surface, which is a measure of the spatial distribution of mass displacement. In a differential surface, the...