Las2DoD: Change Detection Based on Digital Elevation Models Derived from Dense Point Clouds with Spatially Varied Uncertainty

Abstract

The advances of remote sensing techniques allow for the generation of dense point clouds to detect detailed surface changes up to centimeter/millimeter levels. However, there is still a need for an easy method to derive such surface changes based on digital elevation models generated from dense point clouds while taking into consideration spatial varied uncertainty. We present a straightforward method, Las2DoD, to quantify surface change directly from point clouds with spatially varied uncertainty. This method uses a cell-based Welch’s t-test to determine whether each cell of a surface experienced a significant elevation change based on the points measured within the cell. Las2DoD is coded in Python with a simple graphic user interface. It was applied in a case study to quantify hillslope erosion on two plots: one dominated by rill erosion, and the other by sheet erosion, in southeastern United States. The results from the rilled plot indicate that Las2DoD can estimate 90% of the total measured sediment, in comparison to 58% and 70% from two other commonly used methods. The Las2DOD-derived result is less accurate (65%) but still outperforms the other two methods (30% and 48%) for the plot dominated by sheet erosion. Las2DoD captures more low-magnitude changes and is particularly useful where surface changes are small but contribute significantly to the total surface change when summed.