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

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