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

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 7; p. 1537
• Main Authors: Bailey, Gene, Li, Yingkui, McKinney, Nathan, Yoder, Daniel, Wright, Wesley, Washington-Allen, Robert
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2022
• Subjects: Algorithms; Change detection; Confidence intervals; DEM of difference (DoD); Digital Elevation Models; digital elevation models (DEM); hillslope erosion; Lasers; Methods; point cloud; Remote sensing; Rill erosion; Sheet erosion; Standard deviation; terrestrial laser scanning (TLS); Uncertainty; Unmanned aerial vehicles
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14071537

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.