Title | ||
---|---|---|
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. |
Year | DOI | Venue |
---|---|---|
2022 | 10.3390/rs14071537 | REMOTE SENSING |
Keywords | DocType | Volume |
digital elevation models (DEM), DEM of difference (DoD), terrestrial laser scanning (TLS), point cloud, hillslope erosion | Journal | 14 |
Issue | ISSN | Citations |
7 | 2072-4292 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Gene Bailey | 1 | 0 | 0.34 |
Yingkui Li | 2 | 0 | 1.69 |
Nathan McKinney | 3 | 0 | 0.34 |
Daniel Yoder | 4 | 0 | 0.68 |
Wesley Wright | 5 | 0 | 0.68 |
Robert A. Washington-Allen | 6 | 13 | 2.62 |