ToPCAT - Point Cloud Decimation

 
Topographic Point Cloud Analysis Toolkit (ToPCAT) is a software package developed by Brasington et al. (2012) used to decimate (or thin) a point cloud and calculate meaningful statistics for grid defined areas. The process of decimation can reduce computation time and produce meaningful summary outputs (e.g. zMin for modeling bare earth elevations; and detrended standard deviation for modelling surface roughness). 

ET-AL Recommendations to IPC

Concepts

Point Cloud Decimation 

Point cloud decimation (A.K.A. thinning or binning) is common practice when using MBES data to reduce the computation times and memory requirements of their large point clouds (Kearns and Breman 2010). Point decimation is the process of setting a defined grid size and using the point values that are contained within those grids to calculate summary statistics from those points. 
 

Illustration of ToPCAT Workflow


Locally Detrended Standard Deviation

One of the most useful statistics from a point cloud can be those that describe the variance of elevations within a cell. Brasington et al. (2012) developed an algorithm within ToPCAT that reports the variance from a locally detrended surface. This is ensures that only local variations in height are being modeled as is a reasonable proxy for roughness. 

Figure 4 from Brasington et al. (2012) illustrates this workflow (at left). On a cell by cell basis this operation is computationally expensive and is not a standard tool in popular GIS. ToPCAT uses the point values contained within the defined grid size to calculate the minimum, maximum, mean, detrended mean, range, standard deviation, detrended standard deviation, skew, detrended skew, kurtosis, detrended kurtosis, and point density. A minimum number of points is necessary, generally 4, to calculate the detrended standard deviation; the necessity to have more than one point within the cell resolution of analysis is in support of many of the concepts presented in Cell Resolution. Having these statistics in a regularly spaced grid is invaluable for developing a detrended standard deviation raster to represent surface roughness.

In the MBES Toolkit, you can run ToPCAT to export just the locally detrended standard deviation as a proxy roughness surface using the Simple ToPCAT Roughness Tool.

ToPCAT Outputs

ToPCAT, the point cloud thinning toolset recommended by ET-AL, uses the point values contained within the defined grid size to calculate the minimum, maximum, mean, detrended mean, range, standard deviation, detrended standard deviation, skew, detrended skew, kurtosis, detrended kurtosis, and point density. A minimum number of points is necessary, generally 4, to calculate the detrended standard deviation; the necessity to have more than one point within the cell resolution of analysis is in support of many of the concepts presented Determining Grid Resolution. Having these statistics in a regularly spaced grid is invaluable for developing a detrended standard deviation raster to represent surface roughness.

The command-line version of ToPCAT is straight forward to use, but requires a fair degree of pre and post processing. The MBES Toolkit, now makes these pre and post processing steps seamless and allows you to run ToPCAT from an easy to use user interface. The two main commands are ToPCAT Prep and ToPCAT Point Cloud Decimation. 

Visualizing Decimated Point Clouds

MBES Tools - Visualizing Decimation

Resources

Relevant Literature

  • Brasington, J., D. Vericat, and I. Rychkov. 2012. Modeling river bed morphology, roughness, and surface sedimentology using high resolution terrestrial laser scanning. Water Resources Research 48. DOI: 10.1029/2012wr012223.
  • Rychkov, I., J. Brasington, and D. Vericat. 2010. Processing and Modelling on Terrestrial Point Clouds. Institute of Geography and Earth Sciences, University of Wales, Aberystwyth, Wales.
  • Rychkov, I., J. Brasington, and D. Vericat. 2012. Computational and methodological aspects of terrestrial surface analysis based on point clouds. Computers & Geosciences 42:64-70. DOI: 10.1016/j.cageo.2012.02.011.

Other Resources


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