The Planar Surface Statistics Point Cloud Task attributes a user drawn polygon with planar extraction statistics. It defines the best fit plane and computes the quality of fit values, which are stored as attributes on the shape file. It also serves as a diagnostic tool to facilitate the setting of planar filter settings.
Statistics given in the output include:
 Number of the Points (PntCnt)  The total number of LAS points in the input geometry that are used to compute the planar statistics.
 Classes of the Points (ClCnt_#)  The number of points for each classification code(#) that are present in the input geometry.
 Density (PntDen)  Density of the points in a given area, obtained by dividing the number of points by the area of the input polygon.

Normal Vector  The plane's normal vector. The input points will be used to fit an Eigen fit. The plane normal vector is the Eigen vector corresponding to minimum Eigen value. The vector is output as two parameters:
 Azimuth  North being zero and positive angles clockwise – e.g. compass heading. Thus, Azimuth ranges from zero to 359.9999 degrees.
 Slope  Degree of slope of the fitted plane above the horizontal (nadir being 90 degrees). Thus, elevation ranges from zero to 90 degrees.
 Standard Deviation (StdDev)  The standard deviation of the points relative to the defined plane along the normal. This tells us how the point values are "spread out" on the plane. Lesser standard deviation means the points are more tightly clustered about the plane.
 Outside 1 Sigma (Out1Sig)  Number of points that fall outside of the first standard deviation.
 Lie within 1 Sigma (In1Sig)  Number of points that fall within the first standard deviation.
 SqrtEigen1  The square root of the principle Eigen value
 Eigenvalues (Eigen1, Eigen2 and Eigen3)  The three Eigenvalues computed by Principle Component Analysis (PCA).
 Normal  The X, Y, and Z components describing the direction of the normal vector.
 Normal Distance (NormalDis)  Distance of the plane from the origin.
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