Running Statistics on LiDAR data

[ThomasQuigley]

Hi,

Have converted large .las file into multipoint data. My intention is to smooth out the data by a method such as Kriging or IDW as i mean to classify by intensity with a different programme and wish to remove clutter for a better, and clearer presentation. Have ran the IDW but all i get is a raster that is of no use as i need to keep point data for reinsertion back into microstation.

my questions

1. Can I run statistical analysis on .las Lidar data without converting to raster.
2. Once .las file is converted is it possible to view the attribute table with additional fields such as intensity and swath angle?
3. Can anyone suggest a method that eradicates the need to use microstation at all?

Cheers

TQ

[JeffreyEvans]

As a multipart point feature class you do not have access to the attributes that you are interested in. Additionally, without resorting to Python you are very limited in the type of statistical analysis you can perform on lidar point clouds. I really do not understand what you mean by "smooth out the data". You will loose considerable information/precision in the data by generalizing it using interpolation. Different interpolators can behave in undesirable ways given certain spatial structures. IDW will yield very poor results and Kriging is somewhat intractable given the volume of lidar data and also provides less the optimal representation of the point cloud (Evans & Hudak 2007). There are some methods/software that are designed to "thin" the lidar point cloud using proven methods. A very good free software for manipulating lidar data in las format is LASTools (http://www.cs.unc.edu/~isenburg/lastools/). The function "lasthin" will likely achieve "smoothing" the point cloud by reducing the volume of the data. There is also a function that can export las to singlepart shapefiles. I would recommend doing some more research before mucking around with the lidar point measurements and potentially adding considerable bias in to the data. A very simple approach to data reduction is to filter the point cloud by return (e.g., first, last and single returns). 

Also, keep in mind that intensity is not calibrated and can exhibit considerable variation because of the adaptive gain of the sensor. Without calibrating the intensity values any resulting classification would be somewhat erroneous. With very high pulse frequencies (>120MhZ) and dual-phase sensors, I have seen the intensity values become unusable.    

A good starting point on lidar intensity calibration is:
Kaasalainen, S., et. al., (2009) Radiometric Calibration of LIDAR Intensity With Commercially Available Reference Targets. IEEE Transactions on Geoscience and Remote Sensing. 47(2):588-598

Some information on the behavior of interpolators on lidar point clouds
Evans, J.S., & A.T. Hudak (2007). A multiscale curvature algorithm for classifying discrete return lidar in forested environments. IEEE Transactions on Geoscience and Remote Sensing 45(4):1029-1038

[ThomasQuigley]

@jevans02

Thank you for your reply.

I will try the software you have suggested and am grateful for the advice concerning the statistical analysis.

Recently I have bombarded myself with documents and have fully read some and sifted through others, I shall add your recommendations to the top of my pile.

The study of intensity and how metadata and other internal/ external factors affect its value is the core of my research and I shall send you the results of my findings in approximately three months if it interests you.

Thanks

TQ