[R-sig-Geo] Advice on clustering LiDAR point clouds

[Wesley Roberts]

Dear R-sig-Geo,

I am currently looking at clustering a LiDAR point cloud (trees in a plantation forest) using R and have some questions that I hope some of you may be able to answer. 

My method is a two stage approach, firstly I selected potential tree locations by overlaying a static grid on the point data and selecting the maximum value within each grid. These locations were stored as potential tree locations and have been used as sample data in a spatial clustering approach (I would have liked to use a moving filter but could not find a local maximum approach implemented in R). These points and the original data are now being clustered using the clus algorithm in the spatclus package.

My query regards the use of an algorithm developed for disease mapping (Kuldorff's circular zone in 2D) with Lidar data. The density of the lidar points is around 5 per square meter and I am concerned that the algorithm will not be able to identify clusters based on height. I am yet to inspect the results as the clus algorithm is still running so I cant comment on that right now, but I was wondering if anyone on the list had any suggestions wrt the clustering and or segmentation of lidar point clouds using R. I am unwilling to use interpolation as I want to avoid the lengthy process of selecting the correct interpolation procedure and or model and would like to stick with the point cloud.

Any advice on this matter would be greatly appreciated.
Many thanks and kind regards,

Wesley


Wesley Roberts MSc.
Researcher: Earth Observation
Natural Resources & the Environment (NRE)
CSIR
Tel: +27 (0)21 888-2490
Fax: +27 (0)21 888-2693
"To know the road ahead, ask those coming back."
- Chinese proverb



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