Tag Archives: point cloud

Ripley’s K analysis on two forested stands

Bryan Begay

  1. Question asked

Can a point pattern analysis help describe the relationship between the spatial pattern of trees in my area of interest with forest aesthetics? More specifically, how does Ripley’s K function describe forest aesthetics in different parts of the forest on the McDonald-Dunn Forest.

  1. Tool

The main tool that I used for the point pattern analysis was the Ripley’s K function.

  1. Steps taken for analysis

My workflow involved doing preprocessing of the LiDAR data, then creating a canopy height model to obtain an individual tree segmentation. The individual tree segmentation would then allow me to extract tree points with coordinates that could be usable points for the Ripley’s K Function.

 

LiDAR preprocessing

I started off with finding my harvest unit area of interest (Saddleback stand) and finding a nearby riparian stand that would be used to compare the Ripley’s K function outputs.   I create polygons to clip the LiDAR point clouds onto. I found the LiDAR files that were over the AOIs and used the ArcMap Create LAS Dataset (Data Management) to make workable files, then clipped the data sets to the polygons using the Extract LAS (3D analyst) tool. Fusion was used to merge the clipped LiDAR tiles to make one continuous data set for both AOIs. Then I normalized the point cloud with FUSION by using a 2008 DEM raster from DOGAMI, and the FUSION tools ASCII2DTM and Clipdata.

 

CHM, tree segmentation, and Ripley’s K

With the normalized point cloud, a canopy height model (CHM) was created in R-studio, and then an individual tree segmentation was made with an R package called lidR by using a watershed algorithm. The segmented trees were exported as a polygon shapefile that could be used in ArcMap. The Feature to Point tool (Data Management) was used to calculate the centroid of the polygons to identify individual trees as points. The points could then be used in RStudio spatstat package to be used in a Ripley’s K Function. The function was calculated for both Saddleback stand and a nearby riparian area.

  1. Results

The results show that the pattern for both the Saddleback stand and the riparian area were clustered. Both stands observed lines were plotted above the expected line for a random spatial pattern.  The lines were significantly different, being above the higher confidence envelope. The riparian stand has higher levels of clustering compared to Saddleback stand. The Saddleback stand showed a plotted clustering pattern as well, but not to the degree of the riparian stand.

 

  1. Critiques

Some critiques for my analysis would be to use a more robust individual tree segmentation algorithm analysis. For the sake of processing speed and creating delineated polygons with reduced noise, I used a resolution of 1 meter for my CHM. The 1 meter resolution for my CHM smoothed over the tree segmentation, possibly removing potential tree polygons but creating more defined segmented trees. The CHM lower resolution was used with a relatively simple watershed algorithm. Past algorithms I’ve used showed better results than watershed but required more detailed inputs. Another criticism I have is that using the feature to point does not necessarily give me the tree tops, but finds the centers of polygons that the tree segmentation identified as individual trees. Finding a more robust method for determining tree points would be more preferable.