I have an ALS point cloud. I have been able to successfully normalize the height, but am wondering if there's any way to remove/filter some points that are obviously errors/outliers (points below 0 and a handful that are 30-50m or even 100m above the tallest trees). The data I'm using was downloaded from the USGS National Map with only ground points already classified.
If I normalize with the
tin() algorithm and create my chm straight from the normalize point cloud, my chm min/max values are:
values : -5.23, 129.98 (min, max)
and if I normalize with a dtm:
values : -14.65, 129.91 (min, max)
Ultimately, I'm trying to segment trees and generate products of tree heights, canopy cover, tree density, etc. But I can't run my analysis when I know the max tree height will be < 30m across my study site.
If I try to reclassify the ground, will that help with the points below 0? But that won't address the 129m max...
I was thinking maybe I'd be better off smoothing my chm and using the chm/tree tops to segment trees as I was having trouble finding a good way to utilize the