Multiscale Curvature Classification of ground returns in 3-D lidar point
clouds (las files), designed for forested environments
Driections to install
- Install into c:\microdem\mcc_lidar
- Get the file msvcr71.dll from the web, and place it in
- Insure there are no spaces in the path to the bin directory
- If there are problems when you try to run, get the command from the
debug file, run it in a DOS command window, and note the error messages.
Actions to run:
- The first time you run the program, you must identify the EXE file.
- Then pick a directory for the classified files.
- Two parameters must be defined in the command line syntax
to run MCC, the scale ('s') parameter and the curvature threshold ('t'). The
optimal scale parameter is a function of
- scale of the objects (e.g.,
trees) on the ground, and
- sampling interval (post spacing) of the lidar
- This will be threaded, using the number of cores on the Hardware tab of
the options form, if there are a number of files to process.
Extract from mcc-lidar help file on setting the paramters:
- Current lidar sensors are capable of collecting high density data (e.g., 8
pulses/m2) that translate to a spatial sampling frequency (post
spacing) of 0.35 m/pulse (1/sqrt(8 pulses/m2) = 0.35 m/pulse), which
is small relative to the size of mature trees and will oversample larger trees
(i.e., nominally multiple returns/tree). Sparser lidar data (e.g., 0.25 pulses/m2)
translate to a post spacing of 2.0 m/pulse (1/sqrt(0.25 pulses/m2) =
2.0 m/pulse), which would undersample trees and fail to sample some smaller
trees (i.e., nominally <1 return/tree).
- Therefore, a bit of trial and error is warranted to determine the best scale
and curvature parameters to use. Select a las tile containing a good variety of
canopy and terrain conditions, as it's likely the parameters that work best
there will be applicable to the rest of your project area tiles. As a starting
point: if the scale (post spacing) of the lidar survey is 1.5 m, then try 1.5.
Try varying it up or down by 0.5 m increments to see if it produces a more
desirable digital terrain model (DTM) interpolated from the classified ground
returns in the output file. Use units that match the units of the lidar data.
For example, if your lidar data were delivered in units of feet with a post
spacing of 3 ft, set the scale parameter to 3, then try varying it up or down by
1 ft increments and compare the resulting interpolated DTMs. If the trees are
large, then it's likely that a scale parameter of 1 m (3 ft) will produce a
cleaner DTM than a scale parameter of 0.3 m (1 ft), even if the pulse density is
0.3 m (1 ft). As for the curvature threshold, a good starting value to try might
be 0.3 (if data are in meters; 1 if data are in feet), and then try varying this
up or down by 0.1 m increments (if data are in meters; 0.3 if data are in feet).
Very slow classification.
Option on Stats tab of the LIDAR point cloud analysis
You are responsible for:
- Picking the two parameters above. These will be saved as MICRODEM
- Pick the number of threads you want to use, and that number of batch files
can be created. You can close
MICRODEM, or do other things while the classification runs.
- Insure the mcc-lidar files are on your hard disk.
- If you need to debug, use the batch file option, and run the batch
file from the DOS prompt to see error messages.
- You might have to subset the LAS files to get this to run.
- This will treat roofs as ground.
last revision 6/7/2017