A common product of LiDAR data is high resolution Digital Elevation Models (DEM) which are a raster (gridded) product. There are two types of DEM: a Digital Terrain Model (DTM) is a model of the bare earth and doesn’t contain trees or buildings, a Digital Surface Model (DSM) is a model of the surface which includes the top of buildings and trees etc. The difference between these can provide the height of trees and buildings.
The Sorted Pulse Data (SPD) library is a format for storing, and a set of tools for processing, full waveform and discrete return LiDAR data. More details on SPDLib are available from spdlib.org and in the following publications:
- Bunting, P.J., Armston, J., Lucas, R. M., Clewley, D. 2013. Sorted pulse data (SPD) library. Part I: A generic file format for LiDAR data from pulsed laser systems in terrestrial environments. Computers & Geosciences, 56, pp.197–206. (link)
- Bunting, P.J., Armston, J., Clewley, D., Lucas, R. M. 2013. Sorted pulse data (SPD) – Part II: A processing framework for LiDAR data from pulsed laser systems in terrestrial environments. Computers & geosciences, 56, pp.207–215. (link)
For Linux the software can be installed through conda. Once anaconda / miniconda has been installed (Python 3.5) SPDLib and the prerequisites can be installed using:
conda create -n spdlib_env \ -c conda-forge -c rios -c osgeo \ spdlib spd3dpointsviewer tuiview source activate spdlib_env
To create the DTM and DSM the following steps are required.
- Convert to SPD Format
- Classify ground returns
- Interpolate to DTM and DSM
Assuming discrete LiDAR data in LAS 1.2 format:
spdtranslate --if LAS --of SPD -b 10 -x LAST_RETURN \ -i LiDAR.las -o LiDAR_10m.spd
The spdtranslate command can also be used with data in ASCII format. For full waveform data in LAS 1.3 format a separate script is needed – there will be more details on working with waveform data in SPDLib in a future post.
If the LAS file doesn’t have the projection defined properly you can specify it by setting –input_proj and –output_proj to point to WKT file with the correct projection.
Using the -b option creates a spatially indexed point cloud, which is needed for display and many of the other algorithms in SPDLib. In this example a bin size of 10 m is used.
There are multiple algorithms in SPDLib to classify ground returns, one of the recommenced algorithms is a progressive morphology filter [1]. This is available through the command spdpmfgrd.
spdpmfgrd -r 50 --overlap 10 --initelev 0.1 --maxfilter 14 -b 0.5 \ -i LiDAR_10m.spd -o LiDAR_10m_pmfgrd.spd
To view the classified points open the file in SPDPoints viewer, and select ‘Classification’ as the point colour.
Note – you might have problems running SPDPointsViewer if you’re running Linux through VirtualBox.
Within SPDLib there is also an implementation of the multi-scale curvature algorithm [2], created at the US Forest Service. This does a good job at classifying ground returns under a forest canopy while retaining the terrain but it does not differentiate the buildings.
spdmccgrd -r 50 --overlap 10 -i LiDAR_10m.spd -o LiDAR_10m_mccgrd.spd
More details on both these algorithms and the required parameters are available in the SPDLib Documentation and their respective references
Once the points have been classified they need to be interpolated to create a raster DEM. A key parameter is the resolution of the raster which is generated (-b) which needs to be a whole number multiple of the SPD index. For example, if the SPD file has a bin size of 10 m then the the output raster file resolution can be 1, 2 or 5 m but not 3 m. There are different interpolation algorithms available in SPDLib, the Natural Neighbour algorithm is recommended.
# DTM spdinterp --dtm --topo -r 50 --overlap 10 --in NATURAL_NEIGHBOR \ -f GTiff -b 1 -i LiDAR_10m_pmfgrd.spd -o LiDAR_1m_dtm.tif # DSM spdinterp --dsm --topo -r 50 --overlap 10 --in NATURAL_NEIGHBOR \ -f GTiff -b 1 -i LiDAR_10m_pmfgrd.spd -o LiDAR_1m_dsm.tif
The DTM and DSM can be opened in TuiView. To see the differences you can create a colour composite and look at a profile (the DSM and DTM will be shown in a different colour)
# Create virtual raster stack
gdalbuildvrt -separate LiDAR_1m_dtm_dms_stack.vrt \
LiDAR_1m_dtm.tif LiDAR_1m_dsm.tif
# Open composite in TuiView
tuiview --rgb -b 2,1,1 --stddev LiDAR_1m_dtm_dms_stack.vrt
You can also create a hillshade image using GDAL:
gdaldem hillshade -of GTiff LiDAR_1m_dtm.tif \ LiDAR_1m_dtm_hillshade.tif
And view in TuiView:
Using the spdinterp command it is also possible to interpolate a canopy height model (CHM) to provide the height of a forest canopy using –chm.
This post was based on the SPDLib documentation written by Pete Bunting. A PDF of the documentation and example datasets can be downloaded from bitbucket.org/petebunting/spdlib-documentation/
[1] Zhang, K., Chen, S., Whitman, D., Shyu, M., Yan, J., Zhang, C., 2003. A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Transactions on Geoscience and Remote Sensing 41 (4), pp. 872–882.
[2] Evans, J. S., Hudak, A. T., 2007. A multiscale curvature algorithm for classifying discrete return lidar in forested environments. IEEE Transactions on Geoscience and Remote Sensing 45 (4), pp. 1029–1038.
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