nickmcummins - Download one or more lidar tiles:
wget https://coast.noaa.gov/htdata/lidar1_z/geoid12b/data/5008/20131013_usgs_olympic_wa_10TDT310990.laz
- Note the source crs (coordinate reference system, i.e. projection). This 2013 USGS Lidar: Olympic Peninsula (WA) Point Cloud dataset from NOAA uses EPSG 4269. This means that the point cloud coordinates are using latitude/longitude, which is not well supported by many downstream GIS tools. Running
lasinfo https://coast.noaa.gov/htdata/lidar1_z/geoid12b/data/5008/20131013_usgs_olympic_wa_10TDT310990.lazconfirms that the x and y coordinates are in lat/lng:lasinfo (170203) report for 20131013_usgs_olympic_wa_10TDT310990.laz reporting all LAS header entries: file signature: 'LASF' file source ID: 0 global_encoding: 1 project ID GUID data 1-4: 00000000-0000-0000-6C4F-6D7900636970 version major.minor: 1.2 system identifier: 'LAStools (c) by rapidlasso GmbH' generating software: 'lasduplicate (160119) commercia' file creation day/year: 231/2014 header size: 227 offset to point data: 331 number var. length records: 1 point data format: 1 point data record length: 28 number of point records: 25204541 number of points by return: 16034492 6849061 2147980 173008 0 scale factor x y z: 0.0000001 0.0000001 0.001 offset x y z: 0 0 0 min x y z: -123.9223149 47.8406412 248.190 max x y z: -123.9087939 47.8497435 827.800
Thus, re-projecting the .las file is necessary before any further processing can be done. Common CRS systems include UTM based projections, as well as state plane. For this example, I’ve chosen to reproject from lat/lng to NAD 1983 StatePlane Washington South FIPS 4602 Feet as it is what is used by data available through the Puget Sound Lidar Consortium.
Re-projection can be done with PDAL’s filters.reprojection. Create a new JSON pipeline file named lat_lng_WA_S_FIPS_ft_reprojection.pipeline.json with the following contents:
{
"pipeline": [
{
"type": "readers.las",
"compression": "laszip",
"filename": "20131013_usgs_olympic_wa_10TDT310990.laz"
},
{
"type": "filters.reprojection",
"in_srs": "EPSG:4326",
"out_srs": "EPSG:102749"
},
{
"type": "writers.las",
"compression": "laszip",
"scale_x": "0.01",
"scale_y": "0.01",
"scale_z": "0.01",
"offset_x": "auto",
"offset_y": "auto",
"offset_z": "auto",
"filename": "20131013_usgs_olympic_wa_10TDT310990.las"
}
]
}
Now save the file and run the pipeline. This will probably take a while as it involves de-compressing the input .laz file, iterating through and reprojecting every point in the point cloud, and writing the output file to disk. Now, examining the outputted, reprojected .las file with lasinfo 20131013_usgs_olympic_wa_10TDT310990.las will indicate the min/max in the new projection, as well as the z (elevation) values converted into feet:
lasinfo 20131013_usgs_olympic_wa_10TDT310990.las lasinfo (170203) report for 20131013_usgs_olympic_wa_10TDT310990.las reporting all LAS header entries: file signature: 'LASF' file source ID: 0 global_encoding: 0 project ID GUID data 1-4: 00000000-0000-0000-0000-000000000000 version major.minor: 1.2 system identifier: 'PDAL' generating software: 'PDAL 1.5.0 (Releas)' file creation day/year: 211/2017 header size: 227 offset to point data: 690 number var. length records: 3 point data format: 3 point data record length: 34 number of point records: 25204541 number of points by return: 16034492 6849061 2147980 173008 0 scale factor x y z: 0.01 0.01 0.01 offset x y z: 800114.623206489603035 932554.57963061647024 814.270025000000487 min x y z: 800114.62 932554.58 814.27 max x y z: 803498.24 935938.24 2715.87
You should set the scale factor to 0.01 0.01 0.01 instead of 0.000002 0.000002 0.01 as airborne LiDAR has more error than the centimeter resolution this gets you.
Thanks for the pointer, Martin! I’ve updated my post accordingly.