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NAME

       r3.in.lidar  - Creates a 3D raster map from LAS LiDAR points using univariate statistics.

KEYWORDS

       raster3d, import, LIDAR, statistics, conversion, aggregation, binning

SYNOPSIS

       r3.in.lidar
       r3.in.lidar --help
       r3.in.lidar  [-dvopsg]   [input=name]    [file=name]   [n=name]   [sum=name]   [mean=name]
       [proportional_n=name]            [proportional_sum=name]            [return_filter=string]
       [class_filter=integer[,integer,...]]           [base_raster=name]           [zscale=float]
       [intensity_range=min,max]   [intensity_scale=float]   [--overwrite]  [--help]  [--verbose]
       [--quiet]  [--ui]

   Flags:
       -d
           Use base raster actual resolution instead of computational region

       -v
           Use only valid points
           Points invalid according to APSRS LAS specification will be filtered out

       -o
           Override projection check (use current location’s projection)
           Assume that the dataset has same projection as the current location

       -p
           Print LAS file info and exit

       -s
           Scan data file for extent then exit

       -g
           In scan mode, print using shell script style

       --overwrite
           Allow output files to overwrite existing files

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       input=name
           LAS input file
           LiDAR input file in LAS format (*.las or *.laz)

       file=name
           File containing names of LAS input files
           LiDAR input files in LAS format (*.las or *.laz)

       n=name
           Count of points per cell
           Name for output 3D raster map

       sum=name
           Sum of values of point intensities per cell
           Name for output 3D raster map

       mean=name
           Mean of point intensities per cell
           Name for output 3D raster map

       proportional_n=name
           3D raster map of proportional point count
           Point count per 3D cell divided by point count per vertical column

       proportional_sum=name
           3D raster map of proportional sum of values
           Sum of values per 3D cell divided by sum of values per vertical column

       return_filter=string
           Only import points of selected return type
           If not specified, all points are imported
           Options: first, last, mid

       class_filter=integer[,integer,...]
           Only import points of selected class(es)
           Input is comma separated integers. If not specified, all points are imported.

       base_raster=name
           Subtract raster values from the z coordinates
           The scale for z is applied beforehand, the filter afterwards

       zscale=float
           Scale to apply to Z data
           Default: 1.0

       intensity_range=min,max
           Filter range for intensity values (min,max)

       intensity_scale=float
           Scale to apply to intensity values
           Default: 1.0

DESCRIPTION

       The  r.in.lidar  module  is  very  similar to the r3.in.lidar module and many parts of its
       documentation apply also for r3.in.lidar.

        Figure: Proportional count of points per 3D cell. When 50% of all points  in  a  vertical
       column  fall into a given 3D cell, the value is 0.5. Here, the green color was assigned to
       0.5, red to 1 and yellow to 0. The figure shows vertical slices and green color  indicates
       high vegetation while red color indicates bare ground.

NOTES

           ·   This  module is new and partially experimental. Please don’t rely on its interface
               and be critical towards its outputs.  Please report issues on the mailing list  or
               in the bug tracker.

           ·   No  reprojection  is  performed, you need to reproject ahead or use GRASS Location
               which has the right coordinate system which fits with the data.

           ·   Some temporary maps are created but not cleaned up. Use of  --overwrite  might  be
               necessary even when not desired.

           ·   Expects points to have intensity and causing random (undefined) result for related
               outputs (sum, mean, proportional_sum) when the intensity is not  present  but  the
               outputs were requested.

EXAMPLES

   Basic import of the data
       Set  the region according to a 2D raster and adding 3D minimum (bottom), maximum (top) and
       vertical (top-bottom) resolution.
       g.region rast=secref b=80 t=160 tbres=5 -p3
       Now, r3.in.lidar will create the 3D raster of the size given by the computation region:
       r3.in.lidar input=points.las n=points_n sum=points_sum \
           mean=points_mean proportional_n=points_n_prop \
           proportional_sum=points_sum_prop

   Point density vertical structure reduced to the terrain
       Create ground raster:
       r.in.lidar input=points.las output=ground method=mean class_filter=2
       Set vertical extent of computational region to (relative) coordinates above ground:
       g.region rast=secref b=0 t=47 -p3
       Compute point density:
       r3.in.lidar input=points.las n=points_n sum=points_sum \
           mean=points_mean proportional_n=points_n_prop \
           proportional_sum=points_sum_prop \
           base_raster=ground

   Complete workflow for vertical structure analysis
       Compute the point density of points in 2D while setting the output extent to be  based  on
       the  data (-e) and the resolution set to a relatively high number (here 10 map units, i.e.
       meters for metric projections).
       r.in.lidar input=points.las output=points_n method=n -e resolution=10
       This step can be repeated with using different resolutions (and the --overwrite  flag)  to
       determine the resolution for the further analysis.

       The  class_filter  option  should be also provided if only part of the points is analyzed,
       for example class_filter=3,4,5 would be used for low, medium, and high vegetation  if  the
       LAS file follows the usedstandard ASPRS class numbers.

       The  resolution  should  be  suitable  for computing digital elevation model which will be
       computed in the next steps.  Once you decided on the resolution, you can use the 2D raster
       to set the computational region extent and resolution:
       g.region raster=points_n -p3
       class_filter=2 is used to limit
       r.in.lidar input=points.las output=ground_mean method=mean class_filter=2
       The  following  steps  are  not  necessary if the point density is high enough to fill the
       raster continuously.

       Convert the raster to vector point resulting in a decimated point cloud:
       v.to.rast input=ground_mean type=point output=ground_decimated use=z
       Interpolate the ground surface from the decimated ground points:
       v.surf.rst input=ground_decimated elevation=ground
       Now we need to determine upper vertical limit for  the  3D  raster  (the  top  value  from
       g.region  -p3).  This  can  be potentially done with lower resolution. The -d flag ensures
       that the ground raster will be used in its actual resolution regardless of the  resolution
       of the output.
       r.in.lidar input=points.las method=max output=veg_max class_filter=3,4,5 base_raster=ground -d
       With that, we can finally set up the 3D extent and resolution:
       g.region rast=secref b=0 t=40 res=1 res3=1 -p3
       Note  that  the  2D  and  3D  resolutions  are  separate  so  that user can perform the 2D
       calculations on a finer resolution than the 3D calculations. The vertical  resolution  can
       be  and  often  is  set  to  a different value as well. Here we use the same value for all
       resolutions for simplicity.

       Finally, we perform the 3D binning where we count number of points per cell (voxel):
       r3.in.lidar input=points.las n=n class_filter=3,4,5 base_raster=ground -d

SEE ALSO

        r3.in.xyz, r.in.lidar, v.in.lidar, r.to.rast3, r3.to.rast, r3.mapcalc, g.region

REFERENCES

           ·   V. Petras, A. Petrasova, J. Jeziorska, H.  Mitasova  (2016):  Processing  UAV  and
               lidar  point  clouds  in  GRASS  GIS.   XXIII ISPRS Congress 2016 [ISPRS Archives,
               ResearchGate]

           ·   ASPRS LAS format

           ·   LAS library

           ·   LAS library C API documentation

AUTHOR

       Vaclav Petras, NCSU GeoForAll Lab

       Last changed: $Date: 2017-11-25 23:27:34 +0100 (Sat, 25 Nov 2017) $

SOURCE CODE

       Available at: r3.in.lidar source code (history)

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