ArcGis has a new LiDAR dataset at 10.1 which will allow you to view your LiDAR data directly and also see more information than what's in a raster... for example you can add to ArcMap and then filter down the display to just tree classes, or just first returns!
LiDAR data contains much more than just elevation, there's intensity that is stored by default (which can assist where no orthos are available) and they should be classified to ground/non-ground at the very basic; full classification is the most useful (ground, low/medium/high vegetation, buildings, water, bridges, powerlines...) for filtering depending on what you're looking for.
Should you have access to a 3d analyst licence you can convert your LAS dataset to a raster with any cell size you desire or you can view with facets...
IMO I think that it is very wise to get both a raster (use now and with existing processes) and LiDAR data (develop new processes) in a delivery.
You can do this using GDAL, it directly supports XYZ format. It doesn't matter if your coordinates are UTM, gdal_translate will output in the same coordinate system.
So to convert to GeoTIFF is as simple as:
gdal_translate test.xyz test.tif
Look at the GeoTIFF doc for output options (such as compression) and the gdal_translate doc for more usage info. In particular, you should specify what the coordinate system is with the -a_srs parameter.
-a_srs srs_def:
Override the projection for the output file. The srs_def may be any of the usual GDAL/OGR forms, complete WKT, PROJ.4, EPSG:n or a file containing the WKT.
gdal_translate -a_srs EPSG:12345 test.xyz test.tif
Comma/space separated and fixed column widths, with and without a header row are supported.
The supported column separators are space, comma, semicolon and tabulations.
$ head -n 2 test_space.xyz
x y z
146.360047076550984 -39.0631214488636616 0.627969205379486084
$ gdalinfo test_space.xyz
Driver: XYZ/ASCII Gridded XYZ
Files: test_space.xyz
Size is 84, 66
Coordinate System is `'
Origin = (146.359922066953317,-39.062997159090934)
Pixel Size = (0.000250019195332,-0.000248579545455)
Corner Coordinates:
Upper Left ( 146.3599221, -39.0629972)
Lower Left ( 146.3599221, -39.0794034)
Upper Right ( 146.3809237, -39.0629972)
Lower Right ( 146.3809237, -39.0794034)
Center ( 146.3704229, -39.0712003)
Band 1 Block=84x1 Type=Float32, ColorInterp=Undefined
Min=0.336 Max=0.721
$ head -n 2 test_commas.xyz
x, y, z
146.360047076550984, -39.0631214488636616, 0.627969205379486084
$ gdalinfo test_commas.xyz
Driver: XYZ/ASCII Gridded XYZ
etc...
$ head -n 2 test_formatted.xyz
x y z
146.3600471 -39.06312145 0.627969205
$ gdalinfo test_formatted.xyz
Driver: XYZ/ASCII Gridded XYZ
etc...
The only gotchas I'm aware of are:
- The opening of a big dataset can be slow as the driver must scan the whole file to determine the dataset size and spatial resolution; and
The file has to be sorted correctly (by Y, then X).
Cells with same Y coordinates must be placed on consecutive lines. For a same Y coordinate value, the lines in the dataset must be organized by increasing X values. The value of the Y coordinate can increase or decrease however.
$ head -n 5 test.csv
x,y,z
146.3707979,-39.07778764,0.491866767
146.3787985,-39.07157315,0.614820838
146.3637974,-39.07132457,0.555555582
146.3630473,-39.07579901,0.481217861
$ gdalinfo test.csv
ERROR 1: Ungridded dataset: At line 3, too many stepY values
gdalinfo failed - unable to open 'test.csv'.
$ tail -n +2 test.csv| sort -n -t ',' -k2 -k1 > test_sorted.xyz
$ head -n 5 test_sorted.xyz
146.3600471,-39.07927912,0.606096148
146.3602971,-39.07927912,0.603663027
146.3605471,-39.07927912,0.603663027
146.3607971,-39.07927912,0.589507282
146.3610472,-39.07927912,0.581049323
$ gdalinfo test_sorted.xyz
Driver: XYZ/ASCII Gridded XYZ
etc...
Best Answer
Simple method:
Rename the file(s) to csv or asc to see the tables (you may need to edit each one and add a header line), create a feature class from XY table (or make XY event layer) then perform a Point to Raster (your cellsize is 15 metres), this will give you a DEM. With the DEM run GDAL_Contour or Esri contour:
Better method (with extensions):
From the points created from the XY table create a Terrain and do your contours in Esri using Surface Contour (3d Analyst).
You can smooth out contours by resampling your data from 15m to say 5m using a bilinear method.
If you have a few to do you might be best to set your Snap Raster and Cell Size environments, this will ensure the cells line up. It is best to mosaic tiles together then clip out slightly more than you need as contours will end halfway through the last cell.
This is what I got from the tile: