Thats about right. The "last return" lidar data still has quite a bit of leftover vegetation/buildings etc that has to be filtered spatially to get the bare earth you need for contouring. Usually the agency that has acquired the data also post-processes it to produce a clean bare earth DEM or TIN and provides that as one of the products (in addition to the raw "point cloud" data). You also get the return intensity, which is effectively an IR photo of the area at the same posting as the elevation data. Agencies often make contours from the final DEM (usually 2-foot) and provide these in vector formats.
More and more statewide and countywide data is available for free or for a nominal fee nowadays. County GIS departments, some state ones, and major city GIS depts are good places to look. Maryland has statewide coverage that is distributed by county, and each county does it a little differently. Baltimore county for example sells the data (currently $90 for "4 tiles," which covers several km^2). They provide the raw data, intensity, and a bare-earth dem. Cecil and Howard county 2m posting data is available for free from a NOAA coastal erosion website. Pennsylvania is in the process of collecting statewide data that will all be free. The western 1/3 is already done, and the middle 1/3 is due this summer. I did a test with a piece near Pittsburgh for Ed Hicks to compare a recent photo-derive d base to a base from lidar using this data and the contour match was excellent. Connecticut has statewide data at 4m posting, which is very coarse. Its about the same res as you'd get from USGS data, but its more accurate (that is, it matches reality even though the res is low). Louisiana has statewide coverage via FEMA and the Army corps of Engineers for flood mapping. Its all available for free online. Most of North Carolina is available through CLICK, as is some of the LA data, and there is alot of coverage of Puget Sound free online.
From what I've seen, data that is 2m posting or better will get you
the same quality you get from a standard photo-derived basemap. If your average point spacing is much larger than 2m then you start to miss detail in the contours that is important for O. As you go to lower and lower spacing approaching 1m you get incredible ammounts of extra detail - beyond what you need for just the contours.
The Balto county data is just over 1m posting and I can pick out rootstocks under canopy from the hole behind the rootball, large boulders and most trail treads. Other info you can add from the lidar are roads, trails, buildings, clearings, ditches, streams, pits, depressions, knolls and in some cases you can judge vegetation thickness from the quality or lack thereof of the vegetation filtering. You can also tell the difference between evergreen and deciduous, and you can see things under most pine canopy. In one case we were able to map a ruined barbed-wire fence in the middle of the forest using average vegetation height. The fenceline followed an old farm field edge and the average tree height in the old field was 5m shorter than the forest on the other side. You couldn't tell just standing under the trees.
Francis Hogle pointed out to me that one of the tougher problems with the lidar data while drafting is being careful not to put too much detail from the lidar base on the final map, as you can often see things in the lidar that are just too subtle for an orienteer to notice. I can make out 100 year old farm field boundaries under canopy that are just 15cm above the average terrain that are impossible to find when walking through the woods.
Anyways, a number of O maps have already been produced from Lidar - notably QOC's 2007 US Champs map last fall, and a map north of Baltimore that Ted Good and others have fieldchecked for an A-meet next spring. I know Mark Dominie is working with lidar bases in the NE these days. As soon as central and eastern PA are covered by the state, SVO and DVOA will be in basemap heaven. This will exacerbate the current shortage of good fieldcheckers in the US. We're going to see alot more lidar-derived maps from now on.
Regarding cactusmagnet's original question, I looked into the three file formats he listed. Two of them at least seem to be simple ascii text, so might be easy to manipulate. However that globalmapper demo might be a good place for him to start. It claims to be able to read at least one of his 3 formats. Looks like a pretty capable package, but I haven't used it myself. As jj and others pointed out though, most of the USGS DEM data is pretty poor no matter how you slice it. The 30m DEMs are way too low resolution. The 10m DEMs provide about the same resolution (same information content) as the printed USGS topos, so even though its easy to re-contour those at 5m intervals, you haven't gained any new info and they are pretty bad to begin with. I've converted 2-foot vector data into grid, made TINs and then re-contoured at 5m (with smoothing to overcome coarse jaggies). That works pretty well as long as you always keep the limitations of the original data in mind. You never get something for nothing. Changing the contour interval or making the paper USGS data digital doesn't improve the original content - just changes its form. Avoid the USGS data if you can. If its all you've got, go ahead but be very careful with it. Some might suggest a white-paper base would be better than using mis-information to start, but that does require some genuine fieldchecking skill (which I certainly don't have).
There are a number of free GIS tools out there. Notably the GDAL libraries in C or Python, but these do require some operator skill - even just to get an executable program the average user can run. I've been working on a set of simple lidar/gis tools for orienteers which Greg Lennon and James Scarborough have been helping with. These are written in a proprietary language called IDL but its possible to export compiled applications without requiring a license (i.e. its free). We've been testing the first basic tools distributed on mac OSX, windows, and linux. The idea here is not to produce a huge general-purpose GIS library, but to provide the basic tools an average club user would need to make an OCAD basemap from lidar data (and maybe read a few other useful vector formats like shapefiles - although OCAD9 can read shapefiles directly now I think). I'm easliy distracted by shiny objects though, and racing season is looming. Plus my apt is now surrounded by tens of km^2 of lidar basemap, which is very distracting. Greg has some other free contouring and display tools linked at lidarbasemaps.org
, some of which can write DXF for import into OCAD.