After mucking around a bit, I was able to use free tools to browse the contents of the classified LiDAR, then used ArcGIS 9.2 tools from 3D Analyst and ERDAS Imagine to get where I wanted to go with this surface. First, I needed to know how many returns there were, and what each of the classes meant. The LAStools info function helped there. Then I used ArcGIS 9.2 3D Analyst “LAS to multipoint” conversion tool, but selected only the first return. Multipoint was an annoying format because it did not seem to fit anywhere in the cool new ESRI “terrain” feature data type. In the end, I gave up on ESRI terrain and went straight to the classic TIN. For maximum overlap, I did not filter out any specific angle from nadir, taking whatever was sent along from the contractor to Alameda County.
Of course, I had to negotiate the treacherous 3D Analyst menu items that were necessary. Getting multipoint into a TIN required creation of a TIN (obvious, but with blank result) and then the non-obvious choice of “Edit TIN” which effectively accepted the multipoint data that were imported from LAS and allowed me to specify the delunay method of choice. Once canned as a TIN, it was a familiar step to specify a raster gridding. I haven’t found a way to reproject the TIN, so I was still in NAD83 California coordinate US Survey feet, and an assumed NAVD88-Geoid 2003 CONTUS-feet vertical while I tried several grid resolutions. In the end, I was happy with 1 foot gridding.
Then, raster on disk, I was able to reproject to WGS84 UTM zone 10 north meters, and chose bilinear resampling on a 25 cm grid posting interval. Once in my favored projection, I rescaled the Z values to NAVD88-Geoid 2003 CONTUS-meters, and began to examine the need for a bit of grayscale morphological processing. I’ve been a great fan of mathematical morphology for over 20 years, so it was a pleasure to craft a kernel or 3 to compensate for some artifacts. Because the TIN-to-grid was so highly oversampled, I was able to use a combination of a tall, narrow 7×3 kernel for morphological CLOSE, followed by a 3×3 DILATE, and a diamond-shaped 5×5 ERODE to finish off the task. In case this morphological stuff sounds like odd stuff to do, these operators are variations on focal max and focal min convolutions. The results are rather important for my application, as shown in the following images.
First is the reflective DEM surface, and the same with the Open Berkurodam 40-region overlay.
Next are more detailed images, near the Greek Theater, showing why I ran the morphological filtering and also how I was able to mostly conserve building footprint areas while inflating trees. The main artifact attenuated was interlace-type effects at the end of overlapping LiDAR scans. The long axis of the morphologcial CLOSE kernel was perpendicular to these artifacts.
Here is the morpho-filtered reflective DEM, with the 10 cm natural color imagery overlaid.
Next up, I’ll need to figure out how to best use this 25 cm surface. It really seems a shame to use it in the way that I have thus far with terrain megaprims–where using four megaprims per region I have effectively downsampled the terrain to 4.26-meter grid postings. That wasn’t so bad for the bare earth model. Here I’ve got something over 290 times denser with 0.25-meter grid surface samples.
But to use many more than 160 megaprims for the entire 40-region model, I really must automate the placement of the (auto-generated) sculpties. For that, I’ll need to ask around the OpenSim community for advice!