I spent two full years of my life tromping through wilderness, sacrificing life and limb for the most complete data sets humanly possible. We covered miles of remote beaches on foot to install geodetic control monuments and take beach profiles, separated by as little as 10 meter spacing. We brethlessly operated our pricey new boat in less than one meter of water to collect just one more line of multibeam sonar bathymetric data or to get the right angle to see a dock at the end of an inlet with our mobile LiDAR. One of the most trying, and perhaps most dangerous tasks undertaken by our four-person team was the installation of large plywood targets before LiDAR scans. Boat based LiDAR is not yet a commonly employed data collection method, and our team has been executing foot-based GPS surveys for years. We were dead set on ground truthing our new “high-accuracy” toys before we decided to trust them entirely.

A co-worker created large, plywood targets of varying configurations: black and white crosses, X’s, circles, targets, and checker boards. We tested them all, and determined the checker board to show up best after processing the intensity of the returns from a dry dock scan. For the next 12 months, we hiked dozens of these 60 centimeter square plywood nightmares all over the Olympic Peninsula for every scan, placing them at the edge of 100 meter cliffs, then hiking to the bottom to be sure we had even spacing at all elevations. After placing each target (using levels and sledges), we took multiple GPS points of its center to compare with spatial data obtained LiDAR. We collected so much data, other research groups were worried about our sanity.

Then, we finally sat down to look for these targets in the miles and miles of bluff and beach topography collected. Perhaps you already know what’s coming? The targets were completely impossible to find; generously, we could see about one of every ten targets placed. Imagine our devastation (or that of the co-worker who had done most of the hiking and target building).

So the spatial question is rather basic: where are my targets?

I hope to answer the question with a few different LiDAR data sets currently at my disposal. The first is a full LiDAR scan of Wing Point on Bainbridge Island, WA. It’s one of the smaller scans, covering only a few miles of shoreline. Deeper water near the shoreline allowed the boat to come closer to shore, and the data density is expected to be high. We hope to find a few targets, and have GPS data corresponding to their locations. Currently, the file is about 5 times the size recommended by Arc for processing in ArcMap. On first attempts, it will not open in the program. While dividing the file would be easy with the proprietary software used with the LiDAR, I’d like to figure out how to do that with our tools. This will be one of the first mountains to climb.

The second data set is a more recent target test scan. Since my departure and determining the frustrating reality of the plywood targets, the group has found some retired Department of Transportation (DOT) signs. They have used gorilla tape and spray paint to create target patterns, similar to the test done with the original batch. I’ve been given one line of a scan of these new target hopefuls. My goal here is to ascertain the abilities of ArcMap for processing target data and aligning it with GPS points, without the added trials of trying to find the darn targets. Of course, I’m already hitting blocks with this process, as well. Primarily, finding the targets requires intensity analysis. Intensities should be included in the .LAS file I’m opening in ArcMap, but they are not currently revealing themselves. My expectation is that this is related to my inexperience with LiDAR in ArcMap, but that remains to be seen.

Writing this post, I’m realizing that my link to spatial statistics currently seems far in the future. Just viewing the data is going to be a challenge, since the whole process is so new to me. The processing will hopefully result in an error analysis of the resulting target positions, when compared to the confidence of ground collected points. Furthermore, the Wing Point data was taken for FEMA flood control maps, and that sort of hazard map could be constructed once rasters or DEMs are created.

A large part of me is horrified by how much I’ve taken on, deciding to figure out how to use ArcMap for LiDAR processing when my experience with the program is already rather primitive. However, I’m excited to be learning something helpful and somewhat innovative, not to mention helpful to the group for whom I spent so many hours placing targets.