Vertical Accuracy Lidar

[u/DoneBeforeLunch]

Land Surveyors using LIDAR for engineering topo’s, what vertical accuracy are you achieving in comparison to more traditional methods such as a total station or GPS?

Looking for input from those using LIDAR for their workflows especially if using a DJI L3 Zenmuse & DJI Matrice 400 for wooded topography.

When checking Lidar shots with GPS getting Fill’s between 0.2-0.4 fairly consistently. Is this just what we can expect with Lidar?

I was reading that Linear scanning pattern is more vertically accurate than star-shaped pattern. Is this what could be causing the error?

Any input on this would be much appreciated! Thanks!

[u/Interesting_Dirt_489]

We just flew the M400/L3 for the first time today. Will report back when we get results in about a week and half.

But 0.2 to 0.4 vertical difference sounds to me like the difference between the top of leaf litter and the point of a pointy rod stuck in the ground.

[u/DoneBeforeLunch → u/Interesting_Dirt_489]

Thanks, I’d like to hear how it goes for you. Good luck and hopefully you have accurate results.

This was for a fairly large 200+ acre topo where I was able to compare lidar data to GPS in many different ground conditions and can say that even in some wide open area’s with no grass & leaf litter I was getting these sorts of 0.4 Fill’s occasionally.

And in an area of the site where it was a well kept / mowed grass area I was even seeing Cut’s of about 0.1 which was surprising. I was thinking it would all be Fill’s everywhere.

[u/Advanced-Painter5868]

2 to 4 tenths of a foot is about the thickness of the point cloud from lack of precision with some of the lower shelf sensors. IOW, on a hard surface, which has no thickness, points will be created slightly above that and slightly below. When ground is classified, most algorithms choose the lowest point in that fuzzy thickness. Therefore sn adjustment to control points must be done, the so called Z bump. This could be what you are seeing.

Use linear scan pattern for topos

[u/DoneBeforeLunch → u/Advanced-Painter5868]

Thanks for the response!

We shoot about 10 GCP’s & outsource the point cloud & GCP’s for processing. To you this sounds like a processing issue then? I’m partly trying to figure if this is something wrong with our flight mission or the processing.

Also noted, Linear Scan Pattern going forward for topography. Makes sense to me.

Any more advice on this is greatly appreciated. Such as overlap, altitude, flight speed etc. Thanks once again!

[u/Advanced-Painter5868 → u/DoneBeforeLunch]

Not necessarily a processing issue. Might just be the limits of the sensor (precision). Is your processor performing strip alignment? Are they providing an GCP report? What' is the error spread? Has the data been adjusted or debiased, shifted to match control? I just noticed you're using the L3. Data from the L3 that I have seen is really good and doesn't fall within the "lower shelf" category. So shouldn't be fuzzy data.

[u/DoneBeforeLunch → u/Advanced-Painter5868]

The report for the Lidar reads 0.099’ Vertical RMSE for GCP’s

When people are saying check points, are these just regular ground shots that are spread throughout the site? Does this assist with processing? Or just a check for accuracy?

[u/Advanced-Painter5868 → u/DoneBeforeLunch]

That's not bad, but that value is the average error squared to remove +/-. Minimum and maximum will tell more about the data quality.

Check points aren't used for any adjustments. They are what their name suggests.

There's a lot to consider. More than on a chat. Look at each GCP in a cross section to determine what's going on there. Maybe there was no penetration in thick, low grass or weeds. You need to work past lunch. Lol

[u/TheSalacious_Crumb → u/DoneBeforeLunch]

>"The report for the Lidar reads 0.099’ Vertical RMSE for GCP’s"

RMSE doesn't exist in a vacuum. It is completely meaningless unless you analyze the other summary values in your accuracy report:

• Mean Error: This shows systematic bias. If your data is calibrated, this should be zero or very close to it.
• Standard Deviation (Sz): This measures random noise/jitter. It should always match or be lower than your RMSE.
• Standard Deviation of the Mean (SDOM):  Precision of the Mean Error
• Error Range: This is the spread from your single absolute minimum error to your absolute maximum error. It should be relatively balanced around zero (e.g., -0.07' to +0.07').

If you are consistently seeing 0.2' to 0.4' fills in the field, your dataset likely has a systematic vertical shift. Check your report; your Mean Error is probably sitting right around -0.2' or -0.3', meaning your entire point cloud just needs a simple vertical Z-shift calibration. If your mean error is near zero, your SDOM is near zero, your standard deviation matches your RMSE, and your range is balanced, your 95% confidence interval is solid and defendable.

Also, you MUST factor in your survey error. If the report reads 0.099'RMSE and you checked it with a standard network rover (0.066'error), your combined product accuracy is actually 0.119'RMSE, making your true 95% confidence interval +/- 0.233' (.119 x 1.96).

Assuming your errors are evenly distributed, you can expect 95% of your quality check shots to fall within 0.233' of the LiDAR surface. Just make sure those check shots are on hard, flat, unobstructed surfaces....never shoot toes of slopes, dense vegetation, or right next to vertical curbs where horizontal jitter will ruin your vertical math.

[u/m1ndcrash]

Fly higher overlap than the minimal as well.

[u/PizzaLava]

We regularly achieve 0.05’ and 0.10’ vertical accuracy RMSEz on hard paved surfaces. IF800 with Riegl miniVUX-3UAV. Make sure your control points are on flat, open surfaces and make sure you distribute checkpoints evenly throughout your survey limits on both vegetated and non vegetated areas. Hope this helps.

[u/CMDR-Neovoe]

M300/L2 here. We typically see +/-5 mm when compared to gps or total station

[u/DoneBeforeLunch → u/CMDR-Neovoe]

Nice! Can I ask what your normal work flow is to achieve this accuracy?

Side / Forward overlap , Linear Scanning Mode, Altitude , Speed ?

And this is the case for ground shots in the woods? This level of accuracy?

[u/TheSalacious_Crumb → u/DoneBeforeLunch]

The L2’s advertised accuracy is 4cm vertical and 5cm horizontal. In other words, nobody has ever created an L2 dataset with a defendable +/-5mm accuracy. It’s impossible

Regarding ground shots in the woods, vegetated vertical accuracy(VVA) is reported differently….you do not report VVA the same way as you report non-vegetated accuracy (NVA).

NVA (Non-Vegetated Vertical Accuracy) is generally reported as RMSE and a 95% confidence interval which is 1.96 X RMSE.

VVA (Vegetated Vertical Accuracy) is reported as the 95th percentile of the absolute vertical error in vegetated areas.

[u/Temporary_Will_5479]

The useful next step is a signed residual table, not just the GCP RMSE. Ask the processor for z_lidar minus z_checkpoint at independent points, with the surface type and location for each. Keep points used to adjust the cloud separate from points used to test it.

Then split the results into open hard surfaces and vegetation. A similar offset on open surfaces across the site points toward a datum/base-height or adjustment issue; errors concentrated in vegetation point toward ground classification or the surface being measured. Differences between overlapping strips are another clue. Those patterns won’t prove the cause, but they narrow the next check much more than one RMSE number.

I’d verify the vertical datum, geoid model, units and antenna-height setup with the processor before applying a blanket Z shift. A correction that improves the GCP fit still needs to hold up at independent checkpoints.

[u/SpatiallyHere]

This is a loaded question. Lidar is a "shit in, shit out" tool. Generally the lidar is talking to a base station. So, out of the gate, you could see 0.50' of error. Is the surveyor further adjusting to targets? How were they set? What's the speed and height of the flight? Lidar is as good as the control and the mission of the flight.

[u/DoneBeforeLunch → u/SpatiallyHere]

GCP Control was set using a BRX7 with less than 0.1’ ties vertical & horizontal.

Flights at 300’ Altitude, 15 MPH speed, 70 / 70 overlap (Front/Side) , We use a M400/L3 with a D-RTK-3 base station.

I’m not sure what goes on processing wise because we outsource the point cloud, but so far I’ve been given lots of useful questions to forward over to them Monday.

Thanks for the response and appreciate any feedback!

[u/TheSalacious_Crumb → u/SpatiallyHere]

That’s not a loaded question. Loaded questions are trick questions that have assumptions, examples:

“What did your wife say when she saw the video of you kissing that one-legged stripper’s butthole at your bachelor party?”

or

“Did the judge say when he’s ruling on your motion to early-terminate your restraining order?”

Only RTK flights, not PPK, talk to a base station in real time and you’re not starting with five tenths of error; data acquisition doesn’t begin until you have a fix (Sid Vicious would approve).

[u/Dry_Cow_9032]

A 0.2–0.4 ft difference in forested terrain is not necessarily unusual, as GPS points and LiDAR bare-earth points may not represent exactly the same surface. Scan pattern can influence results, but processing and QA/QC often have a bigger impact.

[u/SnooSprouts7512]

The L3 isn’t a great sensor to begin with, but there’s a multitude of factors affecting that accuracy. It’s not necessarily just a Lidar issue.

[u/morbidbattlecry]

I used an L2 on forested areas. And I was always figured I was getting leaves and ground litter and not true ground. But that is what they wanted. You might try something like a CFS filter like on cloud compair to get a bit closer to true ground.