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PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Number of samples: at least five

Length: at least 15 × NPS

Width: at least 15 × NPS

All points in all sampled areas should be included in the

calculation of the standard deviation. A single class from the

classified dataset should be used for this test.

Examining the standard deviation of the terrain elevation

from such surfaces provides a good indication of the relative

accuracy of one lidar point to another. Such a standard

deviation value according to the USGS specification should be

around 7 cm, assuming there is no elevation bias in the data.

However, examining the relative accuracy between swaths

is more complicated as it is difficult to agree on the best and

most economical way to achieve it. Lidar vendors or data

providers usually evaluate the elevation differences in the

entire overlap areas between flight lines. Such practice is

achieved through the evaluation of a color-coded raster map

built from the elevation differences, typically referred to as

the “Dz image.” The interpretation of such a map is either

achieved through visual inspection by an operator or through

an automated analytical evaluation or a hybrid between the

two. However, for end users or their consultants, examining

every single point in the overlap areas may not be practical

and can get very expensive very quickly. Statistically based

methods and sampling techniques have been proven to be

the most efficient method. In most cases, using a sample

can tell you just as much as an exhaustive testing routine.

Calculating the sample size involves a very complicated

statistical concept that may or may not work for geospatial

projects. Therefore, I suggest the following strategy:

1) Evaluate the Dz image, if it is available, to figure out the

unwanted occurrences of gross errors such as instrument

malfunction or other systematic problems.

2) Sample the data by selecting a certain number of overlap

areas between swaths. The sample size can vary by the

project and specifications; however, sampling one-third

the overlap areas between swaths but not less than five

samples may prove to be acceptable strategy. Use the

following formula to select the number of samples:

N = (n-1)/3 but not less than 5

Where,

N = is the number of samples

n = number of swaths or flight lines

3) In each of the sample overlap areas, select 20 locations

in open ground. The 20 locations are to be randomly

sampled using the bare-earth DEM to minimize the effect

of trees and buildings on statistics. The process can be

automated to reduce the manual labor required for the

selection process.

4) Evaluate the difference in elevation of the two flight lines

by examining a discrete point or along a short profile line.

5) Tabulate your results from the 20 samples and compute

RMSE.

6) Examine any sample that results in an RMSE that is

larger than three times the project-specified RMSE.

Discard it if it is due to vegetation or an explainable cause.

7) Evaluate the resulting RMSE from each sample (overlap

area). Examine the samples that results in an RMSE

higher than the threshold; in the case of the USGS

specifications it is 10 cm.

8) Calculate an RMSE from all the RMSEs of the samples.

9) Evaluate the final RMSE computed in step 8 against the

project specification.

10) Use the RMSE computed in step 8 to evaluate the final

relative accuracy against the project specifications.

As to whether to perform your assessment on the tiles base

or the swath base, I recommend the use of the individual

swaths as it prevents confusion from assuming the tiles

preserve the integrity of the final calibration of the data in

the form of swaths. LAS files for the individual swaths can

be added to the delivery; however, if such a deliverable is not

available, all bare-earth returns can be used from the tiles to

examine the relative accuracy between swaths, as the source

of each return is documented in the LAS file.

**Dr. Abdullah is Senior Geospatial Scientist at Woolpert,

Inc. He is the 2010 recipient of the ASPRS Photogrammetric

(Fairchild) Award.

The contents of this column reflect the views of the author, who is

responsible for the facts and accuracy of the data presented herein.

The contents do not necessarily reflect the official views or policies of

the American Society for Photogrammetry and Remote Sensing and/

or Woolpert, Inc.

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