# Precision

Precision, also called intraswath-precision, is a measurement of repeatability on a hard surface target from within a single pass of a scanner. This metric is primarily a factor of the intrinsic calibration and stability of a scanner. It is also greatly impacted by properties of the measured surface.

![An example of how precision is measured in a LiDAR swath](https://content.gitbook.com/content/dEevfLZRIk38LUPwDa4V/blobs/GSDBvFF8YXWwDg3YMecU/Precision_Visualized.PNG)

#### Many factors affect the precision of a dataset, most notably:

* Laser intrinsic quality (Range accuracy, Range Precision, Angular accuracy)
* Target Reflectivity
* Range to target
* Incident angle/scan angle & beam divergence

## Measuring Precision

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Phoenix LiDAR quantifies precision of systems and datasets during testing by replicating\* the USGS methodology and closely conforming with the ASPRS outline.
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1. Utilize a hard surface area within the calibration site, generally a region of flat parking lot or sidewalk.
   1. The area is carefully selected to contain a target of typical real-world signal return at the laser's wavelength, often around 20% reflectivity, avoiding very dark or highly reflective subjects.
2. Within this area, data is sampled from all overlapping flightlines to consider a variety of scan angles.
3. Data is processed independently for each flightline according to the USGS methodology.
4. The per flightline results are summarized by taking an average that characterizes a dataset or scanner.
5. These evaluations are made for a single AGL (above ground level) at a time and results are conveyed as such.

\* PLS typically does not create the vectorized polygons with summarizing attributes as the USGS outline for data delivery.

### Industry References

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Data providers should always reach an agreement with end users about data quality standards and reporting specifications.
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{% tab title="ASPRS Standards Summary" %}
ASPRS (2014) refers to precision as “within-swath accuracy” and outlines the following criteria it’s quantification:

* Evaluated against single swath data.
* The sample area should be a relatively flat and hard surface.
* The sample area should contain single return LiDAR pulses only.
* The test area should not include abrupt changes in reflectivity.
* Compute the difference between 2 raster surfaces: 1 representing the max, and 1 the min elevation within each cell.
* Raster cell size should be twice the nominal point spacing.

The required “within-swath” accuracy of a dataset to meet a certain accuracy class is outlined in the following table taken from the ASPRS guidelines

![Source: ASPRS (2014)](https://content.gitbook.com/content/dEevfLZRIk38LUPwDa4V/blobs/gOfk26ThtjgTPtEb1hL8/Precision_ASPRS.PNG)

ASPRS Positional Accuracy Standards for Digital Geospatial Data, 2014. <http://www.asprs.org/wp-content/uploads/2015/01/ASPRS_Positional_Accuracy_Standards_Edition1_Version100_November2014.pdf>&#x20;

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USGS Base specification refers to “Intraswath precision” or “Smooth surface precision” as a component of relative accuracy. Their guidelines for measurement are similar to ASPRS, with more details on exactly how to execute the methodology. Per USGS, precision must be calculated through a raster cell analysis where:

* *Precision = Range-(Slope x Cellsize x 1.414)* with the following specifications:
  * Range is the difference between min and max elevation (Z) values within a cell.
  * Slope is the maximum slope between a cell and it’s 8 neighbors, calculated using the min Z of each cell.
  * Cell size is the average nominal point spacing(rounded up to an integer) x 2.
* Assessments should be made on hard surfaces with single return pulses only, just like ASPRS.
* The sample area should be approximately 100 pixels, so highly dependent on point density.
* The full width of the swath should be evaluated, including the range of scan angles, if possible. This may be accomplished by sampling multiple areas and data from multiple flightlines.
  * Essentially, it is important to consider scan angle / incident angle to avoid misrepresenting the data.
* The cells precision values should be statistically summarized per sample area (raster) to show:
  * RMSDz
  * Min Precision value
  * Max Precision value

The USGS guidelines place RMSDz values into a scale of Quality Levels, as seen in the following table taken from USGS.

![](https://content.gitbook.com/content/dEevfLZRIk38LUPwDa4V/blobs/v6OPnkJb9i8HQIOd1jOp/Precision_USGS.PNG)

LiDAR Base Specifications, 2021. rev A. <https://www.usgs.gov/core-science-systems/ngp/ss/lidar-base-specification-data-processing-and-handling-requirements#positional-accuracy-validation>
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