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Adopt updated accuracy standards

By NGP Standards and Specifications

The American Society for Photogrammetry and Remote Sensing (ASPRS) has published the second edition of the ASPRS Positional Accuracy Standards for Digital Geospatial Data. The Lidar Base Specification will need to be updated to conform with the second edition. Many of the changes relate to updating the reference, but some others are more impactful. Please read through the list below carefully!

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Status of this revision:

This revision is under review by the Elevation Specifications Review Board and is very likely to change prior to final adoption. 

Introduction

The second edition of the ASPRS Positional Accuracy Standards includes several important updates that impact the requirements in the Lidar Base Specification. Notably, the Root Mean Square Error (RMSE) terms have been renamed, Vegetated Vertical Accuracy (VVA) has had several changes and a project can no longer fail just for VVA, and 95% Confidence Level will no longer be used which impacts Table 4. Many of the changes have indirect impacts that are not directly mentioned in the Lidar Base Specification. For instance, survey errors must now be included in RMSE calculations. This is not directly mentioned in the LBS, but must be followed since the Positional Accuracy Standards are incorporated by reference.  

Summary of changes for ASPRS Positional Accuracy Standards for Digital Geospatial Data, Edition 2, Version 1.0:

Note: this is only a list of the important changes! Please see second edition of the ASPRS Positional Accuracy Standards for discussion and details of these and other changes!

1.Eliminated reference to 95% confidence level as an accuracy measure.
2.Relaxed the accuracy requirement for ground control and checkpoints.
3.Required the inclusion of survey checkpoint accuracy when computing the accuracy of the final product.
4.Removed the pass/fail requirement for Vegetated Vertical Accuracy (VVA) for lidar data.
5.Increased the minimum number of checkpoints required for product accuracy assessment from 20 to 30.
6.Limited the maximum number of checkpoints for large projects to 120.
7.Introduced a new accuracy term: “three-dimensional positional accuracy.“
8.Added Best Practices and Guidelines Addenda.
 
RMSEz has been replaced with RMSEV
RMSEx and RMSEy have been combined into RMSEH

Throughout the LBS:

Change ASPRS 2014 to ASPRS 2023. 

Individual changes:

Current Requirement: 

In LBS 2023 rev. A: Data Processing and Handling:
Absolute Horizontal Accuracy
  • The horizontal accuracy of each lidar project shall be reported using the form specified by the ASPRS (2014): “This data set was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a ___ (cm) RMSEx / RMSEy Horizontal Accuracy Class which equates to Positional Horizontal Accuracy = +/- ___ cm at a 95% confidence level.”

Change to:

Absolute Horizontal Accuracy
  • The horizontal accuracy of each lidar project shall be reported using the form specified by the ASPRS (2023): “This data set was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data, Edition 2 (2023) for a __(cm) RMSEH horizontal positional accuracy class. The tested horizontal positional accuracy was found to be RMSEH = __(cm).”

Current Requirement: 

In LBS 2023 rev. A: Data Processing and Handling: Absolute Vertical Accuracy
  • The minimum NVA and VVA requirements for all data, using the ASPRS methodology, are listed in table 4. Both the NVA and VVA required values shall be met.

Change to:

  • RMSEV for NVA shall be calculated including survey error as described in ASPRS(2023). See the Glossary entry for RMSEV for detailed equations. 
  • The minimum NVA and VVA requirements for all data, using the ASPRS methodology, are listed in table 4. NVA values shall be met, VVA values shall be reported. 

Current Requirement: 

In LBS 2023 rev. A: Data Processing and Handling: Absolute Vertical Accuracy
  • The minimum required thresholds for absolute and relative accuracy may be increased by the USGS–NGP when any of the following conditions are met:

Change to:

(This change is for clarification of "increased")

  • The minimum required thresholds for absolute accuracy and data internal precision may be relaxed by the USGS-NGP when any of the following conditions are met: 

Current Requirement: 

In LBS 2023 rev. A: Delivery: Survey Point Delivery:
  • The GeoPackage shall possess the following minimum attribution:
    • (list of attributions)

Add to list of attributions:

(This change is to add survey point accuracy reporting to the Survey Point GeoPackage. "REAL" is the GeoPackage format for Double)

  • Accuracy – (Real format) The accuracy of the survey point.  

Current Requirement: 

In LBS 2023 rev. A: references:

American Society for Photogrammetry and Remote Sensing (ASPRS), 2014, “Positional Accuracy Standards for Digital Geospatial Data”—Draft revision 5, version 1: Ameri­can Society for Photogrammetry and Remote Sensing, 39 p., accessed October 12, 2014, at http://www.asprs.org/wp-content/uploads/2015/01/ASPRS_Positional_Accuracy_Standards_Edition1_Version100_November2014.pdf.

Change to:

American Society for Photogrammetry and Remote Sensing (ASPRS), 2023, “Positional Accuracy Standards for Digital Geospatial Data”—Edition 2, version 1: American Society for Photogrammetry and Remote Sensing, 25 p., accessed October 20, 2023, at https://publicdocuments.asprs.org/PositionalAccuracyStd-Ed2-V1.

Current Requirement: 

In LBS 2023 rev. A: tables:
Table 4: Absolute vertical accuracy for light detection and ranging data and digital elevation models.
Quality level RMSEz (nonvegetated) (m) NVA at the 95-percent confidence level (m) VVA at the 95th percentile (m)
QL0 ≤0.050 ≤0.098 ≤0.15
QL1 ≤0.100 ≤0.196 ≤0.30
QL2 ≤0.100 ≤0.196 ≤0.30
QL3 ≤0.200 ≤0.392 ≤0.60

Change to:

Table 4: Absolute vertical accuracy for light detection and ranging data and digital elevation models.
Quality level RMSEV (nonvegetated) (m)
QL0 ≤0.050
QL1 ≤0.100
QL2 ≤0.100
QL3 ≤0.200

Current requirement:

In LBS 2023 rev. A: Glossary:

root mean square error (RMSE): The square root of the average of the set of squared differences between dataset coordinate values and coordinate values from an independent source of higher accuracy for identical points. The RMSE is used to estimate the absolute accuracy of both horizontal and vertical coordinates when standard or accepted values are known, as with GPS-surveyed check points of higher accuracy than the data being tested. In the United States, the independent source of higher accuracy is expected to be at least three times more accurate than the dataset being tested.

  • RMSEr  The horizontal root mean square error in the radial direction that includes both x and y coordinate errors.
    • \(\sqrt{(RMSE_x^2+RMSE_y^2)}\)
    •  
    • where: 
    • RSMEx  is the RMSE in the x direction, and
    • RSMEY  is the RMSE in the y direction.
  • RMSEx  The horizontal root mean square error in the x direction (easting).
    • \(\sqrt{\sum{\frac{(x_n-x_n')^2}{N}}}\)
    •  
    • where:
    • xn    is the set of N x  coordinates being evaluated, 
    • x'n   is the corresponding set of check point x coordinates for the points being evaluated,
    • N     is the number of x coordinate check points, and 
    • n     is the identification number of each check point from 1 through N. 
  • RMSEy The horizontal root mean square error in the y direction (northing).
    • \(\sqrt{\sum{\frac{(y_n-y_n')^2}{N}}}\)
    • where:
    • yn    is the set of N y  coordinates being evaluated, 
    • y'n   is the corresponding set of check point y coordinates for the points being evaluated,
    • N     is the number of y coordinate check points, and 
    • n     is the identification number of each check point from 1 through N. 
  • RMSEz The vertical root mean square error in the z direction (elevation).
    • \(\sqrt{\sum{\frac{(z_n-z_n')^2}{N}}}\)
    • where:
    • zn    is the set of N z  values (elevations) being evaluated, 
    • z'n   is the corresponding set of check point elevations for the points being evaluated,
    • N     is the number of z check points, and 
    • n     is the identification number of each check point from 1 through N. 

Change to:

root mean square error (RMSE): The square root of the average of the set of squared differences between dataset coordinate values and coordinate values from an independent source of higher accuracy for identical points. The RMSE is used to estimate the absolute accuracy of both horizontal and vertical coordinates when standard or accepted values are known, as with GPS-surveyed check points of higher accuracy than the data being tested. In the United States, the independent source of higher accuracy is expected to be at least two times more accurate than the dataset being tested. The survey accuracy is included in calculating the vertical accuracy of the dataset. 

  • RMSEH  The horizontal root mean square error in the horizontal direction that includes both x and y coordinate errors.

\[\sqrt{(RMSE_x^2+RMSE_y^2)}\]

  • where: 
    • RSMEx  is the RMSE in the x direction, and
    • RSMEY  is the RMSE in the y direction.
  • RMSEx The horizontal root mean square error.

 \[\sqrt{\sum{\frac{(x_n-x_n')^2}{N}}}\]

  • where:
    • xn    is the set of N x  coordinates being evaluated, 
    • x'n   is the corresponding set of check point x coordinates for the points being evaluated,
    • N     is the number of x coordinate check points, and 
    • n     is the identification number of each check point from 1 through N. 
  • RMSEy The horizontal root mean square error in the y direction (northing).

\[\sqrt{\sum{\frac{(y_n-y_n')^2}{N}}}\]

  • where:
    • yn    is the set of N y  coordinates being evaluated, 
    • y'n   is the corresponding set of check point y coordinates for the points being evaluated,
    • N     is the number of y coordinate check points, and 
    • n     is the identification number of each check point from 1 through N. 
  • RMSEV1 The root mean square error of the first component of vertical error (elevation).

\[\sqrt{\sum{\frac{(z_n-z_n')^2}{N}}}\]

  • where:
    • zn    is the set of N z  values (elevations) being evaluated, 
    • z'n   is the corresponding set of check point elevations for the points being evaluated,
    • N     is the number of z check points, and 
    • n     is the identification number of each check point from 1 through N. 
  • RMSEv  The root mean square vertical error including survey error (V2)

\[(\sqrt{(RMSE_{V_1}^2+RMSE_{V_2}^2)}\]