ASPRS Accuracy Standards for Digital Geospatial Data

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ASPRS Accuracy Standards for Digital Geospatial
Data
February 17-19, 2014 Denver, Colorado, USA

• Dr. David Maune (Dewberry)
• Dr. Qassim Abdullah (Woolpert)
• Hans Karl Heidemann (USGS)
• Doug Smith (ASPRS Photogrammetric Division)
• February 17, 2014

Produced by Diversified Communications
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PERS, December, 2013

• Published as DRAFT FOR REVIEW
• Comments due to committee by Feb 1st
• Revised standards to be submitted to ASPRS Board
  for decision during annual conference in March

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Objectives of New Standards

• Replace existing ASPRS Accuracy Standards for
  Large-Scale Maps (1990), designed for hardcopy
  maps with published scale and contour interval,
  and ASPRS Guidelines, Vertical Accuracy Reporting
  for Lidar Data (2004), with new accuracy
  standards that better address digital orthophotos
  and digital elevation data
• Establish/tighten horizontal accuracy standards
  for survey-grade, mapping-grade, and
  visualization-grade orthophotos and planimetric
  maps
• Establish vertical accuracy standards for a broad
  range of vertical data accuracy classes

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Horizontal Accuracy Standards for Digital
Orthophotos
Pixel size can be in centimeters, inches or
feet Class I refers to highest-accuracy
survey-grade orthophotos Class II refers to
standard, high-accuracy mapping-grade
orthophotos Class III to Class N refer to
lower-accuracy visualization-grade orthophotos
for less-demanding user applications
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Horizontal Accuracy/Quality Examples for Digital
Orthophotos (Hi-Res)
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Horizontal Accuracy/Quality Examples for Digital
Orthophotos (Mid-Res)
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Horizontal Accuracy Standards for Planimetric Maps
RMSExy values must be in centimeters for all Map
Scale Factors Class I refers to highest-accuracy
survey-grade maps Class II refers to standard,
high-accuracy mapping-grade maps Class III to
Class N refer to lower-accuracy
visualization-grade maps for less-demanding user
applications
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Horizontal Accuracy/Quality Examples for
Planimetric Maps (Large-Scale)
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Horizontal Accuracy/Quality Examples for
Planimetric Maps (Medium-Scale)
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Vertical Accuracy Standards for Digital Elevation
Data
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Vertical Accuracy Classes (most demanding)

• Class I, the highest vertical accuracy class, is
  most appropriate for local accuracy
  determinations and tested relative to a local
  coordinate system, rather than network accuracy
  relative to a national geodetic network.
• Class II, the second highest vertical accuracy
  class could pertain to either local accuracy or
  network accuracy.
• Class III elevation data, equivalent to 15-cm
  (6-inch) contour accuracy, approximates the
  accuracy class most commonly used for high
  accuracy engineering applications of fixed or
  rotary wing airborne remote sensing data.

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Vertical Accuracy Classes (LiDAR)

• Class IV elevation data, equivalent to 1-foot
  contour accuracy, approximates Quality Level 2
  (QL2) from the National Enhanced Elevation
  Assessment (NEEA) when using airborne lidar point
  density of 2 points per square meter, and Class
  IV also serves as the basis for USGS 3D
  Elevation Program (3DEP). The NEEAs Quality
  Level 1 (QL1) has the same vertical accuracy as
  QL2 but with point density of 8 points per square
  meter (Class III density). QL2 lidar
  specifications are found in the USGS Lidar Base
  Specification, Version 1.1.
• Class V elevation data are equivalent to that
  specified in the USGS Lidar Base Specification,
  Version 1.0.
• Class VI elevation data, equivalent to 2-foot
  contour accuracy, approximates Quality Level 3
  (QL3) from the NEEA and covers the majority of
  legacy lidar data previously acquired for
  federal, state and local clients.

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Vertical Accuracy Classes (less-accurate)

• Class VII elevation data, equivalent to 1-meter
  contour accuracy, approximates Quality Level 4
  (QL4) from the NEEA.
• Class VIII elevation data are equivalent to
  2-meter contour accuracy.
• Class IX elevation data, equivalent to 3-meter
  contour accuracy, approximates Quality Level 5
  (QL5) from the NEEA and represents the
  approximate accuracy of airborne IFSAR.
• Class X elevation data, equivalent to 10-meter
  contour accuracy, represents the approximate
  accuracy of elevation datasets produced from some
  satellite-based sensors.

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Non-vegetated Vertical Accuracy (NVA)

• Non-vegetated Vertical Accuracy (NVA), i.e.,
  vertical accuracy at the 95 confidence level in
  non-vegetated terrain, is approximated by
  multiplying the RMSEz (in non-vegetated land
  cover categories only) by 1.96.
• This includes survey check points located in
  traditional open terrain (bare soil, sand, rocks,
  and short grass) and urban terrain (asphalt and
  concrete surfaces).
• The NVA, based on an RMSEz multiplier, should be
  used in non-vegetated terrain where elevation
  errors typically follow a normal error
  distribution. RMSEz-based statistics should not
  be used to estimate vertical accuracy in
  vegetated terrain where elevation errors often do
  not follow a normal distribution for unavoidable
  reasons.

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Vegetated Vertical Accuracy (VVA)

• Vegetated Vertical Accuracy (VVA), an estimate of
  vertical accuracy at the 95 confidence level in
  vegetated terrain, is computed as the 95th
  percentile of the absolute value of vertical
  errors in all vegetated land cover categories
  combined, to include tall weeds and crops, brush
  lands, and fully forested.
• For all vertical accuracy classes, the VVA is 1.5
  times larger than the NVA.
• If this VVA standard cannot be met in
  impenetrable vegetation such as dense corn fields
  or mangrove, low confidence area polygons should
  be developed and explained in the metadata as the
  digital equivalent to dashed contours used in the
  past when photogrammetrists could not measure the
  bare-earth terrain in forested areas.
• See Appendix C in the full ASPRS standards for
  low confidence area details.

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Vertical Accuracy/Quality Examples for Digital
Elevation Data
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Appendices

• Appendix A Review of prior standards,
  guidelines, specifications, plus NEEA and 3DEP
• Appendix B Example accuracy/quality examples
  above
• Appendix C ASPRS accuracy testing/reporting
  guidelines
• Appendix D Accuracy formulas and a working
  example of how to test and report the vertical
  accuracy of elevation data for a typical LiDAR
  dataset

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Recommended Number of QA/QC Check Points Based on
Area (km2)
For areas gt2500 km2, add 5 additional check
points, horizontal and/or vertical, for each
additional 500 km2 area. Each additional set of 5
vertical checkpoints for 500 km2 would include 3
check points for NVA and 2 for VVA.
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Error Histogram for sample LiDAR dataset, 20
check points each x 5 land cover categories
Normal error distribution, except two outliers
among 100 check points
Weeds Crops
Fully Forested
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Traditional Accuracy Statistics
This demonstrates why the 95th percentile is used
(rather than RMSEz x 1.9600) in vegetated land
cover categories. 95th percentile errors will
approximate RMSEz x 1.9600 as errors in any land
cover category approach a normal error
distribution Mean errors vary between -2 cm and
4 cm this is excellent for normal distribution
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Comparison of NSSDA, NDEP and new ASPRS
statistics for example dataset
Errors do not approximate a normal distribution
in Weeds Crops, and in Fully Forested, also
causing the Consolidated to fail should we use
RMSEz x 1.9600
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Comparison of NSSDA, NDEP and new ASPRS
statistics for example dataset
By using the 95th percentile for Supplemental
Vertical Accuracy (SVA) and Consolidated Vertical
Accuracy (CVA), the dataset passes, as it should.
The new NVA uses RMSEz x 1.9600 to estimate
vertical accuracy at the 95 confidence level in
non-vegetated categories where errors should be
normal
The new VVA uses the 95th percentile to estimate
vertical accuracy at the 95 confidence level in
combined vegetated categories where errors may
not be normal
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Any Questions?