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Large Scale Elevation Data Assessment of SCOOP 2013 Deliverables

Version 1.1 March 2015


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ACKNOWLEDGEMENTS ............................................................................................................................... 2

EXECUTIVE SUMMARY ................................................................................................................................. 2

1.0 INTRODUCTION ................................................................................................................................. 3

2.0 BACKGROUND .................................................................................................................................. 4

2.1 SOUTH CENTRAL ONTARIO ORTHOPHOTOGRAPHY PROJECT 2013 ......................................................... 4

2.2 ASPRS POSITIONAL ACCURACY STANDARDS FOR DIGITAL GEOSPATIAL DATA (2014) ........................ 4

3.0 ELEVATION DATA PRODUCTION ................................................................................................... 5

3.1 ACQUISITION TECHNIQUES .......................................................................................................................... 5

3.1.1 Semi-Global Matching ................................................................................................................... 5

3.1.2 LiDAR ................................................................................................................................................. 6

3.2 DEM PRODUCTION ...................................................................................................................................... 6

3.2.1 SCOOP DEM .................................................................................................................................... 6

3.2.2 LiDAR DEM ....................................................................................................................................... 6

4.0 ACCURACY ASSESSMENT PROCEDURES ................................................................................... 7

4.1 CHECKPOINT REQUIREMENTS ..................................................................................................................... 7

4.2 TESTING ACCURACY IN GIS ........................................................................................................................ 7

5.0 RESULTS ............................................................................................................................................ 8

5.1 VERTICAL ACCURACY ................................................................................................................................. 8

5.2 REPORTING STATEMENTS ........................................................................................................................... 8

5.2.1 LiDAR DEM Vertical Accuracy .................................................................................................... 8

5.2.2 SCOOP DEM Vertical Accuracy .................................................................................................. 8

6.0 DISCUSSION ....................................................................................................................................... 9

6.1 VERTICAL ACCURACY AND QUALITY OF ELEVATION DATA ....................................................................... 9

6.2 RECOMMENDATIONS .................................................................................................................................. 10

7.0 REFERENCES ................................................................................................................................... 11

APPENDIX A .................................................................................................................................................. 12

APPENDIX B .................................................................................................................................................. 17


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Acknowledgements

Large Scale Elevation Data Assessment of SCOOP 2013 Deliverables was written by Ian

Jeffrey, GIS/Remote Sensing Specialist of the Ganaraska Region Conservation Authority.

Financial support of Large Scale Elevation Data Assessment of SCOOP 2013 was provided by

the Ontario Ministry of Natural Resources and Forestry (OMNRF) as part of the Assessment of

Provincial Conservation Authority Floodplain Mapping Status and Case Studies to Assess Large

Scale Geospatial Data and Hydrology/Hydraulic Models for Floodplain Mapping.

It is also acknowledged that the foundation of knowledge required to complete this report has

been the result of continued technical collaboration between the Ganaraska Region

Conservation Authority and the Grand River Conservation Authority as well as the Ontario

Ministry of Natural Resources and Forestry.

Executive Summary

Conservation Authorities in Ontario are facing challenges in meeting large scale elevation data

needs. The Ontario Imagery Strategy offers a cost effective option for obtaining high-resolution

orthoimagery as well as large scale elevation data. The South Central Ontario

Orthophotography Project (SCOOP) 2013 is the first phase in the current five-year Ontario

Imagery Strategy refresh cycle. This report documents the findings of vertical accuracy

assessments of large scale elevation data delivered as part of SCOOP 2013 using the newly

released American Society of Photogrammetry and Remote Sensing (ASPRS) Positional

Accuracy Standards for Digital Geospatial Data.

Correct citation of this document: Ganaraska Region Conservation Authority. 2015. Large Scale Elevation Data Assessment of SCOOP 2013 Deliverables. Ganaraska Region Conservation Authority. Port Hope, Ontario.


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1.0 Introduction

Assessing the accuracy of a map was for centuries a relatively straightforward endeavour. The

practice involved a hard copy cartographic map, a map scale, a scale ruler, and a set of

procedures whereby the map was assessed as a scaled down version of reality with true

relative geometries. Information to be represented on the map was captured and assessed as

planar coordinates using scale relative to local survey monuments. In this context, mapping was

a practice of representing geometry as a function of scale.

As a result of advancements in the way in which mapping information is acquired, this approach

no longer suffices. Mapping information, known as geospatial data, can now be acquired and

processed using new technologies which enable data applications and analyses previously

impossible. Further, these technologies provide a previously unattainable level of reliability.

Traditional survey methods are still fundamental, but can now be complemented with various

methods of data acquisition involving Real-Time Kinematic Global Navigational Satellite

Systems (RTK GNSS), Light Detection and Ranging (LiDAR), Synthetic Aperture RADAR

(SAR), to name a few. As result of these modern data acquisition methods, data is now

captured, projected, and assessed in terms of absolute accuracy. Observed locations are now

referenced to navigational satellites which are in turn referenced to deep space quasars so

distant that they are effectively stationary in relation to the Earth. Through the refinement of

these positional relationships, the accuracy of a location on the Earth can be determined to the

millimetre level in absolute terms.

As mapping professionals, how do we reconcile this technological leap in data acquisition and

modeling? How do we procure data with any level of guarantee that it will in fact meet the data

requirements of any intended use?

In November 2014, ASPRS released a set of standards which aims to provide a quantitative

framework for testing and reporting the accuracy of geospatial datasets. Under the title of the

ASPRS Positional Accuracy Standards for Digital Geospatial Data, herein referred to as 2014

ASPRS Standards, a detailed standard for how digital geospatial data ought to be tested for

accuracy, as well as how to effectively report the test results, was released.

In Spring 2013, a leaf-off orthoimagery acquisition was made for central Ontario. The results

were delivered in early 2014 including the full suite of data captured and produced in effort to

create processed orthoimagery. As part of this full suite delivery, an unclassified 3D point cloud

extracted from the raw aerial image stereo pairs was received.

The following report aims to apply relevant portions of the 2014 ASPRS Standards to a terrain

model produced from the SCOOP-delivered 3D point cloud.


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2.0 Background

In Ontario, there is a current and pronounced business need for one type of geospatial data

defined as large scale elevation data, herein referred to simply as elevation data. The high costs

associated with acquiring and preparing elevation data present challenges in addressing these

data needs. The Ontario Imagery Strategy offers a unique and cost effective approach to

providing partners with high quality orthoimagery as well as the elevation data generated as part

of the orthorectification process.

Concurrent to the need for elevation data is the parallel need for detailed standards on how to

test and report the accuracy of elevation data and geospatial data in general. The recently

released 2014 ASPRS Standards offer a potential solution to fill this void.

2.1 South Central Ontario Orthophotography Project 2013

SCOOP2013 is the first in a multi-year phased acquisition of high-quality orthoimagery in

southern, and parts of northern, Ontario. SCOOP2013, as well as four subsequent phases

covering other areas of Ontario, are key components of the Ontario Imagery Strategy. The

Strategy holds the mandate to facilitate a multi-year, cross-industry partnership model to provide

high quality orthoimagery and its associated products in a cost effective manner. Projects of this

magnitude are effectively cost-prohibitive for any one organization, thus reinforcing the need for

a provincially-coordinated approach such as the Ontario Imagery Strategy.

2.2 ASPRS Positional Accuracy Standards for Digital Geospatial Data

(2014)

In response to challenges faced by the industry in terms of linking acquisition to end product,

the American Society of Photogrammetry and Remote Sensing (ASPRS) released an updated

and more comprehensive set of specifications called the ASPRS Positional Accuracy Standards

for Digital Geospatial Data (2014). These specifications effectively represent the most thorough

and up-to-date reference for measuring and communicating the accuracy of geospatial data.


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3.0 Elevation Data Production

Elevation data can be acquired in many ways, but once brought into a Geographic Information

Systems (GIS) lab, it becomes geospatial data all the same. These points and lines are then

processed into end products designed to meet defined business data requirements. Perhaps

the most common elevation data product is the digital elevation model (DEM). The DEM is

defined as a bare-earth gridded raster representation of an area of interest georeferenced to the

Earth’s surface. For the purpose of this study, two different DEMs will be assessed using the

2014 ASPRS Standards; one created from LiDAR (LiDAR DEM) and the other from a delivered

point cloud created using Semi-Global Matching (SGM) and delivered as part of the

SCOOP2013 deliverables (SCOOP DEM).

3.1 Acquisition Techniques

The most effective way to acquire high-quality geospatial data over a large area is by way of

remote sensing. Remote sensing – obtaining data of objects indirectly, without physical contact

– can be used to acquire geospatial for use in GIS. There are three main forms of remote

sensing geospatial data acquisition currently in widespread use in Ontario: Light Detection and

Ranging (LiDAR), Semi-Global Matching (SGM), and Synthetic Aperture RADAR (SAR). This

report will compare data recently acquired using LiDAR and SGM.

Generally speaking, each different remote sensing method has its associated strengths and

weaknesses across multiple business purposes. For the purpose of this study, the effectiveness

of obtaining high-quality bare earth data representation will be the principal focus. It should be

kept in mind that there exist many other uses for remote sensing, with the derivation of a bare

earth DEM as just one use. With that being said, a balance must be struck in choosing

appropriate, and cost effective acquisition method(s) for the intended purpose. A thorough

understanding of the strengths and limitations of a given acquisition method enables accuracy

requirements to be met but not significantly exceeded.

3.1.1 Semi-Global Matching

A digital photogrammetric technique, SGM is a refined form of auto-extraction of precise and

accurate 3D masspoint information from overlapping stereo imagery. SGM can be classified

under the broader category of image matching techniques which also include pixel

autocorrelation, or, simply, elevation data extraction. What sets SGM apart from these other

approaches is that it “combines concepts of global and local stereo methods for accurate, pixel-

wise matching at low runtime” (Hirschmuller, 2011). After intensive analysis through

computation, the SGM technique produces data in the form of a point cloud. The use of the

SGM processing technqiue for SCOOP2013 was enabled by using a pushbroom sensor (Leica

ADS80-SH2) for data acquisition instead of the traditional fixed-frame sensor used in previous

Ontario Imagery Strategy acquisitions.


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3.1.2 LiDAR

LiDAR – or Light Detection and Ranging – is an efficient means of acquiring highly-accurate

elevation data for a given area. Whereas RTK GNSS can be used to capture targeted and

discrete features, LiDAR is essentially a scan of the Earth that results in a large dataset that can

be used to derive elevation data products.

The LiDAR system is comprised of a laser scanner mounted in a fixed or rotating-wing aircraft

alongside an inertial measurement unit (IMU) linked to an atomic clock and survey-grade GNSS

unit. In brief, the laser scanner scans the Earth from the belly of an aircraft, while the IMU

records orientation of the aircraft in the sky and the GNSS simultaneously measures the aircraft

location relative to a geodetic datum. The scanner measures the time it takes for each laser

scan pulse emitted to return to the sensor. Given that the speed of light can be considered

infinite due to the close relative proximity of the scanner to the Earth, a direct inference of time

can be determined as a function of distance. What is retrieved from the aircraft upon mission

completion is an irregularly-spaced mass of points known as a point cloud.

3.2 DEM Production

3.2.1 SCOOP DEM

For the purpose of this study, a digital surface model (DSM) point cloud was received from the

SCOOP2013 vendor, created using the semi-global matching technique. This approach

effectively produced an unclassified point cloud at 40 cm ground sample distance (GSD). This

DSM point cloud was imported into the Ganaraska Region Conservation Authority (GRCA)

Remote Sensing Lab and processed in effort to delineated ground points for the purpose of

DEM production. Using Inpho DTMaster software, the DSM point cloud was classified by way of

a three-step automated filtering process. After multiple iterations and manual inspection

routines, a classified ground point cloud was produced. This point cloud was then imported into

ESRI ArcGIS 10.2 software for DEM production. By way of the ESRI Terrain Dataset individual

classified ground point tiles were fused together and interpolated to produce a bare earth raster

DEM at 0.5 m ground resolution.

*See Appendix A for a detailed step-by-step account of the SCOOP DEM production process.

3.2.2 LiDAR DEM

A LiDAR-derived DEM acquired in 2006 at 0.5 m ground resolution of Midtown Creek

Catchment in Cobourg, Ontario was used for comparison in this study.


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4.0 Accuracy Assessment Procedures

4.1 Checkpoint Requirements

As required by the 2014 ASPRS Standards, vertical accuracy is tested “…by comparing the

elevations of the surface represented by the data set with elevations determined from an

independent source of higher accuracy…” which is defined as “…at least three times more

accurate than the required accuracy of the geospatial data set being tested”. This requirement

was satisfied by acquiring survey-grade checkpoints using Real Time Kinematic (RTK) GNSS

data captured in the field at centimetre-level accuracy, and classified by vegetative land cover

type. Data was captured in vegetated and non-vegetated terrain to enable the accuracy to be

tested and to ensure the results would be effectively communicated in terms of the overall

reliability of DEM across the entire area of interest. Remote sensing data capture can become a

challenge in vegetated areas in that the bare earth is obstructed, often resulting in less data

captured for bare earth representation.

A DEM is a continuous, gradient dataset with no discrete positions, thereby ruling out measuring

horizontal accuracy, with vertical accuracy as the sole indicator of its overall reliability. This

context calls for a more robust statistical approach than simply averaging the differences

between test and control datasets. In areas designated as “Non-vegetated” (bare soil, sand,

rocks, and short grass) as well as urban terrain (asphalt and concrete surfaces), elevation errors

can be assumed to follow a normal distribution. Given this assumption, accuracy statistics can

be represented as Non-vegetated Vertical Accuracy (NVA) at a 95% confidence interval.

In areas where vegetative cover is more prevalent (tall weeds and crops, brush lands, and fully

forested areas), error cannot be assumed to follow a normal error distribution. Therefore,

RMSEz-based statistics cannot be used to estimate vertical accuracy in these cases. Instead,

accuracy must be calculated as Vegetated Vertical Accuracy (VVA) at the 95th percentile of the

absolute value of vertical errors.

4.2 Testing Accuracy in GIS

Survey checkpoints were imported into the GIS lab in the form of a projected shapefile point

dataset. Once datums and projections were verified to be consistent across all datasets, the

checkpoint shapefile was used to extract coincident raster values for statistical analysis.


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5.0 Results

5.1 Vertical Accuracy

After statistical analysis of vertical accuracy assessment data, results were reported in the

current NVA (n = 25) and VVA (n = 20) statistics as well as legacy statistics for the purpose of

comparison.

Test DEM

RMSE (m)

LMAS RMSEz Non-Vegetated (cm)

NVA at 95% Confidence Level (cm)

VVA at 95th Percentile (cm)

Vertical Accuracy Class 2014

Equivalent Class 1 Contour Interval per ASPRS 1990 (cm)

Equivalent Class 2 Contour Interval per ASPRS 1990 (cm)

Equivalent contour interval per NMAS (cm)

FGDC National Standard for Spatial Data Accuracy - NSSDA - Vertical - 95% Confidence Level (cm)

LiDAR DEM (NVA)

0.274 0.451 27.4 53.7 N/A X 82.2 41.1 90.1 53.7

SCOOP DEM (NVA)

0.15 0.247 15.0 29.4 N/A X 45.0 22.5 49.3 29.4

LiDAR DEM (VVA)

0.254 0.418 N/A N/A 44.38 X N/A N/A N/A N/A

SCOOP DEM (VVA)

0.188 0.309 N/A N/A 46.02 X N/A N/A N/A N/A

Table 1 Vertical Accuracy Test Results in Current and Legacy Statistical Representations

5.2 Reporting Statements

*Note: Project data was acquired before 2014 ASPRS Standards released. Therefore, data

could not have been acquired to meet a specified accuracy class. For consistency, the reporting

statements are printed in full with the specified accuracy class denote by “X” in all cases.

5.2.1 LiDAR DEM Vertical Accuracy

“This data was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial

Data (2014) for a X cm RMSEz Vertical Accuracy Class. Actual NVA accuracy was found to be

RMSEz = 27.4 cm, equating to +/- 53.7 cm at 95% confidence level. Actual VVA accuracy was

found to be +/- 44.4 cm at the 95th percentile.”

5.2.2 SCOOP DEM Vertical Accuracy

“This data was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial

Data (2014) for an X cm RMSEz Vertical Accuracy Class. Actual NVA accuracy was found to be

RMSEz = 15.0 cm, equating to +/- 29.4 cm at 95% confidence level. Actual VVA accuracy was

found to be +/- 46.0 cm at the 95th percentile.”


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6.0 Discussion

6.1 Vertical Accuracy and Quality of Elevation Data

Vertical accuracy test results show the SCOOP-derived DEM outperforming the LiDAR-derived

DEM in both NVA and VVA for the Midtown Creek Catchment in Cobourg, Ontario. With an NVA

RMSEz value of 0.15 m, the SCOOP-derived DEM falls under the 15-cm Vertical Accuracy

Class as defined in the 2014 ASPRS Guidelines. This class also corresponds with the Level 2

Accuracy Category as defined in the 2009 Ontario Imagery and Elevation Guidelines (LMAS =

0.247 m). By meeting Level 2 mapping accuracy, SCOOP-derived DEM can be sufficient to use

in “densely to moderately populated urban areas that may or may not fall within the Regulated

Flood Line” (Ontario, 2009).

Alternately, the LiDAR DEM with an NVA RMSEz of 0.274 m and LMAS of 0.451 m, falls under

the Level 3 Accuracy Category. This category restricts appropriate use of this data to

“moderately or sparsely populated areas that are primarily surrounded by agricultural and/or

forested lands”. In terms defined by the 2014 ASPRS Standard, the LiDAR DEM falls under the

33.3 cm Vertical Accuracy Class.

Table 2 Vertical Accuracy/Quality Classes for Digital Elevation Data (ASPRS, 2014)


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6.2 Recommendations

The results presented by this report provide sound evidence of the potential for using SCOOP-

derived elevation data for DEM production. Further research to other elements of the 2014

ASPRS Standards would be of benefit prior to applying all elements of the Standards across the

whole suite of SCOOP2013 deliverables. Aerial triangulation standards are an intriguing

component to these new standards, enabling users for the first time to define an aerial imagery

acquisition in terms of the quantified level of accuracy a derivative elevation data products may

be required to meet. Further exploration into automating data production routines to this extent

could potentially yield great benefits in addressing costly imagery and elevation data needs.

Further to the above point, additional research with regards to establishing “appropriate use”

categories, similar to the 2009 Ontario Imagery and Elevation Guidelines, would make for a

powerful complimentary document, particularly with links to the 2014 ASPRS Standards. GNSS

technology, RTK or otherwise, allows for a thread to be drawn through acquisition, processing,

accuracy testing, and metadata. This integrated approach suggests that multiple business areas

can now also be served by coupled imagery/elevation acquisitions, further increasing Return on

Investment.

Lastly, remote sensing has proven to be a powerful means of acquiring high-quality topographic

data, however, regardless of how far data capture technology progresses, there will still be

areas deemed to be “low confidence” due to obstructions in birds-eye sight (among other

causes). Steps to integrate remotely sensed data products with on-the-ground surveying – be it

traditional surveying or land-based laser scanning – offers an effective solution to this persistent

issue.

The findings of this report provide a promising glimpse of what can be achieved by incorporating

and employing modern geospatial data acquisition and modeling technologies to serve well-

defined business data requirements, as well as address pronounced large scale elevation data

gaps across the Province of Ontario.


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7.0 References

ASPRS, 2014. ASPRS Positional Accuracy Standards for Digital Geospatial Data,

Photogrammetric Engineering & Remote Sensing, Volume 81, No. 3, 53 p., URL:

http://www.asprs.org/Standards-Activities.html.

Gehrke, S., Morin, K., Downey, M., Boehrer, N. and Fuchs, T., 2010. Semi-Global Matching: An

Alternative to LiDAR for DSM Generation?, Canadian Geomatics Conference and Symposium

of Commission I, ISPRS, June 2010, Calgary, Canada.

Government of Ontario, 2009. Ontario Imagery and Elevation Acquisitions Guidelines, Queen’s

Printer of Ontario.

Grand River Conservation Authority, 2011. DEM Development, Integrated Large Scale

Hydrology Pilot Tutorials, Cambridge, ON.

Hirschmüller, H., 2011. Semi-Global Matching – Motivation, Developments and Applications,

Invited Paper at the Photogrammetric Week, September 2011 in Stuttgart, Germany, pp. 173-

184.


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Appendix A

SCOOP DEM Production Workflow

Portions of the SCOOP DEM Production Workflow adapted from Grand River Conservation

Authority (2011).

1. Inpho DTMaster: Classify LAZ point cloud

3 step filter process:

o LiDAR_STRONG

1. Non-ground feature removal.

o THINOUT

1. Removal of unnecessary data points.

o GROSS_ERRORS

1. Removal of remaining outlier data points.

Figure 1 Filtered point cloud in Inpho DTMaster (GRCA, 2015)

2. Inpho DTMaster: DSM output (Classified LAS Files)

Export Raw Classified LAS

3. Inpho DTMaster: LAS to MP Feature Class

Export Raw Classified SHP


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Figure 2 Delineated ground points in ArcGIS 10.2 (GRCA, 2015)

4. ESRI ArcGIS 10.2: Create ArcGIS Terrain

Create Temp Terrain from Raw Classified SHP

Figure 3 Dynamic Temporary Terrain Dataset (GRCA, 2015).


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5. ESRI ArcGIS 10.2: Temp Raster DEM/Hillshade

Interpolate to Temp DEM/Hillshade

Figure 4 Temporary DEM ready for visual inspection (GRCA, 2015).

6. ESRI ArcGIS 10.2: Create “issue” areas

Manual inspection of 3D hillshade view.

Manual digitization of apparent issue areas relative to SCOOP Orthoimagery


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Figure 5 Issue areas digitized over Temporary Hillshade (top) and over associated orthoimagery (bottom), (GRCA, 2015)


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7. LAStools: Remove “issue” areas from LAS

Data points which intersect or overlap with issues areas deleted from Raw

Classified LAS

Produces Final Classified LAS, exported to Final Classified SHP

8. ESRI ArcGIS 10.2: Final Terrain

Final Classified SHP imported into Final Terrain Dataset

9. ESRI ArcGIS 10.2: Interpolated to raster DEM/Hillshade

Final Terrain Dataset interpolated to Final DEM

Figure 6 Final DEM of Midtown Creek Catchment - Cobourg, Ontario (GRCA, 2015)


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Appendix B

SCOOP DEM Non-Vegetated Areas Accuracy Test Statistics

PT_ID NORTHING EASTING ELEVATION DESCRIPTIO RASTERVALU Zdiff ZdiffSq SumDiffSq Avg RMSE

91 4871468.945 727142.4908 82.0989 QC-URB 82.17870331 -0.07980331 0.006368568 0.564392744 0.02257571 0.150252

90 4871470.84 727146.8765 82.0196 QC-URB 82.032341 -0.012741 0.000162333

89 4871472.341 727150.778 81.9221 QC-URB 81.97805023 -0.05595023 0.003130428

88 4871473.232 727153.2683 81.8713 QC-URB 81.96041107 -0.08911107 0.007940783

87 4871474.306 727156.1635 81.7931 QC-URB 81.79055786 0.00254214 6.46248E-06

31 4872001.794 727486.5829 90.7368 QC-URB 91.0162735 -0.2794735 0.078105437

30 4872000.931 727484.6651 90.7081 QC-URB 91.04251099 -0.33441099 0.11183071

29 4871999.894 727482.6208 90.6945 QC-URB 90.83576965 -0.14126965 0.019957114

28 4871999.046 727480.8241 90.6787 QC-URB 90.65982056 0.01887944 0.000356433

27 4871998.811 727478.8593 90.6339 QC-URB 90.50608826 0.12781174 0.016335841

26 4871218.409 727277.5039 77.7643 QC-OPEN 77.64246368 0.12183632 0.014844089

25 4871226.623 727275.3185 77.9602 QC-OPEN 78.13951111 -0.17931111 0.032152474

24 4871235.918 727272.9062 78.2404 QC-OPEN 78.27122498 -0.03082498 0.000950179

23 4871244.465 727270.968 78.5021 QC-OPEN 78.47808075 0.02401925 0.000576924

22 4871250.617 727268.0235 78.7216 QC-OPEN 78.71543884 0.00616116 3.79599E-05

21 4871246.766 727253.8513 79.6355 QC-URB 79.56039429 0.07510571 0.005640868

20 4871245.992 727255.666 79.5921 QC-URB 79.51275635 0.07934365 0.006295415

19 4871245.196 727258.1155 79.5639 QC-URB 79.44065857 0.12324143 0.01518845

18 4871234.887 727262.5234 78.9515 QC-URB 78.78374481 0.16775519 0.028141804

17 4871228.105 727265.2553 78.9398 QC-URB 78.91889954 0.02090046 0.000436829

16 4871084.993 727955.6698 76.4625 QC-OPEN 76.57291412 -0.11041412 0.012191278

15 4871084.563 727959.545 76.4513 QC-OPEN 76.62156677 -0.17026677 0.028990773

14 4871080.953 727963.3686 76.4407 QC-OPEN 76.68267822 -0.24197822 0.058553459

13 4871079.058 727967.539 76.4517 QC-OPEN 76.74103546 -0.28933546 0.083715008

12 4871082.237 727971.6115 76.5166 QC-OPEN 76.69683075 -0.18023075 0.032483123


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SCOOP DEM Vegetated Areas Accuracy Test Statistics


71 4872415.804 726880.0316 92.8635 QC-LONG 92.95661163 -0.09311163 0.008669776 0.7036679 0.035183 0.187572

70 4872415.356 726884.5033 92.9315 QC-LONG 93.0522995 -0.1207995 0.014592519

69 4872417.327 726890.0814 92.8746 QC-LONG 92.96954346 -0.09494346 0.009014261

68 4872413.786 726891.1896 93.3017 QC-LONG 93.20089722 0.10080278 0.0101612

67 4872415.461 726896.4028 93.3396 QC-LONG 93.3102417 0.0293583 0.00086191

66 4872432.505 726896.9554 91.9811 QC-BRUSH 91.96899414 0.01210586 0.000146552

65 4872432.905 726904.3706 92.1146 QC-BRUSH 92.06304169 0.05155831 0.002658259

64 4872431.985 726909.6184 92.2252 QC-BRUSH 92.27606964 -0.05086964 0.00258772

63 4872434.551 726911.3254 92.2354 QC-BRUSH 92.26078796 -0.02538796 0.000644549

62 4872435.74 726914.6692 92.4937 QC-BRUSH 92.42679596 0.06690404 0.004476151

11 4871101.376 727957.0285 78.1878 QC-LONG GRASS 78.17700958 0.01079042 0.000116433

10 4871107.616 727955.7401 77.9715 QC-LONG GRASS 78.42925262 -0.45775262 0.209537461

9 4871111.938 727957.4522 77.945 QC-LONG GRASS 78.05821228 -0.11321228 0.01281702

8 4871119.543 727958.3086 77.911 QC-LONG GRASS 77.77492523 0.13607477 0.018516343

7 4871120.857 727958.0668 77.8586 QC-LONG GRASS 78.04078674 -0.18218674 0.033192008

6 4871122.222 727955.8174 77.8634 QC-LONG GRASS 78.08285522 -0.21945522 0.048160594

5 4871339.507 727694.8504 79.665 QC-FOR 79.45975494 0.20524506 0.042125535

4 4871338.963 727699.1068 79.7416 QC-FOR 79.64500427 0.09659573 0.009330735

3 4871348.672 727704.576 80.2336 QC-FOR 79.72570801 0.50789199 0.257954274

2 4871351.109 727700.7811 80.3478 QC-FOR 80.48235321 -0.13455321 0.018104566


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LiDAR DEM Non-Vegetated Areas Accuracy Test Statistics


91 4871468.945 727142.4908 82.0989 QC-URB 81.98999786 0.10890214 0.011859676 1.876473942 0.075058958 0.273968899

90 4871470.84 727146.8765 82.0196 QC-URB 81.81999969 0.19960031 0.039840284

89 4871472.341 727150.778 81.9221 QC-URB 81.73999786 0.18210214 0.033161189

88 4871473.232 727153.2683 81.8713 QC-URB 81.68000031 0.19129969 0.036595571

87 4871474.306 727156.1635 81.7931 QC-URB 81.61000061 0.18309939 0.033525387

31 4872001.794 727486.5829 90.7368 QC-URB 90.62999725 0.10680275 0.011406827

30 4872000.931 727484.6651 90.7081 QC-URB 90.76999664 -0.06189664 0.003831194

29 4871999.894 727482.6208 90.6945 QC-URB 91 -0.3055 0.09333025

28 4871999.046 727480.8241 90.6787 QC-URB 91.18000031 -0.50130031 0.251302001

27 4871998.811 727478.8593 90.6339 QC-URB 91.09999847 -0.46609847 0.217247784

26 4871218.409 727277.5039 77.7643 QC-OPEN 77.56999969 0.19430031 0.03775261

25 4871226.623 727275.3185 77.9602 QC-OPEN 77.80000305 0.16019695 0.025663063

24 4871235.918 727272.9062 78.2404 QC-OPEN 78.16000366 0.08039634 0.006463571

23 4871244.465 727270.968 78.5021 QC-OPEN 78.33999634 0.16210366 0.026277597

22 4871250.617 727268.0235 78.7216 QC-OPEN 78.41999817 0.30160183 0.090963664

21 4871246.766 727253.8513 79.6355 QC-URB 79.44000244 0.19549756 0.038219296

20 4871245.992 727255.666 79.5921 QC-URB 79.38999939 0.20210061 0.040844657

19 4871245.196 727258.1155 79.5639 QC-URB 79.34999847 0.21390153 0.045753865

18 4871234.887 727262.5234 78.9515 QC-URB 78.73000336 0.22149664 0.049060762

17 4871228.105 727265.2553 78.9398 QC-URB 78.72000122 0.21979878 0.048311504

16 4871084.993 727955.6698 76.4625 QC-OPEN 76.13999939 0.32250061 0.104006643

15 4871084.563 727959.545 76.4513 QC-OPEN 76.18000031 0.27129969 0.073603522

14 4871080.953 727963.3686 76.4407 QC-OPEN 76.05999756 0.38070244 0.144934348

13 4871079.058 727967.539 76.4517 QC-OPEN 76 0.4517 0.20403289

12 4871082.237 727971.6115 76.5166 QC-OPEN 76.05999756 0.45660244 0.208485788


20

LiDAR DEM Vegetated Areas Accuracy Test Statistics


71 4872415.804 726880.0316 92.8635 QC-LONG 92.87000275 -0.0065 4.22858E-05 1.294012 0.064701 0.254363

70 4872415.356 726884.5033 92.9315 QC-LONG 92.95999908 -0.0285 0.000812198

69 4872417.327 726890.0814 92.8746 QC-LONG 92.87999725 -0.0054 2.91303E-05

68 4872413.786 726891.1896 93.3017 QC-LONG 93.26000214 0.041698 0.001738712

67 4872415.461 726896.4028 93.3396 QC-LONG 93.27999878 0.059601 0.003552305

66 4872432.505 726896.9554 91.9811 QC-BRUSH 91.83000183 0.151098 0.022830657

65 4872432.905 726904.3706 92.1146 QC-BRUSH 91.94000244 0.174598 0.030484308

64 4872431.985 726909.6184 92.2252 QC-BRUSH 92.12000275 0.105197 0.011066461

63 4872434.551 726911.3254 92.2354 QC-BRUSH 92.15000153 0.085398 0.007292899

62 4872435.74 726914.6692 92.4937 QC-BRUSH 92.43000031 0.0637 0.004057651

11 4871101.376 727957.0285 78.1878 QC-LONG GRASS 77.69999695 0.487803 0.237951816

10 4871107.616 727955.7401 77.9715 QC-LONG GRASS 77.52999878 0.441501 0.194923327

9 4871111.938 727957.4522 77.945 QC-LONG GRASS 77.56999969 0.375 0.140625233

8 4871119.543 727958.3086 77.911 QC-LONG GRASS 77.52999878 0.381001 0.14516193

7 4871120.857 727958.0668 77.8586 QC-LONG GRASS 77.55999756 0.298602 0.089163417

6 4871122.222 727955.8174 77.8634 QC-LONG GRASS 77.62000275 0.243397 0.059242221

5 4871339.507 727694.8504 79.665 QC-FOR 79.41999817 0.245002 0.060025897

4 4871338.963 727699.1068 79.7416 QC-FOR 79.41999817 0.321602 0.103427737

3 4871348.672 727704.576 80.2336 QC-FOR 79.87999725 0.353603 0.125034905

2 4871351.109 727700.7811 80.3478 QC-FOR 80.11000061 0.237799 0.05654855

large scale elevation data assessment of scoop 2013 ...€¦ · large scale elevation data...

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