Jim Thompson, Eric Anderson, & Rob Austin NC State University, Department of Soil Science Jim...

Jim Thompson, Eric Anderson, & Rob AustinNC State University, Department of Soil Science

Jim Thompson, Eric Anderson, & Rob AustinNC State University, Department of Soil Science

NC STATE UNIVERSITY DEPARTMENT OF SOIL SCIENCE

Horizontal Resolution and Data Density Effects on

LIDAR-based DEM

Horizontal Resolution and Data Density Effects on

LIDAR-based DEM

NC STATE UNIVERSITY

OutlineOutline

Digital elevation models (DEM)Digital elevation models (DEM)– Terrain attributes calculated from DEMTerrain attributes calculated from DEM

– Horizontal resolution effectsHorizontal resolution effects

– Data density issuesData density issues

LIDAR Data Density vs. DEM QualityLIDAR Data Density vs. DEM Quality– MethodsMethods

– ResultsResults

– Conclusions Conclusions

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ElevationElevation

400 m 422 m

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Terrain AttributesTerrain Attributes

Slope gradientSlope gradient

Slope aspectSlope aspect

Upslope contributing areaUpslope contributing area

Slope curvatureSlope curvature– Profile curvatureProfile curvature

– Contour curvatureContour curvature

– Total curvatureTotal curvature

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Slope GradientSlope Gradient

0% 17%

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Upslope Contributing AreaUpslope Contributing Area

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Profile CurvatureProfile Curvature

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Topsoil ThicknessTopsoil Thickness

0 m 2 m

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DEM ResolutionDEM Resolution

0% 17%

0% 15%

10 m DEM10 m DEM

30 m DEM30 m DEM

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Horizontal Resolution EffectsHorizontal Resolution Effects

Decreasing the horizontal resolution produces Decreasing the horizontal resolution produces (Thompson and Bell, 2001):(Thompson and Bell, 2001):

– lower slope gradients on steeper slopeslower slope gradients on steeper slopes

– steeper slope gradients on flatter slopessteeper slope gradients on flatter slopes

– narrower ranges in curvaturesnarrower ranges in curvatures

– larger catchment areas in upper landscape positions larger catchment areas in upper landscape positions

– lower catchment areas in lower landscape positionslower catchment areas in lower landscape positions

and leads to the loss of small-scale featuresand leads to the loss of small-scale features

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DEM Horizontal ResolutionDEM Horizontal Resolution

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DEM ResolutionDEM Resolution

0% 17%

0% 15%

10 m DEM10 m DEM

30 m DEM30 m DEM

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LIDAR-Based DEMLIDAR-Based DEM

Interpolation of spot elevation dataInterpolation of spot elevation data

Vertical precision of 0.1 mVertical precision of 0.1 m

Horizontal resolutionHorizontal resolution– Best possible for any given applicationBest possible for any given application

NC Floodplain Mapping Program LIDAR DataNC Floodplain Mapping Program LIDAR Data– Bare earth mass dataBare earth mass data

– 20 ft (~6 m) gridded DEM20 ft (~6 m) gridded DEM

– 50 ft (~15 m) gridded DEM50 ft (~15 m) gridded DEM

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LIDAR Data DensityLIDAR Data Density

4000-35,000 laser pulses per second4000-35,000 laser pulses per second

2-7 returns collected for each laser pulse 2-7 returns collected for each laser pulse

25,000 points per square mile25,000 points per square mile

ABUNDANCE OF DATAABUNDANCE OF DATA

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Data ReductionData Reduction

LIDAR tileLIDAR tile LIDAR tileLIDAR tile

50% Test 50% Test 50% Test 50% Test 50% Validation 50% Validation 50% Validation 50% Validation

25% Test25% Test25% Test25% Test

10% Test10% Test10% Test10% Test

5% Test5% Test 5% Test5% Test

1% Test1% Test 1% Test1% Test

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50 25 10 5 1

% of Original Density

0

10

20

30

40

RM

SE

(cm

)

IDW

OK

50 25 10 5 1

% of Original Density

0

10

20

30

40

RM

SE

(cm

)

IDW

OK

}}10 cm10 cm

IDW vs. OKIDW vs. OK

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ObjectivesObjectives

Produce a series of DEM at different horizontal Produce a series of DEM at different horizontal resolutions at multiple LIDAR data densitiesresolutions at multiple LIDAR data densities

Compare each of these DEM to a DEM Compare each of these DEM to a DEM produced from 100% of the LIDAR dataproduced from 100% of the LIDAR data

Determine the optimum LIDAR point density Determine the optimum LIDAR point density suitable for producing a DEM at a given suitable for producing a DEM at a given horizontal resolutionhorizontal resolution

Evaluate the effects of LIDAR data density on Evaluate the effects of LIDAR data density on the production of DEM at different resolutionsthe production of DEM at different resolutions

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OutlineOutline

Digital elevation models (DEM)Digital elevation models (DEM)– Terrain attributes calculated from DEMTerrain attributes calculated from DEM

– Horizontal resolution effectsHorizontal resolution effects

– Data density issuesData density issues

LIDAR Data Density vs. DEM QualityLIDAR Data Density vs. DEM Quality– MethodsMethods

– ResultsResults

– Conclusions Conclusions

NC STATE UNIVERSITY

Study SiteStudy Site

NC STATE UNIVERSITY


Hofmann ForestHofmann Forest– 32,500 ha forest ecosystem32,500 ha forest ecosystem

– Lower Coastal PlainLower Coastal Plain

– >90% forested>90% forested

– 12 to 20 m above MSL12 to 20 m above MSL

LIDAR DataLIDAR Data– NC Floodplain Mapping ProgramNC Floodplain Mapping Program

– 61 LIDAR tiles61 LIDAR tiles

– 9,000,000 LIDAR points9,000,000 LIDAR points

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100%100%

50%50%

25%25%

10%10%

5%5%

1%1%

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DEM5DEM55050DEM5DEM55050DEM10DEM105050

DEM10DEM105050

Data Reduction and ComparisonData Reduction and Comparison

Data Data ReductionReduction

Data Data ReductionReduction

DEM30DEM305050DEM30DEM305050

Compare Compare ElevationsElevations








Generate DEM Generate DEM (ANUDEM)(ANUDEM)


100% LIDAR100% LIDARData SetData Set





50% LIDAR50% LIDARData SetData Set25% LIDAR25% LIDAR

Data SetData Set

25% LIDAR25% LIDARData SetData Set DEM30DEM302525

DEM30DEM302525 DEM10DEM102525DEM10DEM102525 DEM5DEM52525

DEM5DEM5252510% LIDAR10% LIDARData SetData Set









DEM5DEM511

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DEM ComparisonsDEM Comparisons

Evaluate the effects of data density at each Evaluate the effects of data density at each horizontal resolutionhorizontal resolution

Cell-by-cell comparisonCell-by-cell comparison– Assumed DEM created from the complete LIDAR data set Assumed DEM created from the complete LIDAR data set

were the “best” DEMwere the “best” DEM

– Comparisons always made back to DEM created using the Comparisons always made back to DEM created using the total original data set total original data set

Elevation values compared using paired t-testsElevation values compared using paired t-tests

Differences regressed against data densityDifferences regressed against data density

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Data Reduction and Point DensityData Reduction and Point Density

30 m 10 m 5 m

Data reduction Designation Points/cell Designation Points/cell Designation Points/cell

100 DEM30100 26.16 DEM10100 2.90 DEM5100 0.72

50 DEM3050 13.08 DEM1050 1.45 DEM550 0.36

25 DEM3025 6.54 DEM1025 0.73 DEM525 0.18

10 DEM3010 2.62 DEM1010 0.29 DEM510 0.07

5 DEM305 1.31 DEM105 0.15 DEM55 0.04

1 DEM301 0.26 DEM101 0.03 DEM51 0.01

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Elevation DifferencesElevation Differences

30 m 10 m 5 m

Data reduction Difference (m) P value Difference (m) P value Difference (m) P value

50 -0.01 0.4379 -0.01 0.7680 -0.01 0.1824

25 -0.02 0.1095 -0.02 0.0552 -0.03 0.0145

10 -0.02 0.1830 -0.03 0.0117 -0.04 0.0007

5 -0.03 0.0441 -0.04 0.0086 -0.06 0.0006

1 -0.06 0.0013 -0.04 0.0047 -0.07 0.0001

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All reduced density DEM overestimated All reduced density DEM overestimated elevations for all horizontal resolutionselevations for all horizontal resolutions

As data density decreased, the elevation As data density decreased, the elevation difference increaseddifference increased

As mean points per grid cell decreased, the As mean points per grid cell decreased, the elevation difference increasedelevation difference increased

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0.00 0.10 0.20 0.30 0.40 0.50

Mean points per grid cell

-0.08

-0.06

-0.05

-0.03

-0.02

0.00

Mea

n d

iffe

ren

ce (

m)

5 m5 m

0.00 0.50 1.00 1.50 2.00


-0.08

-0.06

-0.05

-0.03

-0.02

0.00M

ean

dif

fere

nce

(m

)

10 m10 m

0 2 4 6 8 10 12 14


-0.08

-0.06

-0.05

-0.03

-0.02

0.00

Mea

n d

iffe

ren

ce (

m)

30 m30 m

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Data DensityData Density

30 m 10 m 5 m

Data reduction Points/ha P value P value P value

50 145 0.4379 0.7680 0.1824

25 73 0.1095 0.0552 0.0145

10 29 0.1830 0.0117 0.0007

5 15 0.0441 0.0086 0.0006

1 3 0.0013 0.0047 0.0001

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Data Density RequirementsData Density Requirements

55 1010 1515 2020 2525 3030

DEM horizontal resolution (m)DEM horizontal resolution (m)

00

3030

6060

9090

120120

150150

LID

AR

(p

oin

ts h

aL

IDA

R (

po

ints

ha-1-1

)) Excessive data densityExcessive data density

Threshold data densityThreshold data density

Insufficient dataInsufficient data densitydensity

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ConclusionsConclusions

Target resolution of DEM determines the level Target resolution of DEM determines the level of acceptable LIDAR data reductionof acceptable LIDAR data reduction

Higher density LIDAR data needed to model Higher density LIDAR data needed to model higher resolution topographic featureshigher resolution topographic features

Results are a function of landscape Results are a function of landscape morphology and land usemorphology and land use

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DEM PrecisionDEM Precision

0% 17%

0% 20%

0.1 m precision0.1 m precision


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Vertical Precision EffectsVertical Precision Effects

Decreasing the vertical precision of the DEM Decreasing the vertical precision of the DEM produces (Thompson and Bell, 2001): produces (Thompson and Bell, 2001):

– more flat cells (slope gradient = 0 and curvature = 0) more flat cells (slope gradient = 0 and curvature = 0)

– more steep cells more steep cells

– a wider distribution in curvaturesa wider distribution in curvatures

– discretization of elevation values, which leads to discretization of elevation values, which leads to discretization of other terrain attributes and reduced discretization of other terrain attributes and reduced similarity of data setssimilarity of data sets

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DEM Vertical PrecisionDEM Vertical Precision

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DEM PrecisionDEM Precision

0% 17%

0% 20%



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0.00 0.10 0.20 0.30 0.40 0.50


-0.08

-0.06

-0.05

-0.03

-0.02

0.00

Me

an

dif

fere

nc

e (

m)

0.00 0.50 1.00 1.50 2.00


-0.08

-0.06

-0.05

-0.03

-0.02

0.00

Me

an

dif

fere

nc

e (

m)

0 2 4 6 8 10 12 14


-0.08

-0.06

-0.05

-0.03

-0.02

0.00

Me

an

dif

fere

nc

e (

m)

NC STATE UNIVERSITY

0.00 0.10 0.20 0.30 0.40 0.50


-0.08

-0.06

-0.05

-0.03

-0.02

0.00

Mean difference (m)

0.00 0.50 1.00 1.50 2.00


-0.08

-0.06

-0.05

-0.03

-0.02

0.00

Mean di

fferenc

e (m)

0 2 4 6 8 10 12 14


-0.08

-0.06

-0.05

-0.03

-0.02

0.00

Me

an

d

iffe

re

nc

e (m

)

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Collected as point filesCollected as point files

Linear interpolation techniques are needed for Linear interpolation techniques are needed for improved use of LIDAR (Lloyd and Atkinson, improved use of LIDAR (Lloyd and Atkinson, 2002)2002)

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Profile CurvatureProfile Curvature

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100%

1%

Jim Thompson, Eric Anderson, & Rob Austin NC State University, Department of Soil Science Jim...

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