Characterizing regional dikes with LiDAR using ArcGIS 10 by Grontmij

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For those seeking presentations on #LiDAR analysis based on
point clouds; you will not find it in this presentation.

This presentation is about the analysis of raster data, derived
from LiDAR data, that has been created specifically for the
Water Boards in the Netherlands (bare earth, 0.5 m resolution).

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 About two thirds of the Netherlands is vulnerable to flooding
 1953 North Sea flood; 9% of total Dutch farmland flooded, 1835 people
killed, 30000 animals drowned, 47300 buildings damaged
 Long history of reclamation of marshes and fenland, resulting in
some 3,000 polders
 There are about 14.000 km of regional dikes in the Netherlands

Sources:
 Wikipedia, Flood control in the Netherlands
 Wikipedia, North Sea flood of 1953
 Wikipedia, Polders and the Netherlands
 Deltawerken online
 Waternet, Flood control and protection

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 Determination of dike top height for five-year legal assessment
 Determination of dike strength on representative profiles
 Mapping of primary and regional dikes and embankments

Doing that manually is a time consuming effort …

… the alternative is using GIS and LiDAR data

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(Up-to-date Height Model of the Netherlands)

 AHN is funded by the Directorate General for
Public Works and Water Management of the
Dutch Government and the 26 Regional Water
Boards
 The main purpose of AHN is to get a highly
detailed representation of the bare earth.
Objects (houses, trees, etc.) are filtered.

Available
Spring 2012
Source:
Spring 2013
 AHN website, availability of AHN2 (Dutch)

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 Managing more than 600 km of regional dike in the Northern
part of NL and about 66 km of sea dikes.

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 Pre-process AHN2 data
 Correct location of the dike based on AHN2
 Divide line representing the dike in parts of 100m
 Determine the lowest point per 100m (H100Min)
 Create cross profile AHN2 on each H100Min
 Analyze standard profiles
 Determine maximum width Bmax
 Determine the design profile
 Iteration to correct profiles

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 Necessary to filter outliers
 Choose filter small to avoid puling the initial dike track away from
the water -> use a Focal mean 3x3
 Fill up small areas with NoData

Unfiltered Filtered

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 Step size interval on centerline (10m)
 Maximum search tolerance (5m)

 Evaluate several methods: Without step size and search tolerance

 Search highest point in original AHN2
 Search highest point in filtered AHN2
 Determine highest point weighted by distance from
centerline and using a minimal increment (e.g., 2cm/m)

 Correct original centerline to create a new centerline based
on the elevation data.

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 Influence of rising “hinterland” at small dikes, pulling the line
away from the actual dike 1
 Small outliers can create large errors 2
 Resulting centerline is less gradually 3

1

2

3

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 Influence of rising “hinterland” at small dikes remains 1

 Small outliers are removed 2
 Resulting centerline is a bit more gradually 3

1

2

3

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 Influence of rising “hinterland” at small dikes is less apparent 1
 Result looks better, but… creates new inconsistencies 2

2 1

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 Alternative: determine the maximum deviation of two
consecutive points (exclude red point, see 1 )
 Incorrect position of the centerline will have consequences

1

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 The corrected centerline is divided into parts of 100m. When a
part is smaller than 50m it is added to the previous part.
 Gather statistics per part
(min, max, mean, etc)

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 For each part the location of the lowest point H100Min is
determined

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 On each H100Min location a cross profile is drawn (100m)
 Step size (precision) is pixel size of AHN2 (0.5m)
 Store as XYZ and dZ (=distance vs. elevation) lines

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 Distance d (horizontally) versus Z (vertically)
Z (* factor 5)

L R

Distance from H100min (d)
H100min
First NoData L and R from H100min

Classic example of a centerline
that doesn’t follow the highest
part of the dike and causes the
H100min to be located to low.

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 4 standard trapezoid profiles are fitted at the highest position
centered underneath the H100min point
Exaggeration Z-axis is factor 5

4 standard trapezoid profiles defined by widths 1m and 2m versus slopes 1:1.5 and 1:2

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 The highest trapezoid profile is selected. When more than 1
profile have the highest position, the widest is chosen.
 If more than 1 option remains, the one with the slightest
inclination is chosen.

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 Try to extent the best standard trapezoid profile by
determining the maximum width Bmax at that height.

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 Over 6000 locations have been processed
 In about 1300 locations the results were different than expected,
due to errors in the location of the original dike line
 In the second part of the project, code was developed to correct
these situations
Dike is here

Dike is NOT here

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 Does not have to be centered at H100min
 Should not cross NoData (probably water) After

 Should limit search to 5 meter L and R iterations

H100min

Last point
Last point before NoData
before NoData on right side
on left side

Standard trapezoid
L1 before iteration

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 All 4 standard trapezoids should be evaluated
 Best option is selected

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 Bmax is determined for new location of best trapezoid
H100min Max search tolerance 5m

Bmax of best
standard
trapezoid

Best standard trapezoid
after iteration

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Z

 3D view of a location: X
Y

First result
before correction
Second result
after correction
with better position

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 Water Board Noorderzijlvest is using these results to fill their
database, fieldwork would have required many months more.
 Relatively large datasets can be used for this analysis (a raster
dataset of 40Gb was used, but can be much bigger too)
 Process is flexible and can be used for any Water Board

 Generation of centerline should be done implementing the
techniques developed for iteration of the standard trapezoids
 Centerline should have correct position or the results will not
be reliable

ArcGIS 10.1 beta 2 was used for this analysis, but ArcGIS 10.0 or 9.3.x can be used as well.

http://twitter.com/#!/XanderBakker

http://nl.linkedin.com/in/xanderbakker
Xander Bakker
Senior GIS Advisor
Xander [DOT] Bakker [AT] Grontmij [DOT] NL

http://software.grontmij.nl

Grontmij Netherlands BV :: GIS & ICT – GIS Team :: http://www.Grontmij.com :: +31 30 220 79 11

Characterizing regional dikes with LiDAR using ArcGIS 10 by Grontmij

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