1. Extracting cross sections and water levels of minor streams and ditches using LiDAR point data
Jennifer Roelens, Stefaan Dondeyne, Jos Van Orshoven & Jan Diels
Division of Soil- and Water Management
KU Leuven
Contact:

2. Ditch characterization
Start of with the base map for water courses of Flanders
Different classes: …
Ditches contain 30 % of the length of all the mapped water courses of Flanders  6000 km
Add another 30 to 50 % to get all the ditches
Characteristics for the navigable ones and first order: characteristics are known and measured every year
 Even bathymetry
Examples of two ditches
To determine their characteristics in the field  Long time
Why do we want this information?
Ditches have a big impact in and around agricultural fields as they fasten the transport of nutrients to the surface water.
In Flanders we have a big problem with nitrate and fosfor pollution due to the intensive use of manure on our fields.
Navigable
First category
Second category
Third category
Ditch of common interest
Non classified

3. Airborne LiDAR data LiDAR campaign 2013 – 2015, Flanders, Belgium
Winter seasons
16 points/m²
Classification: ground, non-ground, water
Intensity (λ = 1064 nm)
Max xy dist = 0.5 m
RMSExy = 0.10 m
RMSEz = 0.05 m
RGB res = 0.10 m
Luckily, a campaign was set up for the whole of Flanders to get high-resolution topographic data.
Done with airborn LiDAR
Mention characteristics
NIR LiDAR beam: disadvantage
Simultanuously RGB imaging with a resolution of 10 cm
Coupled to the points
Every point has its RGB values

4. ‘The Doode Bemde’ Trimble RTK GPS RMSExy = 0.01 m RMSEz = 0.02 m
Study area: Doode Bemde
Former agricultural area changed to nature reserve
Measured 3 km of ditches and measured 153 cross-sectional profiles using a RTK GPS
Accuracy
No maintance of the ditches, so there is sludge accretion and a lot more vegetation in the ditches
Trimble RTK GPS
RMSExy = 0.01 m
RMSEz = 0.02 m

5. Cross-sectional profiles: point selection
Back to the LiDAR data
Due to the small dimensions of the ditches, it was not possible to use the rasterized images of 1m provided by the Agency for Spatial Information Flanders, so we decided to work with the point data to not get any loss of information for these small elevation differences
We have to get the essential points that make up the cross-sectional profile
To do so, the first step was to remove as much of the data we didn’t need, to improve the speed of our calculations and only to get the essential data
The non-ground points were removed and a longitudal buffer was made around the ditch, so that all the points falling out of this buffer could be removed
As not a lot of points will fall exactly on the exact position of the cross-section, a cross-sectional buffer was implemented. Different buffer widths were used to see if this had an influence on the result.
All points falling into this area were considerd a part of the cross-sectional profile and were projected onto the cross-sectional plane

6. Cross-sectional profiles: modelling
Because we’re working with the point cloud data, a profile had to be fitted on the resulting points
We used the SLM tool for this (Mathworks)
Works with spline functions, you can adjust the parameters to your needs
We used a linear and cubic spline profile
Based on the lowest RMSE value, we decided which one was further used

7. Cross-sectional profiles: characteristics
This is how we calculated the width and cross-sectional area
Two maxima, lowest bank projected on the other  bankfull width
Area under bankfull width as cross-sectional area

8. Cross-sectional profiles: characteristics
A first result I want to discuss is the mean vertical deviation between the extracted and reference profile measured in the field
The vertical deviation goes from - 20 centimetres to over + 20 centimetres
There are only a few overestimations of the profile and I’ll come back to that.
Quite homogeneous along the ditch
If we compare the errors made in the centre and on the banks of the ditches, we can see that the biggest contribution to the error comes from the centre of the ditch (water level). A lot of points were reflected in the centre of the ditch
and a small part contribution of vegetation on the banks
Ditch width
We will not discuss all the results here, because I’d like to go to the next part and give some more information there
R² = 0.87
Mean error = m
-0.20 m to m
-0.10 m to 0 m
0 m to 0.10 m
0.10 m to 0.20 m
>0.20 m

9. Cross-sectional profiles: classification
Data gap > 0.5 m
To know which profiles were dry and which had standing water in them, a classification was made based on the characteristics of the points in the profile.
In a classic example of a ditch profile, the NIR beam will be absorbed and leave a gap in the data profile. To decide if it was a gap caused by the water level, we took a threshold of 0.5 m, because this was the maximum planimetric distance of two points in the LiDAR point cloud
Example on the right
Due to the gap, it was difficult to model the profile curve and large peaks and valleys
For the next class, we wanted to make use of the RGB and intensity data
NDWI values

10. Cross-sectional profiles: classification
Intensity correction (Ahokas et al., 2006 & Kaasalainen et al., 2011) 𝐼 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑒𝑑 = 𝐼 𝑜𝑟𝑖𝑔𝑖𝑛𝑎𝑙 . 𝑅 𝑖 2 𝑅 𝑟𝑒𝑓 cos 𝛼 𝑇 𝐸 𝑇𝑟𝑒𝑓 𝐸 𝑇𝑗 (1)
Corrected intensity values as proxy for NIR band
Calculate normalized indices NDWI & NDVI
𝑁𝐷𝑊𝐼= 𝐺𝑟𝑒𝑒𝑛−𝑁𝐼𝑅 𝐺𝑟𝑒𝑒𝑛+𝑁𝐼𝑅 (2)
𝑁𝐷𝑉𝐼= 𝑁𝐼𝑅−𝑅𝑒𝑑 𝑁𝐼𝑅+𝑅𝑒𝑑 (3)
To do this, first of all the intensity values needed to be corrected in and between flights to be able to compare them with each other for each point in our dataset. This was done with this formula.
Because these corrected intensity values were found to be correlated with target reflectance values in the NIR, the corrected intensity values could be used as proxy for the NIR band.
Out of this, the NDVI and NDWI could be calculated

11. NDWIcentre > NDWIbanks
Data gap > 0.5 m
Data gap < 0.5 m
NDWIcentre > NDWIbanks
Data gap < 0.5 m
Variancecentre < 0.005
Furhter, we will look at profiles that have reflectances in the centre of the ditch
For the second step in the classification, we looked at the difference in NDWI values between the banks and the centre of the ditch.
NDWI centre larger than one on the bank, we were sure that there was water present in the ditch and the reflectances were from a small biofilm or small vegetation in the ditches. The signal of the LiDAR beam footprint that came back was combined with water reflectance.
For the third class, this wasn’t the case. When larger vegetation comes into play, we searched for another variable that would be able to make us differentiate between the dry ditches and wet ditches with vegetation.
As the ditches were al vegetated, the vegetation will cause for a larger variance in elevation than the one were there is standing water.
So now we have four classes where three of them contain standing water. The water level for these ditch profiles was extracted as the elevation of the lowest point in the cross sectional profile
Data gap < 0.5 m
Variancecentre ≥ 0.005
NDWI values

12. Water level
Point WATINA
Date WATINA
Level piezometer (m TAW)
Date LiDAR
Water level LiDAR (m TAW)
Distance to piezometer (m)
Difference water level (m)
DYLB065
26/02/2014
27.04
02/03/2014
26.67
6.66
- 0.37
DYLP047
26.07
26.77
16.86
0.70
DYLP085
28/01/2014
26.89
0.40
- 0.22
DYLP087
26.54
26.64
7.55
0.10
DYLP086
27.08
27.18
- 0.54
The extracted water levels were compared with piezometer measurements from the WATINA network. Network of piezometers of all the nature reserves in Flanders.
In the last columns you see the differences and I saw today that these are in line with the validation of UAV measurements for detecting water levels.
The validation of the measurements would be more accurate if there was data on the exact date of the LiDAr survey itself. Also to estimate better the accuracy of our classification result.

13. Conclusion
LiDAR point data are valuable for efficient ditch characterization
Mean Vertical Deviation 0.14 m  Rel. Area Difference -0.48
Contribution of water level & vegetation
Robust method
Independent of buffer width
Small error variation along ditch
MAD: should compare to agricultural ditches in other situations.
There are studies that try to guess the area under water for larger water courses, but we don’t know if they will also be usefull for small ditches

14. Future research Extract location of ditches Connectivity
Impact on nutrient and water discharge modelling

15. Contact: jennifer.roelens@kuleuven.be
Thank you!
Contact:

16. Cross-sectional profiles: characteristics
Vertical deviation
(b) Mean area
difference

17. Intensity correction