1. Department of Geography
College of Science and Engineering
LIDAR: Case Studies Heiko Balzter
1st EUFAR FP7 Training Course on "ADvanced Digital Remote sensing in Ecology and earth Sciences Summer School (ADDRESSS), Tihany, Hungary, August 2010.

2. Estimating DBH and other tree variables in a Corsican Pine woodland stand from terrestrial laser scanner data Kevin Tansey, Nick Selmes, Andrew Anstee, Nick Tate, Anthony Denniss
RSPSoc 2008, Falmouth, 15-17th September 2008

3. Justification for study
Measurement of tree characteristics and scaling-up to the forest stand is a central feature of both commercial forestry and ecological studies1,2
Reliable inventory data are essential for modern forestry management allowing timber harvest forecasting and growth monitoring
1Russo, S.E. et al., 2007, Ecology Letters, 10,
2Hamilton, G.J., 1975, Forest Mensuration Handbook (Lon. HMSO: Forestry Commission Booklet 39).

4. Methods of inventory Manual inventory on a stand-wise basis
Photogrammetry3,4
SAR and InSAR5
Airborne laser scanner
Tree/stand height6,7, biomass8, and species9
3Korpela, I. et al., 2006, Forest Science, 52, 136–147.
4Næsset, E., 2002, Scandinavian J. Forest Res., 17,
5Balzter, H. et al., 2007, IJRS, 28,
6Lim, K. et al., 2003, Progress in Physical Geography, 27,
7Suárez, J.C. et al., 2005, Computers & Geosciences, 31,
8Lim, K. et al., 2002, Rem. Sens. Agric., Ecosys., and Hydro. IV Conf., Crete, Greece.
9Hill, R.A. et al., 2005, IJRS, 26,

5. Methods of inventory Terrestrial Laser Scanning
What tree variables can be derived?
Canopy structure10,11, height12
Mensuration (full plot/stand or single tree)
How automated are retrievals?
How accurate are retrievals?
10Danson, F.M. et al., 2007, IEEE Geosci. Rem. Sens. Lett., 4,
11Hopkinson, C. et al., 2007, IAPRS Vol. XXXVI, Part 3 / W52,
12Király, G. et al., 2007, IAPRS Vol. XXXVI, Part 3 / W52, pp

6. Study area Martinshaw Wood, Leicester Corsican Pine 52.66 N, -1.25 E
1000 stems/ha

8. Methodology Riegl LMS-Z420i 4 scan positions, placed 90 apart
Angular resolution of 0.12 degrees
point spacing of 0.01m at 5m, 0.04m at 20m
Scan area was 23 x 21m
Clipped/aligned scan of 2.7 million points
Partition between cloud and ground

9. Methodology Segment taken 1.27-1.33m above ground
X and y positions gridded at 0.01m
Circular Hough transformation13
Stem detection and measurement method
Seeks circles m increment
Clustering provided the centre of stem
DBH derived from mean radius of cluster
Non linear, least squares circle and cylinder fits14
Basal area, stem density, height and volume
13Illingworth et al., 1988, Computer. Vision, Graphics, and Image Processing, 44,
14Nocedal et al., 2006, Numerical Optimization (2nd Ed.). New York: Springer

11. DBH (cylinder fitting)
Results
All stems detected in the area
Clustering broke down at edge
Problems with low level branching/occlusions
Stem number
DBH (circle fitting)
DBH (cylinder fitting)
DBH (Hough transform)
Field DBH 1
0.38
0.40 2
0.148
0.16 3
0.31
0.30 4
0.3317
0.36
0.37 5 6 7
0.284
0.29 8
0.20
0.25

12. Results & discussion Cylinder fitting – RMSE = 0.037
Circle fitting – RMSE =
Simonse et al. (2003) reported 0.017m15
Hough transformation – RMSE = m
Time consuming nature of adjusting the threshold values
least squares estimation preferable
15Simonse et al., 2003, Proc. ScandLaser Sci. Work. Airb. Laser Scan. of Forests,

14. Circle fitting
Outlying braches

15. Stem density, basal area, tree height
Stem density and basal area were both measured successfully.
Density was measured at 1031 stems ha-1, and basal area recorded as 73.3 m2.
Measurement of tree top height in a plot of this density not possible.
Tree height could not be measured in the study
Stem taper showed consistent underestimation

16. Conclusion
DBH measured with different techniques compared well with field measurements, and least squares circle fitting proved to be the most precise method with a RMSE of m and short calculation times.
Tree height could neither be measured directly nor inferred from other measurements and, consequently, stem volume could not be calculated.
A key consideration in the feasibility of terrestrial laser scanning for widespread use is the efficiency of the analysis, which can be time consuming.
Aspects of the methodology are highly automated but require fine tuning to ensure success. Once the required thresholds of the various functions were derived, automation worked well,

17. Conclusion
Errors are most likely to occur in stem identification and DBH measurement. With the former, errors will be either false positives resulting from threshold values that are too low and the presence of low branching or ground vegetation, or missed stems resulting from threshold values that are too high.
DBH measurements should consider the degree of outlying points resulting from branching or other vegetation around 1.3 m, as these will interfere with least squares estimation
The use of the Gauss Newton algorithm allows confidence values to prevent the recording of most erroneous DBH values.
Future work will focus on forest and wood quality issues such as a more complete investigation into the detection of stem tapering for buckling of the stems, branch networks and their quality (living, dead/dry, dead) and stem straightness.

18. 3D Visualization of OS MasterMap
using height data from LiDAR
Cici Alexander1,2, Sarah Smith2, Claire Jarvis1, Nicholas Tate1, Kevin Tansey1
1Department of Geography, University of Leicester, LE1 7RH
2Ordnance Survey, Southampton, SO16 4GU

19. Data OS MasterMap LiDAR Point Clouds Drawbacks
No height information of buildings
No details of variation in heights within a building
No roof plan
Includes topographic information on every landscape feature including building footprints in the form of polygons
Light Detection And Ranging
Two returns – 1st pulse
(1st return height) and last pulse (last return height) for each point
Same first and last return heights indicate a non-penetrable object
Drawbacks
No information on building outlines
Ordnance Survey © Crown Copyright. All Rights Reserved
The Land-Form PROFILE digital elevation data from Ordnance Survey with 10m resolution was downloaded from Edina Digimap in NTF format
An ortho-rectified aerial photograph of the study area was used for understanding the study area and for visualization.
Land-Form PROFILE
Aerial Photograph

20. Buildings with Pitched Roofs
Aspect
330o
240o
150o
The four main aspects of buildings were calculated.
These were taken to be perpendicular to one another.
Roof polygons, so-called ‘surfaces’ were classified according to:
(aspect x - 45) < aspect < (aspect x + 45)

21. Visualising Buildings on the Terrain
Visualization from LiDAR points at 16ppm2

22. Comparison of LIDAR Point Densities
16 ppm2
1 ppm2

23. Comparison of LIDAR Point Densities
1 ppm2
16 ppm2
40 ppm2
1 ppm2
16 ppm2
40 ppm2
Flat roof buildings detected
268
299
178
Pitched roof buildings detected
377
417
540
Overall accuracy (529 buildings)
73%
77%
86%

24. Terrestrial Activities

25. Summary An enthralling technology for surface expression and change
Topography and intensity information
Space, airborne and terrestrial
Application domain very wide