Challenges of Estimating
Trees Height via LIDAR
Based on Point CloudStudy of European Larch (Larix decidua)
and Norway Spruce (Picea abies).
Adam Młodzianowski
LIDAR -Light Detection And
Ranging
The goal of the study
• Which of three based on point cloud toppercentiles (95th, 99th, 100th) the mostaccurately predict tree height?
• Check if height measurements based on 100thpercentile overestimate result.
• Investigate the accuracy of segmentationalgorithm.
Why height?
Key attribute estimated in most forest inventories.
Base for quantitative analysis of forests:• biomass, • volume,• carbon stores etc.
Effective management.
Used for calculating indicies e.g. Site Index.
Study area (1)
Góry Stołowe (Table mountains) NP
Study area (2)
Overview of the Spruce plot
Data (1)
Flight height 700 m
Width of strip 430 m
Distance from
adjacent strips
214 m
Coverage of strips 50% ≈ 215 m
Flight speed 120 kn ≈ 216
km/h
Laser pulse
repetition
frequency
100 kHz
Scanning frequency 51 kHz
Scanning angle +/- 18º
Laser scanner data were
collected in the period of
14.08 – 23.09.2007.
Altman's Optech 3100 System
Flight parameters
Data (2)710.580
LIDAR points
459.620
Spruce plot
75.761
Within crowns
1.641Used for
calculation
Methods (1)
Software:
TreesVis
ArcGIS 10
SPSS Statistics 20
Methods (2)
Automatic single tree segmentation
CHM
Median filter
Primarysegmentation
Layer selection
Final filtering
Results (1)
Norway Spruce European Larch
80%
17%
3%
84%
15%
1%
Automatic Single Tree Segmentation
Results (2)
Norway Spruce European Larch
Bias and Root Mean Square Error
-3,09
-1,27-1,01
2,3
1,31 1,21
-3,5
-2,5
-1,5
-0,5
0,5
1,5
2,5
3,5
95th 99th 100th
-1,85
-0,8-0,4
1,68
0,74 0,55
-3,5
-2,5
-1,5
-0,5
0,5
1,5
2,5
3,5
95th 99th 100th
Results (3)
Norway Spruce European Larch
Regression analysis – 100th percentile
�� Linear = 0,978 �
� Linear = 0,952
0,965
0,957
0,9
0,91
0,92
0,93
0,94
0,95
0,96
0,97
0,98
0,99
1
Own study Stephens et al.(2012)
Coefficient of determination
Results (4)
Bias and RMSE Coefficient of determination
Comparison of the results
-0,4
-1,13
-0,14
0,55 0,63
0,98
1,35
-1,5
-1
-0,5
0
0,5
1
1,5
Own study Persson etal. (2002)
Hyyppä etal (2000)
Kwak et al.(2007)
Conclusion
• Small-footprint LIDAR systems have potential forthe estimation of individual tree height of coniferspecies.
• Under given conditions maximum heightpercentile derived from ALS point cloud is themost accurate metrics in tree height estimation.
• Point cloud based metrics tend to underestimateresults.
Further research
• Other tree species have to be investigated –including hardwoods.
• Influence of the stand age on heightestimation.
• Data filtering.
Thank you for attention.
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