Challenges of Estimating Trees Height via LIDAR Based on Point

Challenges of Estimating
Trees Height via LIDAR
Based on Point Cloud
Study 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 top
percentiles (95th, 99th, 100th) the most
accurately predict tree height?
• Check if height measurements based on 100th
percentile overestimate result.
• Investigate the accuracy of segmentation
algorithm.
Why height?
Key attribute estimated in most forest inventories.
Base for quantitative analysis of forests:
• biomass,
• volume,
• carbon stores etc.
Used for calculating indicies e.g. Site Index.
Effective management.
Study area (1)
Góry Stołowe (Table mountains) NP
Study area (2)
Overview of the Spruce plot
Data (1)
Laser scanner data were
collected in the period of
14.08 – 23.09.2007.
Altman's Optech 3100 System
Flight parameters
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
Scanning frequency
100 kHz
Scanning angle
+/- 18º
51 kHz
Data (2)
710.580
459.620
75.761
LIDAR points
Spruce plot
Within crowns
1.641
Used for
calculation
Methods (1)
Software:
TreesVis
ArcGIS 10
SPSS
Statistics 20
Methods (2)
Automatic single tree segmentation
CHM
Median filter
Primary
segmentation
Layer selection
Final filtering
Results (1)
Automatic Single Tree Segmentation
Norway Spruce
European Larch
1%
3%
15%
17%
80%
84%
Results (2)
Bias and Root Mean Square Error
Norway Spruce
European Larch
3,5
2,5
3,5
2,3
2,5
1,31
1,5
1,68
1,21
1,5
0,74
0,5
0,5
-0,5
-0,5
-1,5
-1,27
-1,01
-2,5
-3,5
-3,09
95th
-0,4
-0,8
-1,5
-2,5
0,55
-1,85
-3,5
99th
100th
95th
99th
100th
Results (3)
Regression analysis – 100th percentile
Norway Spruce
Linear = 0,978
European Larch
Linear = 0,952
Results (4)
Comparison of the results
Bias and RMSE
Coefficient of determination
1,35
1,5
0,98
1
0,55
1
0,99
0,98
0,63
0,97
0,5
0,965
0,957
0,96
0
0,95
-0,14
-0,5
-0,4
0,94
0,93
0,92
-1
-1,13
-1,5
0,91
0,9
Own study Persson et Hyyppä et Kwak et al.
al. (2002) al (2000)
(2007)
Own study
Stephens et al.
(2012)
Coefficient of determination
Conclusion
• Small-footprint LIDAR systems have potential for
the estimation of individual tree height of conifer
species.
• Under given conditions maximum height
percentile derived from ALS point cloud is the
most accurate metrics in tree height estimation.
• Point cloud based metrics tend to underestimate
results.
Further research
• Other tree species have to be investigated –
including hardwoods.
• Influence of the stand age on height
estimation.
• Data filtering.
Thank you for attention.
References
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