IntroductionMethods and Results
Ongoing research and summary
Use R! for estimating forest parameters basedon Airborne Laser Scanner Data
Johannes Breidenbach
Forest Research Institute Baden-Württemberg,Department of Biometrics
Forstliche Versuchs- und Forschungsanstalt Baden-WürttembergAbteilung Biometrie und Informatik; Wonnhaldestr. 4; 79100 Freiburg
Fon: +49 (0)761-4018-315; [email protected]
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
Inhalt
1 IntroductionBackgroundAirborne Laser ScanningAnalyzing laser data
2 Methods and ResultsA mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
3 Ongoing research and summary
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
1 IntroductionBackgroundAirborne Laser ScanningAnalyzing laser data
2 Methods and ResultsA mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
3 Ongoing research and summary
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Sample Plot Inventory
Forest inventory → statistical sound information on theenterprize-level (Stands are too small)
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Forest Inventory
Forest inventory → statistical sound information on theenterprize-level (Stands are too small)
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Motivation
High costs
Insufficient information on stand level
Staff reduction
Increased economical interest in timber products
⇒ Greater information-need on the stand level
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Motivation
High costs
Insufficient information on stand level
Staff reduction
Increased economical interest in timber products
⇒ Greater information-need on the stand level
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Motivation
High costs
Insufficient information on stand level
Staff reduction
Increased economical interest in timber products
⇒ Greater information-need on the stand level
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Motivation
High costs
Insufficient information on stand level
Staff reduction
Increased economical interest in timber products
⇒ Greater information-need on the stand level
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Motivation
High costs
Insufficient information on stand level
Staff reduction
Increased economical interest in timber products
⇒ Greater information-need on the stand level
J. Breidenbach Use R! for estimating forest parameters based on ALS
Aim ⇒ Regionalization (small area estimation): From apoint-wise to a wall-to-wall information
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Rechtswert [m]
Hoc
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]
Vorrat [Vfm ha−1]
0454966
FoGIS Bestandesgrenzen
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
1 IntroductionBackgroundAirborne Laser ScanningAnalyzing laser data
2 Methods and ResultsA mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
3 Ongoing research and summary
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Airborne Laser Scanning
http://www.geokosmos.com/technologies/airbornscan.jpg
Active remote sensingsystem
Laser for distancemeasurement
Pointcloud with XYZ-rawdata
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Airborne Laser Scanning
http://www.geokosmos.com/technologies/airbornscan.jpg
Active remote sensingsystem
Laser for distancemeasurement
Pointcloud with XYZ-rawdata
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Airborne Laser Scanning
http://www.geokosmos.com/technologies/airbornscan.jpg
Active remote sensingsystem
Laser for distancemeasurement
Pointcloud with XYZ-rawdata
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Digital Terrain Model
Software: TreesVis, FELIS Uni Freiburg
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Digital Surface Model
Software: TreesVis, FELIS Uni Freiburg
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
1 IntroductionBackgroundAirborne Laser ScanningAnalyzing laser data
2 Methods and ResultsA mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
3 Ongoing research and summary
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Area-based method
Aggregation of laser data on sample plot level → metricsStatical relation between
MetricsSample plot attributes (volume, diameter distribution, treeheight)
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Area-based method
Aggregation of laser data on sample plot level → metricsStatical relation between
MetricsSample plot attributes (volume, diameter distribution, treeheight)
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Area-based method
Aggregation of laser data on sample plot level → metricsStatical relation between
MetricsSample plot attributes (volume, diameter distribution, treeheight)
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Area-based method
Aggregation of laser data on sample plot level → metricsStatical relation between
MetricsSample plot attributes (volume, diameter distribution, treeheight)
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Computation of metrics
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Computation of metrics - basic R
Vegetationshöhe [m]
Häu
figke
it
10 15 20 25 30
010
2030
4050
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Computation of metrics - basic R
Vegetationshöhe [m]
Häu
figke
it
10 15 20 25 30
010
2030
4050 0% Perzentil
25% Perzentil
50% Perzentil
75% Perzentil
100% Perzentil
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Computation of metrics - basic R
Vegetationshöhe [m]
Häu
figke
it
10 15 20 25 30
010
2030
4050 0% Perzentil
25% Perzentil
50% Perzentil
75% Perzentil
100% Perzentil
Mittelwert
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
BackgroundAirborne Laser ScanningAnalyzing laser data
Other predictor variables - ArcGIS
Crown cover
Coniferous proportion
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
A mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
1 IntroductionBackgroundAirborne Laser ScanningAnalyzing laser data
2 Methods and ResultsA mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
3 Ongoing research and summary
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
A mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
Hierarchical data structure
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
A mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
Hierarchical data structure
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
A mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
Linear mixed model - lme
y ij = X ijβ + U i,jγ i + U ijγ ij + εij
with
γ i ∼ N(0, D(1)), γ ij ∼ N(0, D(2)), εij ∼ N(0,Σij)
where
Σij =
σ2y2δij1 · · · Kov(εij1, εijnij )
.... . .
...Kov(εijnij , εij1) · · · σ2y2δ
ijnij
andKov(εijk , εijk ′) = exp(−s/ρ) σyδ
ijk σyδijk ′
J. Breidenbach Use R! for estimating forest parameters based on ALS
Regionalization - Maptools
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Rechtswert [m]
Hoc
hwer
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]
LaubwaldMischwaldNadelwaldLichter BereichStarke vertikale Strukur
FoGIS Bestandesgrenzen
Regionalization - Maptools
3422500 3423000 3423500
5327
000
5327
500
5328
000
Rechtswert [m]
Hoc
hwer
t [m
]
Vorrat [Vfm ha−1]
0454966
FoGIS Bestandesgrenzen
Regionalization - Maptools
3422500 3423000 3423500
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000
5327
500
5328
000
457.13
278.87
199.27
346.96
588.60 412.67
397.51
537.47
431.40
515.96
323.84
521.79
556.95
482.69
428.28
143.29
563.85
261.38
337.48
138.01
391.16
518.55
281.84590.42
134.58
521.74
356.49
608.37
176.22
686.29(67.8)
(0.1)
(3.6)
(3.7)
(1.0)
(0.1) (0.3)
(0.3)
(0.2)
(0.8)
(0.7)
(0.4)
(0.2)
(0.1)
(1.4)
(0.7)
(12.2)
(0.1)
(0.3)
(0.1)
(11.1)
(12.9)
(0.5)
(0.6)(0.1)
(9.4)
(0.2)
(4.5)
(0.2)
(3.5)
(0.1)
506.52
NA
289.10
388.83 282.85
420.70
599.50
582.90
534.70
413.80
542.25
395.82
NA
NA
NA
710.20
183.47
407.66
NA
NA
256.50
200.05514.00
NA
510.70
485.60
805.40
NA
473.55(NA)
(8.7)
(NA)
(NA)
(8.9) (21.9)
(28.3)
(20.8)
(NA)
(NA)
(5.1)
(10.2)
(13.4)
(NA)
(NA)
(NA)
(15.4)
(30.3)
(13.0)
(NA)
(NA)
(NA)
(9.2)(17.4)
(NA)
(36.9)
(NA)
(29.6)
(NA)
(29.4)
Rechtswert [m]
Hoc
hwer
t [m
]
Vorrat [Vfm ha−1]
98.36 − 293.22293.22 − 406.69406.69 − 521.75521.75 − 686.29Stichprobenpunkte
IntroductionMethods and Results
Ongoing research and summary
A mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
1 IntroductionBackgroundAirborne Laser ScanningAnalyzing laser data
2 Methods and ResultsA mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
3 Ongoing research and summary
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
A mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
Modeling I
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
A mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
Modeling II
Generalized additive model for location, scale and shape -GAMLSSBe
y ∼ Weibull(a, b, c), a = 7, b, c > 0
with density
f (y |b, c) =cb
(yb
)c−1exp
[
−(y
b
)c]
,
thenb = h−1(η) und c = h−1(η).
Where a = Location-, b = Scale- and c = Shape parameter,ηi = x ′
i β as well as h = link function.
J. Breidenbach Use R! for estimating forest parameters based on ALS
Qu1: (24,26.8] Qu3: (28.9,32.7] Plots: 20 Trees: 242
Den
sity
0 20 40 60 80
0.00
0.04
0.08
Qu1: (18.2,21.1] Qu3: (25.2,28.9] Plots: 45 Trees: 472
Den
sity
0 20 40 60 80
0.00
0.04
0.08
Qu1: (21.1,24] Qu3: (25.2,28.9] Plots: 33 Trees: 436
DBH (cm)
Den
sity
0 20 40 60 80
0.00
0.04
0.08
Qu1: (15.4,18.2] Qu3: (21.5,25.2] Plots: 58 Trees: 725
0 20 40 60 80
0.00
0.04
0.08
Qu1: (18.2,21.1] Qu3: (21.5,25.2] Plots: 38 Trees: 561
0 20 40 60 80
0.00
0.04
0.08
Qu1: (12.5,15.4] Qu3: (17.7,21.5] Plots: 51 Trees: 676
DBH (cm)0 20 40 60 80
0.00
0.04
0.08
Qu1: (15.4,18.2] Qu3: (17.7,21.5] Plots: 29 Trees: 430
0 20 40 60 80
0.00
0.04
0.08
Qu1: (9.67,12.5] Qu3: (14,17.7] Plots: 40 Trees: 449
0 20 40 60 80
0.00
0.04
0.08
Qu1: (6.81,9.67] Qu3: (10.3,14] Plots: 22 Trees: 228
DBH (cm)0 20 40 60 80
0.00
0.04
0.08
IntroductionMethods and Results
Ongoing research and summary
1 IntroductionBackgroundAirborne Laser ScanningAnalyzing laser data
2 Methods and ResultsA mixed model (lme) for timber volume estimationA GAMLSS for diameter distribution estimation
3 Ongoing research and summary
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
Ongoing research
20 40 60 80
1020
3040
DBH (cm)
Hei
ght (
m)
Bivariate height- anddiameter distribution
Random forests toestimate tree-speciesspecific timber volume(multivariate)
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
Ongoing research
Fi Bu Ta Dgl BAh
Vor
rat (
m3 ha
−1)
050
100
150
200
Bivariate height- anddiameter distribution
Random forests toestimate tree-speciesspecific timber volume(multivariate)
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
Summary
R is well suited for analyzing laser data due to its...functionality to call other programs - shell()flexibility (writing own functions)state-of-the-art statistical methods
Still learning R after 5 years of use...
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
Summary
R is well suited for analyzing laser data due to its...functionality to call other programs - shell()flexibility (writing own functions)state-of-the-art statistical methods
Still learning R after 5 years of use...
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
Summary
R is well suited for analyzing laser data due to its...functionality to call other programs - shell()flexibility (writing own functions)state-of-the-art statistical methods
Still learning R after 5 years of use...
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
Summary
R is well suited for analyzing laser data due to its...functionality to call other programs - shell()flexibility (writing own functions)state-of-the-art statistical methods
Still learning R after 5 years of use...
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
Summary
R is well suited for analyzing laser data due to its...functionality to call other programs - shell()flexibility (writing own functions)state-of-the-art statistical methods
Still learning R after 5 years of use...
J. Breidenbach Use R! for estimating forest parameters based on ALS
IntroductionMethods and Results
Ongoing research and summary
Thanks go to...
Christian Gläser, Drs. Edgar Kublin, MatthiasSchmidt, Arne Nothdurft, Gerald Kändler
You for your attention!
J. Breidenbach Use R! for estimating forest parameters based on ALS