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Waldwachstum Systemanalyse Using R as an environment for automatic extraction of forest growth parameters from terrestrial laser scanner data useR! 2008 Dortmund 13.08.2008 1/ 17 Dr. Hans-Joachim Klemmt Waldwachstum Systemanalyse


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Using R as an environment for automatic extraction of forest growth parameters from terrestrial laser scanner data

useR! 2008 Dortmund 13.08.2008

  • Dr. Hans-Joachim Klemmt
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Contents

  • Introduction
  • Relevant parameters for forest inventory purposes
  • Determination of stem positions
  • Calculation of tree heights
  • Estimation of diameters in breast height (DBH)
  • Calculation of volume of stem axis
  • Performance of the developed system (case study „Selb“)
  • Summary and perspectives

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Terrestrial Laserscanner

90° 6

  • 1

2 m m 60 - 200 m

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R-Package „RLaserForest“

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R-Package RLaserForest: Determination of stem positions (slide 1)

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R-Package RLaserForest: Determination of stem positions (slide 2)

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R-Package RLaserForest: Height calculation and Determination of diameters in breast height (DBH)

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R-Package RLaserForest: Calculation of stem volume

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case study „Selb“

9/ 17 50°09’12‘‘ N 12°11’55‘‘ O

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Images of case study stand

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description of data

measurement in field

  • 37 Norway spruce trees + 13 Scots pine trees

Applicated in RLaserForest 37 Norway spruce trees + 9 Scots pine trees

Norway spruce: mean DBH 38,54 cm (20,65-61,25cm); mean height: 30,9m (21,35-38,32) Scots pine: mean DBH 38,07 cm (31,25-49,9cm); mean height: 32,10m (26,08-34,22)

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Results (case study „Selb“) here: length of stems

Norway spruce Scots pine

  • 0.5

0.0 0.5 1.0

Deviation between manual and terrestrial laser stem length measurement

deviation [m] mean: -0.169 mean: -0.046

15 20 25 30 35 40 15 20 25 30 35 40

Manual measured stem length vs. Terrestrial laserscanner stem length

manual measured stem length [m] stem length by terrestrial laserscanning [m] Scots pine Norway spruce

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1 2 3 4 1 2 3 4

calculated in sections vs. volume by quadrature of cubic spline

volume by cubic spline interpolation of laser data [m³] volume of manual measured sections [m³] Scots pine Norway spruce

Results (case study „Selb“) here: volume of stem axis 13/ 17

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Results

here: attempt to explain the deviation between real and calculated volume

5 10 15 20 25 30 0.00 0.05 0.10 0.15 0.20 0.25 0.

Tree no. 2, Norway spruce

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30 stem length [m] radius [m] stemprofile by cubic spline interpolation manual caliper measured section radii 5 10 15 20 25 0.00 0.05 0.10 0.15 0.20 0.25 0.30

Tree no. 8, Scots pine

stem length [m] radius [m] stemprofile by cubic spline interpolation manual caliper measured section radii 5 10 15 20 25 30 0.00 0.05 0.10 0.15 0.20 0.25 0.30

Tree no. 12, Scots pine

stem length [m] radius [m] stemprofile by cubic spline interpolation manual caliper measured section radii 10 20 30 0.00 0.05 0.10 0.15 0.20 0.25 0.

Tree no. 21, Norway spruce

30 stem length [m] radius [m] stemprofile by cubic spline interpolation manual caliper measured section radii

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Summary and perspectives

TODOs: improvement of volume calculation improvement of diameter estimation for excentric stems afterwards: automated determination of tree species (classification) afterwards: automated separation of crown parameters (spectral clustering) Objective:modular built R-Package „RLaserForest“ for automatic extraction of forest growth relevant inventory parameters by the use of the statistic programming language R 15/ 17

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I want to say thank you to:

  • BaySF: Forstbetrieb Selb (insbes. Herrn Michael

Grosch und Herrn Hubert Fellermeyer) for enabling case study in field

  • LfWwk: Herrn Stefan Seifert, Herrn Thomas Seifert,

Herrn Istvan Pal, Herrn Gerhard Schütze, Frau Andrea Oumeddah as well as Herrn Sebastian Seibold and Herrn Martin Stary

  • colleagues from FMI UHUL from Czech republic

(cooperation within a common Interreg IIIa-project)

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Thank you very much for your attention!

I am looking forward to a fruitful discussion …

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