Using R as an environment for automatic extraction of forest growth parameters from terrestrial laser scanner data
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1 Using R as an environment for automatic extraction of forest growth parameters from terrestrial laser scanner data user! 2008 Dortmund 1/ Dr. Hans-Joachim Klemmt
2 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 ) 2/ 17 Summary and perspectives
3 Terrestrial Laserscanner mm m 3/ 17
4 R-Package RLaserForest 4/ 17
5 R-Package RLaserForest: Determination of stem positions (slide 1) 5/ 17
6 R-Package RLaserForest: Determination of stem positions (slide 2) 6/ 17
7 R-Package RLaserForest: Height calculation and Determination of diameters in breast height (DBH) 7/ 17
8 R-Package RLaserForest: Calculation of stem volume 8/ 17
9 case study Selb 9/ N O
10 Images of case study stand 10/ 17
11 measurement in field description of data 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) 11/ 17 Scots pine: mean DBH 38,07 cm (31,25-49,9cm); mean height: 32,10m (26,08-34,22)
12 Results (case study Selb ) here: length of stems Manual measured stem length vs. Terrestrial laserscanner stem length 12/ 17 stem length by terrestrial laserscanning [m] Scots pine Norway spruce deviation [m] Deviation between manual and terrestrial laser stem length measurement mean: mean: Norway spruce Scots pine manual measured stem length [m]
13 Results (case study Selb ) here: volume of stem axis calculated in sections vs. volume by quadrature of cubic spline 13/ 17 volume of manual measured sections [m³] Scots pine Norway spruce volume by cubic spline interpolation of laser data [m³]
14 Results here: attempt to explain the deviation between real and calculated volume Tree no. 2, Norway spruce Tree no. 21, Norway spruce radius [m] stemprofile by cubic spline interpolation manual caliper measured section radii radius [m] stemprofile by cubic spline interpolation manual caliper measured section radii stem length [m] Tree no. 8, Scots pine stem length [m] Tree no. 12, Scots pine 14/ 17 radius [m] stemprofile by cubic spline interpolation manual caliper measured section radii radius [m] stemprofile by cubic spline interpolation manual caliper measured section radii stem length [m] stem length [m]
15 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
16 I want to say thank you to: BaySF: Forstbetrieb Selb (insbes. Herrn Michael Grosch und Herrn Hubert Fellermeyer) for enabling case study in field 16/ 17 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)
17 Thank you very much for your attention! 17/ 17 I am looking forward to a fruitful discussion
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8) Which of the following species is best adapted to poorly drained sites? a) Bur oak b) Eastern red cedar c) Black ash d) Yellow birch
School Score Wisconsin Envirothon 2004 Forestry Exam Answer the following questions based on the species description and graphic to the right. 1) This tree is the state tree of Wisconsin and is prized