The Use of Terrestrial LiDAR to Characterize Single Trees Jean-François Côté (CWFC) Chhun-Huor Ung (CWFC) Joan Luther (CFS)

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1 The Use of Terrestrial LiDAR to Characterize Single Trees Jean-François Côté (CWFC) Chhun-Huor Ung (CWFC) Joan Luther (CFS) Canadian Woodlands Forum 2013 Fall Meeting

2 Inventory Inventory information essential for decision-making Better information ecological sustainability economic profitability Evolving information needs improved accuracy and precision quantity and quality Reduced costs Inventory Management Products 2

3 Inventory Resource characterization Develop cost-effective enhanced inventory of large areas to support decisions makers Enhanced inventory Squeezing maximum value from each tree harvested Tree forms, fibre attributes, visual quality/defect, etc. Identify these attributes for particular end uses access, harvest and deliver the right raw material to the right mill and markets, at the right time! Allow better monitoring of the resource predict the evolution of it through time 3

4 Tree attributes 1. Tree Diameter 2. Species 3. Stem Taper 4. Tree Form 5. Defects 6. Fibre Attributes 4

5 Tree attributes 1. Tree Diameter 2. Species 3. Stem Taper 4. Tree Form 5. Defects 6. Fibre Attributes Credit: montana.edu 5

6 Tree attributes 1. Tree Diameter 2. Species 3. Stem Taper 4. Tree Form 5. Defects 6. Fibre Attributes 6

7 Tree attributes 1. Tree Diameter 2. Species 3. Stem Taper 4. Tree Form 5. Defects 6. Fibre Attributes 7

8 Tree attributes 1. Tree Diameter 2. Species 3. Stem Taper 4. Tree Form 5. Defects 6. Fibre Attributes Credits: Northern Hardwoods Research Institute 8

9 Tree attributes 1. Tree Diameter 2. Species 3. Stem Taper 4. Tree Form 5. Defects 6. Fibre Attributes Credits: missouri.edu 9

10 Tree attributes 1. Tree Diameter 2. Species 3. Stem Taper 4. Tree Form 5. Defects 6. Fibre Attributes 10

11 Current Limitations Traditional Inventory Good for tree diameter and species Some attributes more difficult to measure height, crown Cost and time increase when adding values branchiness, fine-scale structure Technology to Enhance Inventory Remote Sensing Satellite, Airborne, Terrestrial Predict species, structure Link to tree values 11

12 Terrestrial LiDAR LiDAR: Light Detection And Ranging Airborne & Ground-based Measure time it takes a laser pulse to strike an object and return to the source Map the reflecting objects in high 3D detail 12

13 Terrestrial LiDAR Emission Detection Scan Image Footprint: cm, mm 13

14 Terrestrial LiDAR Multiple views to reduce occlusion Red maple 14

15 Terrestrial LiDAR Co-registered point cloud 15

16 Typical Measurements Cross-section of the trunk at DBH Height = 10.4 m DBH = 40.4 cm DBH measured manually = 40.1 cm 16

17 Stem Profile Stem detection: manually or automatic Credits: TreeMetrics 17

18 Stem Profile 3-D stem profiles from scans Precise estimate of log products 18

19 Stem Profile Optitek of FPInnovations Simulations of log cuttings 19

20 Tree Form Number of stems Forks and crowns with strong branches Curvature of the tree Inclination of the tree Credits: Northern Hardwoods Research Institute 20

21 Tree Form Using Terrestrial LiDAR scans Classify tree forms One step further from taper No. stems Curvature/Inclination Locate forks Credits: Northern Hardwood Research Institute 21

22 Defects Tree vigor helps to: predict products distribution determine harvesting costs suggest a tree removal priority or chose silvicultural alternatives Possible using terrestrial LiDAR scans Detect defects by analysing intensity & range image Still in progress 22

23 Fibre Attributes NL Fibre Project Concrete example (balsam fir, black spruce) Quantify relationships among existing inventory attributes and internal fibre quality attributes of trees and stands Enhance measurement of forest species and structure using new inventory technologies Improve mapping of fibre attributes at landscape scales As one of the objectives Predict wood fibre attributes using new opportunities with terrestrial LiDAR 23

24 Fibre Attributes fibre length wood density radial fibre diameter tangential fibre diameter coarseness microfibril angle modulus of elasticity wall thickness specific surface area *Fibre core analysis and attribute database provided by the CBPPL Forest Industry Competitive Advantage Project 24

25 Fibre Attributes Fibre Attributes = ƒ(species, structure, environment) Terrestrial LiDAR data was used to extract fine scale information on tree structure Architectural modeling Well beyond what is feasible from manual field work leaf area, wood volume distribution profiles branchiness, gaps, etc. Linking fibre attributes with structural variables To what extent can these new variables improve the prediction of fibre attributes? 25

26 Fibre Attributes L-Architect (LiDAR data to tree Architecture) Detailed plot structure reconstructed from TLS scans of individual trees 26

27 Fibre Attributes LeafArea_sim HCPA_grid 7.50 Vol_grid Asym_grid 1.12 LAI_grid FractalDimBC_grid 2.01 Lacunarity_grid 1.35 DBH_geom 0.20 Height_geom HeightToLiveCrown_geom 0.90 CrownWidth_geom 3.02 BranchTot_geom Struck_geom 3.42 DeBell_geom 0.26 TaperCoef_geom 0.02 BranchNo1_geom 3.00 BranchNo2_geom 2.00 BranchNo3_geom 2.00 BranchDia1_geom 0.01 BranchDia2_geom 0.01 BranchDia3_geom 0.03 BranchAng1_geom BranchAng2_geom BranchAng3_geom Dia1_geom 0.02 Dia2_geom 0.11 Weibull_Scale_Leaf_Area Weibull_Shape_Leaf_Area 3.64 Weibull_Scale_Wood_Volume Weibull_Shape_Wood_Volume Inventory RMSE Rel. RMSE R2 Rings Fibre Length Wood Density Rad. Diam Tang. Diam Coarseness Micro Fibre Ang Specific Surface Mod. Of Elastic Wall Thick

28 Fibre Attributes LeafArea_sim HCPA_grid 7.50 Vol_grid Asym_grid 1.12 LAI_grid FractalDimBC_grid 2.01 Lacunarity_grid 1.35 DBH_geom 0.20 Height_geom HeightToLiveCrown_geom 0.90 CrownWidth_geom 3.02 BranchTot_geom Struck_geom 3.42 DeBell_geom 0.26 TaperCoef_geom 0.02 BranchNo1_geom 3.00 BranchNo2_geom 2.00 BranchNo3_geom 2.00 BranchDia1_geom 0.01 BranchDia2_geom 0.01 BranchDia3_geom 0.03 BranchAng1_geom BranchAng2_geom BranchAng3_geom Dia1_geom 0.02 Dia2_geom 0.11 Weibull_Scale_Leaf_Area Weibull_Shape_Leaf_Area 3.64 Weibull_Scale_Wood_Volume Weibull_Shape_Wood_Volume L-Architect RMSE Rel. RMSE R2 Rings Fibre Length Wood Density Rad. Diam Tang. Diam Coarseness Micro Fibre Ang Specific Surface Mod. Of Elastic Wall Thick

29 Fibre Attributes Asymmetry (mean) 1.15 Total No. of Branches (mean) Crown Base Height (mean) 3.25 Crown Proj. Area (mean) 3.28 Crown Width (mean) 2.4 DBH (mean) 0.14 DeBell (branchiness, mean) 0.21 Fractal_Dimension_Box_Counting 2.37 Fraction_Cover 11.6 Height Max. Crown Proj. Area (mean) 6.83 Height (mean) HeightToLiveCrown (mean) 1.36 Knot Total Surface (mean) 0.04 Lacunarity 1.52 LAI LeafArea (mean) PAI Struck (branchiness index, mean) 2.9 TaperCoef (mean) 0.02 Volume (mean) 13 Weibull_Scale_Leaf_Area Weibull_Scale_Wood_Volume Weibull_Shape_Leaf_Area 3.21 Weibull_Shape_Wood_Volume 1.52 Total No. of Whorls (mean) Inventory RMSE Rel. RMSE R2 Fibre Length Wood Density Rad. Diam Tang. Diam Coarseness Micro Fibre Ang Specific Surface Mod. Of Elastic Wall Thick

30 Fibre Attributes Asymmetry (mean) 1.15 Total No. of Branches (mean) Crown Base Height (mean) 3.25 Crown Proj. Area (mean) 3.28 Crown Width (mean) 2.4 DBH (mean) 0.14 DeBell (branchiness, mean) 0.21 Fractal_Dimension_Box_Counting 2.37 Fraction_Cover 11.6 Height Max. Crown Proj. Area (mean) 6.83 Height (mean) HeightToLiveCrown (mean) 1.36 Knot Total Surface (mean) 0.04 Lacunarity 1.52 LAI LeafArea (mean) PAI Struck (branchiness index, mean) 2.9 TaperCoef (mean) 0.02 Volume (mean) 13 Weibull_Scale_Leaf_Area Weibull_Scale_Wood_Volume Weibull_Shape_Leaf_Area 3.21 Weibull_Shape_Wood_Volume 1.52 Total No. of Whorls (mean) L-Architect RMSE Rel. RMSE R2 Fibre Length Wood Density Rad. Diam Tang. Diam Coarseness Micro Fibre Ang Specific Surface Mod. Of Elastic Wall Thick

31 Conclusion Address issues Occlusion, Wind, Beam Divergence Detailed measurement of tree characteristic Well beyond what is feasible from manual field work Stem Profile and Branchiness Beyond the work from trees in open stands plantations or urban parks! Get the Full Structure from the Point Cloud Pass the barrier of the unconnected points of a cloud 31

32 Conclusion Remaining Challenges: Still need to be adapted to our environment Different from urban parks or isolated trees Adding this information will improve Capacity to predict the evolution of the tree and product distribution Capacity to determine harvest cost and make silviculture decisions 32

33 Thank you!! Questions/Comments? 33