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Frontiers in Fuels Science: Frontiers in Fuels Science: Species-Specific Crown Profiles Models from Terrestrial Laser Scanning.

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Presentation on theme: "Frontiers in Fuels Science: Frontiers in Fuels Science: Species-Specific Crown Profiles Models from Terrestrial Laser Scanning."— Presentation transcript:

1 Frontiers in Fuels Science: Frontiers in Fuels Science: Species-Specific Crown Profiles Models from Terrestrial Laser Scanning

2 Key Concepts:  Document accuracy and validity of existing crown biomass equations (Affleck)  Develop new crown biomass/fuel equations for inland northwest tree species (Affleck)  Parameterize crown fuel density profiles over individual stems per species with TLS Stem dimensions Crown characteristics (e.g. shape and density) Where We’ve Come From:  Branch Scale Biomass Using TLS

3  Boles Incremental bole diameters used to generate basal area Diameters could be used predict biomass from revised allometries  Crown profiles differ between species A large body of previous work on crown shapes Laser gives precise measurements and characterize crown High replication using laser Tests using limited sample sizes suggest between species differences are more pronounced than those within species  Biomass is distributed unequally throughout a crown: Both vertically and horizontally Distribution differs between species

4 Vicinity Map Trees sampled Trees & TLS sampled  Connection to Destructive Samples Collect TLS data of sampled trees Collect independent samples concurrently representing: −same species −site conditions  TLS Sampling Objective n > 200

5 Optech Ilris 3 6 D HD Terrestrial Lidar System 10 KHz sampling Point density 1cm or less Local characterization

6 Project Approach:  This project seeks to optimize TLS collection by: Limit scanning time by only sampling a hemisphere Control errors of omission through sampling volume 15m Minimum Distance Zone of Interception Zone of Occlusion Current Thinking:  Literature suggests that estimating biomass from TLS requires: Scanning from multiple angles High resolution Offsetting potential occlusion of data within tree

7 Focal Plane (~4.0mm density) Types of Occlusion:  Penetration of energy through the canopy or objects Angle independent/dependent  Shadowing of canopy elements Depends canopy density Angularity (branches shadowing objects above) Data Resolution a Function of Range:  The instrument is parameterized to collect data at a set resolution at a determined range (focal plane)  Characterize hull of tree  Data decreases in density as it gains range Origin

8 Data Process Flow  Data Collection Largest time commitment (e.g. travel, set up, Etc.) ~15 minutes per scan Dependent on site conditions (e.g. adjacent tree density)  Alignment of Scans (Polyworks) Potentially time consuming  Process Using Lab Developed Applications Designed to begin optimizing tree processing for efficiency and repeatability Single processing flow for applying alignment, calculating bole dimensions, and normalized canopy distance from bole.

9 Three Estimates of Bole Diameter Up The Tree: 1.Three samples of distance from bole centroid 2.Fitted line from selected bole points 3.Modeled from the initial bole diameter at the bottom of the tree

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11 Height (m) Distance (m)

12 Species used in preliminary work Crown Characterization Crown Shapes (profiles and lengths) Biomass Distribution Bole characterization Integrating it all What’s next?

13 Data sample for preliminary analysis 6 Douglas firs (Pseudosuga mensiesii) 3 grand firs (Abies grandis) 1 ponderosa pine (Pinus ponderosa) 1 western larch (Larix occidenatlis)

14 90 th crown width percentile chosen to define outer hull Points at each height interval

15 Rescaled both axes as 0-1 Did this for 6 Douglas firs and 3 grand firs

16 Combined all samples per species into one “uber-tree” each

17 Model Fitting curve smoothing scale of variability exclusion of bole/incorporation of crown base height

18 Douglas Fir (n=6) Grand Fir (n=3) Ponderosa Pine (n=1) Western Larch (n=1)

19 Using some impartial metric to consistently define lower bound of crown length

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22 Hull / void Survivability analysis Hull delineation Within-hull biomass distribution

23 Many different radius measures generated curve fitted constantdist1dist2dist3average dist

24 Potential use in linking TLS data to allometry for biomass prediction Problems to overcome

25  Per Species Apply crown base metric Generate the “uber-tree” Fit crown profile curve Determine hull-void demarcation Determine biomass allocation pattern  For New Trees Need species, DBH, height, crown length Use DBH to calculate biomass Use crown profile function to build outer hull shape Allocate biomass within defined hull

26 Things to think about: Occlusions (of bole, inner vegetation) Best metric for CBH delineation Appropriate scale of variation for crown profile curve Defining hull/void demarcation Distributing biomass within that hull Next (this summer through Spring 2013) More trees, more scanning, more data processing Linkages to Affleck lab measures Exploration of applications beyond fire

27 The Laser Team:  Eric Rowell, Ph.D. Plot scale surface fuels characterization; integration of airborne and terrestrial scanning, fuel consumption.  Tara Umphries, M.S. Quantifying fuel dimensions in a grassland.  Jena Ferrarese, M.S. Measuring conifer crown dimensions and the distribution of biomass within them.  Theodore Adams, M.S. Defining/distributing fuel elements in diffuse shrubs of sagebrush and chamise.

28 Acknowledgements:  Joint Fire Sciences Program  Inland Northwest Growth and Yield Cooperative  Affleck lab  Active Remote Sensing Lab, National Center for Landscape Fire Analysis


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