Frontiers in Fuels Science:

Species-Specific Crown Profiles Models from Terrestrial Laser Scanning

Background

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

Background: Working Hypotheses (TLS)

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

Introduction: Sampling

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

Introduction: TLS Instrument

Optech Ilris 36D HD Terrestrial Lidar System

• 10 KHz sampling

• Point density 1cm or less

• Local characterization

Introduction: TLS Sampling

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

15mMinimum 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

Introduction: TLS Sampling

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

Methods: TLS Collection and Processing

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.

Methods: TLS-Based Bole Measurements

Three Estimates of Bole Diameter Up The Tree:1. Three samples of distance

from bole centroid2. Fitted line from selected

bole points3. Modeled from the initial

bole diameter at the bottom of the tree

Methods: Distance and Tree Cleaning

Methods: Building a Library of Crown ShapesH

eig

ht

(m)

Distance (m)

Analysis

• Species used in preliminary work• Crown Characterization

• Crown Shapes (profiles and lengths)• Biomass Distribution

• Bole characterization• Integrating it all• What’s next?

Analysis

• 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)

http://www.idahoforests.org

Analysis: Crown Profiles

• 90th crown width percentile chosen to define outer hull

• Points at each height interval

Analysis: Crown Profiles

• Rescaled both axes as 0-1

• Did this for 6 Douglas firs and 3 grand firs

Analysis: Crown Profiles

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

Analysis: Crown Profiles

Model Fitting• curve smoothing• scale of variability• exclusion of

bole/incorporation of crown base height

Analysis: Crown Profiles

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

Analysis: Crown Base Height

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

Analysis: Crown Base Height

Analysis: Crown Base Height / Crown Profiles

Analysis: Crown Biomass Distribution

• Hull / void

• Survivability analysis

• Hull delineation

• Within-hull biomass distribution

Analysis: Boles

• Many different radius measures generated

curvefitted

constant dist1 dist2 dist3 averagedist

Analysis: Boles

• Potential use in linking TLS data to allometry for biomass prediction

• Problems to overcome

Analysis: Integrating It All

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

Analysis: Where To Next…

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

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.

Acknowledgements:

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