Influence of crown architecture on prediction
of canopy fuel loads and fire hazard in
ponderosa pine forests of the Black Hills

TARA L. KEYSER, RESEARCH FORESTER, USDA
FOREST SERVICE, SOUTHERN RESEARCH
STATION

FREDERICK (SKIP) W. SMITH, PROFESSOR OF
SILVICULTURE, COLORADO STATE UNIVERSITY
Forests of the Black Hills
 Aspen, lodgepole
pine, burr oak,
green ash, white
spruce, paper birch,
open meadows
 85% ponderosa pine
Forest management in the Hills
Timber cut
volume (million
board ft)
Rank
National Forest
1
Black Hills
99,389
2
Chequamegon/
Nicolet (WI)
78,018
3
Quachita (AR)
67,098
4
NFS in FL
46,503
5
Shasta-Trinity
(CA)
39,837
volume harvested
mmbf
Black Hills National Forest
180
160
140
120
100
80
60
40
20
0
1900
1920
1940
1960
year
1980
2000
2020
Current forest management issues
 Mountain Pine Beetle
 Increasing WUI
 Increase in large-scale
wildfires


~82,500 ha have burned
since 2000 in just 21 fire
events
Jasper Fire ~34,000 ha
Fuel reduction treatments
 Goal – create structures
resistant to the initiation &
spread of crown fire


Reduce surface fuels
Reduce vertical & horizontal
continuity of canopy fuels
Passive crown fire
Active crown fire
Alter canopy fuel structure
 Increase Canopy Base Height (CBH)
 The lowest height at which there is a sufficient amount of
canopy fuel to spread fire into the canopy (Van Wagner 1993)
 Reduces the risk of passive crown fire (torching)
 Decrease Canopy Bulk Density (CBD)
 The density (kg/m3) of foliage and small branches within a
stand
 CBD values is used to make inferences about the continuity of
canopy fuels
 Reduces the risk of active crown fire
Estimating CBH and CBD
 CBD & CBH are not directly measured
 Stand-level variables predicted from fire behavior/effects and
forest growth models using standard forest inventory data
 One of the more widely used models is the Fire and
Fuels Extension to the Forest Vegetation Simulator
(FFE-FVS)
CBH and CBD in FFE-FVS
 Obtaining CBH & CBD values requires an estimate
of crown mass (foliage mass + 0.5*1hr branch
mass) of individual trees ≥1.8 m in height within a
stand

In FFE-FVS, allometric equations used to predict crown mass
for ponderosa pine are based on data from Montana and
Idaho (Brown 1978)
Effective CBD
(Reinhardt and Crookston 2003)
 A canopy fuel profile is
created using the
aggregated weight of
crown fuel within 0.3-m
sections of the canopy
canopy base height = 0.011
canopy bulk density = MAX
 A 4-m running average of
CBD (kg/m3) around
those 0.3-m sections is
calculated
Figure from Reinhardt and Crookston (2003)
Distribution of crown mass in FFE-FVS
 An important underlying assumption used in the prediction
of CBH & CBD is that crown mass is equally distributed
throughout the crown
Distribution of crown mass in the real world
Objectives
1.
Create crown mass equations for ponderosa pine
specific to the Black Hills
2.
Describe and predict the vertical distribution of crown
mass
3.
Examine the effect Black Hills crown mass equations +
distribution models have on estimates of CBD and
CBH
Inventory
 June - August of 2006, 16 stands were located
throughout the BHNF.

One vegetation plot randomly established in each stand.
 Each plot was inventoried: Species, DBH, total
height, height to the base of the live crown (BLC)
recorded for all trees ≥1.8 m tall.
 Within each of the 16 stands/plots, 5 trees were
selected for destructive sampling.
Stand attributes
Min
Max
Density (trees/ha)
286
3780
BA (m2/ha)
5.8
47.2
QMD (cm)
16.1
35.5
Stand Density Index (SDI)
140
1112
Relative density [RD
(SDIobs/SDImax)]
13%
100%
Note: SDImax = 1112
Destructive sampling
 For each section, crown was
separated into:
 Foliage + 1 hr (<-.6 cm) fuels
 10 hr fuels (≥0.6 x <2.54 cm)
 100 hr fuels (≥2.54 x <7.6 cm)
 1000 hr fuels (≥7.62 cm)
Statistical analyses
 Nonlinear regression used to develop allometric equations
based on individual tree attributes for total dry mass of
live foliage & live 1 hour fuels

Y = b0X1b1X2b2 + ε
 The Weibull distribution was used to model the
distribution of total crown fuel mass of individual trees

Crown fuel mass = 1 – exp[-(X/β)α]
X = section of crown
 β = scale parameter
 α = shape parameter

 Linear regression used to develop a system of models to
predict the scale (β) & shape (α) parameters of individual
trees based on individual tree and/or stand-level attributes
Foliage mass
 FOL = 0.0865DBH1.8916
LCR1.1358

R2 = 0.89
 Black Hills equations
predicted, on average,
25% greater foliage mass
than Brown (1978)
1 hr fuel mass
1 hour fuel mass (dry; kg)
1.0
R2 = 0.76
0.8
 1HF = 1.5439 LCR5.6131
 Black Hills equations
predicted, on average, 90%
less 1 hr mass than Brown
(1978)
0.6
0.4
0.2
0.0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Live crown ratio (LCR)
 Weibull distribution
statistics

Scale parameter (β) :


4.4 - 7.9
Shape (α) parameter:

1.4 - <3.6
Proportion of crown fuel biomass
Distribution of crown fuel within individual trees
0.25
0.20
Shape = 1.5
Shape = 2.5
Shape = 3.5
0.15
0.10
0.05
0.00
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
Relative crown depth (0 = top of tree)
Parameter prediction
 β = 7.1386 - 0.0608(HT)

Relative height above BLC
1.0
0.9

0.8
 α = 3.3126 - 0.0214(HT) -
0.7
1.1622(RD)
0.6
0.5

0.4
0.3
0.2
0.1
HT = Tree height
R2 = 0.51
RD=13%
RD=46%
RD=75%
0.0
0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18
Proportion of crown fuel biomass

RD = Relative density
(SDIobs/SDImax)
R2 = 0.71
Impact on CBH estimates
Stand
CBH –
original (m)
CBH –
modified (m)
Stand
CBH –
original (m)
CBH –
modified (m)
1
8.2
7.6
9
5.5
4.9
2
7.3
7.3
10
0.9
0.9
3
2.7
4.3
11
7.0
5.2
4
7.6
6.1
12
9.5
9.5
5
8.8
7.6
13
7.6
3.4
6
6.4
6.1
14
5.5
7.3
7
7.3
7.9
15
2.1
2.4
8
7.9
7.9
16
5.5
4.9
Impact on CBD estimates (kg/m3)
Stand
CBD
(original)
CBD
(modified)
Stand
CBD
(original)
CBD
(modified)
1
0.055
0.120
9
0.044
0.092
2
0.094
0.155
10
0.051
0.083
3
0.195
0.234
11
0.051
0.098
4
0.065
0.122
12
0.075
0.146
5
0.090
0.143
13
0.039
0.093
6
0.098
0.164
14
0.091
0.148
7
0.064
0.100
15
0.121
0.169
8
0.062
0.151
16
0.075
0.101
Fire hazard
 Fire hazard indices (torching and crowning
index) & fire type was assessed using NEXUS 2.0



97% weather conditions
Probable maximum momentary gust (53 km/hr)
Fuel model 5 (shrub fuel model)
Torching Index
 Torching index (TI) = 6.1 m open windspeed at
which fire is carried from the surface into the
crown


Function of: surface fuel loading and moisture content,
foliar moisture content, wind reduction by the canopy,
slope, and CBH (Scott and Reinhardt 2001)
Lower TIs = increased susceptibility to passive
crown fire
Impact of modified CBH on TI
Stand
Original TI
(km/hr)
Modified TI
(km/hr)
Stand
Original TI
(km/hr)
Modified TI
(km/hr)
1
35
32
9
18
14
2
31
31
10
0
0
3
10
10
11
27
16
4
32
23
12
43
43
5
40
32
13
32
0.8
6
24
23
14
18
31
7
31
34
15
0
0
8
34
34
16
18
14
Crowning Index
 Crowning index (CI) = 6.1 m open windspeed at
which active crown fire can occur


Function of: surface fuel moisture content, slope, and CBD
(Scott and Reinhardt 2001)
Lower CIs = increased susceptibility to active
crown fire
Impact of modified CBD on CI
Stand
Original
CI
(km/hr)
Modified CI
(km/hr)
Stand
Original CI
(km/hr)
Modified CI
(km/hr)
1
45
34
9
72
42
2
42
42
10
64
45
3
24
21
11
64
40
4
55
34
12
48
29
5
43
31
13
79
42
6
40
28
14
42
29
7
55
39
15
34
26
8
56
29
16
48
39
Potential fire behavior
Stand
Original
Modified
Stand
Original
Modified
1
PASSIVE
ACTIVE
9
PASSIVE
ACTIVE
2
ACTIVE
ACTIVE
10
PASSIVE
ACTIVE
3
ACTIVE
ACTIVE
11
PASSIVE
ACTIVE
4
PASSIVE
ACTIVE
12
ACTIVE
ACTIVE
5
ACTIVE
ACTIVE
13
PASSIVE
ACTIVE
6
ACTIVE
ACTIVE
14
ACTIVE
ACTIVE
7
PASSIVE
ACTIVE
15
ACTIVE
ACTIVE
8
PASSIVE
ACTIVE
16
ACTIVE
ACTIVE
Conclusions
 Crown mass equations for ponderosa pine in the
Black Hills resulted in substantially different crown
mass estimates than produced by Brown (1978):
 Underestimated
foliage mass by an average of
25%
 Overestimated 1 hr fuel mass by an average of
90%
Conclusions (cont.)
 Using a allometric equations developed for
ponderosa pine in the Hills + a non-uniform
distribution of crown fuel mass resulted in:


Similar estimates of CBH
An average 67% increase in CBD over original methods

Increase ranged from +20 to +140%
Conclusions (cont.)
 Using a threshold of 0.1 kg/m3 for CBD, FVS
misidentified high hazard structures


Original CBD values resulted in only 2 of the 16 stands
possessing a CBD >0.1 kg/m3 threshold
Modified CBD values resulted in an additional 10 stands
possessing a CBD >0.1 kg/m3 threshold
Conclusions (cont.)
 Modified estimates of CBH had little impact on TI
 Modified estimates of CBD resulted in a lowering of
CI for 15 of the 16 stands
 Modified estimates of CBH and CBD resulted in
potential fire type changing from passive to active
crown fire in 8 of the 16 stands
Implications
 Underestimating CBD and fire hazard indices may
result in the misidentification of stands in need of
treatment
 Underestimating CBD could create situations where
fuels treatments do not reduce CBD below the
critical thresholds required to minimize crown fire
hazard
Recommendations
 Widespread use of tree mass allometries be verified for
different tree species and development of local equations
be undertaken where substantial differences in crown
fuel mass estimates occur
 A non-uniform distribution of crown mass be used when
aggregating tree crown mass to identify the position and
amount of canopy mass to calculate CBD as used in fire
prediction models
Actions taken
 Incorporation of new biomass estimates and vertical
distribution models for ponderosa pine in the Black Hills
into FVS is complete (waiting for distribution/release of
update)
 New JFSP funded project implementing similar research
for other fire-prone tree species in the Interior West (Dougfir, lodgepole pine, spruce/fir, P-J)
 Results from study are published in:
 Keyser and Smith (2010) – Forest Science
 JFSP final report #JFSP #06-3-3-13
Acknowledgements
 JFSP funding #06-3-3-1
 Field technicians

Charity Weaver &
Adam Ridley
 Chad Keyser for initial
FORTRAN coding
assistance & Stephanie
Rebain implementation
of results into FFE
 Blaine Cook, Silviculturist,
Black Hills National Forest
 Mike Battaglia and Vicki
Williams