Cli t and
Climate
d Topographic
T
g hi Influences
I fl
on Fire
Fi Regime
R gi
Attributes
Att ib t
in the Northern Cascade Range
Range,
g , Washington
Washington,
g , USA
C Alina
C.
Ali C
Cansler
l 1 and
d Donald
D
ld McKenzie
M K
i 2
Northern Cascade Range Burn Severity Images,
Images 1984-2008
1984 2008
Climate Fire Occurrence
Climate,
Occurrence, and Fire Size
0.5
1.0
1.5
-2
10
8
-1
0
100000
2
2
3
2
R =0.20, P<0.05
10
000
10000
 Area burned was
significantly
i ifi
tl correlated
l t d with
ith
J l maximum
July
i
average
t
temperature
t
and
d JJuly
l
minimum
i i
average relative
l i
humidity.
10
00
10
000
15
1.5
1
July RH
10
00
10
1.0
6
1
10000
100000
10
00
05
0.5
12
14
0
R =0.46, P<0.001
-3
3
-2
2
Snow Water Equivalent
q
 Develop
p a burn severityy atlas for the northern Cascade Range
g based on local field data.
-1
July Temperature
10000
Are
ea Burned (ha)
-1.5
1 5 -1.0
1 0 -0.5
0 5 0.0
00
 Combined model with Julyy
temperature
p
and snow
water equivalent best
explained fire occurrence
2
-3
Snow Water Equivalent
Obj ti
Objectives
PDE* = 0.16, P<0.01
100000
-1.5 -1.0 -0.5 0.0
 Fire occurrence was
significantly correlated with
May 1st snow water
equivalent July maximum
equivalent,
a erage temperature,
average
temperat re and
J l minimum
July
i i
average
relative
l ti humidity.
h idit
4
Num
mber o
of Fire
es perr Yearr
12
10
8
6
2
4
Num
mber o
of Fire
es perr Yearr
12
10
8
6
4
2
Num
mber o
of Fire
es perr Yearr
PDE* = 0.35, P<0.001
14
PDE* = 0.20, P<0.001
14
We examined the influence of annual climate and topography
p g p y on fire occurrence, size, severity,
y and
within-fire severity
yp
pattern across 14,455
,
square
q
kilometers of federallyy managed
g land in the northern Cascade
Range
g of Washington
g
State,, USA. Landsat Thematic Mapper
pp ((LTM)) data were used to quantify
q
y burn severityy for
all fires greater than 10 ha (n = 125) that occurred during a 25 year period (1984
(1984-2008).
2008). Categorical burn
severity images were developed from an index of burn severity (Relative differenced Normalized Burn Ratio)
derived from LTM data and parameterized with data from 639 field plots
plots.
Fires in the northern Cascade Range respond both to local topographical controls and large
large-scale
scale
annual climatic variation.
variation Topographical complexity was positively correlated with patch density and negatively
correlated with within
within-fire
fire spatial aggregation,
aggregation indicating that the within-fire
within fire severity mosaic reflects the
underlying topographic complexity
complexity. Fire size was positively correlated with the proportion of area burned at high
severity and spatial aggregation of the high severity class.
class Summer temperature was positively correlated with
fire occurrence,
occurrence annual area burned,
burned the proportion of area burned at high severity
severity, and spatial aggregation of
th hi
the
high
h severity
it class.
l
The
Th relationship
l ti
hi between
b t
climate
li t drivers
di
and
d fire
fi regime
i
attributes
tt ib t identified
id tifi d in
i this
thi study
t d
adds
dd nuance to
t the
th climate-area
li t
burned
b
d relationship
l ti
hi documented
d
t d in
i previous
i
research.
h
10
000
Ab t
Abstract
t
-1
1
0
1
-1
1
0
1
July
y Temperature
p
2
 Area burn was not
correlated with May 1st
snow water equivalent.
equivalent
3
Julyy RH
The dNBR is the change from prefire to postfire NBR.
NBR
The RdNBR normalizes the dNBR byy the initial image
g reflectance to
account for spatial
p
variation within the image.
g The dNBRoffset corrects
for
o mismatches
s a c es in phenology
p e o ogy between
be ee image
age pairs.
pa s The
e RdNBR
d
has
as
been shown to work better when extrapolating to fires not included in
the original classification (Miller et al.
al 2009).
2009)
1.0
0
0.8
0.6
0.4
0.2
0
0.0
Prop
portion a
at Un
ncha
anged
d Se
everitty
1.0
0
0.8
0.6
0.4
0.2
0
0.0
1
-3
-2
1
1.0
J l Temperature
July
T
t
2
R =0.32, P=0.006
0.8
100 ha
1000 ha
10000 ha
100000 ha
0
Annual Area Burned
100 ha
1000 ha
10000 ha
100000 ha
2
R =0.25, P=0.05
0.6
Annual Area Burned
-1
0.4
 Hi
High
h severity
it spatial
ti l dispersion,
di
i
as
measured by the Normalized
Landscape Shape Index, was
positively correlated with May 1st
snow water equivalent
0
2
R =0.36, P=0.003
100 ha
1000 ha
10000 ha
100000 ha
0..2
Both indices are based on the Normalized Burn Ratio, which uses two
Landsat TM bands, Band 4 (R4, 0.76–0.90μm, near-infrared) and Band
7 ((R7, 2.08–2.35μm,
μ mid-infrared),
) to assess burn severity.
y
-1
High Se
everiity NLSI
N
 Proportion of area burned at
unchanged severity was negatively
correlated with July temperature,
temperature
and positively correlated with
August relative humidity.
humidity
-2
Annual Area Burned
J l Temperature
July
T
t
0.25
5
Geospatial fire occurrence records from federal land management agencies were used to identify all fires over
ten hectares (n = 125) that occurred in the study area during between 1984-2008.
1984 2008 Two remotely sensed indices
of burn severity,
severity the differenced Normalized Burn Ratio (dNBR) (Key and Benson 2006) and the Relative
differenced Normalized Burn Ratio (RdNBR) (Miller and Thode 2007),
2007) were evaluated for use in this study.
study
-3
0.20
0
 P
Proportion
ti off area b
burned
d att high
hi h
severity was positively correlated
with July temperature.
0.1
15
B
Burn
S
Severity
ity Indices
I di
0.10
0
Climate influences both burn severity
and the spatial pattern:
2
R =0.27, P=0.012
100 ha
1000 ha
10000 ha
100000 ha
0.05
0
Methods
Annual Area Burned
0.0
00
 Quantify the relationship between climate and fire regime attributes.
attributes
Climate,
Cli
t S
Severity,
ity and
d
Within-fire Severity
Pattern
 Quantifyy the relationship
p between fire size and fire regime
g
attributes.
P ortio
Prop
on off High Se
everiity Core
C
Area
a
 Quantify the relationship between topography and fire regime attributes
attributes.
Prop
P
portio
on att Hig
gh Se
everrity
*PDE = Proportion of deviance explained in a GLM
-3
3
-2
2
-1
1
0
July Temperature
1
-1.5
15
-1.0
10
-0.5
05
00
0.0
05
0.5
10
1.0
15
1.5
July Temperature
C
Conclusions
l i
Fi ld Validation
Field
V lid ti and
d IImage
g Cl
Classification
ifi ti
 In the northern Cascade Range fire season climate (i.e. July temperature) is more important than
antecedent climate (i.e.
(
spring
p g snowpack)
p
) in p
predicting
g fire occurrence and area burned. Fire
spatial patterns had a weak but significant relationship with both fire season climate and
antecedent climate.
climate
 Climate – Local climate observations from Remote Automated
Weather Stations (RAWS) and snow telemetry station (SNOTEL)
were converted to climate anomalies for analysis
analysis.
 Contagion Index – increases with spatial aggregation
aggregation.
 Aggregation Index – increases with spatial aggregation.
10000
0
1000
10
00
10
150
200
0
250
30
00
1000
10000
1.3
1.4
85
70
1.2
1.5
2
80
8
60
65
70
0
75
7
A k
Acknowledgements
l d
t
55
1.1
1.2
1.3
1.4
 Under expected warmer future climate, fires may not only be larger in size, but may also create a
more spatially
p
y aggregated
gg g
landscape.
p Future research is needed to address how large
g aggregated
gg g
patches of high severity influence species composition,
composition rates of succession,
succession and other ecosystem
functions
functions.
R =0.13,
0 13 P
P=0.001
0 001
60
50
40
1.0
1.5
Topographic Surface-Area Ratio
1.0
1.1
1.2
1.3
1.4
1.5






Topographic Surface-Area Ratio

1.0
2
0.6
0..8
R =0.19,, P<0.001
0.4

10
100
1.0
Fire Size (ha)
1000
10000
100000
Fire Size (ha)
2
2
R = 0.06, P<0.01
0.6
100
1000
Fire Size (ha)
10000
100000
100
1000
Fire Size (ha)
10000
100000


Proportion
p
of area burned at low and
unchanged
g severities decreases.

High severity core area increases.
increases


High severity spatial dispersion, as
measured by the Normalized Landscape
Shape Index ,decreased.


10
Proportion
P
ti off area b
burned
d att high
hi h
severity
it increases.
i
Fire size was not significantly correlated
with spatial aggregation when measured
across all severity classes by the
Contagion Index or the Aggregation
Index
Index.
Alana Lautensleger,
Lautensleger Andrew Larson,
Larson Joe Restaino,
Restaino Seth Cowdery,
Cowdery and Whitney Albright for help in the field
Jim Lutz
Lutz, Research Associate,
Associate School of Forest Resources
Resources, University of Washington
Washington, Seattle
Seattle, WA
Karen Kopper,
Kopper Fire Ecologist
Ecologist, North Cascades National Park,
Park Seattle,
Seattle WA
North Cascades National Park Complex Fire Management
Management, Marblemount,
Marblemount WA
Robert Norheim, GIS Analyst, School of Forest Resources, University of Washington, Seattle, WA
Susan Prichard,, Research Ecologist.
g
Fire and Environmental Applications
pp
Team,, US Forest Service,, and School of Forest
Resources,, Universityy of Washington,
g , Seattle,, WA
Stephen
p
Howard, USGS, EROS Data Center, Sioux Falls, SD
Funding provided by US Forest Service, Pacific Northwest Research Station, through a cooperative agreement with the
University of Washington, School of Forest Resources.
References

0.6
0.8
0.8
8
R =0.41, P<0.001
10
Fire Size, Severity, and
Withi fi Severity
Within-fire
S
ity Pattern
P tt
As fire size increases:
0..2
100000
0.4
 Normalized Landscape Shape Index (NLSI) – decreases with
spatial aggregation
aggregation, and increase with spatial dispersion
dispersion.
100
0.2
 Proportion of landscape made up of core area (area > 90m
from edge of patch).
10
Pro
oportio
on off High
h Sevverity Core
e Area
 Area weighted mean patch size
1.1
T
Topographic
hi S
Surface-Area
f
A
Ratio
R ti
2
0.0
1.0
 P
Proportion
ti off landscape
l d
att high,
hi h moderate,
d t low,
l
and
d
unchanged
h
d severity.
it
 Edge
g densityy – decreases with spatial
p
aggregation.
gg g
2
R =0.22,, P<0.001
0.0
 The spatial pattern of fires
fires, both for the whole landscape and
within a given severity class,
class was quantified using FRAGSTATS
(McGarigal and Marks 1995).
1995) Specific metrics used include:
1.0
 Fire climate interactions affect landscape pattern not just through changes in the annual area
burned but also by influencing the severity mosaic within individual fires.
burned,
fires The influence of climate
on fire
fi can be
b seen directly,
di tl via
i itits effect
ff t on severity
it pattern
tt
relationships,
l ti
hi
and
d iindirectly,
di tl via
i itits
effect on fire size, which in turn affects severity patterns.

Prop
portio
on at High Seve
erity
 Topographical complexity - the “surface to area ratio” was
calculated using digital elevation models (DEMs) data for each fire
(Jenness 2004).
1.5
30
3
 Topographical complexity was not
significantly correlated with class level
spatial pattern.
Quantifying Climate,
Climate Topography,
Topography and Within-fire
Within fire Severity Pattern
1.4
20
0
CBI
Conta
C
agion
n Inde
ex
 Patch density increased and patch shape
becomes less complex,
complex probably due to an
increased proportion of small simple
patches across the landscape (not shown).
shown)
30
3.0
1.3
R =0.09,
0 09 P
P=0.001
0 001
0.4
25
2.5
1.2
2
R =0.08, P=0.001
T
Topographic
hi S
Surface-Area
f
A
Ratio
R ti
0.2
20
2.0
Pro
oportion a
at Uncchang
ged S
Severrity
15
1.5
High Sevverityy NLS
SI
10
1.0
0..8
05
0.5
0.6
00
0.0
1.1
Ag
ggreg
gation Ind
dex
10
000
 The spatial pattern of the whole fire , as
measured by the Contagion Index and
Aggregation Index becomes less spatially
aggregated.
gg g
2
R =0.10, P<0.001
1.0
 Area-weighted
Area weighted mean patch size increased.
increased
0.4
 RdNBR images were used to produce categorical
images for all fires in the study.
study
 Edge density increased.
0..2
 Both indices performed similarly, but RdNBR had
slightly
g y higher
g
classification accuracyy (62%
(
vs. 59%).
)
As topographical complexity increases:
50
00
 Both dNBR and RdNBR models were evaluated based
on model fit and severity class categorization accuracy.
accuracy
Topography and
Withi fi Severity
Within-fire
S
ity Pattern
P tt
0
 DNBR and RdNBR values that would serve as
thresholds between severity classes were determined.
RdNB
R
BR
1500
1
0
1.74
Results
100
Physical measurements of overstory tree canopy scorch,
scorch char height,
height tree mortality,
mortality and surface fuel
consumption (forest plots) were translated into CBI values.
values 388 physical plots were installed on the
70 000 ha Tripod fire
70,000
fire.
 To
T validate
lid t and
d classify
l
if burn
b
severity
it images
i
non-linear
li
RdNBR = 187.1869  126.7CBI
r-squared = 0.470, P < 0.001
regression
i was used
d to
t model
d l dNBR and
d RdNBR as a
f
function
i off CBI.
CBI

Ed
dge D
Denssity
The Composite Burn Index (CBI) combines ecologically significant burn severity variables into one
numeric site index. CBI was employed on 251 plots over 4 fires.
0.0
0

Are
ea-w
weighted Mean
M n Pattch Size
S
 Two methodologies were used to assess burn severity in the field:

Jenness, J. S. 2004. Calculating landscape surface area from digital elevation models. Wildlife Society Bulletin 32:829839.
K
Key,
C
C.H.
H and
dN
N.C.
C Benson.
B
2006
2006. L
Landscape
d
A
Assessment:
t G
Ground
d measure off severity,
it the
th Composite
C
it Burn
B
Index;
I d
and
d
R
Remote
t sensing
i off severity,
it the
th Normalized
N
li d Burn
B
R
Ratio.
ti IIn D
D.C.
C L
Lutes;
t
R
R.E.
E K
Keane; JJ.F.
F Caratti;
C tti C
C.H.
H Key;
K
N.C.
N C Benson;
B
S
S.
Sutherland; and L
L.J.
J Gangi.
Gangi 2006
2006. FIREMON: Fire Effects Monitoring and Inventory System.
System USDA Forest Service
Service, Rocky
Mountain Research Station
Station, Ogden
Ogden, UT
UT. Gen
Gen. Tech
Tech. Rep
Rep. RMRS
RMRS-GTR-164-CD:
GTR 164 CD: LA 1
1-51.
51
McGarigal K
McGarigal,
K., S.A.
S A Cushman,
Cushman M.C.
M C Neel,
Neel and E.
E Ene.
Ene 2002.
2002 FRAGSTATS: Spatial Pattern Analysis Program for
Categorical Maps
Maps. Computer software program produced by the authors at the University of Massachusetts,
Massachusetts Amherst
Amherst.
Available at the following web site: http://www
http://www.umass.edu/landeco/research/fragstats/fragstats.html.
umass edu/landeco/research/fragstats/fragstats html
Miller, J. D. and A. E. Thode. 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the
delta Normalized Burn Ratio (dNBR).
(
) Remote Sensing
g of Environment 109:66-80.
Miller,, J. D.,, E. E. Knapp,
pp, C. H. Key,
y, C. N. Skinner,, C. J. Isbell,, R. M. Creasy,
y, and J. W. Sherlock. 2009. Calibration and
validation of the relative differenced Normalized Burn Ratio (RdNBR)
(
) to three measures of fire severityy in the Sierra
Nevada and Klamath Mountains, California, USA. Remote Sensing of Environment 113:645-656.
1 School
of Forest Resources, University of Washington, Seattle, WA 98195-2100.
2 Pacific Wildland Fire Sciences Lab, USDA Forest Service, 400 N 34th Street, Suite 201, Seattle, WA 98103.