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DEFENSIBLE SPACE OPTIMIZATION FOR PREVENTING WILDFIRE STRUCTURE LOSS IN THE SANTA MONICA
MOUNTAINS
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Defensible Space Optimization for Preventing Wildfire Structure Loss in the Santa Monica Mountains
Amanda J. Miner
Johns Hopkins University
In Partial Fulfillment of the Requirements for the Degree of Master of Science (MS)
Advisors: Gergana Miller, PhD, and Robert S. Taylor, PhD
Submitted in December 2014
Research Funding Provided by the Santa Monica Mountains Fund and the Southern California Research and Learning Center.

DEFENSIBLE SPACE OPTIMIZATION FOR PREVENTING WILDFIRE STRUCTURE LOSS IN THE SANTA MONICA MOUNTAINS
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ACKNOWLEDGEMENTS
I would like to thank Robert Taylor for his guidance, encouragement, wisdom, and provision of quality data resources
throughout the course of this project—and for providing me with so much of his valuable time. I am so grateful for Marti
Witter’s support during this study in innumerable ways. Thank you to Alexandra Syphard and Tess Brennan, for being willing
to share data, knowledge and suggestions with me. I am very grateful to Irina Irvine, who has been a constant source of
encouragement and has allowed me substantial time to work on this. Finally, I want to thank Chris Miner for his patience,
wisdom and time in helping me to learn R and overcome some significant data hurdles.

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Abstract
This study replicated and extended an innovative study by Syphard et al. (2014) that assessed the role of defensible space in
preventing structure loss during wildfire events in San Diego. The geographic extent of this study was the Santa Monica Mountains
National Recreation Area, an area west of Los Angeles. Results of both studies corroborate that defensible space in excess of 30m (100ft)
provided no additional protection to structures. A variety of measurements, calculations, and statistical analysis methods were used to
assess what measure of defensible space provides protection, which terrain variables are correlated, and what additional factors
influence structure loss. Some results correlate with the San Diego study, while others provide insight as to future research needs. Of
particular interest here are the surrounding vegetation types that proved statistically significant, which merit additional study.
Keywords: Defensible space, wildfire, fuel modification, structure loss

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Defensible Space Optimization for Preventing Wildfire Structure Loss in the Santa Monica Mountains
BACKGROUND
The southern California region withstands a persistent risk of wildland fire events that threaten countless lives and structures.
The majority of these wildland fires occur during southern California’s annual “fire season”, which begins as early as May and typically
lasts through the month of December. Winter rains generate dense vegetation and summer droughts transform the vegetation into
highly ignitable fuels, coinciding with a seasonal southwest wind system. This regime, when coupled with southern California’s
substantial human population growth and expansion into fire-prone areas, creates considerable risk of structure loss to wildfire (Keeley
et al. 2013).
There is a prevailing pattern of land use in Southern California that includes “single family homes, master planned
communities, and large-lot ranchettes -- expanding into naturally fire-prone ecosystems”, producing a volatile scenario (Pincetl et al.
2008:25). Large populations are directly adjacent to and intermixed with dangerous fuels, and since “over 95% of all fires on these
landscapes are started by people, there has been a concomitant increase in fire frequency and increased chance of ignitions during Santa
Ana wind events….” (Keeley et al, 2004).
The 2003 fire season is a prime example of how severe the threat of structure loss can become. Beginning on October 21, 2003,
a firestorm comprised of 14 major wildfire events broke out across the southern California region, ranging from Santa Barbara County
all the way down to the US-Mexican border. The fires were finally suppressed on November 4th, after claiming 24 lives and 3,710
structures, burning 750,043 acres, and costing over $3 billion in damages and firefighting expenditure (Blackwell and Tuttle 2004;
Keeley et al. 2013).

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Figure 1: Fires Occurring in the Santa Monica Mountains National Recreation Area during 2003 and 2007
Approach to structure protection
According to Nowicki (2002), “current efforts to protect communities from the threat of [wildfire] are being planned without
consideration for what is actually effective at protecting houses and communities…strategic plans need to utilize the best available
science to develop the most effective and efficient methods for protecting houses and communities”. The effect of vegetation on
structure vulnerability to fire damage “is neither clear-cut nor easily characterized” (Foote et al. 1991).
Though the state of California mandates a single clearance distance measurement for all types of vegetation, this is “insufficient
to fully characterize the hazard that vegetation surrounding a structure presents” (Foote et al. 1991). Even though garden or landscape
vegetation planted adjacent to a structure could be deemed a substantial hazard, “much landscape vegetation clearly does not pose a

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hazard to structures” (Foote et al. 1991). Further, Foote et al. (1991) note that if vegetation standing between a structure and an
approaching fire is not highly flammable, it may even function as a protectant for the structure through absorption of part of the heat
flux and filtering out firebrands.
The state of California endorses the use of defensible space as a strategy for protecting buildings and firefighters, and as such,
it enforces very specific regulations pertaining to vegetation clearance. However, despite California's stringent ‘100 foot’ rule for
defensible space, there is actually little empirical evidence documenting the efficacy of different types of defensible space fuel
modification for protecting structures from wildfire.
Therefore, faced with an acute need to minimize structure losses, alongside uncertainty regarding adequate defensible space
requirements, a focused and scientific analysis to help define them is imperative “in order to avoid inadvertently damaging
adjacent...ecosystems and wildlife habitat…” (Nowicki 2002).

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RESEARCH QUESTIONS & METHODOLOGY
Primary Research Questions:
This study seeks the answers to the same three significant questions posed by the San Diego study;
1) How much defensible space is needed to provide significant protection to homes during wildfires, and is it beneficial to have
more than the legally required 100ft/30m?
2) Does the amount of defensible space needed depend on slope incline, aspect relative to the direction of fire spread, or other
obvious terrain variable?
3) What is the role of defensible space relative to other factors that influence structure loss, such as terrain, fuel types in the
home ignition zone, observable details of construction, and housing density? (Syphard et al. 2014).
1. Data Acquisition And Preparation
The first stage of the study involved locating the required data and preparing it for subsequent calculations. Essential study
data included:
 Structures (including an indication of whether they were damaged, destroyed, or neither damaged nor destroyed in any of the
identified fires)
 Historical Wildfire Perimeters (from 2000 to 2013)
 NAIP Orthographic Photographs (for the year prior to each of the identified fires)
 Parcels
 Santa Monica Mountains National Recreation Area Boundary
 Burn Severity (for each fire)
 3m DEM for the Santa Monica Mountains, from which to generate Slope and ‘southwest-facing’ parcels
 Major and Minor Roads
 Surrounding Vegetation: Wildlife Habitat Classifications and SAMO Vegetation Map classifications
Within the structure data, all structures residing within fire perimeters (occurring between 2000 and 2013) were extracted
into two main ‘Structure’ datasets: Damaged/Destroyed and Unharmed. Although the San Diego study did not include ‘damaged’
structures in their analyses, I decided to include it with the intent to analyze it both concurrently and separately from destroyed
structures. Since my dataset was smaller, I was able to include this additional parameter.

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Syphard et al (2014) randomly selected equal numbers of ‘destroyed’ and ‘not destroyed’ to use from its substantially sized
dataset. However, since the Santa Monica Mountains is a significantly smaller study area, all structures within the park’s boundary
were necessarily used in the analysis. Interestingly, the Santa Monica Mountains contained around the same number of damaged and
destroyed structures as it did unharmed structures. After the structure data was prepared, the datasets were merged into one, and the
specific fires impacting each structure were reviewed. ‘Monitoring Trends in Burn Severity’ data (http://mtbs.gov , US Geological
Survey/US Forest Service) was used to ensure that all structures were located within areas that burned at a minimum of low severity.
2. Defensible Space Measurements
Following the measurement methods used by Syphard et al (based on CALFIRE 2006 guidelines), I used 1 meter resolution
NAIP orthophotography from the year prior to each fire in order to measure the defensible space around structures. Workflow involved
drawing measurements beginning from all 4 orthogonal sides of each structure, until each line intersected with, a) wildland vegetation,
b) trees or shrubs with <10m between canopies, or c) any vegetation touching or overhanging a structure (any such side is immediately
assigned a 0m value). There were two overall sets of calculations performed: a) a measurement of defensible space that falls within each
structure’s parcel boundaries, and b) defensible space that continues to the full measure of defensible space (Figure 2).

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Figure 2: Defensible Space Measurement Methods for the Four Orthogonal Sides of Each Structure (showing allowable measurements for the purposes of this
study). Adapted from Syphard et al. (2014).
These measurements were performed for every structure in my dataset. Wherever I was presented with unusually shaped
structures, or wherever structures presented at an angle, I simply treated all such cases in a consistent manner.
To calculate the percentage of woody vegetation cover, non-woody vegetation cover, and structure area on each parcel, I
generated a 25m fishnet for the whole study area. The surrounding vegetation type at the end of defensible space measurements was
averaged for every structure, using a 30m buffer around structures. Finally, all structures with touching or overhanging vegetation on
one or more sides were recorded, and this set of calculations was joined to the main datasets.

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VARIABLE DEFINITION
Defensible space to the property boundary Measure of defensible space clearance from side of structure to property
boundary, calculated for each of the four orthogonal directions from the
structure and averaged
Total distance of defensible space (beyond parcel boundary) Measure of clearance from side of structure to end of clearance calculated for
four orthogonal directions from structure and averaged
Vegetation cover type at end of defensible space Main vegetation cover type encountered at the end of defensible space
measurement (WHR Vegetation Class and SAMO Veg Map data)
Percent vegetation clearance Percentage of clearance calculated across the entire property
Vegetation overhanging/touching structure Number of sides on which woody vegetation touches structure (assigned a
number between 1 and 4)
Table 1. Defensible Space Variables Measured for Every Structure
3. Calculation Of Each Structure’s Spatial Information
Specific spatial information was extracted for all structures, which included housing density, slope, distance to nearest
major/minor streets, and surrounding fuel type. To assess housing density, a ‘Kernel Density’ analysis was run for all structures with
1500m and 2000m radius parameters, and the results of each were added to structure attribute tables using the ‘Value to Point’ tool.
This tool was used repetitively to add any attributes that were in raster format. Next, values were extracted from a 3m slope raster
(derived from a 3m DEM) and added to each structures’ attribute tables. In accordance with Syphard et al. (2014), slope was sorted
from lowest to highest, and at the midpoint of the dataset all values above the midpoint’s value were assigned a ‘1’ for steep slope, and
all values below the midpoint value were assigned ‘0’ for shallow slope. These assignments were somewhat arbitrary and may not truly
reflect what is steep or shallow on the ground, I decided to use the terms ‘low’ and ‘high’ slope for the purpose of simplicity. ‘Aspect’
was calculated from the same 3m DEM, to generate an index of ‘southwestness (parcels with a southwest-facing aspect)’. Due to the
3m scale used, it was necessary to use the ‘Majority Filter’ tool, after first using the ‘Int’ tool to generate an integer field. I used the
maximum of Eight Neighbors and selected ‘Majority’ for the replacement threshold. This was necessary for achieving a more
‘generalized’ aspect result that was applicable to the general scale of parcels. The ‘Near’ tool was then used to calculate the distance of
each structure to the nearest minor and major streets, based on a pre-existing road-type attribute. This was a data set acquired from
the Santa Monica Mountains NRA, which seemed appropriately designated upon review.

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4. Initial Analysis Of Data Subsets
The entire dataset was sorted into two equal subsets of high and low slope. Within these subsets, groups were created to
reflect the mean distance of defensible space around structures. These groups were further sorted into subgroups reflecting mean
defensible space a) within parcel boundaries, and b) regardless of parcel boundaries. For these two subgroups, the proportion of all
homes destroyed (or damaged) by fire were calculated.
5. Tests For Independence
Pearson’s Chi-squared tests were performed to determine whether the proportion of destroyed structures within different
measurement groups was significant. The first test was based upon 4 equal interval groups within the 30m required distance. These
intervals were at 0-7m, 8-15m, 16-23m, and 24-30m. The second test was based on 3 interval groups to test whether groups with mean
distances of greater than 30m are significantly different to those in the first test. The intervals for this test were at 24-30m, 31-90m, and
>90m. I also generated a relative risk value for each measurement range using MedCalc’s relative risk calculator
(www.medcalc.org/calc/relative_risk.php) based on the results of these groupings (Tables 2 and 3). Relative risk compares the ratio of
the probability of an event occurring in an ‘exposed group’ (in this case damaged or destroyed structures) to the probability of that
same event occurring in a ‘non-exposed’, control group (in this case unharmed structures). This enables a comparison of risk between
groups, and helps to put risk in context. A relative risk of 1 would mean there is no difference in risk between the two groups. If the
relative risk is <1, the event is less likely to occur in the ‘exposed’ group than in the control group, but if it is >1, then the event is more
likely to occur in the ‘exposed’ group than in the control group.
6. Determine a ‘Prescriptive Dose’ of Defensible Space for Structure Survival
Using the software package DRC in R, a dose-response relationship of defensible space with the survival of structures was
calculated. This is an analysis type used in the medical field to quantify the median effective treatment dosage. The test uses a value
known as ‘ED50’, which is commonly used as a measure of the reasonable expectancy of a drug effect. For this study, it will provide the
median effective dose for 50% of the structures receiving the ‘treatment’. First, using the binary dependent variable of ‘destroyed or
damaged/not destroyed or damaged’, the calculation was performed by fitting a log-logistic model with logistic regression. This was
repeated using ‘Destroyed’, ‘Damaged’, and ‘Not Damaged or Destroyed’. Separate analyses were performed for defensible space
measurements within and beyond parcel boundaries.

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7. Consider Defensible Space Relative To Other Variables
The dose response analysis was followed by a series of multiple linear regression analyses. First, using the binary dependent
variables ‘damaged/destroyed’ and ‘unharmed’, I fit a log-logistic model with logistic regression using package MuMIn in R for model
averaging. This provides the coefficient of determination (R2) for each model, which is used to assess whether the model describes the
data well. It provides the ratio of any unexplained residual variance by the model (i.e. the difference between observed and predicted
values) over total observed variance. In order to consider defensible space in relation to other variables, I developed a series of multiple-
generalized linear regression models (GLMs) to consider all possible combinations of predictor variables for best fit. This was done
using AICc to rank the models to select the best model for each region. For each series, I ran one model with WHR Vegetation
Classification (State of California, Wildlife Habitat Relationships) and the other with SAMO Veg Map Classification (NPS, Santa
Monica Mountains NRA) in order to evaluate detailed information about the role of vegetation in structure loss. First performed a
series of logit regressions, performing models with each vegetation classification to the parcel boundary and to the full measure of
defensible space. Then I separated out damaged, destroyed, and unharmed for use in an ordered logit regression series (to the parcel
boundary and to full defensible space, using each vegetation classification).
8. Assess Destroyed Structures According To Surrounding Matrix
For this stage of the analyses, the most common cover type at the end of defensible space measurements was summarized,
based on the majority surrounding cover type from the 4 orthogonal sides. The predominant surrounding vegetation (fuel) type was
assessed for each structure, based upon the California Wildlife Habitat Relationships System (‘WHRTYPE’) classifications and also
the SAMO VegMap classifications. This was achieved by drawing a 30m buffer around each structure, and extracting the most
dominant vegetation type values to the structure point data. The raw data tables from all calculations can be viewed in APPENDIX A.

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RESULTS
9. Categorical Analysis
a) Pearson’s Chi-Squared Tests of Independence
Figure 3: Pearson’s Chi-Squared Tests of Independence, Showing Proportion of Damaged and Destroyed Structures based on Slope (Low or High), P = 0.03316,
X-Squared = 4.4575
Figure 4: Proportion of Damaged or Destroyed Structures based on the Mean Measurement of Defensible Space (up to the Parcel Boundary), P = 0.421, X-squa
red = 5.4431
Defensible Space Measurements

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Figure 5: Proportion of Damaged or Destroyed Structures based on the Mean Measurement of Defensible Space (beyond the Parcel Boundary), P = < 0.3257, X-
Squared = 2.2438
For the Chi-squared tests of independence, there were some significant patterns regarding the proportions of damaged and
destroyed structures among the 4 equal interval groups (0-30m). For measurements up to the parcel boundaries, the majority of
structures fell into the 0-7m group. The number of damaged or destroyed structures dropped off substantially in the 8-15m group and
continued to decline through the 16-23m and 24-30m groups, leaving only a minimal number in the latter group. Therefore, the
proportion of damaged/destroyed structures steadily decreased as defensible space increased for measurements to the parcel boundary.
When measurements were made to the full defensible space, the 3 equal interval groups followed a similar pattern of decreasing
damaged/destroyed structures as defensible space increased. However, the vast majority of structures fell within the 24-40m group,
with only minimal numbers of structures in the 30-60m and >60m groups.
Whether structures were measured to the parcel boundary or to the full defensible space, those that were not damaged were
comparable in number to those that were destroyed in each measurement category, with the exception of the last category in each.
Only damaged structures remained in the last category, whether measured to the parcel boundary or the full defensible space. The Chi-
Squared value for defensible space measurements up to the parcel boundary is x2 = 5.4431 (where >5 is considered significant) with a p-
value of P = 0.1421. However, when measured beyond the parcel boundary, the chi-squared value is 2.2438, and p=0.3257.
Destroyed structures accounted for slightly more than half of all damaged/destroyed structures on low slopes, whereas on high
slopes damaged structures made up well over half of the structures (approximately 60%). The Chi-Squared value for this test was x2=
4.5375 and P=0.03316.
24-30m 30-60m >60m
Defensible Space Measurements

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b) Relative Risk
Slope Type Damaged
or
Destroyed
Unburned Relative
Risk
p-value Damaged
or
Destroyed
Unburned Relative
Risk
p-value
Low Slope
0-7 135 123 1.0463
(0.1469)
P = 0.9639 133 122 1.0430
(0.1464)
P = 0.0036
8-15 7 6 1.0291
(0.6139)
P = 0.9135 10 7 1.1278
(0.7449)
P = 0.5679
16-23 1 3 0.4643
(0.0790)
P = 0.3957 1 3 0.4250
(0.0743)
P = 0.3361
24-30 0 0 0.9333
(0.1238)
P = 0.9466 0 0 1.6667
(0.1552)
P = 0.6732
31-90 0 0 1.0000
(0.0625)
P = 1.0000 0 0 1.00000
(0.0625)
P = 1.0000
60 or 90+ 0 0 1.0000
(0.0625)
P = 1.0000 0 0 1.00000
(0.0625)
P = 1.0000
High Slope
0-7 90 108 0.9095
(0.1274)
P = 0.9247 89 108 0.9040
(0.1266)
P = 0.9199
8-15 3 1 1.6500
(0.9183)
P = 0.0940 1 1 1.1067
(0.2744)
P = 0.8866
16-23 1 1 0.6667
(0.0803)
P = 0.7074 0 1 0.5000
(0.0352)
P = 0.6087
24-30 1 0 0.7759
(0.3466)
P = 0.5370 0 1 1.0000
(0.0335)
P = 1.0000
31-90 0 0 1.00000
(0.0625)
P = 1.0000 0 0 2.0000
(0.0902)
P = 0.6611
60 or 90+ 0 0 1.00000
(0.0625)
P = 1.0000 0 0 1.00000
(0.0625)
P = 1.0000
Table 2. Number of Burned and Unburned Structures within Defensible Space Distance Categories (m), their Relative Risk and Significance (Confidence
Intervals in Parentheses)
The relative risk analysis yielded some interesting patterns (Table 2). For structures on low slopes, the 0-7m and 8-15m groups
had relative risk values of >1 for both measurements of defensible space, meaning that structure damage or destruction is more likely to
occur. There was only one measurement group that had statistical significance from the relative risk calculations; 0-7m on low slope,
when measured to full defensible space (p=0.0036). The only other low slope measurement group that had a relative risk of damage or
destruction was the 24-30m group when measured to the full defensible space. Low slope measurement groups with low relative risk
for damaged or destroyed structures (<1) were 16-23 and 24-30m (to the parcel boundary), and 16-23m (full defensible space). There
was no difference in relative risk between ‘exposed’ and control groups at the 31-90m and 60 or 90+m, regardless of the measure of
defensible space.
Measurements to Parcel Boundary Full Defensible Space Measurements

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On ‘high’ slope, the 0-7m group had less relative risk of structure damage (regardless of defensible space measurement type). The
8-15m groups were the only high slope groups that had a greater relative risk of damage or destruction. The remaining groups either
had less or equal relative risk for damage or destruction when compared to the control group.
10. Effective Treatment Analysis
All Parcels
Effective
Treatment
(n=244)
Parcel Mean
of All
Structures
(n=485)
Effective Treatment
of all Low Slope
Structures (mean
13.91455%) (n=133)
Parcel Mean
of All Low
Slope
Structures
(n=279)
High Slope (mean
37.35952%)
effective treatment
(n=241)
Parcel Mean of
all High Slope
Structures
(n=206)
Defensible
Space within
parcel
1.724548 1.553285 2.072473 1.786952 1.307666 1.236813
Total Distance
of Defensible
Space
1.906474 1.746608 2.195277 1.944078 1.560431 1.479161
Mean Percent
(%) Clearance
on Property
25.209016 24.971134 25.962406 25.781362 24.306306 23.873786
Table 3. Effective Treatment Results Representing the Distance (Meters) and Percent Clearance that Provided Improvement in Structure Survival in the Event
of a Wildfire (based on structures that survived)
Here, ‘effective treatment’ was crudely defined by the mean measurement of unharmed structures. When the mean defensible
space measurements around surviving structures were compared with ‘effective’ defensible space, they were surprisingly similar (Table
3). The low numbers were heavily influenced by the number of structures that had vegetation touching or overhanging structure sides
(recorded as 0m of defensible space per study guidelines). Whether the mean defensible space of surviving structures was measured to
the parcel boundary or to full defensible space, it was in fact slightly lower than the ‘effective’ measurement, regardless of slope type.
The mean effective defensible space treatment of low slope structures was approximately 1m higher than the mean of all low slope
structures, regardless of whether defensible space was measured to the parcel boundary or to the full extent. Likewise, the mean
effective defensible space treatment for high slope structures was slightly higher than the actual parcel mean for high slopes. This too
was the same regardless of the extent of the defensible space measurement.
The calculated effective treatment for mean percent clearance across all parcels was 25.209016%. For parcels on low slope, the
effective mean percent clearance was 25.962406%, and the result for high slope was 24.306306%. The mean percent clearance values of
surviving structures indicate that actual values were almost identical to effective treatment calculations, with <2% difference between
all values.

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Most Frequent
Vegetation Type:
WHR Classification
Most Frequent
Vegetation Type: SAMO
Veg Classification
Second Most Frequent
Vegetation Type: WHR
Classification
Second Most Frequent
Vegetation Type: SAMO
Veg Classification
Defensible Space
Measurement
Urban Urban/Disturbed, Built
Up/Cleared Types
Coastal Sage Scrub Urban Trees or Riparian
Woodland Types
Destroyed Structures Urban (49) Urban/Disturbed, Built
Up/Cleared Types (89)
Coastal Sage Scrub (41) Riparian Woodland Types
(11)
Damaged Structures Urban (61) Urban/Disturbed, Built
Coastal Sage Scrub (40) Riparian Woodland Types
(9)
Unharmed Structures Urban (135) Urban/Disturbed, Built
Coastal Sage Scrub/ Mixed
Chaparral (59/37)
Urban Trees (30)
Table 4: Most frequent vegetation types at the end of all defensible space measurements, and which were most frequent for destroyed, damaged, and unharmed
structures.
The most frequently found vegetation at the end of defensible space measurements was Urban, regardless of vegetation
Classification used (Table 4). The second most frequently found vegetation was Coastal Sage Scrub (WHR Classification) and Urban
Trees and Riparian Woodland Types (SAMO Veg Classification). Urban vegetation types represented by both vegetation
classifications are the most frequently occurring vegetation types associated with destroyed, damaged, and unharmed structures, as
well as the most frequently occurring vegetation at the end of defensible space measurements. Generally speaking, results showed that
damaged and destroyed structures were located within predominantly urban, coastal sage scrub, and riparian woodland type
communities. Unharmed structures were located within largely urban, coastal sage scrub, mixed chaparral, and urban tree vegetation
types.

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11. Dose Response Analysis (DRC in R)
Figure 6: Median Effective ‘Dose’ of Defensible Space (meters) When Measured to the Parcel Boundary
Figure 7: Median Effective ‘Dose’ of Defensible Space (meters) When Full Defensible Space was Measured
When the dose-response analysis was run on defensible space measured to the parcel boundary, the ED50 value indicated a
median effective treatment ‘dosage’ of 16.790 meters. Results for full defensible space measurements were very similar, indicating a
median effective treatment ‘dosage’ of 17.107 meters. This would indicate mean defensible space measurement of 16.95m, regardless of
measurement method.

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12. Generalized Linear Regression Analysis, with Model Averaging (MuMIn in R)
The first set of multiple regression models used WHR Vegetation Classifications. With measurements to the parcel boundary
(Table 5), the most important variables according to their Akaike weights were mean defensible space measurements, distance to major
roads, and slope. Significant variables were the same when full defensible space measurements were used, although the significance of
slope was diminished (Table: 6). The R2 value for Model A with averaging is 0.05602355, and 0.05635483 for Model B with averaging.
When the SAMO Veg Classification was used, the overall model fit was marginally better. For Model C (measurements to the
parcel boundary), the R2 value was 0.06737012 (Table 7). Distance to roads was the only significant variable in this model. Model D
(full defensible space measurements) had the best fit of all models, with an R2 value of 0.06841039, and had 2 significant variables;
slope and distance to major roads (Table 8). There is some debate among sources as to the inference of the R2 measure. Extremely
strong predictors are needed to achieve an R2 score even close to 1, with fit considered ‘excellent’ above 0.2.
While neither of the regressions with model averaging revealed any significance for particular vegetation types, this level of
detail was retrieved in the Ordered Logit results (SECTION 13).
Figures 8 and 9 are final plots showing the trend of defensible space measurements in these models. They show a pattern of
steadily declining fire likelihood as defensible space increases, leveling off between 17 and 24m. These results were the same regardless
of the defensible space measure or vegetation type used.
***p<0.001, **p<0.01, *p<0.05. Standard errors in parentheses
Table 5: Logit Regression Model A (with Averaging), with Measurements to the Parcel Boundary using the WHR Vegetation Classification.

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Table 6: Logit Regression Model B (with Averaging), with Full Defensible Space Measurements using the WHR Vegetation Classification.
Table 7: Logit Regression Model C (with Averaging), with Defensible Space Measurements to the Parcel Boundary using the SAMO VegMap Classification.

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Table 8: Logit Regression Model D (with Averaging), with Full Defensible Space Measurements using the SAMO VegMap Classification.
Figure 8: Logit Regressions with model averaging, using measurements to the parcel boundary (regardless of vegetation type)

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Figure 9: Logit Regressions with model averaging, using full defensible space measurements (regardless of vegetation type)
13. Ordered Logit Regression Analysis
Table 9: Ordered Logit Regression, Damaged, Destroyed, and Undamaged, with WHR Vegetation Classification

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For the ordered logit regression analysis, I separated ‘damaged’ and ‘destroyed’ structures from one another for comparison
with structures that were ‘unharmed’. This provided more in-depth results regarding the significance of surrounding vegetation types.
The first model used the WHR Vegetation Classification, and indicated the strong significance of the following vegetation types:
Coastal Sage Scrub (CSC), Mixed Chaparral (MCH), Urban (URB), and Montane Riparian (MRI). Though this model had a better fit
than the logit regressions (Akaike weights indicated ~996 regardless of defensible space measurement), the only other significant
variable was distance to major roads.
Table 10: Ordered Logit Regression, Damaged, Destroyed, and Undamaged, with SAMO VegMap Classification
When an ordered logit regression was run with the SAMO Veg Map Classification, Upland Tree types, Riparian Woodland
types, and Urban Roads were significant. This model had the best overall model fit (AIC 1000.39 for measurements to parcel boundary,

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and AIC 999.82 for full defensible space). The total explained deviance for the WHR Vegetation Classification was ~968 and for the
SAMO Veg Map Classification it was ~962.
Figure 10: Ordered Logit Regression Plot, Measurements to Parcel Boundary with WHR Vegetation Classification
When plots were drawn from these ordered logit regressions a clear pattern emerged. This was particularly true for the
measurement to the parcel boundary, regardless of vegetation type. The optimum defensible space for either damaged or destroyed
structures is between 21 and 24m. When the model using full defensible space was plotted, a very slightly less confident version of the
same result is presented (i.e. it shows that there is ~2% chance that a structure will still be destroyed with 24m of defensible space, and
~4% chance that it could be damaged).
Figure 11: Ordered Logit Regression Plot, Full Defensible Space Measurements with WHR Vegetation Classification

25
When plots were drawn from the SAMO VegMap Ordered Logit Regressions, they showed the same general trend as
the WHR Vegetation plots, but with slightly less confidence (Figures 12 & 13). When measured to the parcel boundary, the
destroyed structures showed an optimum measurement of 23-24m, with a ~2% chance that a structure would still be destroyed.
Damaged structures indicated the same general measurement, with ~4-5% chance that a structure would still be damaged. When
measured to the full defensible space, the line became much more linear. Though the damaged and destroyed lines ended at 24m,
they showed a ~7% and ~5% chance of being damaged or destroyed, respectively. In the next section we can see that there are
certain vegetation types that could be affecting the data (and perhaps the overall models) due to their low representation.
Figure 12: Ordered Logit Regression Plot, Full Defensible Space Measurements with SAMO VegMap Classification

26
Figure 13: Ordered Logit Regression Plot, Full Defensible Space Measurements with SAMO VegMap Classification
Ordered Logit Plots of Vegetation Variables Only
Figure 14: Ordered Logit Plots for Individual WHR Vegetation Types

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When vegetation types were plotted individually it proved a much greater level of detail about their influence. In the Ordered
Logit Plots for WHR Vegetation types, Urban, Coastal Sage Scrub, and Montane Riparian all indicated an optimum defensible space
measurement of <30m. Annual Grasses and Coast Oak Woodland produced unusual results, although this is likely due to their low
representation in the study (counts of 3 and 7, respectively). Mixed chaparral was the only vegetation type in the WHR classification
that appeared to need slightly longer than the 24m measurement, and more so for ‘damaged’ structures than ‘destroyed’. This could
perhaps be an influence of the ‘critically flammable’ nature of Mixed Chaparral vegetation communities (Bolsinger 1989).
Figure 15: Ordered Logit Plots for Individual SAMO VegMap Types
Individual ordered logit plots for each of the SAMO VegMap types also yielded additional insight. First it must be noted that
there were some strange outputs for a few of the vegetation types. Upland Tree types, Urban Shrubs, and Exotic and/or Invasive types

28
yielded an unusual pattern, but again this is likely due to extremely low representation in the model (counts of 1, 2, and 3, respectively).
Urban Roads were unusual in that the slope trended in the opposite direction when compared to other vegetation types. However, this
indicates that the further a structure is from urban roads the more likely it is to be damaged or destroyed. This makes some sense in
that the structure is more likely to be surrounded by flammable vegetation types as opposed to impervious surface.
CSS types and Urban Tree types both indicated an optimum measurement of ~17m of defensible space, and Urban
Grasses/Herbs and Disturbed Vegetation types indicated a measurement of ~20m. Chaparral types were very close behind, with a
measurement of ~21m. Urban/Disturbed, Built Up, Cleared types was by far the most represented SAMO vegetation group found within
defensible space measurements (376). This suggested that a measurement of ~22m of defensible space would be optimal (but indicated
a <5% chance that a structure could still be destroyed, and a 10% chance that it could still be damaged). Riparian Woodland types
indicated that a defensible space measurement of slightly greater than 24m is needed, with a 5% and 10% chance of being damaged or
destroyed (respectively). Given the trend of the plot lines, however, it is unlikely that they indicate an optimum measurement greater
than 30m.
14. Influence of Touching or Overhanging Vegetation
As surrounding vegetation at the edge of defensible space measurements proved to be an important variable within my results,
it seemed pertinent to evaluate the vegetation that was adjacent to, or overhanging structures. Although the vegetation types
themselves were not recorded (as they were assumed to be exotic/urban), the number of structure sides that had overhanging or
touching vegetation was. The directional sides of the structures that had overhanging or touching vegetation were not recorded, but
the recorded numbers between destroyed, damaged, and non-damaged structures were rather unremarkable, in that non-damaged
structures held a consistently comparable (or higher) count than damaged/ destroyed. This was true regardless of how many sides were
touching or overhanging.

29
DISCUSSION
These results clearly indicate that a defensible space measurement in excess of 30m is not indicated to provide protection to
structures during wildfire events. In fact, the combined results of the dose-response and regression models strongly indicate that
optimal defensible space is between 17 and 22m. However, it must be noted that these results do not account for the defensible space
that is necessary for firefighters to perform their duties. In spite of this, we can still propose that there is no indicated structural benefit
to having more defensible space than the legally required measurement of 30m.
My results did not suggest that defensible space needs were dependent on aspect (‘southwestness’) relative to the direction
of fire spread. Also, results regarding slope were somewhat counterintuitive. However, an important note to make is that, in following
the same method as Syphard et al. (2014) for determining which parcels lie on low or high slopes, the mean value of my ‘low’ slopes
produced a >13% incline. This is classed as a ‘moderate’ slope, so may in fact be mischaracterizing both my ‘low slope’ and ‘high slope’
grouped results.
According to Table 3, the most effective defensible space measurements when measured to the parcel boundary were <2m
regardless of slope or measure of defensible space. This seems remarkably low, but is likely influenced by the proportion of the
structures in the dataset that had vegetation touching or overhanging one or more sides. The authors of the original study automatically
assigned a 0m value to any structure side that was wholly or partially touched by vegetation. Interestingly, overall regression results
did not indicate any significance of overhanging or touching vegetation. Perhaps this would have yielded different results had I split
these into two separate categories. Regardless, this aspect of the study merits some further investigation, particularly in terms of the
type of vegetation involved, as “the hazard of vegetation adjacent to structures has been recognized for some time” (Foote et al. 1991). I
am hopeful that this would yield improved overall model fit.
Overall effective percent clearance was approximately 25% regardless of slope, as there was negligible variation between low
slope, high slope, and all parcels. For some reason, this factor did not play as significant a role in my results as the defensible space
measurements did.
According to the WHR Classification, approximately 50% of all structures were located within an ‘urban’ land cover type,
whereas 77.53% of all structures were within the SAMO Veg Classification of ‘Urban/Disturbed, Built Up/Cleared Types’. It is

30
important to note that ornamental/urban vegetation may produce highly flammable litter, and yet it is permitted as part of defensible
space. Therefore, a further study of urban vegetation type flammability is needed. This should include an assessment of weeds, which
are highly flammable and are promoted whenever land is disturbed. In the fires that occurred in the Santa Monica Mountains,
structures acted as fuel for one another due to close proximity. The flammability of urban vegetation merits some further study, as “the
ignitability of whatever the embers land on, particularly adjacent to the house, is…most critical for propagating the fire within the
property, or igniting the home” (Syphard et al. 2014). Once urban vegetation’s influence is better understood, it should be integrated
into the defensible space strategy and the education of homeowners, especially in very high risk areas.
Another important consideration is whether other factors are causing longer measures of defensible space to be less significant
for structure protection. As Syphard et al. (2014) note, “most homes are not destroyed by the direct ignition of the fire front but rather
due to ember-ignited spot fires, sometimes from fire brands carried as far as several km away” (I). For this reason, it is important that
any assessment of defensible space needs is considered at the ‘landscape’-scale. Other landscape-scale variables that may minimize the
effect of increasing measures of defensible space are distance to major roads, housing density, and slope. It is interesting that distance
to major roads is a significant variable throughout most of this study, but distance to minor roads is not. Roofing and construction
materials are known to play a role, but if we seek more immediate and attainable strategies it seems that the role of urban vegetation in
structure ignitions is a primary research need.
CONCLUSION
Several known factors related to structure loss were included in this study: housing density, distance to roads, percent
clearance of surrounding vegetation, vegetation type, slope, aspect, and defensible space. Related factors to structure loss that were
not included in this study were construction and roofing types, urban vegetation types directly adjacent to structure, presence and
amount of weeds, and fuel loads of vegetation. These are variables that would ideally be included in any subsequent studies, if the
data is available.
With the variables used in this study, the results strongly suggest that defensible space measurements of greater than 30m
provide no additional protection to a structure. The results are similar to those of the San Diego study (Syphard et al. 2014), although
they state that optimum defensible space for structure protection is around 15m. Given that firefighting activities need defensible
space to do their part in structure protection, 15m—or even <30m—may not be a practical measure in many cases. The general rule
of thumb for firefighters is to maintain a safety distance of three times the height of the flame. However, since surrounding vegetation

31
type proved significant in these results, a study of urban vegetation fuel loads (to calculate flame heights) may prove beneficial. With
this information, it may be possible to accurately define optimum defensible space measures according to the surrounding plant
community that meet firefighter protection needs.
Going forward, the results of this study would be best validated if additional data from surrounding fires could be added to
the analysis, whether that is older fire data or an expansion of the study area. If the results were to hold up with data from another
time and/or location, it would certainly be clarifying.
Long term planning for structure protection should be done at the landscape scale—with strategic landscape planning that
considers housing density and clustering, as well as the vegetation intermix (both urban and non-urban). This would ideally include
space for optimum defensible space between and around structures, with careful integration of low fuel load vegetation types. New
development planning should consider all known factors such as distance to roads, southwest aspect and slope. Smart landscape
planning would in fact seek to minimize fire exposure in the first place.
In the short term, the most important action that a homeowner can take to make their property less vulnerable in the event of
a wildfire is the removal of flammable vegetation adjacent to, or overhanging their home. The San Diego study indicated that parcel
clearance is optimal at 40%, whereas this study indicates that 25% is sufficient. Regardless, a homeowner can ‘defend’ their property
most effectively by removing vegetation with the highest fuel load, and minimizing canopy cover. As Syphard et al. (2014) indicate,
defensible space should not be synonymous with ‘clearance’, but rather the removal of the right vegetation types, given that some
vegetation cover can protect structures from radiant heat.
The comparable trend between this and the San Diego study is certainly validating, and implies its applicability across the
southern California region. These methods, and possibly the results themselves, may be pertinent to other regions with persistent
fire regimes.

32
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Syphard AD, Brennan TJ, Keeley JE. 2014. The Role of Defensible Space for Structure Protection During Wildfires. International
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DATA SOURCES
 Structures (including an indication of whether they were damaged, destroyed, or neither damaged nor destroyed in any of
the identified fires), Alexandra Syphard, Conservation Biology Institute, San Diego. Acquired July 2014.
 Historical Wildfire Perimeters (from 2000 to 2013), National Park Service, Santa Monica Mountains National Recreation Area.
Acquired March 2014.
 NAIP Orthographic Photographs (for the year prior to each of the identified fires), National Park Service, Santa Monica Mountains
National Recreation Area. Acquired August 2014.
 Parcels, National Park Service, Santa Monica Mountains National Recreation Area. Acquired August 2014.
 Santa Monica Mountains National Recreation Area Boundary, National Park Service, Santa Monica Mountains National Recreation
Area. Acquired July 2014.
 Burn Severity (for each fire), Monitoring Trends in Burn Severity (MTBS.gov). Acquired August 2014.
 3m DEM for the Santa Monica Mountains (from which to generate Slope and ‘Southwestness’), National Park Service, Santa
Monica Mountains National Recreation Area. Acquired August 2014.
 Major and Minor Roads, National Park Service, Santa Monica Mountains National Recreation Area. Acquired September 2014.
 Surrounding Vegetation: Wildlife Habitat Classifications and SAMO Vegetation Map classifications, National Park Service,
Santa Monica Mountains National Recreation Area. Acquired September 2014 and November 2014, respectively.

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