Sierra Nevada 

Fire Severity Monitoring 

1984-2004 

United States 

Department of 

Agriculture 

Forest Service

Pacific Southwest 

Region 

R5-TP-027 

August 2008 
The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, 

color, national origin, age, disability, and where applicable, sex, marital status, familial status, parental status, religion, sexual 

orientation, genetic information, political beliefs, reprisal, or because all or part of an individual's income is derived from any 

public assistance program. (Not all prohibited bases apply to all programs.) Persons with disabilities who require alternative 

means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA's TARGET 

Center at (202) 720-2600 (voice and TDD). To file a complaint of discrimination, write to USDA, Director, Office of Civil 

Rights, 1400 Independence Avenue, S.W., Washington, D.C. 20250-9410, or call (800) 795-3272 (voice) or (202) 720-6382 

(TDD). USDA is an equal opportunity provider and employer. 
Sierra Nevada 

Fire Severity Monitoring 1984 – 2004 

Lead Agency: USDA Forest Service, Pacific Southwest Region 

Authors: Jay D. Miller 

 Remote Sensing Specialist 

 Fire, Fuels and Aviation Management 

 Pacific Southwest Region 

 Hugh D. Safford 

 Regional Ecologist 

 Pacific Southwest Region 

August 2008 
This page has no content.
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

i

Table of Contents 

Executive Summary..............................................................................................................1

Background......................................................................................................................1

Objectives........................................................................................................................1

Approach .........................................................................................................................1

Findings...........................................................................................................................2

Discussion........................................................................................................................3

Applications.....................................................................................................................5

Introduction..........................................................................................................................6

Background......................................................................................................................6

Objectives........................................................................................................................8

Approach .........................................................................................................................8

Spatial Extent and Time Period Covered......................................................................9

Wildland Fire Use fires.............................................................................................10

Ecological Stratification for Reporting.......................................................................11

Remote Sensing and Field Calibration Data................................................................12

Fire Severity Ratings Based on the Composite Burn Index ..........................................13

Fire Severity Ratings Based on Tree Mortality............................................................15

Stratification by Vegetation Type..............................................................................16

Fire Regime Concept ................................................................................................18

Results................................................................................................................................21

Summary of Fires Mapped..............................................................................................21

Fires from 2000 through 2004 .........................................................................................21

Severity by Vegetation Type...........................................................................................22

Comparison of 1984-2004 fires with SNFPA FEIS analysis.........................................23

Current versus pre-settlement patterns in severity .......................................................25

Severity in Wildland Fire Use (WFU) vs. Wildfires (WF) .................................................26

Trend Analysis...............................................................................................................28

Trend in Severity by Vegetation Type 1984-2004.......................................................29

Trend in Fire Size and High Severity Patch Size 1984-2004 ........................................37

Trend in Fire Size and Burned Area 1908-2006 ..........................................................41

Fire Correlations with Climate...................................................................................44

Applications of Fire Severity Data ......................................................................................49

Appendix A: Methods.........................................................................................................51

Appendix B: Severity by Vegetation Type...........................................................................62

Appendix C: Individual Fire Results...................................................................................79

References...........................................................................................................................97
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

ii

List of Tables 

Table 1. Number of acres by ownership in mapped fires................................................................................................................................10

Table 2. Number of fires within each latitudinal zone....................................................................................................................................12

Table 3. CBI based severity categories..............................................................................................................................................................14

Table 4. CALVEG dominance type groupings.................................................................................................................................................16

Table 5. Historic fire regime types for SNFPA vegetation types (adapted from Sugihara et. al. 2006)................................................20

Table 6. Number and percent acres burned 2000 through 2004 for conifer vegetation types by severity category............................22

Table 7. Number of acres fro m mapped 1984-2004 fires analyzed by regional vegetation type on U.S. Forest Service 

administered lands..........................................................................................................................................................................................23

Table 8. The average percent of acres burned by regional vegetation type, fires 1984-2004 vs. percentages used for the SNFPA 

FEIS..................................................................................................................................................................................................................25

Table 9. Fires used in WFU vs. WF comparison..............................................................................................................................................27

Table 10. Number and percent of acres burned for conifer vegetation types by severity category in WFU and WF fires on Forest 

Service administered land.............................................................................................................................................................................28

Table 11. Regression statistics for results of ARIMA time series modeling of trends in high severity fire and high severity patch 

size, 1984-2004...............................................................................................................................................................................................31

Table 12. Contiguous high severity conifer patches greater than 1000 ac. on U.S. Forest Service administered lands in 1984-2004 

mapped fires....................................................................................................................................................................................................41

Table 13. Fire-climate regression results...........................................................................................................................................................47
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

iii

List of Figures 

Figure 1. Comparison of BAER soil burn severity map to vegetation based severity map.......................................................................7

Figure 2. Location and time period of fires with respect to Landsat path/row location.............................................................................9

Figure 3. Stratification of fires corresponding to latitudinal ecological zones...........................................................................................12

Figure 4. Example field plot photos of CBI based severity categories........................................................................................................14

Figure 5. Typical map of percent basal area mortality....................................................................................................................................15

Figure 6. Hypothetical fire severity distribution curves describing the variation in severity for different fire regimes....................19

Figure 7. Number of acres burned 2000 through 2004 in conifer vegetation types by severity category.............................................21

Figure 8. Comparison of average severity measured in fires 1984-2004 vs. history conditions for three major forest types..........26

Figure 9. Comparison of average percent of acres burned by severity category in each WFU and WF fire.......................................28

Figure 10. Temporal trend in percent area burned at high severity for all forest types combined 1984-2004.....................................30

Figure 11. Trends in percentage of high severity acres for Foothill and Woodland vegetation types 1984-2004..............................33

Figure 12. Trends in percentage of high severity acres for mixed conifer and white fir types 1984-2004...........................................35

Figure 13. Trends in percentage of high severity acres for four Montane vegetation types 1984-2004...............................................36

Figure 14. Percentage of high severity acres for subalpine conifer and lodgepole pine vegetation types 1984-2004........................37

Figure 15. Percent high severity vs. fire size for 1984-2004 mapped fires.................................................................................................38

Figure 16. Maximum high severity conifer patch size on U.S. Forest Service administered lands from each mapped fire vs. fire 

size.....................................................................................................................................................................................................................39

Figure 17. Mean and mean maximu m high severity conifer patch size on U.S. Forest Service administered lands.........................40

Figure 18. Number and total area of fires greater than 100 ac. per year 1908-2006 for the eleven SNFPA Forests..........................43

Figure 19. Mean and maximu m fire size per year 1908-2006 for the eleven SNFPA Forests................................................................44

Figure 20. Global air temperature record normalized to the 1961-1990 average......................................................................................45

Figure 21. Western Regional Climate Center climate regions for California.............................................................................................47

Figure 22. Sierra Nevada region D total precipitation for 1908-2006.........................................................................................................48

Figure 23. Sierra Nevada region D seasonal average temperatures..............................................................................................................48
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

iv
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Executive Summary - 1

Executive Summary 

Background 

This report presents the findings of the Sierra Nevada Forest Plan Amendment (SNFPA) Fire Severity 

Monitoring Program for the period 1984-2004. The Fire Severity Monitoring Program (FSMP) is a 

fundamental component of the SNFPA monitoring plan as originally outlined in the Final Environmental 

Impact Statement (USDA 2001) and confirmed in the Final Supplemental Environmental Impact 

Statement (USDA 2004). Data collected by the FSMP are important for addressing a number of pivotal 

resource management questions in the SNFPA area, as well as for validating underlying assumptions of 

the SNFPA.

Objectives 

The primary objectives of this project were to: 1) reassess estimates of vegetation-based severity used in 

the Sierra Nevada Forest Plan Amendment Final Environmental Impact Statement (SNFPA FEIS) (USDA 

2001) by directly mapping severity for a widespread sample of fires, and 2) quantitatively evaluate 

current spatial and temporal trends in fire regimes in the SNFPA area. 

Approach 

The basic approach was to develop a fire severity atlas for a large and representative sample of fires in the 

SNFPA area using satellite imagery and fire severity mapping methods based on existing US-Geological 

Survey and National Park Service protocols. Field plot data were collected one-year post-fire on fires at a 

variety of locations and in multiple vegetation types in order to calibrate the imagery to the Composite 

Burn Index (CBI) and tree basal area mortality. 

In this report, fire severity data are reported in three ways. First, satellite acquired imagery is used to 

produce continuous distribution curves of vegetation-based severity by vegetation type over multiple 

fires. The distribution curves are compared to conceptual severity distributions assumed to be 

representative of different historical (“pre-settlement”) fire regimes. Secondly, the continuous data are 

divided into four CBI-defined severity categories (high, moderate, low, unburned) for each fire. 

Proportional representation of the different severity categories from the sampled fires from 1984-2004 is 

compared to the severity assumptions produced by the SNFPA Final Environmental Impact Statement 

(FEIS). Trends from 1984-2004 are analyzed (1) in the proportional representation by area of high 

severity vs. other severity classes in a variety of vegetation types, and (2) in patch sizes of high severity 

fire in conifer forests. Wildland fires managed as Wildland Fire Use (WFU) fires since 2001 are also 

compared with wildfires (wildland fires not managed as WFU). Third, the continuous data are divided 

into seven fire severity categories based upon tree basal area mortality and are reported for each fire. All 

analysis by vegetation type, including patch size, was performed only on acres administered by the U.S. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Executive Summary - 2

Forest Service. We also report temporal trends in fire size and annual burned area between 1908 and 2006 

for all fires in the FEIS analysis area using the fire history database for the state of California. Finally, we 

explore relationships of climate to percent high severity fire, number of fires, fire size, and annual burned 

area. 

Findings 

1. Both time series regression and ten-year moving averages document increases in the proportion 

of high severity fire in the majority of the area burned on U.S. Forest Service lands in the SNFPA 

area. However, the proportional area of fires burning at high severity showed strong interannual 

variability. At the beginning of the period of analysis, about 14% of the area affected in forest 

fires burned at high severity; by the beginning of the 21st century the high severity component 

had increased to over 23%. Different forest types showed different patterns within this general 

trend. The ten-year moving average for percent high severity fire in mixed conifer forest shows 

an increase from 17% of fire area on average on U.S. Forest Service lands to about 27% over the 

analysis period. Severity in forested areas dominated by white fir and black oak increased at the 

greatest rate, growing by 200-300% across the analysis period. Fires in lower to middle elevation 

evergreen (live) oak forests were only marginally more severe on average at the end than at the 

beginning of the period, and both high elevation forests and low elevation westside ponderosa 

pine showed no increase in the proportion of high severity fire. 

2. The average size of contiguous areas (“patches”) of stand-replacing fire within conifer forests 

almost doubled across the period of analysis, rising from a mean of about 7 ac. in the first tenyear period to about 13 ac. in the last period. High severity patch size was found to be positively 

related to fire size, which likely has roots in the close correlation between large fire incidence 

and extreme fire weather. 

3. The actual proportion of high severity acres (vs. other severities) in 1984-2004 fires was less in 

conifer forest than the assumed proportions used in the FEIS analysis. The percentages for 

eastside pine forest were comparable, differing by only 5%, but estimates for the high severity 

component in mixed conifer, ponderosa pine, and white fir forests were 44-55% percent higher 

in the SNFPA FEIS. It is not surprising that the FEIS estimates were somewhat higher than the 

results presented here since existing vegetation maps were used to estimate severity as opposed 

to directly measuring severity as was done for this report. Assumptions were made during the 

FEIS analysis about vegetation structure that was not necessarily true. For example, it was 

assumed that all areas converted to plantations within fire perimeters had experienced stand 

replacing fire. 

4. The percentage of high severity acres (vs. other severities) in WFU fires (since 2001) was 13% 

on average as opposed to 19% in non-WFU wildfires. The difference in severity between WFU 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Executive Summary - 3

managed fires and wildfires was primarily mediated through differences in two vegetation types, 

eastside pine and mixed conifer. All fires that were compared occurred in similar vegetation 

types and at approximately the same elevation. However, only one WFU fire and one wildfire 

occurred in the same year. Therefore differences in antecedent moisture conditions may have 

influenced the results of the comparison. 

5. The overall area burned in forest fires and number of fires larger than 100 ac. per year dropped 

from the 1930’s through the middle of the century, but area burned has been increasing at a rapid 

rate since the 1970’s and especially since the early 1980’s. Mean and maximum fire size have 

both risen since the beginning of the fire record, but the increase has entirely occurred over the 

last two or three decades. Current values for mean and maximum fire size appreciably exceed 

those seen over the previous 80 years while annual number of fires over 100 ac. has remained 

relatively low. 

6. Fire size and annual burned area in the first half of the 20th century were negatively correlated 

with winter precipitation and positively correlated with spring maximum temperatures, with the 

correlations shifting in the second half of the century to negative correlation with spring 

precipitation and positive correlations with summer temperatures. In similar fashion, the number 

of fires was also negatively correlated to winter precipitation in the first half of the century, and 

negatively correlated to spring precipitation in the second half of the century. However, unlike 

fire size and annual area burned, the number of fires remained positively correlated with summer 

maximum temperatures over the whole century. For all long-term fire variables, the strength of 

the climate-fire relationship increases considerably from the first to the second half of the record. 

7. Mean annual precipitation over the analysis area increased by approximately 10 inches between 

1908 and 2006. There were no temporal trends in mean maximum temperature, but mean 

minimum temperatures showed significant increases. Precipitation also explained more of the 

variation in fire size and annual burned area over time, while temperature explained less. It may 

be that higher fuel loads due to increased precipitation; longer growing seasons through warmer 

nighttime temperatures, and the general absence of fire has lead to conditions that are now less 

limiting to the occurrence of fire and higher severity fire in the low and middle elevation forests 

in the SNFPA area. 

Discussion 

The results presented here provide the first quantitative demonstration that the extent of forest standreplacing fire is increasing across a significant part of the SNFPA area. Fires in most low and middle 

elevation forest types are currently burning at higher severity than before Euroamerican settlement, and 

the magnitude of that departure is increasing with time. Forest types most affected by increasing fire 

severity are those which 1) form the majority of the lands administered by the U.S. Forest Service; 2) 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Executive Summary - 4

support most remaining habitat for a suite of old-forest obligate carnivores and raptors whose declining 

populations led to the SNFPA in the first place [for example, California spotted owl (Strix occidentalis 

occ.), goshawk (Accipiter gentilis), and fisher (Martes pennanti)]; 3) see the heaviest human resource 

extraction and recreation use; and 4) are experiencing rapid growth in human population. Through their 

growing tendency to kill larger patches of canopy trees, contemporary fires are contributing to increasing 

levels of forest fragmentation. With continuing increases in the extent of high severity fire and high 

severity patch size, post-fire erosion, stream sedimentation, nutrient cycling and natural forest 

regeneration processes will also be increasingly impacted (Pickett and White 1985; Hobbs and others 

1992; Sugihara and others 2006), and human safety is also a rising concern (USDA 2004). The magnitude 

of these effects is still buffered by the fact that most fires are controlled during initial attack (Calkin and 

others 2005), but the number, severity, and size of fires that exceed initial attack capability is increasing 

across the study region. 

Using a 1970-2003 dataset, Westerling and others (2006) showed a dramatic increase in large fire 

frequency in the western US beginning in the mid-1980’s, centered in the northern Rockies and northern 

California (our study area plus adjoining coastal forests). Our data, which are from a smaller spatial scale 

but much broader temporal scale and which include medium-sized fires, corroborate Westerling and 

others’ (2006) findings and show that the post-1980 increase in fire activity is not restricted to fire 

frequency, but extends to fire size and annual burned area as well, at least in the SNFPA area. Although 

our fire severity dataset only begins in 1984, we hypothesize that the increases we see in the extent of 

forest stand-replacing fire in the study region are linked to these longer-term patterns. Like Westerling and 

others (2006) we find a significant relationship between climate and wildland fire activity, but the 

temporal extent of our fire perimeter dataset allows us to discern three important trends in the nature of 

this relationship over time. First, early in the 20th century fire size and annual burned area in the study 

region responded largely to winter precipitation and springtime temperature (which influences snowmelt), 

but these fire variables now respond more directly to precipitation and temperature during the fire season 

itself. Second, fire number, size and annual burned area - and, at least over the last quarter-century, fire 

severity - have been rising in the study region even as regional precipitation has increased. Third, we 

document a strong increase in the relative importance of precipitation and temperature in driving fire size 

and annual burned area over the last century. Heat, oxygen, and fuel are the fundamental extrinsic factors 

regulating fire combustion and maintenance. Precipitation has a direct influence on fire through the 

wetting of fuel. But warming nighttime temperatures lead to an earlier snowmelt, which in turn leads to 

drying of fuels earlier in the year and a longer fire season. Increasing annual precipitation and warmer 

nighttime temperatures also have an indirect positive effect on fire activity due to increased fuels resulting 

from a longer growing season and augmented vegetation growth. Additionally, across California current 

annual burning affects only 6% of the area burned annually before Euroamerican settlement (Stephens 

and others 2007). The temporal patterns we see in the climate-fire relationship are clearly due in part to 

increasing mean nighttime temperatures, but our results suggest a prominent role for increasing levels of 

forest fuels, presumably from a combination of fire suppression and increasing annual precipitation. With 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Executive Summary - 5

respect to fire severity, most of the forested landscape within the SNFPA area historically supported 

relatively high frequencies of low to moderate severity fire and thus fairly low fuel loadings. We 

hypothesize that the pattern of an increasing proportion of high severity fire in SNFPA conifer forests we 

have measured is to a large extent an effect of the current and continuing absence of an agent to remove 

forest fuels at a rate compatible with their accumulation. 

Applications 

A wide variety of Pacific Southwest Region organizations are using the fire severity data produced by this 

project. A partial list includes: the Stewardship and Fireshed Assessment process for post-fire revision of 

fuel data layers, and calibration of fire behavior and fire effects modeling; the SNFPA California fisher 

working group for calibrating and validating model outputs; the Regional Ecology Program for fire 

regime mapping and post-fire inventory stratification; the Remote Sensing Lab for updating vegetation 

maps after fire occurrence; the Regional Center for Post-fire Restoration, for rapid assessment of 

deforested acres within fire perimeters; for assessment of fuels treatment effectiveness to alter fire 

behavior. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction - 6

Introduction 

Background 

Concerns about the effects of fires to communities, old growth forests, and wildlife species dependent on 

late seral conditions, such as the California spotted owl and California fisher, were primary reasons for 

the Sierra Nevada Forest Plan Amendment Final Environmental Impact Statement (USDA 2001) (SNFPA 

FEIS). Fire and fuels is the central issue to all seven of the other topic areas addressed in the FEIS: old 

forests; air quality; sociocultural conditions; soil productivity; noxious weeds; lo wer Westside hardwoods; 

and aquatic, riparian, and meadow habitats. The level of concern regarding threats on habitat for species 

at risk posed by fire and the effects of fire and fuels management is of particular concern. A monitoring 

plan to track vegetation-based fire severity from medium and large fires was developed by Andrea Thode 

and others to measure how the FEIS addressed these issues. We report here on (1) the initial fire severity 

atlas developed to characterize landscape level trends in fire severity for the SNFPA area, and (2) results 

from analysis of that atlas. 

Fire severity is a measure of the consequences of fire to a given resource. The fire severity data presented 

here are based upon the correlation of satellite-acquired imagery with a field-measured composite burn 

index (CBI). The CBI protocol, developed by the USGS and National Park Service incorporates eighteen 

vegetation related variables across all vertical strata (understory, midstory and overstory), four fuel 

variables, and one soil variable (Key and Benson 2005a). Given its heavy weighting toward measures of 

vegetation condition, the CBI is primarily a metric of fire effects on vegetation, rather than soil. Due to 

their view from above, satellites primarily measure conditions in the uppermost structural component of 

the vegetation community. For forested systems the uppermost structural component is the tree canopy. 

Variables that describe tree cover form a fundamental basis for mensuration, analysis, mapping and 

management of forest resources, and are often used as important variables in models of wildlife and plant 

species habitat (USDA 1992; Cade 1997; Zielinski and others 2006). Therefore in addition to CBI, we 

collected individual tree mortality data in our field plots and produced severity map products in units of 

CBI and percent tree basal area mortality. 

Burned Area Emergency Response (BAER) post-fire severity maps are based on severity to soils and 

hydrologic function (USDA 1995; Parsons 2003; Safford and others 2008). BAER soil burn severity maps 

differ in purpose from vegetation burn severity maps (such as those generated and analyzed here) whose 

main purpose is the identification of the extent of stand-replacing fire. Areas mapped as high, medium and 

low severity by a BAER soil burn severity map and a vegetation burn severity map will often not be 

coincident (especially at high severity), and even where they are, their focus is on different aspects of 

ecosystem response to fire (Safford and others 2008). For example, a forested area where all of the trees 

have been killed but the needles remain on the branch would be classified as high severity in a vegetation 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction -7

burn severity map, but as moderate severity in a soil burn severity map, because the needles will provide 

soil cover as they drop (Figure 1). 

Figure 1. Comparison of BAER soil burn sev erity map to v egetation based severity map. 

Beginning in 2006 the Wildland Fire Leadership Council sponsored the national interagency funded 

(USDI and U.S. Forest Service) Monitoring Trends in Burn Severity (MTBS) program. The data 

presented in this report were collected following the general MTBS methodology (developed by USGS 

and the National Park Service), with a few basic differences (Key and Benson 2005b). First, the national 

program is limited to fires greater than 1,000 ac., whereas our effort sampled fires over 100 acres in size. 

Secondly, the mapping methods employed by MTBS follow the methods used by the Remote Sensing 

Applications Center (RSAC) to produce Burned Area Reflectance Classification (BARC) maps for BAER 

teams, which do not correct for pre-fire cover and therefore can under represent stand-replacing fire 

(Miller and Thode 2007; Safford and others 2008). The satellite data used for this report are processed 

slightly differently to remove the pre-fire cover bias; details of the methods used for this report are 

provided in Appendix A and in Miller and Thode (2007). Thirdly, categorical BARC maps produced for 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction - 8

the BAER program are not calibrated or assessed for accuracy using field-sampled data, whereas maps 

presented in this report have been calibrated by field data collected across the SNFPA area. 

The results presented in this report are based upon a sample of fires over 100 acres that occurred in the 

SNFPA area from 1984 through 2004. The MTBS program will deliver data for all fires over 1000 acres 

for the State of California for 1984 through 2004 during Fy2008. Upon delivery of those statewide data, 

results presented in this report for the SNFPA area can be updated (if necessary), and methods used here 

can be applied to other regions of the state. 

Nationally publicized fires over the last couple of decades, beginning with the 1987 California and 1988 

Yellowstone fires and continuing through the 2003 Southern California fires, have led to the prevailing 

opinion that fires are becoming larger and more severe on average (Skinner and Chang 1996; Arno and 

Fiedler 2005). As a result, changes were made in fire policy, as outlined in the National Fire Plan (2001), 

and funding increased for frontline fire fighting resources, fuels treatments, post-fire restoration, and 

community assistance. Most evidence used to support these policy changes has come from analysis of fire 

occurrence data. Erman and Jones (1996) used fire occurrence data for an analysis of fire frequency on 

the Forests within the SNFPA area, but concluded that the hypothesis that larger fires were occurring 

more frequently was unsubstantiated for much of the SNFPA area. Recently Westering and others (2006) 

used climate and fire occurrence data from the Western US to conclude that recent changes in climate 

have promoted increased large fire activity, higher large-fire frequency, longer fire durations, and longer 

fire seasons since the mid-1980’s. Although there is substantial evidence for recent increases in fire size 

and frequency (Erman and Jones’ results notwithstanding), a remaining and perhaps more significant 

question is whether or not there is a trend toward more acres experiencing higher severity fire and 

increased high severity patch size (Erman and Jones 1996; McKelvey and others 1996). The severity atlas 

produced for this report provides the first means for evaluating that question for SNFPA ecosystems, and 

indeed for any similarly sized area in the western United States. 

Objectives 

Analysis used to develop management options for the SNFPA FEIS (USDA 2001) estimated the 

percentage of lethal, mixed-lethal and non-lethal fire by vegetation type that occurred in the SNFPA area 

between 1974 and 1988 (Hermit 1996). Our primary objectives were to reassess those estimates by 

directly mapping severity for as many fires as possible and evaluate current trends in fire regimes for the 

SNFPA area. Additional objectives were to evaluate how well the remote sensing approach developed by 

Key and Benson (2005b) in Glacier National Park worked in SNFPA area and to develop a field data set 

to generate site specific calibration and interpretations of fire severity. 

Approach 

A condensed description of the analysis methods and data are provided here to provide background for 

understanding the detailed results which follow this section. An expanded description of the analysis 

methods and data are detailed in Appendix A. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction -9

Spatial Extent and Time Period Covered 

Our objective was to map all fires over 1000 acres that occurred at least partially on U.S. Forest Service 

administered lands in the SNFPA area between 2000 and 2004, and all fires over 100 acres between 1984 

through 1999. We were not able to map all fires for the entire 1984 through 1999 period due to budget and 

time restraints. We mapped all fires greater than 100 ac. that occurred in the central SNFPA area between 

1984 and 2004 covered by Landsat scenes P43R33, P43R34, and P43R34 (Figure 2). We added to this 

sample a subset of fires greater than 100 ac. that occurred elsewhere in the SNFPA area. We also included 

a complete coverage of all fires greater than 1000 acres that occurred between 2000 and 2004. Figure 2 

depicts the extent and time period covered by Landsat data acquired by the project. We only mapped fires 

that occurred at least partially on U.S. Forest Service administered lands. 

Figure 2. Location and time period of f ires with respect to Landsat path/row location. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction - 10

Considering only fires greater than 100 ac., the size-distribution of sample of fires we mapped is not 

significantly different from the complete fire history for the entire SNFPA area from 1984-2004 (χ

2

 = 

9.051, P = 0.249, df = 7), and mean fire sizes of our sample and the complete SNFPA area record are 

comparable (t = 1.172, P = 0.242, df = 614). There is also no latitudinal gradient in the severity of fires 

less than 1000 ac. in size (R2 = 0.00003), that is, although our sample for fires less than 1000 ac. is more 

inclusive for the central study area, there is no systematic difference in fire severity for small fires 

between the north, central, and south study area that might bias our results. In short, there is no statistical 

reason to believe that our sample of fires is not representative of the entire population of fires that 

occurred in the SNFPA area during the 1984-2004 period. We mapped a total of 197 fires, totaling 

1,192,627 ac. and accounting for 59% of the total area burned in the 1984-2004 time period across the 

SNFPA area. Of the sampled fire area, 71% occurs on U.S. Forest Service (USFS) lands, 29% on lands of 

other jurisdictions (Table 1). We did not include any fires that overlapped (3% of the total mapped area) in 

the analysis involving forest type since we did not have vegetation maps updated at sufficiently frequent 

intervals to capture fire-related vegetation changes. As a result, analyses carried out under the forest type 

stratification use data from 177 of the 197 total fires. All mapped fires were used in all remaining 

analyses. 

Table 1. Number of acres by ownership in mapped f ires 

Ownership Area (ac) 

Priv ate Lands 236,577 

State Lands 26,789 

Other 2,033 

BLM 57,849 

NPS 23,897 

USFS 847,792 

There were no temporal trends in precipitation 1984-2006. Based on means for the Sierra Nevada, the 

1984-2006 period was at the 112-yr mean for precipitation (mean annual precipitation 1984-2006 divided 

by 112-yr mean = 1.002) and precipitation variability (average of annual standard deviations from 112-yr 

mean = 0.327; 1984-2006 mean of standard deviations = 0.326). Mean maximum temperatures did not 

change significantly 1984-2006; with respect to the mean minima, only the mean for June-August 

increased (+1.7o C, R2

 = 0.177, P = 0.046). 

Wildland Fire Use fires 

Fires mapped for this report included fires that were both suppressed as wildfires, and fires that were 

managed as Wildland Fire Use (WFU) fires. WFU fires account for about 4% of the area within all the 

fires mapped for this report. Considering only fires greater than 100 ac. in size, WFU fires account for 

about 3% of the SNFPA area burned as recorded in the state fire history database for the same time 

period. Fires analyzed for this report therefore are representative of the entire population of fires on a 

whole. About 2,900,000 ac. of U.S. Forest Service lands in the SNFPA area are managed as WFU areas, 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction -11

where naturally ignited fires may be allowed to burn without direct suppression, depending on a variety of 

factors including weather, fire location, staffing and budget. In addition, about 80% (approx. 1,000,000 

ac.) of Yosemite and Sequoia-King Canyon National Parks are managed as WFU. Across the broader 

study region, about 13% of all lands are managed under WFU authority, but since these areas are in 

almost all cases high elevation wilderness areas with low fuel loads, WFU fires tend to remain small. 

Since relatively few fires are managed as WFU, the total area burned in WFU fires is very small as well. 

Two studies using similar methodologies to our own have recently demonstrated increased severities of 

fire in WFU-managed areas in Yosemite National Park and the Gila Wilderness in New Mexico. The 

Yosemite study – which is nested within our larger study region – measured a large increase in fire 

severity in the mid-1980’s, but little subsequent rise (Collins 2007). The probability of a fire re-burning a 

previously burned area was limited by the time since last fire, suggesting that natural fire processes in the 

studied watershed were limiting fuels. This is very different from most of the study region as a whole, 

where long-term fire suppression has generated a fuels-rich environment. In the Gila study, Holden and 

others (2007) also saw a significant rise in fire severity in the period 1984-2004, but could not rule out 

precipitation effects in their analysis. 

Ecological Stratification for Reporting 

Fires were stratified geographically by latitude for reporting purposes. The three broad latitudinal regions 

(northern, central, and southern, as shown in Figure 3) correspond to latitudinal breaks between CALVEG 

ecological zones North Interior, North Sierran, and South Sierran (USDA 2005). The number of fires that 

fell within each latitudinal zone is listed in Table 2. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction - 12

Figure 3. Stratif ication of f ires corresponding to latitudinal ecological zones. 

Table 2. Number of fires within each latitudinal zone 

Latitudinal Zone Number of Fires 

Northern 9 

Central 47 

Southern 141 

Remote Sensing and Field Calibration Data 

Fire severity was mapped using imagery acquired by Landsat satellites. The Landsat data were converted 

to the relative differenced Normalized Burn Ratio index (RdNBR) which is correlated to fire effects on 

vegetation (Miller and Thode 2007). The “Normalized Burn Ratio” (NBR) is a measure derived using 

reflectance from Bands 4 and 7 in the Landsat TM 30-m satellite data (Key and Benson 2005b). NBR is 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction -13

sensitive primarily to living chlorophyll and the water content of soils and vegetation, but it is also 

sensitive to lignin, hydrous minerals, ash and char (Elvidge 1990; Kokaly and others 2007). Where prefire images are available, the post-fire NBR measure can be subtracted from a pre-fire measure to give 

“delta” or “differenced” NBR (dNBR) (Key and Benson 2005b). The relative dNBR (RdNBR) is created 

by dividing the dNBR measure by the pre-fire NBR in order to remove the biasing effect of the pre-fire 

vegetation condition (Miller and Thode 2007; Safford and others 2008). 

 

Satellite data obtained one year post-fire and one to two years pre-fire were used to calculate the RdNBR 

index. Field data collected on 14 fires in the SNFPA area during 2002 through 2005 were used to 

“calibrate” the satellite derived RdNBR index to a composite measure of severity and tree based severity 

data. Field data were collected utilizing the FIREMON composite burn index (CBI) methods (Key and 

Benson 2005a) with additional quantitative measures of effects to trees. CBI is primarily a measure of 

severity to vegetation and is calculated as the linear average of fire effects seen in all vegetation strata 

(understory, midstory and overstory), as well as exposed surface soil, and non-photosynthetic surface 

fuels. In addition to CBI, we collected individual tree mortality data in our field plots and produced 

severity map products in units of percent tree basal area mortality. The data are reported here in three 

different ways. First, a continuous measure of severity provided by the relative RdNBR index is used to 

produce severity distribution curves by vegetation type over multiple fires. Secondly, the RdNBR data are 

divided into four broad categories of severity for each fire based upon the composite burn index. Third, 

the RdNBR data for each fire are divided into seven categories of severity based upon tree mortality. The 

assumptions underlying the latter two measures are described in more detail below and in Appendix A. 

Fire Severity Ratings Based on the Composite Burn Index 

The satellite derived RdNBR index was calibrated to the field measured CBI through regression analysis. 

CBI, calculated as a linear average of the severity rating to vegetation (overstory, middlestory and 

understory), and to a much lesser extent, the soil results in a continuous variable ranging between zero 

(unburned) and three (highest severity). Severity is often mapped in broad categories to aid in 

interpretation (DeBano and others 1998). Key and Benson (Key and Benson 2005a) however make no 

recommendation as to specific CBI values for delineating categorical severity ratings. Choosing which 

CBI values to use as thresholds between severity categories is therefore partially subjective judgment. 

Similar but distinct severity maps could be produced depending on management objective, analysis 

criteria, etc. For this analysis we chose to place the thresholds halfway between the values listed on the 

CBI data form for adjacent categories. For example, the CBI data form indicates a “moderate” severity 

occurs when CBI ranges between 1.5 and 2.0, and “high” severity occurs between 2.5 and 3.0. We 

therefore chose 2.25 as the threshold between “moderate” and “high” severity categories. Table 3 lists the 

CBI thresholds used and generalized descriptions of the severity categories. Figure 4 depicts photos 

representative of each severity category taken from actual field validation plots. User map accuracies of 

high severity patches average about 76%, moderate about 63% and unchanged to low about 68% (see 

Appendix A for details). 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction - 14

Table 3. CBI based sev erity categories. 

CBI Value Severity Category Definition 

0 – 0.1 Unchanged One y ear after the f ire the area was indistinguishable f rom pre-fire conditions. 

This does not always indicate the area did not burn. 

0.11 – 1.25 Low Areas where surface f ire occurred with little change in cover and little 

mortality of the vegetation. 

1.26 – 2.25 Moderate A mixture of fire effects on v egetation, ranging f rom low to high, characterized 

by a “mosaic” spatial pattern. 

2.26 – 3.0 High Areas where high to total mortality of the vegetation occurred. 

Figure 4. Example field plot photos of CBI based sev erity categories. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction -15

Fire Severity Ratings Based on Tree Mortality 

Variables that describe tree cover form a fundamental basis for mensuration, analysis, mapping and 

management of forest resources, and are often used as important variables in models of wildlife and plant 

species habitat (USDA 1992; Cade 1997; Zielinski and others 2006). We therefore also developed fire 

severity ratings based upon tree mortality, in contrast to the CBI approach described above. We measured 

tree mortality by size class one year post-fire in the same plots where we measured CBI to develop a 

regression model of percent tree basal area mortality to the RdNBR index. Percent tree basal area 

mortality was calculated as the proportion of total tree basal area of dead trees to pre-fire basal area. Data 

from plots with at least 5% pre-fire tree canopy cover were used in developing the regression model. A 

typical map of percent basal area mortality categorized into seven classes is shown in Figure 5. 

Characteristic of most fires, high severity patches are surrounded by rings of decreasing severity. The 

seven category map shows a steep change gradient typical in the transition area between high severity 

patches and the surrounding low severity. When considering areas with at least 10% pre-fire tree canopy 

cover [the U.S. Forest Service considers 10% to be the lower limit for defining forested areas (Brohman 

and Bryant 2005)], combining the seven categories into three broad classes accuracies for patches greater 

than 75% change in basal area average 80%; accuracies of areas with less than 25% change average about 

70%, and areas between 25 and 75% change in basal area average only about 37% accurate, which is not 

much better than random (see Appendix A for details). Given the high accuracy at identifying high 

severity patches and the low accuracy for areas with 25-75% change, these maps should best be used only 

to locate and analyze areas of high severity fire. 

Figure 5. Ty pical map of percent basal area mortality. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction - 16

Stratification by Vegetation Type 

One of the primary objectives of this report was to reassess the analysis used in the SNFPA FEIS that 

estimated the percentage of lethal, mixed-lethal and non-lethal fire by vegetation type (Hermit 1996). 

That analysis used the first CALVEG vegetation maps developed for California through classification of 

Landsat imagery by the U.S. Forest Service Pacific Southwest Region Remote Sensing Lab (RSL) 

(USDA 2005). The vegetation types were comprised of CALVEG dominance types grouped into nine 

regional types: ponderosa pine, eastside pine, mixed conifer, white fir, pinyon-juniper, black oak, live oak, 

blue oak, and chaparral shrub. We used the most recent version of CALVEG to lump Dominance Type 

into the same nine types as were used in the FEIS and we added four more types: lodgepole pine, red fir, 

riparian and subalpine conifer (Table 4). Since one of the primary objectives of this report was to reassess 

the severity estimates made for the SNFPA FEIS, we aggregated CALVEG types into the same groupings 

that were used in the original FEIS analysis, even though ecologically those groupings may not make the 

most sense. For example, single-leaf pinyon pine and western juniper CALVEG types were lumped 

together for the FEIS analysis even though the two types have distinctly different distributions, and 

possibly different fire regimes. A description of each regional group including a range distribution map 

derived from CALVEG can be found in Appendix B of this report. 

Table 4. CALVEG dominance ty pe groupings 

Dominance Type Description Regional Type Code 

QK Black Oak Black Oak QK 

QD Blue Oak Blue Oak QD 

PD Gray Pine Blue Oak QD 

QL Valley Oak Blue Oak QD 

CJ Brewer Oak Chaparral shrub CS 

CP Bush Chinquapin Chaparral shrub CS 

CC Ceanothus Mixed Chaparral Chaparral shrub CS 

CA Chamise Chaparral shrub CS 

CK Coy ote Brush Chaparral shrub CS 

BM Curlleaf Mountain Mahogany Chaparral shrub CS 

FM Curlleaf Mountain Mahogany (tree) Chaparral shrub CS 

CI Deerbrush Chaparral shrub CS 

BX Great Basin - Mixed Chaparral Transition Chaparral shrub CS 

CG Greenleaf Manzanita Chaparral shrub CS 

CH Huckleberry Oak Chaparral shrub CS 

CQ Lower Montane Mixed Chaparral Chaparral shrub CS 

CE Mountain Misery Chaparral shrub CS 

CY Mountain Whitethorn Chaparral shrub CS 

CN Pinemat Manzanita Chaparral shrub CS 

CB Salal - California Huckleberry Chaparral shrub CS 

CS Scrub Oak Chaparral shrub CS 

CV Snowbrush Chaparral shrub CS 

CX Upper Montane Mixed Chaparral Chaparral shrub CS 

CM Upper Montane Mixed Shrub Chaparral shrub CS 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction -17

Dominance Type Description Regional Type Code 

CL Wedgeleaf Ceanothus Chaparral shrub CS 

CW Whiteleaf Manzanita Chaparral shrub CS 

EP Eastside Pine Eastside Pine EP 

JP Jeff rey Pine Eastside Pine EP 

WP Washoe Pine Eastside Pine EP 

QC Cany on Live Oak Liv e Oak LO 

QW Interior Live Oak Liv e Oak LO 

LP Lodgepole Pine Lodgepole Pine LP 

DP Douglas-Fir - Ponderosa Pine Mixed Conif er MC 

MF Mixed Conif er - Fir Mixed Conif er MC 

MP Mixed Conif er - Pine Mixed Conif er MC 

DF Pacif ic Douglas-Fir Mixed Conif er MC 

PJ Singleleaf Piny on Pine Piny on-Juniper PJ 

WJ Western Juniper Piny on-Juniper PJ 

PP Ponderosa Pine Ponderosa Pine PP 

RF Red Fir Red Fir RF 

WW Western White Pine Red Fir RF 

QX Black Cottonwood Riparian RI 

SB Buckwheat Riparian RI 

QI Calif ornia Buckeye Riparian RI 

QJ Cottonwood - Alder Riparian RI 

QF Fremont Cottonwood Riparian RI 

TA Mountain Alder Riparian RI 

QQ Quaking Aspen Riparian RI 

NR Riparian Mixed Hardwood Riparian RI 

NM Riparian Mixed Shrub Riparian RI 

QE White Alder Riparian RI 

QO Willow Riparian RI 

QY Willow - Alder Riparian RI 

QS Willow - Aspen Riparian RI 

WL Willow (Shrub) Riparian RI 

BP Bristlecone Pine Subalpine Conif er SA 

FP Foxtail Pine Subalpine Conif er SA 

SA Subalpine Conif ers Subalpine Conif er SA 

WB Whitebark Pine Subalpine Conif er SA 

WF White Fir White Fir WF 

Using static vegetation maps to analyze severity by vegetation type over time is of concern since high 

severity events can cause vegetation type change. Ideally we would like to have used vegetation maps that 

pre-dated the first fires we mapped. The first CALVEG map was produced in 1978 by using photointerpretation techniques with Landsat MSS imagery. The scale of that map is 1:250,000 and is therefore 

too broad-scale for the analysis performed for this report. The earliest CALVEG maps of sufficient scale 

date from the early 1990s and were first produced using 30m imagery and image classification 

techniques. Although CALVEG is used as an existing vegetation map, the mapping methodology calls for 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction - 18

not removing any previously productive conifer forest land from the vegetation map – for example, when 

stand replacing events occur the tree density is set to zero but the primary dominance type is not changed 

(Ralph Warbington, personal communication). In essence then, the CALVEG map for California is at 

least partly a “potential vegetation” map. Additionally, the mapping methods used by the RSL have 

greatly improved since the first version of CALVEG, resulting in maps with higher accuracies. Based on 

these considerations, we decided to use the most recent CALVEG data to stratify all but one fire mapped 

for this report. The CALVEG data were inspected for each fire to determine whether fire patterns were 

reflected in the current vegetation map. In only two cases, the 2000 Manter Fire and the 1992 Cleveland 

Fire, was it felt that the current CALVEG data did not adequately represent the pre-fire condition. 

The1999-2000 version of CALVEG was used to stratify the Manter Fire (except for the fire area that 

occurred outside the Forest boundary, which was not mapped in 1999-2000). The 1992 Cleveland 

predated the first usable CALVEG map. The forested land surrounding the Cleveland is predominately 

classified as mixed conifer, but within the fire perimeter the high severity patches are currently classed as 

ponderosa pine since they were replanted with ponderosa pine. We therefore reset all ponderosa pine 

polygons within the fire perimeter to mixed conifer for this analysis. We also eliminated all fires that 

overlapped (3% of the total mapped area) from any vegetation type analysis, thereby minimizing any 

confusion in the analysis due to vegetation type change. A total of 197 fires were mapped for this report. 

After eliminating the overlapping fires, 177 fires remained for use in the analysis by vegetation type. 

Fire Regime Concept 

Fire behavior is a complex function of weather, topography, and fuels. By examining many fires over time 

to account for variation in weather and topography, patterns of how fire interacts with vegetation 

communities can be discerned. Distilling those fire patterns into summaries known as fire regimes helps 

in understanding ecosystem processes at a landscape scale (Agee 1993; Sugihara and others 2006). 

Although there are many attributes that can be used to characterize fire regimes, seven attributes are most 

commonly thought to be most important to ecosystem function: seasonality, fire return interval, size, 

spatial complexity, fireline intensity, type and severity (Sugihara and others 2006). This report 

concentrates primarily on characterizing severity, although trends in fire size and complexity are also 

examined. Sugihara and others (2006) present a set of conceptual distribution curves that describe the 

probability of occurrence for each fire regime attribute. For example, Figure 6 depicts conceptual severity 

distribution curves for five severity types. The x-axis represents the range of values for severity and the yaxis the proportion of the burned area for each type: low, moderate, high, very high, and multiple. 

Sugihara and others (2006) provide the following definitions of each severity type: 

Low Fire Severity Most of the area burns in low-severity fires that produce only slight or no 

modification to vegetation structure; most of the mature individual plants survive....ponderosa 

pine, and blue oak woodlands are often examples of this fire severity pattern. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction -19

Moderate Fire Severity Most of the area burns in fires that are moderately stand modifying, with 

most individual mature plants surviving... Mixed conifer and giant sequoia are typical examples 

of this severity pattern. 

High Fire Severity Fire kills the above ground parts of most individual plants over most of 

the burned area. Most individual plants survive below ground and resprout...many sprouting 

chaparral types are often examples of this fire severity pattern. 

Very High Fire Severity Fires are mostly stand replacing over much of the burned area. All or 

nearly all of the individual mature plants are killed... Lodgepole pine ... and non-sprouting 

chaparral types frequently display this fire severity pattern. 

Multiple Fire Severity The area burned is mostly divided between two distinct fire types: low 

severity and high to very high severity... red fir and white fir forests are often examples of this fire 

severity pattern. 

Figure 6. Hypothetical f ire severity distribution curv es describing the v ariation in severity for different fire regimes. 

(f rom Sugihara and others 2006) 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Introduction - 20

Sugihara and others (2006) use the severity types above to describe - in a general sense - hypothetical 

historic (pre-settlement) fire severity attributes for major Sierra Nevada vegetation types. Table 5 lists the 

historic severity and fire type for each regional vegetation type used in this report except for the riparian 

type which was not discussed by Sugihara and others (2006). Mixed conifer, ponderosa pine, eastside 

pine, and subalpine conifers historically experienced predominantly surface fire at low or low-moderate 

severity in pre-settlement times (that is, as far back as the fire scar record goes). Although “surface fire” 

indicates that the fire front advances primarily through surface fuels, some of the area may experience 

torching of individual trees or groups of trees. According to Sugihara and others (2006), lodgepole pine, 

red fir and white fir historically experienced a range of both fire types and severities (Table 5). 

Table 5. Historic fire regime ty pes for SNFPA v egetation types (adapted f rom Sugihara et. al. 2006) 

Vegetation Type Severity Fire Type 

Black Oak Low-Moderate Surf ace 

Blue Oak Low Surf ace 

Chaparral Shrub High Crown 

Eastside Pine Low Surf ace 

Liv e Oak Low Surf ace 

Lodgepole Pine Multiple Multiple 

Mixed Conif er Low-Moderate Surf ace-Multiple 

Piny on Juniper High Activ e Crown 

Ponderosa Pine Low-Moderate Surf ace 

Red Fir Multiple Multiple 

Subalpine Conif er Low Surf ace 

White Fir Multiple Surf ace-Multiple 

One of the major objectives of this project was to measure as directly as possible the current severity 

distribution curves and compare the distribution of severity for each vegetation type to the estimate made 

in the SNFPA FEIS. The fire severity distribution curves reported in Appendix B by vegetation type were 

derived by summing the Landsat based RdNBR index values over 177 fires from 1984 through 2004. The 

distribution curves were then summarized into four categories of severity (Table 3) and are reported in the 

Results section of this document. The high severity category as used in this document combines the high 

and very high severity categories listed above as described by Sugihara and others (2006). 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results -21

Results 

Summary of Fires Mapped 

Maps by U.S. National Forest displaying all fires mapped for this report are provided in Appendix C 

along with a summary list of all mapped fires. Year, fire name, and direct protection agency are listed for 

each fire. For each fire, Appendix C also gives the number of acres burned by CBI-derived severity 

category, the number of acres in three categories of percent tree basal area mortality, and the percentage of 

each category within the fire perimeter. A total of 2,012,230 acres burned in all fires that occurred at least 

partially on U.S. Forest Service lands in the SNFPA area between 1984 and 2004. The fires mapped for 

this report account for 1,192,627 acres, or 59% of the total number of acres burned during the 1984 

through 2004 period. 

Fires from 2000 through 2004 

For this report we were only able to map a portion of the fires on U.S. Forest Service administered lands 

that occurred from 1984 through 2004 for the entire study area. The sample of fires mapped included all 

fires greater than 1000 acres only from 2000 through 2004. We are therefore only able at this time to 

compare acres burned during the 2000-2004 period. Figure 7 displays and Table 6 lists the number (and 

percent) of acres burned on U.S. Forest Service administered lands by severity category stratified by 

conifer vegetation type and latitudinal zone. 

0

2 0

4 0

6 0

8 0

10 0

12 0

14 0

Eastside Pine

Lodg epole Pine

Mixed Con ife r

Pond ero sa Pine

Wh ite Fir

Total

Eastside Pine

Lodg epole Pine

Mixed Con ife r

Pond ero sa Pine

Re d Fir

Su balpine Con ife r

Wh ite Fir

Total

Eastside Pine

Lodg epole Pine

Mixed Con ife r

Pond ero sa Pine

Re d Fir

Su balpine Con ife r

Wh ite Fir

Total

Northern Central Southe rn

Acres (thous ands )

High

Mode rate

Low

Unchange d

 

Figure 7. Number of acres burned 2000 through 2004 in conifer vegetation ty pes by sev erity category. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 22

Table 6. Number and percent acres burned 2000 through 2004 for conif er v egetation types by severity category. 

Region Vegetation Type Unchanged 

(acres / %) 

Low 

(acres / %) 

Moderate 

(acres / %) 

High 

(acres / %) 

Total 

(acres) 

Northern Eastside Pine 270 / 5 945 / 19 1,610 / 32 2,130 / 43 4,955 

Lodgepole Pine 294 / 24 451 / 36 245 / 20 248 / 20 1,239 

Mixed Conif er 739 / 6 2,309 / 19 3,689 / 31 5,129 / 43 11,867 

Ponderosa Pine 7 / 12 27 / 46 20 / 33 5 / 9 60 

White Fir 850 / 12 1,572 / 22 1,567 / 22 3,010 / 43 6,999 

Total 2,161 / 9 5,306 / 21 7,131 / 28 10,522 / 42 25,120 

Central Eastside Pine 196 / 10 500 / 26 883 / 46 324 / 17 1,903 

Lodgepole Pine 91 / 26 189 / 53 52 / 15 25 / 7 358 

Mixed Conif er 9,070 / 14 19,507 / 29 17,665 / 26 20,542 / 31 66,784 

Ponderosa Pine 139 / 8 566 / 32 751 / 42 321 / 18 1,778 

Red Fir 771 / 32 884 / 37 506 / 21 213 / 9 2,374 

Subalpine Conif er 23 / 21 79 / 72 8 / 7 0 / 0 111 

White Fir 960 / 18 1,833 / 34 1,259 / 24 1,291 / 24 5,342 

Total 11,251 / 14 23,558 / 30 21,124 / 27 22,715 / 29 78,649 

Southern Eastside Pine 2,674 / 10 6,422 / 24 9,528 / 35 8,621 / 32 27,245 

Lodgepole Pine 1,389 / 25 2,813 / 51 993 / 18 335 / 6 5,530 

Mixed Conif er 4,330 / 7 17,391 / 30 19,384 / 33 17,125 / 29 58,230 

Ponderosa Pine 616 / 7 2,682 / 29 4,056 / 44 1,786 / 20 9,140 

Red Fir 4,576 / 20 9,311 / 41 5,550 / 24 3,296 / 14 22,733 

Subalpine Conif er 999 / 26 1,785 / 47 706 / 19 302 / 8 3,792 

White Fir 140 / 3 1,015 / 24 957 / 23 2,032 / 49 4,144 

Total 14,724 / 11 41,419 / 32 41,174 / 31 33,497 / 26 130,814 

Grand Total 28,136 / 12 70,284 / 30 69,429 / 30 66,734 / 28 234,583 

Severity by Vegetation Type 

To the public, fires that are big and fires that are severe, and especially those that are big and severe, are 

perceived simply as “bad”, without reference to the normal nature of fire in the vegetation type(s) in 

question. The Yellowstone fires of 1988 and the Southern California fires of 2003 are two cases where 

large, severe fire complexes caused huge public outcry, but – taking all variables into account, including 

weather and climate – fire size and severity in both cases were well within historical ranges (Romme and 

Despain 1989; Keeley and Fotheringham 2006). In other parts of the West, large, highly severe fires were 

indeed rare in pre-settlement times (for example, most yellow pine-dominated systems), and their 

occurrence today is probably correctly perceived as outside the range of historical variability (Agee 1993; 

Arno and Fiedler 2005). Table 5 provides some reference for the historic distributions of fire severities 

and types among major California vegetation types. As the Table makes clear, high severity fire was a 

major or minor component of fire regimes in many parts of the landscape, but its representation clearly 

differed by vegetation type (with the vegetation type both affecting and being affected by the fire regime). 

In this section, we stratify fire severity over the 1984-2004 period by major vegetation type in order to 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results -23

provide the proper contextual basis for understanding whether the current distribution of low versus high 

severity is indeed effecting undesirable changes to ecosystems in the SNFPA area. 

Mapped fires from 1984 through 2004 were stratified into 13 regional vegetation types (Table 4). The 

Landsat based RdNBR index values were summed by vegetation type over 177 fires from 1984 through 

2004 to create probability distribution curves of severity (see Appendix B). The distribution curves 

provide a measured estimate of the current fire regime severity attributes. Only the portions of the fires 

that occurred on U.S. Forest Service administered lands were included in the distributions. The number of 

acres mapped in each vegetation type for this report is listed in Table 7. The mixed conifer and chaparral 

shrub types were the best represented vegetation types, each with over 150,000 acres. Lodgepole pine, 

riparian and subalpine forests all had less than 7,500 acres. The severity distribution curves were 

summarized into percent area burned in three categories of severity (unchanged to low, moderate, and 

high) and are compared to the percentages listed in the SNFPA FEIS in the next section. 

Table 7. Number of acres f rom mapped 1984-2004 f ires analy zed by regional vegetation ty pe on U.S. Forest Service 

administered lands. 

Vegetation Type Total Acres 

Black Oak 14,623 

Blue Oak 18,623 

Chaparral Shrub 158,734 

Eastside Pine 57,918 

Liv e Oak 53,321 

Lodgepole Pine 7,355 

Mixed Conif er 240,306 

Piny on Juniper 39,274 

Ponderosa Pine 61,054 

Red Fir 29,849 

Riparian 3,554 

Subalpine Conif er 4,333 

White Fir 23,853 

Comparison of 1984-2004 fires with SNFPA FEIS analysis 

The percentage of acres burned in each vegetation type by severity category was used in the SNFPA FEIS 

to evaluate the management alternatives presented in the Plan. Technology to directly map fire severity at 

the landscape scale did not exist at the time, however. The fire history GIS layer developed for the 

California spotted owl EIS was used in combination with the first comprehensive CALVEG map to 

estimate the number of acres burned by severity category. The CALVEG map, however, only represented 

post-fire conditions, and since no pre-fire vegetation data were available, fire severity could only be 

inferred from stand structure as mapped in CALVEG. Plantations and areas of non-stocked vegetation 

within known fire perimeters were assumed to have experienced high severity, stand-replacing events. 

Low density stands with an open canopy structure that showed signs of some loss of large trees were 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 24

assumed to have experienced moderate severity fire. Areas within fire perimeters that had dense stands of 

large trees and full canopies were assumed to have been affected by low severity fire (Hermit 1996). 

The average percentage of acres burned by CBI severity category during the 1984-2004 period and the 

percentages used in the SNFPA FEIS (USDA 2001) are shown in Table 8 for each regional vegetation 

type. Since the lowest severity category used in the SNFPA FEIS was “non-lethal”, the unchanged and 

low CBI-based severity categories were added together to make one category for comparative purposes; 

low severity and non-lethal, moderate severity and mixed lethal, and high severity and lethal are all 

assumed for our purposes to be synonymous. Note that the SNFPA FEIS did not include estimates of 

severity for lodgepole pine, red fir, riparian, and subalpine conifer. We also do not have the raw acreages 

for the SNFPA FEIS analysis, and thus we cannot compute a statistical measure of the strength of the 

association (for example, χ

2

) between the two datasets. 

Qualitatively, eastside pine is the only vegetation type where the severity distributions from the 1984-

2004 fires are very similar to the FEIS estimates (Table 8). Aside from pinyon pine, the percentage of 

high severity acres in all conifer forest types was estimated to be higher in the SNFPA FEIS than was 

actually measured in fires from 1984-2004. Correspondence between the SNFPA estimates and our 

measurements for high severity fire were fairly close for eastside pine, differing by only 5%. However, 

SNFPA estimates of high severity fire for the mixed conifer, ponderosa pine, and white fir vegetation 

types were 44-55% percent higher than in the actual 1984-2004 fire record; in these types, SNFPA 

estimates for non-lethal and mixed-lethal fire percentages were both lower than measured by our method. 

The percentage of high severity acres for blue oak agrees closely between the SNFPA FEIS and the 1984-

2004 record, but black oak and live oak types differ by 78% and 47%, respectively. The SNFPA FEIS 

estimates also suggest that chaparral experiences almost exclusively high severity fire. In this case, we 

can’t actually compare our estimates to SNFPA estimates: our measurements are biased by the dominance 

of resprouting shrubs since our mapping protocol calls for using imagery and field data acquired one year 

post-fire. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results -25

Table 8. The av erage percent of acres burned by regional v egetation type, f ires 1984-2004 vs. percentages used f or 

the SNFPA FEIS. 

Regional Fires 1984-2004 SNFPA FEIS 

Vegetation Type Unchanged to Low Moderate High Non-Lethal Mixed Lethal 

Black Oak 40 37 23 10 85 5 

Blue Oak 77 21 2 95 4 1 

Chaparral Shrub 31 41 28 1 4 95 

Eastside Pine 31 32 37 26 (25)* 37 (35)* 42 (40)* 

Liv e Oak 48 34 18 40 (36)* 60 (55)* 10 (9)* 

Lodgepole Pine 73 18 9 

Mixed Conif er 40 31 29 34 21 45 

Piny on Juniper 32 25 43 9 (9)* 85 (83)* 8 (8)* 

Ponderosa Pine 37 37 26 30 (30)* 31 (31)* 38 (39)* 

Red Fir 64 23 13 

Riparian 61 24 15 

Subalpine Conif er 76 17 7 

White Fir 42 24 34 33 18 49 

* SNFPA-FEIS columns do not all add up to 100% (for example, in Li ve Oak they add to 110%). This is a feature of the original table 

and we have left it as originally published (USDA 2001) – the values in parentheses are those values which result if the published 

errors are standardized. 

Current versus pre-settlement patterns in severity 

For the most part, current patterns in fire severity are very different from those patterns which 

characterized SNFPA area forests before Euroamerican settlement in the mid 19th century (Sudworth 

1900; Leiberg 1902; Skinner and Chang 1996; Weatherspoon and Skinner 1996; Sugihara and others 

2006). Figure 8 shows the average severity in fires during the period 1984-2004 versus “reference” 

conditions, idealized severity distributions from the pre-Euroamerican settlement period, derived from 

modeling carried out for the interagency LANDFIRE program. Based on a multiyear series of regional 

workshops with fire scientists, LANDFIRE built nonequilibrial, aspatial state and transition models of 

idealized pre-settlement disturbance regimes to feed national- and regional-scale assessments of current 

departures from pre-settlement “reference” conditions (LANDFIRE; The Nature Conservancy and others 

2006; Rollins and Frame 2006). One output of the LANDFIRE reference models is an estimate of the 

proportion of fires occurring at low, moderate and high severity. We used these proportions as a tentative 

best-estimate of pre-settlement conditions, and compared them to the area proportions of fire severity 

classes we sampled in the SNFPA area from 1984 to 2004. We also compared our overall severity 

proportions (summing all forest types) for the northern SNFPA area against 19th century values reported in 

1902 by Leiberg (1902) for the area between 38o 55’ N and 40o 10’ N latitude. Leiberg (1902) suggests 

that much of the high severity fire he recorded was due to miners who carelessly let fires go, and his 

description suggests that pre-settlement fires may have had even less high severity than what he tallied. In 

Figure 8, the most “departed” forest systems are those at lower to middle elevations which were 

historically dominated by yellow pines (Jeffrey pine, ponderosa pine) and characterized by high 

frequency/low severity fire regimes; currently, the mean percent area of fires burning at high severity in 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 26

these forest types is 5 to 8 times greater than in the pre-settlement reference models. Lower to middle 

elevation fir-dominated forests (for example, white fir) on moister sites are also burning at different 

severities and frequencies than before settlement, but to a lesser degree than the pine systems. On the 

contrary, contemporary high elevation forests (for example, red fir) appear to be burning within or near 

the historic range of variability for severity (Figure 8). These results further confirm general patterns 

reported from different forest types across western North America (Agee 1993; Weatherspoon and 

Skinner 1996; Schoennagel and others 2004; Arno and Fiedler 2005; Noss and others 2006; Sugihara and 

others 2006). Comparing our composite results for the northern SNFPA area against Leiberg (1902), who 

published an estimate of 19th century fire severity patterns in 1902, we find an almost 300% increase in 

the occurrence of stand replacing fire over the last two centuries. 

0

10

20

30

40

50

60

70

80

90

100

Current

Ref.

Current

Ref.

Current

Ref.

Current

Ref.

Current

Leiberg

1902

E-side Pine Pond. Pine White Fir Red Fir N. SNFPA

area

Percent of Burned Area

Low

Moderate

High

Figure 8. Comparison of av erage severity measured in fires 1984-2004 vs. history conditions for three major forest 

ty pes in the SNFPA area, and a composite for the Northern SNFPA area. Error bars are +/- 33% of the mean.

Severity in Wildland Fire Use (WFU) vs. Wildfires (WF) 

Wildland fire use (WFU) became a regularly used management strategy in the SNFPA area in 1999. Most 

fires managed as WFU do not typically occur in the same years as wildfires (WF) not managed as WFU 

since antecedent weather conditions factor into the decision whether to allow a fire to be managed as 

WFU or not. Additionally, our database is limited in the number of WFU fires due to the short time WFU 

has been implemented on U.S. Forest Service administered lands in the SNFPA area. All fires that were 

managed as WFU during the 1999 through 2004 period occurred on the Stanislaus and Sequoia National 

Forests. It is therefore difficult to directly compare fire effects between the two management strategies 

under the exact same conditions, but some preliminary observations can be made. Severity in WFU fires 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results -27

from 1999 through 2003 (no WFU fires occurred in 2004) was compared with severity in wildfires from 

1988 through 2004.The wildfires used in this comparison occurred at approximately the same elevation 

and in the same geographic region as the WFU fires (Table 9). The percentage of acres in WFU fires that 

experienced high severity averaged 13% as opposed to 20% in WF fires (Figure 9). Eastside pine and 

mixed conifer were the two regional conifer vegetation types where the WFU vs. WF difference was most 

pronounced (Table 10). The high severity component was approximately equal between WFU and WF 

fires in red fir and subalpine conifer. There were not enough acres burned in either WFU or WF fires for 

lodgepole pine, ponderosa pine or white fir to make comparisons. 

Table 9. Fires used in WFU vs. WF comparison 

Year Fire WFU 

1999 Deer Yes 

1999 Hiram Yes 

2003 Albanita Yes 

2003 Cooney Yes 

2003 Hooker Yes 

2003 Kibbie Yes 

2003 Mountain Cmplx Yes 

2003 Mud Yes 

2001 Silv er Yes 

2003 Summit Yes 

2003 West Kern Yes 

2003 Whit Yes 

1997 Choke No 

2004 Crag No 

1988 Desk No 

1988 Fawn No 

1990 Lily No 

1988 Obelisk No 

1992 Rainbow No 

2002 Spi3Sourgrass No 

2001 White No 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 28

0

5

10

15

20

25

30

35

40

45

Unchanged Low Mo derate High

Percent of Burned Area

WFU

WF

Figure 9. Comparison of av erage percent of acres burned by sev erity category in each WFU and WF fire. 

Table 10. Number and percent of acres burned for conif er v egetation types by severity category in WFU and WF f ires 

on U.S. Forest Service administered land. 

 WFU Fires WF Fires 

Regional 

Vegetation Type 

Unchanged 

to Low(%) 

Moderate 

(%) 

High 

(%) Acres 

Unchanged 

to Low(%) 

Moderate 

(%) 

High 

(%) Acres 

Eastside Pine 53 38 9 2479 36 29 35 636 

Lodgepole Pine 66 26 8 1448 77 22 1 73 

Mixed Conif er 56 35 9 10220 54 26 20 9614 

Ponderosa Pine 0 59 35 6 210 

Red Fir 56 31 13 8855 71 19 11 2837 

Subalpine Conif er 63 31 5 656 94 5 1 307 

White Fir 100 0 0 10 9 65 26 8 

Trend Analysis 

Seven fire regime attributes that contribute in fundamental ways to ecosystem function are; seasonality, 

fire return interval, fire size, spatial complexity, fireline intensity, fire type and fire severity (Sugihara and 

others 2006). Most analyses of fire regimes in the past have concentrated on characterizing fire size 

through fire perimeter data, fire return interval through tree ring analysis of fire scared trees, and fire type 

by examining stand age. The severity atlas produced for this report provides the first means for evaluating 

severity and spatial complexity at the landscape level. We examined the trend in percent high severity 

acres and high severity patch size from 1984 through 2004. We also examined trends from 1908 through 

2006 in number of fires and fire size using the Pacific Southwest Region fire history database. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results -29

Trend in Severity by Vegetation Type 1984-2004 

We examined the trend in percent high severity acres by regional vegetation type from 1984 through 

2004. We do not include the regional chaparral shrub type because rapid resprouting of many chaparral 

species makes pre- vs. one year post-fire comparisons difficult. Autoregressive Integrated Moving 

Average (ARIMA) time series regression (Box and Jenkins 1970; Shumway 1988) was used to calculate 

trends in the percent of fire area burning at high severity per year and high severity patch size over the 

1984-2004 period. We transformed all percent severity data by arcsin-square root and all area data by log 

to meet statistical assumptions of normality, and therefore the linear models appear curve-linear in the 

figures. Because of the high interannual variability in most of these datasets, we also portray results using 

a ten-year running mean of the annual data for graphic depiction of the decadal trend. The running means 

smooth the annual data and effectively removes the effects of cyclical and seasonal variability (Porkess 

1991). In our discussion, we use ten-year running means to only compare average values at the beginning 

and end of the analysis period. All statistical significance reported in the discussion is based on time series 

regression analysis. Only U.S. Forest Service administered lands are considered; private lands and lands 

managed by other public agencies are not included. Note that total burned area varies widely among the 

different regional vegetation types (Table 7), and certain types simply did not experience enough fire 

between 1984 and 2004 to develop a robust statistical pattern. As a rule of thumb, we feel that a sample 

size of about 15 fires (N = 15) and 20,000 acres burned (or 3% of the total burned area) is an approximate 

minimum for development of a “meaningful” pattern and therefore did not develop rigorous statistical 

time series models for vegetation types that fell below that level (Lodgepole pine, riparian, and subalpine 

conifers). 

Trends in the proportional area of fires burning at high severity showed strong interannual variability, 

demonstrated by the gray squares in Figure 10. However, the increasing slope of the time-series 

regression [solid line in Figure 10; R2 = 0.353; P(linear)=0.011; Table 11] documents significant increases 

in fire severity in forest types that make up the majority (~70%) of the burned area we surveyed. At the 

beginning of the period of analysis, a 10-yr average (the 10-yr mean is depicted with white squares in 

Figure 10) of about 14% of the area affected in forest fires in the broader study area burned at high 

severity (forest stand replacement); 21 years later the high severity component was approaching 23% of 

fire area. The pattern of inter-annual variability in severity is robust and not a result of the sample of fires 

we mapped, which can be discerned from number of acres mapped each year as shown by the white 

diamonds in Figure 10. Different forest types showed different patterns within this general trend, with the 

proportion of high severity fire increasing at an average-or-above rate for most low- and middle elevation 

forest types (for example, mixed conifer, white fir, black oak), but at a below-average rate or not at all for 

high elevation forest types (for example, red fir). Therefore in the following sections we discuss trends for 

each regional vegetation type in groups based upon elevation zone. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 30

All Forest Types

0

10

20

30

40

50

1980 1985 1990 1995 2000 2005

Year

P ercent High Severity

100

1000

10000

100000

A cres

% High Severity 10Yr Moving Avg Predicted Mapped Burned Area

R2 = 0.353; P (Linear)=0.011

Figure 10. Temporal trend in percent area burned at high severity for all f orest types combined (except pinyonjuniper) 1984-2004 with the best-fit regression function, 10-y r moving average for % high sev erity, and burned area 

mapped (right-hand Y-axis). Pictured P v alue ref ers to the linear trend. The data were best f it by a 1st order 

autoregressive function, P(AR1) = 0.035, adj. R2

 = 0.281.
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Table 11. Regression statistics f or results of ARIMA time series modeling of trends in high sev erity f ire and high severity patch size, 198

All 

Forests 

Mixed 

Conifer 

Ponderosa 

Pine 

Eastside 

Pine Red fir White fir Black oak Blue oak Live oak Pinyon Junipe

N 20 20 19 17 14 14 16 15 18 12 df e 17 17 17 15 12 8 14 11 16 10 Parameter estimates Sigma-sq 0.011 0.017 0.022 0.035 0.138 0.016 0.092 0.008 0.010 0.025 Intercept 0.191 0.197 0.255 0.138 0.315 0.003 0.069 0.011 0.143 -0.019 Linear 0.008 0.008 -0.001 0.010 0.007 0.019 0.013 0.013 0.008 0.018 AR1 -0.473 -0.546 -0.611 -1.284 -1.198 AR2 -1.585 -0.755 AR3 -1.174 AR4 -0.786 P (linear) 0.011 0.025 0.789 0.152 0.237 <0.0001 0.002 <0.0001 0.059 0.021 P (AR1) 0.035 0.017 0.006 0.000 <0.0001 P (AR2) 0.001 0.002 P (AR3) 0.003 P (AR4) 0.001 Statistics of fit Mean square error 0.009 0.019 0.019 0.031 0.019 0.023 0.008 0.012 0.009 0.020 Root mean square error 0.097 0.016 0.139 0.175 0.138 0.151 0.092 0.109 0.094 0.143 Mean absolute % error 26.948 53.077 31.557 56.254 30.869 52.279 28.070 269.487 33.186 106.104 Mean absolute error 0.078 0.106 0.111 0.158 0.095 0.108 0.070 0.093 0.076 0.118 R-SQ 0.353 0.356 0.252 0.158 0.114 0.462 0.522 0.249 0.214 0.426 adj R-SQ 0.281 0.281 0.158 0.074 0.040 0.126 0.488 0.044 0.165 0.368 Akaike Inf ormation Criterion -92.164 -77.194 -68.883 -55.190 -53.694 -40.948 -74.468 -58.466 -80.993 -42.658 Schwarz Baysian Criterion -89.030 -74.206 -66.050 -53.524 -52.416 -37.113 -72.923 -55.633 -79.213 -41.688 Percent high severity by vegetation type was arcsin-square root tr ansformed before anal ysis, patch size was log transfor med.
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 32

Foothill and Woodland Vegetation Zones 

All four regional vegetation types that fall in the foothill and woodland zones exhibit increasing trends in 

the percentage of high severity fire between 1984 and 2004 (Figure 11). However, the blue oak type trend 

line is largely influenced by the 2000 Highway fire where 8 out of a total of 18 acres in the blue oak type 

were high severity. If the Highway fire were considered an anomaly, the trend line for blue oak would be 

flat. Only five fires mapped between 1984 through 1995 occurred in the pinyon juniper type and nine 

years had no fires mapped. We did not map any fires between 1984 and 2000 on the Modoc or Sequoia 

National Forests where a significant amount of the pinyon juniper type exists (Figures 2 and B-9). Fires 

mapped from 1995-2004 were typically 20-40% high severity; the increasing trend may be a mapping 

anomaly, given the spatial coverage of the mapping and that pinyon juniper is thought to typically 

experience a stand replacing fire regime (Table 5; Sugihara and others 2006). Although oak species in the 

SNFPA area resprout after fire, increasing severity in oak dominated vegetation may be of some 

management concern, as hardwood species are a centerpiece of the SNFPA FEIS (USDA 2001) and the 

success of stump sprouting is known to relate negatively to fire severity (Plumb and Gomez 1983). At the 

same time, increasing severity may also benefit oak species by decreasing conifer competition 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Figure 11. Trends in percentage of high severity acres f or Foothill and Woodland v egetation types 1984-2004 with the best-f it regressioav erage, and burned area mapped (right-hand Y-axis). Percent high severity data were transformed by an arcsin-square root transf ormaregression. Pictured P v alue ref ers to the linear trend. Black oak and Live oak were best f it by linear f unctions; Blue oak with a 1st order 

Black Oak

0

1020304050

1980 1985 1990 1995 2000 2005

Year

Percent

10100100010000

Acres

% High Severity 10Yr Moving Av g Predicted Mapped Burned AcresR2 = 0.522; P (Linear)=0.002

Blue Oak

0

10203040506070

1980 1985 1990 1995 2000 

Year

Percent

% High Severity 10Yr Moving Avg Predicted Mapp

R2 = 0.249; P (Linear)<0.0001

Live Oak

0

1020304050

1980 1985 1990 1995 2000 2005

Year

Percent

10100100010000100000

Acres

% High Sev erity 10Yr Moving Avg Predicted Mapped Burned Ac res

R2 = 0.214; P (Linear)=0.059

Pinyon Juniper

0

10203040506070

1980 1985 1990 1995 20

Year

Percent

% High Severity 10Yr Moving Avg Predicted MapR2 = 0.426; P(Linear)=0.021
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 34

Montane Zone 

Mixed conifer and white fir vegetation types both saw increasing trends in the percentage of high severity 

fire between 1984 and 2004 (Figure 12). Trends of high severity fire in eastside pine, red fir, ponderosa 

pine and riparian types were generally weak or flat (Figure 13). We felt the riparian type had too few 

acres mapped to form a meaningful trend (Table 7), and therefore did not perform a rigorous statistical 

analysis of the trend. Mixed conifer and white fir had the strongest and most robust statistical 

relationships to increasing severity and these type make up almost half (47%) of the burned forested area 

we surveyed (Table 7). It is thought that mixed conifer historically experienced predominantly low to 

moderate fire severity (Table 5; Sugihara and others 2006). A temporal trend toward higher severity fire in 

the mixed conifer type would be of management concern as it is the dominant conifer type in the SNFPA 

area and serves as primary habitat to most of the species of concern addressed by the SNFPA FEIS 

(USDA 2001). Eastside pine and red fir time series were best fit with linear models (Table 11). Ponderosa 

pine time series was best fit with a 1st order autoregressive function (P = 0.006). Although the slopes of 

the models for eastside pine and red fir are positive, and slightly decreasing for ponderosa pine, none of 

the slopes are statistically significant and therefore may not represent actual trends. The lack of trend in 

westside ponderosa pine appears to contradict well-documented patterns of increasing fire severity in 

ponderosa pine forest in other parts of the Southwest (Allen and others 2002; Schoennagel and others 

2004; Arno and Fiedler 2005). Ponderosa pine in the SNFPA area is generally a seral species, and ingrowth of shade-tolerant conifers due to fire suppression is resulting in a steady loss of forest classified as 

“ponderosa pine”; some of the pattern may thus be artifactual. In addition, ponderosa pine forest occupies 

the most heavily populated elevation belt of the SNFPA area, which results in rapid response times for fire 

control agencies and relatively few fires exceed initial attack capability. Ponderosa pine and eastside pine 

are thought to have historically experienced a low severity surface fire regime (Sugihara and others 2006). 

Since the current distribution of high severity fire for both types is much higher than what occurred presettlement (Figure 8), it is likely that these types were already out of their historical range before 1984. 

Red fir however, appears to have experienced a percentage of high severity fire typical of historic 

conditions (Figure 8). The slight upward, but not statistically significant, trend over the analysis period 

may be an anomaly due to the larger amount of acres mapped later in the analysis period, which in turn 

may be due to an increase in the number of acres burned under WFU since 2000 (Table 9 and Table 10). 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results -35

Figure 12. Trends in percentage of high severity acres f or mixed conif er and white f ir types 1984-2004 with the bestf it regression f unction, 10-yr mov ing av erage, and burned area mapped (right-hand Y-axis). Percent high severity 

data were transf ormed by an arcsin-square root transf ormation bef ore computing the regression. Pictured P v alue 

ref ers to the linear trend. Mixed conifer was best fit by 1st order autoregressiv e f unction; white f ir with a 4th order 

autoregressive function.

Mixed Conifer

0

10

20

30

40

50

60

1980 1985 1990 1995 2000 2005

Year

Percent

100

1000

10000

100000

Acres

% High Severity 10Yr Moving Avg Predic ted Mapped Burned Acres

R

2

 = 0.356; P (Linear)=0.025

White Fir

0

10

20

30

40

50

60

70

1980 1985 1990 1995 2000 2005

Year

Percent

1

10

100

1000

10000

Acres

% High Severity 10Yr Mov ing Avg Pr edicted Mapped Burned Acres

R2 = 0.462; P (Linear)<0.0001
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 36Figure 13. Trends in percentage of high severity acres f or f our Montane vegetation ty pes 1984-2004 with the best-fit regression f unctionand burned area mapped (right-hand Y-axis). Percent high sev erity data were transf ormed by an arcsin-square root transf ormation bef oregression. Pictured P v alue ref ers to the linear trend. Eastside pine and red f ir f its were best fit with linear trends that were not signif icanacres mapped; ponderosa pine was best fit with a 1st order autoregressiv e f unction (P=0.006).

Eastside Pine

0

102030405060

1980 1985 1990 1995 2000 2005

Year

Percent

110100100010000100000

Acres

% High Severity 10Yr Moving Avg Mapped Burned Acres

Ponderosa Pine

0

102030405060708090

1980 1985 1990 1995 2000 

Year

Percent

% High Severity 10Yr Moving Avg Predicted Mappe

R2 = 0.252; P (Linear)=0.789

Red Fir

0

102030405060

1980 1985 1990 1995 2000 2005

Year

Percent

110100100010000100000

Acres

% High Severity 10Yr Moving Avg Mapped Burned Acres

Riparian

0

102030405060708090

1980 1985 1990 1995 20

Year

Percent

% High Severity 10Yr Moving Avg Mapped Burne
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results -37

Subalpine Zone 

Area and sample size are very low for both subalpine conifer and lodgepole pine (Table 7) and do not 

permit a rigorous statistical analysis of the temporal trend in high severity. 

Figure 14. Percentage of high severity acres f or subalpine conifer and lodgepole pine v egetation types 1984-2004, 

10-y r moving average, and burned area mapped (right-hand Y-axis). 

Trend in Fire Size and High Severity Patch Size 1984-2004 

Fire management policy in the SNFPA area since the beginning of the 20th century has been to limit the 

number of acres burned (Husari and McKelvey 1996). Fire frequency and size have long been the 

preferred quantitative measures of fire patterns (for example McKelvey and Busse 1996), since they are 

easy to determine from suppression perimeters. However, the percentage of high severity acres in the 

1984-2004 mapped fires only correlates weakly with fire size (Figure 15). 

Subalpine Conifer

0

10

20

30

40

1980 1985 1990 1995 2000 2005

Year

Percent

0

1

10

100

1000

10000

Acres

% High Severity 10Yr Moving Avg Mapped Burned Acres

Lodgepole Pine

0

5

10

15

20

25

1980 1985 1990 1995 2000 2005

Year

Percent

1

10

100

1000

10000

Acres

% High Severity 10Yr Moving Avg Mapped Burned Acres
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 38

R = 0.318; p<.0001

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

2 2.5 3 3.5 4 4.5 5 5.5

Log (Fire Size)

Percent High Severity 

Figure 15. Percent high severity vs. fire size f or 1984-2004 mapped fires. All acres in each f ire were included. 

Percent high severity data were transformed by a standard arcsin-square root transf ormation and area data log 

transf ormed before computing the regression. 

Fire size alone is most likely not the fire regime variable of most concern. All regime variables are 

interrelated and fully understanding how fire interrelates to ecosystems can only be accomplished by 

examining as many regime variables as possible (Sugihara and others 2006). Severity may be the variable 

that bests describes fire effects to the biological and physical components of the ecosystem, but fire size 

and frequency are also critical to spatiotemporal patch dynamics, which can have very important 

connections to propagule flow, sedimentation, habitat availability, migration, etc. 

High severity patch size is probably a metric of more concern to post-fire recovery since patch size and 

severity control the number of surviving individuals and distance to seed sources, which in turn influences 

succession processes (Pickett and White 1985; Turner and others 1998). By most accounts, before 

Euroamerican arrival fires in most conifer forests in the SNFPA area were not typified by large patches of 

high severity fire (Sudworth 1900; Sugihara and others 2006). However as shown in Figure 16, the size of 

the maximum high severity patch in conifer forests was fairly well correlated with fire size for the fires 

mapped from 1984-2004 for this report. Ponderosa and eastside pine historically experienced primarily 

low severity and low complexity fires with very few small high severity patches. Subalpine conifers saw 

low severity fire with few high severity patches. Lodgepole pine, white fir and red fir were thought to 

have experienced at least some fire with large high severity patches (Sugihara and others 2006). However 

lodgepole pine, white fir and red fir were minor components of the total conifer acres mapped for this 

report (Table 7). 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results -39

-1

0

1

2

3

4

5

2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

Log (Fire Size)

Log (Max High Severity C onifer Patch Size)

R = 0.627; P <.0001

Figure 16. Maximum high severity conif er patch size on U.S. Forest Service administered lands f rom each mapped 

f ire vs. f ire size. Both axes are Log transformed. 

We examined the mean and mean maximum patch size by year for high severity patches in conifer 

forests, excluding pinyon-juniper, in the mapped fires from 1984-2004 (Figure 17). Only conifer acres on 

U.S. Forest Service administered lands were included in the analysis. Patches less than 900 m2 were 

eliminated from the analysis since our minimum mapping unit was the Landsat 30m pixel size. 

Regressions of patch size were significantly influenced by the 1994 Cottonwood fire. We removed the 

1994 Cottonwood Fire from the patch size analysis as an outlier, as its mean patch size was nine standard 

deviations higher than the 1984-2004 mean and the maximum high severity patch was more than twice 

the size of any other high severity patch measured in the mapped fires (Table 12). The average size of 

contiguous areas (“patches”) of stand-replacing fire within conifer forest fires almost doubled across the 

period of analysis, rising from a mean of about 7 ac. in the first ten-year period to about 13 ac. in the last 

period (area data log-transformed, R2 = 0.325, P = 0.011). Assuming circular patch geometry, the average 

radius of these high severity patches has increased from about 320 ft to about 420 ft. The mean maximum 

high severity patch size has also increased over the period of record, from about 110 ac. at the beginning 

of the record, to 290 ac. in the most recent ten year period (area data log-transformed, R2

 = 0.353, P = 

0.007). 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 40

Figure 17. Mean and mean maximum high severity conif er patch size on U.S. Forest Service administered lands in 

the 1984-2004 mapped fires excluding the 1994 Cottonwood fire. Ten y ear moving average is for previous 10 years, 

display ed only for visual comparison. Patch size data log-transformed bef ore computing the regression. Pictured P

v alue ref ers to the linear trend. 

0

5

10

15

20

25

30

1980 1985 1990 1995 2000 2005

Year

Acres

Mean Patch S ize 10 Yr Moving Avg 10^Predicted

R2 = 0.325; P =0.011

0

100

200

300

400

500

600

700

800

900

1000

1980 1985 1990 1995 2000 2005

Year

Acres

Mean Maximum Patch Size 10 Yr Moving Avg 10^Predicted

R2 = 0.353; P =0.007
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results -41

Table 12. Contiguous high sev erity conifer patches greater than 1000 ac. on U.S. Forest Serv ice administered lands 

in 1984-2004 mapped f ires. 

Year Fire Name High Severity 

Patch Size (ac) 

1994 Cottonwood 10736 

2002 McNally 4751 

2000 Storrie 4544 

2002 McNally 3282 

2001 Blue 2086 

1987 Paper Cmplx 1856 

2002 McNally 1557 

2001 Stream 1553 

1987 Larson Cmplx 1438 

2002 McNally 1362 

1994 Cottonwood 1275 

1992 Ruby 1242 

1992 Clev eland 1059 

2004 Power 1049 

2001 Star 1032 

2000 Manter 1021 

Trend in Fire Size and Burned Area 1908-2006 

Since our capability to directly measure percent high severity and high severity patch size for past fires is 

tied to the launch date of Landsat, we are currently unable to examine longer term trends in severity, but 

we can examine longer trends in fire size and burned area. The most comprehensive data on fire activity 

available since the beginning of the settlement era are historical fire records kept by the land management 

agencies. Fortunately California has one of the best and most complete fire history databases in the U.S., 

with the earliest records dating from before the 20th century. 

We used the California fire history database jointly maintained by the California Department of Forestry, 

U.S. Forest Service, and National Park Service examine trends in number of fires and fire size. There are 

known problems with that database, such as sizes of all fires are not accurate and many fires less than 10 

ac. are not included. However, the number of fires over 100 ac. and sizes of the largest fires are most 

likely accurate enough for a SNFPA area wide trend analysis. We began our trends analyses in 1908, as 

the data for 1906 and 1907 are very incomplete, and we restricted our analyses to fires greater than 100 

ac, as small fires tend to be under-reported in the database (McKelvey and others 1996) and we were only 

interested in analyzing fires that exceed initial attack capability. We analyzed all fires greater than 100 

acres in size that intersected the eleven Forests within the SNFPA area during the 1908-2006 period, 

which totaled 2,170. We used 10-year running means of the log-transformed dependent variables to 

graphically explore long-term trends. Figure 18 shows the number of fires per year for the SNFPA area 

from 1908 through 2006. The ten year running average used to smooth the data clearly indicates a peak in 

the number of fires around 1920. There were significantly fewer fires between the early 1940s and early 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 42

1970s. Since the 1970s the number of fires has been increasing. While more than one factor may have 

contributed to the 30 years of relatively few fires, the advent of modern fire fighting suppression 

techniques after the Second World War, including smokejumpers, air tankers, and a military styled fire 

suppression organization, most likely contributed to the reduction in fire count (Pyne 1982). There also 

appears to be a reduction in the running average of the maximum fire size over the same 30 year period 

with an increasing trend in maximum size beginning either in the 1970s or 1980s (Figure 19). 

Interestingly, the ten year running average in Figure 19 shows a temporal oscillation in average fire size 

(with a periodicity of 15-20 years) ending in an upward trend from the early 1980s through present. 

Between 1940 and 1970 there does not appear to be a reduction in average fire size with the reduction in 

maximum fire size and number of fires, indeed the overall trend for the whole 1908-2006 period is up 

(R2=0.089; P=0.003). This would be consistent with the idea that human management of disturbances is 

most likely to affect the tails of the distribution rather than the mean. Although more muted, the 

oscillation pattern can also be seen in the ten year running average of the maximum fire size and number 

of fires. Fire activity in the Sierra Nevada has been shown to be coupled to interannual and interdecadal 

climatic variability such as the Pacific Decadal Oscillation (PDO) and El Niño-Southern Oscillation 

(ENSO) (Swetnam 1993; Taylor and Beaty 2005). The oscillations evident in the ten year moving 

averages may therefore be related to climate variability. The upwards trend in maximum fire size since the 

mid to late 1980s appears to be uncharacteristic when compared to the beginning of the century (Figure 

19). 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results -43

Figure 18. Number and total area of f ires greater than 100 ac. per y ear 1908-2006 for the eleven SNFPA Forests. 

Ten y ear moving average is for previous 10 years, display ed only f or v isual comparison. 

0

20

40

60

80

100

120

1900 1920 1940 1960 1980 2000 2020

Year

Number of Fires Moving 10yr Avg

0

50

100

150

200

250

300

1900 1920 1940 1960 1980 2000 2020

Year

Acres (thousands)

Total Area Burned 10yr Moving Avg
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 44

Figure 19. Mean and maximum f ire size per y ear 1908-2006 for the eleven SNFPA Forests. Ten y ear moving 

av erage is f or prev ious 10 y ears, displayed only for visual comparison. 

Fire Correlations with Climate 

Research into climate change has shown that global air temperature has been increasing (Brohan and 

others 2006). When normalized to the 1961-1990 mean, the global air temperature record is above the 

mean and rising since the beginning of 1980’s (Figure 20). Westerling and others (2006) have recently 

shown that changes in climate have lead to increased large fire occurrence in the western U.S. since the 

early to mid 1980s. These changes in climate and fire occurrence appear to coincide with the increases we 

see in the maximum and average fire size in the SNFPA area (Figure 19). 

0

2

4

6

8

10

12

1900 1920 1940 1960 1980 2000 2020

Year

Acres (thousands)

Mean Fire Size 10yr Moving Avg

0

20

40

60

80

100

120

140

160

1900 1920 1940 1960 1980 2000 2020

Year

Acres (thousands)

Maximum Fire Size 10yr Moving Avg
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results -45

Figure 20. Global air temperature record normalized to the 1961-1990 av erage. (from University of East Anglia, UK; 

http://www.cru.uea.ac.uk/cru/inf o/warming/) 

To more closely examine fire-climate relationships we acquired the California region D climate data from 

the Western Regional Climate Center which covers the western slope of the Sierra Nevada (Figure 21). 

Total precipitation, and mean minimum and maximum temperatures were grouped into standard seasons: 

Dec-Jan-Feb (winter), March-Apr-May (spring), June-July-Aug (summer), and Sept-Oct-Nov (fall). The 

time series was divided into three temporal groups to determine whether different climate variables were 

correlated to fire size and count in the early (1908-1956), late (1957-2006) and very late (1982-2006) 

portions of the study period; these temporal groups were generated by splitting the data set in half and 

then in half again, and were not made based on any a priori assumptions. Regressions were performed on 

the climate variables against the number, maximum and mean fire size, and total burned area for the years 

1908 through 2006, and percent high severity fire seen in the major conifer forests 1984-2004 (Table 13). 

In the 1984-2004 period, fire severity in yellow pine-dominated forests in the SNFPA area (ponderosa 

pine, dry mixed conifer, Jeffrey pine) was best explained by springtime temperature minima, while 

severity in moister and higher elevation forest types dominated primarily by fir species (moist mixed 

conifer, true firs, subalpine) was best explained by spring and summer precipitation (Table 13). Climatic 

correlations were strongest for forest types with a fir component. Between 1908 and 2006, the annual 

number of recorded fires, fire size and annual burned area in the study region were all positively related to 

summer temperatures and negatively related to winter precipitation. Annual number of fires was also 

negatively related to spring precipitation. However, splitting the temporal record into early (1908-1956), 

and late (1957-2006) periods shows that a shift in climate correlations has occurred. In the early period, 

most fire variables were characterized by negative correlations with winter precipitation and positive 

correlations with spring temperatures, shifting in the late period to negative correlations with spring 

precipitation and positive correlations to summer temperatures (Table 13). Exceptions were mean fire size 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 46

which was correlated with only spring temperatures in the early period and annual number of fires which 

was positively correlated to summer temperatures for both early and late periods. For all four fire 

variables, the strength (R2) of the climate-fire relationship increases considerably from the first to the 

second half of the record (Table 13). Across the 99 year record, the proportion of variance in the fire 

variables explained by climate has more than doubled, from 11-27% in the early record to 34-52% in the 

late record. Fire size and burned area are increasing in concert with rising temperatures and precipitation, 

but whereas variance in fire size and annual burned area was primarily explained by winter precipitation 

and spring temperatures at the beginning of the record, it is now primarily explained by spring 

precipitation and summer temperatures (Table 13). Regional annual average precipitation during this 

period has increased approximately 10 inches (Figure 22), with most of the increase occurring in the 

spring (3.2 in, R2=0.036, P=0.06) and fall (3.5 in, R2=0.07, P=0.008). There were no temporal trends in 

mean maximum temperature over the 1908-2006 period (Figure 23), but all four seasonal measurements 

of mean minimum temperatures showed significant increases led by June-August ( +3.16o F, R2 = 0.299, 

P < 0.001). 

Heat, oxygen, and fuel are fundamental factors controlling fire combustion. Precipitation’s direct 

influence on fire is negative, through the wetting of fuel, and overall precipitation is increasing in the 

study area (Figure 22). But increasing amounts of precipitation is falling in the form of rain due to 

increasing temperatures. Increasing nighttime minimum temperatures are leading to earlier snowmelt, 

which in turn deepens the summer drought, drying fuels earlier in the year and lengthening the fire 

season. Increasing annual precipitation and warmer nighttime temperatures also have an indirect positive 

effect on fire activity due to increased fuels resulting from a longer growing season and augmented 

vegetation growth. All of which in concert with effective fire suppression is resulting in an increase in the 

percentage of high severity fire in middle and low elevation xeric conifer forests. Conversely, the 

percentage of high severity fire in mesic and high elevation conifer forests is primarily directly related to 

the amount of spring precipitation. It appears that the trend in precipitation and temperature occurred over 

the entire century, yet mean and max fire size, and annual area burned have increased primarily since the 

1980’s to levels higher than those seen during the period of modern suppression methods while number of 

fires has remained relatively low (Figure 18 and Figure 19). It may be that higher fuel loads due to 

increased precipitation; longer growing seasons through warmer nighttime temperatures, and absence of 

fire has lead to conditions that are less limiting to fire size. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results -47

Figure 21. Western Regional Climate Center climate regions for Calif ornia. (from Western Regional Climate Center 

http://www.wrcc.dri.edu/monitor/cal-mon/index.html) 

Table 13. Fire-climate regression results 

Dependent Variable* Period Winter 

precip

Spring 

precip

Spring 

max 

temp

Spring 

min 

temp

Summer 

max 

temp

Summer 

min 

temp

P R2

Dry Mixed Conifer 1984-2004 0.715 0.000 0.512

Ponderosa Pine 1984-2004 0.495 0.023 0.245

Jeff rey Pine 1984-2004 0.380** 0.089 0.144

Moist Mixed Conif er 1984-2004 -0.732 0.000 0.537

Red Fir 1984-2004 -0.539 0.011 0.290

Subalpine 1984-2004 -0.537 0.012 0.287

Number of Fires 1908-2006 -0.232 -0.288 0.359 0.000 0.359 

1908-1956 -0.397 0.331 0.001 0.266 

1957-2006 -0.424 0.532 0.000 0.523 

Mean Fire Size 1908-2006 -0.215 0.323 0.264 0.000 0.253 

1908-1956 0.328 0.021 0.108 

1957-2006 -0.351 0.368 0.000 0.339 

Max Fire Size 1908-2006 -0.222 0.244 0.313 0.000 0.278 

1908-1956 -0.296 0.349 0.004 0.217 

1957-2006 -0.424 0.413 0.000 0.396 

Annual Burned Area 1908-2006 -0.253 0.345 0.320 0.000 0.388 

1908-1956 -0.360 0.360 0.001 0.269 

1957-2006 -0.462 0.499 0.000 0.523 

* Dependent variable for forest types 1984-2004 is percent area of fires burning at high severity. 

Values in climate columns are standardized regression coefficients. 

All predictor parameter estimates are significant at P < 0.05 except **. 

Winter = Dec-Feb, Spring = Mar-May, Summer = Jun-Aug, Fall = Sep-Nov 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Results - 48

10

20

30

40

50

60

70

80

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

P rec ipitation (in)

Figure 22. Sierra Nev ada region D total precipitation f or 1908-2006. Dashed line indicates linear trend (R2=0.068, 

P=0.009). 

Figure 23. Sierra Nev ada region D seasonal av erage temperatures (a) maximum, (b) minimum. JJA=June-August, 

SON=Sept-Nov, MAM=March-May , and DJF=December-February. Dashed line indicates linear trend.

Maximum Temperatures

40

50

60

70

80

90

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Temperature (F)

JJA

SON

MAM

DJF

A

Minimum Temperatures

20

25

30

35

40

45

50

55

60

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Temperature (F)

JJA

SON

MAM

DJF

B
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Applications -49

Applications of Fire Severity Data 

While vegetation based fire severity mapping is useful for measuring patterns of fire severity and trends 

over time, the greatest utility is in addressing important resource management questions. Current uses of 

vegetation based severity maps, both within Region 5 and nationally, include updating fuels and wildlife 

habitat layers to evaluation of vital underlying assumptions in project to regional scale planning analysis 

for NEPA. These assumptions include: effectiveness of fuel treatments, changes in wildlife habitat and 

vegetation structure and composition from fire, fire behavior and effects modeling, and fire regime 

condition class modeling. Another application is the broad scale evaluation of fire effects of WFU fires in 

comparison with wildfires. A limited comparison has been provided in this report. There is also the 

potential to improve information available for post-fire restoration and salvage related to vegetation, since 

most current BAER mapping is more focused on soils and hydrologic function. Since the middle of 2006 

the Region 5 reforestation silviculturist has been using maps calibrated in units of percent tree basal area 

mortality developed for this report for post-fire reforestation planning within one month post-fire. Postfire reforestation planning methods developed in Region 5 are currently being deployed in other regions. 

There are several on-going applications of the vegetation based fire severity mapping by the Stewardship 

and Fireshed Assessment process. They are utilizing the severity maps to modify fuels layers post-fire as 

well as vegetation structure and composition. They are also utilizing the severity maps to calibrate their 

fire behavior and fire effects modeling assumptions. An obvious application that is currently under 

utilized is the objective evaluation of the effectiveness of fuel treatments. Examples of how severity data 

produced by this project can be used for assessing fuels treatment effectiveness to alter fire behavior can 

be found in Fites and others(2007), and Finney and others (2005). Using severity maps from historic fires 

to validate a fire risk model for determining the placement of fire fighter safety zones is a novel 

application being developed on the Salmon-Challis NF. Occurrence of crown and lethal surface fire was 

correlated to vegetation structure, topography, historic fire regime, and fire regime condition class to 

develop a predictive model of where extreme fire behavior could occur on the landscape. The resulting 

model of extreme fire risk was then validated by comparing areas of high risk from crown fire to severity 

patterns mapped in historic fires. 

The above examples are only a sample of management questions that could be addressed using vegetation 

severity data. Many more applications will be envisioned when more people become acquainted with the 

data. Below are examples of additional questions that we believe could be addressed using severity data. 

! Ecosystem / Landscape level monitoring – Through historical severity data determine where and 

why high severity fire occurring. For example, does high severity fire occur in stands of large 

trees (old growth), or is it in young stands? What are the implications to the usage of suppression 

tactics and placement of fuels treatments? 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Applications - 50

! EIS and NEPA planning – Use the distribution of fire severity by category (low, moderate, and 

high) in past prescribed fires to predict effects in future prescribed fires for NEPA planning. 

! Wildlife habitat monitoring - Severity data can be used to monitor how fires (severity and acres 

burned) are affecting habitat quality parameters; snag recruitment for Black-backed Woodpecker, 

for example. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix A: Methods - 51 

Appendix A: Methods 

Satellite Derived Index 

Recently the normalized burn ratio (NBR) has gained considerable attention, mostly in the United States, 

for mapping fire scars (Miller and Yool 2002; Brewer and others 2005; Epting and others 2005; Key and 

Benson 2005b). Most often a post-fire NBR image is subtracted from a pre-fire NBR image in a change 

detection methodology to derive the differenced NBR (dNBR) (Key and Benson 2005b). NBR is sensitive 

primarily to living chlorophyll and the water content of soils and vegetation, but it is also sensitive to 

lignin, hydrous minerals, ash and char (Elvidge 1990; Kokaly and others 2007). Absolute differenced 

images must be calibrated on each individual fire to ensure accurate results however, and absolute change 

images can under represent high severity fire in heterogeneous landscapes (Miller and Yool 2002; Key 

and Benson 2005b; Zhu and others 2006; Miller and Thode 2007). A relative dNBR (RdNBR) image 

created by dividing the dNBR measure by a function of the pre-fire NBR to remove the biasing effect of 

the pre-fire condition was therefore used to map severity to vegetation for this report (Miller and Thode 

2007; Safford and others 2008). An additional advantage to RdNBR is that a single set of thresholds 

(calibrations) can be used to develop categorical classifications for fires (at least those occurring in 

similar vegetation types) without acquiring field data on each fire (Zhu and others 2006; Miller and Thode 

2007). 

Data processing of the Landsat data included converting raw digital numbers to at sensor reflectance as 

described by Chander and Markham (2003). Pre- and post-fire image pairs were matched by anniversary 

date as close as possible to minimize sun angle effects and differences in phenology. Images from June 

through August were used to map 96% of the fires mapped for this project. All post-fire images were 

acquired one growing season after fire occurrence to match the date of field sampling (Key and Benson 

2005a). No atmospheric scattering algorithm was applied to the data since the NBR employs only near 

and middle infrared wavelengths that are minimally affected by atmospheric scattering (Avery and Berlin 

1992). Satellite values were not corrected for topographic shading since NBR is a ratio and topographic 

effects cancel when atmospheric scattering is minimal (Ekstrand 1996). NBR values were multiplied by 

1000 and converted to integer format to match procedures established by Key and Benson (2005b). A 

focal mean algorithm was used to average pixel values in a 3x3 pixel window to match the 90 meter 

diameter field plots. The dNBR for each fire was normalized to account for inter-annual differences in 

precipitation by subtracting the average dNBR value sampled from an unburned area outside the fire 

perimeter. 

Field Data 

Field data collected on 18 fires in the SNFPA area during 2002 through 2005 were used to “calibrate” the 

satellite derived index. The field protocol measured fire effects primarily to vegetation in 90 meter 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

52- Appendix A: Methods 

diameter circular plots. Field measurements employed the composite burn index (CBI) protocol 

developed by Key and Benson (2005a) supplemented with additional qualitative measures on trees: 

species; diameter breast height; tree height; canopy height; percent canopy torched, scorched and green; 

crown class; and char height. The supplemental measurements allowed us to derive specific vegetation 

related relationships to the satellite data like percent basal area mortality. CBI values were not collected 

the first field season (four fires). The qualitative measurements were made on all fires. The CBI protocol 

calls for sampling one year post-fire to allow for first year mortality due to fire effects and vegetation 

recovery therefore all field data were collected the summer after each fire occurred. 

Composite Burn Index (CBI) Based Classification 

The CBI was developed by Key and Benson (2005a) as a field measure of the average burn condition 

found in a plot. The CBI protocol as depicted by the field data sheet in Figure A-1, records fire effects 

derived from ocular estimates in five strata: 1) surface fuels and soils; (2) herbs, low shrubs and trees less 

than 1 meter; (3) tall shrubs and trees 1 to 5 meters; (4) intermediate trees; and (5) big trees. Each stratum 

incorporates four or five variables that are visually estimated and ranked between zero and three. Values 

for each stratum or all strata can be averaged to create a severity index value for understory and/or 

overstory components as well as the whole plot. Total CBI values used for this study were derived by 

summing scores from all measured values and dividing by the number of values measured. CBI values 

range between zero (unburned) and three (highest severity). Since the CBI is a field based protocol, 

regression analysis of field measured values to the satellite derived RdNBR index was used to develop 

categorical classes of severity (Miller and Thode 2007). The CBI protocol provides a consistent 

methodology for quickly assessing the relative severity at a location, allowing a larger number of 

locations to be evaluated than would a more quantitative protocol. Two major disadvantages of the CBI 

protocol however are: 1.) variability in CBI values can be high since the measurements are ocular 

estimates (Korhonen and others 2006); and 2.) CBI does not result in a measurement that is familiar to 

most resource managers. 

The CBI based maps are based on the severity to vegetation, in contrast to the Burned Area Emergency 

Response (BAER) team maps, which are focused on severity to soils and hydrologic function (Parsons 

2003; Safford and others 2008). BAER severity maps can look similar to vegetation severity maps since 

fire intensity and severity are often correlated, but not always. Figure A-2 contrasts a typical BAER 

severity map with a CBI based severity map. Since BAER teams focus on hydrologic function they can 

categorize areas of high vegetation mortality as low severity if the soil surface is not exposed, which can 

happen, for example, when dead conifers drop their needles. 

The vegetation severity categories reported in four categories of “unchanged”, “low”, “moderate”, and 

“high” are based upon CBI field data. We prefer to label the lowest severity class “unchanged” instead of 

“unburned”. Since we measure severity after one growing season, it is therefore difficult sometimes to 

distinguish areas which have recovered after very low severity fire from unburned areas via satellite 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix A: Methods - 53 

imagery. Field measured CBI values range between zero (unburned) and three (highest severity). 

Choosing which CBI values to use as thresholds between severity categories is somewhat of a value 

judgment. Similar but distinct severity maps could be produced depending on management objective, 

analysis criteria, etc. For this report we chose to place the thresholds halfway between the values listed on 

the CBI data form for adjacent categories. For example, the CBI data form indicates a “moderate” 

severity occurs when CBI ranges between 1.5 and 2.0, and “high” severity occurs between 2.5 and 3.0. 

We therefore chose 2.25 as the threshold between “moderate” and “high” severity categories. The 

regression analysis of all CBI plot values with the satellite derived RdNBR index presented in Miller and 

Thode (2007) was used to determine thresholds for classifying satellite collected values into severity 

categories (Table A-1). Since the U.S. Forest Service considers a minimum of 10% tree cover to be the 

minimum to define forested areas we overlaid field plots with at least 10% pre-fire tree canopy cover with 

the regression model from Miller and Thode (2007) in Figure A-3 and computed the confusion matrix 

shown in Table A-2. The high severity category had the highest producer’s and user’s accuracies, which is 

what we desire since the high severity areas are where the greatest ecological impacts and most post-fire 

management activities occur. Producer’s accuracy, a description of map omission error, indicates the 

probability that a field plot had the correct class on the map; while user’s accuracy, a description of 

commission error, is the probability that the class of a pixel on the map actually represents that category 

on the ground. The accuracy of the moderate severity category is the lowest, which is not surprising 

considering the typically high variability of moderately burned areas, and that the satellite is looking 

down and summing fire effects both vertically and horizontally over a 30m x 30m area. Producer’s 

accuracy for the high severity category is higher for areas with more than 20% pre-fire tree canopy cover 

(Table A-3). Mapping vegetation with sparse cover has historically been a remote sensing challenge since 

wavelengths used for the detection of vegetation are also influenced by the amount of exposed soil, parent 

substrate, soil water content, and in the case of fire post-fire ash cover (Huete 1988; Rogan and Yool 

2001; Kokaly and others 2007). 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

54- Appendix A: Methods 

BURN SEVERITY -- COMPOSITE BURN INDEX (BI) 

PD - Abridged Examiners: Fire Name:

Registration Code Project Code Plot Number

Field Date mmdd yyyy / / Fire Date mmyyy y /

Plot Aspect Plot % Slope UTM Zone

Plot Radius Overstory UTM E plot center GPS Datum

Plot Radius Understory UTM N plot center GPS Error (m)

Number of Plot Photos Plot Photo IDs

BI – Long Form % Burned 20 m Plot = % Burned 30 m Plot = Fuel Photo Series =

BURN SEVERITY SCALE

No Effect Low Moderate High FACTOR

STRATA

RATING FACTORS

0.0 0.5 1.0 1.5 2.0 2.5 3.0 SCORES

A. SUBSTRATES

% Pre-Fire Cover: L itter = Duff = Soil/Rock = Pre-Fire Depth (inches): Litter = Duff = Fuel Bed =

Litter/Lig ht Fuel Consumed Un chang ed -- 50 % litter -- 1 00% litter >80% light fuel 98% Light Fuel

Duff Un chang ed -- Ligh t char -- 50% loss deep char -- C onsu med

Mediu m Fuel, 3 -8 in. Un chang ed -- 2 0% con sumed -- 40 % consumed -- >60% loss, deep ch

Heavy Fu el, > 8 in. Un chang ed -- 10% loss -- 25% loss, deep char -- >40% loss, deep ch

Soil Cover/Color Un chang ed -- 10% change -- 4 0% chang e -- >80% change

B. HERBS, LOW SHRUBS AND TREES LESS THAN 1 METER: 

Pre-Fire Cover = Enhanced Grow th Factor = 

% Fo liage Altered (blk-brn) Un chang ed -- 30% -- 80% 95 % 100% + branch loss

Freq uency % Livin g 100% -- 90% -- 50% < 2 0% None

C olo nizers Un chang ed -- Low -- Mod erate High-Low Lo w to None

Spp. Co mp . - Rel. Abund. Un chang ed -- Little change -- Moderate chang e -- High change

C . TA LL SHR U BS A N D T REE S 1 TO 5 M ETER S:

Pre-Fire Cover = Enhanced Growth Factor = 

% Fo liage Altered (blk-brn) 0% -- 20% -- 60-90 % > 9 5% Signifcnt b ranch loss

Freq uency % Livin g 100% -- 90% -- 30% < 1 5% < 1%

% Change in Co ver Un ch an ged -- 15% -- 70% 90% 100%

Spp. Co mp . - Rel. Abund. Un ch an ged -- Little change -- Moderate chang e -- High Change

D . INT ERM ED IA TE TR EES (S UB CA N OPY, POLE -S IZED TR EES )

Pre-Fire % Cover = Pre-Fire Number Living = Pre-Fire Number Dead =

% Gr een (Unaltered ) 100% -- 80% -- 40% < 1 0% None

% Black (Torch) None -- 5 -20% -- 60% > 8 5% 10 0% + branch loss

% Brown (Scorch/Gird le) None -- 5 -20% -- 40-80 % < 40 or > 80% Non e due to torch

% Canopy Mortality None -- 15% -- 60% 80% %100

Ch ar Height None -- 1.5 m -- 2.8 m -- > 5 m

Post Fire: %Girdled = %Felled = %Tree Mortality =

E. B IG TRE ES (UPPER C AN OPY , DOM IN AN T, C OD OMN A NT T RE ES)

Pre-Fire % Cover = Pre-Fire Number Living = Pre-Fire Number Dead =

% Gr een (Unaltered ) 100% -- 95% -- 50% < 1 0% None

% Black (Torch) None -- 5 -10% -- 50% > 8 0% 10 0% + branch loss

% Brown (Scorch/Gird le) None -- 5 -10% -- 30-70 % < 30 or > 70% Non e due to torch

% Canopy Mortality None -- 10% -- 50% 70% %100

Ch ar Height None -- 1.8 m -- 4 m -- > 7 m

Post Fire: %Girdled = %Felled = %Tree Mortality =

C omm uni ty N otes/C om ments: CB I = Su m of Scores / N R ated: Sum of S cores N R ated CBI

U nd ers tory (A +B +C )

Overstory (D + E)

T otal Plot (A + B+C +D + E)

% Estimators: 20 m Plot: 314 m2 1% = 1x3 m 5% = 3x5 m 10% = 5x6 m After , Key and Benson 1999, USGS NRMSC, Glacier Field Station.

3 0 m Plot: 707 m2 1% = 1x7 m (<2x4 m) 5% = 5x7 m 10% = 7x10 m Version 3.0 May 18, 2004

Strata an d Factors are d efined in FIREMON Landscape Assessment, Chapter 2, and on accompanying BI “ch eat sheet”. www.fire.org/firemon/lc.htm

∑=

N =

X =

∑=

N =

X =

∑=

N =

X =

∑=

N =

X =

∑=

N =

X =

BURN SEVERITY -- COMPOSITE BURN INDEX (BI) 

PD - Ab rid ged Examiners: Fire Name:

Registration Code Project Code Plot Number

Field Date mmdd yyyy / / Fire Date mmyyy y /

Plot Aspect Plot % Slope UTM Zone

Plot Radius Overstory UTM E plot center GPS Datum

Plot Radius Understory UTM N plot center

BURN SEVERITY -- COMPOSITE BURN INDEX (BI) 

PD - Ab rid ged Examiners: Fire Name:

Registration Code Project Code Plot Number

Field Date mmdd yyyy / / Fire Date mmyyy y /

Plot Aspect Plot % Slope UTM Zone

Plot Radius Overstory UTM E plot center GPS Datum

Plot Radius Understory UTM N plot center GPS Error (m)

Number of Plot Photos Plot Photo IDs

B I – L ong Form % Burned 20 m Plot = % Burned 30 m Plot = Fuel Photo Series =

BURN SEVERITY SCALE

N o Effect Low Mod erate High FA C TOR

GPS Error (m)

Number of Plot Photos Plot Photo IDs

B I – L ong Form % Burned 20 m Plot = % Burned 30 m Plot = Fuel Photo Series =

BURN SEVERITY SCALE

N o Effect Low Mod erate High FA C TOR

STRATA

RA TIN G FAC TOR S

0.0 0.5 1.0 1.5 2.0 2.5 3.0 S CO RES

A . SU BST RA TES

% Pre-Fire Cover: L itter = Duff = Soil/Rock = Pre-Fire Depth (inches): Litter = Duff = Fuel Bed =

STRATA

RA TIN G FAC TOR S

0.0 0.5 1.0 1.5 2.0 2.5 3.0 S CO RES

A . SU BST RA TES

% Pre-Fire Cover: L itter = Duff = Soil/Rock = Pre-Fire Depth (inches): Litter = Duff = Fuel Bed =

Litter/Lig ht Fuel Consumed Un chang ed -- 50 % litter -- 1 00% litter >80% light fuel 98% Light Fuel

Duff Un chang ed -- Ligh t char -- 50% loss deep char -- C onsu med

Mediu m Fuel, 3 -8 in. Un chang ed -- 2 0% con sumed -- 40 % consumed -- >60% loss, deep ch

Heavy Fu el, > 8 in. Un chang ed --

Litter/Lig ht Fuel Consumed Un chang ed -- 50 % litter -- 1 00% litter >80% light fuel 98% Light Fuel

Duff Un chang ed -- Ligh t char -- 50% loss deep char -- C onsu med

Mediu m Fuel, 3 -8 in. Un chang ed -- 2 0% con sumed -- 40 % consumed -- >60% loss, deep ch

Heavy Fu el, > 8 in. Un chang ed -- 10% loss -- 25% loss, deep char -- >40% loss, deep ch

Soil Cover/Color Un chang ed -- 10% change -- 4 0% chang e -- >80% change

B. HE RB S, LOW SHR U BS A N D T RE ES LES S THA N 1 M ETER : 

Pre

10% loss -- 25% loss, deep char -- >40% loss, deep ch

Soil Cover/Color Un chang ed -- 10% change -- 4 0% chang e -- >80% change

B. HE RB S, LOW SHR U BS A N D T RE ES LES S THA N 1 M ETER : 

Pre-Fire Cover = Enhanced Grow th Factor = 

% Fo liage Altered (blk-brn) Un chang ed -- 30% -- 80% 95 % 100% + branch loss

Freq uency % Livin g 100% -- 90% -- 50% < 2 0% None

C olo nizers Un chang ed -- Low -- Mod erate High-Low Lo w to None

-Fire Cover = Enhanced Grow th Factor = 

% Fo liage Altered (blk-brn) Un chang ed -- 30% -- 80% 95 % 100% + branch loss

Freq uency % Livin g 100% -- 90% -- 50% < 2 0% None

C olo nizers Un chang ed -- Low -- Mod erate High-Low Lo w to None

Spp. Co mp . - Rel. Abund. Un chang ed -- Little change -- Moderate chang e -- High change

C . TA LL SHR U BS A N D T REE S 1 TO 5 M ETER S:

Pre-Fire Cover = Enhanced Growth Factor = 

% Fo liage Altered (blk-brn) 0% -- 20% -- 60-90 % > 9 5% Signifcnt b ranch loss

Spp. Co mp . - Rel. Abund. Un chang ed -- Little change -- Moderate chang e -- High change

C . TA LL SHR U BS A N D T REE S 1 TO 5 M ETER S:

Pre-Fire Cover = Enhanced Growth Factor = 

% Fo liage Altered (blk-brn) 0% -- 20% -- 60-90 % > 9 5% Signifcnt b ranch loss

Freq uency % Livin g 100% -- 90% -- 30% < 1 5% < 1%

% Change in Co ver Un ch an ged -- 15% -- 70% 90% 100%

Spp. Co mp . - Rel. Abund. Un ch an ged -- Little change -- Moderate chang e -- High Change

Freq uency % Livin g 100% -- 90% -- 30% < 1 5% < 1%

% Change in Co ver Un ch an ged -- 15% -- 70% 90% 100%

Spp. Co mp . - Rel. Abund. Un ch an ged -- Little change -- Moderate chang e -- High Change

D . INT ERM ED IA TE TR EES (S UB CA N OPY, POLE -S IZED TR EES )

Pre-Fire % Cover = Pre-Fire Number Living = Pre-Fire Number Dead =

% Gr een (Unaltered ) 100% -- 80% -- 40% < 1 0% None

% Black (Torch) None --

D . INT ERM ED IA TE TR EES (S UB CA N OPY, POLE -S IZED TR EES )

Pre-Fire % Cover = Pre-Fire Number Living = Pre-Fire Number Dead =

% Gr een (Unaltered ) 100% -- 80% -- 40% < 1 0% None

% Black (Torch) None -- 5 -20% -- 60% > 8 5% 10 0% + branch loss

% Brown (Scorch/Gird le) None -- 5 -20% -- 40-80 % < 40 or > 80% Non e due to torch

% Canopy Mortality None -- 15% -- 60% 80% %100

Ch ar Height None -- 1.5 m -- 2.8 m -- > 5 m

5 -20% -- 60% > 8 5% 10 0% + branch loss

% Brown (Scorch/Gird le) None -- 5 -20% -- 40-80 % < 40 or > 80% Non e due to torch

% Canopy Mortality None -- 15% -- 60% 80% %100

Ch ar Height None -- 1.5 m -- 2.8 m -- > 5 m

Post Fire: %Girdled = %Felled = %Tree Mortality =

E. B IG TRE ES (UPPER C AN OPY , DOM IN AN T, C OD OMN A NT T RE ES)

Pre-Fire % Cover = Pre-Fire Number Living = Pre-Fire Number Dead =

% Gr een (Unaltered ) 100% -- 95% -- 50% < 1 0% None

Post Fire: %Girdled = %Felled = %Tree Mortality =

E. B IG TRE ES (UPPER C AN OPY , DOM IN AN T, C OD OMN A NT T RE ES)

Pre-Fire % Cover = Pre-Fire Number Living = Pre-Fire Number Dead =

% Gr een (Unaltered ) 100% -- 95% -- 50% < 1 0% None

% Black (Torch) None -- 5 -10% -- 50% > 8 0% 10 0% + branch loss

% Brown (Scorch/Gird le) None -- 5 -10% -- 30-70 % < 30 or > 70% Non e due to torch

% Canopy Mortality None -- 10% -- 50% 70% %100

% Black (Torch) None -- 5 -10% -- 50% > 8 0% 10 0% + branch loss

% Brown (Scorch/Gird le) None -- 5 -10% -- 30-70 % < 30 or > 70% Non e due to torch

% Canopy Mortality None -- 10% -- 50% 70% %100

Ch ar Height None -- 1.8 m -- 4 m -- > 7 m

Post Fire: %Girdled = %Felled = %Tree Mortality =

C omm uni ty N otes/C om ments: CB I = Su m of Scores / N R ated: Sum of S cores N R ated CBI

U nd ers tory (A +B +C )

Ch ar Height None -- 1.8 m -- 4 m -- > 7 m

Post Fire: %Girdled = %Felled = %Tree Mortality =

C omm uni ty N otes/C om ments: CB I = Su m of Scores / N R ated: Sum of S cores N R ated CBI

U nd ers tory (A +B +C )

Overstory (D + E)

T otal Plot (A + B+C +D + E)

% Estimators: 20 m Plot: 314 m2 1% = 1x3 m 5% = 3x5 m 10% = 5x6 m After , Key and Benson 1999, USGS NRMSC, Glacier Field Station.

3 0 m Plot: 707 m2 1% = 1x7 m (<2x4 m) 5% = 5x7 m 10% = 7x10 m Version 3.0 May 18, 2004

Strata an d Factors are d efined in FIREMON Landscape Assessment, Chapter 2, and on accompanying BI “ch eat sheet”. www.fire.org/firemon/lc.htm

∑=

N =

X =

∑=

N =

X =

∑=

N =

X =

∑=

N =

X =

∑=

N =

X =

Figure A-1. CBI data form. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix A: Methods - 55 

Figure A-2. Typical BAER and CBI based severity maps. A.) Severity map resulting from calibrating Landsat imagery 

with CBI data. B.) BAER burn severity map based upon soil characteristics. 

Table A-1. CBI Categories 

Severity 

Category 

CBI 

Threshold 

RdNBR 

Threshold 

Definition 

Unchanged 0 – 0.1 Less than 69 One year after the fire the area was indistinguishable from pre-fire 

conditions. This does not always indicate the area did not burn. 

Low 0.11 – 1.25 69 – 315 Areas where surface fire occurred with little change in cover and 

little mortality of the vegetation. 

Moderate 1.26 – 2.25 316 – 640 A mixture of effects ranging between low and high on the 

vegetation in a mosaic pattern. 

High 2.26 – 3.0 Greater than or 

equal to 641 

Areas where high to complete mortality of the vegetation 

occurred. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

56- Appendix A: Methods 

-500

0

500

1000

1500

0.00 0.50 1.00 1.50 2.00 2.50 3.00

CBI

RdNB R

Figure A-3. Regression model of RdNBR to CBI overlaid with plots greater than 10% pre-fire tree cover. 

Table A-2. CBI Classification confusion matrix 

Severity Category 

Low to 

Unchanged Moderate High Total 

User's 

Accuracy (%) 

Low to Unchanged 153 70 2 225 68.0 

Moderate 50 140 32 222 63.1 

High 2 41 138 181 76.2 

Total 205 251 172 628 

Producer's Accuracy (%) 74.6 55.8 80.2 68.6 

Note: Columns = Reference (field collected CBI values in plots with at least 10% pre-fire tree canopy cover) 

Table A-3. CBI severity map producer’s accuracies as a function of pre-fire tree canopy cover. 

Pre-fire Tree 

Canopy Cover 

(%) 

Low to 

Unchanged Moderate High 

1-10 83 52 45 

10-20 85 43 56 

20-40 79 58 73 

40-60 70 62 80 

60-80 63 76 83 

80-100 51 68 74 

Note: Columns represent the number of plots with trees. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix A: Methods - 57 

Percent Tree Basal Area Mortality Based Classification 

The CBI is a composite measure of severity from all strata of vegetation structure. However, many forest 

management activities are based upon fire effects to trees alone. We therefore also report severity in units 

of percent tree basal area mortality. Tree mortality by diameter size class was sampled in the same field 

plots where CBI data were collected. The mortality data were used to develop a nonlinear regression 

model of percent mortality of total tree basal area to the satellite derived RdNBR (Figure A-4; R2 = 

0.5528; P<.0001). The model was used to categorize percent basal area mortality into seven mortality 

categories shown in Table A-4. Figure A-5 compares a typical CBI based severity map to a map of percent 

basal area mortality. Characteristic of most fires, patches indicated by the highest severity category are 

surrounded by rings of decreasing severity, sometimes only one pixel wide (pixels are 30m square). The 

seven category map shows the steep change gradient typical in the transition area between high patches 

and the surrounding low severity. Table A-5 shows the confusion matrix using plots with more than 10% 

pre-fire tree canopy cover since the U.S. Forest Service considers areas with at least 10% tree cover to be 

forested (Brohman and Bryant 2005). The seven mortality categories were consolidated into three 

categories for reporting accuracies to more closely follow typical low, moderate and high classes, and 

since accuracies for the categories spanning the moderate severity range of 25-75% were poor. As with 

the CBI based values, the highest mortality category of greater than 75% basal area had the highest 

producer’s and user’s accuracies at about 80%. The moderate basal area mortality category of 25-75% had 

the lowest accuracies; lower than the accuracy of the moderate severity category of the CBI maps and not 

much better than would be expected by a random classification. The low accuracy of the 25-75% 

mortality category is not only due to the high spatial variability of moderate fire patterns, but of the high 

variability of tree versus shrub and herbaceous cover as reflected by the increase in user’s accuracy as the 

percentage of pre-fire tree cover increases. The CBI is a composite measurement of severity accounting 

for all vegetation strata, where as the basal area mortality is only a measure of trees. Since the satellite 

index is highly sensitive to chlorophyll and is a measurement integrated over both horizontal and vertical 

space, CBI values more closely represent what is measured by the satellite except where tree canopy is 

dense enough to obstruct the view of the understory. Producer’s accuracy is over 80% for the high 

severity category except when pre-fire tree canopy cover is less than 20% (Table A-6), due to the higher 

percentage of understory vegetation and soil exposed to view of the satellite when tree cover is low. Due 

to the low accuracy of the 25-75% category these maps should only be used to identify and analyze 

patches of high severity (>75% mortality). 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

58- Appendix A: Methods 

-500

0

500

1000

1500

0 10 20 30 40 50 60 70 80 90 100

P erce nt Change in Basa l Area

RdNBR

RdNBR=143.7+405 .1*AS IN(SQRT(%Ch angeinBA /100))

Figure A-4. Nonlinear regression model of RdNBR to field measured percent tree basal area mortality for plots with 

more than 5% pre-fire tree cover. 

Table A-4. Percent change in tree basal area categories 

Percent Tree 

Basal Area 

Mortality 

RdNBR Threshold 

0 Less than 144 

1 – 10% 144 – 273 

11 – 25% 274 – 355 

26 – 50% 356 – 461 

51 – 75% 462 – 567 

76 – 90% 568 – 649 

91 – 100% Greater than or equal to 650 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix A: Methods - 59 

Figure A-5.Typical CBI based severity map compared to a percent basal area mortality map. A.) Severity map 

resulting from CBI derived thresholds. B.) Map of percent basal mortality resulting from a regression model of field 

measured mortality to RdNBR. 

Table A-5. Percent change in tree basal area classification confusion matrix 

Severity Category <25% 25-75% >75% Total 

User's 

Accuracy (%) 

<25% 244 84 12 340 71.8 

25-75% 76 79 47 202 39.1 

>75% 23 50 278 351 79.2 

Total 327 196 325 848 

Producer's Accuracy (%) 71.1 37.1 82.5 67.3 

Note: Columns = Number of plots with at least 10% pre-fire tree canopy cover) 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

60- Appendix A: Methods 

Table A-6. Percent change in basal area categories producer’s and user’s accuracies as a function of pre-fire tree 

canopy cover. 

Producer’s Accuracy User’s Accuracy 

Pre-fire Tree 

Canopy Cover (%) <25% 25-75% >75% <25% 25-75% >75% 

1-10 36 67 61 67 10 77 

10-20 71 20 56 83 5 80 

20-40 64 34 81 79 26 74 

40-60 68 43 89 78 35 87 

60-80 76 37 88 68 53 77 

80-100 87 35 83 51 65 76 

Note: Columns represent the number of plots with trees. 

Trends in percent high severity and patch size, 1984-2004 

Time series regression was used to calculate trends in the percent of fire area burning at high severity per 

year and high severity patch size over the 1984-2004 period, using SAS 9.1 (2003). We tested for data 

normality using Q-Q plotting and standard statistical tests (Lilliefors and Shapiro-Wilks). We transformed 

all percent severity data by arcsin-square root and all area data by log to meet statistical assumptions of 

normality. We fit Autoregressive Integrated Moving Average (ARIMA) time domain regressions to the 

severity data, using the Box-Jenkins (Box and Jenkins 1970; Shumway 1988) systematic technique for 

model identification and estimation. We fit linear trend models to severity data stratified by our regional 

vegetation types (including all forest types combined), and used Box-Jenkins techniques to determine if 

autoregressive, moving average, or difference operators improved the fit. We compared model adequacy 

using the Akaike Information Criterion (AIC) and Schwarz Bayes Criterion (SBC) as measures of 

goodness-of-fit (Shumway 1988). The patch size analysis only includes conifer forests, excluding pinyonjuniper. The minimum measurable patch size was 900 m2

, as our minimum mapping unit was the Landsat 

30m pixel size. We removed one fire from the patch size analysis as an outlier, the Cottonwood Fire of 

1994, as its maximum and mean high severity patch sizes were fully nine standard deviations higher than 

the 1984-2004 mean. Because of the high interannual variability in most of these datasets, we also portray 

results using a ten-year running mean of the annual data for graphic depiction of the decadal trend. We 

chose a ten-year window for our running mean calculation because (1) temporal autocorrelation statistics 

among the fire severity data showed a maximum at ten years, (2) 10 years is approximately the length of a 

half-cycle of the Pacific Decadal Oscillation, which has demonstrated temporal effects on fire activity in 

Northern California (Taylor and Beaty 2005), and (3) we wished to have at least 10 points to track mean 

trends across our analysis period. 

Pre-settlement reference conditions for fire severity 

Our graphs of idealized mean proportions of pre-Euroamerican settlement fire severities by vegetation 

type are based on nonequilibrial, aspatial state and transition models developed by the interagency 

LANDFIRE and FRCC programs for national and regional mapping of fuels and fire regimes 

(LANDFIRE; Hann and others 2005; The Nature Conservancy and others 2006; Long and others 2006; 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix A: Methods - 61 

Pratt and others 2006). The eastside pine reference conditions are based on LANDFIRE Biophysical 

Setting model BPS 0610310 (California Montane Jeffrey Pine Woodland), the ponderosa pine reference 

on model BPS 0310270 (Mediterranean California Dry-Mesic Mixed Conifer Forest and Woodland), the 

white fir reference on model BPS 0610280 (Mediterranean California Mesic Mixed Conifer Forest and 

Woodland), and the red fir reference on model BPS 061032b (Mediterranean California Red Fir Forest 

and Woodland). 

Trends in area burned and fire occurrence, 1908-2006 

We used the California fire history database jointly maintained by the California Department of Forestry 

and Federal land management agencies in California to investigate trends in number of fires, fire size and 

total burned area per year. This database contains fire perimeter (and other) information in GIS form for 

all fires greater than 10 ac. back to 1950, and somewhat larger fires (size depending on agency reporting 

the fire) before that date, back to 1906. We began our trends analyses in 1908, as the data for 1906 and 

1907 are very incomplete, and we restricted our analyses to fires greater than 100 ac, as small fires tend to 

be under-reported in the database (McKelvey and others 1996) and fires over 100 ac. represent those fires 

that exceed initial attack capability. We analyzed all fires that intersected the eleven Forests within the 

SNFPA area for the 1908-2006 period, which totaled 2,170. We used 10-year running means of the logtransformed dependent variables to graphically explore long-term trends. 

Climate analysis 

To examine fire-climate relationships we acquired California Region D (Sierra Nevada) climate data 

summaries from the Western Regional Climate Center (WRCC 2007). Few of our fires occurred outside 

of Region D, so we used this climate dataset in all of our analyses. The monthly climate variables we 

included in our analysis included total precipitation, and mean minimum and maximum temperatures. The 

monthly data were grouped into seasons: Dec-Jan-Feb (winter), March-Apr-May (spring), June-July-Aug 

(summer), and Sept-Oct-Nov (fall). Stepwise linear regressions (P(enter) < 0.10, P(remove) < 0.05) for all 

subsets of the independent data were performed on the climate variables against the number, maximum 

and mean fire size for the years 1908 through 2006. We checked for data linearity through residual 

plotting and tested for normality using Q-Q plotting and standard statistical tests (Lilliefors and ShapiroWilks). Percent data were arcsin-square root transformed; fire number, size and area were log 

transformed; and precipitation variables were square root transformed. To assess data collinearity we 

examined variance inflation factors: in none of the final models did the VIF exceed 1.5 for any 

independent variable. The time series was also divided into two temporal groups of equal length to 

determine whether different climate variables were correlated to fire size and count in the early (1908-

1956) and late (1957-2006) portions of the period of record; this split was not made based on any a priori

assumptions. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

62- Appendix B: Severity by Vegetation Type 

Appendix B: Severity by Vegetation Type 

One of the primary objectives of this project was to reassess the analysis used in the SNFPA FEIS (USDA 

2001) to estimate the percentage of lethal, mixed-lethal and non-lethal fire by vegetation type (Hermit 

1996). That analysis used the first CALVEG vegetation maps developed for California through 

classification of Landsat imagery by the U.S. Forest Service Pacific Southwest Region Remote Sensing 

Lab (RSL). The vegetation types were comprised of CALVEG dominance types grouped into nine 

regional types: ponderosa pine, eastside pine, mixed conifer, white fir, pinyon-juniper, black oak, live oak, 

blue oak, and chaparral shrub. The most recent version of CALVEG was used to lump Dominance Type 

into the same nine types as were used in the FEIS. In addition, we added four more types: lodgepole pine, 

red fir, riparian and subalpine conifer (Table 8). 

Using static vegetation maps to analyze severity by vegetation type over time is of concern since high 

severity events can cause vegetation type change. Ideally we would like to have used vegetation maps that 

pre-dated the first fires we mapped. The earliest CALVEG maps of a scale matching Landsat TM used to 

generate the severity data date from the early 1990s and were the first produced using 30m imagery and 

image classification techniques. Although CALVEG is used as an existing vegetation map, the mapping 

methodology calls for not removing any previously productive conifer forest land from the vegetation 

map, for example, when stand replacing events occur the tree density is set to zero but the primary 

dominance type is not changed (Ralph Warbington, personal communication). In essence then, the 

CALVEG map for California is at least partly a “potential vegetation” map. Additionally, the mapping 

methods used by the RSL have greatly improved since the first version of CALVEG, resulting in maps 

with higher accuracies. Based on these considerations, we decided to use the latest CALVEG data to 

stratify all but one fire mapped for this report. The CALVEG data were inspected for each fire to 

determine whether fire patterns were reflected in the current vegetation map. In only two cases, the 2000 

Manter Fire and the 1992 Cleveland Fire, was it felt that the current CALVEG data did not adequately 

represent the pre-fire condition. The 1999-2000 version of CALVEG was used to stratify the Manter Fire 

(except for the fire area that occurred outside the Forest boundary, which was not mapped in 1999-2000). 

The 1992 Cleveland predated the first usable CALVEG map. The forested land surrounding the Cleveland 

is predominately classified as mixed conifer, but within the fire perimeter the high severity patches are 

currently classed as ponderosa pine since they were replanted with ponderosa pine. We therefore reset all 

ponderosa pine polygons within the fire perimeter to mixed conifer for this analysis. We also eliminated 

all fires that overlapped (3% of the total mapped area) from any vegetation type analysis, thereby 

minimizing any confusion in the analysis due to vegetation type change. A total of 197 fires were mapped 

for this project. After eliminating the overlapping fires, 177 fires remained for use in the analysis by 

vegetation type. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix B: Severity by Vegetation Type - 63 

The Landsat based RdNBR index values were summed by vegetation type over 177 fires from 1984 

through 2004 to create probability distribution curves of severity. The distribution curves provide a 

measured estimate of the current fire severity that has occurred by vegetation type. Only the portions of 

the fires that occurred on U.S. Forest Service administered lands were included in the distributions to 

eliminate differences in management strategies between other Federal land management agencies, private 

owners, and the U.S. Forest Service. 

The following sections describe the geographic distribution of each vegetation type within the SNFPA 

area and severity distribution curves for each vegetation type computed from the 1984-2004 fires. These 

distribution curves are summarized in Table 8 of the Results section of the main report. The distribution 

curves are derived by subtracting post-fire from pre-fire satellite images. Therefore, the vertical line on 

the x-axis represents zero, or where the pre- and post-fire images are equal. The x-axis to the right of the 

vertical line generally corresponds with the magnitude of fire severity (severity increases from left to right 

along the axis). The x-axis to the left of the vertical line represents areas where vegetation in image pixels 

was “greener” after the fire than before. Some of the pixels that fall to the left of the vertical line may 

have burned at low severity and actually experienced vegetation response that increased greenness one 

year after the fire. Other increases in greenness may be attributed to factors such as image noise and interannual variation in precipitation. An attempt is made during image processing to normalize the imagery so 

that pixels outside each fire have approximately the same image values. 

Black Oak 

The black oak regional vegetation type is composed solely of the black oak CALVEG type. Black oak 

occurs primarily in the lower montane zone on the west slopes of the Sierra Nevada. It is sparsely 

distributed and primarily found in areas of higher insolation and higher fire frequency (Barbour and 

Major 1988) (Figure B-1). Sugihara and others (2006) indicate that black oak historically experienced 

primarily low to moderate surface fire. The low to moderate mode seen in the probability distribution 

curve from the 1984-2004 fires shown in Figure B-1 corresponds broadly with that assessment, although 

23% of burned acres did experience high severity fire (Table 8). 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

64- Appendix B: Severity by Vegetation Type 

Figure B-1. Geographic distribution and fire severity probability distribution curve for the black oak regional 

vegetation type. 

Blue Oak 

The blue oak regional vegetation type is composed of the blue oak, valley oak, and gray pine CALVEG 

types; gray pine is included in this group because it is a common associate of blue oak, and only rarely 

dominates the canopy on its own (Barbour and Major 1988). These vegetation types occur at lower 

elevations in the foothill shrub and woodland zone on west side of the Sierra Nevada (Figure B-2). 

Sugihara and others (2006) indicate that blue oak historically experienced low severity surface fire. The 

low severity mode seen in the probability distribution curve from the 1984-2004 fires shown in Figure B2 corresponds with that assessment. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix B: Severity by Vegetation Type - 65 

Figure B-2. Geographic distribution and fire severity probability distribution curve for the blue oak regional vegetation 

type. 

Chaparral Shrub 

The regional chaparral shrub type is composed of a very broad grouping of types as defined by CALVEG 

(Table 4), covering the entire elevation range of the Sierra Nevada (Figure B-3). Sugihara and others 

(2006) indicate that chaparral shrub types historically experienced primarily stand replacing fire. 

Therefore the historic severity probability distribution curve should have a mode in the high severity 

range. However, the distribution curve computed from the 1984-2004 fires has a mode in the upper 

moderate severity range. Many chaparral shrub species resprout after fire, although a number of species 

of manzanita and ceanothus do not. Since the severity data for this report were derived from one-year 

post-fire imagery, the distribution curve shown in Figure B-3 almost certainly under represents the 

amount of stand replacing fire experienced, as significant resprouting has usually occurred by that time. It 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

66- Appendix B: Severity by Vegetation Type 

is difficult to use our one-year post-fire measurements to make comparisons between our results and the 

SNFPA estimates for fire severities in chaparral. A better comparison would perhaps be made from 

severity assessments using immediate post-fire imagery. 

Figure B-3. Geographic distribution and fire severity probability distribution curve for the chaparral shrub regional 

vegetation type. 

Eastside Pine 

Eastside pine, Jeffrey pine, and Washoe pine CALVEG types were combined into one regional vegetation 

type. These types primarily occur in the montane zone on the east side of the Sierra Nevada and Modoc 

Plateau, but a minor component also occurs in the upper montane zone on the west slope (Barbour and 

Major 1988; Figure B-4). Sugihara and others (2006) indicate that eastside pine and Jeffrey pine 

historically experienced low severity surface fire. The low to low-moderate severity mode seen in the 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix B: Severity by Vegetation Type - 67 

probability distribution curve from the 1984-2004 fires shown in Figure B-4 corresponds at least broadly 

with that assessment. However, west side Jeffrey pine is found primarily on rocky, low productivity sites, 

and historically supported a more mixed severity regime with longer fire return intervals. The low 

severity mode seen in the probability distribution curve from the 1984-2004 fires shown in Figure B-4 

corresponds with that assessment. There is a secondary high severity mode in the 1984-2004 probability 

distribution curve, indicating that 37% of the area experienced high severity fire (Figure B-4 and Table 8). 

A variety of factors appear to have contributed to the high number of high severity acres (in comparison 

to the presumed historic condition [Sugihara and others 2006]). One is clearly the inclusion of west side 

Jeffrey pine stands with the east side types in our analysis (for example, the McNally Fire included 3200 

acres of severely burned west side Jeffrey pine). Another is the well-documented in-growth of younger 

cohorts of Jeffrey pine and shade tolerant species like white fir into many east side pine stands, which can 

increase severity by “laddering” fire into the tree canopy. Finally, the eastside pine type is characterized 

by the presence of Great Basin shrubs such as sagebrush and bitterbrush which have a high severity 

regime (USDA 2005; Sugihara and others 2006), and in some places high densities of these shrubs may 

account for higher severities. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

68- Appendix B: Severity by Vegetation Type 

Figure B-4. Geographic distribution and fire severity probability distribution curve for the eastside pine regional 

vegetation type. 

Live Oak 

The live oak regional vegetation type is composed of the canyon live oak and interior live oak CALVEG 

types; these both occur in the foothill shrub and woodland zone on the west side of the Sierra Nevada 

(Figure B-5). The low severity mode seen in the probability distribution curve from the 1984-2004 fires 

shown in Figure B-5 agrees with the assessment made by Sugihara and others (2006) that live oak 

historically experienced low severity surface fire. Although a significant percentage of acreage also 

experienced moderate to high severity fire (Figure B-5 and Table 8), it should be noted that forty-four 

percent of those acres occurred in a single event, the 2002 McNally Fire. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix B: Severity by Vegetation Type - 69 

Figure B-5. Geographic distribution and fire severity probability distribution curve for the live oak regional vegetation 

type. 

Lodgpole Pine 

The lodgepole pine regional vegetation type is composed solely of the lodgepole pine CALVEG type. 

Lodgepole pine primarily occurs in the subalpine region in the Sierra Nevada (Figure B-6). Sugihara and 

others (2006) indicate that lodgepole pine historically experienced multiple severity and fire types, as 

opposed to a primarily low severity surface fire regime for other subalpine conifer forests (Table 5). We 

therefore placed lodgepole pine in a separate regional vegetation type from the other subalpine conifers 

for this analysis. The severity distribution curve in Figure B-6 indicates that lodgepole pine experienced 

primarily low severity fire in the 1984-2004 period, much like the other subalpine conifers (Figure B-7). 

It may be that our twenty year period of record is of insufficient duration to fully characterize the high 

severity component within the lodgepole pine fire regime, as fire return intervals for this type are 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

70- Appendix B: Severity by Vegetation Type 

estimated to be many decades to several hundred years (Keeley 1981; Sugihara and others 2006; Caprio 

2007). It should also be noted that Sierra Nevada lodgepole pine is generally non-serotinous and the 

limited fire history data we do have, combined with modern fuel profiles, suggest that the “classic” high 

severity picture we have of Rocky Mountain lodgepole pine does not apply to the Sierra Nevada 

(Critchfield 1957; Parker 1986). 

Figure B-6. Geographic distribution and fire severity probability distribution curve for the lodgepole pine regional 

vegetation type. 

Subalpine Conifer 

The subalpine conifer regional vegetation type includes subalpine conifers, bristlecone, foxtail, and 

whitebark pine CALVEG types. The subalpine conifers primarily occur in the subalpine region of the 

central and southern Sierra Nevada (Figure B-7). The severity distribution curve shown in Figure B-7 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix B: Severity by Vegetation Type - 71 

indicates that subalpine conifers experienced primarily low severity fire during the 1984-2004 fires, 

which corresponds with the presumed historical fire severity pattern as depicted by Sugihara and others 

(2006) (Table 5). 

Figure B-7. Geographic distribution and fire severity probability distribution curve for the subalpine conifer regional 

vegetation type. 

Mixed Conifer 

The mixed conifer regional vegetation type includes the mixed conifer-fir, mixed conifer-pine, Douglas 

fir, and Douglas fir-ponderosa pine CALVEG types. The mixed conifer type is the most widespread 

conifer type, primarily occurring in the lower and middle montane zones of the Sierra Nevada. Mixed 

conifer stands occurring on the eastside were also included in this grouping (Figure B-8), although they 

show some differences in species composition and structure (Barbour and Major 1988). Sugihara and 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

72- Appendix B: Severity by Vegetation Type 

others (2006) indicate that mixed conifer historically experienced primarily low to moderate severity fire 

and surface to multiple fire types (Table 5). The severity distribution curve in Figure B-8 that although 

mixed conifer experienced primarily low to moderate severity fire during the period 1984-2004, 28% of 

the fire acres burned under high severity conditions (Figure B-8 and Table 8). 

Figure B-8. Geographic distribution and fire severity probability distribution curve for the mixed conifer regional 

vegetation type. 

Pinyon Juniper 

The pinyon juniper regional vegetation type includes the single-leaf pinyon pine and western juniper 

CALVEG types. The single-leaf pinyon pine type occurs primarily on the eastside of the southern and 

central Sierra Nevada and the western juniper type occurs primarily on the Modoc Plateau and at upper 

elevations in the southern Sierra Nevada (Figure B-9). Sugihara and others (2006) indicate that pinyon 

pine historically experienced primarily high severity fire (Table 5). The severity distribution curve in 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix B: Severity by Vegetation Type - 73 

Figure B-9 indicates two modes of severity, one high severity and a lower mode at low to unchanged. 

Both pinyon pine and western juniper series typically occur either with shrubs, such as big sagebrush, or 

sparse understory vegetation (Barbour and Major 1988; Sawyer and Keeler-Wolf 1995). Where shrubs are 

absent, low severity fire may dominate unless winds are strong enough to promote active crown fire, 

which could result in the bimodal distribution shown in Figure B-9. 

Figure B-9. Geographic distribution and fire severity probability distribution curve for the pinyon juniper regional 

vegetation type. 

Ponderosa Pine 

The ponderosa pine type includes only the ponderosa pine CALVEG type which occurs primarily in the 

lower montane zone on the western slopes of the Sierra Nevada (Figure B-10). Most of the ponderosa 

pine in the northern and central SNFPA area lies outside U.S. Forest Service boundaries: those acres are 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

74- Appendix B: Severity by Vegetation Type 

therefore not included in the distribution curve shown in Figure B-10. Sugihara and others (2006) indicate 

that ponderosa pine historically experienced low severity surface fire (Table 5). Current severity appears 

to be higher than under presumed pre-settlement conditions: our distribution curve indicates about 30% 

high severity between 1984 and 2004 (although most fire was low to moderate severity; Table 8). Note 

also that many areas historically dominated by ponderosa pine now support mixed stands of pine with 

shade tolerant species. These areas are currently mapped as mixed conifer (see above) and experience 

somewhat more high severity fire than those areas which continue to be dominated by ponderosa pine 

(Figure B-10). 

Figure B-10. Geographic distribution and fire severity probability distribution curve for the ponderosa pine regional 

vegetation type. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix B: Severity by Vegetation Type - 75 

Red Fir 

The red fir regional vegetation type includes the red fir and western white pine CALVEG types. The red 

fir type occurs in the upper montane zone of the Sierra Nevada (Figure B-11). Sugihara and others (2006) 

indicate that red fir historically experienced multiple severity and fire types (Table 5). The severity 

distribution curve from the 1984-2004 fires indicates red fir experienced primarily low severity fire 

during that time period. Thirteen percent experienced high severity fire, as evidenced by the small knee in 

the distribution curve (Figure B-11 and Table 8). 

Figure B-11. Geographic distribution and fire severity probability distribution curve for the red fir regional vegetation 

type. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

76- Appendix B: Severity by Vegetation Type 

Riparian 

The riparian regional vegetation type includes a large group of CALVEG types that normally occur in 

riparian zones (Table 4). The riparian zone types occur sparsely in the montane zone of the eastern Sierra 

Nevada (Figure B-12). Sugihara and others (2006) do not include any discussion of these vegetation types 

for the Sierra Nevada. The severity distribution curve from the 1984-2004 fires indicates that riparian 

types experienced primarily low severity fire although 39% experienced moderate to high severity during 

that time period (Figure B-12 and Table 8). The regional riparian type may not be adequately sampled to 

characterize its severity distribution curve: our sample consisted of only 3500 acres, the smallest area of 

any of the types we analyzed (Table 7). 

Figure B-12. Geographic distribution and fire severity probability distribution curve for the riparian regional vegetation 

type. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix B: Severity by Vegetation Type - 77 

White Fir 

The white fir regional vegetation type includes only the white fir CALVEG type. The white fir type 

occurs in the montane region in the northern and central Sierra Nevada and in the Warner Mountains 

(Figure B-13). Sugihara and others (2006) indicate that white fir historically experienced low to moderate 

severity and surface or multiple fire types (Table 5). The severity distribution curve shown in Figure B-13 

indicates that white fir did experience primarily low severity fire during the 1984-2004 fires, but a 

secondary mode occurs in the distribution curve indicating that 34% experienced high severity (Table 8). 

The CALVEG white fir type is more or less pure white fir, described as being mostly in north-facing 

pockets and around lakes. It is mapped as occurring between the mixed conifer pine and mixed conifer fir 

types on south and west aspects, and between mixed conifer pine and red fir on north and east aspects (H. 

Gordon, pers. comm.). This tends to be a moister vegetation type than most mixed conifer and ponderosa 

pine stands and hence fire return intervals would be expected to be longer and severity skewed somewhat 

more toward mixed and high severity fire. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

78- Appendix B: Severity by Vegetation Type 

Figure B-13. Geographic distribution and fire severity probability distribution curve for the white fir regional vegetation 

type. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix C: Individual Fire Results - 79 

Appendix C: Individual Fire Results 

Figures C-1 through C-10 display the four category severity data, by National Forest, for all fires mapped 

in the SNFPA area for this report. When multiple fires occur in the same location, the data for the oldest 

fire are displayed. Perimeters of all fires in the fire history database are shown for each Forest, color 

coded as to whether they occurred before or after 1984, the earliest date mapped by this project. Table C-1 

lists all fires included in this report. Year, fire name, and direct protection agency are listed as identifying 

fields for each fire. Names were not recorded in the regional fire history database for all fires. When the 

fire name was missing a name was derived by concatenating year, state, unit, and local number. The 

number of acres burned by CBI derived severity category, number of acres in three categories of percent 

tree basal area mortality, and the percentage of each category within the fire perimeter are given for each 

fire. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

80- Appendix C: Individual Fire Results 

Figure C-1. Fires mapped on the Eldorado NF. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix C: Individual Fire Results - 81 

Figure C-2. Fires mapped on the Inyo NF. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

82- Appendix C: Individual Fire Results 

Figure C-3. Fires mapped on the Lassen NF. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix C: Individual Fire Results - 83 

Figure C-4. Fires mapped on the Modoc NF. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

84- Appendix C: Individual Fire Results 

Figure C-5. Fires mapped on the Plumas NF. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix C: Individual Fire Results - 85 

Figure C-6. Fires mapped on the Sierra NF. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

86- Appendix C: Individual Fire Results 

Figure C-7. Fires mapped on the Sequoia NF. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix C: Individual Fire Results - 87 

Figure C-8. Fires mapped on the Stanislaus NF. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

88- Appendix C: Individual Fire Results 

Figure C-9. Fires mapped on the Lake Tahoe Basin Management Unit. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix C: Individual Fire Results - 89 

Figure C-10. Fires mapped on the Tahoe NF. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

90- Appendix C: Individual Fire Results Table C-1. Individual fire results 

Year Fire Name Protection Agency Unit Unchanged (ac/%) Low (ac/%) Moderate (ac/%) High (ac/%) %BA Mortality <25 (ac/%) %BA Mortal

25-75 (ac/%

1984 1984CATOI00000031 USF TOI 1296 / 13 1920 / 20 2800 / 29 3799 / 39 3581 / 36 1873 / 1984 1984CATOI00000134 USF TOI 4094 / 23 3958 / 23 5814 / 33 3620 / 21 8764 / 50 3862 / 1984 1984NVTOI00000136 USF TOI 48 / 16 100 / 34 77 / 26 70 / 24 161 / 55 49 / 1984 BASIN USF SNF 228 / 40 257 / 46 74 / 13 4 / 1 507 / 90 48 1984 COW CDF BDU 2038 / 63 1183 / 37 4 / 0 0 / 0 3225 / 100 0 1984 GREEN GULCH CDF NEU 55 / 6 135 / 14 213 / 23 536 / 57 210 / 22 142 / 1984 PIUTE USF STF 94 / 62 35 / 23 17 / 11 6 / 4 132 / 87 13 1985 1985CACCD0000J913 BLM CCD 79 / 10 120 / 15 322 / 39 301 / 37 226 / 28 212 / 1985 1985CAENF00000066 USF ENF 93 / 27 68 / 20 71 / 21 109 / 32 172 / 51 42 / 1985 1985CAINF00000070 USF INF 62 / 27 103 / 44 56 / 24 13 / 5 174 / 75 40 / 1985 1985CAINF00005307 USF INF 7 / 4 17 / 10 22 / 13 127 / 73 27 / 15 14 1985 1985CASTF00000112 USF STF 56 / 28 77 / 38 60 / 30 8 / 4 142 / 70 40 / 1985 8 MILE CDF AEU 469 / 53 226 / 25 188 / 21 9 / 1 731 / 82 132 / 1985 BACKBONE CDF FKU 266 / 78 74 / 22 1 / 0 0 / 0 341 / 100 0 1985 BIG CREEK USF SNF 328 / 56 182 / 31 75 / 13 3 / 1 528 / 90 51 1985 MAMMOTH USF SNF 210 / 26 383 / 47 143 / 18 74 / 9 629 / 78 89 / 1985 RIVER USF STF 59 / 17 179 / 51 91 / 26 22 / 6 255 / 72 60 / 1986 1986CAENF00000082 USF ENF 185 / 30 216 / 35 153 / 25 60 / 10 433 / 70 103 / 1986 1986CATNF00000013 USF TNF 1816 / 88 247 / 12 13 / 1 0 / 0 2069 / 100 6 1986 1986CATOI00000029 USF TOI 84 / 2 535 / 15 1044 / 29 1919 / 54 756 / 21 692 / 1986 DEER USF SNF 2347 / 18 3175 / 24 4766 / 36 3105 / 23 6053 / 45 3169 / 1986 RIVERSIDE USF STF 30 / 24 44 / 35 42 / 34 10 / 8 81 / 64 30 / 1986 SYCAMORE USF SNF 1021 / 64 546 / 34 26 / 2 0 / 0 1577 / 99 15 1987 1987CATNF00000176 USF TNF 192 / 9 783 / 36 719 / 33 481 / 22 1087 / 50 471 / 1987 1987CATOI00000119 USF TOI 628 / 9 746 / 11 1776 / 27 3503 / 53 1518 / 23 1077 / 1987 BIG USF TNF 69 / 6 400 / 36 373 / 34 257 / 23 538 / 49 234 / 1987 CHAWANAKEE USF SNF 175 / 10 746 / 41 745 / 41 136 / 8 1034 / 57 489 / 1987 CHINA USF SNF 58 / 17 124 / 37 124 / 37 27 / 8 198 / 60 87 / 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix C: Ind

Year Fire Name Protection Agency Unit Unchanged (ac/%) Low (ac/%) Moderate (ac/%) High (ac/%) %BA Mortality <25 (ac/%) %BA Mortal

25-75 (ac/%

1987 CLAVEY 87 CMPLX USF STF 1240 / 14 2249 / 25 3523 / 40 1850 / 21 3890 / 44 2323 / 1987 EVER USF STF 26 / 5 29 / 5 99 / 18 401 / 72 64 / 12 59 / 1987 HAMM 87 CMPLX USF STF 3134 / 9 9508 / 28 13609 / 40 7773 / 23 14348 / 42 8800 / 1987 HASLOE 87 CMPLX USF STF 193 / 3 1213 / 17 2821 / 40 2754 / 39 1673 / 24 1764 / 1987 JARREL USF STF 336 / 18 1050 / 56 425 / 23 65 / 3 1485 / 79 281 / 1987 LARSON 87 CMPLX USF STF 4304 / 9 15377 / 32 19244 / 40 9676 / 20 22274 / 46 12946 / 1987 LAUREL USF INF 76 / 6 151 / 13 672 / 56 293 / 25 264 / 22 475 / 1987 PAPER CDF TCU 522 / 11 1881 / 41 1863 / 41 313 / 7 2787 / 61 1261 / 1987 PAPER 87 CMPLX USF STF 4284 / 11 10254 / 27 12884 / 34 10511 / 28 16299 / 43 8470 / 1987 RIVER II USF STF 251 / 34 278 / 38 190 / 26 20 / 3 572 / 77 128 / 1988 1988CAINF00005323 USF INF 360 / 56 263 / 41 25 / 4 0 / 0 634 / 98 15 1988 1988NVTOI00000135 USF TOI 777 / 41 631 / 33 257 / 14 238 / 12 1457 / 77 170 1988 BACKBONE CDF FKU 229 / 92 20 / 8 0 / 0 0 / 0 249 / 100 0 1988 BRIDGE CDF TCU 1309 / 18 1675 / 23 2513 / 35 1656 / 23 3302 / 46 1673 / 1988 CLEARINGHOUSE USF STF 1076 / 41 823 / 32 596 / 23 103 / 4 1993 / 77 377 / 1988 DESK USF SNF 145 / 57 96 / 38 13 / 5 0 / 0 245 / 96 9 1988 EL PORTAL USF SNF 49 / 29 73 / 43 48 / 28 0 / 0 132 / 77 38 / 1988 FAWN USF SNF 40 / 20 107 / 53 55 / 27 1 / 0 161 / 79 36 / 1988 GARNET USF SNF 1127 / 48 596 / 25 526 / 23 87 / 4 1808 / 77 364 / 1988 LAKE USF SNF 212 / 79 54 / 20 0 / 0 0 / 0 267 / 100 0 1988 MIDDLE FORK USF STF 87 / 81 20 / 19 0 / 0 0 / 0 107 / 100 0 1988 OBELISK USF SNF 1862 / 24 2908 / 38 2114 / 28 754 / 10 5126 / 67 1397 / 1988 USFS ASSIST 3 CDF NEU 319 / 51 167 / 27 121 / 19 20 / 3 505 / 80 86 / 1989 1989CATOI00000067 USF TOI 49 / 38 40 / 31 25 / 19 16 / 12 94 / 73 16 / 1989 BALCH USF SNF 1389 / 15 2728 / 30 3365 / 37 1498 / 17 4600 / 51 2211 / 1989 BURROUGH CDF FKU 297 / 18 904 / 56 408 / 25 10 / 1 1321 / 82 270 / 1989 POWERHOUSE USF SNF 3065 / 25 4179 / 34 3648 / 30 1277 / 10 7763 / 64 2312 / 1990 1990CATNF00000090 USF TNF 88 / 27 59 / 18 63 / 20 112 / 35 154 / 48 38 / 1990 1990CATOI00000094 USF TOI 65 / 28 90 / 39 74 / 32 4 / 2 169 / 72 55 / 1990 1990CATOI00000109 USF TOI 8 / 4 48 / 25 84 / 43 54 / 28 66 / 34 45 / 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

92- Appendix C: Individual Fire Results 

Year Fire Name Protection Agency Unit Unchanged (ac/%) Low (ac/%) Moderate (ac/%) High (ac/%) %BA Mortality <25 (ac/%) %BA Mortal

25-75 (ac/%

1990 A-ROCK USF YNP 985 / 5 4695 / 26 5699 / 31 6795 / 37 6508 / 36 3627 / 1990 COTTONWOOD CDF TCU 128 / 5 628 / 25 921 / 37 826 / 33 876 / 35 600 / 1990 KIRCH USF SNF 830 / 22 1385 / 36 1154 / 30 431 / 11 2387 / 63 764 / 1990 LILLY USF SNF 7 / 4 34 / 20 64 / 37 70 / 40 49 / 28 41 / 1990 SAVAGE USF SNF 130 / 7 510 / 26 768 / 39 581 / 29 722 / 36 487 / 1991 1991CAENF000012A2 USF ENF 188 / 64 104 / 36 0 / 0 0 / 0 292 / 100 0 1991 1991CASTF00000003 USF STF 131 / 68 58 / 30 4 / 2 0 / 0 192 / 99 2 1991 1991NVTOI00000091 USF TOI 778 / 90 76 / 9 8 / 1 0 / 0 857 / 99 4 1992 1992CATOI00000160 USF TOI 15 / 11 35 / 27 31 / 24 50 / 38 53 / 40 19 / 1992 ABERDEEN USF INF 5 / 1 48 / 9 157 / 28 343 / 62 64 / 11 79 / 1992 CLEVELAND USF ENF 1780 / 8 2726 / 12 5402 / 23 13176 / 57 5001 / 22 3356 / 1992 GULCH FIRE CDF TCU 914 / 5 2860 / 16 8460 / 47 5913 / 33 4458 / 25 5416 / 1992 ITALIAN USF SNF 808 / 36 950 / 42 457 / 20 30 / 1 1845 / 82 309 / 1992 RAINBOW USF INF 1653 / 19 2686 / 31 2138 / 25 2106 / 25 4614 / 54 1377 / 1992 RUBY USF STF 60 / 1 389 / 9 870 / 20 3020 / 70 528 / 12 525 / 1993 1993CAINF00000035 USF INF 10 / 2 49 / 9 366 / 68 111 / 21 70 / 13 202 / 1993 BACKBONE CDF FKU 100 / 91 10 / 9 0 / 0 0 / 0 109 / 100 0 1993 ROAD USF ENF 79 / 24 143 / 43 68 / 20 44 / 13 232 / 70 46 / 1993 WHITE DEER USF SQF 98 / 40 140 / 56 10 / 4 0 / 0 241 / 97 6 1994 BIG CREEK USF SNF 1141 / 19 1458 / 24 1982 / 33 1393 / 23 2852 / 48 1360 / 1994 BROKEN USF STF 42 / 31 25 / 19 24 / 18 42 / 32 69 / 53 15 / 1994 COTTONWOOD USF TNF 6630 / 14 7811 / 16 12298 / 25 22053 / 45 15859 / 33 7957 / 1994 CREEK CDF TCU 42 / 3 479 / 32 707 / 47 272 / 18 616 / 41 460 / 1994 CRYSTAL USF TNF 647 / 8 871 / 11 2144 / 28 4100 / 53 1689 / 22 1315 / 1994 FOUR LANE CDF FKU 56 / 23 59 / 25 63 / 27 60 / 25 122 / 51 40 / 1994 GARLIC USF SNF 364 / 48 393 / 52 4 / 1 0 / 0 760 / 100 1 1994 HIRSCHDALE USF TNF 116 / 10 203 / 18 567 / 50 259 / 23 373 / 33 369 / 1994 KELSEY CDF AEU 197 / 16 315 / 25 289 / 23 468 / 37 546 / 43 186 / 1994 POWERHOUSE #2 USF SNF 161 / 35 260 / 56 44 / 10 0 / 0 433 / 93 33 1994 SECATA USF SNF 300 / 45 265 / 40 102 / 15 0 / 0 594 / 89 72 / 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix C: Ind

Year Fire Name Protection Agency Unit Unchanged (ac/%) Low (ac/%) Moderate (ac/%) High (ac/%) %BA Mortality <25 (ac/%) %BA Mortal

25-75 (ac/%

1995 HELESTER USF TNF 62 / 9 305 / 46 153 / 23 140 / 21 389 / 59 102 / 1995 MEEKS USF TMU 60 / 37 49 / 30 24 / 15 30 / 18 112 / 69 15 1995 MILL USF ENF 5 / 4 29 / 23 42 / 34 50 / 40 40 / 31 26 / 1995 POLE USF INF 501 / 11 679 / 15 1169 / 27 2036 / 46 1303 / 30 764 / 1995 POWERHOUSE #2 CDF FKU 349 / 53 223 / 34 81 / 12 1 / 0 594 / 91 57 1995 WUKSACHI NPS KNP 2559 / 70 1069 / 29 50 / 1 3 / 0 3648 / 99 28 1996 AUTUMN HILLS USF TOI 175 / 4 491 / 13 1961 / 50 1277 / 33 812 / 21 1209 / 1996 BELLI USF TOI 733 / 11 3399 / 50 2199 / 32 495 / 7 4590 / 67 1502 / 1996 BIG CREEK USF STF 58 / 52 29 / 26 25 / 22 0 / 0 90 / 81 21 / 1996 COLEVILLE USF TOI 242 / 9 494 / 19 1263 / 48 652 / 25 843 / 32 827 / 1996 COOKS USF PNF 12 / 1 191 / 16 451 / 38 538 / 45 262 / 22 300 / 1996 MANUAL CDF TCU 157 / 34 226 / 48 83 / 18 0 / 0 401 / 86 58 / 1996 MT. JACKSON USF TOI 10 / 1 112 / 12 310 / 34 487 / 53 149 / 16 203 / 1996 ROGGE USF STF 4903 / 23 6566 / 31 8659 / 41 1247 / 6 12780 / 60 6292 / 1996 STUMPFIELD CDF MMU 1142 / 28 1442 / 35 1140 / 28 378 / 9 2743 / 67 731 / 1996 TRIMMER2 CDF FKU 3339 / 53 2223 / 36 669 / 11 16 / 0 5751 / 92 456 1997 CHOKE USF SQF 394 / 10 1083 / 27 1194 / 30 1312 / 33 1637 / 41 772 / 1998 ROUGH USF SNF 91 / 9 406 / 42 450 / 46 21 / 2 617 / 64 312 / 1998 TOM USF INF 305 / 8 298 / 8 1614 / 45 1378 / 38 666 / 19 884 / 1999 BEAN CREEK USF PNF 137 / 7 830 / 45 671 / 36 201 / 11 1090 / 59 469 / 1999 BUCKS USF PNF 3550 / 10 16275 / 47 11162 / 32 3589 / 10 21937 / 63 7345 / 1999 DEER USF STF 67 / 18 143 / 40 142 / 39 11 / 3 240 / 66 98 / 1999 DEHAVEN CDF TGU 12692 / 74 3816 / 22 560 / 3 3 / 0 16733 / 98 331 1999 DEVILS GAP USF PNF 187 / 12 600 / 39 318 / 20 445 / 29 841 / 54 205 / 1999 DIVISION USF INF 1061 / 32 1687 / 51 490 / 15 41 / 1 2876 / 88 327 / 1999 FLORISTON CDF NEU 96 / 25 61 / 16 143 / 37 89 / 23 164 / 42 91 / 1999 GULLY USF TOI 12 / 6 153 / 81 24 / 13 0 / 0 177 / 94 12 1999 GUN II CDF TGU 37205 / 61 14569 / 24 6642 / 11 2683 / 4 53066 / 87 4273 1999 HIRAM USF STF 641 / 23 1091 / 39 813 / 29 284 / 10 1892 / 67 538 / 1999 LIGHTNING #31 CDF TCU 12 / 8 43 / 29 50 / 34 42 / 28 61 / 42 31 / 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

94- Appendix C: Individual Fire Results 

Year Fire Name Protection Agency Unit Unchanged (ac/%) Low (ac/%) Moderate (ac/%) High (ac/%) %BA Mortality <25 (ac/%) %BA Mortal

25-75 (ac/%

1999 LOOKOUT USF PNF 336 / 12 1348 / 50 695 / 26 316 / 12 1823 / 68 453 / 1999 PIDGEON USF PNF 523 / 11 2761 / 58 1075 / 22 431 / 9 3487 / 73 704 / 1999 PILOT USF STF 364 / 9 674 / 16 1252 / 30 1872 / 45 1172 / 28 744 / 1999 WEST USF TOI 15 / 6 119 / 44 136 / 50 4 / 1 154 / 56 107 / 2000 ARROW CREEK USF TOI 401 / 14 1060 / 37 1201 / 41 237 / 8 1646 / 57 837 / 2000 AZUSA USF INF 81 / 10 274 / 32 359 / 43 131 / 15 406 / 48 235 / 2000 GOLDEN2 USF TOI 254 / 14 340 / 19 607 / 33 613 / 34 638 / 35 369 / 2000 HARLEY CDF TCU 119 / 63 61 / 32 10 / 5 0 / 0 184 / 96 6 2000 HIGHWAY CDF FKU 81 / 11 256 / 36 294 / 42 78 / 11 380 / 54 208 / 2000 KING CDF KRN 2440 / 47 994 / 19 973 / 19 818 / 16 3566 / 68 640 / 2000 MANTER USF SQF 10756 / 14 12125 / 15 21564 / 27 35153 / 44 25223 / 32 14045 / 2000 MILLWOOD USF SQF 38 / 14 80 / 30 82 / 30 72 / 27 128 / 47 52 / 2000 MITCHELL USF TOI 143 / 23 180 / 29 302 / 48 4 / 1 361 / 57 214 / 2000 SAWMILL USF INF 25 / 7 92 / 26 197 / 55 43 / 12 129 / 36 121 / 2000 SENECA USF TOI 155 / 13 558 / 46 479 / 39 24 / 2 802 / 66 343 / 2000 STORRIE USF PNF 12098 / 21 16633 / 29 12141 / 21 15785 / 28 30596 / 54 7880 / 2001 BELL USF MDF 414 / 13 613 / 20 1051 / 34 1035 / 33 1154 / 37 711 / 2001 BLUE USF MDF 8005 / 23 8183 / 24 8205 / 24 10284 / 30 17433 / 50 5430 / 2001 BRICEBURG BLM BLM 50 / 6 234 / 30 372 / 48 117 / 15 336 / 44 257 / 2001 CRATER USF INF 1090 / 19 2283 / 40 2053 / 36 276 / 5 3968 / 70 1288 / 2001 CREEK FIRE CDF MMU 1455 / 13 2249 / 20 4381 / 39 3294 / 29 4128 / 36 2750 / 2001 DARBY CDF TCU 857 / 6 4685 / 32 5774 / 40 3260 / 22 6358 / 44 3922 / 2001 DEVIL CDF LMU 58 / 1 694 / 16 1659 / 38 1993 / 45 137 / 72 22 / 2001 FLORISTON CDF NEU 76 / 40 54 / 28 37 / 19 24 / 12 530 / 21 259 / 2001 GAP USF TNF 214 / 8 264 / 10 422 / 17 1655 / 65 2349 / 55 1116 / 2001 HIGHWAY USF SQF 515 / 12 1579 / 37 1696 / 40 457 / 11 533 / 31 455 / 2001 HIGHWAY 70 CDF BTU 123 / 7 335 / 19 689 / 40 594 / 34 4461 / 31 4546 / 2001 MARTIS CDF NEU 880 / 6 2901 / 20 6886 / 48 3788 / 26 894 / 32 1424 / 2001 MCLAUGHLIN USF INF 74 / 3 593 / 21 1919 / 69 211 / 8 517 / 84 62 / 2001 MOORE CDF TCU 193 / 31 308 / 50 88 / 14 26 / 4 141 / 58 75 / 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

Appendix C: Ind

Year Fire Name Protection Agency Unit Unchanged (ac/%) Low (ac/%) Moderate (ac/%) High (ac/%) %BA Mortality <25 (ac/%) %BA Mortal

25-75 (ac/%

2001 MUSIC USF SNF 30 / 12 94 / 38 111 / 45 10 / 4 1619 / 38 1109 / 2001 NORTH FORK USF SNF 226 / 5 1162 / 27 1676 / 39 1206 / 28 2636 / 31 1535 / 2001 POE CDF BTU 694 / 8 1654 / 20 2398 / 28 3730 / 44 375 / 63 74 / 2001 ROCK CREEK USF TNF 135 / 23 221 / 37 114 / 19 127 / 21 103 / 89 13 / 2001 SALT USF ENF 33 / 29 61 / 53 22 / 19 0 / 0 178 / 100 0 2001 SILVER USF SNF 65 / 37 112 / 63 1 / 1 0 / 0 5550 / 32 3197 / 2001 STAR USF ENF 842 / 5 4001 / 23 4869 / 28 7423 / 43 1045 / 24 968 / 2001 STREAM USF PNF 312 / 7 576 / 13 1443 / 33 2019 / 46 186 / 46 91 / 2001 TREASURE USF TNF 52 / 13 121 / 30 135 / 33 97 / 24 125 / 48 57 / 2001 WHITE USF STF 26 / 10 90 / 34 83 / 31 65 / 25 949 / 22 1087 / 2002 BIRCH USF INF 182 / 7 154 / 6 444 / 16 1979 / 72 370 / 13 278 / 2002 BOREL USF SQF 1338 / 38 1565 / 45 574 / 16 26 / 1 3074 / 88 380 / 2002 CANNON USF TOI 3096 / 11 3649 / 13 6520 / 24 14108 / 52 7321 / 27 4131 / 2002 CONE USF LNF 199 / 9 513 / 24 540 / 26 847 / 40 802 / 38 353 / 2002 ELLIS 2 USF ENF 71 / 27 130 / 49 59 / 22 7 / 3 212 / 79 41 / 2002 FULLER USF INF 373 / 5 950 / 14 2462 / 36 3044 / 45 1545 / 23 1615 / 2002 GONDOLA USF TMU 55 / 8 189 / 27 317 / 46 129 / 19 277 / 40 226 / 2002 HUNTER USF ENF 124 / 18 318 / 46 201 / 29 56 / 8 472 / 68 136 / 2002 MCNALLY USF SQF 15259 / 10 36922 / 24 57785 / 38 42830 / 28 59303 / 39 39187 / 2002 PAIUTE USF INF 60 / 14 101 / 23 161 / 37 111 / 26 181 / 42 104 / 2002 PIPER USF INF 11 / 6 19 / 10 22 / 11 143 / 73 33 / 17 14 2002 PLUM USF ENF 757 / 40 714 / 38 319 / 17 106 / 6 1529 / 81 205 / 2002 ROCK CREEK 2 USF SNF 252 / 51 239 / 48 6 / 1 0 / 0 495 / 100 1 2002 SAINT PAULI USF ENF 22 / 6 124 / 36 156 / 45 47 / 13 172 / 49 111 / 2002 SHOWERS USF TMU 8 / 3 81 / 26 139 / 45 80 / 26 104 / 34 97 / 2002 SPI #3 SOURGRASS CDF TCU 122 / 17 409 / 57 160 / 22 24 / 3 561 / 79 111 / 2003 ALBANITA_HOOKER USF SQF 514 / 11 2392 / 50 1527 / 32 385 / 8 3176 / 66 1041 / 2003 BASIN USF SQF 47 / 5 164 / 16 275 / 28 512 / 51 248 / 25 187 / 2003 COD FISH USF TNF 179 / 20 439 / 50 222 / 25 38 / 4 659 / 75 156 / 2003 COONEY USF SQF 223 / 11 712 / 34 722 / 35 421 / 20 1045 / 50 476 / 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

96- Appendix C: Individual Fire Results 

Year Fire Name Protection Agency Unit Unchanged (ac/%) Low (ac/%) Moderate (ac/%) High (ac/%) %BA Mortality <25 (ac/%) %BA Mortal

25-75 (ac/%

2003 DEXTER USF INF 376 / 15 1360 / 54 596 / 24 195 / 8 1858 / 74 380 / 2003 KIBBIE CMPLX NPS YNP 622 / 9 2058 / 30 2983 / 43 1278 / 18 3103 / 45 2067 / 2003 MOUNTAIN CMPLX USF STF 1269 / 29 1651 / 38 1003 / 23 397 / 9 3106 / 72 678 / 2003 MUD USF STF 401 / 9 1117 / 25 1717 / 39 1223 / 27 1744 / 39 1179 / 2003 ROYAL USF TMU 188 / 52 175 / 48 0 / 0 0 / 0 363 / 100 0 2003 SAGEHEN USF INF 74 / 7 161 / 15 231 / 21 643 / 58 262 / 24 143 / 2003 SALT CDF AEU 50 / 19 158 / 59 52 / 19 7 / 3 217 / 81 37 / 2003 SUMMIT USF INF 415 / 7 2301 / 38 2509 / 41 883 / 14 3176 / 52 1701 / 2003 WEST KERN NPS KNP 2252 / 28 4226 / 52 1452 / 18 199 / 2 6854 / 84 960 / 2003 WHIT USF STF 78 / 7 527 / 49 387 / 36 90 / 8 683 / 63 261 / 2003 WILLIAMS NPS KNP 847 / 23 2127 / 57 647 / 17 123 / 3 3151 / 84 418 / 2003 WOODLOT NPS YNP 143 / 28 251 / 49 113 / 22 0 / 0 431 / 85 74 / 2004 CRAG UFS SQF 61 / 7 266 / 30 330 / 37 240 / 27 375 / 42 223 / 2004 DEEP UFS SQF 347 / 11 668 / 21 948 / 29 1261 / 39 1110 / 34 604 / 2004 EARLY UFS STF 87 / 5 450 / 25 811 / 46 422 / 24 644 / 36 521 / 2004 FREDS UFS ENF 275 / 3 1060 / 13 2538 / 32 4024 / 51 1629 / 21 1669 / 2004 POWER UFS ENF 1119 / 7 3825 / 22 5722 / 33 6499 / 38 5689 / 33 3853 / 2004 STRAYLOR CDF LMU 227 / 7 751 / 22 1115 / 33 1325 / 39 1125 / 33 746 / 2004 TUOLUMNE UFS STF 21 / 3 105 / 14 282 / 37 347 / 46 149 / 20 181 / 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

References - 97 

References 

Agee, J.K. (1993). Fire ecology of Pacific Northwest forests. Washington, D.C., Island Press. 

Allen, C.D., Savage, M., Falk, D.A., Suckling, K.F., Swetnam, T.W., Schulke, T., Stacey, P.B., Morgan, P., 

Hoffman, M. and Klingel, J.T. (2002). Ecological restoration of Southwestern ponderosa pine 

ecosystems: A broad perspective. Ecological Applications 12(5): 1418-1433. 

Arno, S.F. and Fiedler, C.E. (2005). Mimicking Nature's Fire: Restoring Fire-Prone Forests in the West. 

Washington, D.C., Island Press. 

Avery, T.E. and Berlin, G.L. (1992). Fundamentals of Remote Sensing and Airphoto Interpretation. Upper 

Saddle River, NJ, Prentice Hall. 

Barbour, M.G. and Major, J. (1988). Terrestrial Vegetation of California. Sacramento, CA, USA, 

California Native Plant Society. 

Box, G.E.P. and Jenkins, G.M. (1970). Time Series Analysis, Forecasting, and Control. San Francisco, 

CA, Holden Day. 

Brewer, K.C., Winne, J.C., Redmond, R.L., Opitz, D.W. and Mangrich, M.V. (2005). Classifying and 

mapping wildfire severity: A comparison of methods. Photogrammetric Engineering and Remote 

Sensing 71(11): 1311-1320. 

Brohan, P., Kennedy, J.J., Haris, I., Tett, S.F.B. and Jones, P.D. (2006). Uncertainty estimates in regional 

and global observed temperature changes: a new dataset from 1850. Journal of Geophysical 

Research 111(D12106). 

Brohman, R. and Bryant, L., Eds. (2005). Existing Vegetation Classification and Mapping Technical 

Guide, Gen. Tech. Report WO-67, USDA Forest Service, Washington Office, Ecosystem 

Management Coordination Staff. 

Cade, B.S. (1997). Comparison of tree basal area and canopy cover in habitat models: subalpine forest. 

Journal of Wildlife Management 61(2): 326-335. 

Calkin, D.E., Gebert, K.M., Jones, J.G. and Neilson, R.P. (2005). Forest Service large fire area burned and 

suppression expenditure trends, 1970–2002. Journal of Forestry 103: 179-183. 

Caprio, A.T. (2007). Fire History of Lodgepole Pine of Chagoopa Plateau, Sequoia and Kings Canyon 

National Parks. Proceedings of the 2002 Fire Conference on Managing Fire and Fuels in the 

Remaing Wildlands and Open Spaces of the Southwestern United States, December 2-5, 2002, 

San Diego, CA, USDA Forest Service, Pacific Southwest Research Station, Albany, CA, USA. 

Chander, G. and Markham, B.L. (2003). Revised Landsat 5 TM radiometric calibration procedures, and 

post calibration dynamic ranges. IEEE Transactions on Geoscience and Remote Sensing 41: 

2674-2677. 

Collins, B.M. (2007). Natural Wildfires in Sierra Nevada Wilderness Areas. Berkeley, University of 

California. Ph.D. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

References - 98

Critchfield, W.B. (1957). Geographic variation in Pinus contorta. Harvard University Publications 3: 1-

118. 

DeBano, L.F., Neary, D.G. and Ffolliott, P.F. (1998). Fire's Effects on Ecosystems. New York, John Wiley 

and Sons, Inc. 

Ekstrand, S. (1996). Landsat TM-based forest damage assessment: correction for topographic effects. 

Photogrammetric Engineering and Remote Sensing 62(2): 151-161. 

Elvidge, C.D. (1990). Visible and near infrared reflectance characteristics of dry plant materials. 

International Journal of Remote Sensing 11(10): 1775-1795. 

Epting, J., Verbyla, D. and Sorbel, B. (2005). Evaluation of remotely sensed indices for assessing burn 

severity in interior Alaska using Landsat TM and ETM+. Remote Sensing of Environment 96(3-

4): 328-339. 

Erman, D.C. and Jones, R. (1996). Fire Frequency Analysis of Sierra Forests. Sierra Nevada Ecosystems 

Project: Final Report to Congress. Davis, CA, University of California. vol II, chap. 42: 1139-

1153. 

Finney, M.A., McHugh, C.W. and Grenfell, I.C. (2005). Stand- and landscape-level effects of prescribed 

burning on two Arizona wildfires. Canadian Journal of Forest Research 35(7): 1714-1722. 

Fites, J., Campbell, M., Reiner, A. and Decker, T. (2007). Fire Behavior and Effects Relating to 

Suppression, Fuel Treatments, and Protected Areas on the Antelope Complex and Wheeler Fire. 

USDA Forest Service, Adaptive Management Services Enterprise Team. 41. 

Hann, W., Havlina, D. and Shlisky, A. (2005). Interagency Fire Regime Condition Class Guidebook, 

version 1.2. Interagency and The Nature Conservancy fire regime condition class website. USDA 

Forest Service, US Department of the Interior, The Nature Conservancy, and Systems for 

Environmental Management. www.frcc.gov. 

Hermit, R. (1996). Fire Analysis - California Spotted Owl EIS: Frequency of Burning and Severity of 

Burning. USDA Forest Service. Sierra Nevada Framework Project. Unpublished Report. 

Hobbs, S.D., Tesch, S.D., Owston, P.W., Stewart, R.E., Tappeniner, J.C. and Wells, G.E., Eds. (1992). 

Reforestation practices in southwestern Oregon and northern California. Corvallis, OR, Forestry 

Research Laboratory, Oregon State University. 

Holden, Z.A., Morgan, P., Crimmins, M.A., Steinhorst, R.K. and Smith, A.M.S. (2007). Fire season 

precipitation variability influences fire extent and severity in a large southwestern wilderness 

area, United States. Geophysical Research Letters 34: L16708, doi:10.1029/2007/GL030804. 

Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25: 295-

309. 

Husari, S.J. and McKelvey, K.S. (1996). Fire-Management Policies and Programs. Sierra Nevada 

Ecosystems Project: Final Report to Congress. Davis, CA, University of California. vol. II, chap. 

40: 1101-1117. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

References - 99 

Keeley, J.E. (1981). Reproductive cycles and fire regimes. Proceedings - Conference on fire regimes and 

ecosystem properties. H. A. Mooney, T. M. Bonnicksen, N. L. Christensen, J. E. Lotan and W. A. 

Reiners, Eds., USDA Forest Service. General Technical Report, WO-26: 594. 

Keeley, J.E. and Fotheringham, C.J. (2006). Wildfire management on a human-dominated landscape: 

California chaparral wildfires. Wildfire - a century of failed forest policy. G. Wuerthher, Ed. 

Covelo, CA, Island Press: 69-75. 

Key, C.H. and Benson, N.C. (2005a). Landscape assessment: ground measure of severity, the Composite 

Burn Index. FIREMON: Fire Effects Monitoring and Inventory System. D. C. Lutes, R. E. Keane, 

J. F. Caratti et al, Eds. Ogden, UT, USDA Forest Service, Rocky Mountain Research Station. 

General Technical Report, RMRS-GTR-164-CD:LA1-LA51. 

Key, C.H. and Benson, N.C. (2005b). Landscape assessment: remote sensing of severity, the Normalized 

Burn Ratio. FIREMON: Fire Effects Monitoring and Inventory System. D. C. Lutes, R. E. Keane, 

J. F. Caratti et al, Eds. Ogden, UT, USDA Forest Service, Rocky Mountain Research Station. 

General Technical Report, RMRS-GTR-164-CD:LA1-LA51. 

Kokaly, R.F., Rockwell, B.W., Hiare, S.L. and King, T.V.V. (2007). Characterization of post-fire surface 

cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and 

multispectral remote sensing. Remote Sensing of Environment 106: 305-325. 

Korhonen, L., Korhonen, K.T., Rautiainen, M. and Stenberg, P. (2006). Estimation of forest canopy cover: 

A comparison of field measurement techniques. Silva Fennica 40(4): 577-588. 

LANDFIRE. from http://www.landfire.gov. 

Leiberg, J.B. (1902). Forest conditions in the northern Sierra Nevada, California. U.S. Government 

Printing Office, Washington D.C., USA. 

Long, D., Losensky, B.J. and Bedunah, D. (2006). Developing the LANDFIRE Vegetation and 

Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project. The 

LANDFIRE Prototype Project: nationally consistent and locally relevant geospatial data for 

wildland fire management. M. G. Rollins and C. K. Frame, Eds. Fort Collins, CO, USDA Forest 

Service, Rocky Mountain Research Station. General Technical Report, RMRS-GTR-175: 217-

276. 

McKelvey, K.S. and Busse, K.K. (1996). Twentieth Century Fire Patterns on Forest Service Lands. Sierra 

Nevada Ecosystems Project: Final Report to Congress. Davis, CA, University of California. vol. 

II, chap. 41: 1119-1138. 

McKelvey, K.S., Skinner, C.N., Chang, C., Erman, D.C., Hussari, S.J., Parsons, D.J., van Wagtendonk, 

J.W. and Weatherspoon, C.P. (1996). An Overview of Fire in the Sierra Nevada. Sierra Nevada 

Ecosystems Project: Final Report to Congress. Davis, CA, University of California. vol. II, chap. 

37: 1033-1040. 

Miller, J.D. and Thode, A.E. (2007). Quantifying burn severity in a heterogeneous landscape with a 

relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment 

109(1): 66-80. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

References - 100

Miller, J.D. and Yool, S.R. (2002). Mapping forest post-fire canopy consumption in several overstory 

types using multi-temporal Landsat TM and ETM data. Remote Sensing of Environment 82(2-3): 

481-496. 

Noss, R.F., Franklin, J.F., Baker, W.L., Schoennagel, T. and Moyle, P.B. (2006). Managing fire-prone 

forests in the western United States. Frontiers in Ecology and the Environment 4(9): 481-487. 

Parker, A.J. (1986). Persistaence of Lodgepole Pine Forests in the Central Sierra Nevada. Ecology 67: 

1560-1567. 

Parsons, A. (2003). Draft Burned Area Emergency Rehabilitation (BAER) soil burn severity definitions 

and mapping guidelines. USDA Forest Service, Remote Sensing Applications Center, Salt Lake 

City, UT. 12. 

Pickett, S.T.A. and White, P.S., Eds. (1985). The ecology of natural disturbance and patch dynamics. New 

York, NY, Academic Press. 

Plumb, T.R. and Gomez, A.P. (1983). Five southern California oaks: identification and postfire 

management. USDA Forest Service, Pacific Southwest and Range Experment Stations, Berkeley, 

CA. General Technical Report. GTR-PSW-71: 56. 

Porkess, R. (1991). The Harper Collins dictionary of statistics. New York, NY, Harper Collins Publishers. 

Pratt, S., Holsinger, L. and Keane, R.E. (2006). Using Simulation Modeling to Assess Historical 

Reference Conditions for Vegetation and Fire Regimes for the LANDFIRE Prototype Project. The 

LANDFIRE Prototype Project: nationally consistent and locally relevant geospatial data for 

wildland fire management. M. G. Rollins and C. K. Frame, Eds. Fort Collins, CO, USDA Forest 

Service, Rocky Mountain Research Station. General Technical Report, RMRS-GTR-175: 277-

314. 

Pyne, S.J. (1982). Fire in America: A cultural history of wildland and rural fire. Seattle, University of 

Washington Press. 

Rogan, J. and Yool, S.R. (2001). Mapping fire-induced vegetation depletion in the Peloncillo Mountains, 

Arizona and New Mexico. International Journal of Remote Sensing 22(16): 3101-3121. 

Rollins, M.G. and Frame, C.K. (2006). The LANDFIRE Prototype Project: nationally consistent and 

locally relevant geospatial data for wildland fire management. USDA Forest Service, Rocky 

Mountain Research Station, Fort Collins, CO. General Technical Report. RMRS-GTR-175. 

Romme, W.H. and Despain, D. (1989). Historical perspective on the Yellowstone fires of 1988. 

BioScience 39: 695-699. 

Safford, H.D., Miller, J., Schmidt, D., Roath, B. and Parsons, A. (2008). BAER soil burn severity maps do 

not measure fire effects to vegetation: a reply to Odion and Hanson. Ecosystems 11: 1-11. 

SAS (2003). SAS 9.1 for Windows. Cary, NC, SAS Institute Inc. 

Sawyer, J.O. and Keeler-Wolf, T. (1995). A Manual of California Vegetation. Sacramento, CA, California 

Native Plant Society. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

References - 101 

Schoennagel, T., Veblen, T.T. and Romme, W.H. (2004). The interaction of fire, fuels, and climate across 

Rocky Mountain forests. BioScience 54: 661-676. 

Shum way, R.H. (1988). Applied Statistical Times Series Analysis. Englewood Cliffs, NJ, Prentice Hall. 

Skinner, C.N. and Chang, C. (1996). Fire Regimes, Past and Present. Sierra Nevada Ecosystems Project: 

Final Report to Congress. Davis, CA, University of California. vol. II, chap. 38: 1041-1069. 

Stephens, S.L., Martin, R.E. and Clinton, N.E. (2007). Prehistoric fire area and emissions from 

California’s forests, woodlands, shrublands and grasslands. Forest Ecology and Management 251: 

205-216. 

Sudworth, G.B. (1900). 21st Annual report of the U.S. Geological Survey. U.S. Government Printing 

Office, Washington D.C., USA. Part 5: 505-561. 

Sugihara, N.G., van Wagtendonk, J.W., Shaffer, K.E., Fites-Kaufman, J. and Thode, A.E., Eds. (2006). 

Fire in California's Ecosystems. Berkeley, CA, University of California Press. 

Swetnam, T.W. (1993). Fire history and climate change in giant sequoia groves. Science 262: 885-889. 

Taylor, A.H. and Beaty, R.M. (2005). Climatic influences on fire regimes in the northern Sierra Nevada 

mountains, Lake Tahoe Basin, Nevada, USA. Journal of Biogeography 32(3): 425-438. 

The Nature Conservancy, USDA Forest Service, and Department of the Interior (2006). LANDFIRE 

Vegetation Dynamics Modeling Manual, Version 4.1. Boulder, CO. 

Turner, M.G., Baker, W.L., Peterson, C.J. and Peet, R.K. (1998). Factors influencing Succession: Lessons 

from large, infrequent natural disturbances. Ecosystems 1: 511-523. 

USDA (1992). Forest Service Resource Inventories: An Overview. USDA Forest Service: Forest 

Inventory, Economics, and Recreation Research, Washington, DC. 

USDA (1995). Burned Area Emergency Rehabilitation handbook. USDA Forest Service, Washington, 

D.C. Forest Service Handbook 2509.13, Amendment No. 2509.13-95-7. 

USDA (2001). Sierra Nevada Forest Plan Amendment Final Environmental Impact Statement. USDA 

Forest Service, Pacific Southwest Region. 

USDA (2004). Sierra Nevada Forest Plan Amendment Final Supplemental Environmental Impact 

Statement. USDA Forest Service, Pacific Southwest Region. Technical Report. R5-MB-046. 

USDA. (2005). CALVEG Zones and Alliances - Vegetation Descriptions. Retrieved January, 2007, from 

http://www.fs.fed.us/r5/rsl/projects/classification/. 

Weatherspoon, C.P. and Skinner, C.N. (1996). Landscape-Level Strategies for Forest Fuel Management. 

Sierra Nevada Ecosystem Project: Final report to congress. Davis, CA, University of California. 

vol II, chap 56: 1471-1492. 

Westerling, A.L., Hidalgo, H.G., Craven, D.R. and Swetnam, T.W. (2006). Warming and earlier spring 

increase Western U.S. forest wildfire activity. Science 313: 940-943. 
Sierra Nevada Fire Severit y Monitoring 1984 – 2004 

References - 102

WRCC. (2007). California Climate Tracker. from http://www.wrcc.dri.edu/monitor/calmon/index.html. 

Zhu, Z., Key, C.H., Ohlen, D. and Benson, N.C. (2006). Evaluate Sensitivities of Burn-Severity Mapping 

Algorithms for Different Ecosystems and Fire Histories in the United States. Joint Fire Science 

Program. JFSP 01-1-4-12: 36. 

Zielinski, W.J., Truex, R.L., Dunk, J.R. and Garman, T. (2006). Using forest inventory data to assess 

fisher resting habitat suitability in California. Ecological Applications 16(3): 1010-1025.