Quantifying Fire Potential of Dry Forest Types using ArcGIS and FCCS
Holly Mouser and Dr. Ernesto Alvarado (advisor), University of Washington School of Environmental and Forest Sciences, Autumn 2015
Natural resource management, whether by Euro-American or tribal influence,
has an impact on landscape dynamics. Studies of tribal versus non-tribal forest
management suggest that there are discrepancies between management types
in similar physiographic regions with regard to species composition and
structure. Wilder (2014) predicated his research upon these findings and found
specific discrepancies on two study sites under differing ownership and
management within the South Eastern Cascade physiographic region: one site in
the U.S.D.A. Forest Service Naches Ranger District and one site in the Yakama
Nation’s Tribal Forest. Differences such as stand structure, forest cover types, and
potential vegetation were identified.
Wildland fire hazard and risk analysis can help resource managers plan forest
health restoration activities and prioritize action for ecologically vulnerable areas
(Hessburg 2007a). This project is an extended analysis of research performed by
Wilder (2014) and offers wildland fire hazard awareness with the opportunity for
resource management professionals to explore appropriate dry forest health
restoration activities.
Results
Decision support tools aid in wildland restoration decision-making and help to increase
the sustainability of dry forest ecosystems. U.S.D.A. Forest Service’s Fuel
Characterization Classification System (FCCS) is a decision support program recognized
for its ability to characterize actual fuelbeds and subsequent fire effects such as fire
behavior, one of the primary variables of fire danger (Hessburg 2007a; Keane 2008).
Esri’s ArcGIS is a widely used program for serving the needs of a geographic
information system, as it does in this project. All data used in this analysis is sourced
from the results of Wilder’s Quantifying landscape spatial patterns: A collaborative
forest management framework for tribal and federal lands (2014). Biophysical setting
(BPS) GIS layers were used from Wilder’s quantification of Naches Ranger District and
Yakama Tribal Forest study sites.
Available Fire Potential (AFP), Crown Fire Potential (CFP), and Surface Fire Potential
(SFP) are quantified in FCCS by indices ranging from 0-9.
1. Calculate area of unique BPS classes in a new field within BPS raster layers for each
study site. Where cci = cell count of a given cover type and 30m is the cell size, area
in hectares (ha) is calculated as follows:
1 ℎ𝑎
𝐴𝑟𝑒𝑎 ℎ𝑎 = 𝑐𝑐𝑖 × (30 𝑚 × 30𝑚) ×
10,000 𝑚2
2. Determine the closest match of each vegetation cluster to an FCCS fuelbed using
text information provided in the raster attribute table and documentation found
within FCCS.
Descriptive statistics of Both Sites following analysis in FCCS
Naches
Pre-management era South Eastern Cascade dry forests were frequented by fires
of high, mixed, and low severity (Hessburg 2007b). For millennia, indigenous
peoples have occupied lands and utilized natural resources of the Pacific
Northwest. Euro-American settlement of the region began around 1850,
imposing a drastic change in resource management compared to indigenous
peoples’ use of the land. “Total suppression” fire management policies were
enacted by the federal government starting in 1911 with the Weeks Act and
continued for several decades under various other acts of government.
Methods
Yakama
Introduction
Surface_Reaction Surface_Spread Surface_Flamelength CFP AFP SFP
Mean
3.484
4.855
3.131
2.482 5.340 4.855
Standard deviation
1.661
2.299
1.413
1.917 3.918 2.299
Standard error
0.429
0.593
0.365
0.495 1.012 0.593
Variance
2.758
5.283
1.998
3.674 15.349 5.283
Mean
4.142
4.875
3.649
3.266 6.317 4.875
Standard deviation
1.598
1.917
1.605
1.523 2.778 1.917
Standard error
0.604
0.724
0.607
0.576 1.050 0.724
Variance
2.554
3.674
2.575
2.319 7.717 3.674
The following graphics are FCCS outputs of AFP, CFP, and SFP symbolized by gradients
in ArcGIS:
Special acknowledgement to Tmth-Spusmen Wilder for allowing his data to be
used in this project.
3. Summarize each unique FCCS fuelbed by the total area that it occupies within each
study site, input these values into FCCS when prompted, and run the program.
Naches Ranger District study area (red) and Yakama Tribal Forest study area
(green) relative to Washington state.
Discussion
References
Hessburg PF., Reynolds KM., Keane RE., James KM., & Salter RB. (2007 A).
Evaluating wildland fire danger and prioritizing vegetation and fuels
treatments. Forest Ecology and Management, (247), 1-17. Retrieved September
1, 2015, from ScienceDirect.
Hessburg PF., James KM., & Salter RB. (2007 B). Re-examining fire severity
relations in pre-management era mixed conifer forests: inferences from
landscape patterns of forest structure. Landscape Ecology, (22), 5-24. DOI:
10.1007/s10980-007-9098-2
Keane, R., Drury, S., Karau, E., Hessburg, P., & Reynolds, K. (2008). A method for
mapping fire hazard and risk across multiple scales and its application in fire
management. Ecological Modelling, 221(2010), 2-18.
doi:10.1016/j.ecolmodel.2008.10.022
Wilder T. (2014). Quantifying landscape spatial patterns: A collaborative forest
management framework for tribal and federal lands (Order No. 1563142).
Available from Dissertations & Theses @ University of Washington WCLP.
(1566673065).
4. Download the “Potentials” result file (already in CSV format), load it into the same
data frame as the BPS raster layers, and perform a Join By Attribute based on the
FCCS approximation field. Symbolize any of the newly quantified data as necessary.
FCCS is recognized for its ability to characterize actual fuelbeds and
subsequent fire effects (Hessburg 2007a). Fire effects can be likened to fire
behavior and fire hazard, two of the primary variables of fire danger (Hessburg
2007a; Keane 2008). Under these interpretations, the results of this analysis
present findings relevant to fire behavior and fire hazard. Surface fire behavior
and potentials, crown fire potential, and available fire potential – the response
variables chosen as the quantitative results for this analysis – are collectively
the FCCS equivalent of fire behavior and fire hazard metrics.
Potentials remain constant among unique fuelbed IDs regardless of the study
site in which they occur. This is because those values are not weighted by area
but by environmental inputs such as wind speed and humidity, which were left
at the program defaults for both study sites. In the scenario that
environmental inputs were changed, we could have seen differing values for
each fuelbed number between study sites. Differing values for potentials could
increase the probability for realized differences in fire behavior and fire hazard
between study sites. Quantifying geospatial effects of neighboring fuelbeds on
fire dynamics and smoke composition and production would be the most
natural opportunity for extended analysis after this project.