This document discusses how to identify and mitigate wildfire risk in urban areas. It explains that risk is a function of hazard and exposure, where hazard is based on fuel levels and exposure is the probability of a fire starting. The author describes using GIS data on terrain, vegetation and canopy characteristics to map crown bulk density, which is then input into a fire modeling program to simulate random fires and map burn probabilities as a measure of exposure and overall risk. Communities at high risk are then identified and strategies discussed to reduce risk, such as selectively removing tree canopy to lower crown bulk density and future fire risk. In summary, the document outlines a GIS and modeling based approach to identify wildfire risk areas and strategies to mitigate risk in

1. Location, Location, Location:
Finding and Mitigating Wildfire Risk
in a Wildland-Urban Sea
Dr. Joseph White, Department of Biology
Baylor University
Waco, Texas

3. Semantics: Hazard vs. Risk
• Hazard
– Any real or potential condition that can cause injury
• Fuels
• Risk
– The potential for loss to occur given the realization
of a hazard
– Risk = Hazard × Exposure
• Exposure is the probability of the fruition of a hazardous
situation
• Loss includes houses AND habitat!

4. Hazard = Fuels

5. Austin, TX
Western Austin, TX
Fuel map derived from spectral
partitioning of Landsat-5 Thematic
Mapper data

6. GIS Data
Terrain
Vegetation Type
Canopy
Characteristics

7. Crown Bulk Density (CBD) Mapping
LiDaR

8. FLAMMAP: Burn Probabilities= Exposure
• Predicts long-term fire potential utilizing
randomized ignition points for iterative
prediction of burned areas
• “The burn probability for a given pixel is
an estimate of the likelihood that a pixel
will burn given a random ignition within
the study area and … is not an estimate
of the future likelihood of a wildfire…”
Ager et al. 2007

9. • Dry, hot, south winds
• 500,000 random fires
• Fire size varies
Simulate!

10. Risk Realized

11. Communities at Risk Mapped

12. Risk Reduction
• FLAMMAP simulation shows
– Risk ∝ CBD
𝑇𝑟𝑒𝑒 𝐶𝑎𝑛𝑜𝑝𝑦 𝑅𝑒𝑚𝑜𝑣𝑎𝑙 (%) =
𝛽 𝑅𝑖 − 𝑅 𝑜
𝐶𝐵𝐷 𝑀𝑎𝑥
∙ 100.0
– R0 = risk at time 0
– R1 = risk at time 1
– β = Coefficient ranging from 0.4
to 1.7 depending on house type

13. “Urban forests are economic goods. When income increases the demand [for
urban forests] will rise as well.”
P. Zhu and Y. Zhang. 2008. Landscape and Urban Planning 84:293-300.

14. Acknowledgements
• Research supported provided by U.S. Fish and Wildlife
– Carl Schwope, Deborah Holle, Carl Sexton
• Research supported provided by City of Austin
– William Conrad, Glen Gillman, Lisa, O’Donnell, Scott Rowin
• Bowman Environmental
– Bill Gabler, Deborah Blackburn, Cliff Ladd
• Center for Spatial Research @ Baylor
– Jonathan Cook, Patricia Spiller, Bruce Byars
• Spatial Ecology Lab @ Baylor