Wildfire Risk Assessment of the Los Padres National Forest.
By Kyle Somoano, Dean Christiansen, Keenan Sanders�University of California, Santa Barbara
METHODS
FUTURE DIRECTIONS
RESULTS
Figure 1. Label in 24pt Arial.
ABSTRACT
ENVIRONMENTAL DATA ANALYSIS
CONCLUSIONS
CONTACT
In this research project, we will be using ArcGIS Pro to produce a wildfire risk assessment map of the Los Padres National Forest (LPNF). This map exposes need for preemptive planning for fires, protecting homes, and saving lives. To determine fire risk we will analyze the fire history, vegetation types, precipitation data, and fire fighting infrastructure across Los Padres National Forest.
Our first task with the data was to measure the Euclidian distance from the nearest fire station to areas throughout the National Forest. Next, we used vegetation data to categorize areas based on their flammability, where barren land is the lowest, forest area is moderate, shrubland/chaparral is high, finally grassland with extreme. We also divided areas up based on how recent, if any, their last fire was. Finally, we used California’s annual precipitation average, to distinguish areas based on their dryness. For each variable we ranked their fire vulnerability status on a four-point scale, four being the most vulnerable and one the least vulnerable. To make our final map, we added all the variables together with the raster calculator.
Dean Christiansen
deanchristiansen@ucsb.edu
Kyle Somoano
kylesomoano@gmail.com
Keenan Sanders
keenansanders@ucsb.edu
INTRODUCTION
In December of 2017, California had the largest recorded fire in the state’s history, the Thomas Fire, which burned over 440 square miles and cost over 2.2 billion dollars in damages. It started in areas heavily dominated by invasive grasses near Santa Paula, and then spread to the Los Padres National Forest near Ojai. That area was extremely vulnerable and spread quickly due to high winds. The fire showed how vulnerable the LPNF is to wildfires and that there is a need for more data-driven forest planning that will help minimize the impacts of wildfires. There are a lot of variables that go into the development and spread of fire, which can make measuring vulnerability fires unpredictable. However, through research of fire characteristics and variables, we can help minimize the destructive implications of wildfires.
INFRASTRUCTURE DATA ANALYSIS
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Map 1. Vegetation types
Map 2. Burn Scars
ACKNOWLEDGEMENTS
Data Sources:
Cal Fire
USDA Forest Service
Special Thanks:
We would like to thank teaching assistants Meilin Shi, Jing Xu, Jiwon Baik, and professor Krzysztof Janowicz for their help and guidance.
Map 3. Precipitation values
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The proposed analysis of the Los Padres Forest presents multiple challenges. While the data sourced for this research is the most recent, it only ranges to 2011 for the vegetation layer and does not include invasive grass cover, which are extreme fire hazards. Given the active fire history of the forest, this might not give us an accurate representation of current vegetation cover, recovering areas, and healthy regions in the past decade. That being said, this risk assessment is intended to be used for the upcoming 2021 fire season only. The results show that the majority of the forest is deemed high and extreme risk. We recommend that the State of California take preventative measures to mitigate these risks.
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Map 4. Fire Station distances
Map 5 is the visual representation of our results, which is based on combining the 4 other data sets. The granularity for our final map was scaled to 500 by 500 meter raster data. Based on our results, we found that the areas in the center and northern part of the southern section of the Los Padres Forest is the most at risk for a wildfire. Due to recent fires near the city of Santa Barbara, we found that most of the surrounding area now has a low chance of having another wildfire.
Map 5. Map of Final Risk Assessment .
Mean: 10.73
Median: 11.04
The natural jenks for each risk level are:
Low: 5.37- 8.79
Moderate: 8.80-10.49
High: 10.50-11.79
Extreme: 11.80-14.76
Chart 1. Histogram of risk values
Chart 1. This chart displays the range of pixel risk values. The High risk category contains the most pixels. Extreme contained the second most pixels followed by the moderate and lastly low category.