A Powerful Ally: Camera Networks in Wildfire Prevention

Did you know that over the past four decades the amount of land burned annually in forest fires has been increasing [1]? This growing problem requires novel solutions. To fight forest fires, we need to know when and where they are happening…. But with 766 million acres of forest land in the United States [2], there's a lot to keep track of! Additionally, as you might guess, newer smaller fires are easier to fight than fires that have been raging for a long time [1]. This is where camera networks come in. 

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Figure 1

This graph from Mohapatra and Trinh (2022) shows the trend of land being burned by forest fires, increasing from the years 1984 to 2020 [1].

How does a camera network help prevent wildfires? A large set of cameras across a region send still or video feed back to the people in charge of preventing destructive forest fires [1]. This kind of network allows firefighters to track one of the first visible signs of a fire – smoke [3]. 

There are benefits to using camera network technology to detect wildfires. For instance,  larger and more distant regions can be easily viewed for evidence of wildfire. In fact, technology has been shown to do this particular job even more effectively than humans can [1]. Why is that?

When community members spot wildfires, a firefighter needs to see the fire in person before a plan to contain the fire can be made [4]. Because of the time necessary to travel to the site and assess, this method allows the fire to worsen before any firefighting plan can be put in place. Alternatively, when fires can be identified and observed by firefighters using cameras, the firefighting can start before the fire has long to worsen [4]. However, certain weather can make camera networks less effective, as can obstructions in front of the camera's view [5]. These drawbacks make it even more important that other measures–such as controlled burns and fire safety education–are also taken to prevent large wildfires. 

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Figure 2

These pictures, from California’s North Bay, are real-life examples of the kind of images that firefighters using wildfire camera networks might see [6].

ALERTCalifornia is a California-wide project created by UC San Diego that uses a network of over a thousand high-definition cameras to help keep people safer from wildfires and other natural disasters [7]. The network is used to quickly help people make informed safety decisions, if and when a wildfire happens. In fact, UC San Diego has partnered with CALFIRE to make sure that firefighters can receive this life-saving information as soon as possible [8]. In previous years, the ALERTWildfire camera network operated on its own but has since become part of the more comprehensive ALERTCalifornia project [7]. This real-life example shows how camera network technology can make wildfire information more accessible and help put out fires faster.

At the Robotic-Assisted Wood Waste Removal Project, we also use cameras–in a different way–to help prevent the danger and destruction of large scale wildfires. While camera networks help locate forest fires in smaller, less dangerous stages so they can be put out, we aim to prevent unintentional forest fires in the first place. Both firefighting solutions use cameras and advanced technology, providing great examples of how technology can promote environmental goals.

Click here to learn more about the ALERTCalifornia project:

https://alertcalifornia.org/about/ 

Sources:

1. Mohapatra, A., & Trinh, T. (2022). Early wildfire detection technologies in practice—a review. Sustainability, 14(19), 12270. https://doi.org/10.3390/su141912270 

2. Oswalt, S. N., Smith, W. B., Miles, P. D., & Pugh, S. A. (2019). Forest resources of the United States, 2017: A technical document supporting the forest service 2020 RPA assessment. United States Department of Agriculture. https://doi.org/10.2737/wo-gtr-97 

3. Shi, J., Wang, W., Gao, Y., & Yu, N. (2020). Optimal placement and intelligent smoke detection algorithm for wildfire-monitoring cameras. IEEE Access, 8, 72326–72339. https://doi.org/10.1109/ACCESS.2020.2987991 

4. Stipaničev, D., Štula, M., Krstinić, D., Šerić, L., Jakovčević, T., & Bugarić, M. (2022). Site-specific wildfire risk index in Croatian wildfire monitoring and surveillance system. ICFBR 2022. https://doi.org/10.3390/environsciproc2022017034 

5. Alkhatib, A. AA. (2013). Smart and low cost technique for forest fire detection using wireless sensor network. International Journal of Computer Applications, 81(11), 12–18. https://doi.org/10.5120/14055-2044  

6. Callahan, M. (2019, August 8). New wildfire detection camera installed on Pole Mountain, Sonoma Coast’s highest peak. Petaluma Argus-Courier. https://www.petaluma360.com/article/news/new-wildfire-detection-camera-installed-on-pole-mountain-sonoma-coasts-hi/?artslide=1 

7. Scully, C. (2023, May 3). Alertcalifornia launches to provide essential tools to understand and adapt to natural disasters. UC San Diego Today. https://today.ucsd.edu/story/alertcalifornia-launches-to-provide-essential-tools-to-understand-and-adapt-to-natural-disasters  

8. ALERTCalifornia. (n.d.). ALERTCalifornia. University of California San Diego. Retrieved July 25, 2024, from https://alertcalifornia.org/