I know a lot about forest fires in California. I lived in the California mountain forests for eight years, including several years when our home was threatened by fire and we were asked to evacuate.

During my time as a mountain forest resident, I was doing some writing for the local paper that included going out to fire lines and talking with fire coordinators. The flames were not far away, and the firefighting was dangerous.

The fires that devastated California in November 2018 left over 70 people dead and over 1,000 people unaccounted for. That doesn't begin to describe the economic and environmental damage, nor the stark reality that fires like this, given global warming, are likely to be the norm from now on.

All of this supports an increased use of UAVs and other geospatial technologies to help fight fires and also plan logistics. One approach to this hybrid geospatial blend is the teaming of drones with satellite imagery.

“Satellite imagery and drones can provide officials with a wealth of real-time data and information that can assist with major event management,” says Natan Barak, CEO and founder of mPrest Systems, which proves analytics software for IoT devices. “The satellite imagery can provide a layout of what an area looked like before and after a disaster, as well as the location of streets, power lines and other critical infrastructure that might not be visible after a disaster hits. Drones can provide a closer, more in depth look of what is going on in a disaster zone.”

By utilizing a combination of satellite imagery and drone surveillance, disaster response teams can use artificial intelligence (AI) to process the data that is collected by satellite imagery, drones and sensors. Together, the data gathered and the analytics performed can help predict the path and severity of destruction, in addition to assisting with the planning of mitigation techniques. This data intelligence advantage enables officials to prepare and protect critical infrastructure, people and property.

Drones can also complement satellite imagery by covering periods of time that the satellites can’t. For example, a satellite might only be available during specific time windows. Since drones are available on demand, they can cover those time windows when satellites are unavailable. This means that data collection and analytics are uninterrupted.

Drones can also fly lower than satellites and be more effective for tasks such as providing data and analytics for vegetation management.

“Drones and satellites can work together to streamline major event management,” says Barak. “The key is using an application that is vendor agnostic and allows officials to gather data from thousands of sensors and systems, including satellite imaging and drone patrol. In this way, data can be analyzed centrally, so officials have a single situational awareness picture.”

In the field, there are several disaster fighting applications that the satellite imagery and drone combination can be applied to.

Sensors can provide information about rising water levels, spikes in energy consumption, increased wind levels and unusual ground motions. They can also provide data on critical asset health so that government and utility officials can make informed real-time decisions. Once data is collected, artificial intelligence can use advanced algorithms to analyze the collected data, often in combination with historical data and data from third parties. The more holistic the data, the greater accuracy that can be afforded to decision-making.

“Here is an example of how all of this different data can come together,” says Barak. “Prior to the wildfire season, officials can leverage previous history and simulation tools to plan in advance for possible wildfire scenarios. Officials can then identify high-risk areas based on weather sensors and [the] time-of-year to remove vegetation and other obstacles from critical infrastructure to decrease the damage in the event of wildfire outbreak. During and following a wildfire, officials can receive notifications of wildfire incidents. Based on the fire’s location, forecasted wind speeds and weather, the application can predict the fire’s propagation.

“The application can identify power and utility infrastructure (lines, transformers, breakers, etc.), as well as critical facilities (schools, hospitals, etc.) that are in the fire’s path. Officials can then assign restoration crews and materials based on location and damage assessment, together with electronic maps. Real-time and filed data inputs can be used to create an optimal restoration plan that accelerates recovery times and reduces restoration costs.”

For firefighters, government agencies, surveyors, and others engaged in rapid response, the combination of satellite imagery, drones and AI can go a long way to improve disaster mitigation.

Current best practices include:

  • Employing predictive analytics for disaster response planning and for identification of resource gaps in materials, equipment, crews and supporting infrastructure. 
  • Investing in geospatial and Internet of Things (IoT) technologies that are interoperable with each other (no small task since many IoT devices have their own proprietary operating systems).
  • Pursuing tool integration through a single platform that can produce a holistic picture of an event by being able to accept and analyze a variety of data coming in from different technology sources and devices.
  • Employee training in new technology and business processes that they might not be familiar with.