Measuring the economic impact of early bushfire detection
The fires that occurred over the 2019/20 Australian summer were unprecedented in scale and had a devastating impact on large parts of Australia.
In this paper we estimate the economic costs of bushfires between 2020 and 2049 and the potential reduction in these costs from investments in early fire detection systems.
Under various plausible climate change related scenarios the costs of fires over the next 30 years will be considerable, up to $2.2billion per year, or $1.2billion per year in Net Present Value terms.
Even with conservative estimates of the reduction in the number of economically damaging fires due to earlier fire detection, the reduction in the costs of fires over the next 30 years is considerable. Under plausible scenarios of change leading to a growth in large fires (which almost all scientists expect it will) and early detection leading a reduction in the probability of large fires, then we predict an economic benefit of around $14.4billion, or $8.2billion in Net Present Value terms.
Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images
Pioneering networks of cameras that can search for wildland fire signatures have been in development for some years (High Performance Wireless Research & Education Network— PWREN cameras and the ALERT Wildfire camera). While these cameras have proven their worth in monitoring fires reported by other means, we have developed a functioning prototype system that can detect smoke from fires usually within 15 min of ignition, while averaging less than one false positive per day per camera.
FUEGO – Fire Urgency Estimator in Geosynchronous Orbit – A Proposed Early-Warning Fire Detection System
Current and planned wildfire detection systems are impressive but lack both sensitivity and rapid response times. A small telescope with modern detectors and significant computing capacity in geosynchronous orbit can detect small (12 m2) fires on the surface of the earth, cover most of the western United States (under conditions of moderately clear skies) every few minutes or so, and attain very good signal-to-noise ratio against Poisson fluctuations in a second.
Hence, these favorable statistical significances have initiated a study of how such a satellite could operate and reject the large number of expected systematic false alarms from a number of sources.
Here we present both studies of the backgrounds in Geostationary Operational Environmental Satellites (GOES) 15 data and studies that probe the sensitivity of a fire detection satellite in geosynchronous orbit. We suggest a number of algorithms that can help reduce false alarms, and show efficacy on a few.
Early detection and response would be of true value in the United States and other nations, as wildland fires continue to severely stress resource managers, policy makers, and the public, particularly in the western US.
Here, we propose the framework for a geosynchronous satellite with modern imaging detectors, software, and algorithms able to detect heat from early and small fires, and yield minute-scale detection times.