Background
Over the past 30 years, the total area destroyed by wildfires has increased roughly by a factor of three to four. Over the same period, the number of wildfires has declined from a record high number of 96,385 in 2006 to 58,985 in 2021. This implies that wildfires now are more likely to spread at a faster rate and, therefore, cause more damage and take more resources and time to be extinguished than before.
To minimize the risk of escalation of a fast-growing wildfire, it is not only essential to quickly detect the fire but also to make an early assessment of how fast the fire perimeter is expanding. Infrared (IR) images collected by instruments on NASA Earth-observing satellites are useful in tracking the movement of a wildfire's perimeter for a week or longer. However, these images are not useful for early wildfire detection and growth assessment because of the coarse resolution (>375 m), low acquisition frequency (every 12 h) and processing delay (2.5 h).
To address the current limitations of using IR satellite data for detection and real-time mapping of wildfires and to reduce the cost and increase the efficiency of using this technology on the ground, we plan to propose the following approach to NASA: (1) build an inexpensive imaging system that is optimized for detection and real-time mapping of wildfires, (2) develop and train machine learning data analysis tools for rapid on-board image analysis and prediction of spread, and (3) deploy the system on a constellation of small satellites to establish a facility to rapidly detect and track wildfires. In the current IR&D program we are developing a prototype of the sensor imaging and demonstrate its potential for accelerated detection and real-time mapping of wildfires based on test data and wildfire modeling.
Approach
The IRD work package consists of seven tasks.
- In the first task, the program team defines the requirements for the fire detection system capable of detecting wildfires at high resolution and design the Task 5 test setup.
- In Task 2, assemble and integrate the breadboard systems. All cameras are tested and co-aligned to a reference target so that they observe the same field of view.
- In Task 3, use an existing monochromatic IR source to calibrate the system for narrow bandwidths centered at various wavelengths.
- The next task involves thermal calibrations of the system using known heat sources. The calibration data are used to map measured incident radiation to source radiant power.
- In Task 5, perform two sets of full-scale fire experiments. The purpose of the first set is to determine the fraction within the area covered by one pixel that needs to be burning for the fire to be detected. The second set determine how well the system can track a growing fire, in particular shortly after detection.
- In Task 6, the team uses models of various levels of complexity to simulate several well-documented wildfires. The simulation results will be used to interpolate between 12-hour NASA satellite images. The idea is to show that the increased image acquisition frequency of our system would likely have resulted in earlier detection and containment.
Accomplishments
Tasks 1 and 2 have been completed. Task 1 defined the requirements for the system and Task 2 involved integrating the prototype instrument. Unfortunately, Task 2 was delayed by six months due to supply chain issues, difficulties in obtaining a suitable lens, camera vendor (Visimid) delays, and updates to the camera design after the mounting fixture was procured (which required some in-house modifications to the fixture).
Task 5 is the two sets of fire experiments. The first set of tests was performed in October 2023 and follow-on tests were executed in January of 2024.
For Task 6, we currently are revisiting the fire modeling and are exploring new ideas to improve agreement between the predicted and observed final burnt area. Although several adjustments to the input data have resulted in improved agreement, the FARSITE model still over-predicts the fire spread in the Southern direction (against the wind but uphill) and significantly under-predicts the fire spread in the opposite direction. To further improve the agreement, we will now modify the weather stream input files to account for the effect of wind gusts, which are not reflected in the automated weather station data. To improve the model predictions, we plan to do a sensitivity analysis of the spotting sub-model input parameters.
Task 7 will be completed in early 2024, after which the results of the program will be incorporated into a proposal to NASA to develop a prototype flight unit as the next step in the program.