The airborne Compact Fire Imager (CFI) for measurements across the entire fire lifecycle

Fires continue to grow hotter, faster, and more destructive in a warmer world. There is an urgent need for new observations of extreme fires to understand and anticipate rapid changes in fire risk, to detect and track individual fire events, and to evaluate fire impacts on ecosystems and communities. Here, we propose to develop a new instrument, the Compact Fire Imager (CFI), which will deliver unsaturated multi-spectral measurements at high spatial resolution in a form factor that is compatible with the size, weight, and power (SWaP) constraints for NASA's next-generation airborne platforms. Our team will also design and implement an onboard processing system to deliver fire products in near-real time for fire management, a critical step to enable the autonomous operation of CFI on unmanned aerial systems (UAS), including high altitude long endurance (HALE) platforms under development to support NASA's Wildland FireSense Project. We propose to develop and deliver CFI, a pushbroom instrument with six spectral bands between the shortwave infrared (SWIR) and thermal infrared (TIR), including two channels in the mid-wave infrared (MWIR) specifically designed to detect and characterize flaming and smoldering fires. CFI builds on the design and performance of the dual-band Compact Thermal Imager (CTI) that collected more than 15 million images from the International Space Station (ISS) in 2019, including thousands of fires. CFI leverages the stability and proven performance of innovative Strained-Layer Superlattice (SLS) detector technology on CTI with four specific improvements for fire science and applications: 1) a larger format SLS detector array that improves cross-track resolution and swath width, 2) a custom butcher block filter that provides six specific bands for fire science and applications, 3) a custom optical design that leverages the latest infrared glass technology, and 4) an enhanced processor card that supports instrument operation and onboard fire detection using machine learning (ML) algorithms. CFI?s six bands provide critical inputs for fire detection and characterization. Our team has a proven track record of algorithm development and data product delivery for active fires, burned area, and fire carbon emissions. New measurements from CFI will supply real-time data to fire managers and critical new insights regarding the fine-scale details of fire behavior and ecosystem impacts that cannot be captured with existing airborne or satellite sensors. The proposed onboard processing approach leverages commercial off-the-shelf (COTS) components and existing software workflows developed by our team for routine instrument operation, including commanding and data handling. This allows our team to focus on the design and implementation of new ML algorithms for onboard processing that leverage CFI?s unique spectral channels and the spatial resolution, swath width, and repeat observations possible with airborne platforms. The baseline design includes a GPU to accelerate ML algorithms that draw upon existing packages such as TensorFlow lite to provide optimal performance while meeting the stringent SWaP constraints for the overall CFI design. The proposed CFI instrument and onboard processing system primarily targets two research topics in A.53: 1) Enhance capabilities of existing science instruments needed for monitoring pre-fire, active-fire, and post-fire environments; 2) Reduce mass and power of instruments for accommodation by next-generation small spacecraft and aerial platforms,? with additional attention to the use of ML for onboard processing (Topic 5). Our team is uniquely qualified to develop and deliver the CFI instrument and analyze and interpret CFI data from airborne deployments as part of NASA's Wildland FireSense Project. In addition, the proposed effort has broad support from US agency partners with operational roles for active fire management (USFS, NOAA) and post-fire assessment (USGS).

Enhances the capabilities for existing science instruments for monitoring pre-fire, active-fire, and post-fire situations, reduces the power and mass of these instruments, and enables unprecedented observations in support of wildfire science through distributed observing systems and the information technologies needed for their support.

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