UAS-mounted Canopy Penetrating Radar-Tag System for Understory Fuel Sensing

Pre-fire assessments and interventions, such as long-term fire projections, fuel treatments, and prescribed burning operations, rely heavily on the accurate characterization of forest fuels across large areas. However, remote sensing approaches offer limited vertical resolution to characterize understory fuels obscured by the canopy layer, resulting in errors sometimes even more than 100% in predicting fire perimeter and burned areas [1]. This proposal targets this challenge via a scalable and accurate understory fuel sensing platform, complementing (sub)orbital systems. This will be achieved via passive Radio-Frequency (RF) tags designed by lead PI [anonymized] that seamlessly pair up with radar systems for higher sensing resolution. In this application, the tags will be distributed sparsely (e.g., every 50 - 200 meters) throughout areas of interest, such as Wildland Urban Interfaces, with no need for power or communication infrastructure. The tags are then remotely interrogated from a UAS-mounted radar, and their RF channel is used to characterize understory fuels. As such, the RF tags act as ground references, similar in concept to NISAR calibration corner reflectors, but in a much more scalable setting, to collectively enable separation between understory and canopy signal echoes. The research efforts under this proposal include commoditizing the tag-radar prototypes for highly reliable in-field testing and developing physics-informed models for generalizing RF signatures of tag reflections to different types of fuels in diverse forestry sites. The key intuition is that vegetative dielectric and moisture content will alter the RF signatures in both frequency and time domains, which will be learned using a physics-guided synthesizer. We will also develop a framework for integrating our sparse but accurate sensing data with landscape-scale orbital data (e.g., UAVSAR, NISAR) to deliver wall-to-wall fuel maps using label propagation techniques. This framework will facilitate the application of mixed-model statistical analysis in future stakeholder management and fire-fuel research activities. We will work with three potential stakeholders (anonymized) who are leading prescribed burn activities or serve as prescribed fire burn bosses. We will also work closely with these teams to use our proposed system during a prescribed burn and field experiment and show the impact of large-scale high-resolution fuel characterizations. FireTech Significance: This project dramatically improves our ability to estimate fuel moisture across large areas involved in pre-fire fuel treatments and prescribed burning operations. The fuel characteristics resulting from this project will be used as inputs for the models and projections (e.g., BEHAVE, FOFEM, FVS) used by burn managers. Finally, the proposed RF tags will provide a new data type complementary to (sub)orbital SAR data and augment existing NASA data products with accurate ground truthing. To the best of our knowledge, there is currently no technique with such high spatial measurement capability of fuel moisture, attesting to the game-changing potential for this work in fire ecology. Team: This interdisciplinary work builds on the PIs' expertise in wireless sensing (PI #1), wildfire analytics (Co-I #2), antenna and RFIC (Co-I #3), control systems (Co-I #4), remote sensing (Co-I #5), and wildland fire (Co-I #6). The team's previous work has advanced on several of the proposed tasks, e.g., designing passive RF tags, developing statistical and machine learning models for biomass classification, and a UAS-based forest sensing platform. [1] Mutlu et al. ""Sensitivity analysis of fire behavior modeling with LIDAR-derived surface fuel maps"" Forest Ecology & Management 2008

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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