WAMI for Support of Wildland Fire Science, Management, and Disaster Mitigation

We propose to adapt and demonstrate a low-swap infrared wide area motion imagery (WAMI) system for support of the mission of fighting active fires. WAMI consists of low-rate, high-pixel-count video in the mid-wave infrared (MWIR) over large areas. While its applications have been primarily for defense and security domains, based on our conversations with USFS wildland fire management stakeholders in this effort, it is exceptionally well suited to fire detection and monitoring even in its current, defense-focused, design and that the capability would improve the safety and effectiveness of firefighting efforts. It provides a new form of data to emerging analysis capabilities that can increase the efficiency and capacity of fire managers. The data provided would also the wildfire modeling community. BlackKite is a podded sensor that includes processing, storage, and communications in a compact, lightweight package. The system is controlled and utilized in flight through a digital data link, with real-time imagery and products available on demand through cell or ad hoc networks. Once on the ground, all data collected in the flight (up to 6 hours continuous) are available for download and further processing. Features of BlackKite support of the Fire Services Mission: Mid-Wave Infrared: Sensitive to small hot-spots High Resolution: GSDs between 0.25 and 1 m decreases need for spectral discrimination High Collection Rate: 100's of km2/hr are possible High Revisit Rate: Revisit rates of 1 Hz, while maintaining >10km2 coverage Precision Geolocation: <5m GLE in the orbiting mode Low SWAP: < 40lb Remote Operation: via MPU-5 Wave Relay radio Easy Integration: uses NATO 14"" Munitions Rack lugs or DT-1-11 Dovetail Real-Time and DVR Capabilities: flexible data model allows asynchronous operations; data can be accessed via phone or ad hoc networks. The current BlackKite system can fly both persistent orbiting and mapping missions. WAMI systems image large areas at medium resolution and at less than full video frame rates. The combination of image resolution and frame rate is designed to serve important functions in automated processing systems like tracking vehicles or monitoring the progress of fires. In these cases, higher resolution and frame rates are not critical to the function. The resulting system allows a broad access to the area of interest by multiple users to look at images and products either from the present or from prior times in the flight. * Data Acquisition and Early Utility Analysis. Execute ≥ 100 hours of data acquisition time covering the hot phases of fire-fighting campaigns. We will provide direct support to operations and will use data to develop products and tools. We propose that the sensor be mounted on a USFS (or contractor) aircraft, so that it operates for the fire season to expand the data set. * Software products and compatibility - Modify our ESRI ARC-GIS-based tools and software to be more specifically adapted to USFS needs and will adapt USFS tools and methods for product development to maximize the utility of the system. We will add a high dynamic range mode which will provide larger dynamic ranges to avoid blooming while maintaining high sensitivity. * Quantification of Benefit -- Use collected data to quantify the value of high-dynamic range imaging and temporal and spatial resolution in fire detection, tracking, and prediction. * Machine learning - Apply machine learning techniques to the collected data, along with other associative data, to provide tactical predictions for firefighting managers.

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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