The Next Generation Fire System: Capability Demonstrations and Early Results

Satellite remote sensing is a vital tool for fire detection and characterization; however, exploitation of earth observation satellites has been limited by the lack of a sensor agnostic method for detecting thermal anomalies under a wide range of background conditions, including cloud cover. To address existing detection limitations, a new sensor agnostic algorithm known as the Next Generation Fire System (NGFS) is being developed by NOAA. The new algorithm is based on the well-established VOLcanic Cloud Analysis Toolkit (VOLCAT) algorithm and can be applied to virtually any satellite sensor with imaging capabilities, at least one midwave infrared (~4 μm) channel, and two longwave infrared channels in the 10-13 μm part of the spectrum, regardless of orbit. The excess midwave infrared radiation associated with a thermal anomaly is used to retrieve the radiative power to support fire intensity and emissions modeling. The NGFS is designed to operate under clear or cloudy sky conditions, with application for cloud-affected scenes enabled through use of robust detection metrics and a novel atmospheric correction procedure. In this presentation, we will discuss ongoing efforts at the University of Wisconsin-Madison to enhance the capabilities of the NGFS algorithm and to develop higher order features, such as automated alerting for new thermal anomaly detections, active fire situational awareness tools that are tolerant of cloud cover, terrain correction of satellite imagery and fire detections, and benchmarking activities. Results from several case studies using GOES and VIIRS observations will also be shown to demonstrate the capabilities of the NGFS for fire detection and monitoring.