Bridging EOS Remote Sensing Measurements and Fire Emissions, Smoke Dispersion, and Air Quality DSS in the Eastern US

Fire emissions, smoke dispersion, and air quality are very important for fire fighting and planing of prescribed burning. BlueskyRAINS (BSR) is a comprehensive and state-of-the-art Decision Support System (DSS) for fire managers and air quality managers to plan fuels treatments and support state air quality smoke regulatory actions, especially related to prescribed fires. BSR has been created by a close collaboration of land management and air quality regulator users. The primary inputs of BSR system are fuel properties (fuel moisture, fuel temperature and fuel loading) and fire characteristics (burned area, fire location and active fires). Field measurement of these parameters has limitations in spatial coverage, spatial and temporal resolution, and requires high costs. However, these parameters can be retrieved using satellite remote sensing efficiently, and a system to bridge satellite remote sensing and the BSR DSS can enhance and improve the capabilities of BSR significantly. To reach that goal, some efforts are needed to investigate how to retrieve fuel properties and fire characteristics using satellite remote sensing and how to integrate remote sensing data processing system and BSR flexibly. George Mason University (GMU) has been collaborating with partners on BSR-RS, a system to support the effort by developing a capacity to obtain necessary fuel and fire properties and monitor smoke dispersion using satellite remote sensing (RS) products (Qu et al., 2005). The USDA Forest Service and other agencies are immigrating the system to the eastern states. Our system generates real-time and composite data products of fuel properties and fire characteristics based on NASA/GSFC MODIS Direct Broadcast (DB) measurements (Dasgupta et al., 2005) and NASA RS data products, such as those from MODIS, MOPITT, TOMS, OMI and AIRS. The techniques for integrating our system and BSR are investigated so as to feed our data products to BSR efficiently and enhance BSR. The experimental implementation of the integrated system will be conduced at a couple of USDA Forest Service research stations. Detailed technical approaches for bridging EOS Remote Sensing Measurements and BSR DSS in the eastern states are discussed in this paper.