Bridging EOS remote sensing measurements and fire emissions, smoke dispersion, and air quality DSS in the Eastern US

Abstract

Fire eniissions, smoke dispersiotl. ancl air quality are very important for fire fighting and planing of prescribed burning. BlueskyRATNS (BSR) is a comprehenisive and state-of-the-art Decision Support System (DSS) for fire managers and air quality managers to plan fiiels treatments and support state air qiiality smoke regulatory actions, especially related to prescribed fires. BSR has been created by a close collaboration of land rnatiageliiel1t and air quality regulator users. The primary inputs of BSR system are fuel properties (fuel moisture, fuel temperature and fuel loading) and firc characteristics (burned area. tire 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 bridgc satellite remote sensing and the BSR DSS can enhance and improve the capabilities of BSR significantly. To reach that goal. soliie efforts are needed to investigate how to retrieve properties and fire cliaracteristics using satellite retilote sensing and how to integrate remote scnsing data processing system and BSR flexibly. Gcorgc Mason University(C MU) 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 ct al.. 2005). The USDA Forest Service and other agencies are integrating the system to the eastern states. Our system generates real-time and composite data products of fuel properties and fire characteristics based on NASAfGSFC MODIS Direct Broadcast (DB) measurements (Dasgupta et al., 2005) and NASA RS data products, such as those from MODIS, MOPITT, TOMS, OM1 and AIRS. The tecl~niqiies for integrating oul system and BSR are investigated so as to feed our data products to BSR efficiently and enhance BSR. The expeimental implentation of the integrated system will be conduced as a couple of USDA Forest Service research stations. Detailed technical approaches for bridging EOS Remote Sensing Measuremenls and BSR DSS in the eastern states are discussed ill this paper.

Citation

Qu, John J.; Hao, Xianjun; Yang, Ruixin; Dasgupta, Swarvanu; Bhoi, Sanjeeb; Kafatos, Menas. 1999. Bridging EOS remote sensing measurements and fire emissions, smoke dispersion, and air quality DSS in the Eastern US. Fairfax, VA: George Mason University: 1-5.