Strategies for lidar characterization of particulates from point and area sources

Michael D. Wojcik; Kori D. Moore; Randal S. Martin; Jerry Hatfield

doi:10.1117/12.865079

22 October 2010 Strategies for lidar characterization of particulates from point and area sources

Michael D. Wojcik, Kori D. Moore, Randal S. Martin, Jerry Hatfield

Proceedings Volume 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII; 78240R (2010) https://doi.org/10.1117/12.865079
Event: SPIE Remote Sensing, 2010, Toulouse, France

Abstract

Use of ground based remote sensing technologies such as scanning lidar systems (light detection and ranging) has gained traction in characterizing ambient aerosols due to some key advantages such as wide area of regard (10 km²), fast response time, high spatial resolution (<10 m) and high sensitivity. Energy Dynamics Laboratory and Utah State University, in conjunction with the USDA-ARS, has developed a three-wavelength scanning lidar system called Aglite that has been successfully deployed to characterize particle motion, concentration, and size distribution at both point and diffuse area sources in agricultural and industrial settings. A suite of massbased and size distribution point sensors are used to locally calibrate the lidar. Generating meaningful particle size distribution, mass concentration, and emission rate results based on lidar data is dependent on strategic onsite deployment of these point sensors with successful local meteorological measurements. Deployment strategies learned from field use of this entire measurement system over five years include the characterization of local meteorology and its predictability prior to deployment, the placement of point sensors to prevent contamination and overloading, the positioning of the lidar and beam plane to avoid hard target interferences, and the usefulness of photographic and written observational data.

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Michael D. Wojcik, Kori D. Moore, Randal S. Martin, and Jerry Hatfield "Strategies for lidar characterization of particulates from point and area sources", Proc. SPIE 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, 78240R (22 October 2010); https://doi.org/10.1117/12.865079

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KEYWORDS

LIDAR

Sensors

Atmospheric particles

Remote sensing

Calibration

Agriculture

Environmental sensing

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