skip to main content

SciTech ConnectSciTech Connect

Title: Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data

An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.
 [1] ;  [2] ;  [2]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. National Center for Atmospheric Research, Boulder, CO (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 0739-0572
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Journal of Atmospheric and Oceanic Technology
Additional Journal Information:
Journal Volume: 33; Journal Issue: 4; Journal ID: ISSN 0739-0572
American Meteorological Society
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING observational techniques and algorithms; algorithms; data processing; lidars/lidar observations; mathematical and statistical techniques; classification