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Izvestiya, Atmospheric and Oceanic Physics

, Volume 54, Issue 9, pp 1334–1340 | Cite as

Landsat Land Use Classification for Assessing Health Risk from Industrial Air Pollution

  • B. M. BalterEmail author
  • D. B. Balter
  • V. V. Egorov
  • M. V. Stalnaya
  • M. V. Faminskaya
METHODS AND MEANS OF SATELLITE DATA PROCESSING AND INTERPRETATION

Abstract

This study investigated to what extent risk estimates can be modified by involving in the process readily available space data and well-tested processing methods. We classify Landsat data for these types using the support vector algorithm and small characteristic training sites for each type. The dispersion modeling problem, unlike most classification tasks, is tolerant of unclassified areas. We show that the classification obtained is better than available global maps based on MODIS or Landsat. For a large chemical plant, we perform dispersion modeling and calculate the maximal hourly concentrations and acute risk from the main pollutant. We compare several versions of calculated risk based on the surface parameters assessed from global maps and variants of Landsat classification to show that the latter are twice as accurate as the former (with a ~20 and ~40% error, respectively). Risk estimates are shown to vary considerably (by ~25%) depending on the yearly set of Landsat data used, so that using multi-year data is a must, unless land use changes considerably over the period. Thus, in assessing the hazard from air pollution from any specific plant, which is an obligatory procedure for establishing the plant’s sanitary protection zone and obtaining the pollutant emission permit, it is desirable to use Landsat data.

Keywords:

Landsat AERMOD pollutant dispersion surface roughness classification 

Notes

ACKNOWLEDGMENTS

This work was supported by the Russian Foundation for Basic Research, project no. 16-07-00170, and Russian Ministry of Education and Science (task 1.9328.2017).

REFERENCES

  1. 1.
    Grekousis, G., Mountrakis, G., and Kavouras, M., An overview of 21 global and 43 regional land-cover mapping products, Int. J. Remote Sens., 2015, vol. 36, no. 21, pp. 5309–5335.CrossRefGoogle Scholar
  2. 2.
    U.S. EPA, User’s Guide for the AMS/EPA Regulatory Model—AERMOD, US EPA, 2004.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • B. M. Balter
    • 1
    Email author
  • D. B. Balter
    • 1
  • V. V. Egorov
    • 1
  • M. V. Stalnaya
    • 1
  • M. V. Faminskaya
    • 2
  1. 1.Space Research Institute, Russian Academy of SciencesMoscowRussia
  2. 2.Russian State Social UniversityMoscowRussia

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