Skip to main content

Satellite Imagery Data for Global Health and Epidemiology

  • Chapter
  • First Online:
Statistical Methods for Global Health and Epidemiology

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

Abstract

In this chapter, we describe commonly accessible sources of satellite imagery data free of charge for research. Exemplary data include lightening for development level, PM2.5 for pollution, temperature, recitation deforestation. We cover the sources of such data, methods to access, and utilization of them as a measure of the macro-environment in research, overall and zoom in down to specific country, district and community/neighborhood levels. Examples are used to illustrate the process, including R codes, screen shots, and tables.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Behnke, J, Mitchell, A, & Ramapriyan, H. (2018). NASA’s earth observing data and information system - Near-term challenges. In: 9th PV2018 Conference, United Kingdom: Harwell.

    Google Scholar 

  • Dockery, D. W., Pope, C. A., 3rd, Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., … Speizer, F. E. (1993). An association between air pollution and mortality in six U.S. cities. The New England Journal of Medicine, 329, 1753–1759.

    Article  Google Scholar 

  • European Space Agency (ESA). (2014). Overview: Copernicus monitoring system. Retrieved from March 4 http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Overview3

  • Holben, B. N., Eck, T. F., Slutsker, I., Tanre, D., Buis, J. P., Setzer, A., … Smirnov, A. (1998). AERONET - a federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment, 66, 1–16.

    Article  Google Scholar 

  • Hu, Z. (2009). Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease. International Journal of Health Geographics, 8, 27.

    Article  Google Scholar 

  • Hu, Z., & Rao, K. R. (2009). Particulate air pollution and chronic ischemic heart disease in the eastern United States: A county level ecological study using satellite aerosol data. Environmental Health, 8, 26.

    Article  Google Scholar 

  • Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., & Pozzer, A. (2015). The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525, 367–371.

    Article  Google Scholar 

  • Rank, R. (2011). Comprehensive large array-data stewardship system: Infrastructure and architecture improvements for NPP and GOES-R. In Seventh Annual Symposium on Future Operational Environmental Satellite Systems, Washington State Convention Center.

    Google Scholar 

  • Sorek-Hamer, M., Just, A. C., & Kloog, I. (2016). Satellite remote sensing in epidemiological studies. Current Opinion in Pediatrics, 28, 228–234.

    Article  Google Scholar 

  • Takaku, J., & Tadono, T. (2017). Quality updates of ‘Aw3d’ global Dsm generated from Alos prism. In 2017 IEEE International Geoscience and Remote Sensing Symposium (Igarss) (pp. 5666–5669).

    Google Scholar 

  • Takaku, J., Tadono, T., Tsutsui, K., & Ichikawa, M. (2016). Validation of ‘Aw3d’ global Dsm generated from alos prism. In Xxiii Isprs Congress, Commission Iv. (Vol. 3, pp. 25–31).

    Google Scholar 

  • Thomson, M. C., Ukawuba, I., Hershey, C. L., Bennett, A., Ceccato, P., Lyon, B., & Dinku, T. (2017). Using rainfall and temperature data in the evaluation of National Malaria Control Programs in Africa. The American Journal of Tropical Medicine and Hygiene, 97, 32–45.

    Article  Google Scholar 

  • United Nations Environment Programme (UNEP). (2018). Why does UN Environment matter? Retrieved from https://www.unenvironment.org/about-un-environment/why-does-un-environment-matter

  • USGS. (2013). Earth explorer help document. In USGS (Ed.).

    Google Scholar 

  • World Health Organization (WHO). (2018). Ambient (outdoor) air quality and health. Retrieved from October 1 http://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health

  • Yang, F. K., Wang, Y., Tao, J. H., Wang, Z. F., Fan, M., de Leeuw, G., & Chen, L. F. (2018). Preliminary investigation of a new AHI aerosol optical depth (AOD) retrieval algorithm and evaluation with multiple source AOD measurements in China. Remote Sensing, 10, 748.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chen, H., Ponpetch, K. (2020). Satellite Imagery Data for Global Health and Epidemiology. In: Chen, X., Chen, (.DG. (eds) Statistical Methods for Global Health and Epidemiology. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-35260-8_2

Download citation

Publish with us

Policies and ethics