Skip to main content
Log in

Smoke detections and visibility estimation using Himawari_8 satellite data over Sumatera and Borneo Island Indonesia

  • Published:
Spatial Information Research Aims and scope Submit manuscript

Abstract

Smoke as the one of weather hazard that contains large pollutant and affect the major live aspects: health, tourism, transportation and climate. Due to its regular appearance in Maritime Continent Indonesian area South East Asia, it is important to assess the satellite remote sensing Himawari_8 data to detect smoke and model the horizontal visibility as the smoke proxy. Using RGB (red, green, blue) combination, maximum likelihood and backward selection of multiple regression were used to detect and to develop the horizontal visibility model. RGB aerosol and RGB day natural color visually sees only the thick smoke with horizontal visibility observe below 1600 m. The best horizontal visibility model [with significant level 95% (probability < 0.05)] was develop from combination of band 3 (0.64 µm); band 7 (3.9 µm); and band 14 (11.2 µm) with root means square error value is about 404 m and correlation value is about 0.69.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. METAR is observational data report in abbreviated plain language intended for dissemination and use beyond the aerodrome of origin [26].

  2. \(RMSE = \left( {\frac{{\mathop \sum \nolimits_{1}^{N} \left( {O - M} \right)\left( {O - M} \right)^{2} }}{N}} \right)^{{{\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-0pt} \!\lower0.7ex\hbox{$2$}}}} \quad (1)\) where N is the number data, O is the prevailing visibility over airport (observation), and M is the visibility modeled from single or multiple regression [36].

References

  1. Duncan, B. N., Martin, R. V., Staudt, A. C., Yevich, R., & Logan, J. A. (2003). Interannual and seasonal variability of biomass burning emissions constrained by satellite observations. Journal of Geophysical Research, 108, ACH-1. https://doi.org/10.1029/2002JD002378.

    Google Scholar 

  2. Gaveau, D. L., Salim, M., Hergoualc’h, K., Locatelli, B., Sloan, S., Wooster, M., et al. (2014). Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: Evidence from the 2013 Sumatran fires. Scientific Reports, 4, 6112. https://doi.org/10.1038/srep06112.

    Article  Google Scholar 

  3. Betha, R., Behera, S. N., & Balasubramanian, R. (2014). 2013 Southeast Asian Smoke Haze: Fractionation. Environmental Science and Technology, 48, 4327–4335. https://doi.org/10.1021/es405533d.

    Article  Google Scholar 

  4. Khan, M. F., Latif, M. T., Saw, W. H., Amil, N., Nadzir, M. S. M., Sahani, M., et al. (2015). Fine particulate matter associated with monsoonal effect and the responses of biomass fire hotspots in the tropical environment. Atmospheric Chemistry and Physics Discussions, 15, 22215–22261. https://doi.org/10.5194/acpd-15-22215-2015.

    Article  Google Scholar 

  5. Heil, A., Langmann, B., & Aldrian, E. (2007). Indonesian peat and vegetation fire emissions: Study on factors influencing large-scale smoke haze pollution using a regional atmospheric chemistry model. Mitigation and Adaptation Strategies for Global Change, 12, 113–133. https://doi.org/10.1007/s11027-006-9045-6.

    Article  Google Scholar 

  6. Li, Z., Li, J., Menzel, W. P., Schmit, T. J., & Ackerman, S. A. (2007). Comparison between current and future environmental satellite imagers on cloud classification using MODIS. Remote Sensing of Environment, 108, 311–326. https://doi.org/10.1016/j.rse.2006.11.023.

    Article  Google Scholar 

  7. Hillger, D., Kopp, T., Seaman, C., Miller, S., Lindsey, D., et al. (2015). User validation of VIIRS satellite imagery. Remote Sensing, 8(1), 11. https://doi.org/10.3390/rs8010011.

    Article  Google Scholar 

  8. Lensky, I. M., & Rosenfeld, D. (2008). Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT). Atmospheric Chemistry and Physics, 8, 6739–6753.

    Article  Google Scholar 

  9. Kokhanovsky, A., & Leeuw, G. (2009). Satellite aerosol remote sensing over land (pp. 2–4). Chichester: Springer/Praxis Publishing. https://doi.org/10.1007/978-3-540-69397-0.

    Book  Google Scholar 

  10. Shin, D. H., Lee, H., Jung, J. S., Lee, K. H., Noh, Y. M., Mu, D., et al. (2009). Optical and microphysical properties of severe haze and smoke aerosol measured by integrated remote sensing techniques in Gwangju, Korea. Atmospheric Environment, 43, 879–888. https://doi.org/10.1016/j.atmosenv.2008.10.058.

    Article  Google Scholar 

  11. Bessho, K., Date, K., & Hayashi, M. (2016). An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. Journal of the Meteorological Society of Japan, 2, 151–183. https://doi.org/10.2151/jmsj.2016-009.

    Article  Google Scholar 

  12. Wang, J., Song, W., Wang, W., Zhang, Y., & Liu, S. (2011). A new algorithm for forest fire smoke detection based on MODIS data in Heilongjiang province. In 2011 international conference on remote sensing, environment and transportation engineering (pp. 5–8). https://doi.org/10.1109/RSETE.2011.5964042.

  13. Rosenfeld, D., & Lens, (1998). Satellite-based insights into precipitation formation processes in continental and maritime convective clouds. Bulletin of the American Meteorological Society, 79, 2457–2476.

    Article  Google Scholar 

  14. Kaufman, Y. J., & Tanré, D. (2003). Aerosol Measurements. In J. R. Holton, J. A. Curry, & J. A. Pyle (Eds.), Encyclopedia of atmospheric science (pp. 1939–1956). Amsterdam: Elsevier Science Ltd.

    Google Scholar 

  15. Li, Z. Q., Khananian, A., Fraser, R. H., & Cihlar, J. (2001). Automatic detection of fire smoke using artificial neural networks and threshold approaches applied to AVHRR imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(9), 1859–1870. https://doi.org/10.1109/36.951076.

    Article  Google Scholar 

  16. Giacco, F., Thiel, C., Pugliese, L., Scarpetta, S., & Marinaro, M. (2010). Uncertainty analysis for the classification of multispectral satellite images using SVMs and SOMs. IEEE Transactions on Geoscience and Remote Sensing, 48(10), 3769–3779. https://doi.org/10.1109/TGRS.2010.2047863.

    Article  Google Scholar 

  17. Cetin, M., Kavzoglu, T., & Musaoglu, N. (2004). Classification of multi-spectral, multi-temporal and multi-sensor images using principal componenet analysis and artificial neural network: Beykoz case (pp. 0–5). Estambul: Cartesio.org. Retrieved from http://cartesia.org/geodoc/isprs2004/comm4/papers/480.pdf.

  18. Li, X., Song, W., Lian, L., & Wei, X. (2015). Forest fire smoke detection using back-propagation neural network based on modis data. Remote Sensing, 7(100501), 4473–4498. https://doi.org/10.3390/rs70404473.

    Article  Google Scholar 

  19. Duda, T., & Canty, M. (2002). Unsupervised classification of satellite imagery: Choosing a good algorithm. International Journal of Remote Sensing, 23(11), 2193–2212. https://doi.org/10.1080/01431160110078467.

    Article  Google Scholar 

  20. Peterson, D., Hyer, E., & Wang, J. (2013). A short-term predictor of satellite-observed fire activity in the North American boreal forest: Toward improving the prediction of smoke emissions. Atmospheric Environment, 71, 304–310. https://doi.org/10.1016/j.atmosenv.2013.01.052.

    Article  Google Scholar 

  21. Ristiyono, L., Danoedoro, P., & Marfai, M. A. (2016). Kajian Klasifikasi Berbasis Obyek Untuk Pemetaan Bangunan Yang Berisiko Gempabumi Di Bantul, Daerah Istimewah Yogyakarta. Majalah Geografi Indonesia, 30(1), 68–75.

    Article  Google Scholar 

  22. Indrawati, L., Hartono, H., & Sunarto, S. (2009). Klasifikasi Pohon Keputusan Untuk Kajian Perubahan Penggunaan Lahan Kota Semarang Menggunakan Citra Landsat TM/ETM+. Majalah Geografi Indonesia, 23(2), 109–123. https://www.ingentaconnect.com/content/doaj/02151790/2016/00000023/00000002/art00002.

  23. Mecikalski, J. R., Berendes, T. A., Feltz, W. F., Bedka, K. M., Bedka, S. T., Murray, J. J., et al. (2007). Aviation applications for satellite-based observations of cloud properties, convection initiation, in-flight icing, turbulence, and volcanic ash. Bulletin of the American Meteorological Society, 88(10), 1589–1607. https://doi.org/10.1175/bams-88-10-1589.

    Article  Google Scholar 

  24. Smith, A. M. S., & Wooster, M. J. (2005). Remote classification of head and backfire types from MODIS fire radiative power and smoke plume observations. International Journal of Wildland Fire, 14(3), 249–254. https://doi.org/10.1071/WF05012.

    Article  Google Scholar 

  25. Heil, A., & Goldammer, G. (2001). Smoke-haze pollution: A review of the 1997 episode in Southeast Asia. Regional Environmental Change, 2, 24–37. https://doi.org/10.1007/s101130100021.

    Article  Google Scholar 

  26. ICAO. (2011). Doc. 8896 Manual of Aeronautical Meteorological Practice (Ninth Edit). Montreal, Quebec, Canada

  27. ICAO. Meteorological Service for International Air Navigation (2013).

  28. Wang, Y., & Field, R. D. (2004). Trends in atmospheric haze induced by peat fires in Sumatra Island, Indonesia and El Nino phenomenon from 1973 to 2003. Geophysical Research Letters, 31, 1–4. https://doi.org/10.1029/2003GL018853.

    Article  Google Scholar 

  29. Ismanto, H., Hartono, H., & Marfai, M. A. (n.d.). Spatiotemporal visibility characteristics impacted by forest and land fire over airports in Sumatera and Borneo Island Indonesia. Queastiones Geographicae Journal (Submitted), 1–14 (In Press).

  30. Tosca, M. G., Randerson, J. T., Zender, C. S., Nelson, D. L., Diner, D. J., & Logan, J. A. (2011). Dynamics of fire plumes and smoke clouds associated with peat and deforestation fires in Indonesia. Journal of Geophysical Research, 116, 1–14. https://doi.org/10.1029/2010JD015148.

    Google Scholar 

  31. Gultepe, I., Pearson, G., Milbrandt, J. A., Hansen, B., Platnick, S., Taylor, P., et al. (2009). The fog remote sensing and modelling field project. Bulletin of the American Meteorological Society, 90(Mar), 341–359. https://doi.org/10.1175/2008bams2354.1.

    Article  Google Scholar 

  32. Ferguson, S. A., Collins, R. L., Ruthford, J., & Fukuda, M. (2003). Vertical distribution of nighttime smoke following a wildland biomass fire in boreal Alaska. Journal of Geophysical Research, 108(D23), 4743. https://doi.org/10.1029/2002JD003324.

    Article  Google Scholar 

  33. Heintzenberg, J. (2003). Physics and chemistry of aerosols. In J. R. Holton, J. A. Curry, & J. A. Pyle (Eds.), Encyclopedia of atmospheric science (pp. 34–40). Amsterdam: Elsevier Science Ltd.

    Chapter  Google Scholar 

  34. Shukla, B. P., & Pal, P. K. (2009). Automatic smoke detection using satellite imagery: Preparatory to smoke detection from Insat-3D. International Journal of Remote Sensing, 30(1), 9–22. https://doi.org/10.1080/01431160802226059.

    Article  Google Scholar 

  35. Wang, J., Ge, C., Yang, Z., Hyer, E. J., Reid, J. S., Chew, B. N., et al. (2013). Mesoscale modeling of smoke transport over the Southeast Asian Maritime Continent: Interplay of sea breeze, trade wind, typhoon, and topography. Atmospheric Research, 122, 486–503. https://doi.org/10.1016/j.atmosres.2012.05.009.

    Article  Google Scholar 

  36. McCue, M. H. (2007). Validation and development of existing and new RAOB-based warm-season convective wind forecasting tools for CAPE CANAVERAL Air Force and Kennedy Space Center. Plymouth: Plymouth State University.

    Google Scholar 

  37. Li, B., & Hou, L. (2015). Discuss on satellite-based particulate matter monitoring technique. In ISPRS—International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol. XL-7/W3, pp. 219–223). https://doi.org/10.5194/isprsarchives-xl-7-w3-219-2015.

  38. Kessner, A. L., Wang, J., Levy, R. C., & Colarco, P. R. (2013). Remote sensing of surface visibility from space : A look at the United States East Coast. Atmospheric Environment, 81, 136–147. https://doi.org/10.1016/j.atmosenv.2013.08.050.

    Article  Google Scholar 

  39. Hyslop, N. P. (2009). Impaired visibility: The air pollution people see. Atmospheric Environment, 43(1), 182–195. https://doi.org/10.1016/j.atmosenv.2008.09.067.

    Article  Google Scholar 

Download references

Acknowledgements

This work as a part of Ph.D. research that was supported by Education and Training Center of Indonesian Meteorological Climatological and Geophysical Agency. We thank to Mr. Andersen Panjaitan as the Chief of Satellite SubDivision of BMKG for providing Himawari_8 satellite data and Mr. Retnadi Heru Jatmiko (UGM) and Mr Alpon Sepriando (BMKG) for valuable discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Muh Aris Marfai.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ismanto, H., Hartono, H. & Marfai, M.A. Smoke detections and visibility estimation using Himawari_8 satellite data over Sumatera and Borneo Island Indonesia. Spat. Inf. Res. 27, 205–216 (2019). https://doi.org/10.1007/s41324-018-0225-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41324-018-0225-8

Keywords

Navigation