Assessment of forest fire effects based on automated processing of Earth remote sensing imager

  • Andrew I. Valasiuk Belgosles, 27 Čyhunačnaja Street, 1 building, Minsk 220089, Belarus
  • Antonina A. Topaz Belarusian State University, 4 Niezaliežnasci Avenue, Minsk 220030, Belarus https://orcid.org/0000-0002-9105-6119
DOI: https://doi.org/10.33581/2521-6740-2022-1-57-70

Abstract

The article presents a study of the automated detection specifics within forest-covered areas traversed by fires based on the different time satellite imagery from the Sentinel-2A and Sentinel-2B using the differential normalised burn ratio index (dNBR) for the pre-fire and post-fire periods the calculation. The studies carried out on the research topic are given and a review of the currently functioning forest fire monitoring systems has been implemented. The urgency of the development and testing of an automated system for assessing the forest fire consequences using open source software and Earth remote sensing data has been substantiated. It has been established that the differential index dNBR, calculated from the Sentinel-2A and Sentinel-2B satellite images captured on different dates makes it possible to effectively detect burned-out areas. It is shown that the Python ecosystem makes it possible to successfully create systems for automated processing of Earth remote sensing data. A prototype of a system for the automated detection of forest-covered areas traversed by fires has been developed, based on the materials of different dates satellite imagery from Sentinel-2A and Sentinel-2B spacecraft. The flowchart of the algorithm of processing Earth remote sensing data using the proposed system was presented. For the Sentinel-2 satellite images for the dates before and after the fire, the differential index dNBR was calculated, the analysis of the results of which showed a close correlation of the dNBR index with the degree of burnout of the territory. A schematic map of the areas affected by the fire has been drawn up and the accuracy of identifying burnedout areas has been assessed by calculating the confusion matrix. An assessment of the effectiveness of the automated system for identifying areas affected by forest fires, ways of its modernisation and improvement, as well as the prospects for implementation in production has been carried out. It is noted that the results of the created system have high reliability indicators. At the same time, the need was revealed to increase the sensitivity of the system when identifying areas that have undergone partial burnout. A variant of improving the algorithms used in the work by introducing the multilevel Otsu’s method, intended to significantly increase the sensitivity of the system, has been proposed.

Author Biographies

Andrew I. Valasiuk, Belgosles, 27 Čyhunačnaja Street, 1 building, Minsk 220089, Belarus

engineer at the department of remote sensing and monitoring

Antonina A. Topaz, Belarusian State University, 4 Niezaliežnasci Avenue, Minsk 220030, Belarus

PhD (geography), docent; associate professor at the department of geodesy and aerospace cartography, faculty of geography and geoinformatics

References

  1. Bariev ER, Zolotoy SA, Kotov SG, Kudryashov AN, Semenov OA. Sovershenstvovanie tekhnicheskikh sredstv povysheniya operativnosti obnaruzheniya prirodnykh pozharov [Improvement of technical means for increasing the efficiency of detecting natural fires]. Minsk: Belarusian State Centre for Accreditation, Ministry of Emergencies of the Republic of Belarus; 2009. 174 p. Russian.
  2. Key CH, Benson N, Ohlen D, Howard S, McKinley R, Zhu Z. The normalized burn ratio and relationships to burn severity: ecology, remote sensing and implementation. In: Greer JD, editor. Rapid delivery of remote sensing products. Proceedings of the Ninth Forest Service remote sensing applications conference; 2002 April 8–12; San Diego, USA [CD-ROM]. Bethesda: American Society for Photogrammetry and Remote Sensing; 2002. 1 CD-ROM: 4 3/4 in.
  3. Tran BN, Tanase MA, Bennett LT, Aponte C. Evaluation of spectral indices for assessing fire severity in Australian temperate forests. Remote Sensing. 2018;10(11):1680. DOI: 10.3390/rs10111680.
  4. French NHF, Kasischke ES, Hall RJ, Murphy KA, Verbyla DL, Hoyet EE, et al. Using Landsat data to assess fire and burn severity in the North American boreal forest region: an overview and summary of results. International Journal of Wildland Fire. 2008;17(4):443–462. DOI: 10.1071/WF08007.
  5. Harris S, Veraverbeke S, Hook S. Evaluating spectral indices for assessing fire severity in chaparral ecosystems (southern California) using MODIS/ASTER (MASTER) airborne simulator data. Remote Sensing. 2011;3(11):2403–2419. DOI: 10.3390/rs3112403.
  6. SERVIR’s Forest Fire Detection and Monitoring System in Nepal [Internet]. SERVIR, 2012 May 10 [cited 2020 February 27]. Available from: https://servirglobal.net/Global/Articles/Article/1143/servirs-forest-fire-detection-and-monitoring-system-in-nepal.
  7. Belyaev AI, Ershov DV, Lupyan EA, Romanyuk BV, Sukhinin AI, Tashchilin SA. [National system for collecting, processing and analysing information on wildfires and its interface with international and regional information networks level]. In: Upravlenie lesnymi pozharami na ekoregional’nom urovne. Materialy Mezhdunarodnogo nauchno-prakticheskogo seminara; 9–12 sentyabrya 2003 g.; Khabarovsk, Rossiya [Materials of the International scientific and practical seminar; 2003 September 9–12; Khabarovsk, Russia]. Moscow: Aleks; 2004. p. 156–166. Russian.
  8. Loupian EA, Bartalev SA, Ershov DV, Kotel’nikov RV, Balashov IV, Burtsev MA, et al. Satellite data processing management in Forest Fires Remote Monitoring Information System (ISDM-Rosleskhoz) of the Federal Agency for Forestry. Current Problems in Remote Sensing of the Earth from Space. 2015;12(5):222–250. Russian.
  9. Fire Information for Resource Management System (FIRMS) [Internet]. 2020 [cited 2020 February 29]. Available from: https://firms.modaps.eosdis.nasa.gov/.
  10. Global Wildfire Information System (GWIS) [Internet]. 2020 [cited 2020 February 29]. Available from: https://gwis.jrc.ec.europa.eu/.
  11. European Forest Fire Information System (EFFIS) [Internet]. 2020 [cited 2020 February 29]. Available from: https://effis.jrc.ec.europa.eu/.
  12. ScanEx Fire Monitoring Service in Russia [Internet]. 2010 July 6 [cited 2021 June 13]. Available from: https://www.scanex.ru/company/smi/scanex-fire-monitoring-service-in-russia1903/.
  13. MODIS сomponents [Internet]. 2020 [cited 2021 September 18]. Available from: https://modis.gsfc.nasa.gov/about/components.php.
  14. OpenStreetMap [Internet]. 2020 [cited 2020 February 28]. Available from: https://www.openstreetmap.org/.
  15. Committee on Land Resources, Geodesy and Cartography under the Council of Ministers of the Republic of Belarus. Nacyjanal’ny atlas Belarusi [National atlas of Belarus]. Mjasnikovich MU, Kazulin AU, Shymaw UM, Pirozhnik II, Cjejrjefman KA, Pashkevich MF, et al., editors. Minsk: Belkartagrafija; 2002. 292 p. Belarusian.
  16. Regulation (EU) No. 377/2014 of the European Parliament and of the Council of 3 April 2014 establishing the Copernicus Programme and repealing Regulation (EU) No. 911/2010 (Text with EEA relevance). Official Journal of the European Union. 2014;L122:44–66.
  17. Sentinel-2 – the operational Copernicus optical high resolution land mission [Internet]. European Space Agency, 2013 [cited 2021 April 16]. Available from: https://esamultimedia.esa.int/docs/S2-Data_Sheet.pdf.
  18. Sentinel-2 [Internet]. 2021 [cited 2021 February 28]. Available from: https://sentinel.esa.int/web/sentinel/missions/sentinel-2.
  19. U. S. Geological Survey. Landsat – Earth observation satellites (version 1.2, April 2020): USGS Fact Sheet 2015-3081 [Internet]. 2020 [cited 2021 June 15]. Available from: https://doi.org/10.3133/fs20153081.
  20. Belarusian Spacecraft (BS) [Internet]. Geoinformation systems, 2021 [cited 2021 February 28]. Available from: https://gis.by/en/tech/bka.
  21. St. Laurent AM. Understanding open source and free software licensing. Sebastopol: O’Reilly Media; 2008. 231 p.
  22. Eo-learn [Internet]. 2020 [cited 2020 February 28]. Available from: https://eo-learn.readthedocs.io/en/latest/.
  23. Index DataBase: a database for remote sensing indices [Internet]. The IDB Project, 2011– [cited 2020 March 21]. Available from: https://www.indexdatabase.de/db/i-single.php?id=53.
  24. Stehman SV. Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment. 1997;62(1):77–89. DOI: 10.1016/S0034-4257(97)00083-7.
  25. ENVI [Internet]. L3Harris Geospatial Solutions, 2020 [cited 2020 March 22]. Available from: https://www.l3harrisgeospatial.com/Software-Technology/ENVI.
  26. Henry F, Herwindiati DE, Mulyono S,Hendryli J. Sugarcane land classification with satellite imagery using logistic regression model. IOP Conference Series: Materials Science and Engineering. 2017;185:012024. DOI: 10.1088/1757-899X/185/1/012024.
  27. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. 1979;9(1):62– 66. DOI: 10.1109/TSMC.1979.4310076.
  28. Ping-Sung Liao, Tse-Sheng Chen, Pau-Choo Chung. A fast algorithm for multilevel thresholding. Journal of Information Science and Engineering. 2001;17(5):713–727.
Published
2022-05-26
Keywords: forest fires, monitoring, Earth remote sensing data, Sentinel-2 satellite, eo-learn Python framework
Supporting Agencies The authors are grateful to Sinergise Laboratory for Geographical Information Systems (Slovenia) for the kindly provided access to the API of Sentinel Hub (https://www.sentinel-hub.com) during the research.
How to Cite
Valasiuk, A. I., & Topaz, A. A. (2022). Assessment of forest fire effects based on automated processing of Earth remote sensing imager. Journal of the Belarusian State University. Geography and Geology, 1, 57-70. https://doi.org/10.33581/2521-6740-2022-1-57-70
Issue
No 1 (2022): Journal of the Belarusian State University. Geography and Geology
Section
Geography

Copyright (c) 2022 Journal of the Belarusian State University. Geography and Geology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

The authors who are published in this journal agree to the following:

  1. The authors retain copyright on the work and provide the journal with the right of first publication of the work on condition of license Creative Commons Attribution-NonCommercial. 4.0 International (CC BY-NC 4.0).
  2. The authors retain the right to enter into certain contractual agreements relating to the non-exclusive distribution of the published version of the work (e.g. post it on the institutional repository, publication in the book), with the reference to its original publication in this journal.
  3. The authors have the right to post their work on the Internet (e.g. on the institutional store or personal website) prior to and during the review process, conducted by the journal, as this may lead to a productive discussion and a large number of references to this work. (See The Effect of Open Access.)