Везде

RAS PresidiumИсследование Земли из космоса Earth Research from Space

  • ISSN (Print) 0205-9614
  • ISSN (Online) 3034-5405

Classification of Sources of Climate-Active Gas Emissions Based on Satellite Data Using Machine Learning Methods

You can
PII
S3034540525060016-1
DOI
10.7868/S3034540525060016
Publication type
Article
Status
Published
Authors
V.G. Bondur
  • Affiliation: AEROCOSMOS Research Institute for Aerospace Monitoring
N.V. Feoktistova
  • Affiliation: AEROCOSMOS Research Institute for Aerospace Monitoring
O.S. Voronova
  • Affiliation: AEROCOSMOS Research Institute for Aerospace Monitoring
Volume/ Edition
Volume / Issue number 6
Pages
3-19
Abstract
Using machine learning methods based on attribute data from satellite information products, a classification of thermal anomalies associated with wildfires and gas flares in Eastern Siberia and the Far East was conducted. Based on the analysis of seven different machine learning models, three models with the best performance metrics were selected (Random Forest, Extreme Gradient Boosting, and Categorical Boosting). Introduction of additional features such as meteorological data and spatiotemporal characteristics of thermal anomalies improved the training performance of the selected models by 12–25% for MODIS satellite data and by 12–21% for VIIRS satellite data. Ensemble modeling approaches were used to improve classification performance. The best two ensemble models were tested on new data. The model for MODIS data correctly classified 88.6% of wildfires and 86.4% of gas flares, while the model for VIIRS data correctly classified 97.6% of wildfires and 97.2% of gas flares. The obtained results allow for the exclusion of thermal anomalies caused by gas flares, which are falsely identified as wildfires when analyzing fire activity, and to improve the accuracy of emission estimates for climate-active gases and aerosols associated with fires.
Keywords
природные пожары спутниковые данные космический мониторинг пожарные эмиссии газовые факелы машинное обучение
Date of publication
21.03.2026
Year of publication
2026
Number of purchasers
0
Views
5

References

  1. 1. Алсынбаев К.С., Брыксин В.М., Евтюшкин А.В., Ерохин Г.Н., Козлов А.В. Оценка мощности факельных установок по сжиганию попутного нефтяного газа на основе обработки космоснимков MODIS // Вестник Балтийского федерального университета им. И. Канта. 2013. Вып. 10. С. 131–137.
  2. 2. Alsynbaev K.S., Bryksin V.M., Evtyushkin A.V., Erokhin G.N., Kozlov A.V. Estimation of the capacity of flare units for burning associated petroleum gas based on the processing of MODIS space images // Bulletin of the Immanuel Kant Baltic Federal University. 2013. Issue 10. P. 131–137. (in Russian)
  3. 3. Бондур В.Г. Космический мониторинг эмиссий малых газовых компонент и аэрозолей при природных пожарах в России // Исследования Земли из космоса. 2015. № 6. С. 21–35. https://doi.org/10.1134/S0001433816090103.
  4. 4. Bondur V.G. Satellite monitoring of trace gas and aerosol emissions during wildfires in Russia // Izvestiya, Atmospheric and Oceanic Physics. 2016. V. 52. № 9. P. 1078–1090. https://doi.org/10.1134/S0001433816090103.
  5. 5. Бондур В.Г., Гинзбург А.С. Эмиссия углеродсодержащих газов и аэрозолей от природных пожаров на территории России по данным космического мониторинга // Доклады академии наук. 2016. Т. 466. № 4. С. 473–477. https://doi.org/10.7868/S0869565216040186
  6. 6. Bondur V.G., Ginzburg A.S. Emission of Carbon-Bearing Gases and Aerosols from Natural Fires on the Territory of Russia Based on Space Monitoring // Doklady Earth Sciences. 2016. V. 466. № 2. P. 148–152. https://doi.org/ 10.1134/S1028334X16020045
  7. 7. Бондур В.Г., Гордо К.А., Кладов В.Л. Пространственно-временные распределения площадей природных пожаров и эмиссий углеродсодержащих газов и аэрозолей на территории Северной Евразии по данным космического мониторинга // Исследование Земли из космоса. 2016. № 6. С. 3–20. https://doi.org/10.7868/S0205961416060105
  8. 8. Bondur V.G., Gordo K.A., Kladov V.L. Spacetime distributions of wildfire areas and emissions of carbon-containing gases and aerosols in northern Eurasia according to satellite-monitoring data // Izvestiya, Atmospheric and Oceanic Physics, 2017, V. 53. № 9. P. 859–874. https://doi.org/10.1134/S0001433817090055.
  9. 9. Бондур В.Г., Гордо К.А. Космический мониторинг площадей, пройденных огнем, и объемов эмиссий вредных примесей при лесных и других природных пожарах на территории Российской Федерации // Исследование Земли из космоса. 2018. № 3. С. 41–55. https://doi.org/10.7868/S020596141803003X
  10. 10. Bondur V.G., Gordo K.A. Satellite monitoring of burnt-out areas and emissions of harmful contaminants due to forest and other wildfires in Russia // Izvestiya, Atmospheric and Oceanic Physics, 2018. V. 54. № 9. P. 955–965. https://doi.org/10.1134/S0001433818090104
  11. 11. Бондур В.Г., Зима А.Л., Феоктистова Н.В. Долговременный спутниковый мониторинг различных типов природных пожаров и эмиссий климатически активных газов и аэрозолей от них на территории России и ее крупных регионов / Исследование Земли из космоса. 2024. № 5. C. 19–34. https://doi.org/10.31857/S0205961424050021
  12. 12. Bondur V.G., Zima A.L., Feoktistova N.V. Long-Term Satellite Monitoring of Various Types of Wildfires and Wildfire-Induced Emissions of Climate-Active Gases and Aerosols in Russia and in Its Large Regions // Izvestiya, Atmospheric and Oceanic Physics, 2024. V. 60. № 12. P. 1494–1506. https://doi.org/10.1134/S0001433825700161.
  13. 13. Вивчар А.В., Моисеенко К.Б., Панкратова Н.В. Оценки эмиссий оксида углерода от природных пожаров в Северной Евразии в приложении к задачам атмосферного переноса и климата // Известия РАН. Физика атмосферы и океана. 2010. Т. 46. № 3. С. 307–320.
  14. 14. Vivchar A.V., Moiseenko K.B., Pankratova N.V. Estimates of carbon monoxide emissions from wildfires in northern Eurasia for airquality assessment and climate modeling// Izvestiya, Atmospheric and Oceanic Physics, 2010, V. 46 P. 281–293. https://doi.org/10.1134/S0001433810030023
  15. 15. Горячкин Б.С., Чечнев А.А. Анализ чувствительности метрик бинарной классификации к дисбалансу данных // E-Scio. Научный электронный журнал. 2021. № 4(55). С. 23–34.
  16. 16. Goryachkin B.S., Chechnev A.A. Analysis of sensitivity of binary classification metrics to data imbalance // E-Scio. Scientific Electronic Journal. 2021. № 4(55). P. 23–34. (in Russian)
  17. 17. Протопопова В.В., Габышева Л.П. Пирологическая характеристика растительности в лесах Центральной Якутии и ее динамика в постпожарный период. Природные ресурсы Арктики и Субарктики, 25, 3, 2018, С. 80–86. https://doi.org/10.31242/2618-9712-2018-25-3-80-86
  18. 18. Protopopova V.V., Gabysheva L.P. Pyrological characteristic of vegetation in forests of Central Yakutia and its dynamics in post-fire period // Arctic and Subarctic natural resources. 2018. V 25. № 3. P. 80–86. https://doi.org/10.31242/2618-9712-2018-25-3-80-86. (in Russian)
  19. 19. Попов Н.В., Говор И.Л., Гитарский М.Л. Эмиссия парниковых газов от сжигания попутного нефтяного газа в России // Метеорология и гидрология. 2021. № 5. С. 54–61. https://doi.org/10.52002/0130-2906-2021-5-54-61.
  20. 20. Popov N.V., Govor I.L., Gitarskii M.L. Greenhouse Gas Emission from Combustion of Associated Petroleum Gas in Russia // Russ. Meteorol. Hydrol. 2021. V. 46, P. 325–330. https://doi.org/10.3103/S1068373921050071
  21. 21. Слепцова М.И. Роль углеводородов в Якутии // Физико-технические проблемы добычи, транспорта и переработки органического сырья в условиях холодного климата. 2024, №1. С. 103–107. https://doi.org/10.24412/cl-37255-2024-1-103-107
  22. 22. Sleptsova M.I. The role of hydrocarbons in Yakutia // Physical and technical problems of extraction, transport and processing of organic raw materials in cold climate conditions. 2024, № 1. P. 103–107. https://doi.org/10.24412/cl-37255-2024-1-103-107 (in Russian)
  23. 23. Тимофеева С.С., Гармышев В.В. Экологические последствия лесных пожаров на территории Иркутской области // Экология и промышленность России. 2017. Т. 21. № 3. С. 46–49.
  24. 24. Timofeeva S.S., Garmyshev V.V. Ecological consequences of forest fires in the Irkutsk region // Ecology and industry of Russia. 2017. V. 21. № 3. P. 46–49. (in Russian)
  25. 25. Третий оценочный доклад об изменениях климата и их последствиях на территории Российской Федерации // Под ред. В.М. Катцова (Росгидромет). СПб.: Наукоемкие технологии, 2022. 676 с.
  26. 26. Third Assessment Report on Climate Change and its Consequences in the Russian Federation // Ed. by V.M. Kattsov (Roshydromet). St. Petersburg: Science-Intensive Technologies, 2022. 676 p. (in Russian)
  27. 27. Ahmad F., Waseem Z., Ahmad M., and Ansari M.Z. Forest Fire Prediction Using Machine Learning Techniques // 2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON), New Delhi, India, 2023. P. 705–708. https://doi.org/10.1109/REEDCON57544.2023.10150867.
  28. 28. Alexandropoulos S.-A.N., Aridas C.K., Kotsiantis S.B., Vrahatis M.N. (2019). Stacking Strong Ensembles of Classifiers. In: MacIntyre J., Maglogiannis I., Iliadis L., Pimenidis E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2019. IFIP Advances in Information and Communication Technology, vol. 559. Springer, Cham. https://doi.org/10.1007/978-3-030-19823-7_46
  29. 29. Bhushan T. A Detailed Review on Decision Tree and Random Forest. Bioscience Biotechnology Research Communications. 2020. V. 13. № 14. P. 245–248. https://doi.org/10.21786/bbrc/13.14/57.
  30. 30. Briem G.J., Benediktsson J.A., Sveinsson J.R. Multiple Classifiers Applied to Multisource Remote Sensing Data. // IEEE Trans. Geosci. Remote Sens. 2002. P. 2291–2299. https://doi.org/10.1109/TGRS.2002.802476
  31. 31. Bondur V.G., Gordo K.A., Voronova O.S., Zima A.L., Feoktistova N.V. Intense Wildfires in Russia over a 22-Year Period According to Satellite Data. Fire 2023, V. 6, № 3. P. 14. https://doi.org/10.3390/fire6030099
  32. 32. Bot K., Borges J.G. A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support. Inventions. 2022. V. 7. № 1. P. 30. https://doi.org/10.3390/inventions7010015
  33. 33. Elvidge C.D., Bazilian M.D., Zhizhin M., Ghosh T., Baugh K.E., Hsu F.-C. The potential role of natural gas flaring in meeting greenhouse gas mitigation targets // Energy Strategy Reviews. 2018. V. 20. P. 156–162. https://doi.org/10.1016/j.esr.2017.12.012
  34. 34. Ester M., Kriegel H.-P., Sander J., Xu X., Simoudis E., Han J., Fayyad U.M. A density-based algorithm for discovering clusters in large spatial databases with noise // Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. AAAI Press, 1996. P. 226–231. CiteSeerX https://doi.org/10.1.1.121.9220. ISBN 1-57735-004-9.
  35. 35. Ferreira A., Figueiredo M. M.A.T. (2012). Boosting Algorithms: A Review of Methods, Theory, and Applications. In: Zhang, C., Ma, Y. (eds) Ensemble Machine Learning. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9326-7_2
  36. 36. Ganaie M.A., Hu M., Malik A.K., Tanveer M., Suganthan P.N. Ensemble deep learning: a review // Eng. Appl. Artif. Intell. 2022. V. 115. P. 47. 105151. https://doi.org/10.1016/j.engappai.2022.105151
  37. 37. Giglio L., Schroeder W., Justice C.O. The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 2016. P. 31–41. ISSN 0034-4257, https://doi.org/10.1016/j.rse.2016.02.054.
  38. 38. Giglio L. VIIRS/JPSS1 Active Fires 6-Min L2 Swath 375m V002. 2024, distributed by NASA EOSDIS Land Processes Distributed Active Center. https://doi.org/10.5067/VIIRS/VJ114IMG.002
  39. 39. IPCC, 2022: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA. 3056 p. https://doi.org/10.1017/9781009325844.
  40. 40. Kazanskiy N., Khabibullin R., Nikonorov A., Khonina S. A Comprehensive Review of Remote Sensing and Artificial Intelligence Integration: Advances, Applications, and Challenges. Sensors. 2025; V. 25. № 19. P. 42. 5965. https://doi.org/10.3390/s25195965
  41. 41. Lappalainen H., Petäjä T., Kujansuu J., Kerminen V., Skorokhod A., Kasimov N., Bondur V. et al. Pan Eurasian Experiment (PEEX) – a research initiative meeting the grand challenges of the changing environment of the northern pan-eurasian arctic- boreal areas // Geography. Environment. Sustainability. 2014. № 2(7). P. 13–48. https://doi.org/10.24057/2071-9388-2014-7-2-13-48
  42. 42. Li F., Zhang X., Kondragunta S., Csiszar I. Comparison of fire radiative power estimates from VIIRS and MODIS observations. Journal of Geophysical Research: Atmospheres, 2018, V. 123. № 9. P. 4545–4563. https://doi.org/10.1029/2017JD027823
  43. 43. Li R., Tao M., Zhang M., Chen L., Wang L., Wang Y. et al. Application potential of satellite thermal anomaly products in updating industrial emission inventory of China. Geophysical Research Letters, 2021, V. 48. № 8. e2021GL092997. https://doi.org/10.1029/2021GL092997
  44. 44. Likas A., Vlassis N., Verbeek J.J. The global k-means clustering algorithm. Pattern Recognit. 2003, V. 36. № 2. P. 451–461. https://doi.org/10.1016/S0031-3203 (02)00060-2
  45. 45. Muñoz S.J. ERA5-Land monthly averaged data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 2019. https://doi.org/10.24381/cds.68d2bb30
  46. 46. Nikolaychuk O., Pestova J., Yurin A. Wildfire Susceptibility Mapping in Baikal Natural Territory Using Random Forest. Forests 2024, V. 15. № 1. P. 27. 170. https://doi.org/10.3390/f15010170
  47. 47. Pérez-Porras F.-J., Triviño-Tarradas P., Cima-Rodríguez C., Meroño-de-Larriva J.-E., García-Ferrer A., Mesas-Carrascosa F.-J. Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires. Sensors 2021, V. 21. № 11. P. 19. 3694. https://doi.org/10.3390/s21113694
  48. 48. Ponomarev E.I., Zabrodin A.N., Shvetsov E.G., Ponomareva T.V. Wildfire Intensity and Fire Emissions in Siberia. Fire 2023, V. 6. № 7. P. 15. 246. https://doi.org/10.3390/fire6070246
  49. 49. Rodell M., Houser P.R., Jambor U., Gottschalck J., Mitchell K., Meng C.-J., Arsenault K., Cosgrove B., Radakovich J., Bosilovich M., Entin J.K., Walker J.P., Lohmann D., Toll D. The Global Land Data Assimilation System. Bulletin of the American Meteorological Society. 2004. V. 85. № 3. P. 381–394. https://doi.org/10.1175/BAMS-85-3381
  50. 50. Sarkar M.S., Majhi B.K., Pathak B., Biswas T., Mahapatra S., Kumar D., Bhatt I.D., Kuniyal J.C., Nautiyal S. Ensembling machine learning models to identify forest fire-susceptible zones in Northeast India // Ecological Informatics, Volume 81, July 2024, 102598. P. 16. https://doi.org/1016/j.ecoinf.2024.102598
  51. 51. Shirazi Z., Wang L., Bondur V.G. Modeling Conditions Appropriate for Wildfire in South East China – A Machine Learning Approach. Front. Earth Sci. V. 9. 622307. 2021. https://doi.org/10.3389/feart.2021.622307
  52. 52. Zhizhin M., Matveev A., Ghosh T., Hsu F.-C., Howells M., Elvidge C. Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire. Remote Sensing. 2021; V. 13. № 16. Р. 30. 3078. https://doi.org/10.3390/rs13163078
QR
Translate

Indexing

Scopus

Scopus

Scopus

Crossref

Scopus

Higher Attestation Commission

At the Ministry of Education and Science of the Russian Federation

Scopus

Scientific Electronic Library