Peer-reviewed articles 17,970 +


Title: BURNED AREA PREDICTION USING SMOKE PLUME DETECTION FROM HIGH SPATIAL RESOLUTION IMAGERY
BURNED AREA PREDICTION USING SMOKE PLUME DETECTION FROM HIGH SPATIAL RESOLUTION IMAGERY

PlumX Article Metrics by Elsevier
Julia Ahlen
10.5593/sgem2023/2.1/s08.19
10.5593/sgem2023/2.1
1314-2704
978-619-7603-57-6
English
23
2.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Geoinformatics
The fast-spreading wildfire engulfs the dense parched flora and all obstructions in its way, transforming a woodland into a volatile reservoir of combustible materials. Once ignited, fires can expand at a velocity of up to 23 km/h. As flames spread across vegetation and woodlands, they have the potential to become self-sustaining, propagating sparks and embers that can spawn smaller fires miles away. The proximity of the burning materials to the observer has a direct impact on the density of smoke produced by the fire. This relationship is crucial for fire management teams and emergency responders and helps them assess the severity of a fire, predict its behavior, and make informed decisions regarding evacuation measures, resource allocation, and the protection of affected communities and ecosystems. Drones are valuable tools in the fight against forest fires. They can capture high-resolution imagery, thermal imaging, and video footage, supplying insights into the properties, behavior, and direction of the fire. By employing classical image processing techniques, it is possible to analyze these images and promptly determine the extent of land cover affected.
According to the Swedish Civil Contingencies Agency, more than 25000 ha of forest burned down during the period of 2012-2021, which resulted in severe damage costs. The presence of a reliable and easily accessible smoke detection and assessment tool could significantly reduce the impact of wildfires. This study utilizes low and mid-level image processing techniques to analyze the domain of wildfires, leveraging smoke properties to estimate the extent of land affected by the flames.
[1] Aguilera, R., Corringham, T., Gershunov, A. et al. Wildfire smoke impacts respiratory health more than fine particles from other sources: observational evidence from Southern California. Nat Commun 12, 1493 (2021). https://doi.org/10.1038/s41467-021-21708-0
[2] Kuna, Ch.Varada Rajulu & Nandanwar, Anand & M C, Kiran. (2013). Evaluation of Smoke Density on Combustion of Wood Based Panel Products. International Journal of Materials and Chemistry. 2. 225-228. 10.5923/j.ijmc.20120205.07.
[3] Ba, R.; Chen, C.; Yuan, J.; Song, W.; Lo, S. SmokeNet: Satellite Smoke Scene Detection Using Convolutional Neural Network with Spatial and Channel-Wise Attention. Remote Sens. 2019, 11, 1702. https://doi.org/10.3390/rs11141702
[4] Larsen A, Hanigan I, Reich BJ, Qin Y, Cope M, Morgan G, Rappold AG. A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication. J Expo Sci Environ Epidemiol. 2021 Feb;31(1):170-176. doi: 10.1038/s41370-020-0246-y. Epub 2020 Jul 27. PMID: 32719441; PMCID: PMC7796988.
[5] C. -L. C. Huang and T. Munasinghe, Exploring Various Applicable Techniques to Detect Smoke on the Satellite Images, 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 5703-5705, doi: 10.1109/BigData50022.2020.9378466.
[6] Guo, M.; Li, J.; Wen, L.; Huang, S. Estimation of CO2 Emissions from Wildfires Using OCO-2 Data. Atmosphere 2019, 10, 581. https://doi.org/10.3390/atmos10100581
[7] S. Chaturvedi, P. Khanna, A. Ojha, A survey on vision-based outdoor smoke detection techniques for environmental safety, ISPRS Journal of Photogrammetry and Remote Sensing, 185, 2022, Pp 158-187.
[8] Ahlen, J. Smoke and fog classification in forest monitoring using high spatial resolution images. 22nd International Multidisciplinary Scientific GeoConference SGEM 2022, July, 2022, Albena, Bulgaria, 2022, Vol. 22, s. 131-138
[9] Schweizer, D.; Preisler, H.; Entwistle, M.; Gharibi, H.; Cisneros, R. Using a Statistical Model to Estimate the Effect of Wildland Fire Smoke on Ground Level PM2.5 and Asthma in California, USA. Fire 2023, 6, 159. https://doi.org/10.3390/fire6040159
[10] Tian, Y.; Wu, Z.; Li, M.; Wang, B.; Zhang, X. Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images. Remote Sens. 2022, 14, 4431. https://doi.org/10.3390/rs14184431
[11] Gao, Y.; Cheng, P. Forest fire smoke detection based on visual smoke root and diffusion model. Fire Technol. 2019, 55, 1801–1826. [Google Scholar]
[12] Liu, Y. et al. (2022). Smoke Plume Dynamics. In: Peterson, D.L., McCaffrey, S.M., Patel-Weynand, T. (eds) Wildland Fire Smoke in the United States. Springer, Cham. https://doi.org/10.1007/978-3-030-87045-4_4
This work is funded by Geospatial Information Science Group at the University of Gavle, Sweden. The imagery is provided by Specialist manager Prevention/Fire engineer at Vastervik’s municipality. The unit for emergency services and community protection.
conference
https://doi.org/10.5593/sgem2023/2.1/s08.19
Download PDF
Proceedings of 23rd International Multidisciplinary Scientific GeoConference SGEM 2023
23rd International Multidisciplinary Scientific GeoConference SGEM 2023, 03 - 09 July, 2023
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.
145-152
03 - 09 July, 2023
website
9099
smoke, detection, drone images, burned area calculation