Forest Fire Disaster Risk Analysis using Sentinel 2 and Landsat Images Case Study: Al-Qoubaiyat and Tyre regions, Lebanon

Year 2022, Volume: 3 Issue: 2, 84 - 94, 30.12.2022

Mohamed Issa , Mohammad Abboud

https://doi.org/10.48053/turkgeo.1177843

Abstract

Fires are considered a threat to the world with all its components and sectors. Recently, it is noticeable an increase in these fires that hit many countries, especially in Lebanon which is considered a country, rich in forests. A forest fire can be naturally caused by either global warming or high temperature. On the other hand, it may be caused by man-made via factories and glass waste. Fires cause great damage to the environment and may lead to human death. Unfortunately, the fire that broke out in AL-Qoubaiyat and Tyre in Lebanon, have been witnessed and caused great damage to the environment, human losses, etc. In this study, a study of fire risk management for those two study areas, will be analyzed using two types of data (Landsat-8 and Sentinel-2) for AL-Qoubaiyat case study, whereas it will be between (Landsat-7 and Sentinel-2) for the Tyre case study. The Analysis will be done by using the Normalized Burn Ration (NBR), Differenced Normalized Burn Ration (NBR) along with all type of required atmospheric corrections. According to our study, it was found advisable to monitor fire risk management using Sentinel-2 L2A data since the atmospheric correction is already performed on it but for L1C data the Sen2Cor python must be used to apply atmospheric correction. Furthermore, the Sentinel-2 L2A data analysis gave more precise results than Landsat-8 by about 2% in Sour case study and 5.7 % in AL-Qoubaiyat case study. Hoping that this method will help in tracking fires, disaster risk reduction, and help in classifying burn severity accompanied with calculating the area corresponding to each class.

Keywords

GIS, DEM, DRR, Forest Fire, Satellite Remote Sensing, Lebanon, NBR, Burn Index

References

• Ali, E. (2020). Geographic Information System (GIS): Definition, Development, Applications & Components. Department of Geography, Ananda Chandra College. India.
• Rutkay, A., Kalkan, K., & Gürsoy, Ö. (2020). Determining the forest fire risk with sentinel 2 images. Turkish Journal of Geosciences, 1(1), 22-26.
• Haddad, E.A., Farajalla, N., Camargo, M., Lopes, R.L., & Vieira, F.V. (2014). Climate change in Lebanon: Higher-order regional impacts from agriculture. Region, 1(1), 9-24. Chu, T., & Guo, X. (2013). Remote sensing techniques in monitoring post-fire effects and patterns of forest recovery in boreal forest regions: A review. Remote Sensing, 6(1), 470-520.
• Navarro, G., Caballero, I., Silva, G., Parra, P.C., Vázquez, Á., & Caldeira, R. (2017). Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. International Journal of Applied Earth Observation and Geoinformation, 58, 97-106.
• Ryu, J.H., Han, K.S., Hong, S., Park, N.W., Lee, Y.W., & Cho, J. (2018). Satellite-based evaluation of the post-fire recovery process from the worst forest fire case in South Korea. Remote Sensing, 10(6), 918.
• Jovanović, D., Govedarica, M., Sabo, F., Bugarinović, Ž., Novović, O., Beker, T., & Lauter, M. (2015). Land cover change detection by using remote sensing: A case study of Zlatibor (Serbia). Geographica Pannonica, 19(4), 162-173.
• Keeley, J.E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested usage. International journal of wildland fire, 18(1), 116-126.
• Korhonen, L., Packalen, P., & Rautiainen, M. (2017). Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote sensing of environment, 195, 259-274.
• Mueller-Wilm, U., Devignot, O., & Pessiot, L. (2016). Sen2Cor configuration and user manual. Telespazio VEGA Deutschland GmbH: Darmstadt, Germany.
• Rozario, P.F., Madurapperuma, B.D., & Wang, Y. (2018). Remote sensing approach to detect burn severity risk zones in Palo Verde National Park, Costa Rica. Remote Sensing, 10(9), 1427.
• Sabuncu, A., & Ozener, H. (2018). Evaluating and Comparing NDVI and NBR Indices Performance for Burned Areas in Terms of PBIA and OBIA in Aegean Region Turkey. FIG Congress, 2018
• Walz, Y., Maier, S.W., Dech, S.W., Conrad, C., & Colditz, R.R. (2007). Classification of burn severity using Moderate Resolution Imaging Spectroradiometer (MODIS): A case study in the jarrah‐marri forest of southwest Western Australia. Journal of Geophysical Research: Biogeosciences, 112(G2).