Calculation of Fireline Intensity Using Remote Sensing and Geographic Information Systems: 2021 Milas-Karacahisar Fire
Öz
Aim of the study: The objective of this study is to calculate fireline intensity using remote sensing and geographic information systems, to investigate relationship between fireline intensity and VIIRS active fire data, and to develop a practical fireline intensity estimation model.
Material and methods: The Visible Infrared Imaging Radiometer Suite (VIIRS) active fire/hotspot data provided by Suomi National Polar Orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration (NOAA-20) satellites were used to estimate the rate of fire spread. Fuel consumption was estimated using Sentinel-2 images, stand type maps and surface and available crown fuel loading models for Turkish red pine (Pinus brutia Ten.). The fireline intensity was then calculated using Byram’s (1959) fireline intensity equation.
Main results: The results indicated that the number of VIIRS active fire data was well correlated with fireline intensity, rate of fire spread and fuel consumption. The calculated fireline intensity ranged between 175.0 and 47597.2 kW/m with an average value of 9280.4 kW/m. The number of VIIRS active fire data alone explained 72% of the variation in fireline intensity.
Highlights: Satellite based products can be effectively used to calculate fireline intensity through estimating rate of fire spread and fuel consumption easily and effectively in burned areas.
Anahtar Kelimeler
Forest fires, VIIRS, Sentinel-2, rate of spread, fuel consumption, fireline intensity
Kaynakça
- Alexander, M.E. (1982). Calculating and Interpreting Forest Fire Intensities. Canadian Journal of Botany-Revue Canadienne De Botanique, 60(4), 349-357.
- Alexander, M.E., & Cruz, M.G. (2018). Fireline Intensity. In S. L. Manzello (Ed.), Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, Springer International Publishing, 1-8.
- Andela, N., van der Werf, G. R., Kaiser, J. W., van Leeuwen, T. T., Wooster, M. J., & Lehmann, C. E. R. (2016). Biomass burning fuel consumption dynamics in the tropics and subtropics assessed from satellite. Biogeosciences, 13(12), 3717-3734.
- Baysal, I. (2021). Vertical Crown Fuel Distributions in Natural Calabrian Pine (Pinus brutia Ten.) Stands. Croatian Journal of Forest Engineering, 42(13), 301-312.
- Baysal, İ., Yurtgan, M., Küçük, Ö., & Öztürk, N. (2019). Estimation of Crown Fuel Load of Suppressed Trees in Non-treated Young Calabrian Pine (Pinus brutia Ten.) Plantation Areas. Kastamonu University Journal of Forestry Faculty, 19(3), 350-359.
- Bradstock, R.A., & Auld, T.D. (1995). Soil Temperatures During Experimental Bushfires in Relation to Fire Intensity: Consequences for Legume Germination and Fire Management in South-Eastern Australia. Journal of Applied Ecology, 32(1), 76-84.
- Byram, G.M. (1959). Combustion of forest fuels. In K. P. Davis (Ed.), Forest Fire: Control and Use, MCGraw-Hill, 61-89.
- Chen, J., Li, R., Tao, M., Wang, L.T., Lin, C., Wang, J., . . . Chen, L. (2022). Overview of the performance of satellite fire products in China: Uncertainties and challenges. Atmospheric Environment, 268, 118838.
- Cole, F.V., & Alexander, M.E. (1995). Head fire intensity class graph for FBP System Fuel Type C-2 (Boreal Spruce). Alaska Department of Natural Resources, Division of Forestry, Fairbanks, AK and Natural Resources Canada, Canadian Forest Service, Edmonton, AB.
- Coskuner, K.A. (2022a). Assessing the performance of MODIS and VIIRS active fire products in the monitoring of wildfires: a case study in Turkey. iForest - Biogeosciences and Forestry, 15(2), 85-94.