Evaluating spectral indices for estimating burned areas in the case of Izmir / Turkey

Authors : Birsen KESGİN ATAK, Ebru ERSOY TONYALOĞLU

Pages : 49-59

Doi:10.31195/ejejfs.657253

View : 44 | Download : 10

Publication Date : 2020-03-06

Article Type : Research Paper

Abstract :Mapping and determination of fire damaged areas in an accurate and prompt way is essential for identifying environmental losses caused by fires, post-fire management activities and planning strategies. In this context, this study aims to evaluate the performance spectral indices for discriminating burned and unburned areas in the immediate post-fire environment in the case of Gaziemir, Buca and Karabağlar districts of Izmir metropolitan city where one of the forest fires occurred in the 18rd August 2019. For this, whilst a Sentinel 2A insert ignore into journalissuearticles values(26th August 2019); was used to map burned / unburned areas as the reference dataset, two Landsat 8 satellite images insert ignore into journalissuearticles values(7th and 28th August 2019); were used for the calculation of spectral indices. The spectral indices of normalised difference vegetation index insert ignore into journalissuearticles values(NDVI);, atmospherically resistant vegetation index insert ignore into journalissuearticles values(ARVI);, two versions of normalised burn ratio insert ignore into journalissuearticles values(NBR and NBR2); and burnt area index insert ignore into journalissuearticles values(BAI); were calculated for the selected two dates as well as pre-fire and post-fire temporal differences in those indices. For the performance comparison of spectral indices, binary maps of burned and unburned areas were created and separability index insert ignore into journalissuearticles values(SI); was calculated for pre/post-fire differenced spectral indices. Our results suggest that NBR2, NDVI and ARVI had the highest potential for discriminating burned areas, respectively. Even though the value of separability indices was different from each other where NBR and BAI had the lowest values, that doesn’t necessarily mean these indices cannot discriminate burned areas, since the separation of burned and unburned areas highly depend on spatio-temporal circumstances like vegetation types and time lags between image acquisition dates.
Keywords : Remote sensing, spectral indices, burned area mapping, Landsat 8, Sentinel 2A

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