Utilization of hyperspectral remote sensing imagery for improving burnt area mapping accuracy

Abstract

Wildfires pose a direct threat when occurring close to populated areas. Additionally, their significant carbon and climate feedbacks represent an indirect threat on a global, long-term scale. Monitoring and analyzing wildfires is therefore a crucial task to increase the understanding of interconnections between fire and ecosystems, in order to improve wildfire management activities. This study investigates the suitability of 232 different red / near-infrared band combinations based on hyperspectral imagery of the DESIS sensor with regard to burnt area detection accuracy. It is shown that the selection of wavelengths greatly influences the detection quality, and that especially the utilization of lower near-infrared wavelengths increases the yielded accuracy. For burnt area analysis based on the Normalized Difference Vegetation Index (NDVI), the optimal wavelength range has been found to be 660 - 670 nm and 810 - 835 nm for the red band and near-infrared band, respectively.