With the development of optoelectronic technology, low-light-level (LLL) imaging technology and its application have recently become a research focus.In LLL remote sensing images,it is difficult to completely separate forest fires from industrial combustion and urban heat sources based on radiance or temperature alone. Meanwhile,because of frequent data saturation in the low-light band, the existing fire detection products only provide detection information. To identify forest fires among various heterogeneous heat sources with high brightness and further improve the pixel-level characterization of fire detection products, a new spectral index, the enhanced noctilucent fire disturbance index (ENFDI),is proposed based on the principle that surface temperature decreases with the increasing vegetation density and the fact that vegetation and urban surfaces are negatively correlated. According to the results, ENFDI enhances the differences in spectral characteristics between forest fires and city lights and improves the ability of forest fire identification under low-light conditions. The ENFDI of forest fires is greater than1.073, which is considerably higher than that of urban lighting areas (0.840–1.073). This index can also effectively reduce the impact of easy saturation of night-time light (NTL). The correlation (R) between ENFDI and the mid-and far-infrared brightness temperature difference is as high as 0.94–0.97, which is considerably higher than that of NTL (0.82–0.83). Moreover, ENFDI is relatively stable and is not affected by lunar phases; forest fires at night are identified with or without moonlight. The forest fires identified using the ENFDI show a good overall correspondence with the NPP/VIIRS active fire product (VNP14IMG), with a positional tolerance of within 628 m. Therefore, ENFDI is sensitive, stable and accurate for identifying forest fires. Further, it may serve as a feasible reference for achieving further pixel-level characterization of fires.