DMSP-OLS night-light images have provided an effective way to monitor cities on a global scale. These images can separate urban areas and other human activities from the background without light with correct spatial measurement. However, due to saturation and blooming problems, the ability of these images to describe urban features is limited, especially in urban cores. Therefore, in this research, a new index, ILNANLI, has been developed and presented by integrating LST and NDVI for the adjusted night-light index. This index has two general goals: reducing the effects of saturation in the city centres, increasing the variety of brightness pixel values of night-light images, and reducing the blooming in the surrounding areas. The ILNANLI has been developed in such a way that it has put the indicators and relationships together, taking into account the characteristics of different regions of the city (such as buildings, vegetation, and soil) in such a way that in every urban environment (temperate and dry and semi-desert) to be successful. Another feature is its easy implementation and high success. In fact, the prominent feature of this index, which distinguishes it from the rest of the indices, is that it has been able to work successfully in arid and semi-desert areas in addition to temperate regions. The evaluation results showed that the average overall accuracy of ILNANLI in mapping and extracting built-up areas with automatic thresholds in cities with different climates has effectively increased by 8% compared to other indexes. Also, the examination of transects shows that the proposed index is more effective than the other four indexes in increasing the diversity of values and reducing blooming. Therefore, as a favourable option with easy implementation, the proposed index can be used to reduce the effect of saturation and blooming and study the urban structure at local scales.
