Remote Sensing Extraction Method of Tailings Ponds in Ultra-Low-Grade Iron Mining Area Based on Spectral Characteristics and Texture Entropy

With the rapid development of the steel and iron industry, ultra-low-grade iron ore has been developed extensively since the beginning of this century in China. Due to the high concentration ratio of the iron ore, a large amount of tailings was produced and many tailings ponds were established in the mining area. This poses a great threat to regional safety and the environment because of dam breaks and metal pollution. The spatial distribution is the basic information for monitoring the status of tailings ponds. Taking Changhe Mining Area as an example, tailings ponds were extracted by using Landsat 8 OLI images based on both spectral and texture characteristics. Firstly, ultra-low-grade iron-related objects (i.e., tailings and iron ore) were extracted by the Ultra-low-grade Iron-related Objects Index (ULIOI) with a threshold. Secondly, the tailings pond was distinguished from the stope due to their entropy difference in the panchromatic image at a 7 × 7 window size. This remote sensing method could be beneficial to safety and environmental management in the mining area