Coal resources are an important guarantee for socioeconomic development, but the exploitation of coal resources is often accompanied by serious damage to the ecological environment. There is therefore an urgent need to monitor the ecological environment of mining areas. In the current monitoring processes, while the thresholding method segments remote sensing index images to extract target features, threshold values are usually determined by empirical judgment or other methods. Such subjectiveness undermines the accuracy of the method and its application for long-term monitoring. To address the issue, we propose a normal distribution probability-based threshold segmentation method. It can greatly reduce the influence of subjective factors and improve the accuracy of feature extraction. This method was tested to Landsat 8 data over the Xiaolongtan mining area, southwest China for determining the threshold values of normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and normalized difference coal mine index (NDCMI) for extracting vegetation, water, and coal respectively. The test result was consistent with our field observation of the mining area. It is concluded that the proposed probability-based threshold segmentation method is practical and effective and can be used for monitoring the ecological environment of mining areas.

thresholding segmentation,NDVI,MNDWI,NDCMI,coal,mining area