Automated Seasonal Separation of Mine and Non Mine Water Bodies From Landsat 8 OLI/TIRS Using Clay Mineral and Iron Oxide Ratio

Abstract

Opencast mining has huge effects on water pollution for several reasons. Fresh water is heavily used to process ore. Mine effluent and seepage from various mine related areas especially tailing reservoir, increase water pollution immensely. Monitoring and classification of mine water bodies, which have such environmental impacts, have several research challenges. In the past, land cover classification of a mining region detects mine and non mine water bodies simultaneously. Water bodies inside surface mines have different characteristics from other water bodies. In this paper, a novel method has been proposed to differentiate mine and non mine water bodies over the seasons, which does not require to set a threshold value manually. Here, water body regions are detected over the entire scene by any classical water body detection algorithm. Further, each water body is treated independently, and reflectance properties of a bounding box over each water body region are analyzed. In the past, there were efforts to use clay mineral ratio (CLM) to separate mine and non mine water bodies. In this paper, it has been observed that iron oxide ratio (IO) can also separate mine and non mine water bodies. The accuracy is observed to increase, if the difference of CLM and IO is used for segregation. The proposed algorithm separates these regions by taking into account seasonal variations. Means of differences of CLM and IO of each bounding box have been clustered using K-means clustering algorithm. The automation provides precision and recall for mine, and non mine water bodies as [77.83%, 76.55%] and [75.18%, 75.84%], respectively, using ground truths from high-definition Google Earth images.