  Remotely sensing the spatial distribution of exposed coal (EC) is significant
for understanding the footprints of mining activities. However, widely
applicable methods for the identification of EC surfaces remain inadequate
because the choices of recent methods confront the diverse EC types and
backgrounds. Therefore, this study proposed a new Automated Coal Mapping Index
(ACMI) which was empirically formulated by an iterative process of identifying
parameters that maximize the separability of EC and non-EC surfaces. The
performance of ACMI was tested in six study areas worldwide with different
landscape types and coal types. Based on the visual inspection, ACMI was more
effective in highlighting EC surfaces and suppressing non-EC surfaces than the
existing methods. Compared with the sample points obtained through direct
interpretation, ACMI obtained better EC mapping results than previous methods
with the F1 score and overall accuracy (OA) no less than 0.91 and 93.20% across
all the selected Landsat images of the study areas, respectively. In addition,
ACMI was demonstrated to have a stable optimal threshold and 0 can serve as its
default threshold. The default threshold makes EC mapping using ACMI an
automated process. The new index has the potential to support a variety of
mining-activity-related studies, such as the identification of mining
disturbances and illegal mining detection at multi-spatial-temporal scales.
