# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 4 Results and Discussion
### Comparison with Other Methods  
To assess the applicability of the BCI, this study selected regions (Figure 10) with varying natural background conditions and levels of human activity in the Inner Mongolia Autonomous Region of China. The study conducted a comparative analysis of the bare coal identification capabilities of the BCI and existing indices NDCI (All NDCI results presented were derived from the combination of the SWIR1 band and the NDCI) [29], OMCI [30], and ECI [31]. The basic information for each region is shown in Table 3.  
Figure 10: R1–R8 location distribution. Figure 9: Application of BCI in other regions. (**A1**): Manglai coal mine. (**A2**): Haerwusu coal mine. (**A3**): Haolailiang coal mine and Hengbo coal mine. (**A4**): Zhanihe coal mine. (**A5**): Yimin coal mine. (**A6**): Huoshatu coal mine. In this paper, various bare coal detection methods were initially applied to obtain calculation results for four regions with better natural background conditions. To facilitate comparability of the results calculated by different indices, all results were normalized, as shown in Figure 11. It is evident that the BCI accurately distinguishes bare coal from other land covers across diverse complex environments. The detection results of the BCI appear finer and smoother in regions R1 and R3. In the circular area of R2, the BCI successfully differentiates bare coal from water. However, we also recommend excluding water areas before applying the BCI because the spectral characteristics of water can vary significantly due to different component contents, leading to the \"foreign objects with the same spectrum\" phenomenon, which may affect BC detection results. The ability of the BCI to eliminate impermeable surfaces is demonstrated in R4. Although some man-made structure pixels were still misclassified as bare coal pixels, these pixels can be removed through morphological processing and by setting a minimum pixel retention threshold. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Region**} & **Natural Background** & **Level of Human** & \multirow{2}{*}{**Topography**} \\  & **Conditions** & **Activity** & \\ \hline R1 & \(\surd\) & \(\surd\) & \(\surd\) \\ R2 & \(\surd\) & \(\surd\) & \(\times\) \\ R3 & \(\surd\) & \(\times\) & \(\surd\) \\ R4 & \(\surd\) & \(\times\) & \(\times\) \\ R5 & \(\times\) & \(\times\) & \(\surd\) \\ R6 & \(\times\) & \(\surd\) & \(\surd\) \\ R7 & \(\times\) & \(\surd\) & \(\times\) \\ R8 & \(\times\) & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}  
* Notes: \(\surd\) denotes good natural background conditions (grass, water, bare soil), intense level of human activity (mms, disturbance of land-use change), and flat topography (shadow); \(\times\) is the opposite.  
\end{table} 
Table 3: Comparison of R1-R8 basic conditions. Figure 11: Results of different indices calculated for four regions with good natural background conditions. The red and black regions represent the extent of bare coal, while the blue areas highlight the sections of focused contrast. R5-R8 (Figure 12) represent the calculation results for areas with poor natural background conditions. It is evident that NDCI is significantly influenced by vegetation, making it difficult to distinguish bare coal from other land covers in the R1-R4 results. However, in areas without surrounding vegetation (R5-R8), NDCI can more accurately identify bare coal, effectively distinguishing it from bare soil, impermeable surfaces, and water areas. This makes NDCI suitable for regions with high human activity and poor ecological quality. OMCI is heavily disturbed by bare soil and impermeable surfaces, yielding ideal results only in specific areas (R4, R7, R8). Therefore, when using this index to identify bare coal, it is necessary to combine OMCI with more detailed and reasonable thresholds to distinguish bare coal from other features.