**4. Discussion**

An effective way of accomplishing adequate environmental management of mining areas requires the integration of remote sensing methods and Geographic Information Systems. Remote sensing provides image analysis fundamentals while a Geographic Information System offers spatial data analysis and geo-visualization tools. If these are exploited in a proper way, then continuous monitoring of mining activity can lead to efficient reclamation. In addition, freely-available data and open-source software drastically facilitates the efforts in this direction. This study utilized both of them in an effort to develop a comprehensive and at the same time rapid methodology for identifying mining areas and precisely delineating their boundaries. Of course, this approach can be beneficial for a multitemporal analysis in order to evaluate mining expansion.

The implemented approach for image segmentation evaluation that is demonstrated in this study does not require ground truth data, since it is an unsupervised method that is characterized by two features included in the following. Each segment should be internally homogeneous (weighted variance metric) and at the same time discrete from its adjacent segments (Moran's I spatial autocorrelation index). These two indicators are calculated for each spectral band and then combined into a global evaluation metric, the objective function. The main advantage of this approach is its robustness, since it exploits well-established statistical methods. However, since it is a global evaluation metric, it may not perform well when two segmentation results depict very similar performance but have dissimilar local error distributions. An approach that is capable of quantifying both locally and globally segmentation performance may be more suitable for the aforementioned situation.
