Surface mining activities, exploitation of ore, and stripping and dumping of the overburden cause changes in the land cover and land use of a mine area. The area of land disturbance can be very large in the case of surface coal mining, due to the nature of the coal extraction process. Sustainable mining requires continuous monitoring of changes in land cover and land use induced by the mining activities. This is essentially important for identifying the long-term impacts of mining on the environment and on land cover in order to provide necessary mine closure and reclamation measures. In this sense, digital image classification provides a powerful tool for obtaining rigorous data, and reduces the cost of field measurements in time and money, particularly when dealing with large areas. Various remote sense data records and image classification techniques serve different features for numerous purposes. The selection of a suitable data and image classification method is significant for ensuring the effective use of information extracted from the satellite images, e.g. land-use classes. This paper proposes a methodology for identifying land-use change in surface coal mines using multi-temporal high-resolution satellite images. The methodology has been implemented for identifying, quantifying and analysing the spatial response of landscape to surface mining activities in the Goynuk, Bolu surface mine in Turkey, from 2004 to 2008. The methodology essentially entails (i) acquiring data, (ii) preprocessing the data, (iii) image classification using the maximum likelihood classification algorithm (iv) accuracy assessment and (v) change detection analysis depending on class-based approaches. The results show that the methodology can be utilised effectively in monitoring land-use change in surface coal mining areas. It also provides essential input for planning mine reclamation and closure activities.