This study applies remote sensing techniques for monitoring non-ferrous metal smelting impacts in the extreme environment of northern Siberia. Ground and at-satellite reflectance and normalized difference vegetation index (NDVI) values for different vegetation types have been compared and a hybrid supervised-unsupervised classification of Landsat TM data performed, based on field and ancillary data. This has allowed us to distinguish several degrees of vegetation damage in tundra and forests. However, it was difficult to differentiate between some significant classes, such as damaged grass tundra and sparse dead larch forests with a grass understorey. We suggest possible refinement of our results, including the combination of images taken at different phenological stages and from different sensors. However, it should be noted that the north-Siberian environment presents unusually severe limitations of optical-infrared satellite observation possibilities and problems in imagery interpretation. Standard indicators of vegetation vigour, such as NDVI, widely applied at lower latitudes, become less informative in highly variable tundra and pre-tundra ecosystems.