Significant environmental impacts of mining activities connected with high-sulfur materials result from the production of acid mine drainage and potentially toxic elements, which easily migrate to adjacent ecosystems due to the typical absence of vegetation on spoil heaps and toeslope talus mantle. In this paper, we present the results of the first comprehensive study of the ecosystems affected by acidic and metal-enriched (Al, Ca, Co, Cu, Fe, Mg, Mn, Ni, and Zn) mine drainage conducted at spoil heaps and adjacent talus mantle under semihumid climate conditions within the Moscow Brown Coal Basin (Central Russian Upland, Tula Region, Russia). A total of 162 samples were collected, including 98 soil samples, 42 surface water samples, and 22 plant samples (aerial tissues of birch). Coal talus mantle materials of Regosols were characterized by the increased concentration of water-soluble Ca, K, Mg, and S, and all mobile fractions of Al, Co, S, and Zn. The chemical composition of birch samples within the zones affected by acid mine drainage differed insignificantly from those in the unpolluted ecosystems with black soils, due to the high tolerance of birch to such conditions. Differences between the affected and undisturbed sites in terms of the chemical composition decreased in the following order: waters > soils > plants. The geochemical characterization of plants and soils in coal mining areas is essential for the mitigation of negative consequences of mining activities.

Rocket launches are a source of anthropogenic disturbance to Central Kazakhstan ecosystems. Multistage launch vehicles (LV) are used for orbit insertion of different spacecrafts from the Baikonur cosmodrome (Republic of Kazakhstan). The launch consists of stages during which the rocket separates successively, with pieces falling to the ground along the flight route of the LV. Regions of first stage falling located in Central Kazakhstan endure the most intensive impact. First stage fallings lead to mechanical and pyrogenic disturbance and pollution by fuel components. We characterized vegetation changes at the first stage falling sites of ‘Proton-M’ rocket carriers during two growing seasons. Spontaneous revegetation by ruderal plant communities occurs after falling. First stage falling sites have lower vegetation cover and species diversity. Ceratocarpus arenarius is a dominant species in plant communities at the sites that have been affected by first stage falling. After winter rocket launches vegetation is less deteriorated at the falling sites than after spring and summer launches. The recovery process in plant communities is considerably faster at falling sites corresponding to winter rocket launches. Nomenclature: S.K. Cherepanov (1995).

?????? ?????, ???????? ??????????????, ???????? ???????, ????? ?????? ???????? ???????, ????? ?????? ???????? ??????????????, ??????, ??????, ??????, ?????? ???????, ?????? ?????????, ?????? ??????, ?????? ????????, ??????? ??????? ????, ??????????? ???????? ??????????????, ????????-??????????????, ????????? ??????, ???????? ??????, ??????? ?????????, ??????? ?????????????, ?????? ?????, ???????????? ????????????, ????????????????, ?????????, ????? ????, ??????????? ????, ???????? ????, ????????

In arid lands of Central Kazakhstan, fires commonly occur due to both man-made (e.g. space rocket launches) and natural factors. Remote sensing is the best method to identify burned landscapes and to assess their long-term dynamics. In this paper, we assessed total areas and seasonal features of fires using Landsat 8 OLI data of high spatial and temporal resolution, corrected for atmospheric effects. We defined data requirements, considered various spectral indices, and evaluated their suitability for automated delineation of burned areas. Spectral indices included special burn indices (NBRT, MIRBI, NBR, NBR2, BAI) and vegetation indices (MSAVI, MSAVI2, MTVI2, GEMI3) that allowed us to determine the boundaries and severity of fires indirectly based on the state of vegetation. Our study showed that burned areas were most accurately identified using indices of NBR2 (normalized burn ratio 2) and MSAVI2 (modified soil adjusted vegetation index 2). They were extracted using image segmentation and natural breaks classification methods. We have assessed MSAVI2 and NBR2 segmentation results with machine learning metrics, i.e. for Soyuz drop zone (landing area of rocket stages) precision of NBR segmentation equaled 99.5%, recall − 99.5%, accuracy − 98.5%. For Proton drop zone NBR segmentation precision equaled 99.3%, recall − 99.7%, accuracy − 99.4%. MSAVI2 results are less accurate. The accuracy of segmentation significantly depended on the vegetation state which was predetermined by the frequency and severity of previous fires within certain territory.