Arctic permafrost landscapes are in rapid transition and strongly affected by climate warming. Remote sensing methods can help for a better understanding and monitoring of those landcover changes. Common features of arctic permafrost landscapes are thermokarst lakes and drained lake basins. They play an important role for the geomorphological, hydrological and the ecological development of arctic landscapes. The change of habitat characteristics of arctic permafrost lowlands also effects the local biodiversity. Deepening our understanding of processes associated with drainage events and drained basins in the arctic environment is crucial for numerous applications (e.g., landscape models).The study area is located on the Yamal peninsula in Northern Russia, Siberia. The peninsula can be categorized into a discontinuous and continuous permafrost tundra region. Yamal is covered by different tundra vegetation communities, thaw lakes, wetlands and river floodplains. Drained lake basins differ between the regions in their frequency of occurrence and in their size. We selected several drained lake basins on the Yamal peninsula representing a North-South climatic gradient and different drained lake basin development stages. Some drained lake basins are close to infrastructure. Human activity on Yamal does comprise not only gas infrastructure projects but extensive reindeer herding serves as main traditional land use form.Drained lake basins and associated landscape dynamics such as changes in surface water area and vegetation cover can be monitored from space and described with different remote sensing indices. The different indices can be calculated from multiple satellite images on an annual and inter-annual level. In detail, the selected drained lake basins are evaluated at the peak of the growing season (between July 1. and August 31.) and inter-annual landcover dynamics from 2016 up to present. For this thesis multispectral imagery data was used, which was received from Sentinel-2 and Landsat-8 satellites. The derived data was used to calculate a range of different landcover metrics such as Normalized Difference Vegetation Index (NDVI) and Tasseled Cap (TC) indices. The Tasseled Cap coefficients were adjusted to the corresponding satellite and the spectral indicators for brightness, greenness and wetness were calculated.The results were analysed by comparing the different sites, focusing on the connection of the studied parameters and site-specific factors (such as relative basin age, hydrological connectivity). In addition, comparisons are made to a landcover classifications devel-oped within the ESA DUE Globpermafrost and Permafrost cci projects which are based on fusion of Sentinel-1 and 2 data using machine learning. The results will advance the understanding of drained lakes greening and the corresponding change of flora and fauna and biodiversity.