Recent and projected future climate warming strongly affects permafrost stability over large parts of the terrestrial Arctic with local, regional and global scale consequences. The monitoring and quantification of permafrost and associated land surface changes in these areas is crucial for the analysis of hydrological and biogeochemical cycles as well as vegetation and ecosystem dynamics. It is also necessary for improved understanding of the consequences of climate change on past and present permafrost stability and potential future landscape conditions. Changes within vulnerable permafrost landscapes occur on different spatial and temporal scales and can result from both press (gradual) and pulse (event-driven) disturbances. Press disturbances that result from top down permafrost thaw have the potential to impact large expanses of the Arctic that will in turn change vegetation communities and hydrological conditions. Furthermore, destabilization of coastal regions has been identified as critical for the carbon cycle, but also for many Arctic communities built on permafrost terrain. Rapid thaw processes associated with land surface subsidence (thermokarst) and erosion in regions underlain by ice rich permafrost are a strong indicator of permafrost degradation. Typical thermokarst-related press disturbances include thaw slumps, detachment slides, degrading ice wedges, and thermokarst lake dynamics. Various stages of the thermokarst lake cycle that include thaw subsidence, ponding, lake formation, -expansion, and -drainage as well as vegetation succession affect vast stretches of coastal lowlands. In recent years, tundra fires have been recognized as an important disturbance which may trigger widespread permafrost degradation in severely burned areas. Due to the size and remoteness of the Arctic, a large portion of potentially ecosystem-critical disturbance processes and landscape shifts remain undetected. Remote sensing methods are therefore important for understanding and quantifying these landscape dynamics. So far, most remote sensing studies of landscape change processes in the Arctic have been carried out at high spatial resolution for local scales or low to moderate spatial resolutions for regional scales. In most cases, the temporal resolution of high spatial resolution studies has been very limited, often including comparisons between a few snapshot datasets only. Dynamic assessments with repeat acquisitions over longer periods have so far largely relied on MODIS and AVHRR data at spatial resolutions of 250 to 1000m. At this resolution, many characteristic disturbances, indicating initial landscape change, may remain unnoticed due to their small spatial extent. Therefore, detection and quantification of the abundance, distribution, and frequency of disturbances over large areas with a high spatial and temporal resolution has been very limited so far. In this study, we present a methodology for monitoring landscape change in Alaska’s northern and western tundra lowlands, across a 500,000 km2 region, based on time-series analysis of the Landsat archive (TM, ETM+, and OLI) from 1985 to 2015. With a highly automated processing chain, we are able to analyze several thousand Landsat scenes, which allows for a multi-scaled spatio-temporal analysis at 30 meter spatial resolution. The processing system has been designed to handle nearly all working steps from data download to the final product with minimal user interaction. All necessary processing steps are carried out automatically, including data extraction, masking, reprojection, subsetting, data stacking, and calculation of multi-spectral indices. These indices, e.g. Landsat Tasseled Cap and NDVI among others, are used as proxies for land surface conditions, such as vegetation status, moisture or albedo. Finally, time-series analysis is applied to each multi-spectral index and each pixel over the entire observation period of around 30 years, from 1985 to 2015. The resulting dataset consists of several information layers, e.g. linear trend parameters of each multi-spectral index, which allows for the detailed analysis, but also visualization of land surface properties and processes over very large areas during the past three decades. Numerous processes, such as coastal and fluvial dynamics, lake area changes, fires, and also anthropogenic influences are detected. Numerous landscape changes were detected in northern and western Alaska that resulted from a myriad of disturbances such as thermokarst formation, coastal and lake bluff erosion, lake drainage, fires, and landscape wetting or drying, which have not been described in the appropriate spatial and temporal context before. With the high temporal density of the dataset it is even possible to determine the timing of initiation of pulse disturbances, such as lake drainage or tundra fires. Furthermore, the dataset allows for an assessment of post-disturbance successional trajectories across large permafrost, environmental, and climatic gradients. Due to the high level of automation and the use of freely available Landsat data, the spatial extent of the analysis is transferable and scalable, which enables the comparison of different sites, across similar Arctic landscapes. With the presented processing chain and the resulting information, we envision enhancing the understanding of the response of permafrost landscapes to a warming climate as well as the quantification of changes in the frequency, magnitude, impact, and trajectory of various land surface disturbances in Arctic tundra regions. The combination of high temporal and spatial resolution in conjunction with freely available data and highly scalable tools provide a great opportunity for the analysis, monitoring and future projection of permafrost landscapes. With increasing computational capabilities and the launch and progress of further non-commercial earth-observation missions, such as Sentinel-2, we envision a pan-Arctic scale monitoring of rapidly changing permafrost regions.