Land-use is transforming habitats across the globe, thereby threatening wildlife. Large mammals are especially affected because they require large tracts of intact habitat and functioning corridors between core habitat areas. Accurate land-cover data is critical to identify core habitat areas and corridors, and medium resolution sensors such as Landsat 8 provide opportunities to map land cover for conservation planning. Here, we used all available Landsat 8 imagery from launch through December 2014 to identify large mammal corridors and assess their quality across the Caucasus Mountains (> 700,000 km2). Specifically, we tested the usefulness of seasonal image composites (spring, summer, fall, and winter) and a range of image metrics (e.g., mean and median reflectance across all clear observations) to map nine land-cover classes with a Random Forest classifier. Using image composites from all four seasons yielded markedly higher overall accuracy than using single-season composites (8% increase) and the inclusion of image metrics further improved the classification significantly. Our final land-cover map had an overall accuracy of 85%. Using our land-cover map, we parameterized connectivity models for three generic large mammal groups and identified wildlife corridors and bottlenecks within corridors with cost-distance modeling and circuit theory. Corridors were numerous (in total, 85, 131, and 132 corridors for our three mammal groups, respectively), but often had bottlenecks or high average cost along the least-cost path, indicating limited functioning. Our findings highlight the potential of Landsat 8 composites to support connectivity analyses across large areas, and thus to contribute to conservation planning, and serve as an early warning system for biodiversity loss in areas where on-the-ground monitoring is challenging, such as in the Caucasus.