Long-term Land Cover Dataset of the Mongolian Plateau Based on Multi-source Data and Rich Sample Annotations

Abstract. The Mongolian Plateau (MP), with its unique geographical landscape and cultural features, plays an important role in the safety of the regional ecological environment and sustainable development in North Asia. Land-cover data form the basis of MP studies; however, global-scale data products often fail to reflect regional characteristics. Despite improvements in data resolution, the level of detail remains insufficient for MP studies. This study aimed to improve the land cover classification system for MP. Based on multi-source data and extensive sample annotations, we constructed a high-quality land cover dataset for the MP and analyzed regional land cover changes. In this study, we used Landsat 5 and 8 images as primary data, which were supplemented by NASADEM, nighttime light data, ESA WorldCover, and other multi-source data for auxiliary analysis. The MP was divided into 13 major land cover categories: forest, shrub, meadow steppe, real steppe, desert steppe, wetland, water, cropland, built areas, bare area, desert, sand, and ice. For these categories, we manually labeled the samples for seven periods: 1990, 1995, 2000, 2005, 2010, 2015, and 2020. We constructed a set of image features and used a random forest model to train and predict land-cover data from 1990 to 2020. The overall accuracy of the land-cover products was 83.9 %, with a kappa coefficient of 0.817. Analysis of land cover changes over the past 30 years revealed that 65.04 % of land cover types in the MP remained unchanged from 1990 to 2020. The sandy area decreased annually, with a significant trend of bare areas transitioning to vegetation. The land-cover dataset produced in this study includes land-cover data and labeled samples for seven periods, exhibiting high application potential. It can be used for research on land-cover changes and related resource and environmental issues in the MP, providing data to support ecological protection and natural resource management in the region.