Long-Term Spatiotemporal Information Extraction of Cultivated Land in the Nomadic Area: A Case Study of the Selenge River Basin

The Mongolian Plateau, a region where nomadic and agrarian civilizations intersect, exemplifies regional sustainable development and natural resource utilization through the spatiotemporal distribution of cultivated land. However, large-scale, long-term, high-precision extraction of cultivated land has not been systematically conducted in this area. This study integrated remote sensing technology with machine learning methodologies to develop an automated extraction process based on spectral, textural, and topographical features. We monitored changes in cultivated land across eight time periods from 1990 to 2023 within the Selenge River Basin, utilizing Google Earth Engine and 3527 scenes derived from Landsat and Sentinel satellite imagery. The area of cultivated land fluctuated between 6332.78 km² and 14,799.22 km², representing 2.26% to 5.29% of the total area. Cultivated land exhibited a significant decline prior to 2005 and gradually increased after 2010, largely influenced by agricultural policy reforms. Traditional nomadic areas showed a spatial pattern of reconstruction, characterized by a significant transformation to agricultural land. The overall accuracy exceeded 90%, and kappa coefficients remained above 0.83. Consistency checks and comparisons of different integration methods further validate the feasibility and reliability of the research methods and results. This approach holds promise for application across the entire Mongolian Plateau and other arid and semi-arid regions for monitoring cultivated land dynamics.