The Detection of Past and Future Land Use and Land Cover Change in Ugam Chatkal National Park, Uzbekistan, Using CA-Markov and Random Forest Machine Learning Algorithms
DOI:
https://doi.org/10.23917/forgeo.v38i2.4221Keywords:
LULC, CA-Markov, Uzbekistan, Random ForestAbstract
This comprehensive study investigates land use and land cover (LULC) changes in Ugam Chatkal Na-tional Park, Uzbekistan, over a 30-year period from 1993 to 2022 with Landsat satellite images. Utili-zing advanced CA-Markov and Random Forest machine learning algorithms, it meticulously analyzes historical data to understand past trends and projects future LULC changes. According to remote sen-sing analysis of the past, our findings show the sharp decline of glacier land cover from 2105 km2 to 1334 km2 in the Ugam Chatkal National Park, replaced by tree cover (from 327 km2 in 1993 to 450 km2 in 2022), rangelands (1259 km2 in 1993 to 1355 km2 in 2020), and rocks (from 834 km2 in 1993 to 1390 km2 in 2022). Agriculture, water and bare land witnessed some fluctuations but did not change significantly. At the same time, the region experienced some urbanization, raising the urban area from 50 km2 in 1993 to 90 km2 after 29 years. The article suggests three possible scenarios for the future of the region: “hard”, “soft” and “bad” scenarios. Land cover change predictions are done in TerrSet software with the CA-Markov model for four decades: 2035, 2045,2055 and 2065. Hard and soft sce-narios predict similar patterns for the future: a decline in glacier cover and a rise of tree cover, rock and rangelands, with a slight increase in agriculture and urban classes. Whereas “bad” scenario, which incorporates rapid urbanization and agricultural expansion for the study area, forecasts a climb of the urban area until 415 km2 (8% of the territory) until 2065, and 286 km2 for agriculture.
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