Quantifying Uncertainty in Projections of Desertification in Central Asia Using Bayesian Networks

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 665 - 665

Published: Feb. 15, 2025

Desertification presents major environmental challenges in Central Asia, driven by climatic and anthropogenic factors. The present study quantifies desertification risk through an integrated approach using Bayesian networks the ESAS model, offering a holistic perspective on dynamics. Four key variables—vegetation cover, precipitation, land-use intensity, soil quality—were incorporated into model to evaluate their influence desertification. A probabilistic was developed gauge with simulations conducted at 200 geospatial points. Hazard maps for 2030–2050 were produced under climate scenarios SSP245 SSP585, incorporating projected changes. All procedures assessment, mapping, downscaling performed Google Earth Engine platform. findings suggest 4% increase 11% SSP585 2050, greatest threats observed western regions such as Kazakhstan, Uzbekistan, Turkmenistan. Sensitivity analysis indicated that vegetation quality exerts strongest desertification, reflected Vegetation Quality Index (VQI) ranging from 1.582 (low Turkmenistan) 1.692 (very low Kazakhstan). comparison of models revealed robust alignment, evidenced R2 value 0.82, Pearson correlation coefficient 0.76, RMSE 0.18. These results highlight utility effective tool assessment scenario analysis, underscoring urgency targeted land management proactive adaptation. Although reclaimed opportunities afforestation sustainable agriculture, carefully considering potential trade-offs biodiversity ecosystem services remains essential.

Language: Английский

Redefining the modified Water Benefit-Based Ecological Index to evaluate the impact of cropland expansion on the ecological environment in an arid area

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: unknown, P. 143730 - 143730

Published: Sept. 1, 2024

Language: Английский