The continuing decrease of sandy desert and sandy land in northern China in the latest 10 years

Abstract

Timely monitoring of the sandy desert and sandy land (SDSL) dynamics and their driving factors is vital for ecological restoration policy-making to combat the expansion of the SDSL. Northern China, being the principal implementation area of ecological restoration projects, is ecologically fragile and encompasses almost all of the SDSL regions in China. To explore and compare the dominant driving factors for SDSL changes by extracting both aeolian activity information and vegetation indices, the SDSL datasets in northern China from 2010 to 2020 were first constructed in this study by the human–computer interactive interpretation method based on the series Landsat image data. The spatiotemporal dynamic characteristics of SDSL were subsequently analyzed, and the dominant driving factors of SDSL changes were further explored via the geodetector model by the fractional vegetation cover trend and the difference of aeolian activity index. The results showed a net decrease of 3,780.25 km2 in the total SDSL area between 2010 and 2020, and the decrease rate was 2.44 times higher in the shorter period of 2015–2020 compared to 2010–2015. The spatial differentiation between the western and eastern parts of the Helan Mountains was prominently exhibited by the relatively higher changed SDSL area in the eastern part (62.74% for aeolian activity weakened and 83.32% for aeolian activity intensified) and the increasingly changed SDSL area in the western part (from 33.09% to 39.60% in aeolian activity weakened and from 13.02% to 19.60% in aeolian activity intensified). Especially, the increased aeolian activity intensified SDSL area has happened on the southern edge of the Gurbantunggut Desert and along both sides of the Tarim River from 2015 to 2020 deserve attention. The co-driving of the multiple driving factors dominated the change of SDSL from 2010 to 2020, with the county mean value of the difference in aeolian activity index (MDAAI) and the county mean value of the trend in fractional vegetation cover (MFVCT) as dependent variables, respectively. Climatic factors of the mean annual average temperature, the mean annual cumulative precipitation, and the mean annual aridity index have a relatively larger explanatory power for the change of SDSL in both MDAAI and MFVCT. Anthropogenic factors of the mean annual afforestation area and the mean annual number of large livestock and sheep and goats (year-end) have a greater explanatory power in MDAAI, while the mean annual number of large livestock and sheep and goats (year-end) and the mean annual population density in MFVCT. In particular, the dependent variable MDAAI is better at identifing the dominant drivers of aeolian activity intensified SDSL area compared to MFVCT, with anthropogenic factors playing a stronger dominant role.