Dynamic monitoring of aeolian desertification based on multiple indicators in Horqin Sandy Land, China

Aeolian desertification has become one of the most serious environmental and socioeconomic problems facing the world today. Quantitative remote sensing technology is an important means to achieve the development trends of aeolian desertified land (ADL). To compensate for the shortcomings in the time scale of Landsat The matic Mapper and other high-spatial-resolution remote sensing data, this study introduces Moderate Resolution Imaging Spectroradiometer time series data and products to invert the monitoring indicators of ADL. The QUEST (quick, unbiased, and efficient statistical tree) classification method was used to establish the extraction model of ADL based on multiple indicators. The ADL time series dataset was extracted from 2000 to 2015, and the characteristics of ADL and its spatial-temporal dynamics were analyzed. These results were combined with meteorological data and socioeconomic statistics to discuss the main factors influencing ADL. The results showed that, by the end of 2015, the total area of ADL was 32,633 km(2), accounting for 26.02% of the study area. The slight, moderate, severe, and extremely severe ADL accounted for 51.39%, 34.11%, 10.31%, and 4.20%, respectively. The total area of ADL decreased significantly at a rate of 2388.60 km(2) y(-1) from 2000 to 2015. The decreasing area was dominated by the slight and moderate ADL. The reversal of ADL exhibited significant correlations with an increase of annual precipitation and a decrease of annual maximum wind velocity (p < 0.01). The impact of annual maximum wind velocity on ADL is more important than annual precipitation. Increases in population density and the number of livestock did not promote the development of ADL. A series of ecological protection projects and policies created advantageous conditions for the reversal of ADL. This research provides a new method for monitoring ADL and useful information for controlling and managing aeolian desertification in this region. (C) 2018 Elsevier B.V. All rights reserved.