Spatio-temporal changes in vegetation structure and its driving factors based on multi-resolution remote sensing images in arid and semi-arid regions

Arid and semi-arid regions occupy 41% of the global land area and support more than 38% of the global population. With the limited precipitation and atmospheric condensation, the survival of natural vegetation here mainly depends on the shallow/deep soil water and groundwater. The natural ecological barrier formed by natural vegetation plays an important role in protecting the stability of artificial oases and maintaining ecological security in desert areas here. Based on multi-source and multi-resolution remote sensing images (MODIS, Landsat, GF-2), as well as meteorological data, Gravity Recovery and Climate Experiment (GRACE) data, and groundwater table depth (GTD) data, this study took Xinjiang, China, as a research area to accurately characterize the spatio-temporal changes of its vegetation structure (vegetation coverage, vegetation index) and quantify its driving factors. The results showed as following:

(1) At the scale of the whole of Xinjiang, the modified three-band maximum gradient difference (TGDVI) method was proposed to improve the extraction accuracy of fractional vegetation cover (FVC) in desert areas. It showed that the average FVC of Xinjiang had an increasing trend as a whole, with a growth rate of 0.19%∙a^–1 from 2003 to 2020. The influence of temperature on FVC was mainly concentrated in spring and autumn, while precipitation and groundwater storage (GWS) were the main factors in summer. In summer, GWS was the main factor affecting FVC of shrubland and cropland, and precipitation had the greatest impact on FVC of meadow grassland. With the hydrothermal conditions becoming wetter, the influence of FVC on temperature in Xinjiang gradually decreases, but that on precipitation increases. The influence of GWS on FVC increased from arid to semi-arid conditions, and then it decreased from semi-arid to humid conditions.

(2) At the scale of inland river basin, taking the Sangong River Basin (SRB) as an example, this study proposed to use deep learning methods based on GF-2 images to accurately extract the distribution of desert woody plants, and combined with Landsat 5\7\8 images to establish high-precision time series data set of woody plants vegetation index. It found that the difference between woody plant coverage (WFC) and vegetation coverage (FC) was a fine indicator for delineating desert-oasis ecotone. In this study, this range was about 1.6 km m from the oasis. Increasing GTD inhibited desert vegetation growth, while Enhanced Vegetation Index (EVI) changes in croplands were closely related to the expansion of agricultural areas rather than GTD. In the desert of lower reaches of SRB, the growth of woody plants was constrained as GTD increased.

This study indicated that groundwater was a critical influencing factor in maintaining vegetation survival in arid and semi-arid regions at both regional and watershed scales, especially for woody plants. This was of great significance to the sustainable utilization of water resources and the protection of the ecological environment here.