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Sciences in Cold and Arid Regions ›› 2021, Vol. 13 ›› Issue (1): 43-52.doi: 10.3724/SP.J.1226.2021.20030

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Response of revegetation to climate change with meso- and micro-scale remote sensing in an arid desert of China

Guang Song1,2,BingYao Wang1,JingYao Sun1,3,YanLi Wang1,3,XinRong Li1,2()   

  1. 1.Shapotou Desert Research and Experimental Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Science, Lanzhou, Gansu 730000, China
    2.Key Laboratory of Stress Physiology and Ecology in Cold and Arid Regions of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    3.University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-05-09 Accepted:2020-10-26 Online:2021-02-28 Published:2021-02-07
  • Contact: XinRong Li E-mail:lxinrong@lzb.ac.cn

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Abstract:

The revegetation protection system (VPS) on the edge of the Tengger Desert can be referred to as a successful model of sand control technology in China and even the world, and there has been a substantial amount of research on revegetation stability. However, it is unclear how meso- and micro-scale revegetation activity has responded to climatic change over the past decades. To evaluate the relative influence of climatic variables on revegetation activities in a restored desert ecosystem, we analysed the trend of revegetation change from 2002 to 2015 using a satellite-derived normalized difference vegetation index (NDVI) dataset. The time series of the NDVI data were decomposed into trend, seasonal, and random components using a segmented regression method. The results of the segmented regression model indicate a changing trend in the NDVI in the VPS, changing from a decrease (-7×10-3/month) before 2005 to an increase (0.3×10-3/month) after 2005. We found that precipitation was the most important climatic factor influencing the growing season NDVI (P <0.05), while vegetation growth sensitivity to water and heat varied significantly in different seasons. In the case of precipitation reduction and warming in the study area, the NDVI of the VPS could still maintain an overall slow upward trend (0.04×10-3/month), indicating that the ecosystem is sustainable. Our findings suggest that the VPS has been successful in maintaining stability and sustainability under current climate change conditions and that it is possible to introduce the VPS in similar areas as a template for resistance to sand and drought hazards.

Key words: vegetation activity, climate change, NDVI trend, restored ecosystem, meso- and micro-scale

Figure 1

Overview of the study area. (a) Locations of the Tengger Desert (red area), study area (red line area) and Baotou-Lanzhou Railway; (b) landscape of the desert before restoration; (c) vegetation restoration using 1m×1m straw checkerboards combined with drought-tolerant shrubs; (d) the VPS status after more than 50 years of restoration"

Figure 2

Plot of the monthly mean NDVI and its seasonal correction trend (trend), seasonal variation (seasonal), and residual components. The black dashed line represents a simple linear fit, and the light grey dashed line and the light magenta dashed line represent segmented regression before and after the change point (2005), respectively"

Figure 3

Cumulative sum chart of the NDVI standard deviation. Yellow and light blue represent the areas before and after the change point"

Figure 4

Interannual variations in the average (a) growing season, (b) spring, (c) summer and (d) autumn NDVI and the corresponding climate variables. The black dashed line indicates simple linear fits during the period 2002-2015. The light grey and light magenta dashed lines represent segment fits before and after the change point of the NDVI trend, respectively"

Table 1

Partial correlation analysis results of the NDVI in the growing season and the NDVI in spring, summer and autumn"

ItemsSRNSMNATN
CoefficientPCoefficientPCoefficientP
GSN0.5430.0450.6570.0110.5910.026

Table 2

Partial correlation analysis results between the NDVI and climate factors in the growing season and in each season"

ItemsPrecipitationTemperatureRadiation
CoefficientPCoefficientPCoefficientP
GSN0.553*0.042-0.2430.4660.5440.067
SRN0.2350.4620.0960.7670.4310.032
SMN0.688**0.007-0.600*0.023-0.2340.465
ATN0.3970.201-0.622*0.0180.3300.295
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