Relationship between vegetation coverage and climate change in semi-arid sandy land and the significance to ecological construction

doi:10.7522/j.issn.1000-694X.2020.00089

Journal of Desert Research ›› 2021, Vol. 41 ›› Issue (1): 183-194.DOI: 10.7522/j.issn.1000-694X.2020.00089

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Relationship between vegetation coverage and climate change in semi-arid sandy land and the significance to ecological construction

Xuyang Wang(), Yulin Li(), Jie Lian, Yulong Duan, Lilong Wang

Naiman Desert Research Station，Northwest Institute of Eco-Environment and Resources，Chinese Academy of Sciences，Lanzhou 730000，China

Received:2020-06-10 Revised:2020-09-03 Online:2021-01-29 Published:2021-01-29
Contact: Yulin Li

Abstract

Abstract:

The four major sandy lands in the north of China include the Mu Us Sandy Land， Hunshandake Sandy Land， Horqin Sandy Land and Hulunbuir Sandy Land， where are typical areas for desertification development and key areas for desertification control in semi-arid area. Analysis on the temporal and spatial evolution of vegetation coverage in these four sandy lands and their response mechanisms to climatic change is of great strategic and scientific importance to the protection of biodiversity and ecological security in ecologically fragile areas of north China. Based on the analysis of Normalized Difference Vegetation Index （NDVI） and the corresponding climate data during 1998-2018， it is found that the four major sandy land areas have large inter-annual fluctuations in NDVI. The vegetation coverage of the Mu Us Sandy Land and Horqin Sandy Land generally tend to improve， with 75.22% and 42.06% of the area vegetation being improved， respectively. The vegetation coverage of the Hunshandake Sandy Land and Hulunbeier Sandy Land showed a non-significant decline， the corresponding degraded proportions were 60.04% and 51.4%， respectively. As the four major sandy lands are mainly distributed in typical arid and semi-arid climate zones， the vegetation growth is mainly affected by precipitation， and the synergistic effect of temperature and precipitation cannot be ignored. In addition， the NDVI showed highest positive correlation with continuous humidity index （CWD） in Hulunbuir Sandy Land （r=0.429）， while the highest negative correlation coefficient between NDVI and continuous drought index （CDD） was found in Hunshandake Sandy Land （r=-0.264）， indicating that the vegetation growth in Hulunbuir Sandy Land and Hunshandake Sandy Land are most sensitive to the continuous humid and continuous drought events， respectively. In the end， based on the vegetation-driven zones of different sandy lands， we suggest that the enclosure and grazing prohibition and aerial seeding afforestation should continue to be implemented in the non-climate-driven areas of the Mu Us Sand Land， and the main measures of enclosure should be adopted in the climate-driven areas. Ecological migration and the “retaining land with land” model should be implemented in Hunshandake Sandy Land； Strict enclosure measures should be taken for the grassland with sparse forest in Horqin Sandy Land while taking into account the development of economic benefits； In the Hulunbuir Sandy Land， we must adhere to the combination of biological and engineering measures to establish an ecological development pattern of “peripheral closure， marginal governance， and internal development”.

Key words: sandy lands, vegetation coverage, spatiotemporal evolution

CLC Number:

Q948.15

Xuyang Wang, Yulin Li, Jie Lian, Yulong Duan, Lilong Wang. Relationship between vegetation coverage and climate change in semi-arid sandy land and the significance to ecological construction[J]. Journal of Desert Research, 2021, 41(1): 183-194.

Figures/Tables 10

Fig.1 Location of the study area

Fig.2 Spatial distribution of the multi-year mean normalized-difference vegetation index (NDVI) in semi-arid sandy land of China from 1998 to 2018

Fig.3 The inter-annual variations of the normalized-difference vegetation index (NDVI) in semi-arid sandy land of China from 1998 to 2018

Fig.4 Spatial pattern change of the normalized-difference vegetation index (NDVI) in semi-arid sandy land of China from 1998 to 2018

Fig.5 Spatial distribution of partial correlations between the multi-year mean normalized-difference vegetation index (NDVI) and the mean annual temperature in semi-arid sandy land of China from 1998 to 2018

Fig.6 Spatial distribution of partial correlations between the multi-year mean normalized-difference vegetation index (NDVI) and the mean total annual precipitation in semi-arid sandy land of China from 1998 to 2018

Fig.7 Spatial distribution of the multiple-correlation coefficient between the multi-year mean normalized-difference vegetation index (NDVI) and the two climate drivers (temperature and precipitation) in semi-arid sandy land of China from 1998 to 2018

Table 1 The criteria used for classification of the driving factors for dynamic changes of the normalized-difference vegetation index (NDVI).

NDVI变化驱动因子		C1	C2	C3
气候因子	[T+P]⁺	P < 0.01	P < 0.01	P < 0.05
	T	P < 0.01	P ≥ 0.01	P < 0.05
	P	P ≥ 0.01	P < 0.01	P < 0.05
	[T+P]	P ≥ 0.01	P ≥ 0.01	P < 0.05
非气候因子	NC	P ≥ 0.01	P ≥ 0.01	P ≥ 0.05

Fig.8 Spatial distribution of the effects of the driving factors on changes in the multi-year normalized-difference vegetation index (NDVI) in semi-arid sandy land of China from 1998 to 2018

Table 2 Correlation between normalized-difference vegetation index (NDVI) and extreme precipitation index

指数	呼伦贝尔沙地	浑善达克沙地	科尔沁沙地	毛乌素沙地
CWD	0.429^**	0.362^**	0.068	0.138
CDD	-0.225	-0.264^**	-0.004	-0.189^*

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Relationship between vegetation coverage and climate change in semi-arid sandy land and the significance to ecological construction

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