Spatiotemporal Pattern and Driving Force Analysis of Vegetation Variation in Altay Prefecture based on Google Earth Engine

doi:10.5814/j.issn.1674-764x.2021.06.002

Abstract

Abstract:

Quantitative evaluation and driving mechanism analysis of vegetation dynamics are essential for promoting regional sustainable development. In the past 20 years, the ecological environment in Altay Prefecture has changed significantly due to global warming. Meanwhile, with increasing human activities, the spatiotemporal pattern and driving forces of vegetation variation in the area are uncertain and difficult to accurately assess. Hence, we quantified the vegetation growth by using the Normalized Difference Vegetation Index (NDVI) on the Google Earth Engine (GEE). Then, the spatiotemporal patterns of vegetation from 2000 to 2019 were analyzed at the pixel scale. Finally, significance threshold segmentation was performed using meteorological data based on the correlation analysis results, and the contributions of climate change and human activities to vegetation variation were quantified. The results demonstrated that the vegetation coverage in Altay Prefecture is mainly concentrated in the north. The vegetation areas representing significant restoration and degradation from 2000 to 2019 accounted for 24.08% and 1.24% of Altay Prefecture, respectively. Moreover, spatial correlation analysis showed that the areas with significant correlations between NDVI and temperature, precipitation and sunlight hours accounted for 3.3%, 6.9% and 20.3% of Altay Prefecture, respectively. In the significant restoration area, 18.94% was dominated by multiple factors, while 3.4% was dominated by human activities, and 1.74% was dominated by climate change. Within the significant degradation area, abnormal degradation and climate change controlled 1.07% and 0.17%, respectively. This study revealed the dynamic changes of vegetation and their driving mechanisms in Altay Prefecture, and can provide scientific support for further research on life community mechanism theory and key remediation technology of mountain-water-forest-farmland-lake-grass in Altay Prefecture.

Key words: vegetation variation, climate change, human activities, driving mechanism, Google Earth Engine, Altay Prefecture

HE Yuchuan, XIONG Junnan, CHENG Weiming, YE Chongchong, HE Wen, YONG Zhiwei, TIAN Jie. Spatiotemporal Pattern and Driving Force Analysis of Vegetation Variation in Altay Prefecture based on Google Earth Engine[J]. Journal of Resources and Ecology, 2021, 12(6): 729-742.

Figures/Tables 12

Fig. 1 Overview of the study area

Fig. 2 Flowchart of significance threshold segmentation

Fig. 3 Spatial distribution of NDVI in Altay Prefecture Note：The bottom left in Fig. 3b is the F test result (P<0.05); The statistical graphs in the upper right corner of Fig. 3 are the percentage of each data level in the legend on the right in the entire area.

Table 1 Vegetation cover classification of Altay Prefecture

NDVI	Vegetation cover classification	Area (km²)	Proportion (%)
NDVI<0.2	Low coverage	64716	56.1
0.2≤NDVI<0.4	Medium and low coverage	14253	12.4
0.4≤NDVI<0.6	Medium coverage	8363	7.2
0.6≤NDVI<0.8	Medium and high coverage	7833	6.8
0.8≤NDVI	High coverage	20146	17.5

Table 2 NDVI trend grading table

Slope	NDVI trend grading	Area (km²)	Proportion (%)
< -0.0100	Serious degradation	183	0.2
-0.0100 - -0.0050	Moderate degradation	451	0.4
-0.0050 - -0.0010	Slight degradation	5084	4.3
-0.0010 - 0.0010	Essentially unchanged	52531	44.8
0.0010 - 0.0050	Slight restoration	51090	43.6
0.0050 - 0.0100	Moderate restoration	4279	3.6
> 0.0100	Obvious restoration	3586	3.1

Fig. 4 The NDVI trends in different counties of Altay Prefecture from 2000 to 2019

Fig. 5 The variation trends of meteorological factors. A, B and C respectively represent air temperature, precipitation and sunshine hours; 1, 2, 3 and 4 respectively represent the multi-year mean, temporal trend, broken line chart of average trend and histogram at the pixel scale of the change rates.

Fig. 6 Spatial distribution diagrams of correlation coefficients between NDVI and meteorological factors of temperature (a), precipitation (b), and sunlight hours (c). Note: The inset maps in the bottom left corner of each show the t test results for each factor (P<0.05) and the statistical graphs in the upper right corner of Fig. 4 are the percentage of each data level in the legend on the right in the entire area.

Fig. 7 Spatial distribution diagrams of significantly correlated r values between NDVI and temperature (T, red), precipitation (P, green) and sunlight hours (S, blue).

Fig. 8 Spatial distribution and proportions of the driving forces

Fig. 9 Temporal trend of livestock production

Fig. 10 The evaluation chart of significant changes led by human factors. A and C indicate areas where cultivated land has increased, B indicates artificial reservoir areas, 1, 2 and 3 respectively represent NDVI distribution in 2000, NDVI distribution in 2019, and the numbers of pixels with different NDVI values.

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