Abstract
Since the implementation of the great western development strategy in 2000, the ecological environment in the western region of China has been significantly improved. In order to explore the temporal and spatial characteristics of vegetation coverage in the western region, this paper adopted the method of Maximum Value Composite (MVC) to obtain the mean Normalized Difference Vegetation Index (NDVI) of vegetation on the basis of the Moderate-resolution Imaging Spector audiometer (MODIS) data of 2000/2005/2010/2015/2018. Thereafter, the spatio-temporal differentiation characteristics of vegetation in western China were analyzed. The results show that: (1) According to the time characteristics of vegetation coverage in the western region, the average annual NDVI value of vegetation coverage in the growing season in the western region fluctuated between 0.12 and 0.15, among which that of 2000 to 2010 fluctuated more greatly but did not show obvious change trend. (2) Based on Sen trend and Mann-Kendall test analysis, the area of vegetation coverage improvement in the western region from 2000 to 2018 was larger than that of significant vegetation degradation. (3) From the perspective of global autocorrelation coefficient, Moran’s I values were all positive from 2000 to 2018, which indicates that the vegetation coverage in the west showed strong positive autocorrelation in each period. According to the average value and coefficient of variation of vegetation coverage, the vegetation coverage was lower in 2000, its internal variation was smaller, and the vegetation coverage increased with time. According to the local spatial autocorrelation analysis, the vegetation coverage levels in different regions varied greatly. (4) The standard deviation ellipse method was used to study the spatial distribution and directional transformation of vegetation. It makes the result more intuitive, and the three levels of gravity center shift, direction shift, and angle shift were considered: the vegetation growth condition in the spatial aggregation area improved in 2015; the standard deviation ellipses in 2000 and 2018 overlapped and shifted eastward, which indicates that the vegetation coverage conditions in the two years were similar and got ameliorated.
Highlights
In the international approaches to vegetation degradation, generally Remote Sensing (RS), Global Position System (GPS), and Geographic Information System (GIS) are combined to monitor vegetation degradation
In terms of research methods, scholars use linear trend method, R/S analysis method, spatial interpolation method, spatial autocorrelation, standardized ellipse, etc., which is of important practical significance for vegetation and ecological environment protection in the research area [18]; besides, from the perspectives of climate change, remote sensing image interpretation, field vegetation survey and vegetation degradation characteristics and laws in mining areas [19], scholars analyzed the dynamic evolution of vegetation at different spatial scales by path analysis and other mathematical statistics and spatial analysis methods [20,21]
The Band Math operation expression of vegetation coverage((b1 lt 0.031766) * 0 + (b1 gt 0.522991) * 1 + (b1ge 0.031766 and b1 le 0.522991) * ((b1 − 0.031766)/(0.522991 − 0.031766) and the calculation method of annual maximum mean are as follows: In the formula, Pi is the average of the monthly maximum Normalized Difference Vegetation Index (NDVI) of the pixels from May to September, n is the number of regional pixels, verage is the normalized coefficient of vegetation coverage, and the reference value is 0.0121165124 [24,25]
Summary
In the international approaches to vegetation degradation, generally Remote Sensing (RS), Global Position System (GPS), and Geographic Information System (GIS) are combined to monitor vegetation degradation. Remote sensing technology can extract many vegetation parameters such as surface coverage and vegetation coverage from multi-scale, multi-temporal, and multi-band remote sensing images, and has become an important means to study the spatial distribution and dynamic evolution of vegetation [13]. In terms of research methods, scholars use linear trend method, R/S analysis method, spatial interpolation method, spatial autocorrelation, standardized ellipse, etc., which is of important practical significance for vegetation and ecological environment protection in the research area [18]; besides, from the perspectives of climate change, remote sensing image interpretation, field vegetation survey and vegetation degradation characteristics and laws in mining areas [19], scholars analyzed the dynamic evolution of vegetation at different spatial scales by path analysis and other mathematical statistics and spatial analysis methods [20,21]. Thereby, the dynamic change law of vegetation in the western region were revealed more comprehensively, locating the background status for the later analysis of the influencing factors of vegetation degradation, providing basis for vegetation restoration and ecological environment protection in the western region, and providing reference and a scientific basis for vegetation degradation monitoring and decision-making of degraded land remediation in west China
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