Spatiotemporal Pattern of Vegetation Ecology Quality and Its Response to Climate Change between 2000–2017 in China

Abstract

Vegetation ecology quality (VEQ) is an important indicator for evaluating environmental quality and ecosystem balance. The VEQ in China has changed significantly with global warming and gradual intensification of human activities. It is crucial to research the spatiotemporal characteristics of VEQ and its response to climate change in China. However, most previous studies used a single indicator to reflect VEQ in China, which needs to combine the effects of multiple indicators to reveal its variation characteristics. Based on the six remote sensing indicators, fractional vegetation cover, leaf area index, net primary productivity, vegetation wetness, land surface temperature, and water use efficiency of vegetation, the vegetation ecology quality index (VEQI) was constructed by principal component analysis in this paper. The spatio-temporal distribution and trend characteristic of VEQ within disparate ecosystems in China from 2000 to 2017 were studied. How continuous climate change affected VEQ over time was also analyzed. The results showed that the differences in spatial distribution between the excellent and poor VEQ regions were significant, with the proportion of excellent regions being much larger than that of poor regions. The VEQ has been ameliorated continuously during the past 18 years. Simultaneously, the VEQ would be ameliorated persistently in the future. Differences in the distribution and variation trend of VEQ occurred in disparate ecosystems. The VEQ of broadleaved forest was the best, while that of shrubs and arctic grassland ecosystem was the worst. The VEQ characteristics were different in disparate climate zones, with the best VEQ in the tropical monsoon climate zone and the worst in the plateau mountain climate zone. Except for desert vegetation and paddy field-dominated vegetation, VEQ of other ecosystems were significantly negatively correlated with altitude. Generally, moderate precipitation and temperature were favorable to improve VEQ in China. VEQ during the peak growing season was negatively correlated with temperature and positively correlated with precipitation, and the influence of precipitation on VEQ was stronger than that of temperature. Our results can be used to enact relevant management measures and policies.

Spatial-temporal changes and driving factors of eco-environmental quality in the Three-North region of China

“…(1) Trend analysis based on the Theil-Sen median trend method and MK test As a robust method of trend calculation, the Theil-Sen median trend method is often used in conjunction with the MK test to detect the variation trend of long-term sequence data (Li et al, 2021a;Jiang et al, 2022). The calculation formula of the Theil-Sen median (β) is as follows: RSEI RSEI median (2000 2020),…”

“…The dynamic analysis of time series remote sensing images is important for identifying the changes and internal laws of eco-environmental quality. The existing research methods for long-term sequences are numerous and have been used frequently, providing reference for their application to the spatial-temporal analysis of eco-environmental quality (Sun et al, 2020;Li et al, 2021a;Li et al, 2021b;Xu et al, 2021;Jiang et al, 2022). The linear trend analysis method is commonly used to describe the trend and change of time series data by obtaining the slope to estimate the magnitude of the variation trend through regression analysis (Duan et al, 2011;Deng et al, 2019;Xu et al, 2021).…”

“…However, this method has limited stability and is sensitive to anomalous data. The Theil-Sen median is a nonparametric statistical trend estimator that is often combined with the Mann-Kendall (MK) test for the trend analysis of long-term sequence data (Li et al, 2021a;Zhang et al, 2021b;Jiang et al, 2022). Compared with the linear trend analysis method, the Theil-Sen median trend method combined with the MK test has high calculation efficiency and robustness to error, which effectively reduces the influence of data outliers and improves the accuracy of test results.…”

“…Compared with the linear trend analysis method, the Theil-Sen median trend method combined with the MK test has high calculation efficiency and robustness to error, which effectively reduces the influence of data outliers and improves the accuracy of test results. In addition, the Hurst exponent can be calculated to predict the future trend of time series data (Li et al, 2021a;Li et al, 2021b;Rivas-Tabares et al, 2021), and the coefficient of variation (Li et al, 2021b;Zhang et al, 2021b), Moran index (Xiong et al, 2021) and change vector analysis (Xu et al, 2019;Sun et al, 2020) can be effectively used to understand the spatial evolutionary characteristics of vegetation.…”

Temporal and Spatial Changes in Vegetation Ecological Quality and Driving Mechanism in Kökyar Project Area from 2000 to 2021

“…Using EOS/MODIS vegetation parameter products and meteorological observation data, this study explored the temporal and spatial changes in the NDVI, the fractional vegetation cover (FVC), and the NPP in the Kökyar Project Area from 2000 to 2021 and constructed a comprehensive vegetation ecological quality index (VEQI) [20] to comprehensively analyze the vegetation quality in the study area. Then, based on the Landsat series of satellite data, the random forest algorithm built into the Google Earth Engine (GEE) [21] platform was used to construct the annual LULC and RSEI of the Kökyar Project Area in order to evaluate the temporal and spatial changes in the ecological quality in the Kökyar Project Area from 2000 to 2021.…”