The response of vegetation phenology change to climate change effects in the Northern China has been reported in the past several decades. Phenological change is a critical understanding in terrestrial carbon cycling. This study aims to investigate linear and nonlinear change trends and nonlinear response change trends in climate in vegetation phenology over Northern China in the last three decades. We analyzed the vegetation phenology over the Northern China by the new released Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVIg) dataset from 1982 to 2006.Results show that based on linear method, we can found that SOS was gradually advanced, EOS gradually delayed, and then LOS gradually lengthened. But on the basis of nonlinear method, phenological trends in the SOS, EOS and LOS are not continuous, we can found extended LOS with advanced SOS and delayed EOS before the turning point (TP) of spring SOS and autumn EOS trends and shortened LOS with delayed SOS and advanced EOS after the turning point (TP) of spring SOS and autumn EOS trends.
The Agro-Pastoral Transitional Zone in Northern China (hereafter APTZNC) is situated in an arid/semi-arid area, and is one of the most vulnerable areas in the world subject to climate change. Annual integrated the NASA Global Inventory Modeling and Mapping Studies (hereafter GIMMS) Normalized Difference Vegetation Index (hereafter ΣNDVI) and annual rainfall were used in this study. Meanwhile, the dynamics of ΣNDVI and rain-use efficiency (hereafter RUE) were predicted during the period, through the use of the Mann-Kendall nonparametric test and linear regression temporal trend analysis. The tendency of desertification under different precipitation scenarios was also analyzed. The results showed that annual ΣNDVI and rainfall were not significantly correlated in most sections of the study area, yet opposite results were observed for a smaller percentage of the study area (p<0.01). Changes in vegetation productivity may increase, whereas a significant decrease in a small pixel proportion was observed. The northeast and central sections of the study area are characterized by positive trends in RUE slope values, contrary to what was observed in the southwestern sections of the study area. The results fit well with the findings through ΣNDVI and RUE. Rainfall in the range of 200-500 mm can be seen as a threshold value as the desertification trend decreases and vegetation restoration capacity is enhanced with increasing rainfall.
With the fast urbanization process, how does the vegetation environment change in one of the most economically developed metropolis, Shanghai in East China? To answer this question, there is a pressing demand to explore the non-stationary relationship between socio-economic conditions and vegetation across Shanghai. In this study, environmental data on vegetation cover, the Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery in 2003 were integrated with socio-economic data to reflect the city’s vegetative conditions at the census block group level. To explore regional variations in the relationship of vegetation and socio-economic conditions, Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models were applied to characterize mean NDVI against three independent socio-economic variables, an urban land use ratio, Gross Domestic Product (GDP) and population density. The study results show that a considerable distinctive spatial variation exists in the relationship for each model. The GWR model has superior effects and higher precision than the OLS model at the census block group scale. So, it is more suitable to account for local effects and geographical variations. This study also indicates that unreasonable excessive urbanization, together with non-sustainable economic development, has a negative influence of vegetation vigor for some neighborhoods in Shanghai.
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