| The greenhouse effect caused by greenhouse gas emissions has made climate change a major issue in the world. Among these greenhouse gases, carbon dioxide is the largest contributor to global warming. Urban green space, which makes up the majority of the natural components in an urban ecosystem, plays a crucial role in the regulation of the global carbon cycle and the mitigation of atmospheric carbon dioxide. A quantitative study of carbon stock in an urban green space can help to better understand the role of urban green space in reducing greenhouse gas emissions of carbon dioxide in the global carbon cycle, and to provide a theoretical basis for the planning and management of urban green space.Remote sensing not only helps estimate vegetation carbon stock in an immediate, effective and large-scale way, but also helps monitor dynamic changes of carbon stock in the long run. Therefore, remote sensing data can be used to estimate urban green space carbon stock and its spatial distribution with less time and effort.The present study aims at estimating the situation of changes in aboveground carbon(AGC) stock of Xi’an’s urban green space with the application of remote sensing and field investigation data. First of all, biomass models of eleven shrub species were developed in the urban area of Xi’an city. Then, with the help of the carbon data from a field survey and Landsat image attained in 2010, an estimation model of a univariate model relating spectrally derived vegetation indices and AGC stock was developed. Meanwhile, based on the Landsat images obtained in 2004, 2010 and 2013, the total AGC stock in the urban green space of Xi’an was estimated in the year 2004, 2010 and 2013, respectively. Furthermore, the characteristics of spatiotemporal dynamics of aboveground carbon stock and its potential causes were analyzed in the paper. The main results were as follows:(1) By using the regression analysis method, the equations were developed for organs and total biomass of eleven common greening shrub species(Ligustrum quihoui, Buxus bodinieri, Berberis thunbergii var. atropurpurea, Buxus megistophylla, Photinia serrulata, Pittosporum tobira, Hibiscus syriacus, Nandina domestica, Lagerstroemia indica, Syringa oblata and Forsythia suspensa) in Xi’an city. The organ and total biomass optimal models of the eleven shrubs were power functional model(CAR model) except for the leaf biomass model of Buxus megistophylla and Nandina domestica which were logarithmic functional models(VAR model). The independent variable Buxus megistophylla for the optimal biomass model of branch, root and individual was basal diameter, while height was for the organs and individual of Lagerstroemia indica and the leaf of Nandina domestica, basal diameter square multiplied by height was for the organ and individual of Photinia serrulata, Syringa oblate and Forsythia suspense, and the leaf of Pittosporum tobira. Orther shrub species were crown-related factors in the independent variable selection. The independent variable Buxus bodinieri and Berberis thunbergii var. atropurpurea for the optimal biomass model of the organs and individual were crown diameter multiplied by height and canopy volume, respectively.(2) Remotely-sensed vegetation indices(Normalized difference vegetation index, Ratio vegetation index, Difference vegetation index, Soil adjusted vegetation index, Modified soil adjusted vegetation index and Renormalized difference vegetative index) were used to develop a regression equation between vegetation indices and AGC stock of urban green space based on field investigation data and Xi’an Landsat image attained in 2010. The power functional model of NDVI with the determination coefficient was the highest at R2=0.711 in all fitting models with vegetation index. The regression relation of equation was significant(F test, P<0.001). Through accuracy evaluation, the prediction accuracy was 86.3%. The results showed that NDVI was correlated moderately well with AGC stock in urban green space, and it was the most reliable to estimate AGC stock in urban green space.(3) The model with AGC-NDVI was then used to estimate the AGC stock of urban green space of Xi’an city in 2004, 2010 and 2013, respectively. The AGC stock in the urban green space of Xi’an in 2004, 2010 and 2013 were 73843 t, 126621 t and 200076 t, respectively; the AGC stock densities in 2004, 2010 and 2013 were 1.62 t/ha, 2.77 t/ha and 4.38 t/ha, respectively. Most AGC stock density values in the study area in 2004 were in the range of 0.0-1.0 t/ha, and few areas were in high AGC stock densities. The number of mean AGC stock values over 1.0 t/ha in 2010 and 2013 greatly increased compared to that in 2004. The number of the AGC stock density values over 12.0 t/ha in 2013 was large compared to 2004 and 2010. Overall, the AGC stock of urban green space was on the increase in Xi’an over the past 9 years and the distribution of urban green space AGC stock in Xi’an became more balanced.(4) Great changes of AGC stock and density at different levels of urbanization gradient took place in Xi’an’s urban green space from 2004 to 2013. Besides, both the AGC stock and the AGC stock density were on a gradual increase within the 1st ring road, between the 1st and the 2nd ring road and between the 2nd ring road and the beltway in 2004-2013. In addition, the AGC stock density was always the highest between the 2nd ring road and the beltway, while it was the lowest within the 1st ring road in 2004, 2010 and 2013. The AGC stock densities were shown the obvious gradient feature along the east-west, south-north, southeast-northwest, northeast-southwest gradient transects and the gradient buffer zones in 2004, 2010 and 2013.(5) There was no significant relation between the changes of AGC stock in Xi’an’s urban green space and precipitation. Policy orientation, major ecological greening projects such as “transplanting big trees into the city” and the World Horticultural Exposition were found out to have an important impact on changes in the spatiotemporal patterns of AGC stock. |
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