| Carbon cycle of urban ecosystem is an important part of carbon verification of terrestrial ecosystem, quantitative and qualitative research of urban carbon sources/sinks has vital significance for carbon cycle.But the study of carbon sources/sinks focused on the homogeneous underlying of ecosystem, while urban ecosystem as a major source of atmosphere carbon dioxide, few research has been done in this area. This paper investigated urban system on the basis of eddy covariance technique, observating carbon dynamic flux of south Xuzhou. Based on improved CASA model and GSMSR model, Net Ecosystem Productivity(NEP) in urban area was retrieved, integrating with remote sensing data and meteorological data as input. Carbon sources/sinks was analyzed using simulated NPP at south of Xuzhou in 2014-2015, temporal-spatial characteristics and influencing factors were analyzed with eddy covariance data.This will be helpful for assessing region ecological environment quality, reasonable development and protection in Xuzhou city. The main research is listed as the following:(1)The fundamental mechanism of eddy covariance technique was researched, Comprehensive utilization of axis rotation, crosswind correction, data sieving at night realized data quality controlling and evaluation. On the basis of improved observation, threshold of friction velocity was set as 0.22m/s by test method of averaged piecewise. According to fragmentary data, artificial neural network algorithm was applied to interpolate the incomplete data. Reliable data obtained by pretreatment, representing the relatively real exchange processes of this region.(2)Basic principles of CASA model was researched. Aiming at difficulty in obtaining of evapotranspiration, the calculation of water stress index was improved by NDWI, making optimization in model. Optimized parameters including daily solar radiation, NDVI, temperature were chose to retrieved NPP based on the CASA model in 2014-2015, while geostatistical model of soil respiration(GSMSR) was developed to measure the heterotrophic respiration. On this basis, NEP was obtained by NPP and heterotrophic respiration.(3)In order to study on reliability analysis and accuracy evaluation, correlation between simulated NEP and eddy covariance data was analysis. Based on principles of carbon contributive area, radius of footprint was determined as 1500 m. Correlation analysis of mean NEP within the radius and observes displayed that highest related coefficient was 0.627 and 0.823 in 2014&2015, respectively. Meanwhile, for the sake of making sure of dependability on model, simulated data was compared to existing research results, the average NPP of south of Xuzhou in 2014&2015 is 508.06--12?? amg C and 343.735 12--?? amg C, respectively. It is indicated that the retrived NPP based on remote sensing and CASA model was accorded with the NPP tendency internal China mainland estimated by many scholars.(4)Characteristics of urban carbon sources/sinks was studied, temporal-spatial analysis on NPP was conducted by modified model in annual, quarterly, monthly. The results showed that annual mean NPP estimated by HJ-1 satellite and Landsat8 is 372.67--12?? amg C, 312.06 12--?? amg C in 2014; and it was 301.72 12--?? amg C, 346.96--12?? amg C in 2015, respectively. From monthly mean value, Yunlong Mountain, Quanshan Park, Zhushan scenic, Lali Mountain and cropland in suburb were carbon sinks, downtown with concentrated buildings was carbon sources. All the analysis showed that eddy covariance technology can accuratly monitoring the carbon cycle of ubarn system, improved CASA model combined with GSMSR has strong reliability in supervising the carbon sources/sinks in large range of urban.(5)Analysis on meteorological factors related to carbon flux was proceeded, conclusions demonstrated that: temperature and flux present quadratic relativity correlation, while there was negative correlation among atmospheric humidity and carbon flux. Influencers of wind direction and speed revealed that southeast and southwest of eddy covariance station had a significant influence on carbon flux. On the other hand, image classification of underlying within footprint region can interpret origin of carbon sources/sinks in detail. |
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