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You C.,East China Normal University | You C.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Gao Z.,CAS Yantai Institute of Coastal Zone Research | Ning J.,CAS Yantai Institute of Coastal Zone Research
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014

This study examined the dynamic changes of the Bohai Sea coastline in recent 20 years through spatial-temporal analysis using combined data from remote sensing and GIS technology. Three standard false color remote sensing images derived from visual interpretation and the vectorization from artificial methods are adopted to complete the extraction of the Bohai Sea coastline information. The results show that the Bohai Sea coastline has an increasing trend from 1990 to 2010, especially with the fastest growth during 2000 to 2010.The coastlines along the Liaoning and Shandong Provinces generally had a growing trend, while the shoreline along the Beijing-Tianjin-Hebei region changes most rapidly. These analyses have suggested that human influence is the key factor in causing the significant changes of the Bohai Sea coastlines in recent years. © 2014 SPIE. Source


Wang M.,East China Normal University | Wang M.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Liu C.,East China Normal University | Liu C.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014

An online visual analytical system based on Java Web and WebGIS for air quality data for Shanghai Municipality was designed and implemented to quantitatively analyze and qualitatively visualize air quality data. By analyzing the architecture of WebGIS and Java Web, we firstly designed the overall scheme for system architecture, then put forward the software and hardware environment and also determined the main function modules for the system. The visual system was ultimately established with the DIV + CSS layout method combined with JSP, JavaScript, and some other computer programming languages based on the Java programming environment. Moreover, Struts, Spring, and Hibernate frameworks (SSH) were integrated in the system for the purpose of easy maintenance and expansion. To provide mapping service and spatial analysis functions, we selected ArcGIS for Server as the GIS server. We also used Oracle database and ESRI file geodatabase to store spatial data and non-spatial data in order to ensure the data security. In addition, the response data from the Web server are resampled to implement rapid visualization through the browser. The experimental successes indicate that this system can quickly respond to user's requests, and efficiently return the accurate processing results. © 2014 SPIE. Source


Zhou C.,East China Normal University | Zhou C.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Shi R.,East China Normal University | Shi R.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 5 more authors.
International Journal of Remote Sensing | Year: 2013

As one of the major greenhouse gases, atmospheric carbon dioxide (CO2) concentrations have been monitored by both top-down satellite observations and air sampling systems on surface stations. The Atmospheric Infrared Sounder (AIRS) on board NASA's Aqua low Earth orbit (LEO) satellite is a high-resolution infrared sounder that has been in operation for more than 10 years. The World Data Centre for Greenhouse Gases (WDCGG) archives and provides data on CO2 and other greenhouse gases measured mainly from surface stations. In this article, we focus on the correlation between the two different sources of CO2 data and the influencing factors. In general, we find that a linear positive correlation occurs at most stations. However, the variation in the correlation coefficient is large, especially for stations in the Northern Hemisphere. The station's location, including its latitude, longitude, and altitude, is an important influencing factor because it determines how much its CO2 measurements are influenced by human activities. We also use root mean square difference (RMSD) and bias as evaluation indicators and find that they have similar trends like correlation coefficients. © 2013 © Taylor & Francis. Source


Yang J.,East China Normal University | Yang J.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Liu C.,East China Normal University | Liu C.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014

FAO56 Penman-Monteith equation, Hargreaves-Samani equation and Priestley-Taylor equation were used to estimate the reference crop evapotranspiration(ETo) in the North China Plain during the summer and winter, using climatology data from 1960 to 2013 including daily mean wind speed, average relative humidity, sunshine percentage, mean, maximum and minimum temperature at 10 weather stations over the North China Plain. By comparing the Hargreaves-Samani equation and Priestley-Taylor equation with FAO56 Penman-Monteith equation, we found that there existed interdependency between ETo derived from the former two equations and FAO56 Penman-Monteith equation. The interdependency in summer is higher than that in winter. In summer, the average pearson's correlation coefficient between ETo calculated by Hargreaves-Samani equation and FAO56 Penman-Monteith equation is 0.81, and the average pearson's correlation coefficient of ETo calculated by Priestley-Taylor equation and FAO56 Penman-Monteith equation is 0.87, while the corresponding pearson's correlation coefficient for them in winter is 0.69 and 0.51. Respectively, for the ETo calculation in summer, interdependency between Priestley-Taylor equation and FAO56 Penman-Monteith equation is higher than that between Hargreaves-Samani equation and FAO56 Penman-Monteith equation, and vice versa for winter. © 2014 SPIE. Source


Du J.,East China Normal University | Du J.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | Liu C.,East China Normal University | Liu C.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014

In this work, a soil moisture data assimilation scheme was developed based on the Community Land Model Version 3.0 (hereafter CLM) and Ensemble Kalman Filter. Soil moisture in the 1st soil layer was assimilated into CLM to evaluate the improvements of land surface process simulation. The results indicated that the assimilation system could improve the model accuracy effectively. It can transfer the variations of shallow soil layer's moisture to the deep soil and make great improvements to the soil water and heat status in an overall level. The system could improve the soil moisture accuracy from the 1st soil layer to the 6th soil layer by 50%. According to this experiment, the transfer depth of soil moisture was from 40 cm to 60 cm. After assimilation, the correlation coefficient of latent heat flux observation and simulation increased from 0.68 to 0.91 and the RMSE dropped from 86.7 W/m2 to 45.7 W/m2. For the sensible heat flux, the correlation coefficient increased from 0.69 to 0.80 and the RMSE reduced from 105.1 W/m2 to 71.3 W/m2. It was feasible and significant to assimilate soil moisture remote sensing products. © 2014 SPIE. Source

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