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The amount and growth rate of carbon emissions have been accelerated on a global scale since the industrial revolution (1800), especially in recent decades. This has resulted in a significant influence on the natural environment and human societies. Therefore, carbon emission reduction receives continuously increasing public attention and has long been under debate. In this study, we made use of the land-use specific carbon emission coefficients from previous studies and estimated the land-use carbon emissions and carbon intensities of the Yangtze River Delta Urban Agglomeration (YRDUA)—which consists of 26 eastern Chinese cities—from Landsat image data and socio-economic statistics in 1995, 2005, and 2015. In addition, spatial autocorrelation models including both global and local Moran's I were used to analyze the spatial autocorrelation of carbon emissions and carbon intensities. It was found that (1) the YRDUA witnessed a rapidly increasing trend for net carbon emissions and a decreasing trend for carbon intensity over the two decades; (2) the spatial differences in carbon intensity had gradually narrowed, but were large in carbon emissions and had gradually increased; and (3) the carbon emissions in 2005 and 2015 had significant spatial autocorrelations. We concluded that (1) from 1995 to 2015 in the YRDUA, carbon emissions were large for the cities along the Yangtze River and carbon intensities were high for Anhui province's resource-based cities, while both carbon emissions and carbon intensities were small for Zhejiang province's cities; (2) over two decades, the increase in carbon emissions from urban land was approximately twice the increase in urban land area. Our study can provide useful insights into the assignment of carbon reduction tasks within the YRDUA. [ABSTRACT FROM AUTHOR]

The Advanced Spaceborne Thermal-Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is important to a wide range of geographical and environmental studies. Its accuracy, to some extent associated with land-use types reflecting topography, vegetation coverage, and human activities, impacts the results and conclusions of these studies. In order to improve the accuracy of ASTER GDEM prior to its application, we investigated ASTER GDEM errors based on individual land-use types and proposed two linear regression calibration methods, one considering only land use-specific errors and the other considering the impact of both land-use and topography. Our calibration methods were tested on the coastal prefectural city of Lianyungang in eastern China. Results indicate that (1) ASTER GDEM is highly accurate for rice, wheat, grass and mining lands but less accurate for scenic, garden, wood and bare lands; (2) despite improvements in ASTER GDEM2 accuracy, multiple linear regression calibration requires more data (topography) and a relatively complex calibration process; (3) simple linear regression calibration proves a practicable and simplified means to systematically investigate and improve the impact of land-use on ASTER GDEM accuracy. Our method is applicable to areas with detailed land-use data based on highly accurate field-based point-elevation measurements. [ABSTRACT FROM AUTHOR]