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The nighttime stable light (NSL) images on board the Operational Line-scan System (OLS) of the Defense Meteorological Satellite Program (DMSP) are useful for extracting large-scale built-up urban areas. However, most NSL-based studies are presently empirical threshold-based approaches. They always overestimate the areas of built-up land in urban regions because of the 'blooming' effect of NSL; and overlook small patches in developing towns where the NSL is much lower. In this study, a neighborhood statistics analysis (NSA) method is developed on the basis of the relative spatial variations between neighboring built-up and non-built-up pixels in DMSP-OLS images. It is applied to extract the built-up areas of eight cities in the Pearl River Delta in 1996Delta in , 2000Delta in , 2005Delta in , and 2009. The validations indicate that the total areas of the NSA-mapped results are highly correlated with those from Landsat TM/ETM+ data (R 2 = 0.94; p < 0.001). The comparison results, which are evaluated by landscape indices (the landscape shape index (LSI), the contiguity index (CONTIG), and the perimeter area ratio (PARA)), also show good correlations (R 2 > 0.46; p < 0.001). In addition, the total NSL of the built-up urban areas is significantly correlated with the statistical population data (R 2 = 0.62; p < 0.001), which indirectly confirms the effectiveness of our proposed method.Keywords: DMSP-OLS nighttime stable light (NSL); built-up urban area; urban expansion; remote sensing IntroductionThirty years of rapid urbanization in China have resulted in a 3.37-fold increase in builtup areas (Fang 2009;Lu et al. 2008 Lu et al. , 2012. Large amounts of natural lands in cities are being replaced by artificial surfaces, which cause numerous environmental and ecological problems (Milesi et al. 2003;Imhoff et al. 2004;Su et al. 2010;Chen et al. 2012). Therefore, accurately monitoring the regional expansion of built-up areas in China is essential (He et al. 2006;Liu et al. 2012 (Cao et al. 2009;Liu et al. 2012;Bhatti and Tripathia 2014), are commonly used. However, because of the limited geographic coverage of medium-and high-resolution images, these methods require a large number of scenes to cover the regional and national scales (Liu et al. 2003(Liu et al. , 2005(Liu et al. , 2010Zhang, He, and Liu 2014).Nighttime stable light (NSL) images on board the Operational Line-scan System (OLS) of the Defense Meteorological Satellite Program (DMSP), which uses a low-light detecting sensor to detect city night lights (Elvidge et al. 1997(Elvidge et al. , 1999, were first demonstrated to be capable of mapping built-up urban areas by Croft (1978) and Kramer (1994). Subsequent studies demonstrated the superiority of DMSP-OLS NSL in extracting the built-up urban areas at global (Elvidge et al. 1997(Elvidge et al. , 1999(Elvidge et al. , 2007 (Imhoff et al. 1997a(Imhoff et al. , 1997bSmall, Pozzi, and Elvidge 2005;Lu et al. 2008;Cao et al. 2009;Shi et al. 2014;Wei et al. 2014). However, most NSL-based studies presently use empir...

Provincial carbon emissions research is necessary for China to realize emissions reduction targets. Two-level decomposition model based on the Kaya identity was applied to uncover the main driving forces for the energy related carbon emissions in Shandong province from 1995 to 2011, an important energy base in China. Coal consumption is still the biggest contributor to the increased carbon emissions in Shandong. Decomposition results show that the affluence effect is the most important contributors to the carbon emissions increments. The energy intensity effect is the dominant factor in curbing carbon emissions. The emission coefficient effect plays an important negative but relatively minor effect on carbon emissions. Based on the local realities, a series of environment-friendly mitigation policies are raised by fully considering all of these influencing factors. Sustainable mitigation policies will pay more attention to the low-carbon economic development along with the significant energy intensity reduction in Shangdong province.

Urban expansion models are useful tools to understand urbanization process and have been given much attention. However, urban expansion is a complicated socioeconomic phenomenon that is affected by complex and volatile factors involving in great uncertainties. Therefore, the accurate simulation of the urban expansion process remains challenging. In this paper, we make an attempt to solve such uncertainty through a reversal process and view urban expansion as a process wherein the urban landscape overcomes resistance from other landscapes. We developed an innovative approach derived from the minimum cumulative resistance (MCR) model that involved the introduction of a relative resistance factor for different source levels and the consideration of rigid constraints on urban expansion caused by ecological barriers. Using this approach, the urban expansion ecological resistance (UEER) model was created to describe ecological resistance surfaces suitable for simulating urban expansion and used to simulate urban expansion in Guangzhou. The study results demonstrate that the ecological resistance surface generated by the UEER model comprehensively reflects ecological resistance to urban expansion and indicates the spatial trends in urban expansion. The simulation results from the UEER-based model were more realistic and more accurately reflected ecological protection requirements than the conventional MCR-based model. These findings can enhance urban expansion simulation methods.

In the aftermath of the global financial crisis of 2008, China witnessed gradual shrinkage of cities in the Pearl River Delta (PRD). In this study, we introduce the concept of economic resilience to analyse urban growth and shrinkage in the context of a rapidly-urbanising region. Multiple regression analysis is performed to explore the determinants of economic resilience in the PRD. By measuring resistance in the shrinking phase and recoverability in the growing phase in a group of cities in the PRD, this study distinguishes four scenarios and investigates their characteristics from a spatial perspective. The results demonstrate that the financial crisis had a severe and asymmetric influence on this area, indicating more than 15% of cities are faced with shrinking. The spatial distribution of economic resilience indicates a centre-periphery pattern, that is, high economic resilience in the inner ring and low economic resilience in the outer ring of the PRD. The service economy is found to play a significant role in promoting urban economic resilience. Results imply that sound economic policies for enhancing resilience: both poor local financial status and a high degree of export concentration adversely impact resistance, while upgrading the manufacturing economy and stimulating of industrial innovation are conducive to improve recoverability.

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