Effectively evaluating the effects of urban forms on CO2 emissions has become a hot topic in socioeconomic sustainable development; however, few studies have been able to explore the urban form-CO2 emission relationships from a multi-perspective view. Here, we attempted to analyze the relationships between urban forms and CO2 emissions in 264 Chinese cities, with explicit consideration of the government policies, urban area size, population size, and economic structure. First, urban forms were calculated using the urban land derived from multiple-source remote sensing data. Second, we collected and processed CO2 emissions and three control variables. Finally, a correlation analysis was implemented to explore whether and to what extent the spatial patterns of urban forms were associated with CO2 emissions. The results show that urban form irregularity had a more significant impact on CO2 emissions in low-carbon pilot cities than in non-pilot cities. The impact of the complexity of urban forms on CO2 emissions was relatively significant in the small- and large-sized cities than in the medium-sized cities. Moreover, urban form complexity had a significant correlation with CO2 emissions in all of the cities, the level of which basically increased with the population size. This study provides scientific bases for use in policy-making to prepare effective policies for developing a low-carbon economy with consideration of the associations between urban forms and CO2 emissions in different scenarios.
The rapid development of global industrialization and urbanization has resulted in a great deal of electric power consumption (EPC), which is closely related to economic growth, carbon emissions, and the long-term stability of global climate. This study attempts to detect spatiotemporal dynamics of global EPC using the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime stable light (NSL) data. The global NSL data from 1992 to 2013 were intercalibrated via a modified invariant region (MIR) method. The global EPC at 1 km resolution was then modeled using the intercalibrated NSL data to assess spatiotemporal dynamics of EPC from a global scale down to continental and national scales. The results showed that the MIR method not only reduced the saturated lighted pixels, but also improved the continuity and comparability of the NSL data. An accuracy assessment was undertaken and confined that the intercalibrated NSL data were relatively suitable and accurate for estimating EPC in the world. Spatiotemporal variations of EPC were mainly identified in Europe, North America, and Asia. Special attention should be paid to China where the high grade and high-growth type of EPC covered 0.409% and 1.041% of the total country area during the study period, respectively. The results of this study greatly enhance the understanding of spatiotemporal dynamics of global EPC at the multiple scales. They will provide a scientific evidence base for tracking spatiotemporal dynamics of global EPC. (C) 2016 Elsevier Ltd. All rights reserved.
The nighttime light data records artificial light on the Earth's surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were released by the Earth Observation Group of National Oceanic and Atmospheric Administration's National Geophysical Data Center (NOAA/NGDC). As new-generation data, NPP-VIIRS data have a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. This study aims to investigate the potential of NPP-VIIRS data in modeling GDP and EPC at multiple scales through a case study of China. A series of preprocessing procedures are proposed to reduce the background noise of original data and to generate corrected NPP-VIIRS nighttime light images. Subsequently, linear regression is used to fit the correlation between the total nighttime light (TNL) (which is extracted from corrected NPP-VIIRS data and DMSP-OLS data) and the GDP and EPC (which is from the country's statistical data) at provincial-and prefectural-level divisions of mainland China. The result of the linear regression shows that R-2 values of TNL from NPP-VIIRS with GDP and EPC at multiple scales are all higher than those from DMSP-OLS data. This study reveals that the NPP-VIIRS data can be a powerful tool for modeling socioeconomic indicators; such as GDP and EPC.
The Arctic coastal plain is covered with numerous thermokarst lakes. These lakes are closely linked to climate and environmental change through their heat and water budgets. We examined the intralake thermal structure at the local scale and investigated the water temperature pattern of lakes at the regional scale by utilizing extensive in situ measurements and multidate Landsat-8 remote sensing data. Our analysis indicates that the lake skin temperatures derived from satellite thermal sensors during most of the ice-free summer period effectively represent the lake bulk temperature because the lakes are typically well-mixed and without significant vertical stratification. With the relatively high-resolution Landsat-8 thermal data, we were able to quantitatively examine intralake lateral temperature differences and gradients in relation to geographical location, topography, meteorological factors, and lake morphometry for the first time. Our results suggest that wind speed and direction not only control the vertical stratification but also influences lateral differences and gradients of lake surface temperature. Wind can considerably reduce the intralake temperature gradient. Interestingly, we found that geographical location (latitude, longitude, distance to the ocean) and lake morphometry (surface size, depth, volume) not only control lake temperature regionally but also affect the lateral temperature gradient and homogeneity level within each individual lake. For the Arctic coastal plain, at regional scales, inland and southern lakes tend to have larger horizontal temperature differences and gradients compared to coastal and northern lakes. At local scales, large and shallow lakes tend to have large lateral temperature differences relative to small and deep lakes.
China's rapid industrialization and urbanization have resulted in a great deal of CO2 (carbon dioxide) emissions, which is closely related to its sustainable development and the long term stability of global climate. This study proposes panel data analysis to model spatiotemporal CO2 emission dynamics at a higher resolution in China by integrating the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime stable light (NSL) data with statistic data of CO2 emissions. Spatiotemporal CO2 emission dynamics were assessed from national scale down to regional and urban agglomeration scales. The evaluation showed that there was a true positive correlation between NSL data and statistic CO2 emissions in China at the provincial level from 1997 to 2012, which could be suitable for estimating CO2 emissions at 1 km resolution. The spatiotemporal CO2 emission dynamics between different regions varied greatly. The high-growth type and high-grade of CO2 emissions were mainly distributed in the Eastern region, Shandong Peninsula and Middle south of Liaoning, with clearly lower concentrations in the Western region, Central region and Sichuan-Chongqing. The results of this study will enhance the understanding of spatiotemporal variations of CO2 emissions in China. They will provide a scientific basis for policy-making on viable CO2 emission mitigation policies. (C) 2015 Elsevier Ltd. All rights reserved.
The cargo handling capacity of a port is the most basic and important indicator of port size. Based on the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light data and panel model, this study attempts to estimate the cargo handling capacity of 28 coastal ports in China using satellite remote sensing. The study confirmed that there is a very close correlation between DMSP-OLS nighttime light data and the cargo handling capacity of the ports. Based on this correlation, the panel data model was established for remote sensing-based estimation of cargo handling capacity at the port and port group scales. The test results confirm that the nighttime light data can be used to accurately estimate the cargo handling capacity of Chinese ports, especially for the Yangtze River Delta Port Group, Pearl River Delta Port Group, Southeast Coastal Port Group, and Southwest Coastal Port Group that possess huge cargo handling capacities. The high accuracy of the model reveals that the remote sensing analysis method can make up for the lack of statistical data to a certain extent, which helps to scientifically analyze the spatiotemporal dynamic changes of coastal ports, provides a strong basis for decision-making regarding port development, and more importantly provides a convenient estimation method for areas that have long lacked statistical data on cargo handling capacity.