Jinpei Ou

Quantifying the relationship between urban forms and carbon emissions using panel data analysis

Carbon emissions are increasing in the world because of human activities associated with the energy consumptions for social and economic development. Thus, attention has been paid towards restraining the growth of carbon emissions and minimizing potential impact on the global climate. Currently there has also been increasing recognition that the urban forms, which refer to the spatial structure of urban land use as well as transport system within a metropolitan area, can have a wide variety of implications for the carbon emissions of a city. However, studies are limited in analyzing quantitatively the impacts of different urban forms on carbon emissions. In this study, we quantify the relationships between urban forms and carbon emissions for the panel of the four fastest-growing cities in China (i.e., Beijing, Shanghai, Tianjin, and Guangzhou) using time series data from 1990 to 2010. Firstly, the spatial distribution data of urban land use and transportation network in each city are obtained from the land use classification of remote sensing images and the digitization of transportation maps. Then, the urban forms are quantified using a series of spatial metrics which further used as explanatory variables in the estimation. Finally, we implement the panel data analysis to estimate the impacts of urban forms on carbon emission. The results show that, (1) in addition to the growth of urban areas that accelerate the carbon emissions, the increase of fragmentation or irregularity of urban forms could also result in more carbon emissions; (2) a compact development pattern of urban land would help reduce carbon emissions; (3) increases in the coupling degree between urban spatial structure and traffic organization can contribute to the reduction of carbon emissions; (4) urban development with a mononuclear pattern may accelerate carbon emissions. In order to reduce carbon emissions, urban forms in China should transform from the pattern of disperse, single-nuclei development to the pattern of compact, multiple-nuclei development.

Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data

Recently, the stable light products and radiance calibrated products from Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) have been useful for mapping global fossil fuel carbon dioxide (CO2) emissions at fine spatial resolution. However, few studies on this subject were conducted with the new-generation nighttime light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite, which has a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. Therefore, this study performed the first evaluation of the potential of NPP-VIIRS data in estimating the spatial distributions of global CO2 emissions (excluding power plant emissions). Through a disaggregating model, three global emission maps were then derived from population counts and three different types of nighttime lights data (NPP-VIIRS, the stable light data and radiance calibrated data of DMSP-OLS) for a comparative analysis. The results compared with the reference data of land cover in Beijing, Shanghai and Guangzhou show that the emission areas of map from NPP-VIIRS data have higher spatial consistency of the artificial surfaces and exhibit a more reasonable distribution of CO2 emission than those of other two maps from DMSP-OLS data. Besides, in contrast to two maps from DMSP-OLS data, the emission map from NPP-VIIRS data is closer to the Vulcan inventory and exhibits a better agreement with the actual statistical data of CO2 emissions at the level of sub-administrative units of the United States. This study demonstrates that the NPP-VIIRS data can be a powerful tool for studying the spatial distributions of CO2 emissions, as well as the socioeconomic indicators at multiple scales.

Assessing the impacts of urban sprawl on net primary productivity using fusion of Landsat and MODIS data

Urbanization has profoundly altered the terrestrial ecosystem carbon cycle, especially the net primary productivity (NPP). Many attempts have been made to assess the influence of urbanization on NPP at coarse resolutions (e.g., 250m or larger), which may ignore many smaller and highly fragmented urban lands, and to a large extent, underestimate the NPP variations induced by urban sprawl. Hence, we attempted to analyze the NPP variations influenced by urban sprawl at a fine resolution (e.g., 30m), toward which the accuracy of NPP was improved using remotely sensed data fusion algorithm. In this paper, this assumption was tested in the Pearl River Delta of China. The land cover datasets from the Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) were acquired to quantify the urban sprawl. The synthetic Normal Differential Vegetation Index (NDVI) data was obtained by fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI via spatiotemporal fusion algorithm. The Carnegie-Ames-Stanford Approach (CASA) model was driven by land cover map, synthetic NDVI and meteorological data to estimate the 30-m resolution NPP. Then, we analyzed the influence of urban sprawl on 30-m resolution NPP during the period of 2001-2009. Additionally, we also simulated the spatiotemporal change of future urban sprawl under different scenarios using the Future Land Use Simulation (FLUS) model, and further analyzed its influence on 30-m resolution NPP. Our results showed that the accuracy of 30-m resolution NPP from synthetic NDVI is better than 500-m resolution NPP from MODIS NDVI. The loss in 30-m resolution NPP due to urban sprawl was much higher than 500-m resolution NPP. Moreover, the harmonious development scenario, characterized by a reasonable size of urban sprawl and a corresponding lower NPP loss from 2009 to 2050, would be considered as a more human-oriented and sustainable development strategy.

Mapping Global Fossil Fuel Combustion CO 2 Emissions at High Resolution by Integrating Nightlight, Population Density, and Traffic Network Data

Quantification of global fossil fuel carbon dioxide (CO2) emissions at fine spatial resolution is emerging as a critical need in climate change research and policy-making. Numerous studies have constructed CO2 emission inventories by using spatial proxies, such as radiance-calibrated nightlight and population density, to downscale national emission data into finer spatial scales. However, only using nightlight imagery and population density datasets cannot sufficiently explain the spatial characteristics of potential emission sources from transportation. In this study, we integrated nighttime imagery, population density, and traffic network data to estimate CO2 emission, and performed a linear regression model with corrective measure to create a high-resolution global grid of fossil fuel carbon emissions in 2010. The experimental results show that the model which considers all these factors (nighttime lights, population density, and traffic network data) exhibited a more reasonable distribution of CO2 emission than those involving only one or two factors. Besides, in contrast to previous studies on the actual statistical data of CO2 emissions at the level of subadministrative units of mainland China and the United States, the correlation coefficient of our inventory is significantly larger than those of the other two inventories, while both the mean absolute error and root-mean-squared error of our inventory are the smallest values among the three inventories. The inventory established in this study shows strong agreement with subnational-level CO2 emission statistics. The resulting dataset and corresponding methods would be of immediate use to global climate managers and policy-making communities.

Assimilating multi-source remotely sensed data into a light use efficiency model for net primary productivity estimation

Abstract High spatiotemporal resolution satellite data are necessary for the retrieval of vegetation indexes, such as Normalized Difference Vegetation Index (NDVI), to be assimilated into the Carnegie-Ames-Stanford Approach (CASA) model for net primary productivity (NPP) estimation, especially in the growing season. However, current remotely sensed data cannot accurately monitor vegetation changes at high spatiotemporal resolution. To consider both temporal and spatial information, spatiotemporal fusion models have been developed to obtain the temporal information from high temporal resolution data (e.g., MODIS) together with the spatial information from high spatial resolution data (e.g., Landsat). In this paper, synthetic NDVI images with the spatial resolution of Landsat data and the temporal resolution of MODIS data were first produced using spatiotemporal fusion models. Next, phenological features were extracted from synthetic NDVI time series data to improve land cover classification accuracy. Finally, we evaluated the approach of assimilating the synthetic NDVI and land cover classification map into the CASA model for synthetic NPP estimation. The results revealed that the accuracy of the synthetic NPP was better than NPP estimation from non-fusion NDVI data, and improving the land cover classification accuracy could improve the accuracy of the synthetic NPP estimation. Furthermore, the monthly synthetic NPP showed a significant exponential relationship with the temperature, rainfall, and solar radiation of the current and previous month.