House vacancy rate (HVR) is an important index in assessing the healthiness of residential real estate market. Investigating HVR by field survey requires a lot of human and economic resources. The nighttime light (NTL) data, derived from Suomi National Polar-orbiting Partnership, can detect the artificial light from the Earth surface, and have been used to study social-economic activities. This paper proposes a method for estimating the HVR in metropolitan areas using NPP-VIIRS NTL composite data. This method combines NTL composite data with land cover information to extract the light intensity in urbanized areas. Then, we estimate the light intensity values for nonvacancy areas, and use such values to calculate the HVR in corresponding regions. Fifteen metropolitan areas in the United States have been selected for this study, and the estimated HVR values are validated using corresponding statistical data. The experimental results show a strong correlation between our derived HVR values and the statistical data. We also visualize the estimated HVR on maps, and discover that the spatial distribution of HVR is influenced by natural situations as well as the degree of urban development.
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.
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.
Poverty is a chronic worldwide dilemma that can seriously hamper human sustainable development, which is closely related to economic growth, environmental protection, ecological restoration, and sustainable utilization of resources. Accurately and effectively identifying and evaluating poverty has become an important prerequisite for allowing Chinese governments to make reasonable poverty reduction and alleviation policies. Thus, using Chongqing as a study area, the purpose of this study was to analyze poverty from multiple viewpoints based on multiple data sources. First, a comprehensive poverty index (CPI) was developed by combining nighttime light data, the digital elevation model (DEM), the normalized differential vegetation index (NDVI), and point of interest (POI) data to map poverty at a 500-m spatial resolution. Then, the performance of the CPI was validated with poverty-stricken villages, Google Earth images, and the multidimensional poverty index (MPI). Finally, spatial autocorrelation analysis was used to explore the spatial distribution of poverty across county and town levels. The results revealed that the CPI could provide an effective way of identifying the spatial distribution of poverty when compared with three validated indexes. Most of the rich counties were in the center of Chongqing, whereas the poor counties were located in the northeast and southeast of Chongqing. The Global Moran's I index showed that there were significantly positive spatial autocorrelations of poverty, and that the spatial autocorrelation of poverty was more significant at the town level compared to the county level. Among the selected factors, the POI cost distance was the most import factor for assessing poverty. Our study will be valuable for providing scientific references for the government to implement precise poverty alleviation methods with differentiated policies in China. (C) 2020 Elsevier Ltd. All rights reserved.
Accurate spatial distribution information on gross domestic product (GDP) is of great importance for the analysis of economic development, industrial distribution and urbanization processes. Traditional administrative unit-based GDP statistics cannot depict the detailed spatial differences in GDP within each administrative unit. This paper presents a study of GDP spatialization in Ningbo City, China based on National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL) data and town-level GDP statistical data. The Landsat image, land cover, road network and topographic data were also employed as auxiliary data to derive independent variables for GDP modelling. Multivariate linear regression (MLR) and random forest (RF) regression were used to estimate GDP at the town scale and were assessed by cross-validation. The results show that the RF model achieved significantly higher accuracy, with a mean absolute error (MAE) of 109.46 million China Yuan (CNY).km(-2) and a determinate coefficient (R-2 =3D 0.77) than the MLR model (MAE =3D 161.8 million CNY.km(-2), R-2 =3D 0.59). Meanwhile, by comparing with the estimated GDP data at the county level, the town-level estimated data showed a better performance in mapping GDP distribution (MAE decreased from 115.1 million CNY.km(-2) to 74.8 million CNY.km(-2)). Among all of the independent variables, NTL, land surface temperature (Ts) and plot ratio (PR) showed higher impacts on the GDP estimation accuracy than the other variables. The GDP density map generated by the RF model depicted the detailed spatial distribution of the economy in Ningbo City. By interpreting the spatial distribution of the GDP, we found that the GDP of Ningbo was high in the northeast and low in the southwest and formed continuous clusters in the north. In addition, the GDP of Ningbo also gradually decreased from the urban centre to its surrounding areas. The produced GDP map provides a good reference for the future urban planning and socio-economic development strategies. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
With more record-breaking skyscrapers built in big cities around the world, horizontal urban sprawl no longer dominates the research of urbanization rather than the vertical growth of cities. In such a context, the urban heat island problem cannot be understood by solely studying the impact of the horizontal urban expansion because the 3D structure of the urban landscape could severely alter the natural heat flux transport over the land surface and thus lead to bigger heat island problems. In addition to our current knowledge of impact of 2D landscape changes on urban thermal dynamics, it is crucial to understand the effects of 3D landscape pattern on the thermal environment, in order to maintain a sustainable and eco-friendly urban development. This study investigated the 2D/3D landscape pattern metrics and their association with the land surface temperature (LST) changes in a case study area of Shanghai City using the extreme gradient boosting (XGBoost) regression model and Sharpley Additive exPlanations (SHAP) interpretation method based on datasets of land cover and digital surface model (DSM). Major findings include, 1) 3D landscape pattern metrics could better describe the undulation and heterogeneity of urban surface and were essential when explaining the variation of LST compared with conventional 2D landscape pattern metrics, 2) Low-rise and high-rise buildings tend to alleviate LSTwhile buildings with medium height heating the surroundings; 3) the cooling effect of vegetation was significantly strong; 4) different urban functional types impact the surface temperature in the way determined by their 3D urban landscape pattern. These findings may help urban planners and landscape designers achieve the goal ofminimizing urban heat island using computer models of 3D urban structure. (C) 2020 Elsevier B.V. All rights reserved.
Spatially explicit and reliable data on poverty is critical for both policy makers and researchers. However, such data remain scarce particularly in developing countries. Current research is limited in using environmental data from different sources in isolation to estimate poverty despite the fact that poverty is a complex phenomenon which cannot be quantified either theoretically or practically by one single data type. This study proposes a random forest regression (RFR) model to estimate poverty at 10 km x 10 km spatial resolution by combining features extracted from multiple data sources, including the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) nighttime light (NTL) data, Google satellite imagery, land cover map, road map and division headquarter location data. The household wealth index (WI) drawn from the Demographic and Health Surveys (DHS) program was used to reflect poverty level. We trained the RFR model using data in Bangladesh and applied the model to both Bangladesh and Nepal to evaluate the model's accuracy. The results show that the R-2 between the actual and estimated WI in Bangladesh is 0.70, indicating a good predictive power of our model in WI estimation. The R-2 between actual and estimated WI of 0.61 in Nepal also indicates a good generalization ability of the model. Furthermore, a negative correlation is observed between the district average WI and the poverty head count ratio (HCR) in Bangladesh with the Pearson Correlation Coefficient of -0.6. Using Gini importance, we identify that proximity to urban areas is the most important variable to explain poverty which contribute to 37.9% of the explanatory power. Compared to the study that used NTL and Google satellite imagery in isolation to estimate poverty, our method increases the accuracy of estimation. Given that the data we use are globally and publicly available, the methodology reported in this study would also be applicable in other countries or regions to estimate the extent of poverty.
China's rapid urbanization has contributed to a massive agricultural land loss that could threaten its food security. Timely and accurate mapping of urban expansion and urbanization-related agricultural land loss can provide viable measures to be taken for urban planning and agricultural land protection. In this study, urban expansion in China from 2001 to 2013 was mapped using the nighttime stable light (NSL), normalized difference vegetation index (NDVI), and water body data. Urbanization-related agricultural land loss during this time period was then evaluated at national, regional, and metropolitan scales by integrating multiple sources of geographic data. The results revealed that China's total urban area increased from 31,076 km(2) in 2001 to 80,887 km(2) in 2013, with an average annual growth rate of 13.36%. This widespread urban expansion consumed 33,080 km(2) of agricultural land during this period. At a regional scale, the eastern region lost 18,542 km(2) or 1.2% of its total agricultural land area. At a metropolitan scale, the Shanghai-Nanjing-Hangzhou (SNH) and Pearl River Delta (PRD) areas underwent high levels of agricultural land loss with a decrease of 6.12% (4728 km(2)) and 6.05% (2702 km(2)) of their total agricultural land areas, respectively. Special attention should be paid to the PRD, with a decline of 13.30% (1843 km(2)) of its cropland. Effective policies and strategies should be implemented to mitigate urbanization-related agricultural land loss in the context of China's rapid urbanization.
The total ozone column (TOC) observations derived from the Ozone Mapping and Profiler Suit (OMPS) on-board the Suomi National Polar-orbiting Partnership (Suomi NPP) spacecraft were recently released for monitoring ozone in the stratosphere. Two kinds of TOC data which are derived using the NPOESS algorithm (OMPS-NPOESS) and the TOMS Version 7 algorithm (OMPS-TOMS) are provided. So far, few studies have been conducted to validate the accuracy of the OMPS TOC data. In this letter, we validate a 1 year OMPS-derived TOC data set (from March 2012 to February 2013) by comparing them with ground-based spectroradiometer data. We also examine the difference of the data derived using OMPS-NPOESS and OMPS-TOMS algorithms. Our results show a moderate correlation between OMPS-derived TOC and ground-based data, and the average relative difference between the two data sets (OMPS data minus the ground-based data) is negative, which indicates an underestimation of the TOC in the OMPS data. Such relative difference is not globally consistent: smaller values (around -10% to 0%) are found in low latitudinal areas (from 30 degrees S to 30 degrees N), whereas comparatively larger differences (around -25% to -15%) are detected in data on the mid-high latitudinal areas. A comparison of the two different algorithms suggests that the OMPS-TOMS-derived data have a generally better accuracy than those from OMPS-NPOESS.
Urban hierarchies are closely related to economic growth, urban planning and sustainable urban development. Due to the limited availability of reliable statistical data at fine scales, most existing studies on urban hierarchy characterization failed to capture the detailed urban spatial structure information. Previous studies have demonstrated that night time light data are correlated with many urban socio-economic indicators and hence can be used to characterize urban hierarchies. This paper presents a novel method for studying urban hierarchies from night time light data. Night time light data were first conceptualized as continuous mathematical surfaces, termed night time light surfaces. From the morphology of these surfaces the corresponding surface networks were derived. Hereafter, a night time light intensity (NTLI) graph was defined to describe the morphology of the surface network. Then, structural similarity between the night time light surfaces of any two different cities was calculated via a threshold-based maximum common induced graph searching algorithm. Finally, urban hierarchies were defined on the basis of the structural similarities between different cities. Using the 2015 annual NPP-VIIRS night time light data, the urban hierarchies of 32 major cities in China were successfully examined. The results are highly consistent with the reference urban hierarchies.