Kaifang Shi

Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

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 R2 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.

Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas

The first global night-time light composite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) day–night band carried by the Suomi National Polar-orbiting Partnership (NPP) satellite were released recently. So far, few studies have been conducted to assess the ability of NPP-VIIRS night-time light composite data to extract built-up urban areas. This letter aims to evaluate the potential of this new-generation night-time light data for extracting urban areas and compares the results with Defense Meteorological Satellite Program–Operational Linescan System (DMSP-OLS) data through a case study of 12 cities in China. The built-up urban areas of 12 cities are extracted from NPP-VIIRS and DMSP-OLS data by using statistical data from government as reference. The urban areas classified from Landsat 8 data are used as ground truth to evaluate the spatial accuracy. The results show the built-up urban areas extracted from NPP-VIIRS data have higher spatial accuracies than those from DMSP-OLS data for all the 12 cities. These improvements are due to the relatively high spatial resolution and wide radiometric detection range of NPP-VIIRS data. This study reveals that NPP-VIIRS night-time light composite data would provide a powerful tool for urban built-up area extraction at national or regional scale.

Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China

Poverty has appeared as one of the long-term predicaments facing development of human society during the 21st century. Estimation of regional poverty level is a key issue for making strategies to eliminate poverty. This paper aims to evaluate the ability of the nighttime light composite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) carried by the Suomi National Polar-orbiting Partnership (NPP) Satellite in estimating poverty at the county level in China. Two major experiments are involved in this study, which include 1) 38 counties of Chongqing city and 2) 2856 counties of China. The first experiment takes Chongqing as an example and combines 10 socioeconomic variables into an integrated poverty index (IPI). IPI is then used as a reference to validate the accuracy of poverty evaluation using the average light index (ALI) derived from NPP-VIIRS data. Linear regression and comparison of the class ranks have been employed to verify the correlation between ALI and IPI. The results show a good correlation between IPI and ALI, with a coefficient of determination (R2) of 0.8554, and the class ranks of IPI and API show relative closeness at the county level. The second experiment examines all counties in China and makes a comparison between ALI values and national poor counties (NPC). The comparison result shows a general agreement between the NPC and the counties with low ALI values. This study reveals that the NPP-VIIRS data can be a useful tool for evaluating poverty at the county level in China.

Modeling and mapping total freight traffic in China using NPP-VIIRS nighttime light composite data

In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light composite data were released. Up to present, few studies have been conducted to evaluate the ability of NPP-VIIRS data to estimate the amount of freight traffic. This paper provides an exploratory evaluation on the NPP-VIIRS data for estimating the total freight traffic (TFT) in China, in comparison with the results derived from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime stable light composite data. We first corrected the original NPP-VIIRS data by employing a simple method to remove the outliers. The total nighttime light (TNL) which is measured by the sum value of all pixels from the nighttime light composite data was then regressed on TFT at the provincial level of China. Finally, the spatial distribution patterns of TFT were produced from the corrected NPP-VIIRS and DMSP-OLS data, respectively, and validated by the TFT statistics of 244 prefectures. The results have demonstrated that the corrected NPP-VIIRS data are more suitable for modeling TFT in China than the DMSP-OLS data.

Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS Nighttime Light Composite Data

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.

A New Approach for Detecting Urban Centers and Their Spatial Structure With Nighttime Light Remote Sensing

Urban spatial structure affects many aspects of urban functions and has implications for accessibility, environmental sustainability, and public expenditures. During the urbanization process, a careful and efficient examination of the urban spatial structure is crucial. Different from the traditional approach that relies on population or employment census data, this research exploits the nighttime light (NTL) intensity of the earth surface recorded by satellite sensors. The NTL intensity is represented as a continuous mathematical surface of human activities, and the elemental features of urban structures are identified by analogy with earth’s topography. We use a topographical metaphor of a mount to identify an urban center or subcenter and the surface slope to indicate an urban land-use intensity gradient. An urban center can be defined as a continuous area with higher concentration or density of employments and human activities. We successfully identified 33 urban centers, delimited their corresponding boundaries, and determined their spatial relations for Shanghai metropolitan area, by developing a localized contour tree method. In addition, several useful properties of the urban centers have been derived, such as 9% of Shanghai administrative area has become urban centers. We believe that this method is applicable to other metropolitan regions at different spatial scales.

Surface Water Mapping from Suomi NPP-VIIRS Imagery at 30 m Resolution via Blending with Landsat Data

Monitoring the dynamics of surface water using remotely sensed data generally requires both high spatial and high temporal resolutions. One effective and popular approach for achieving this is image fusion. This study adopts a widely accepted fusion model, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), for blending the newly available coarse-resolution Suomi NPP-VIIRS data with Landsat data in order to derive water maps at 30 m resolution. The Pan-sharpening technique was applied to preprocessing NPP-VIIRS data to achieve a higher-resolution before blending. The modified Normalized Difference Water Index (mNDWI) was employed for mapping surface water area. Two fusion alternatives, blend-then-index (BI) or index-then-blend (IB), were comparatively analyzed against a Landsat derived water map. A case study of mapping Poyang Lake in China, where water distribution pattern is complex and the water body changes frequently and drastically, was conducted. It has been revealed that the IB method derives more accurate results with less computation time than the BI method. The BI method generally underestimates water distribution, especially when the water area expands radically. The study has demonstrated the feasibility of blending NPP-VIIRS with Landsat for achieving surface water mapping at both high spatial and high temporal resolutions. It suggests that IB is superior to BI for water mapping in terms of efficiency and accuracy. The finding of this study also has important reference values for other blending works, such as image blending for vegetation cover monitoring.

Integration of Bayesian regulation back-propagation neural network and particle swarm optimization for enhancing sub-pixel mapping of flood inundation in river basins

ABSTRACT Sub-pixel mapping of flood inundation (SMFI) is one of the hotspots in remote sensing and relevant research and application fields. In this study, a novel method based on the integration of Bayesian regulation back-propagation neural network (BRBP) and particle swarm optimization (PSO), so-called IBRBPPSO, is proposed for SMFI in river basins. The IBRBPPSO–SMFI algorithm was developed and evaluated using Landsat images from the Changjiang river basin in China and the Murray-Darling basin in Australia. Compared with traditional SMFI methods, IBRBPPSO–SMFI consistently achieves the most accurate SMFI results in terms of visual and quantitative evaluations. IBRBPPSO–SMFI is superior to PSO–SMFI with not only an improved accuracy, but also an accelerated convergence speed of the algorithm. IBRBPPSO–SMFI reduces the uncertainty in mapping inundation in river basins by improving the accuracy of SMFI. The result of this study will also enrich the SMFI methodology, and thereby benefit the environmental studies of river basins.