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Huang, X., Wang, C., & Lu, J. (2019). Understanding Spatiotemporal Development of Human Settlement in Hurricane-prone Areas on U.S. Atlantic and Gulf Coasts using Nighttime Remote Sensing. Natural Hazards and Earth System Sciences, , 1–22.
Abstract: Hurricanes, as one of the most devastating natural disasters, have posed great threats to people in coastal areas. A better understanding of spatiotemporal dynamics of human settlement in hurricane-prone areas is demanded for sustainable development. This study uses the DMSP/OLS nighttime light (NTL) data sets from 1992 to 2013 to examine human settlement development in areas with different levels of hurricane proneness. The DMSP/OLS NTL data from six satellites were intercalibrated and desaturated with AVHRR and MODIS optical imagery to derive the vegetation-adjusted NTL urban index (VANUI), a popular index that quantifies human settlement intensity. The derived VANUI time series was examined with the Mann-Kendall test and Theil-Sen test to identify significant spatiotemporal trends. To link the VANUI product to hurricane impacts, four hurricane-prone zones were extracted to represent different levels of hurricane proneness. Aside from geographic division, a wind-speed weighted track density function was developed and applied to historical North Atlantic Basin (NAB)-origin storm tracks to better categorize the four levels of hurricane proneness. Spatiotemporal patterns of human settlement in the four zones were finally analyzed. The results clearly exhibit a north-south and inland-coastal discrepancy of human settlement dynamics. This study also reveals that both the zonal extent and zonal increase rate of human settlement positively correlate with hurricane proneness levels. The intensified human settlement in high hurricane-exposure zones deserves further attention for coastal resilience.
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Letu, H., Hara, M., Tana, G., Bao, Y., & Nishio, F. (2015). Generating the Nighttime Light of the Human Settlements by Identifying Periodic Components from DMSP/OLS Satellite Imagery. Environ Sci Technol, 49(17), 10503â10509.
Abstract: Nighttime lights of the human settlements (hereafter, “stable lights”) are seen as a valuable proxy of social economic activity and greenhouse gas emissions at the subnational level. In this study, we propose an improved method to generate the stable lights from Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) daily nighttime light data for 1999. The study area includes Japan, China, India, and other 10 countries in East Asia. A noise reduction filter (NRF) was employed to generate a stable light from DMSP/OLS time-series daily nighttime light data. It was found that noise from amplitude of the 1-year periodic component is included in the stable light. To remove the amplitude of the 1-year periodic component noise included in the stable light, the NRF method was improved to extract the periodic component. Then, new stable light was generated by removing the amplitude of the 1-year periodic component using the improved NRF method. The resulting stable light was evaluated by comparing it with the conventional nighttime stable light provided by the National Oceanic and Atmosphere Administration/National Geophysical Data Center (NOAA/NGDC). It is indicated that DNs of the NOAA stable light image are lower than those of the new stable light image. This might be attributable to the influence of attenuation effects from thin warm water clouds. However, due to overglow effect of the thin cloud, light area in new stable light is larger than NOAA stable light. Furthermore, the cumulative digital numbers (CDNs) and number of light area pixels (NLAP) of the generated stable light and NOAA/NGDC stable light were applied to estimate socioeconomic variables of population, electric power consumption, gross domestic product, and CO2 emissions from fossil fuel consumption. It is shown that the correlations of the population and CO2FF with new stable light data are higher than those in NOAA stable light data; correlations of the EPC and GDP with NOAA stable light data are higher those in the new stable light data.
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Li, S., Cheng, L., Liu, X., Mao, J., Wu, J., & Li, M. (2019). City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data. Energy, 189, 116040.
Abstract: Accelerating urbanization has created tremendous pressure on the global environment and energy supply, making accurate estimates of energy use of great importance. Most current models for estimating electric power consumption (EPC) from nighttime light (NTL) imagery are oversimplified, ignoring influential social and economic factors. Here we propose first classifying cities by economic focus and then separately estimating each category’s EPC using NTL data. We tested this approach using statistical employment data for 198 Chinese cities, 2015 NTL data from the Visible Infrared Imaging Radiometer Suite (VIIRS), and annual electricity consumption statistics. We used cluster analysis of employment by sector to divide the cities into three types (industrial, service, and technology and education), then established a linear regression model for each city's NTL and EPC. Compared with the estimation results before city classification (R2: 0.785), the R2 of the separately modeled service cities and technology and education cities increased to 0.866 and 0.830, respectively. However, the results for industrial cities were less consistent due to their more complex energy consumption structure. In general, using classification before modeling helps reflect factors affecting the relationship between EPC and NTL, making the estimation process more reasonable and improving the accuracy of the results.
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Li, X., Ge, L., & Chen, X. (2013). Detecting Zimbabwe's Decadal Economic Decline Using Nighttime Light Imagery. Remote Sensing, 5(9), 4551–4570.
Abstract: Zimbabweâs economy declined between 2000 and 2009. This study detects the economic decline in different regions of Zimbabwe using nighttime light imagery from the Defense Meteorological Satellite Programâs Operational Linescan System (DMSP-OLS). We found a good correlation (coefficient = 0.7361) between Zimbabweâs total nighttime light (TNL) and Gross Domestic Product (GDP) for the period 1992 to 2009. Therefore, TNL was used as an indicator of regional economic conditions in Zimbabwe. Nighttime light imagery from 2000 and 2008 was compared at both national and regional scales for four types of regions. At the national scale, we found that nighttime light in more than half of the lit area decreased between 2000 and 2008. Moreover, within the four region types (inland mining towns, inland agricultural towns, border towns and cities) we determined that the mining and agricultural sectors experienced the most severe economic decline. Some of these findings were validated by economic survey data, proving that the nighttime light data is a potential data source for detecting the economic decline in Zimbabwe.
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Li, X., Xu, H., Chen, X., & Li, C. (2013). Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China. Remote Sensing, 5(6), 3057–3081.
Abstract: Historically, the Defense Meteorological Satellite Programâs Operational Linescan System (DMSP-OLS) was the unique satellite sensor used to collect the nighttime light, which is an efficient means to map the global economic activities. Since it was launched in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite has become a new satellite used to monitor nighttime light. This study performed the first evaluation on the NPP-VIIRS nighttime light imagery in modeling economy, analyzing 31 provincial regions and 393 county regions in China. For each region, the total nighttime light (TNL) and gross regional product (GRP) around the year of 2010 were derived, and a linear regression model was applied on the data. Through the regression, the TNL from NPP-VIIRS were found to exhibit R2 values of 0.8699 and 0.8544 with the provincial GRP and county GRP, respectively, which are significantly stronger than the relationship between the TNL from DMSP-OLS (F16 and F18 satellites) and GRP. Using the regression models, the GRP was predicted from the TNL for each region, and we found that the NPP-VIIRS data is more predictable for the GRP than those of the DMSP-OLS data. This study demonstrates that the recently released NPP-VIIRS nighttime light imagery has a stronger capacity in modeling regional economy than those of the DMSP-OLS data. These findings provide a foundation to model the global and regional economy with the recently availability of the NPP-VIIRS data, especially in the regions where economic census data is difficult to access.
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