Abstract: Despite the importance of geospatial analysis of energy use in buildings, the data available for such exercises is limited. A potential solution is to use geospatial information, such as that obtained from satellites, to disaggregate building energy use data to a more useful scale. Many researchers have used satellite imagery to estimate the extent of human activities, including building energy use and population distribution. Much of the reported work has been carried out in rapidly developing countries such as India and China where urban development is dynamic and not always easy to measure. In countries with less rapid urbanization, such as the United States, there is still value in using satellite imagery to estimate building energy use for the purposes of identifying energy efficiency opportunities and planning electricity transmission. This study evaluates nighttime light imagery obtained from the VIIRS instrument aboard the SUOMI NPP satellite as a predictor of building energy use intensity within states, counties, and cities in the United States. It is found that nighttime lights can explain upwards of 90% of the variability in energy consumption in the United States, depending on conditions and geospatial scale. The results of this research are used to generate electricity and fuel consumption maps of the United States with a resolution of less than 200 square meters. The methodologies undertaken in this study can be replicated globally to create more opportunities for geospatial energy analysis without the hurdles often associated with disaggregated building energy use data collection.
Keywords: remote sensing
Abstract: Various scientific researches were conducted to monitor human activities and natural phenomena with the availability of various night time satellite data such as Defense Meteorological Satellite Program (DMPS). Population growth especially in a faster growing economy like China is an important indicator for assessing socio-economic development, urban planning and environmental management. Thus, spatial distribution of population is instrumental in assessing growth and developmental activities in Beijing city of China. The satellite observation data derived from Defense Meteorological Satellite Program (DMSP) was utilized to estimate population density through the measurement of light flux with radiometric recording. The data was calibrated using C0, C1, C2 parameters before processing. Population density of Beijing city was estimated using light volume of this calibrated data. Regression analysis between urban population and light volume revealed high correlation (r2=0.89)r2=0.89). Thus, population density can effectively be estimated using light intensity. The model used for estimating urban population density can effectively be utilized for other major cities of the world.
Keywords: Remote Sensing
Abstract: I study the manipulation of GDP statistics in weak and non-democracies. I show that the elasticity of official GDP figures to nighttime lights is systematically larger in more authoritarian regimes. This autocracy gradient in the night-lights elasticity of GDP cannot be explained by differences in a wide range of factors that may affect the mapping of night lights to GDP, such as economic structure, statistical capacity, rates of urbanization or electrification. The gradient is larger when there is a stronger incentive to exaggerate economic performance (years of low growth, before elections or after becoming ineligible for foreign aid) and is only present for GDP sub-components that rely on government information and have low third-party verification. The results indicate that yearly GDP growth rates are inflated by a factor of between 1.15 and 1.3 in the most authoritarian regimes. Correcting for manipulation substantially changes our understanding of comparative economic performance at the turn of the XXI century.
Keywords: Economics; Remote Sensing
Abstract: Urban extent data play an important role in urban management and urban studies, such as monitoring the process of urbanization and changes in the spatial configuration of urban areas. Traditional methods of extracting urban-extent information are primarily based on manual investigations and classifications using remote sensing images, and these methods have such problems as large costs in labor and time and low precision. This study proposes an improved, simplified and flexible method for extracting urban extents over multiple scales and the construction of spatiotemporal models using DMSP/OLS nighttime light (NTL) for practical situations. This method eliminates the regional temporal and spatial inconsistency of thresholding NTL in large-scale and multi-temporal scenes. Using this method, we have extracted the urban extents and calculated the corresponding areas on the county, municipal and provincial scales in China from 2000 to 2012. In addition, validation with the data of reference data shows that the overall accuracy (OA), Kappa and F1 Scores were 0.996, 0.793, and 0.782, respectively. We increased the spatial resolution of the urban extent to 500 m (approximately four times finer than the results of previous studies). Based on the urban extent dataset proposed above, we analyzed changes in urban extents over time and observed that urban sprawl has grown in all of the counties of China. We also identified three patterns of urban sprawl: Early Urban Growth, Constant Urban Growth and Recent Urban Growth. In addition, these trends of urban sprawl are consistent with the western, eastern and central cities of China, respectively, in terms of their spatial distribution, socioeconomic characteristics and historical background. Additionally, the urban extents display the spatial configurations of urban areas intuitively. The proposed urban extent dataset is available for download and can provide reference data and support for future studies of urbanization and urban planning.
Keywords: Remote Sensing
Abstract: Administering an urban boundary (UB) is increasingly important for curbing disorderly urban land expansion. The traditionally manual digitalization is time-consuming, and it is difficult to connect UB in the urban fringe due to the fragmented urban pattern in daytime data. Nighttime light (NTL) data is a powerful tool used to map the urban extent, but both the blooming effect and the coarse spatial resolution make the urban product unable to meet the requirements of high-precision urban study. In this study, precise UB is extracted by a practical and effective method using NTL data and Landsat 8 data. Hangzhou, a megacity experiencing rapid urban sprawl, was selected to test the proposed method. Firstly, the rough UB was identified by the search mode of the concentric zones model (CZM) and the variance-based approach. Secondly, a buffer area was constructed to encompass the precise UB that is near the rough UB within a certain distance. Finally, the edge detection method was adopted to obtain the precise UB with a spatial resolution of 30 m. The experimental results show that a good performance was achieved and that it solved the largest disadvantage of the NTL data-blooming effect. The findings indicated that cities with a similar level of socio-economic status can be processed together when applied to larger-scale applications.
Keywords: Remote Sensing