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L.Imhoff, M.; Lawrence, W.T.; Stutzer, D.C.; Elvidge, C.D. |
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Title |
A technique for using composite DMSP/OLS “City Lights” satellite data to map urban area |
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Journal Article |
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1997 |
Publication |
Remote Sensing of Environment |
Abbreviated Journal |
Remote Sensing of Environment |
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61 |
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3 |
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361-370 |
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Remote Sensing |
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Abstract  |
A Tresholding technique was used to convert a prototype “city lights” data set from the National Oceanic and Atmospheric Administration's National Geophysical Data Center (NOAAINGDC) into a map of “urban areas” for the continental United States. Thresholding was required to adapt the Defense Meteorological Satellite Program's Operational Linescan System (DMSPIOLS)-based NGDC data set into an urban map because the values reported in the prototype represent a cumulative percentage lighted for each pixel extracted from hundreds of nighttime cloud screened orbits, rather than any suitable land-cover classification. The cumulative percentage lighted data could not be used alone because the very high gain of the OLS nighttime photomultiplier configuration can. lead to a pixel (2.7X2.7 km) appearing “lighted” even with very low intensity, nonurban light sources. We found that a threshold of %89% yielded the best results, removing ephemeral light sources and “blooming” of light onto water when adjacent to cities while still leaving the dense urban core intact. This approach gave very good results when compared with the urban areas as defined by the 1990 U. S. Census; the “urban” area from our analysis being only 5% less than that of the Census. The Census was also used to derive population.- and housing-density statistics for the continent-wide “city lights” analysis; these averaged 1033 persons/km2 and 426 housing units/ king, respectively. The use of a nighttime sensor to determine the location and estimate the density of population based on light sources has proved feasible in this exploratory effort. However, issues concerning the use of census data as a benchmark for evaluating the accuracy of remotely sensed imagery are discussed, and potential improvements in the sensor regarding spatial resolution, instrument gain, and pointing accuracy are addressed. |
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0034-4257 |
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GFZ @ kyba @ |
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2220 |
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Li, S.; Cheng, L.; Liu, X.; Mao, J.; Wu, J.; Li, M. |
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City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data |
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Journal Article |
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2019 |
Publication |
Energy |
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Energy |
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189 |
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116040 |
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Energy; Remote Sensing; China; electric power consumption; Night lights; Nighttime light; VIIRS-DNB |
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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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School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China |
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0360-5442 |
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2672 |
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Li, K.; Chen, Y. |
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Title |
A Genetic Algorithm-Based Urban Cluster Automatic Threshold Method by Combining VIIRS DNB, NDVI, and NDBI to Monitor Urbanization |
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2018 |
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Remote Sensing |
Abbreviated Journal |
Remote Sensing |
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10 |
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2 |
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277 |
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Remote Sensing |
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Accurate and timely information related to quantitative descriptions and spatial distributions of urban areas is crucial to understand urbanization dynamics and is also helpful to address environmental issues associated with rapid urban land-cover changes. Thresholding is acknowledged as the most popular and practical way to extract urban information from nighttime lights. However, the difficulty of determining optimal threshold remains challenging to applications of this method. In order to address the problem of selecting thresholds, a Genetic Algorithm-based urban cluster automatic threshold (GA-UCAT) method by combining Visible-Infrared Imager-Radiometer Suite Day/Night band (VIIRS DNB), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) is proposed to distinguish urban areas from dark rural background in NTL images. The key point of this proposed method is to design an appropriate fitness function of GA by means of integrating between-class variance and inter-class variance with all these three data sources to determine optimal thresholds. In accuracy assessments by comparing with ground truth—Landsat 8 OLI images, this new method has been validated and results with OA (Overall Accuracy) ranging from 0.854 to 0.913 and Kappa ranging from 0.699 to 0.722 show that the GA-UCAT approach is capable of describing spatial distributions and giving detailed information of urban extents. Additionally, there is discussion on different classifications of rural residential spots in Landsat remote sensing images and nighttime light (NTL) and evaluations of spatial-temporal development patterns of five selected Chinese urban clusters from 2012 to 2017 on utilizing this proposed method. The new method shows great potential to map global urban information in a simple and accurate way and to help address urban environmental issues. |
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2072-4292 |
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GFZ @ kyba @ |
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2340 |
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Tilottama, G., Sutton, P. C., & Elvidge, C. D. |
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Informal Economy and Remittance Estimates of India Using Nighttime Imagery |
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2010 |
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International Journal of Ecological Economics & Statistics |
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17 |
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Remote Sensing; Economics |
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Accurate estimates of the magnitude and spatial distribution of both formal and informal economic activity have many useful applications. Developing alternative methods for making estimates of these economic activities may prove to be useful when other measures are of suspect accuracy or unavailable. This research explores the potential for estimating the formal and informal economy for India using known relationships between the spatial patterns of nighttime satellite imagery and economic activity in the United States (U.S.). Regression models have been developed between spatial patterns of nighttime imagery and Adjusted Official Gross State Product (AGSP) for the states of the U.S. The slope and intercept parameters derived from the regression models of the U.S. were blindly applied to India, resulting in an underestimation of Gross State Income (GSI) for each state and Union Territory (UT) of India because of the lower level of urbanization in India in comparison to the U.S. However, a comparison of estimated GSI from the nighttime lights image and the official Gross State Product (GSP) of the states and UTs of India indicates a high correlation between them (r = 0.93). The different levels of urbanization (i.e. percent of population in urban areas) in the U.S. and India are used to adjust the Estimated Gross Domestic Income (EGDI) by multiplying by the ratio of the percentage of the population in urban areas for the two countries. This gives the Adjusted Estimated Gross Domestic Income of India (AEGDI), which is compared with the official Gross National Income (GNI) estimates of India’s states and UTs. The results suggest that the magnitude of India’s informal economy and the inflow of remittances are 150 percent larger than their existing official estimates in the GNI. |
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IDA @ intern @ |
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2554 |
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Chen, X.; Jia, X.; Pickering, M. |
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Title |
A Nighttime Lights Adjusted Impervious Surface Index (NAISI) with Integration of Landsat Imagery and Nighttime Lights Data from International Space Station |
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2019 |
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International Journal of Applied Earth Observation and Geoinformation |
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International Journal of Applied Earth Observation and Geoinformation |
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83 |
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101889 |
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Remote Sensing |
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Accurate mapping of impervious surface is essential for both urbanization monitoring and micro-ecosystem research. However, the confusion between impervious surface and bare soil is the major concern due to their high spectral similarity in optical imagery. Integration of multi-sensor images is considered to offer a better capacity for distinguishing impervious surface from background. In this paper, a new impervious surface index namely nighttime light adjusted impervious surface index (NAISI), which integrates information from Landsat and nighttime lights (NTL) data from International Space Station (NTL-ISS), is proposed. Parallel to baseline subtraction approaches, NAISI integrate the information from the first component of principal component (PC) transformation of NTL-ISS, the Soil Adjusted Vegetation Index (SAVI) and the third component of tasseled cap transform (TC3) of the Landsat data. Visual interpretation and quantitative indices (SDI, Kappa and overall accuracy) were adopted to elevate the accuracy and separability of NAISI. Comparative analysis with NTL derived light intensity, optical indices, as well as existing optical-NTL indices were conducted to examine the performance of NAISI. Results indicate that NAISI achieves a more promising capability in impervious surface mapping. This demonstrates the superiority of integration of optical and nighttime lights information for imperviousness detection. |
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0303-2434 |
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GFZ @ kyba @ |
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2658 |
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