| Records |
| Author |
Tripathy, B.R.; Sajjad, H.; Elvidge, C.D.; Ting, Y.; Pandey, P.C.; Rani, M.; Kumar, P. |
Title  |
Modeling of Electric Demand for Sustainable Energy and Management in India Using Spatio-Temporal DMSP-OLS Night-Time Data |
Type |
Journal Article |
| Year |
2018 |
Publication |
Environmental Management |
Abbreviated Journal |
Environ Manage |
| Volume |
61 |
Issue |
4 |
Pages |
615-623 |
| Keywords |
Remote Sensing |
| Abstract |
Changes in the pattern of electric power consumption in India have influenced energy utilization processes and socio-economic development to greater extent during the last few decades. Assessment of spatial distribution of electricity consumption is, thus, essential for projecting availability of energy resource and planning its infrastructure. This paper makes an attempt to model the future electricity demand for sustainable energy and its management in India. The nighttime light database provides a good approximation of availability of energy. We utilized defense meteorological satellite program-operational line-scan system (DMSP-OLS) nighttime satellite data, electricity consumption (1993-2013), gross domestic product (GDP) and population growth to construct the model. We also attempted to examine the sensitiveness of electricity consumption to GDP and population growth. The results revealed that the calibrated DMSP and model has provided realistic information on the electric demand with respect to GDP and population, with a better accuracy of r (2) = 0.91. The electric demand was found to be more sensitive to GDP (r = 0.96) than population growth (r = 0.76) as envisaged through correlation analysis. Hence, the model proved to be useful tool in predicting electric demand for its sustainable use and management. |
| Address |
Department of Geography, Jamia Millia Islamia, New Delhi, 110025, India. pavan.jamia@gmail.com |
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English |
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0364-152X |
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PMID:29282533 |
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GFZ @ kyba @ |
Serial |
2484 |
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| Author |
Tan, M.; Li, X.; Li, S.; Xin, L.; Wang, X.; Li, Q.; Li, W.; Li, Y.; Xiang, W. |
| Title |
Modeling population density based on nighttime light images and land use data in China |
Type |
Journal Article |
| Year |
2018 |
Publication |
Applied Geography |
Abbreviated Journal |
Applied Geography |
| Volume |
90 |
Issue |
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Pages |
239-247 |
| Keywords |
Remote Sensing |
| Abstract |
Population change is a key variable that influences climate change, ecological construction, soil and water use, and economic growth. Census data are always point data, whereas planar data are often required in scientific research. By using nighttime light (NTL) images and land use data, combined with the fifth and sixth census data of China at the county level, we carried out spatial matching on the population of each county, respectively, and established population density diagrams of China for 2000 and 2010, which had a spatial resolution of 1 × 1 km. The method proposed in this paper is relatively simple and has a high simulation precision. The results showed that during the first ten years of the 21st century, there are some remarkable characteristics in Chinese population spatial pattern change: 1) the “disappearance” of intermediate-density regions; namely, areas with a population density between 500 and 1500 persons/km2 have decreased by 41% during the ten years; 2) continuous growth of high-density regions; namely, areas with a population density of more than 1500 persons/km2 have increased by 76%; 3) an expansion tendency of low-density regions similar to high-density regions. |
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0143-6228 |
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GFZ @ kyba @ |
Serial |
2481 |
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| Author |
Du, M.; Wang, L.; Zou, S.; Shi, C. |
| Title |
Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data |
Type |
Journal Article |
| Year |
2018 |
Publication |
Remote Sensing |
Abbreviated Journal |
Remote Sensing |
| Volume |
10 |
Issue |
12 |
Pages |
1920 |
| Keywords |
Remote Sensing |
| Abstract |
The vacant house is an essential phenomenon of urban decay and population loss. Exploration of the correlations between housing vacancy and some socio-environmental factors is conducive to understanding the mechanism of urban shrinking and revitalization. In recent years, rapidly developing night-time remote sensing, which has the ability to detect artificial lights, has been widely applied in applications associated with human activities. Current night-time remote sensing studies on housing vacancy rates are limited by the coarse spatial resolution of data. The launch of the Jilin1-03 satellite, which carried a high spatial resolution (HSR) night-time imaging camera, provides a new supportive data source. In this paper, we examined this new high spatial resolution night-time light dataset in housing vacancy rate estimation. Specifically, a stepwise multivariable linear regression model was engaged to estimate the housing vacancy rate at a very fine scale, the census tract level. Three types of variables derived from geospatial data and night-time image represent the physical environment, landuse (LU) structure, and human activities, respectively. The linear regression models were constructed and analyzed. The analysis results show that (1) the HVRs estimating model using the Jilin1-03 satellite and other ancillary geospatial data fits well with the Census statistical data (adjusted R2 = 0.656, predicted R2 = 0.603, RMSE = 0.046) and thus is a valid estimation model; (2) the Jilin1-03 satellite night-time data contributed a 28% (from 0.510 to 0.656) fitting accuracy increase and a 68% (from 0.359 to 0.603) predicting accuracy increase in the estimate model of the housing vacancy rate. Reflecting socio-economic conditions, the luminous intensity of commercial areas derived from the Jilin1-03 satellite is the most influential variable to housing vacancy. Land use structure indirectly and partially demonstrated that the social environment factors in the community have strong correlations with residential vacancy. Moreover, the physical environment factor, which depicts vegetation conditions in the residential areas, is also a significant indicator of housing vacancy. In conclusion, the emergence of HSR night light data opens a new door to future microscopic scale study within cities. |
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2072-4292 |
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GFZ @ kyba @ |
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2124 |
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| Author |
Aubé, M.; Simoneau, A.; Wainscoat, R.; Nelson, L. |
| Title |
Modeling the effects of phosphor converted LED lighting to the night sky of the Haleakala Observatory, Hawaii |
Type |
Journal Article |
| Year |
2018 |
Publication |
Monthly Notices of the Royal Astronomical Society |
Abbreviated Journal |
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| Volume |
478 |
Issue |
2 |
Pages |
1776-1783 |
| Keywords |
Skyglow |
| Abstract |
The goal of this study is to evaluate the current level of light pollution in the night sky at the Haleakala Observatory on the island of Maui in Hawaii. This is accomplished with a numerical model that was tested in the first International Dark Sky Reserve located in Mont-Mégantic National Park in Canada. The model uses ground data on the artificial light sources present in the region of study, geographical data, and remotely sensed data for: 1) the nightly upward radiance; 2) the terrain elevation; and, 3) the ground spectral reflectance of the region. The results of the model give a measure of the current state of the sky spectral radiance at the Haleakala Observatory. Then, using the current state as a reference point, multiple light conversion plans are elaborated and evaluated using the model. We can thus estimate the expected impact of each conversion plan on the night sky radiance spectrum. A complete conversion to white (LEDs) with (CCT) of 4000K and 3000K are contrasted with a conversion using (PC) amber (LEDs). We include recommendations concerning the street lamps to be used in sensitive areas like the cities of Kahului and Kihei and suggest best lighting practices related to the color of lamps used at night. |
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0035-8711 |
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GFZ @ kyba @ |
Serial |
1907 |
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| Author |
Kumar, P.; Sajjad, H.; Joshi, P.K.; Elvidge, C.D.; Rehman, S.; Chaudhary, B.S.; Tripathy, B.R.; Singh, J.; Pipal, G. |
| Title |
Modeling the luminous intensity of Beijing, China using DMSP-OLS night-time lights series data for estimating population density |
Type |
Journal Article |
| Year |
2018 |
Publication |
Physics and Chemistry of the Earth, Parts A/B/C |
Abbreviated Journal |
Physics and Chemistry of the Earth, Parts A/B/C |
| Volume |
109 |
Issue |
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Pages |
31-39 |
| 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. |
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1474-7065 |
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GFZ @ kyba @ |
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1934 |
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