| Records |
| Author |
Sarkar, S. |
| Title |
Rapid assessment of cyclone damage using NPP-VIIRS DNB and ancillary data |
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Journal Article |
| Year |
2021 |
Publication |
Natural Hazards (Dordrecht, Netherlands) |
Abbreviated Journal |
Nat Hazards (Dordr) |
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Pages |
in press |
| Keywords |
Remote Sensing |
| Abstract |
Rapid damage assessment of natural disasters is essential for the fast recovery and strategic post-disaster reconstructions. In the present study, National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB)-extracted night-time lights (NTL) data were explored for damage assessment caused by extremely severe cyclonic storm 'AMPHAN' that struck one of the most populous regions in India. The disaster impact was measured on two parameters: population and crop land area, where NTL density and population density were found to be strongly correlated (r (2) > 0.8). From power outage intensity, three 'crisis zones' indicating the severity of cyclone damage were identified. Finally, the assessment found that the total affected population and crop land area were nearly 70% and 66%, respectively, of the study area. Therefore, NPP-VIIRS DNB image-based rapid damage assessment is potentially a useful tool for generating first hand information about the physical damages caused by extreme events. Supplementary Information: The online version of this article (10.1007/s11069-020-04477-9) contains supplementary material, which is available to authorized users. |
| Address |
Department of Geography, Indraprastha College for Women, University of Delhi, Delhi, 110054 India.grid.8195.50000 0001 2109 4999 |
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English |
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0921-030X |
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PMID:33424122; PMCID:PMC7782053 |
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no |
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UP @ altintas1 @ |
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3321 |
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Puttanapong, N.; Martinez Jr. A. M.; Addawe, M.; Bulan, J.; Durante, R. L.; Martillan, M. |
| Title |
Predicting Poverty Using Geospatial Data In Thailand |
Type |
Journal Article |
| Year |
2020 |
Publication |
ADB Economics Working Paper Series |
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630 |
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Remote Sensing |
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Poverty statistics are conventionally compiled using data from household income and expenditure survey or living standards survey. This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. In particular, geospatial data examined in this study include night light intensity, land cover, vegetation index, land surface temperature, built-up areas, and points of interest. The study also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of an area’s population who are living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered in this study, perhaps due to its capability to fit complex association structures even with small and medium- sized datasets. Moving forward, additional studies are needed to investigate whether the relationships observed here remain stable over time, and therefore, may be used to approximate the prevalence of poverty for years when household surveys on income and expenditures are not conducted, but data on geospatial correlates of poverty are available. |
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UP @ altintas1 @ |
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3320 |
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Raisal, A. Y.; Hidayat, M.; Hermawan, L.; Rakhmadi, A. J. |
| Title |
The Effect of the Installation Angle of the Sky Quality Meter on the Night Sky Brightness and the Beginning of the Fajr Prayer Time |
Type |
Journal Article |
| Year |
2020 |
Publication |
Indonesian Review of Physics |
Abbreviated Journal |
IRiP |
| Volume |
3 |
Issue |
2 |
Pages |
35-39 |
| Keywords |
Natural sky brightness |
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Measuring the brightness of the night sky and determining the start of Fajr prayer times can be done using SQM. Observations were made at OIF UMSU with coordinates 3o 34' 55.06“ N and 98o 43' 17.09” E. The sky brightness was measured using three SQMs mounted facing the zenith, eastern horizon, and western horizon. The night sky brightness values for SQM directed to the zenith, eastern horizon, and western horizon are 18.23 mpsas, 15.82 mpsas, and 15.47 mpsas. The beginning of fajr prayer time produced by SQM is after the beginning of fajr prayer time obtained using the Accurate Times concerning the Sun's altitude 18o below the horizon. The difference obtained by SQM directed to the zenith, eastern horizon, and western horizon is 29.5 minutes, 36.7 minutes, and 39.5 minutes. In other words, the beginning of Fajr prayer time used in Indonesia is earlier than it should be. |
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2621-2889 |
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UP @ altintas1 @ |
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3319 |
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| Author |
Jiang, Y. |
| Title |
Asian cities: spatial dynamics and driving forces |
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Journal Article |
| Year |
2021 |
Publication |
The Annals of Regional Science |
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in press |
| Keywords |
Remote Sensing |
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This paper introduces a new city-level panel dataset constructed using satellite nighttime light imagery and grid population data. The data contain over 1500 cities covering 43 Asian and Pacific countries/economies from 1992 to 2016. With the dataset, we perform a variety of analyses for the region as a whole as well as the five largest countries in the region. The exercise produces some novel evidence on several policy-relevant topics including urbanization status and patterns, relations between urbanization and economic growth, evolution of urban systems, primate cities, testing Zipf’s law and Gibrat’s law, the drivers of city growth, and emergence of city clusters. |
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UP @ altintas1 @ |
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3318 |
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Hofer, M.; Sako, T.; Martinez Jr., A.; Addawe, A.; Bulan, J.; Durante, R. L.; Martillan, M. |
| Title |
Applying Artificial Intelligence On Satellite Imagery To Compile Granular Poverty Statistics |
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Journal Article |
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2020 |
Publication |
ADB Economics Working Paper Series |
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629 |
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Remote Sensing |
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The spatial granularity of poverty statistics can have a significant impact on the efficiency of targeting resources meant to improve the living conditions of the poor. However, achieving granularity typically requires increasing the sample sizes of surveys on household income and expenditure or living standards, an option that is not always practical for government agencies that conduct these surveys. Previous studies that examined the use of innovative (geospatial) data sources such as those from high- resolution satellite imagery suggest that such method may be an alternative approach of producing granular poverty maps. This study outlines a computational framework to enhance the spatial granularity of government-published poverty estimates using a deep layer computer vision technique applied on publicly available medium-resolution satellite imagery, household surveys, and census data from the Philippines and Thailand. By doing so, the study explores a potentially more cost-effective alternative method for poverty estimation method. The results suggest that even using publicly accessible satellite imagery, in which the resolutions are not as fine as those in commercially sourced images, predictions generally aligned with the distributional structure of government-published poverty estimates, after calibration. The study further contributes to the existing literature by examining robustness of the resulting estimates to user-specified algorithmic parameters and model specifications. |
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UP @ altintas1 @ |
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3317 |
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