Koel Roychowdhury

Night Lights and Economic Activity in India: A study using DMSP-OLS night time images

This paper investigates the association between night lights and GDP estimates for India at the district level. While many studies are finding a high degree of association between economic activity as measured through the Gross Domestic Product (GDP) and night lights internationally, there is a lack of understanding of whether and how night light data are correlated with economic activity at the sub-national level in emerging economies. This achieves more significance in economic monitoring and policy-making as estimates of GDP are not available at geographically disaggregated level, and even if available there is a large time lag involved before they are released. Stable light data obtained from night time images of 2008 captured by Defense Meteorological Satellite Program – Operational Linescan System (DMSP-OLS) satellite are used in the study. The data records artificial lights from human habitations from the earth surface and is a surrogate of the level of development of an area. The data on GDP at the district level for the year 2008 have been sourced from Indicus Analytics that has used data from government sources and a method of estimation suggested by the Central Statistical Office of the Government of India. Using multinomial non-linear regression techniques the paper finds that indeed GDP at the district level is significantly explained by night lights in the area. It also finds that the non-linearity is much stronger for metropolitan cities where GDP levels are far higher than a linear model can explain. Conversely, in areas where agriculture and forestry activities are higher, the use of night lights in a linear model overestimates the GDP.

A Comparison of High and Low Gain DMSP/OLS Satellite Images for the Study of Socio-Economic Metrics

The Operational Linescan System (OLS) onboard the Defense Meteorological Satellite Program (DMSP) group of satellites, unlike other passive remote sensing sensors, is capable of recording the emissions from artificial lights on the earth surface. Along with detecting light from forest fires, shipping fleets and gas flares, the OLS sensor also records the light emitted from cities at night. This paper reports on a study that uses the DMSP Operational Linescan (DMSP-OLS) images with fixed gain settings of 20 dB and 50 dB to model selected metrics used in the Indian census for the state of Maharashtra. The study firstly looks into the utility of non-composited single fixed gain radiance calibrated DMSP-OLS products for proposing a method which might help to build a surrogate method for Indian census. Several parameters are considered in this analysis, with detailed focus on population density, total population and proportion of households with electricity access for 35 districts within the state of Maharashtra. Results show that spatial scale plays an important role in selection of the images and gains. Secondly, this study provides a relative assessment of gain setting for the DMSP-OLS images in an urban Indian context. Images with a gain of 50 dB prove suitable for larger areas while those with a gain of 20 dB give better results at a smaller spatial scale. Statistical analysis and residual maps of spatial distribution of total population and population density validate the result.

Delineating urban, suburban and rural areas using Landsat and DMSP-OLS night-time images

The number and size of urban settlements are increasing in all the continents of the world at a rapid pace. Urban sprawl is associated not only with changes in landcover and area, but also ecological, climate and social transformations. Mapping the growth and spread of urban areas is important. Remote sensing has long been used to map human settlements. Today the availability of a large number of satellites and sensors, determining the appropriate image to map urban area is a research area itself. This study compares two satellite images: Landsat Enhanced Thematic Mapper data and Defence Meteorological Satellite Program, Operational Linescan System image to map the urban footprint of the city of Hyderabad, India. Landsat ETM data is captured during the daytime and gives spectral reflectance values while the DMSP-OLS data captures artificial lights from human settlements at night and produces brightness information. The results show an accuracy of more than 90% in the classification and delineation of urban, suburban and rural landcover types. This study shows that in addition to spectral reflectance captured by satellites from different features on the earth surface during the daytime, differences in the degree of brightness of the lights emitted from urban areas at night is also an effective indicator in delineating landcover types.

Assessing the utility of DMSP/OLS night-time images for characterizing indian urbanization

Urbanization, that is the movement of population from rural to urban locations, is a process that has been occurring for hundreds of years, but is increasingly prevalent in todays world. In 2008 most of the global population was resident in urban areas. It has been predicted that in the coming years, an increasing number of people will be living in cities; especially in the developing countries within in Asia and Latin America. This study considers the case of India, the second most populated country in the world, with a present total population exceeding 1 billion. It focuses on the state of Maharashtra (including the mega-city of Mumbai and its surrounds - the largest in India with a population of approximately 18.1 million). The Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) is a spaceborne system that detects visible light and thermal emissions of the earth at night. The data is collected nightly, on a global basis. The aim of this paper is to correlate the values obtained from the radiance calibrated DMSP/OLS night-time images of 2001 with population data. The spatial resolution of DMSP/OLS images is approximately 1 Km. This paper asks the question over what range of spatial scales does DMSP/OLS have utility in retrieving metrics of urbanization.

Tracking the relationship between changing skyline and population growth of an Indian megacity using earth observation technology

Abstract Temporal analysis of Landsat-TM imageries reveals a saturated state of Kolkata (Calcutta) Metropolitan Area. However, the city has witnessed accelerated growth in real estate construction in recent past. This study applies digital photogrammetry to quantify the changes in Kolkata’s skyline. Recently, released SRTM DEM of 1 Arc Second and a digital surface model derived from WorldView-1 stereo images were used to account for the past and recent surface heights, respectively. Consequently, this paper examines whether the sustained addition in housing capacity has been necessarily driven by a growth in the urban population/number of households. Results show that 40.31% of the area experienced vertical growth, majorly by replacing older dwellings with taller apartment blocks. Further analysis reveals that part of these newly added residences has remained unoccupied as they were purchased by non-resident Indians for using as a second home or was never sold due to recent economic slowdown.

The role of satellite data in census: Case study of an Indian State

Countries, such as India, conduct a census collection every ten years. Currently census in India is carried out manually, therefore suffering from a number of shortcomings including inconsistency issues, the Modifiable Areal Unit Problem (MAUP) and large temporal acquisition timeframes. This paper proposes a surrogate census method using satellite images captured at night by DMSP-OLS satellites to overcome some of these drawbacks. The lights on the earth surface captured by this satellite represent areas of human habitation. Correlations between stable lights and brightness information with available census metrics from the last Indian census (2001) were calculated using bootstrapping techniques. Linear regression and multivariate analyses were subsequently performed and models proposed for each of the selected census metrics (e.g population density, number of households per square Kilometre, percentage of households with cars, jeeps and vans, Per Capita District Domestic Product (PCDDP) and urban population density) with results ranging from r 2 of 0.8 to 0.9 at the 95% confidence interval. Census metrics unavailable at spatial scales lower than districts were also predicted using the proposed models and maps were derived showing the predicted measures. The results demonstrate that DMSP-OLS night-time images may be successfully used to estimate census variables in real time.

Indian census using satellite images: Can DMSP-OLS data be used for small administrative regions?

India conducts its census every ten years. Census data is collected manually in India with enumerators visiting every household in the country. Being such a vast country (in terms of area) and with a population of more than 1 billion, manual data collection is a laborious and expensive process. In response, this paper proposes a surrogate census method using DMSP-OLS night-time images. The study focuses on smaller administrative regions such as sub-districts (or taluks as they are known in the country) in the state of Maharashtra. Models are proposed using selected census metrics, and mean and standard deviation of stable lights and brightness information as obtained from the satellite images. The adjusted r2 values range from 0.2 to 0.8 at 95% confidence interval, with the majority of the metrics being moderately correlated (with r2 between 0.4 and 0.7). Generally it was found that the observed lights and brightness of big rural settlements from DMSP-OLS images have the potential for predicting certain census metrics. However, unlike larger areas such as districts where DMSP-OLS night-time images adequately predict census metrics, at the sub-district level the results need to be supplemented and validated with other information sources such as survey reports.

Nexus of Health and Development: Modelling Crude Birth Rate and Maternal Mortality Ratio Using Nighttime Satellite Images

Health and development are intricately related. Although India has made significant progress in the last few decades in the health sector and overall growth in GDP, there are still large regional differences in both health and development. The main objective of this paper is to develop techniques for the prediction of health indicators for all the districts of India and examine the correlations between health and development. The level of electrification and district domestic product (DDP) are considered as two fundamental indicators of development in this research. These data, along with health metrics and the information from two nighttime satellite images, were used to propose the models. These successfully predicted the health indicators with less than a 7%-10% error. The chosen health metrics, such as crude birth rate (CBR) and maternal mortality rate (MMR), were mapped for the whole country at the district level. These metrics showed very strong correlation with development indicators (correlation coefficients ranging from 0.92 to 0.99 at the 99% confidence interval). This is the first attempt to use Visible Infrared Imaging Radiometer Suite (VIIRS) (satellite) imagery in a socio-economic study. This paper endorses the observation that areas with a higher DDP and level of electrification have overall better health conditions.