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

Abstract

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.

How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique

“…Methods involving the use of satellite imagery data have proven effective in classifying settlement types. The use of spectral reflectance and night-time lights data obtained from satellite imagery are methods that are effective in delineating settlement types 42. Night-time imagery data are also effective in modelling health indicators (like crude birth rates) at subnational levels, making it useful when predicting such health metrics, as a strong correlation between health and development (like level of electrification and district domestic product) has been shown to exist 43…”

Planning without Baseline Information: Delimitation of Urban and Rural Settlements in Oé-Cusse Ambeno, Timor-Leste

“…Other current techniques conduct distinct analysis based on several levels of detail and using different amounts of data. These techniques focus on analyzing land use patterns (Rashed and Jürgens 2010), using remote sensing and GIS (Sudhira et al 2004;Li and Yeh 2004), using Landsat and the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) nighttime images (Imhoff et al 1997;Roychowdhury et al 2011Roychowdhury et al , 2009, or using a grid approach (van Leeuwen 2006).…”

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

“…It has been used to estimate the population of the countries of the world [10][11][12] and in estimating population without access to electricity [13]. The first global map of the GDP purchasing power parity (GDP-PPP) [14] and gas flares [15] used radiance calibrated DMSP-OLS data to map urban and suburban extent around cities [16,17]. At the smaller spatial scale, the DMSP-OLS dataset was used to estimate population and other socio-economic metrics for the state of Maharashtra in India [7,[18][19][20][21] and GDP estimates [22].…”