A technique for using composite DMSP/OLS “City Lights” satellite data to map urban area

Imhoff, Marc L.; Lawrence, William T.; Stutzer, David; Elvidge, Christopher D.

doi:10.1016/s0034-4257(97)00046-1

Cited by 532 publications

(326 citation statements)

References 6 publications

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“…Since we need to qualitatively study the evolution of global nighttime light in nearly two decades on a large geographic scale, it is necessary to inter-calibrate ) and re-project (Imhoff et al, 1997) each image correspondingly (Cauwels et al, 2014). In the examination of basic scaling properties of nighttime light clusters, we extracted regions with a vast territory and a huge population like China and Contiguous United States (CONUS) as our ROI.…”

Section: Methodsmentioning

confidence: 99%

Allometric scaling, size distribution and pattern formation of natural cities

Li¹,

Wang²,

Zhang³

et al. 2015

Palgrave Commun

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There has been a strong interest in more cogent definitions on economies of scale to reveal general urban growth laws and to develop urban performance metrics. Unstructured data, including satellite images, will provide us with new sources to do so by defining cities as aggregates of human activities. Such a uniform definition on the basis of nighttime light clusters is more consistent and economically meaningful than administrative or official boundaries. In comparison with patterns of city systems found in traditional census data, we examined the allometric scaling, size distribution and fractal geometry of natural cities. It can be concluded from our empirical analysis on regional, country and continental scales that a super-linear scaling between lightness and area with a stable exponent across different low light threshold levels generally holds for natural cities. But Zipf 's Law does not always apply over the whole range of lightness thresholds. Furthermore, we build a model based on the simple geometric matching mechanism to reproduce the self-organized formation process of nighttime light patterns. The statistical properties including allometries, size distributions and fractal geometries generated by our model are in good agreement with empirical evidence. These findings have profound implications for understanding the effects of simple aggregation behaviour in primitive stages of city formation and the urbanization process.

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Section: Methodsmentioning

confidence: 99%

Allometric scaling, size distribution and pattern formation of natural cities

Li¹,

Wang²,

Zhang³

et al. 2015

Palgrave Commun

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show abstract

“…DMSP-OLS imagery has a tendency to overestimate urban areas as a consequence of overglow, but also underestimate where no electricity is present. Imhoff [33] investigated different thresholding techniques to compensate for the overglow problem and through comparison of their data with those of the US census bureauthey found no differences between the two areal assessments. Sutton et al [34] used a radiance-calibrated data set to study urban sprawl in the US.…”

Section: Urban Studiesmentioning

confidence: 99%

Remote Sensing in Social Science Research~!2009-12-28~!2010-03-26~!2010-06-25~!

Hall¹

2010

TORMSJ

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Abstract:Since the early days of satellite remote sensing in the 1950's, accessibility, quality, and scope of remote sensing image data has been continuously improving, making it a rich data source with a wide range of applications. Today, the use of remote sensing techniques and data is commonplace within many disciplines in the natural sciences. Although there are quite a few examples of remote sensing to be found in the social sciences, developments here have, on the whole, been less pronounced. This paper investigates 1) how remote sensing data has been put to use in social science studies, and 2) how social science could better utilize the huge potential of remote sensing data. The first part of the paper gives an overview of existing types of remote sensing techniques and data collection. The second part consistsof a review of social science applications of remote sensing data. In the conclusions it is argued that remote sensing data is at its most valuable in the social sciences when used in combination with traditional methods such as surveys, public records, interviews and direct observation.

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“…Consequently, DMSP/OLS NTL imagery has been a very important resource to characterize and map urban areas [19][20][21][22]. However, it suffers from saturation and overglow effects, as well as its coarse resolution [23].…”

Section: Introductionmentioning

confidence: 99%

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

Zhang

Thau

et al. 2015

Remote Sensing

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Urban areas play a very important role in global climate change. There is increasing need to understand global urban areas with sufficient spatial details for global climate change mitigation. Remote sensing imagery, such as medium resolution Landsat daytime multispectral imagery and coarse resolution Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light imagery, has provided a powerful tool for characterizing and mapping cities, with advantages and disadvantages. Here we propose a framework to merge cloud and cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution, through a Normalized Difference Urban Index (NDUI) to make full use of them while minimizing their limitations. We modify the maximum NDVI value multi-date image compositing method to generate the cloud and cloud shadow-free Landsat NDVI composite, which is critical for generating a global NDUI. Evaluation results show the NDUI can effectively increase the separability between urban areas and bare lands as well as farmland, capturing large scale urban extents and, at the same time, providing sufficient spatial details inside urban areas. With advanced

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A technique for using composite DMSP/OLS “City Lights” satellite data to map urban area

Cited by 532 publications

References 6 publications

Allometric scaling, size distribution and pattern formation of natural cities

Allometric scaling, size distribution and pattern formation of natural cities

Remote Sensing in Social Science Research~!2009-12-28~!2010-03-26~!2010-06-25~!

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

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