Spatial analysis of global urban extent from DMSP-OLS night lights

Small, Christopher; Pozzi, F.; Elvidge, C. D.

doi:10.1016/j.rse.2005.02.002

Cited by 576 publications

(366 citation statements)

References 14 publications

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“…We chose to use population density rather than other proxies, such as the human footprint (30) or night lights (31,32), because density is not as strongly correlated with the gross domestic product as the latter. Population density is an important driver of many threats to biodiversity and is one of the only variables currently available for the whole Basin at the within-country spatial scale.…”

Section: Methodsmentioning

confidence: 99%

Between-country collaboration and consideration of costs increase conservation planning efficiency in the Mediterranean Basin

Kark

Levin

Grantham

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

175

203

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The importance of global and regional coordination in conservation is growing, although currently, the majority of conservation programs are applied at national and subnational scales. Nevertheless, multinational programs incur transaction costs and resources beyond what is required in national programs. Given the need to maximize returns on investment within limited conservation budgets, it is crucial to quantify how much more biodiversity can be protected by coordinating multinational conservation efforts when resources are fungible. Previous studies that compared different scales of conservation decision-making mostly ignored spatial variability in biodiversity threats and the cost of actions. Here, we developed a simple integrating metric, taking into account both the cost of conservation and threats to biodiversity. We examined the Mediterranean Basin biodiversity hotspot, which encompasses over 20 countries. We discovered that for vertebrates to achieve similar conservation benefits, one would need substantially more money and area if each country were to act independently as compared to fully coordinated action across the Basin. A fully coordinated conservation plan is expected to save approximately US$67 billion, 45% of total cost, compared with the uncoordinated plan; and if implemented over a 10-year period, the plan would cost Ϸ0.1% of the gross national income of all European Union (EU) countries annually. The initiative declared in the recent Paris Summit for the Mediterranean provides a political basis for such complex coordination. Surprisingly, because many conservation priority areas selected are located in EU countries, a partly coordinated solution incorporating only EU-Mediterranean countries is almost as efficient as the fully coordinated scenario. biodiversity costs ͉ complementarity ͉ vertebrates

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Section: Methodsmentioning

confidence: 99%

Between-country collaboration and consideration of costs increase conservation planning efficiency in the Mediterranean Basin

Kark

Levin

Grantham

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

175

203

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“…Because of the fine spatial resolution of Landsat TM/ETM+ data (30 × 30 m 2 ), built-up urban areas extracted from the Landsat TM/ETM+ images are feasible and acceptable for evaluating the mapping results of NSL data (Cao et al 2009;He et al 2006;Henderson et al 2003;Small, Pozzi, and Elvidge 2005;Sutton, Cova, and Elvidge 2006). In this article, a decision-tree method was developed to extract the built-up urban areas from Landsat TM/ETM+ images based on the commonly used spectral-analysis algorithms.…”

Section: Appendix 2 Extracting Built-up Urban Areas From Landsat Tm/mentioning

confidence: 99%

“…Subsequent studies demonstrated the superiority of DMSP-OLS NSL in extracting the built-up urban areas at global (Elvidge et al 1997(Elvidge et al , 1999(Elvidge et al , 2007 (Imhoff et al 1997a(Imhoff et al , 1997bSmall, Pozzi, and Elvidge 2005;Lu et al 2008;Cao et al 2009;Shi et al 2014;Wei et al 2014). However, most NSL-based studies presently use empirical threshold-based approaches (e.g., Imhoff et al 1997aImhoff et al , 1997bHenderson et al 2003;Milesi et al 2003).…”

Section: Introductionmentioning

confidence: 99%

“…In comparison, they set empirical thresholds to extract built-up urban areas of different sizes and NSL intensities. Therefore, the threshold methods not only overestimate the built-up area in urban regions because of 'blooming' NSL but also miss small patches of built-up lands in developing towns, where the NSL is much lower (e.g., Imhoff et al 1997a;Owen 1998;Sutton et al 2001;Lawrence et al 2002;Henderson et al 2003;Small, Pozzi, and Elvidge 2005;Cao et al 2009). …”

Section: Introductionmentioning

confidence: 99%

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A new method for extracting built-up urban areas using DMSP-OLS nighttime stable lights: a case study in the Pearl River Delta, southern China

Chen

Wang

et al. 2015

GIScience & Remote Sensing

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The nighttime stable light (NSL) images on board the Operational Line-scan System (OLS) of the Defense Meteorological Satellite Program (DMSP) are useful for extracting large-scale built-up urban areas. However, most NSL-based studies are presently empirical threshold-based approaches. They always overestimate the areas of built-up land in urban regions because of the 'blooming' effect of NSL; and overlook small patches in developing towns where the NSL is much lower. In this study, a neighborhood statistics analysis (NSA) method is developed on the basis of the relative spatial variations between neighboring built-up and non-built-up pixels in DMSP-OLS images. It is applied to extract the built-up areas of eight cities in the Pearl River Delta in 1996Delta in , 2000Delta in , 2005Delta in , and 2009. The validations indicate that the total areas of the NSA-mapped results are highly correlated with those from Landsat TM/ETM+ data (R 2 = 0.94; p < 0.001). The comparison results, which are evaluated by landscape indices (the landscape shape index (LSI), the contiguity index (CONTIG), and the perimeter area ratio (PARA)), also show good correlations (R 2 > 0.46; p < 0.001). In addition, the total NSL of the built-up urban areas is significantly correlated with the statistical population data (R 2 = 0.62; p < 0.001), which indirectly confirms the effectiveness of our proposed method.Keywords: DMSP-OLS nighttime stable light (NSL); built-up urban area; urban expansion; remote sensing IntroductionThirty years of rapid urbanization in China have resulted in a 3.37-fold increase in builtup areas (Fang 2009;Lu et al. 2008 Lu et al. , 2012. Large amounts of natural lands in cities are being replaced by artificial surfaces, which cause numerous environmental and ecological problems (Milesi et al. 2003;Imhoff et al. 2004;Su et al. 2010;Chen et al. 2012). Therefore, accurately monitoring the regional expansion of built-up areas in China is essential (He et al. 2006;Liu et al. 2012 (Cao et al. 2009;Liu et al. 2012;Bhatti and Tripathia 2014), are commonly used. However, because of the limited geographic coverage of medium-and high-resolution images, these methods require a large number of scenes to cover the regional and national scales (Liu et al. 2003(Liu et al. , 2005(Liu et al. , 2010Zhang, He, and Liu 2014).Nighttime stable light (NSL) images on board the Operational Line-scan System (OLS) of the Defense Meteorological Satellite Program (DMSP), which uses a low-light detecting sensor to detect city night lights (Elvidge et al. 1997(Elvidge et al. , 1999, were first demonstrated to be capable of mapping built-up urban areas by Croft (1978) and Kramer (1994). Subsequent studies demonstrated the superiority of DMSP-OLS NSL in extracting the built-up urban areas at global (Elvidge et al. 1997(Elvidge et al. , 1999(Elvidge et al. , 2007 (Imhoff et al. 1997a(Imhoff et al. , 1997bSmall, Pozzi, and Elvidge 2005;Lu et al. 2008;Cao et al. 2009;Shi et al. 2014;Wei et al. 2014). However, most NSL-based studies presently use empir...

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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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Spatial analysis of global urban extent from DMSP-OLS night lights

Cited by 576 publications

References 14 publications

Between-country collaboration and consideration of costs increase conservation planning efficiency in the Mediterranean Basin

Between-country collaboration and consideration of costs increase conservation planning efficiency in the Mediterranean Basin

A new method for extracting built-up urban areas using DMSP-OLS nighttime stable lights: a case study in the Pearl River Delta, southern China

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

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