A global map of urban extent from nightlights

Zhou, Yuyu; Smith, Steve; Zhao, Kaiguang; Imhoff, Marc L.; Thomson, Allison M.; Bond‐Lamberty, Ben; Asrar, Ghassem; Zhang, Xuesong; He, Chunyang; Elvidge, Christopher D.

doi:10.1088/1748-9326/10/5/054011

Cited by 258 publications

(165 citation statements)

References 37 publications

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“…We use the global urban area map at 1-km spatial resolution developed by Zhou et al (52). The map is based on a cluster-based method to estimate optimal thresholds for mapping urban extent using DMSP/OLS NTL to account for regional variations in urban clusters (53).…”

Section: Methodsmentioning

confidence: 99%

Global urban signatures of phenotypic change in animal and plant populations

Alberti

Correa

Marzluff

et al. 2017

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

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390

309

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Humans challenge the phenotypic, genetic, and cultural makeup of species by affecting the fitness landscapes on which they evolve. Recent studies show that cities might play a major role in contemporary evolution by accelerating phenotypic changes in wildlife, including animals, plants, fungi, and other organisms. Many studies of ecoevolutionary change have focused on anthropogenic drivers, but none of these studies has specifically examined the role that urbanization plays in ecoevolution or explicitly examined its mechanisms. This paper presents evidence on the mechanisms linking urban development patterns to rapid evolutionary changes for species that play important functional roles in communities and ecosystems. Through a metaanalysis of experimental and observational studies reporting more than 1,600 phenotypic changes in species across multiple regions, we ask whether we can discriminate an urban signature of phenotypic change beyond the established natural baselines and other anthropogenic signals. We then assess the relative impact of five types of urban disturbances including habitat modifications, biotic interactions, habitat heterogeneity, novel disturbances, and social interactions. Our study shows a clear urban signal; rates of phenotypic change are greater in urbanizing systems compared with natural and nonurban anthropogenic systems. By explicitly linking urban development to traits that affect ecosystem function, we can map potential ecoevolutionary implications of emerging patterns of urban agglomerations and uncover insights for maintaining key ecosystem functions upon which the sustainability of human wellbeing depends.ecoevolution | urbanization | ecosystem function | sustainability | anthropocene E merging evidence of phenotypic change on contemporary timescales challenges the assumption that evolution only occurs over hundreds or thousands of years. Anthropogenic changes in ecological conditions can drive evolutionary change in species traits that can alter ecosystem function (1-3). However, the reciprocal and simultaneous outcomes of such interactions have only begun to emerge (4). Despite increasing evidence that humans are major drivers of microevolution, the role of human activities in such dynamics is still unclear. Might human-driven evolution lead to ecosystem change with consequences for human well-being within contemporary timescales (5, 6)?To address this question, human-driven phenotypic change must be considered in the context of global rapid urbanization. In 1950, 30% of the world's population lived in urban settlements (7). By 2014, that figure had risen to 54%, and by 2050 it is expected to reach 66% (7). By 2030, urban land cover is forecast to increase by 1.2 million km 2 , almost tripling the global urban land area of 2000 (8). Urbanization drives systemic changes to socioecological systems by accelerating rates of interactions among people, multiplying connections among distant places, and expanding the spatial scales and ecological consequences of human activities to glo...

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Section: Methodsmentioning

confidence: 99%

Global urban signatures of phenotypic change in animal and plant populations

Alberti

Correa

Marzluff

et al. 2017

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

Self Cite

390

309

View full text Add to dashboard Cite

show abstract

“…Among three popular methods, both the LOT and VANUI approaches require ancillary data to determine optimal thresholds [23,32,33]. However, numerous human and computational resources are needed to obtain one-time accurate ancillary data for large-scale studies.…”

Section: Innl-svm Provides a Reliable Approach For Rapidly And Accuramentioning

confidence: 99%

“…However, numerous human and computational resources are needed to obtain one-time accurate ancillary data for large-scale studies. Therefore, it is difficult to extract urban land information for multiple years at large spatial scales using either the LOT or VANUI method [23,32,33]. By contrast, INNL-SVM can automatically determine thresholds for training sample selection and SVM classification according to regional characteristics of nighttime light, vegetation coverage, and LST, increasing its efficiency for extracting urban land area at large scales and over long time series [5,8].…”

Section: Innl-svm Provides a Reliable Approach For Rapidly And Accuramentioning

confidence: 99%

Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods

Dou

Liu

et al. 2017

Remote Sensing

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Abstract:Timely and accurate extraction of urban land area using the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data is important for urban studies. However, a comprehensive assessment of the existing methods for extracting urban land using VIIRS nighttime light data remains inadequate. Therefore, we first reviewed the relevant methods and selected three popular methods for extracting urban land area using nighttime light data. These methods included local-optimized thresholding (LOT), vegetation-adjusted nighttime light urban index (VANUI), integrated nighttime lights, normalized difference vegetation index, and land surface temperature support vector machine classification (INNL-SVM). Then, we assessed the performance of these methods for extracting urban land area based on the VIIRS nighttime light data in seven evaluation areas with various natural and socioeconomic conditions in China. We found that INNL-SVM had the best performance with an average kappa of 0.80, which was 6.67% higher than the LOT and 2.56% higher than the VANUI. The superior performance of INNL-SVM was mainly attributed to the integration of information on nighttime light, vegetation cover, and land surface temperature. This integration effectively reduced the commission and omission errors arising from the overflow effect and low light brightness of the VIIRS nighttime light data. Additionally, INNL-SVM can extract urban land area more easily. Thus, we suggest that INNL-SVM has great potential for effectively extracting urban land with VIIRS nighttime light data at large scales.

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“…Since urbanized (i.e., built-up area) land represents less than 1% of the Earth's land surface (Liu et al, 2014;Zhou et al, 2015), efforts to increase resiliency, adaptive capacity, and habitability of cities will require substantial investments over small areas that will impact a disproportionately large fraction of society. This issue is particularly prominent for regions whose local relative share of global urban population continues to rise.…”

Section: Introductionmentioning

confidence: 99%

A Global Geospatial Ecosystem Services Estimate of Urban Agriculture

et al. 2018

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Though urban agriculture (UA), defined here as growing of crops in cities, is increasing in popularity and importance globally, little is known about the aggregate benefits of such natural capital in built‐up areas. Here, we introduce a quantitative framework to assess global aggregate ecosystem services from existing vegetation in cities and an intensive UA adoption scenario based on data‐driven estimates of urban morphology and vacant land. We analyzed global population, urban, meteorological, terrain, and Food and Agriculture Organization (FAO) datasets in Google Earth Engine to derive global scale estimates, aggregated by country, of services provided by UA. We estimate the value of four ecosystem services provided by existing vegetation in urban areas to be on the order of $33 billion annually. We project potential annual food production of 100–180 million tonnes, energy savings ranging from 14 to 15 billion kilowatt hours, nitrogen sequestration between 100,000 and 170,000 tonnes, and avoided storm water runoff between 45 and 57 billion cubic meters annually. In addition, we estimate that food production, nitrogen fixation, energy savings, pollination, climate regulation, soil formation and biological control of pests could be worth as much as $80–160 billion annually in a scenario of intense UA implementation. Our results demonstrate significant country‐to‐country variability in UA‐derived ecosystem services and reduction of food insecurity. These estimates represent the first effort to consistently quantify these incentives globally, and highlight the relative spatial importance of built environments to act as change agents that alleviate mounting concerns associated with global environmental change and unsustainable development.

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A global map of urban extent from nightlights

Cited by 258 publications

References 37 publications

Global urban signatures of phenotypic change in animal and plant populations

Global urban signatures of phenotypic change in animal and plant populations

Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods

A Global Geospatial Ecosystem Services Estimate of Urban Agriculture

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