satellite inventory of human settlements using nocturnal ...ols is intensiﬁed at night, permitting...

Global Change Biology (1997) 3, 387–395

Satellite inventory of human settlements using nocturnalradiation emissions: a contribution for the globaltoolchest

C H R I S T O P H E R D . E LV I D G E , * ‡ K I M B E R LY E . B A U G H , † V I N I T A R U T H H O B S O N , †E R I C A . K I H N , ‡ H E R B E R T W. K R O E H L , ‡ E T H A N R . D AV I S † and D AV I D C O C E R O §*Desert Research Institute, University of Nevada System, Reno, NV 89506, †Cooperative Institute for Research inEnvironmental Sciences, University of Colorado, Boulder, CO 80303, ‡Solar-Terrestrial Physics Division, NOAA NationalGeophysical Data Centre, 325 Broadway, Boulder, CO 80303, §Department of Geography, Universidad de Alcala, 28801 Alcalade Henares, Spain

Abstract

Time series data from the Defense Meteorological Satellite Program (DMSP) OperationalLinescan System (OLS) have been used to derive georeferenced inventories of humansettlements for Europe, North and South America, and Asia. The visible band of theOLS is intensified at night, permitting detection of nocturnal visible-near infraredemissions from cities, towns, and villages. The time series analysis makes it possible toeliminate ephemeral VNIR emission sources such as fire and to normalize for differencesin the number of cloud-free observations. An examination of the area lit (km2) for 52countries indicates the OLS derived products may be used to perform the spatialapportionment of population and energy related greenhouse gas emissions.

Keywords: night-time lights, human settlements

Received 10 July 1996; revision received 20 January 1997; accepted 7 March 1997

Introduction

Much of global change research is dedicated to discerning land cover changes under differing scenarios. Census dataand documenting the impacts of human activities on from individual countries are collected using differentnatural systems (Houghton et al. 1995). Human popula- methods, timetables, and reporting units, making ittion numbers have expanded from ™ 750 million in the impossible to assemble a consistent global product withmid-1700s to 5.8 billion in 1996 and are expected to high spatial resolution. As a result, the basic reportingdouble in the next 45 years (Population Reference Bureau unit of the recently released Global Population Database1994). Human activities which are known to be cumulat- (GPOPDB) of the U.S. Bureau of Census, Internationalively altering the global environment include greenhouse Program Centre, is 2000 km2.gas emissions from fossil fuel consumption, air and water In the late 1960s Tobler (1969) demonstrated that humanpollution, and land cover/land use change. Far from population could be estimated with a high degree ofbeing evenly distributed across the land surface, to a great accuracy by measuring the areal extent of human settle-extent human activities with environmental consequences ments observed from satellite photography generated byare concentrated in or near human population centres. the Gemini manned space flight program. By modelling

Surprisingly, there is no up-to-date global map indicat- settlements as circular areas, empirical relationships wereing location, areal extent, and population of human developed for estimating populations. The coefficientssettlements. Such a data set would be quite useful for a for these relationships were found to vary regionally,variety of global change research applications related to which was attributed to societal differences in the organ-anthropogenic greenhouse gas emissions, assessment of ization of spatial activity. Tobler (1969) speculated thatthe cumulative impacts of human activities on terrestrial high spatial resolution satellite imagery could be used toand aquatic ecosystems, and modelling future land use/ perform a global inventory of human settlements. While

a methodology was established, the application of satelliteremote sensing to derive the global distribution of humanCorrespondence: Christopher D. Elvidge, tel 11/303-497-6121,

fax 11/303-497-6513, e-mail [email protected] population has not proceeded due to the expense and

© 1997 Blackwell Science Ltd. 387

388 C H R I S T O P H E R D . E LV I D G E et al.

logistics associated with acquiring and analysing a global data in analogue form. A digital archive for the DMSP-OLS data was established in mid-1992 at the NOAAcoverage of high spatial resolution satellite imagery, such

as Landsat or SPOT. National Geophysical Data Centre.There are two spatial resolution modes in which dataInstead of relying on high spatial resolution imagery

to observe the location and areal extent of human settle- can be acquired. The full resolution data, having nominalspatial resolution of 0.56 km, is referred to as ‘fine’. Onments, we are producing a global inventory of human

settlements on a 1 km grid from light-intensified night- board averaging of five by five blocks of fine dataproduces ‘smoothed’ data with a nominal spatial reso-time data acquired by the Defense Meteorological Satellite

Program (DMSP) Operational Linescan System (OLS). lution of 2.7 km. Most of the data received by NOAA-NGDC is in the smooth spatial resolution mode.Details on the sensor and algorithms employed have

been described by Elvidge et al. (1997). The detection The potential use of DMSP-OLS data for the inventoryof human settlements and energy consumption patternslimits of the product appear to be in the 100–200 person

range in the USA. We expect that the detection limits rise has been noted since the 1980s (Welch 1980 and Foster1983). Sullivan (1989) produced a 10 km-resolution globalin societies which use less outdoor lighting. In this article

we present images of the night-time lights of Europe and image of OLS observed VNIR emission sources usingfilm data. The global map published by Sullivan (1989)South America. In addition, we examine the relationship

between area lit, population, and energy related carbon was derived from single dates of OLS imagery, selectedbased on the presence of large number cloud-free VNIRemissions for 52 countries.emission sources and mosaiced into a global product. Asa result, many of the features presented in areas such as

BackgroundAfrica are ephemeral VNIR emissions from fires. Theseearly studies with OLS data relied on the analysis of filmSince the early 1970s the U.S. Air Force Defense Meteoro-

logical Satellite Program has operated polar orbiting strips, which limited the studies on population andenergy to a small number of sites.platforms carrying cloud imaging satellite sensors capable

of detecting clouds using two broad spectral bands: The algorithms required to produce georeferencedimages of permanent light sources using time series ofvisible – near infrared (VNIR) and thermal infrared (TIR).

The program began with the SAP (Sensor Aerospace digital DMSP-OLS data were only recently developed(Elvidge et al. 1997). NOAA-NGDC is currently producingVehicle Electronics Package) which were flown from 1970

to 76. The current generation of OLS sensors began flying the first systematic global inventory of human settle-ments, using OLS data from the 1994 to 1995-time period.in 1976 and are expected to continue flying until ™ 2010.

The OLS is an oscillating scan radiometer designed forcloud imaging with two spectral bands (VIS and TIR)

Materials and methodsand a swath of ™ 3000 km. The ‘VIS’ bandpass straddlesthe visible and near-infrared (VNIR) portion of the spec- DMSP-OLS data from night-time orbits covering Europe,

North and South America, and Asia acquired with lesstrum (0.5–0.9 µm). Night-time passes of the DMSP occurbetween 09.30 and 10.30 hours. Satellite attitude is stabil- than half lunar illumination conditions between 1 October

1994 and 30 April 1995 were extracted from the digitalized using four gyroscopes (three axis stabilization), astar mapper, Earth limb sensor and a solar detector. The archive and analysed to derive the georeferenced images

of human settlements. Sufficient orbits were used toOLS VIS band signal is intensified at night using aphotomultiplier tube (PMT), for the detection of moonlit ensure that all land areas had 20–100 cloud-free observa-

tions. We use data acquired with less than 50% lunarclouds. The low light sensing capabilities of the OLS atnight permit the measurement of radiances down to 10–9 illumination for two reasons: (i) During these nights the

VNIR band gain on the OLS is set to its highest monthlywatts/cm2/sr/um. This is more than four orders ofmagnitude lower than the OLS daytime VIS band or the level, permitting detection of smaller light sources present

on the Earth’s surface. And (ii) with low levels of lunarVNIR bands of other sensors, such as the NOAA AVHRRor the Landsat Thematic Mapper. The OLS has a unique illumination it is possible to avoid inclusion of moonlight

reflectance off of clouds and water, which can be confusedcapability to detect the VNIR emissions sources presentat the Earth’s surface, including cities, towns, villages, with VNIR emissions from human settlements. Typical

OLS gain settings are at a level which results in saturatedgas flares, and fires Croft (1973, 1978, 1979). While DMSPdata were not officially classified, digital data were not visible band data for most cities. Therefore, the resulting

products indicate the location and frequency of observa-available or preserved during the first 20 years of theprogram. An OLS film archive at the University of tion for VNIR emission sources, but not the actual bright-

ness. Based on the frequency, we are able to eliminateColorado, National Snow and Ice Data Centre providedthe scientific community with access to a subset of OLS ephemeral emission sources such as fire.

© 1997 Blackwell Science Ltd., Global Change Biology, 3, 387–395

S A T E L L I T E I N V E N T O R Y O F H U M A N S E T T L E M E N T S 389

Table 1. Country-by-country population and energy-related carbon emissions

Country Digraph Population (1995) Area lit (1994–95) square Energy related carbon emissionskilometres in gigagrams (1993)

China CH 1,198,062,729 322,071 129,000India IN 936,461,577 209,888 59,600United States US 263,033,968 1,119,043 924,000Brazil BR 160,737,489 233,609 64,100Japan JA 125,200,170 146,882 215,000Mexico MX 93,985,848 144,587 79,700Germany GE 82,947,817 70,058 162,000Turkey TU 61,437,246 61,498 23,900United Kingdom UK 58,360,677 73,151 107,000France FR 57,862,334 106,388 90,800Italy IT 57,383,872 88,294 101,000Ukraine UP 51,088,930 53,259 75,500Spain SP 39,117,865 98,782 45,300Poland PL 38,589,294 41,130 18,100Columbia CO 36,200,251 44,245 13,500Argentina AR 34,292,742 84,056 33,300Peru PE 24,087,372 18,859 5870Romania RO 21,924,216 15,506 28,300Venezuela VE 21,004,773 54,337 28,400Netherlands NT 15,478,038 13,649 54,700Chile CI 14,161,216 22,086 8800Ecuador EC 10,890,950 13,413 4370Greece GR 10,494,142 27,113 14,100Belarus BO 10,398,187 12,829 20,900Belgium BE 10,136,118 11,814 26,400Hungary HU 10,072,312 11,548 12,600Portugal PO 9,864,697 16,530 10,100Sweden SW 8,846,542 69,537 13,100Bulgaria BU 8,573,601 10,403 8030Austria AU 7,988,416 12,034 13,100Bolivia BL 7,896,254 9287 3020Azerbaijan AJ 7,616,038 8569 13,600Switzerland SZ 7,163,877 8634 12,400Paraguay PA 5,358,198 7194 645Georgia GG 5,281,313 2380 2600Denmark DA 5,228,717 14,151 9750Finland FI 5,099,117 52,112 10,400Moldovia MD 4,459,435 2833 3390Norway NO 4,362,046 53,286 8020Lithuania LH 3,661,469 5468 3830Ireland EI 3,570,949 9195 5040Armenia AR 3,463,914 1319 1040Uruguay UY 3,222,716 7385 1380Albania AL 3,207,033 1496 726Latvia LG 2,506,569 4862 2100Estonia EN 1,477,796 4698 891Cyprus CY 736,261 3528 1570Guyana GY 723,774 1118 306Suriname NS 429,544 1000 392Luxembourg LU 409,385 832 1980Iceland IC 268,081 3865 615French Guyana FG 145,270 722 231



Fig. 1 Night-time lights of Europe.

We have used the one kilometre equal area grid of given to the complete removal of fires from the data ofSouth America.the Interrupted Goode Homolosine Projection (IGHP)

Population estimates for individual countries (Table 1)developed for the NASA-USGS Global 1 km AVHRRfor the year 1995 were extracted from the U.S. Censusproject (Goode 1925 and Steinwand 1993; Eidenshink &Bureau International Data Base. Gigagrams of energy-Faundeen 1995). Each OLS orbit was analysed using therelated carbon emissions for the year 1993 were estimatedalgorithms described by Elvidge et al. (1997) to outlineby multiplying 1993 national consumption of refinedthe extent of coverage, positions of clouds, and positionspetroleum products (e.g gasoline, kerosene, etc.), naturalof VNIR emission sources. From these data, two georefer-gas and coal fuel (compiled by the U.S. Departmentenced images were produced: (i) count of cloud-free dataof Energy, Energy Information Administration) by theobservations, and (ii) count of cloud-free observations ofcarbon contents listed by the U.S. Office of TechnologyVNIR emission sources. These two images are ratioedAssessment (1991).and the resulting value is multiplied by 100 to yield a

measure of the frequency with which lights were observedthat is normalized for spatial variation in the number of

Results and discussioncloud-free observations. Thresholds were applied to filterout fires and other ephemeral VNIR emission sources. The night-time lights of Europe and South America areThe ‘area lit’ in square kilometres for individual countries presented in Figs 1 and 2, respectively. Note the extreme(Table 1) were derived by summing the number grid cells density of lights in Western European countries whenwithin the country boundaries which had a VNIR light compared to the South American countries. Although

less distinct, a similar visual observation can be madesource at least 10% of the time. Particular attention was



Fig. 2 Night-time lights of SouthAmerica.

regarding the density of lights in Western and Eastern numerical values for these anomalies are striking. Norwayand Moldova have nearly the same population (4.4Europe. Offshore lights can be observed in the North

Sea, where oil and gas production sites are active. Another million), yet Norway has almost 19 times the area lit.The USA has about one fourth the population of China,area of offshore lights can be observed in the Atlantic

Ocean east of Argentina, where fleets of fishing boats use but has nearly three times the area lit. Argentina andColombia each have about 35 million people, butbanks of lighting to attract and capture squid.

Figure 3 shows the area lit in each country vs. 1995 Argentina has almost double the area lit. Brazil has twicethe population of Germany, but more than three timespopulation estimates. Note that there is a generally linear

relationship, with the area lit expanding as population the area lit. There are multiple reasons why a countrywould have an anomalously large area lit, includingincreases. Visually evident outliers with anomalously

high area lit relative to population include the USA, artifacts of the sensor spatial resolution and data pro-cessing. Brazil has a much larger number of individualNorway, Sweden, Finland, and Iceland. Outliers with low

area lit values relative to population include: China, cities and towns than countries like Germany. Examina-tion of the night-time lights indicates that for smallerIndia, Georgia, Moldova, Albania, and Armenia. The



Fig. 3 Area lit (km2) vs. 1995 population estimates for the 52 Fig. 4 Area lit (km2) vs. estimates of 1993 carbon emissions fromcountries. fossil fuel consumption for 52 countries.

cities and towns the area lit as currently derived with a to scatter on Fig. 4 are differences in the apportionmentof fossil fuel products between thermal production offixed frequency threshold is substantially larger than the

actual area of lighting present at the Earth’s surface. This electricity and industrial products. For instance, 44% ofGerman petroleum consumption is in non-fuel distillate,situation arises due to the random placement of OLS

pixels in the time series over what are essentially point while the same figure for the USA is 18%.sources of light. These edge effects are also presentsurrounding large cities, but the effect in expanding the

Conclusionarea lit is more pronounced with small towns. Culturalpreferences for the generous use of street lighting may The distribution of human populations across the Earth’s

surface has been identified as one of the key data setsbe a factor accounting for the area lit anomalies in certaincountries. The Scandinavian countries may have more required for global change research (Clarke & Rhind

1992). At present, the only digital maps having globaloutdoor lighting to compensate for the long periods ofwinter darkness. coverage showing cultural features, such as cities, towns

and villages are digitized versions of paper maps, whichThe countries with low area lit values relative topopulation are all developing countries, where the extent are typically years (or decades) out of date. Census data

from individual countries cannot be pooled to provide aof electrification is limited and even in areas with electri-city there may be curtailed use of outdoor lighting. In suitable data set detailing the spatial distribution of

human population due to wide variations in nationalother countries, mountainous conditions, small countrysize, or government protection of farmland are factors census timetables, methods and accuracies. The locations

and areal extents of cities and towns can be extractedwhich may constrain the expansion of urban areas.Figure 4 shows the relationship found between area lit from high spatial resolution remotely sensed data, such

as Landsat or SPOT. However, a single coverage of theand estimates of 1993 carbon emissions from fossil fuelconsumption. Here the scatter can be explained, at least South American continent with Landsat would require

737 scenes. Many more scenes would be required toin part, by the differences in the sources of electric power.Norway, Sweden, France and Brazil generate more than ensure complete coverage with cloud-free data. The task

of acquiring and analysing wall-to-wall Landsat cover-95% of their electric power using hydroelectric andnuclear sources, resulting in shifts in their positions from ages to produce or update maps for cultural features

would be immense and is apparently cost prohibitive.Fig. 3 relative to countries like the United Kingdom andGermany, which are use a high percentage of fossil fuels We have developed a methodology for producing

georeferenced images indicating location and extent offor electric power generation. Another factor contributing



Croft TA (1973) Burning waste gas in oil fields. Nature, 245,human settlements based on satellite observations of375–376.persistent VNIR emission sources present at the Earth’s

Croft TA (1978) Nighttime images of the earth from space.surface. The relationship between area lit, population,Scientific American, 239, 68–79.and energy related carbon emissions has been examined

Croft TA (1979) The Brightness of Lights on Earth at Night, Digitallyfor 52 countries with a total population of 3.5 billionRecorded by DMSP Satellite. Stanford Research Institute Final

inhabitants, representing 61% of the total world popula-Report Prepared for the U.S. Geological Survey.

tion. As with the work of Tobler (1969) it was found Eidenshink JC, Faundeen JL (1994) The 1-km AVHRR globalthat there are substantial variations between individual land data set: First stages in implementation. Internationalcountries in terms of the area lit per increment of either Journal of Remote Sensing, 15, 3443–3462.population or carbon emissions. These variations would Elvidge CD, Baugh KB, Kihn EA, Kroehl HW, Davis ER (1997)

Mapping city lights with nighttime data from the DMSPhave to be accounted for as part of any spatial apportion-Operational Linescan System, Photogrammetric Engineeringment of population or greenhouse gas emissions basedand Remote Sensing, 63, 727–734.on the OLS products.

Flari V, Lazaridou-Dimitriadou M (1995) The impact of nocturnalSpatially referenced data on the extent of nocturnallight pulses on the activity pattern of terrestrial snails (Helixillumination may also be used to model or evaluatelucorum) entrained to a photoperiod of 12 h light: 12 h dark.impacts of artificial lighting on biological systems. ACanadian Journal of Zoology, 73, 1214.

growing body of research indicates that nocturnal lightingFoster JL (1983) Observations of the Earth using nighttime visible

has biological consequences on humans (Koller et al. imagery. International Journal of Remote Sensing, 4, 785–791.1994), plants (Salisbury 1992 and Gaudreau et al. 1994), Gaudreau L, Charbonneau J, Vezina L-P, Gosselin A (1994)and animals (Flari & Lazaridou-Dimitriadou 1995). Photoperiod and photosynthetic flux influence growth and

We are proceeding in the assembly of night-time lights quality of greenhouse-grown lettuce. Horticultural Science,29, 1285.products for other land areas. Because the night-time

Goode JP (1925) The Homolosine projection: A new device forlights data use the same 1 km reference grid as the NASA-portraying the Earth’s surface entire. Annals of the AssociationUSGS-IGBP Global 1 km AVHRR land cover productsof American Geographers, 115, 119–125.(Belward 1996), it should be relatively easy for scientists

Houghton JT, Meira Filho LG, Bruce J, Lee H, Callander BA,to make combined use of the two data sets. We areHaites E, Harris N, Maskell K (1995) Climate Change 1994:currently developing a companion global product ofRadiative Forcing of Climate Change and an Evaluation of the IPCC

radiance calibrated night-time lights derived from OLSIS92 Emission Scenarios. Press Syndicate of the University of

acquisitions made with reduced gain settings to avoid Cambridge, Cambridge.sensor saturation. These ‘low-gain’ data acquisitions are Koller M, Haerma M, Lailinen JT, Kundi M (1994) Differentmade through special request to the U.S. Air Force. In patterns of light exposure in relation to melatonin and cortisolthe longer term, we anticipate producing periodic updates rhythms and sleep of night workers. Journal of Pineal Research,

16, 127.of the night-time lights, for observation of the expansionPopulation Reference Bureau (1994) World Population: Towardsof human settlements. Data sets may be obtained by

the Next Century. Washington, DC.contacting the first-named author.Salisbury FB (1992) Limiting factors and maximum yields: a

controlled ecological life-support system (CELSS). In: PlantPhysiology (eds Salisbury FB, Ross CW). WadsworthAcknowledgementsPublishing, Belmont, CA.

Steinwand DR (1993) Mapping raster imagery into theThe authors appreciate the DMSP program office and the U.S.interrupted Goode Homolosine Projection. InternationalAir Force Global Weather Central for providing NOAA-NGDC

with DMSP data used in this research. Journal of Remote Sensing, 15, 3463–3472.Sullivan WTIII (1989) A 10 km resolution image of the entire

night-time Earth based on cloud-free satellite photographsin the 400–1100 nm band. International Journal of RemoteReferencesSensing, 10, 1–5.

Belward AS (ed.) (1996) The IGBP-DIS global 1 km land cover data Tobler WR (1969) Satellite confirmation of settlement sizeset (DISCover) Proposal and Implementation Plans, International coefficients. Area, 1, 31–34.Geosphere-Biosphere Program, Data and Information System, US Office of Technology Assessment (1991), Changing by Degrees:Working paper 13, Toulouse. Steps to Reduce Greenhouse Gases, OTA-O-482, U.S.

Clarke JT, Rhind DW (eds) (1992) Population Data and Global Government Printing Office, Washington, DC.Environmental Change. International Social Science Council, Welch R (1980) Monitoring urban population and energyHuman Dimensions of Global Environmental Change utilization patterns from satellite data. Remote Sensing of

Environment, 9, 1–9.Programme, Report no. 3, Paris.



Appendix

The night-time lights of USA, Mexico/Central America/ portions of 1001 orbits of 1994–1995 DMSP-OLS data,Caribbean, China and the Indian sub-continent, derived processed by NOAA National Geophysical Data Center.from cloud-free

USA

Mexico and Central America



China

Indian sub-continent


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