An Exploration of using Nighttime
Satellite Imagery from the DMSP OLS
for Mapping Population and Wealth
in Guatemala
Paul C.Sutton
psutton@du.edu
Department of Geography
University of Denver
Outline
• Motivation: Why do this? Is is worthwhile?
• Brief Summary of DMSP OLS image processing
• How Nighttime images can be used to map urban areas
and estimate urban populations.
• How Nighttime images can be used to estimate and map
economic activity
• How Nighttime images may be used to estimate Human
Impact on the environment
Why Use Nighttime Imagery to Map/Model
Demographic and Socio-Economic Phenomena?
• Social, Economic, and Behavioral Demographic Data are
the major gaps to be filled in globally integrated geoinformation
• Existing Information is degrading due to increasing
human mobility, and the fact that a growing proportion of
the earth’s population live in developing countries which
can’t afford to conduct accurate censuses
• Spatially referenced demographic information is a vital
component of studies of: Hazard Planning and Response,
Sustainability and Development Issues, and countless
other cross-disciplinary investigations
System Overview
Defense Meteorological Satellite Program
Operational LineScan System (DMSP OLS)
Two sun-synchronous polar orbiting satellites (865 km orbit)
Observations at 1) ~ Dawn & Dusk, 2) ~ Noon & Midnight
Pixel Size: smoothed ~2.4 km2, fine ~ 0.5 km2, Swath Width ~3000 km
Two Bands: 1) Panchromatic VNIR, 2) Thermal Infrared
Dynamic Range: VNIR more than 4 orders of magnitude larger than traditional
sensors optimized for daytime observation
(e.g. sees light from reflected moonlight to reflected sunlight)
Data available from early 1970’s to Present, Digital Archive est. in 1992
Data Products derived from imagery (hyper-temporal mosaicing):
% cloud cover, % light observed, Fires, Lantern Fishing,
Gas Flares, City Lights, Radiance Calibrated City Lights,
Atmospherically corrected radiance calibrated city lights
Example of Cloud Screening over Italy
VNIR over Italy
Thermal over Italy
A comment on aggregation & scale:
This is a 1 km2 pixel in Denver, Colorado
Nighttime Satellite Imagery
Fires, Fishing, Flares, & City Lights
Forest fires in Australia
Lantern Fishing
In Japan
Gas Flares in the
Persian Gulf
City Lights along the Nile
Mapping Population
• Ln(area) vs. Ln(pop) regression method for
Estimating the Population of Urban Clusters
• Intra-Urban measures of population density:
Light Intensity as a proxy for Population Density
• Works better in countries with high % of
population in urban areas.
• Rural Electrification in Guatemala probably
reduces utility of these methods.
Light Intensity from DMSP OLS imagery
matched with Photographs using GPS
1) Central Guatemala City (DN= 500)
2) San Juan Ixcoy, Huehue. (DN=46)
3) Santa Eulalia, Huehue. (DN=31)
5) Flores, Peten
(DN=301)
4) Coban, Alta Verapaz (DN=181)
6) Nahuala, Solola
(DN=148)
7) Soloma, Huehue.
(DN =87 )
8) Northern Guatemala City
(DN= 215)
Night Lights and Pop Den in Guatemala
DMSP OLS
Nighttime Image
of Guatemala
Population Density
of Guatemala from
LandScan
R2 = .59
Ln(population) = 3.047 + 1.1463Ln(Area)
Population Density
from LandScan
Pixel Regression
(Pts are uPixels)
Light Intensity from DMSP OLS
Mapping Economic Activity
and GDP per Capita
• Just as Night Lights are a proxy measure of population
they are also a proxy measure of Economic Activity.
• Again, relationship is far from perfect (see next slide);
however, Light intensity can be used as a proxy
measure of GDP.
• GDP of Guatemala ~50 Billion; 25% apportioned to
dark area to account for agriculture, remaining 75%
apportioned based on light intensity.
• Using LandScan Population Density dataset and
dividing it into this map of GDP produces a map of
GDP per Capita.
Scatterplot of Night Light Energy &
PPP of GDP for 208 nations
Global map of Marketed Economic Activity as
measured by Nighttime Satellite Image Proxy
Dividing
‘map’ of
GDP
from
DMSP
by Pop
Density
from
Landscan
Guatemala’s
GDP per Capita
(U.S. Dollars)
0-999
1,000
2,000
3,000
4,000
5,000
6-10,000
11-50,000
51-100,000
Over 100,000
GDP per Capita
in Guatemala
2
at ~1 km
spatial resolution
Using Nighttime Imagery to Create an
“Environmental Sustainability Index”
• Measure Environmental Endowment of
Nations using Ecosystem Service Value of
Nation’s Lands
• Measure Human Impact of Nation from
DMSP OLS nighttime Image
• Divide The above measures to create and
ESI (Environmental Sustainability Index)
Measuring Human ‘Impact’
• What data can be used in the I = P*A*T formulation?
• If you use Population for P, GDP/Capita for Affluence,
and CO2 Emissions/GDP for Technology, then ‘Impact’
simplifies to total CO2 emissions
• Daily & Ehrlich used Energy Consumption per Capita to
capture the A*T
• “Impact” is a function of both population size and
individual consumption levels
• Nighttime Imagery from the DMSP OLS correlates with
Population, Energy Consumption, CO2 emissions, and
GDP and may be the best spatially explicit, single
variable, measure of ‘Impact’
Ecosystem Service Valuation:
IGBP to Nature Conversion Table
IGBP Class
Nature Paper Interpretation
Evergreen Needleleaf Forest
Temperate Forest
Evergreen Broadleaf Forest
Tropical Forest
Deciduous Needleleaf Forest
Value
IGBPcode Money
302
1
302
2007
2
2007
Temperate Forest
302
3
302
Deciduous Broadleaf Forest
Temperate Forest
302
4
302
Mixed Forest
25% Tropical, 75% Temperate
728.25
5
728.25
Closed Shrublands
Grass/Rangelands
232
6
232
Open Shrublands
Grass/Rangelands
232
7
232
Woody Savannas
50% Temperate Forest, 50% Grass/Rangelands
267
8
267
Savannas
Grass/Rangelands
232
9
232
Grasslands
Grass/Rangelands
232
10
232
Permanent Wetlands
50% Tidal Marsh/Mangrove, 50% Swamp/Floodplain
14785
11
14785
Croplands
Cropland
92
12
92
Urban
Urban
13
0
Cropland Natural Vegetation Mosaic
50% Cropland, 50% Grassland/Rangeland
14
162
Snow and Ice
Ice/Rock
N/A
15
0
Barren or Sparsely Vegetated
Desert
N/A
16
0
Water Bodies
Lakes/Rivers
17
8498
N/A
162
8498
Global map of ‘Non-Market’ economic
activity from ecosystem services
Deriving The Eco-Value / Night Light Energy
Environmental Sustainability Index
National
Index
Value
Value of given Nation’s
Ecosystem Services as estimated
by Costanza and measured by
USGS 1 km2 Global Land Cover Grid
Amount of Light Energy seen in
Nighttime Satellite Imagery from
Defense Meteorological Satellite Program’s
Operational Linescan System (DMSP OLS)
This index is similar to the inverse of population density
e.g. ‘square kilometers of land per person’
However; ‘square kilometers of land’ is adjusted by the
land’s ecosystem service value; and, ‘per person’ is
measured by the nighttime satellite imagery provided by
the DMSP OLS
A representation of the datasets used to calculate
Eco-Value and Impact from around Central America
Belize
Guatemala
Honduras
Nicaragua
Evergreen Needleleaf Forest
Evergreen Broadleaf Forest
Deciduous Needleleaf Forest
Deciduous Broadleaf Forest
Open Shrublands
Closed Shrublands
Woody Savannas
Grasslands
Permanent Wetlands
Croplands
Urban
Cropland / Natural Vegetation
Water
El Salvador
Costa Rica
Global 1 km2 IGBP Land-Cover Dataset
Country
Belize
Nicaragua
Honduras
Guatemala
Costa Rica
El Salvador
DMSP-OLS ‘Earth at Night’ dataset
Population (1996) Eco-Value/Night Light
224,000
261,306
4,351,000
184,308
5,751,000
97,093
11,241,000
62,085
3,466,000
24,959
5,935,000
9,896
Local Rank
6
5
4
3
2
1
Conclusions
• DMSP OLS nighttime imagery shows a great
deal of promise for myriad applications such as
population estimation, mapping of economic
activity, and measuring human impact on the
environment.
• More Validation and fine tuning of models is
needed.
• Issues of spatial scale of measurement still
problematic.