Download Satellite Mapping of Global Urban Population

Mapping Anthropogenic Activities
from Earth Observation Data
Christopher Doll, Jan-Peter Muller
Workshop on Gridding Population Data
Columbia University, New York
Tuesday 2nd May 2000
DEPARTMENT OF GEOMATIC ENGINEERING
Overview
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Scientific Justification
Mapping Socio-economic parameters from
Night-time Data
Night-lights and Datasets over the UK
Initial Conclusions
Future Research Directions
DEPARTMENT OF GEOMATIC ENGINEERING
Scientific Justification
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Global population remains poorly defined across
the Earth’s surface (Clark & Rhind, 1992)
Human activity affects both the atmosphere and
the surrounding terrestrial/coastal environs
Global change has many manifestations and
effects on human life
– flooding and landslides (Venezuela 10/99, Mozambique 2/2000)
» Thousands of people killed and displaced
– Storms over Western Europe 12/99, US Hurricanes
» Billions of $ insurance loss
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Satellite monitoring provides the best opportunity
to survey changing population rapidly, albeit
indirectly through land use changes
DEPARTMENT OF GEOMATIC ENGINEERING
Mapping Anthropogenic
Parameters from DMSP-OLS Data
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Doll, Muller & Elvidge.
Ambio May 2000
Global relationships
established by country
level correlation of lit area
and CO2 emissions (CDIAC)
Lit area remapped from 30’’
to 1 with a % lit value in
each cell
Relationship applied to
new 1 map with areal
approximation into 10
latitudinal zones
DEPARTMENT OF GEOMATIC ENGINEERING
Mapping CO2 from DMSP-OLS
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kT of Carbon
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Global Image
gridded to 1º and %
lit figure assigned.
Result compared
with CDIAC 1995
map
Similar distribution,
but magnitudes are
lower than CDIAC
~25%
DEPARTMENT OF GEOMATIC ENGINEERING
CO2 Emission difference Map
CDIAC - OLS
DEPARTMENT OF GEOMATIC ENGINEERING
Total Lit Area (by country) vs.
Purchasing Power Parity GDP
provided by WRI (1995)
DEPARTMENT OF GEOMATIC ENGINEERING
Mapping Economic Activity
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Purchasing
Power Parity
GDP used as a
more equal
measure
GDP map uses
night-lights to
distribute
relationship at
1° resolution
Total of global
economy
figure of $22.1
trillion cf.
$27.7 trillion
Intl $, from
WRI figures
DEPARTMENT OF GEOMATIC ENGINEERING
Night-lights within the UK
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Doll & Muller(2000)
ISPRS2000, July 2000
Amsterdam
Bartholomew’s 1:250 000
road network map
– 22 road classes grouped by
road type in standard road
atlases
DN Value
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Institute of Terrestrial
Ecology (ITE) land cover
map (25 classes) at 25m
derived from Landsat
imagery
– 1km summary product giving
% coverage of each class
– ‘Urban’ and ‘Suburban & rural
infrastructure’ classes of
interest
Gridded 200m Population
data from UK government
DEPARTMENT OF GEOMATIC
1991ENGINEERING
census
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Night-lights and the UK Road Network
(Bartholomew’s 1:250 000)
Radiance; x10-10 W.cm2.m-1.sr-1
DEPARTMENT OF GEOMATIC ENGINEERING
Night-lights and Road Density
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3000
2800
2600
Minor Roads
2200
B-Roads
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2000
Trunk Roads & Primary
Dual C/W
1800
1600
Non Primary A-Roads
1400
1200
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Motorw ays
1000
800
Cumulative Distance
600
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400
200
0
0
42
83
13
3
19
0
25
3
32
2
39
7
47
6
56
1
65
0
74
3
84
0
94
10 1
4
12 5
01
Road Density (m .km -2)
2400
Radiance (x10-10 W.cm 2.um -1.sr -1)
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Non-primary ARoads dominate in
urban areas
B-Roads also peak
in city centres
No comprehensive
central list exists of
lit road sections for
the UK
Assumes all roads
are lit
Will make road
density map and
compare to gridded
population
DEPARTMENT OF GEOMATIC ENGINEERING
Night-lights and other
parameters over London at 1km
Suburban/Rural infrastructure (ITE) Gridded Population (1991 census)
Population.km-2
% coverage
Urban (ITE)
DMSP-OLS Radiance Calibrated Night-lights
Radiance; W.cm2.m-1.sr-1
60 km
65 km
DEPARTMENT OF GEOMATIC ENGINEERING
Land cover-Population
Relationships
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European
Cities
43% of urban+suburban land cover  2000
people/km2  DCW urban layer
Less obvious relationship with radiance
– Single threshold overestimates some
settlements, but omits others
Doll & Muller (RSS99) estimated country-level
urban population for 12 countries to within 97%
Potential to examine population morphology of
urban centres
All distributions appear to behave like self-critical
phenomena
Log Suburb. cover
Log Radiance
1km pixels for the UK
DEPARTMENT OF GEOMATIC ENGINEERING
Population density cf. DMSP-OLS radiance
Which is best to map urban areas?
Population.km-2
DN Value
DEPARTMENT OF GEOMATIC ENGINEERING
Initial Conclusions
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Mapping urban area from night-time data has
significant advantages over other RS data
sources
– But DMSP-OLS data is coarse, 2.7km re-sampled to 1km may
not be fine enough
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Need to distinguish between urban and rural
light sources
– Consider the use of population density to map urban area
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Population mapping with radiance calibrated
data appears to offer a lot of potential
– Data set flexible to a much wider range of methodologies
DEPARTMENT OF GEOMATIC ENGINEERING
Future Research Directions
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Trial acquisition of night-time data from NASA-EOS
(Terra) sensors planned in May/June
– MODIS (250m sensitive band)
– MISR (possible analysis of directional effects)
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Assess the potential and limitations in accuracy
and reliability of city lights to map global
population distribution within urban areas
including
– How Temporally stable are coefficients?
– Next step to try to extrapolate 1km distributions rather than just
produce aggregated (country-level) statistics
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Develop better classification techniques for nightlight data
– Adaptive Pixel allocation algorithm (ADAPIX)
– Assign urban/rural classification based on pixel’s position within
a cluster (country-dependent)
DEPARTMENT OF GEOMATIC ENGINEERING
Modelling approach:
Adaptive Pixel Allocation Algorithm
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Low
radiance
pixel near
the centre of
town
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Low radiance
pixel out of
town
Multiple orbit
compositing can
cause small urban
areas to look larger
Pixels of equal, low
radiance can occur
in different locations,
though unlikely to
have same
population density
Algorithm will
assess pixel class
based on the size of
its cluster and
distance from centre
175 km
DEPARTMENT OF GEOMATIC ENGINEERING
Thank you for your time
Christopher Doll; [email protected]
Los Angeles at night- 1988
DEPARTMENT OF GEOMATIC ENGINEERING