mapping anthropogenic activities from earth observation data

DEPARTMENT OF GEOMATIC ENGINEERING

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


Overview

Scientific Justification Mapping Socio-economic parameters

from Night-time Data Night-lights and Datasets over the UK Initial Conclusions Future Research Directions


Scientific Justification

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

Satellite monitoring provides the best opportunity to survey changing population rapidly, albeit indirectly through land use changes


Mapping Anthropogenic Parameters from DMSP-OLS

Data 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


Mapping CO2 from DMSP-OLS

Global Image gridded to 1º and % lit figure assigned.

Result compared with CDIAC 1995 map

Similar distribution, but magnitudes are lower than CDIAC ~25%

kT of Carbon


CO2 Emission difference Map CDIAC - OLS


Total Lit Area (by country) vs. Purchasing Power Parity GDP

provided by WRI (1995)


Mapping Economic Activity

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


DN Value

Night-lights within the UK 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

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 1991 census


Night-lights and the UK Road Network

(Bartholomew’s 1:250 000)

Radiance; x10-10 W.cm2.m-1.sr-1


Night-lights and Road Density

Non-primary A-Roads 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


65 km

Night-lights and other parameters over London at

1km

% coverage

Urban (ITE)

Suburban/Rural infrastructure (ITE)Gridded Population (1991 census)

DMSP-OLS Radiance Calibrated Night-lights

Population.km-2

Radiance; W.cm2.m-1.sr-1

60 km


Land cover-Population Relationships

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

1km pixels for the UK

European Cities

Log

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og

Rad

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ce

Log

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bu

rb.

cove

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Population density cf. DMSP-OLS radiance

Which is best to map urban areas?

DN ValuePopulation.km-2


Initial Conclusions 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

Need to distinguish between urban and rural light sources

– Consider the use of population density to map urban area

Population mapping with radiance calibrated data appears to offer a lot of potential

– Data set flexible to a much wider range of methodologies


Future Research Directions 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)

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

Develop better classification techniques for night-light data

– Adaptive Pixel allocation algorithm (ADAPIX)– Assign urban/rural classification based on pixel’s position

within a cluster (country-dependent)


Modelling approach:Adaptive Pixel Allocation

Algorithm 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

Low radiance pixel out of

town

Low radiance

pixel near the centre

of town

175 km


Thank you for your time Christopher Doll; [email protected]

Los Angeles at night- 1988

mapping anthropogenic activities from earth observation data

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