mapping anthropogenic activities from earth observation data
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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
DEPARTMENT OF GEOMATIC ENGINEERING
Overview
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
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
DEPARTMENT OF GEOMATIC ENGINEERING
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
DEPARTMENT OF GEOMATIC ENGINEERING
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
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
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
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
DEPARTMENT OF GEOMATIC ENGINEERING
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
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
DEPARTMENT OF GEOMATIC ENGINEERING
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
DEPARTMENT OF GEOMATIC ENGINEERING
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
Rad
ian
ceL
og
Rad
ian
ce
Log
Su
bu
rb.
cove
r
DEPARTMENT OF GEOMATIC ENGINEERING
Population density cf. DMSP-OLS radiance
Which is best to map urban areas?
DN ValuePopulation.km-2
DEPARTMENT OF GEOMATIC ENGINEERING
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
DEPARTMENT OF GEOMATIC ENGINEERING
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)
DEPARTMENT OF GEOMATIC ENGINEERING
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
DEPARTMENT OF GEOMATIC ENGINEERING
Thank you for your time Christopher Doll; [email protected]
Los Angeles at night- 1988