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Mapping Anthropogenic Activities from Earth Observation Data

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

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Mapping Anthropogenic Activities from Earth Observation Data

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  1. 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

  2. Overview • Scientific Justification • Mapping Socio-economic parameters from Night-time Data • Night-lights and Datasets over the UK • Initial Conclusions • Future Research Directions

  3. 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

  4. 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

  5. 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

  6. CO2 Emission difference Map CDIAC - OLS

  7. Total Lit Area (by country) vs. Purchasing Power Parity GDP provided by WRI (1995)

  8. 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

  9. 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

  10. Night-lights and the UK Road Network(Bartholomew’s 1:250 000) Radiance; x10-10 W.cm2.m-1.sr-1

  11. 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

  12. 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

  13. Land cover-Population Relationships 1km pixels for the UK • 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 Radiance Log Radiance European Cities Log Suburb. cover

  14. DN Value Population density cf. DMSP-OLS radianceWhich is best to map urban areas? Population.km-2

  15. 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

  16. 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)

  17. 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 near the centre of town Low radiance pixel out of town 175 km

  18. Thank you for your timeChristopher Doll; cdoll@ge.ucl.ac.uk Los Angeles at night- 1988

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