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Remote Sensing of Urban Landscapes and contributions of remote sensing to the Social Sciences

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Title: Remote Sensing of Urban Landscapes and contributions of remote sensing to the Social Sciences


1
Remote Sensing of Urban Landscapes and
contributions of remote sensing to the Social
Sciences
2
Urban-Suburban Land Use
  • Urban and suburban expansion
  • almost 1/2 the Earths population lives in cities
  • rapid expansion of urban centers and their
    peripheries
  • impacts on land cover, societal structure of the
    cities, population distribution, land use
    characteristics
  • interconnectivity of cites at large scales

3
Urban remote sensing
  • High spatial resolution data are needed
  • Temporal and spectral resolution are typically
    not a significant requirement for most
    applications
  • Ancillary data typically used (census data)
  • Can measure variables such as urban extent,
    housing density, structure type, urban vegetation
    cover, air quality, change detection

4
  • Temporal and spatial resolution requirements vary
    depending on applications
  • short term (event-scale, sub-annual) vs. long
    term (interannual)
  • high spatial resolution (lt 1m) vs. medium spatial
    resolution (15-30m)
  • (see Jensen Fig. 12-1)

5
High Spatial Resolution Sensors
  • QuickBird (65cm B/W, 4m multispectral)
  • IKONOS (1m B/W, 4m multispectral)
  • SPOT (2.5m - 20m multispectral)
  • ASTER (15 -30m multispectral)
  • Landsat ETM (15m B/W, 4m multispectral)

6
Delineation of Urban Areas
  • Difficult to do because urban areas are diverse
    and complex
  • Boundaries between urban and suburban are not
    always clear
  • Lack of a consistent definition of what is
    urban
  • administrative boundaries
  • population density, etc.

7
Balitmore, MD Well-developed city
center Diffuse boundary between urban and
natural environment
Landsat TM multi-spectral image
8
Las Vegas, NV Indistinct city center Distinct
boundary between urban and natural environments
Landsat TM multi-spectral image
9
Riyadh, Saudi Arabia Intermediate case
10
Demographic/Socioeconomic Patterns
  • Census data lack spatial details and are
    infrequently updated (not globally available)
  • Remote sensing is useful for monitoring urban
    growth in developing countries
  • Need ancillary data plus repeat temporal coverage
    from remote sensing
  • Important to integrate physical and socioeconomic
    variables

11
Example
  • Pozzi and Small (2002) produced a study of
    relationship between population density (from US
    census) and vegetation cover (from Landsat TM)

12
NYC Population Density
NYC Vegetation Fraction
(source US Census)
(source Landsat TM)
Linear inverse correlation between population and
vegetation fraction
13
Urban Heat Island Monitoring
  • Project ATLANTA (Atlanta Land-use Analysis
    Temperature and Air-quality)
  • Uses remote sensing to observe, measure, and
    monitor impacts of rapid urban growth

14
ATLAS Thermal Images of Atlanta, Georgia
Atlanta - Daytime Image
Atlanta - Nighttime Image
15
City Lights Imagery
  • Uses visible band of the Operational Linescan
    System (on board the DMSP satellite)
  • Useful for making global inventories of human
    settlements
  • Spatial resolution of 1km
  • Relationships between city lights and
    socioeconomic variables such as population
    density, economic activity, electric power
    consumption, etc.

16
Earth Lights from OLS
17
Measurements of Pollution in the Troposphere
(MOPITT)
Carbon monoxide plumes from China 22 km spatial
resolution, 640 km FOV
18
Disaster Monitoring
  • Volcanic eruptions
  • Tornados
  • Hurricanes
  • Oil spills
  • Earthquakes
  • War/terrorism
  • Floods

19
ASTER image of Maryland tornado path
before
after
20
AVHRR image of Hurricane Floyd September 1999
21
RADARSAT image of oil spill
22
ERS-2 Interferometric SAR Mapping of Ground
Displacement
23
Bam, Iran Earthquake destruction IKONOS image
from 12/27/2003
24
Flooding in Dresden, Germany August 22,
2002
QuickBird Satellite Image 65 cm spatial
resolution
25
Epidemiology
  • Cholera virus attaches to zooplankton (copepods)
    and phytoplankton. Plankton plumes emanating from
    the Ganges are being monitored
  • SST and plankton can be monitored in Bay of
    Bengal to track this
  • Hanta virus (carried by mice) correlates to
    changes in precipitation (El Nino) and vegetation
    cover, especially grasses
  • NDVI can be used to track these changes
  • Townshend et al. found that Ebola outbreaks
    corresponded to changes in land use and seasonal
    climate patterns

26
Landsat TM map of land cover near Kikwit, Zaire
(location where Ebola outbreaks were first
reported in 1995) pinkcleared areas greenjungle
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