Remote Sensing of Urban Landscapes and contributions of remote sensing to the Social Sciences.
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Transcript of Remote Sensing of Urban Landscapes and contributions of remote sensing to the Social Sciences.
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Remote Sensing of Urban Landscapes and
contributions of remote sensing to the
Social Sciences
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Urban-Suburban Land Use
• Urban and suburban expansion– almost 1/2 the Earth’s 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
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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
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Temporal and spatial resolution requirements vary depending on applications:
•short term (event-scale, sub-annual) vs. long term (interannual)
•high spatial resolution (< 1m) vs. medium spatial resolution (15-30m)
(see Jensen Fig. 12-1)
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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)
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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.
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Balitmore, MD: Well-developed city center
Diffuse boundary between urban and natural environment
Landsat TM multi-spectral image
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Las Vegas, NV:Indistinct city center
Distinct boundary between urban and natural environments
Landsat TM multi-spectral image
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Riyadh, Saudi Arabia:Intermediate case
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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
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Example
Pozzi and Small (2002) produced a study of relationship between population density (from US census) and vegetation cover (from Landsat TM)
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NYC Population DensityNYC Vegetation Fraction
(source: US Census) (source: Landsat TM)
Linear inverse correlation between population and vegetation fraction
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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
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Atlanta - Daytime ImageAtlanta - Nighttime Image
ATLAS Thermal Images of Atlanta, Georgia
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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.
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Earth Lights from OLS
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Measurements of Pollution in the Troposphere (MOPITT)
Carbon monoxide plumes from China22 km spatial resolution, 640 km FOV
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Disaster Monitoring• Volcanic eruptions• Tornados• Hurricanes• Oil spills• Earthquakes• War/terrorism• Floods
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ASTER image of Maryland tornado pathbefore
after
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AVHRR image of Hurricane Floyd
September 1999
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RADARSAT image of oil spill
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ERS-2 Interferometric SAR Mapping of Ground Displacement
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Bam, IranEarthquake destruction
IKONOS image from 12/27/2003
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QuickBird Satellite Image: 65 cm spatial resolution
Flooding in Dresden, Germany August 22, 2002
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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
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Landsat TM map of land cover near Kikwit, Zaire (location where Ebola outbreaks were first reported in 1995)
pink=cleared areasgreen=jungle