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

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

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

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)

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)

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.

Balitmore, MD: Well-developed city center Diffuse boundary between urban and natural environment Landsat TM multi-spectral image

Las Vegas, NV: Indistinct city center Distinct boundary between urban and natural environments Landsat TM multi-spectral image

Riyadh, Saudi Arabia: Intermediate case

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

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

NYC Population DensityNYC Vegetation Fraction (source: US Census)(source: Landsat TM) Linear inverse correlation between population and vegetation fraction

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

Atlanta - Daytime ImageAtlanta - Nighttime Image ATLAS Thermal Images of Atlanta, Georgia

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.

Earth Lights from OLS

Measurements of Pollution in the Troposphere (MOPITT) Carbon monoxide plumes from China 22 km spatial resolution, 640 km FOV

Disaster Monitoring Volcanic eruptions Tornados Hurricanes Oil spills Earthquakes War/terrorism Floods

ASTER image of Maryland tornado path before after

AVHRR image of Hurricane Floyd September 1999

RADARSAT image of oil spill

ERS-2 Interferometric SAR Mapping of Ground Displacement

Bam, Iran Earthquake destruction IKONOS image from 12/27/2003

QuickBird Satellite Image: 65 cm spatial resolution Flooding in Dresden, Germany August 22, 2002

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

Landsat TM map of land cover near Kikwit, Zaire (location where Ebola outbreaks were first reported in 1995) pink=cleared areas green=jungle