Chapter 2 EMR

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Remote Sensing the Urban Environment
Dr. John R. Jensen Department of
Geography University of South Carolina Columbia,
SC 29208
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Urban Remote Sensing Users

• Zoning regulation
• Commerce and economic development
• Tax assessor
• Transportation and utilities
• Parks, recreation, and tourism
• Emergency management
• Real Estate
• Developers

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Remote Sensing Data Collection and Processing to
Provide Basic Human Spatial Services

• Problem Lack of appreciation by some agencies
  that the vast majority of urban/suburban
  applications require high spatial resolution
  data. Vast resources spent on medium to coarse
  resolution global climate change datasets.
• In the United States, most urban/suburban
  applications are considered the domain of
  commercial photogrammetric engineering firms.

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U.S. Global Change Research Program Funding by
Agency in Millions for 2005 Fiscal Year (USGCRP,
2005)
does not include other federally or privately
sponsored research funding in environmental
science and engineering.
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Remote Sensing Data Collection and Processing to
Provide Basic Human Spatial Services

• Observation Several spatial datasets such as
  road centerlines, front-door geographic location
  and/or building footprints are part of basic
  human spatial services that should be provided
  by tax dollars so that we have equitable societal
  access to basic human services, including timely
  
• Emergency medical services (911) to my
  address
• Fire department response
• Law enforcement
• Contact during a natural, technological,
  or terrorist-
• induced disaster when it may be difficult
  to contact me
• via phone or email (e.g., Graniteville,
  SC train wreck).

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Recent Disasters Requiring Immediate Emergency
Response

• Norfolk Southern
• Graniteville, SC
• January 6, 2005
• Chlorine gas
• 11 deaths
• hundreds injured

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Recent Disasters Requiring Immediate Emergency
Response
La Conchita, California January 10, 2005 Mudslide
LIDAR courtesy of Airborne 1, Inc.
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Remote Sensing Data Collection and Processing to
Provide Basic Human Spatial Services

• Stop the non-equitable existing situation where
  every city, county, or state handles the basic
  human spatial services problem independently,
  differently, and inefficiently.

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Remote Sensing Data Collection and Processing to
Provide Basic Human Spatial Services

• Rural communities with modest tax bases do not
  have the financial resources to provide basic
  spatial human services.
• Solution 1 Provide a government-supported, high
  spatial resolution satellite remote sensing
  system that collects public domain data for the
  entire country. Value-added commercial firms
  could process the data to city/county/state
  specifications.
• Solution 2 Government-sponsored initiative that
  supports commercial data collection and
  processing to provide such services (e.g., the
  NAPP program ClearView NextView). Hasnt worked
  yet.

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Republic of South Africa water resources are
managed using the National Water Act
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• Republic of South Africa water resources are
  managed to provide
• Basic Human Needs Reserve
• Ecological Reserve

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The amount of electromagnetic radiance L recorded
within the instantaneous-field-of-view (IFOV) of
a remote sensing system (i.e., a single silver
halide crystal in an aerial photograph, a picture
element in a digital image, or a unique LIDAR
masspoint) is a function of

• where,
• l wavelength (spectral response measured in
  various bands or at specific frequencies),
• sx,y,z x,y,z location of the picture element
  and its size (x,y),
• t time (temporal information when, how long,
  and how often data were acquired),
• q set of angles that describe the geometric
  relationship between the radiation source (e.g.,
  the Sun), the terrain target of interest (e.g., a
  wheat field), and the sensor system,
• P polarization of back-scattered energy
• W radiometric resolution (precision) at which
  the data are recorded by the sensor

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Lab Spectroradiometer Reflectance Characteristics
of Urban Materials
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Temporal and Spatial Characteristics of Urban
Attributes and Remote Sensing Systems
Temporal and spatial resolution requirements
necessary to extract socio-economic and
some biophysical information for selected
urban/suburban attributes are presented. The
goal is to relate the information requirements
with the current and proposed remote sensing
systems to determine if there are
substantive gaps in capability.
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Temporal and Spatial Characteristics of Urban
Attributes and Remote Sensing Systems
Observations There are a number of remote
sensing systems that currently provide some of
the desired urban/socio-economic information when
the spatial resolution required is poorer than 5
x 5 m and the temporal resolution is between 1
and 55 days. As demonstrated, very high
spatial resolution data (lt1 x 1 m) is required to
satisfy many of the socio-economic data
requirements. This is especially true for urban
areas in developing countries.
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Urban Remote Sensing

• Minimum spatial resolution of 0.25 5 m
• Minimum of four pixels within an object to
  identify
• (one-half the width of the smallest dimension -
• i.e., 5 m wide mobile homes require at least
  2.5 m data)
• Role of shape, size, texture, orientation,
  pattern, shadow
• Land use vs. land cover?

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Urban Remote Sensing
Activity-based classification Land use or
activity information is often more valuable than
land cover information. We require the capability
to provide descriptions of primitive objects in
the scene (e.g., roads, kilns, smoke stacks,
conveyor belts, piles of raw materials) and
utilize inductive learning to synthesize this
information and identify the activity taking
place.
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Urban/Suburban Applications and the Minimum
Remote-Sensing Resolutions Required (Jensen and
Cowen, 1999 Jensen, 2005).
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Clear polygons represent the spatial and temporal
characteristics of selected urban attributes
Temporal Resolution in minutes
Gray boxes depict the spatial and temporal
characteristics of the remote sensing systems
that may be used to extract the required urban
information
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Selected Databases from South Carolinas Spatial
Data Infrastructure
Spatial Data Infrastructure The logical way to
collect, organize, disseminate, and use
geographic information for sustainable
development.
Ortho-rectified Imagery
Hydrology
Elevation
Transportation
Digital Orthophotography
Land cover
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The American Planning Association developed the
Land-Based Classification System that contains
detailed definitions of urban/suburban land-use.
The system incorporates information derived in
situ and using remote sensing techniques. This is
an oblique aerial photograph of a mall in
Ontario, CA. Hypothetical activity and structure
codes associated with this large parcel are
identified. Site development and ownership
information attribute tables are not shown.
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Anderson (USGS) Classification Levels
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Anderson (USGS) Classification Levels
1 Urban or Built-up 11 Residential 111
Single-Family Residential 1111 House,
houseboat, hut, tent 1112 Mobile home 112
Multiple-Family Residential 1121
Duplex 1122 Triplex 1123 Apartment Complex
or Condominium 1124 Mobile home (trailer) park
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Urban Minimum Resolution Requirements
Land Use/Cover Temporal Spatial Spectral USGS
Level 1 5-10 yrs 20-100 m VIS-NIR USGS Level
2 5-10 yrs 5-20 m VIS-NIR USGS Level 3 3-5
yrs 1-5 m Pan-VIS-NIR USGS Level 4 1-3
yrs 0.25-1 m Pan
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Land Use /Land Cover
Relationship between sensor system spatial
resolution and land use/land cover class
Temporal Spatial
Resolution Resolution L1
- USGS Level I 5 - 10 years 20 - 100 m L2 -
USGS Level II 5 - 10 years 5 - 20 m L3 -
USGS Level III 3 - 5 years 1 - 5 m L4
- USGS Level IV 1 - 3 years 0.25 - 1 m
Spatial Resolution in meters
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1974 1,040 urban hectares 1994 3,263
urban hectares 315 increase
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Urban/Suburban Temporal Resolution Considerations

• Phenomenological Stages of Development
• Original state
• Partial or complete clearing
• Land subdivision
• Roads
• Buildings
• Landscaping

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Stages of Development
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Sun City Hilton Head
1994
1996
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Digital Orthophotos of An Area near Atlanta,
Georgia
1993
1999
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Single-family Residential
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Single-family Residential
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Single-family Residential
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Single-family Residential
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Single-family Residential
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Single-family Residential
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Single-family Residential?
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Single-family Residential
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Single-family Residential
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Single-family Residential
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Multiple-family Residential
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Multiple-family Residential
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Multiple-family Residential
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Digital Frame Camera Imagery of Harbor Town,
Hilton Head, SC
1 x 1 ft spatial resolution
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Encroachment of a dune field on an urban area
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Urban Hydrology Extraction of Impervious
Surfaces
Impervious surfaces
USGS NAPP 1 1 m DOQQ of an area in North
Carolina
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Building and Cadastral (Property Line)
Infrastructure
Derived from 0.3 x 0.3 m (1 x 1 ft.) spatial
resolution stereoscopic, panchromatic aerial
photography.
Temporal Spatial
Resolution Resolution B1 - building
per., area, vol., height 1 - 2 years 0.25
- 0.5 m B2 - cadastral (property lines)
1 - 6 month 0.25 - 0.5 m
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Building and Cadastral (Property Line)
Infrastructure
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Extraction of Building Infrastructure Using
Soft-Copy Photogrammetric Techniques
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Urban Infrastructure of Rosslyn, Virginia Derived
Using Soft-Copy Photogrammetric Techniques
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Panchromatic 3 x 3-in Image of Popular Bluff, MO
Obtained on February 15, 2000 at 5,000 ft AGL
Using A Digital Array Panoramic Camera with
32,000 x 8,000 Detectors
Swath width 1.5 mi
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Courtesy of Image America, Inc.
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Remote Sensing Data Collection and Processing to
Provide Basic Human Spatial Services
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Remote Sensing Data Collection and Processing to
Provide Basic Human Spatial Services
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Remote Sensing Data Collection and Processing to
Provide Basic Human Spatial Services
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Transportation Infrastructure
Irmo, S.C. TIGER road network updated using SPOT
10 x 10 m data
Bridge assessment using high resolution oblique
photography
Parking/traffic studies require high
spatial/temporal resolution
Temporal Spatial
Resolution Resolution T1
- general road centerline 1 - 5 years 1
- 30 m T2 - precise road width
1 - 2 years 0.25 - 0.5 m T3 - traffic
count studies (cars, planes etc.) 5 - 10 min
0.25 - 0.5 m T4 - parking studies
10 - 60 min 0.25 - 0.5 m
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Transportation
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Transportation
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Transportation
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Transportation
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Transportation
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Transportation- Military
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IKONOS Panchromatic Stereopair of Columbia, SC
Airport
November 15, 2000
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Verification of the Strategic Arms Reduction
Treaty (START)
Signed in 1991, START required in part that the
United States and Russia, Byelarus, Kazakhstan,
and Ukraine reduce the total number of strategic
nuclear delivery vehicles (inter-continental and
submarine-launched ballistic missiles plus heavy
bombers) to 1,600. Two-hundred seventeen B-52s
had to be destroyed by Dec. 15, 1994. The B-52s
were dismantled at the Aircraft Maintenance and
Regeneration Center at Davis-Monthan Air Force
Base, Tucson, AZ. A 6.5-ton blade deployed from a
crane was used to dismantle the aircraft. The
parts remained in place for 90 days so that
treaty signatories could use satellite
overflights to verify the destruction
(Wetterhahn, 1995 Air and Space).
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Transportation
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Transportation
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IKONOS Panchromatic
Panchromatic Sharpened Near-infrared
Columbia, SC on October 15, 2000
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Utility Infrastructure
West Berlin, Germany (13,000). Utility companies
often digitize the location of every pole,
manhole, transmission line and the facilities
associated with each.
Temporal Spatial
Resolution Resolution U1
- general utility centerline
1 - 5 years 1 - 30 m U2 - precise
utility line width 1 - 2
years 0.25 - 0.6 m U3 - locate poles,
manholes, substations 1 - 2 years 0.25
- 0.6 m
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Utility
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Utilities
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Digital Elevation Model Creation
Urban DEMs are usually created from high spatial
resolution data. The DEM and orthophoto of
Columbia, SC were produced from 16,000
stereoscopic photography using soft-copy
photogrammetric techniques.
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• Check points
• Sanborn
• NASA Verification
• and Validation Team

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LIDAR-derived Bare Earth Digital Elevation Model
Control Study Area
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Orthophotograph 1 x 1 m
First Return rasterized using inverse distance
weighting (IDW)
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First Return rasterized using IDW
First Return analytical hill-shading
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Last Return rasterized using IDW
Last Return analytical hill-shading
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First Return rasterized using IDW
Last Return rasterized using IDW
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Masspoints Used to Create LIDAR-derived IDW Bare
Earth DEM
Masspoint data voids introduced during tree
removal
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Bare Earth rasterized using IDW
Bare Earth analytical hill-shading
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LIDAR-derived IDW Bare Earth DEM overlaid with
Contours
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LIDAR-derived TIN Bare Earth DEM overlaid with
Contours
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• Check points
• Sanborn (95 points)
• NASA Verification
• and Validation Team

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First Return
Last Return
Bare Earth
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First Return
Last Return
Intensity
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Color-coded intensity
Intensity
1st return elevation
Color-coded intensity draped onto 1st return
elevation
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• Classification of Landcover based on
  LIDAR-derived Elevation, Slope, and Intensity
• blue buildings
• green grass
• pink vegetation

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Population Estimation and Socioeconomic
Characteristics
Nairobi, Kenya informal housing
Single and multiple family residences in
Columbia, S. C.
Konso village in southern Ethiopia
Temporal Spatial
Resolution Resolution S1 - local
population estimation 5 - 7 years 0.25 -
5 m S2 - regional/national population
estimation 5 - 15 years 5 - 20
m S3 - quality of life indicators 5 - 10
years 0.25 - 30 m
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Socioeconomic Characteristics

• Population Estimations
• Energy Demand and Conservation
• Quality of Life Indicators
• Building
• Lot
• Adjacent Amenities
• Adjacent Hazards

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Remote Sensing Assisted Population Estimation
Population estimation can be performed at the
local, regional, and national level based
on counts of individual dwelling units,
measurement of land areas, and land use
classification.
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Remote Sensing Assisted Population Estimation
Dwelling Unit Estimation Technique
Assumptions imagery must be of sufficient
spatial resolution (0.3 - 5 m) to identify
individual structures even through tree cover and
whether they are residential, commercial, or
industrial buildings some estimate of the
average number of persons per dwelling unit
must be available, and it is assumed all
dwelling units are occupied.
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Urban/surburban attributes that may be extracted
from remote sensor data and used to assess
housing quality and/or quality of life
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Human Habitation in Much of the Undeveloped World
Usually Requires High Spatial Resolution Imagery
to Estimate Population or Extract Quality of Life
Indicators
Farm in the altiplano adjacent to La Paz, Bolivia
at 4,100 m above sea level. Grain has been
harvested and arranged in rows of sheaves. Piles
of stones (cairns) have a light center with a
darker border of weeds and shrubs.
New Venice Village, Santa Marta, in the La
Magdelena Province of Colombia South America. The
people built lake dwellings to be closer to their
fishing grounds. Buildings are separated by 10 to
30 m channels to allow boat traffic in all
directions.
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 Framework Foundation Data for Sustainable
Development
Worldwide distribution of nighttime lights
derived from the Defense Meteorological Satellite
Program Operational Linescan System
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 Framework Foundation Data for Sustainable
Development
Settlement lights in Africa derived from the
Defense Meteorological Satellite Program
Operational Linescan System. Using a reference
set of stable lights, it is possible to identify
new settlements or expansion/contraction of
existing settlements.
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Automated building counts
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Disaster Emergency Response
Pre-Hurricane Hugo Sullivans Is., S.C. July 15,
1988 1 x 1 m panchromatic
Post-Hurricane Hugo Oct. 23, 1989 1 x 1
m panchromatic
Temporal Spatial
Resolution
Resolution DE1 - pre-emergency imagery 1 - 5
years 1 - 5 m DE2 - post-emergency imagery
12 hr - 2 days 0.25 - 2 m DE3 -
damaged housing stock 1 - 2 days 0.25 - 1
m DE4 - damaged transportation 1 - 2 days 0.25
- 1 m DE5 - damaged utilities 1 - 2 days 0.25
- 1 m
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Disaster Emergency Response
Overturned tanker in Anchorage, Alaska.
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Earthquake damage near Northridge, California,
January 22, 1994.
Landslide cutting off Santa Clara River in
California.
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Tornado Damage
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Post-Attack IKONOS Image of the World Trade
Center in New York City
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Pre-and Post IKONOS Images of the World Trade
Center in New York City
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LIDAR-derived Digital Elevation Model of the
World Trade Center in New York City
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IKONOS Panchromatic Sharpened Near-infrared
Image Overlayed on a USGS Digital Elevation Model
Columbia, SC October 15, 2000
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Public Service
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IKONOS Imagery of Columbia, SC Obtained on
October 28, 2000
Panchromatic 1 x 1 m
Pan-sharpened multispectral 4 x 4 m
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Public Service
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Public Service
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Public Service
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Public Service Education
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Public Service Education
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Commercial Banking
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Commercial Banking/Services
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Commercial Retail
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Commercial Retail
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Commercial Retail
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Commercial Retail
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Commercial Retail
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Commercial Retail
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Commercial Housing
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Commercial Retail, Banking, Services
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Commercial Cemetery
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Recreation
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Recreation
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Recreation
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Recreation
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Recreation
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Recreation
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Recreation
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Recreation
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Recreation
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Recreation
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Recreation
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Recreation
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Recreation
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Recreation
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Processing Chemical
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Processing Chemical
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Processing Chemical
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Processing Chemical
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Processing Chemical
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Processing Chemical
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Processing Chemical
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Processing Chemical
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Processing Chemical
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Processing Mechanical
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Processing Mechanical
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Processing Mechanical
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Processing Mechanical
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Processing Mechanical
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Industry Extractive
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Industry Extraction
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Industry Extraction
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Industry Extraction
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Processing Heat
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Processing Heat
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Meteorological Data
GOES East image of Hurricane Hugo 244 p.m. EDT
Sept. 21, 1989
Temporal Spatial
Resolution
Resolution M1 - weather prediction
3 25 min 1 - 8 km M2 - current
temperature 3 25 min
1 - 8 km M3 - current precipitation
5 10 min 1 x 4 km M4 - immediate
severe storm warning 5 10 min 1 x 4
km M5 - monitoring urban heat islands
12 - 24 hr 5 x 30 m
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Energy Demand and Conservation
Daytime high resolution (0.3 x 0.3 m) aerial
photography of a gymnasium
Nighttime 0.3 x 0.3 m thermal infrared imagery
(8 - 14 mm)
Temporal Spatial
Resolution
Resolution E1 - energy demand and production
potential 1 - 5 years 0.25 - 1 m E2 -
building insulation surveys 1 - 5 years 1
- 5 m
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Critical Environmental Area Assessment
Sun City, S.C. Digitized NAPP Jan. 22, 1994 2.5 x
2.5 m (0.7 - 0.9 mm)
CAMS Band 6 Sept. 23, 1996 2.5 x 2.5 m (0.7 -
0.69 mm)
Temporal Spatial
Resolution
Resolution C1 - stable sensitive environments
1 - 2 years 1 - 10 m C2 - dynamic
sensitive environments 1 - 6
months 0.25 - 2 m
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Improved Digital Image Processing Algorithms

• Urban information extraction Algorithms should
• move beyond per-pixel classification,
• involve inductive classification and machine
• learning, and
• take into account site, association, and
  contextual
• information beyond just minimal landscape
  
• ecology spatial statistics.

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The End