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Chapter 2 EMR

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Title: Chapter 2 EMR


1
Remote Sensing the Urban Environment
Dr. John R. Jensen Department of
Geography University of South Carolina Columbia,
SC 29208
Jensen, 2006
2
Urban Remote Sensing Users
  • Zoning regulation
  • Commerce and economic development
  • Tax assessor
  • Transportation and utilities
  • Parks, recreation, and tourism
  • Emergency management
  • Real Estate
  • Developers

Jensen, 2006
3
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.

Jensen, 2006
4
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.
5
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).

6
Recent Disasters Requiring Immediate Emergency
Response
  • Norfolk Southern
  • Graniteville, SC
  • January 6, 2005
  • Chlorine gas
  • 11 deaths
  • hundreds injured

Jensen, 2006
7
Recent Disasters Requiring Immediate Emergency
Response
La Conchita, California January 10, 2005 Mudslide
LIDAR courtesy of Airborne 1, Inc.
Jensen, 2006
8
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.

Jensen, 2006
9
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.

10
Republic of South Africa water resources are
managed using the National Water Act
Jensen, 2006
11
  • Republic of South Africa water resources are
    managed to provide
  • Basic Human Needs Reserve
  • Ecological Reserve

Jensen, 2006
12
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

Jensen, 2006

13
Lab Spectroradiometer Reflectance Characteristics
of Urban Materials
Jensen, 2006
14
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.
Jensen, 2006
15
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.
Jensen, 2006
16
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?

Jensen, 2006
17
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.
18
Urban/Suburban Applications and the Minimum
Remote-Sensing Resolutions Required (Jensen and
Cowen, 1999 Jensen, 2005).
Jensen, 2006
19
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
Jensen, 2006
20
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
Jensen, 2006
21
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.
Jensen, 2006
22
Anderson (USGS) Classification Levels
Jensen, 2006
23
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
Jensen, 2006
24
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
Jensen, 2006
25
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
Jensen, 2006
26
Jensen, 2006
27
1974 1,040 urban hectares 1994 3,263
urban hectares 315 increase
Jensen, 2006
28
Urban/Suburban Temporal Resolution Considerations
  • Phenomenological Stages of Development
  • Original state
  • Partial or complete clearing
  • Land subdivision
  • Roads
  • Buildings
  • Landscaping

Jensen, 2006
29
Stages of Development
Jensen, 2006
30
Sun City Hilton Head
1994
1996
Jensen, 2006
31
Digital Orthophotos of An Area near Atlanta,
Georgia
1993
1999
Jensen, 2006
32
Single-family Residential
Jensen, 2006
33
Single-family Residential
Jensen, 2006
34
Single-family Residential
Jensen, 2006
35
Single-family Residential
Jensen, 2006
36
Single-family Residential
Jensen, 2006
37
Single-family Residential
Jensen, 2006
38
Single-family Residential?
Jensen, 2006
39
Single-family Residential
Jensen, 2006
40
Single-family Residential
Jensen, 2006
41
Single-family Residential
Jensen, 2006
42
Multiple-family Residential
Jensen, 2006
43
Multiple-family Residential
Jensen, 2006
44
Multiple-family Residential
Jensen, 2006
45
Digital Frame Camera Imagery of Harbor Town,
Hilton Head, SC
1 x 1 ft spatial resolution
Jensen, 2006
46
Encroachment of a dune field on an urban area
Jensen, 2006
47
Urban Hydrology Extraction of Impervious
Surfaces
Impervious surfaces
USGS NAPP 1 1 m DOQQ of an area in North
Carolina
Jensen, 2006

48
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
49
Building and Cadastral (Property Line)
Infrastructure
Jensen, 2006
50
Extraction of Building Infrastructure Using
Soft-Copy Photogrammetric Techniques
Jensen, 2006
51
Urban Infrastructure of Rosslyn, Virginia Derived
Using Soft-Copy Photogrammetric Techniques
Jensen, 2006
52
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
Jensen, 2006
Courtesy of Image America, Inc.
53
Remote Sensing Data Collection and Processing to
Provide Basic Human Spatial Services
54
Remote Sensing Data Collection and Processing to
Provide Basic Human Spatial Services
55
Remote Sensing Data Collection and Processing to
Provide Basic Human Spatial Services
56
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57
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
58
Transportation
Jensen, 2006
59
Transportation
Jensen, 2006
60
Transportation
Jensen, 2006
61
Transportation
Jensen, 2006
62
Jensen, 2006
63
Transportation
Jensen, 2006
64
Transportation- Military
Jensen, 2006
65
IKONOS Panchromatic Stereopair of Columbia, SC
Airport
November 15, 2000
Jensen, 2006
66
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).
Jensen, 2006
67
Transportation
Jensen, 2006
68
Transportation
Jensen, 2006
69
IKONOS Panchromatic
Panchromatic Sharpened Near-infrared
Columbia, SC on October 15, 2000
Jensen, 2006
70
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
71
Utility
Jensen, 2006
72
Utilities
Jensen, 2006
73
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.
74
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75
  • Check points
  • Sanborn
  • NASA Verification
  • and Validation Team

Jensen, 2006
76
LIDAR-derived Bare Earth Digital Elevation Model
Control Study Area
Jensen, 2006
77
Orthophotograph 1 x 1 m
First Return rasterized using inverse distance
weighting (IDW)
Jensen, 2006
78
First Return rasterized using IDW
First Return analytical hill-shading
Jensen, 2006
79
Last Return rasterized using IDW
Last Return analytical hill-shading
Jensen, 2006
80
First Return rasterized using IDW
Last Return rasterized using IDW
Jensen, 2006
81
Masspoints Used to Create LIDAR-derived IDW Bare
Earth DEM
Masspoint data voids introduced during tree
removal
Jensen, 2006
82
Bare Earth rasterized using IDW
Bare Earth analytical hill-shading
Jensen, 2006
83
LIDAR-derived IDW Bare Earth DEM overlaid with
Contours
Jensen, 2006
84
LIDAR-derived TIN Bare Earth DEM overlaid with
Contours
Jensen, 2006
85
  • Check points
  • Sanborn (95 points)
  • NASA Verification
  • and Validation Team

Jensen, 2006
86
First Return
Last Return
Bare Earth
Jensen, 2006
87
First Return
Last Return
Intensity
Jensen, 2006
88
Color-coded intensity
Intensity
1st return elevation
Color-coded intensity draped onto 1st return
elevation
Jensen, 2006
89
  • Classification of Landcover based on
    LIDAR-derived Elevation, Slope, and Intensity
  • blue buildings
  • green grass
  • pink vegetation

Jensen, 2006
90
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
91
Socioeconomic Characteristics
  • Population Estimations
  • Energy Demand and Conservation
  • Quality of Life Indicators
  • Building
  • Lot
  • Adjacent Amenities
  • Adjacent Hazards

Jensen, 2006
92
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.
Jensen, 2006
93
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.
Jensen, 2006
94
Urban/surburban attributes that may be extracted
from remote sensor data and used to assess
housing quality and/or quality of life
Jensen, 2006
95
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.
Jensen, 2006
96
 Framework Foundation Data for Sustainable
Development
Worldwide distribution of nighttime lights
derived from the Defense Meteorological Satellite
Program Operational Linescan System
Jensen, 2006
97
 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.
Jensen, 2006
98
Automated building counts
Jensen, 2006
99
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
100
Disaster Emergency Response
Overturned tanker in Anchorage, Alaska.
Jensen, 2006
Earthquake damage near Northridge, California,
January 22, 1994.
Landslide cutting off Santa Clara River in
California.
101
Tornado Damage
Jensen, 2006
102
Post-Attack IKONOS Image of the World Trade
Center in New York City
103
Pre-and Post IKONOS Images of the World Trade
Center in New York City
104
LIDAR-derived Digital Elevation Model of the
World Trade Center in New York City
105
Jensen, 2006
106
IKONOS Panchromatic Sharpened Near-infrared
Image Overlayed on a USGS Digital Elevation Model
Columbia, SC October 15, 2000
Jensen, 2006
107
Public Service
Jensen, 2006
108
IKONOS Imagery of Columbia, SC Obtained on
October 28, 2000
Panchromatic 1 x 1 m
Pan-sharpened multispectral 4 x 4 m
109
Public Service
Jensen, 2006
110
Public Service
Jensen, 2006
111
Public Service
Jensen, 2006
112
Public Service Education
Jensen, 2006
113
Public Service Education
Jensen, 2006
114
Commercial Banking
Jensen, 2006
115
Commercial Banking/Services
Jensen, 2006
116
Commercial Retail
Jensen, 2006
117
Commercial Retail
Jensen, 2006
118
Commercial Retail
Jensen, 2006
119
Commercial Retail
Jensen, 2006
120
Commercial Retail
Jensen, 2006
121
Commercial Retail
Jensen, 2006
122
Commercial Housing
Jensen, 2006
123
Commercial Retail, Banking, Services
Jensen, 2006
124
Commercial Cemetery
Jensen, 2006
125
Recreation
Jensen, 2006
126
Recreation
Jensen, 2006
127
Recreation
Jensen, 2006
128
Recreation
Jensen, 2006
129
Recreation
Jensen, 2006
130
Recreation
Jensen, 2006
131
Recreation
Jensen, 2006
132
Recreation
Jensen, 2006
133
Recreation
Jensen, 2006
134
Recreation
Jensen, 2006
135
Recreation
Jensen, 2006
136
Recreation
Jensen, 2006
137
Recreation
Jensen, 2006
138
Recreation
Jensen, 2006
139
Processing Chemical
Jensen, 2006
140
Processing Chemical
Jensen, 2006
141
Processing Chemical
Jensen, 2006
142
Processing Chemical
Jensen, 2006
143
Processing Chemical
Jensen, 2006
144
Processing Chemical
Jensen, 2006
145
Processing Chemical
Jensen, 2006
146
Processing Chemical
Jensen, 2006
147
Processing Chemical
Jensen, 2006
148
Processing Mechanical
Jensen, 2006
149
Processing Mechanical
Jensen, 2006
150
Processing Mechanical
Jensen, 2006
151
Processing Mechanical
Jensen, 2006
152
Processing Mechanical
Jensen, 2006
153
Industry Extractive
Jensen, 2006
154
Industry Extraction
Jensen, 2006
155
Industry Extraction
Jensen, 2006
156
Industry Extraction
Jensen, 2006
157
Processing Heat
Jensen, 2006
158
Processing Heat
Jensen, 2006
159
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
160
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
161
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
162
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.

163
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