Transcript No Slide Title

Development of a Model to Quantify
and Map Urban Growth
Emily H. Wilson, James D. Hurd, Daniel L. Civco
Center for Land Use Education and Research (CLEAR)
Department of Natural Resources Management & Engineering
The University of Connecticut
U-4087, Room 308, 1376 Storrs Road
Storrs, CT 06269-4087
Outline
•
•
•
•
•
•
•
Introduction
Justification and Background
Urban Growth Classes
Methods
Examples of Growth Types
Applications
Considerations and Conclusion
Introduction
Northeast
Applications of
Useable
Technology
In
Land planning for
Urban
Sprawl
A NASA Regional
Earth Science
Applications Center
(RESAC)
Introduction
Our RESAC Mission
To make the power of remote sensing technology
available, accessible and useable to local land use
decision makers as they plan their communities.
To educate the general public on the value and utility of
geospatial technologies, particularly RS
information.
Introduction
NAUTILUS Research
Better land cover mapping and change
detection
Urban growth models and metrics
Forest fragmentation models
and metrics
Improved impervious
cover estimates
Introduction
NAUTILUS Research
BetterIndex
land cover
mapping
change
“A Forest Fragmentation
to Quantify
theand
Rate
of
detection
Forest Change”, James D. Hurd
Urban growth models and metrics
Forest fragmentation models
and metrics
Natural Resources: EcologicalImproved
Modeling
impervious
Thursday, April 25 at 10:30 AM
cover estimates
Outline
• Introduction
• Justification and Background
– Justification: Negative Impacts of Urban
Sprawl
– The “Urban Sprawl” Term
– Characteristics of a Good Model
•
•
•
•
•
Urban Growth Classes
Methods
Examples of Growth Types
Applications
Considerations and Conclusion
Justification
Negative Impacts of Sprawl
• Environmental Degradation
– Increased impervious surfaces and
degraded water quality
– Loss of wildlife habitat and wildlife
– Poor air quality
• City Degradation
• Social Degradation
Justification
Negative Impacts of Sprawl
• Environmental Degradation
• City Degradation
– More traffic and longer commutes
– Higher taxes
– Loss of local businesses
– Inner-city decay
• Social Degradation
Justification
Negative Impacts of Sprawl
• Environmental Degradation
• City Degradation
• Social Degradation
– Decline in economic opportunity
– Loss of sense of place and community
– Reduced access to open space
– Reduced social interaction that
threatens the way people live together
Background
What is Urban Sprawl?
Four Ways to Define
•
•
•
•
Using a Quantitative Indicator
Using a Qualitative Indicator
Attitudinal Definition
Specific Landscape Patterns
Background
Problems with the “sprawl”
term
• No universally accepted definition of sprawl
• Not all urban growth is considered to be
sprawl
• The same development can be considered
sprawl to some and not to others
• The use of the term “urban sprawl” has a
negative connotation and not all urban
growth is necessarily unhealthy
• Some types of growth are actually remedies
for sprawl
Background
Urban growth model instead of
urban sprawl model
• Allows us to quantify the amount of
land that has changed to urban uses
• Lets the user decide what he or she
considers to be urban sprawl
Background
Characteristics of a Good Model
• Spatially detailed data with fine spatial grain
and avoids spatial averaging
• Examines the whole landscape and assesses
urban growth in all areas
• Displays the emergence of growth over time
• Broadly available to allow for regional planning
• Has historical depth and is consistent over time
• Is quantifiable
• Maintains spatial pattern and configuration
• Is intuitive, interpretable, easy to calculate, and
does not require much data input
Background
Limitations of Other Methods
• Some other techniques:
– Average change data over a geographic area
– View statistics according to a sharp boundary
– Calculate information over a given area
resulting in a loss of spatial integrity
• This model
– Information is calculated per pixel resulting in
a map that provides a more realistic
representation of the landscape
– Maintains spatial pattern and arrangement
– The output urban growth map identifies how
much and what kind of change has occurred,
and its relation to other landscape features
Background
Benefits of Using Satellite
Imagery
• Satellite data are spatially registered
with reasonable spatial grain or
resolution
• Satellite imagery provides an excellent
data source for landscape coverage and
scenes do not stop at political
boundaries
• More than three decades of data
(Landsat) is available
Outline
• Introduction
• Justification and Background
– Urban Growth Classes
• Infill
• Expansion
• Outlying
– Isolated
– Linear Branching
– Clustered Branching
•
•
•
•
Methods
Examples of Growth Types
Applications
Considerations and Conclusion
Background
Urban Growth Classes
• Infill
• A non-urban pixel is converted to urban and is
surrounded by at least 40% existing urban pixels
• Defined as the development of a small area
surrounded by existing developed land
• Expansion
• Outlying
– Isolated
– Linear Branching
– Clustered Branching
Background
Urban Growth Classes
• Infill
• Expansion
• Characterized by a non-urban pixel being converted
to urban use and surrounded by no more than 40%
existing urban pixels.
• Defined as the spreading out of urban land cover
from existing developed land
• Outlying
– Isolated
– Linear Branching
– Clustered Branching
Background
Urban Growth Classes
• Infill
• Expansion
• Outlying
• Characterized by a change from non-urban to urban
land cover occurring beyond existing urban areas
– Isolated
– Linear Branching
– Clustered Branching
Background
Urban Growth Classes
• Infill
• Expansion
• Outlying
– Isolated
• One or several non-urban pixels away from an
existing urban area is/are converted to urban use
• Defined as a new, small area of construction
surrounded by non-urban land and some distance
from other developed areas
– Linear Branching
– Clustered Branching
Background
Urban Growth Classes
• Infill
• Expansion
• Outlying
– Isolated
– Linear Branching
• Defined as a new road, corridor, or linear
development surrounded by non-urban and some
distance from other urban areas
• Different from an isolated growth in that the pixels
that changed to urban are connected in a linear
fashion
– Clustered Branching
Background
Urban Growth Classes
• Infill
• Expansion
• Outlying
– Isolated
– Linear Branching
– Clustered Branching
• A new urban growth that is neither linear nor
isolated, but instead a cluster or a group
• Defined as a new, large and dense development in a
previously undeveloped area
Outline
•
•
•
•
Introduction
Justification and Background
Urban Growth Classes
Methods
–
–
–
–
–
Fragmentation Model
Urban Change Map
Urban Growth Map
“Clump” Step
Graphical Interface
• Examples of Growth Types
• Applications
• Considerations and Conclusion
Methods
Methods
• Roots of the Urban Growth Model lie in a
forest fragmentation model developed by
Riitters et al (2000) that assigns forest pixels
to five fragmentation categories: interior,
edge, perforated, transitional, and patch
• One idea was utilized for the urban growth
model – that of proportion of forest (Pf)
• Pf is created from a moving window that
quantifies the total number of forest pixels in
the window as compared to total number of
non-water pixels
Methods
Methods
• Adaptations:
– Perforated and edge combined
and called perforated (Pf>0.6)
– Patch and transitional combined
and called patch (Pf <= 0.6)
– Input image changed from
forest/non-forest binary image
to non-urban/urban binary
image
• Pf is further referred to as the
proportion of non-urban
Riitters et al, 2000
Methods
Overall Methodology
Land Cover
Maps
Non-urban
Fragmentation Maps
Date 1
Date 1
Date 2
Date 2
Map of
Change
Map of Urban
Growth
Methods
Land Cover Maps
Date 2
Date 1
3 class minimum:
- Water
- Urban
- Non-urban
Land Cover
Urban
Agriculture
Deciduous
Forest
Coniferous
Forest
Water
Wetland
Barren
Methods
Overall Methodology
Land Cover
Maps
Non-urban
Fragmentation Maps
Date 1
Date 1
Date 2
Date 2
Map of
Change
Map of Urban
Growth
Methods
Non-urban Fragmentation Map
• Input:
– Land cover map that separates urban, non-urban and
water
– User can choose what land cover types are “urban”
• Each center pixel is classified as:
– Interior non-urban: All pixels in 5x5 window are non-urban
– Perforated non-urban: Between 60%
and 100% of pixels in 5x5 window are
non-urban
– Patch non-urban: Fewer than 60% of
pixels in a 5x5 window are non-urban
Methods
Non-urban Fragmentation Map
Date 1
Date 2
Fragmentation
Water
Urban
Interior
Patch
Perforated
Methods
Overall Methodology
Land Cover
Maps
Non-urban
Fragmentation Maps
Date 1
Date 1
Date 2
Date 2
Map of
Change
Map of Urban
Growth
Methods
Map of Change
Use fragmentation maps from 2 dates
Change Class
Date 1
Date 2
Urban – No change
Urban
Urban
NO CHANGE CLASSES
Water – No change
Water
Water
Interior – No change
Interior
Interior
Fragmentation Class –
No change
Perforated/patch
Same fragmentation class as first
date
REGROWTH CLASSES
Change within
fragmentation class
Perforated/patch
Fragmentation class different from
first date
Urban to fragmentation class
Urban
Perforated/patch
Change to Interior
Urban
Interior
Interior to Urban
Interior
Urban
Interior to Perforated
Interior
Perforated
Interior to Patch
Interior
Patch
Perforated to Urban
Perforated
Urban
Patch to Urban
Patch
Urban
(many cases are classification errors)
Methods
Map of Change
Change
Water – no change
Urban – no change
Interior – no change
Fragmentation – no change
Interior to Urban
Interior to Perforated
Interior to Patch
Change within fragmentation
Perforated to Urban
Patch to Urban
Change to Interior
Methods
Overall Methodology
Land Cover
Maps
Non-urban
Fragmentation Maps
Date 1
Date 1
Date 2
Date 2
Map of
Change
Map of Urban
Growth
Methods
Map of Change
Significant Changes
Type of Growth
Patch to urban
In-fill Growth
Perforated to urban
Expansion Growth
“Outlying” Growth
Interior to Urban
Isolated
Linear Branching
Clustered Branching
Methods
Outlying Growth Defined
• Isolated
• Requires a small area of change many interior-toperforated pixels and few or no interior-to-patch pixels
• Linear Branching
• Not limited by size
• Occurs when there is an extended border between the
outlying growth pixels and the non-urban pixels requires
many interior-to-perforated pixels
• The number of interior-to-patch pixels limits the linear class.
• Clustered Branching
• Occurs when outlying growth pixels are close together
requires interior-to-patch pixels (opposite of linear branch)
• Pixels are close together low occurrence of interior-toperforated pixels
Methods
Urban Growth Map
Urban Growth
Urban
Water
Interior
In-fill
Expansion
Isolated
Linear Branch
Clustered Branch
Methods
“Outlying” Growth Problem
• The Problem
– All growth areas were bordered by linear
and/or isolated pixels
– Most linear branches had some isolated
and/or cluster pixels
– The resulting image was confusing with
minimal meaning
• The Solution
– Each outlying growth should be one type
(isolated OR linear branching OR
clustered branching)
– Group each area of “outlying” growth type
change and assign only one type
Interior
In-fill
Expansion
Isolated
Linear Branch
Clustered Branch
Methods
“Clump” Rules
First: “clump” the outlying growth pixels based on neighbors
Then: Determine what each clump should be
Within Each Clump
More isolated pixels than linear branch or clustered branch
Isolated
More clustered branch pixels than isolated or linear branch
Clustered
Branch
More linear pixels than isolated or clustered
AND number of cluster pixels  0.25
number of outlying pixels
Clustered
Branch
More linear pixels than isolated or clustered
AND number of cluster pixels
 0.25
number of outlying pixels
Linear Branch
Methods
“Outlying” Growth Clumped
Urban Change
Clump
IKONOS
Urban Growth
Urban Growth After Clump
Methods
“Outlying” Growth Clumped
Urban Change
Clump
IKONOS
Urban Growth
Urban Growth
After Clump
Methods
Transferability
• The methods and graphical models for
each step in the creation of the urban
growth map had been developed
• We wanted to make it easy to
implement for our other data as well as
data of others
• Developed a user interface for ERDAS
Imagine
Methods
Urban Growth Graphical Interface
•
•
•
•
•
Outline
Introduction
Justification and Background
Urban Growth Classes
Methods
Examples of Growth Types
–
–
–
–
–
In-Fill
Expansion
Isolated Growth
Linear Branch
Clustered Branch
• Applications
• Considerations and Conclusion
Growth Types
In-fill Growth
Date 1
TM
Land Cover
Change
Date 2
TM
Fragmentation
Land Cover
Fragmentation
Growth
Growth Types
Date 1
TM
Expansion Growth
Land Cover
Date 2
Fragmentation
Change
Growth
IKONOS
TM
Land Cover
Fragmentation
Growth Types
Isolated Growth
Date 1
TM
Land Cover
Date 2
Fragmentation
Change
Growth
IKONOS
TM
Land Cover
Fragmentation
Growth Types
Date 1
TM
Linear Branching Growth
Land Cover
Date 2
Fragmentation
Change
Growth
IKONOS
TM
Land Cover
Fragmentation
Growth Types
Date 1
TM
Clustered Branching Growth
Land
Cover
Date 2
Fragmentation
Change
Growth
Digital
Orthophoto
TM
Land
Cover
Fragmentation
Outline
•
•
•
•
•
•
Introduction
Justification and Background
Urban Growth Classes
Methods
Examples of Growth Types
Applications
–
–
–
–
–
–
–
Fragmentation of forest and/or open space
Agricultural Land Lost
Forest Land Lost
Natural Resource Issues
Landscape Dynamics and Decision Making
Visualizations
Urban Growth Dynamics
• Considerations and Conclusion
Applications
Applications
Fragmentation of Forest and/or Open Space
1985
19851990
19901995
19951999
2000
IKONOS
Applications
Applications
Agricultural Land Lost
1985 Land Cover
1985-1990 Growth
2000 Spring IKONOS
1985 TM
1990 TM
Applications
Applications
Date 1 Forest Land Lost and Growth Dynamics
1985
1990
1995
1999
Landsat TM/ETM
Land Cover
Fragmentation
Change
Urban
Growth
Applications
Applications
Natural Resources and Growth Dynamics
1985 Land
Cover
1985 TM
1985-1990
Growth
1990 TM
1990-1995
Growth
1995 TM
1995-1999
Growth
1999 ETM
2000 Spring
IKONOS
Applications
Applications
Landscape Dynamics and Decision Making
• Town officials can
view the collective
results of many sitelevel decisions
• Users can predict
what the effect of
pending or proposed
developments might
be on the town
landscape
Colchester, CT
1985
1985-1999
Growth
Applications
Applications
Visualizations and Growth Dynamics
Outline
•
•
•
•
•
•
•
Introduction
Justification and Background
Urban Growth Classes
Methods
Examples of Growth Types
Applications
Considerations and Conclusion
Conclusion
Considerations
• Model was developed using pixel-bypixel Landsat TM-derived 30 meter
classification of the Salmon River
watershed in Connecticut
– Varying moving window sizes, classification
techniques and regions have not been
thoroughly tested
– Image registration
– Snapshot of a point in time can be
misleading
Conclusion
Conclusions
• Remote sensing derived land cover
information can be an excellent data source
for examining, quantifying, categorizing and
mapping urban growth.
• Past techniques have measured the amount
of urban change but have failed to adequately
categorize it.
• This model provides a systematic,
standardized, and replicable methodology
that can be used to describe the urbanization
process that provides insight into changing
and emerging landscapes and patterns.
Acknowledgement
National Aeronautics and Space Administration
Grant NAG13-99001/NRA-98-OES-08 RESACNAUTILUS, Better Land Use Planning for the
Urbanizing Northeast: Creating a Network of ValueAdded Geospatial Information, Tools, and Education
for Land Use Decision Makers.
Northeast Applications of Useable Technology In Land planning for Urban Sprawl
This presentation
is available at
resac.uconn.edu
Development of a Model to Quantify
and Map Urban Growth
Emily H. Wilson, James D. Hurd, Daniel L. Civco
Center for Land Use Education and Research (CLEAR)
Department of Natural Resources Management & Engineering
The University of Connecticut
U-4087, Room 308, 1376 Storrs Road
Storrs, CT 06269-4087
Methods
“Clump” Rules
Interior-to-urban
Isolated
Linear
Branching
Clustered
Branching
Few (<5)
More (>4)
Many (>4)
Interior-to-patch
None
Few (<2)
At least 1
(>1)
Interior-toperforated
Many (>4)
Many (>3)
Few (<4)
Growth Types
Date 1
TM
Expansion Growth
Land Cover
Fragmentation
Date 2
Change
TM
Land Cover
Fragmentation
Growth
Background
A Good Urban Growth Model
•
•
•
•
•
•
Is quantifiable
Displays the emergence of urban growth over time
Has historical depth and is consistent over time
Displays spatially detailed data with fine spatial grain
Maintains spatial pattern and configuration
Examines the whole landscape, is broadly available to allow
for regional planning and assesses urban growth in all areas
• Avoids spatial averaging
• Is intuitive, interpretable, easy to calculate, and does not
require much data input
Applications
Applications
Visualizations and Growth Dynamics
Background
Four Ways to Define Sprawl
• Quantitative Indicator
– Change in population density where land
consumption occurs faster than population growth
– Increased vehicle miles of travel (VMT) or vehicle
hours of travel (VHT)
– Poor accessibility
– Low housing density
– Proportion of jobs in the city is greater than the
proportion of population in the city
• Qualitative Indicator
• Attitudinal Definition
• Landscape Pattern
Background
Four Ways to Define Sprawl
• Quantitative Indicator
• Qualitative Indicator
– Consumption of resources and land in excess of
what is needed for development
– Scattering of urban settlement over the rural
landscape
– Unplanned growth
• Attitudinal Definition
• Landscape Pattern
Background
Four Ways to Define Sprawl
• Quantitative Indicator
• Qualitative Indicator
• Attitudinal Definition
– Unhealthy growth with negative impacts
– Self-destructive growth that costs money,
consumes land, causes traffic problems, and
creates social inequity and isolation
– The great urban explosion
• Landscape Pattern
Background
Four Ways to Define Sprawl
•
•
•
•
Quantitative Indicator
Qualitative Indicator
Attitudinal Definition
Landscape Pattern
– Low density development with dependence on cars
– Geographic separation of work, home, school, and
shopping
– Scattered residential lots in outlying areas
– Multi-lot, planned housing developments on new
access roads in outlying areas
– Large, pedestrian-unfriendly commercial strips
Applications
Applications
• Fragmentation of forest and/or open
space
• Agricultural Land Lost
• Forest Land Lost
• Natural Resource Issues
• Landscape Dynamics and Decision
Making
• Visualizations
• Urban Growth Dynamics
Growth Types
Examples of Growth Types
•
•
•
•
•
In-Fill
Expansion
Isolated Growth
Linear Branch
Clustered Branch
Background
Urban Growth Classes
• Infill
• Expansion
• Outlying
– Isolated
– Linear Branching
– Clustered Branching