university of wisconsin-milwaukee geography 403 guest lecture: urban remote sensing rama prasada...
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University of Wisconsin-Milwaukee
Geography 403Guest Lecture: Urban Remote Sensing
Rama Prasada MohapatraPhD Candidate
Department of GeographySpring 2010
University of Wisconsin-Milwaukee
Outline
1. Traditional Remote Sensing Applications2. Urban Remote Sensing: New Challenges3. Urban Land Use Classification4. The Vegetation-Impervious Surface-Soil Model5. Population Estimation6. Urban Growth Monitoring7. Urban Growth Modeling
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1. Traditional Remote Sensing Applications
Vegetation (bio-geography)
Vegetation indexBiomass estimationLeaf area index (LAI) estimationYield prediction
GeologyOil inventory
Soil scienceClimate studiesEtc.
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Fieldspectrometry
Truck-mounted imaging radar
Vegetation (bio-geography)1. Traditional Remote Sensing Applications
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Wildfires (ASTER data)San Bernardino Mountains, California, October 28, 2003
1. Traditional Remote Sensing Applications
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Wildfires (MODIS)Los Angeles, California,
October 27, 2003.
1. Traditional Remote Sensing Applications
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0
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0.4
0.6
0.8 S
pect
ral r
efle
ctan
ces
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4
Wavelength(um)
Landsat 5 TM
EO-1 Hyperion
Landsat 7 ETM+
EO-1 ALI
Green Vegetation
Senescent vegetation
Bare soil
Band 2
Band 3Band 4
Band 5 Band 7
Band 1
2. Urban Remote Sensing: New Challenges- Spectral issues
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40 km
The study of the Earth requires many different levels of detail.
Global forecastsimulations useresolutions in the 40 to 200 kilometer range.
2. Urban Remote Sensing: New Challenges- Scale issues
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10 kilometer resolution ischaracteristic of someatmosphericmeasurementsfromgeosynchronousorbit.
10 km
2. Urban Remote Sensing: New Challenges- Scale issues
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1 kilometer resolution ischaracteristicof weathersatellite Earthimages fromgeosynchronousorbit.
1 km
2. Urban Remote Sensing: New Challenges- Scale issues
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30 m
30 meters is
the resolution
of a Landsat
image.
2. Urban Remote Sensing: New Challenges- Scale issues
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1 m
Some recentlow Earth orbitcommercial andEarth resourcesatellites haveresolutionsapproaching1 meter.
2. Urban Remote Sensing: New Challenges- Scale issues
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one-meter resolution
(sharpened 4 meter) satellite
image
11:46 a.m. EDT Sept.
12, 2001
2. Urban Remote Sensing: New Challenges- Scale issues
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2. Urban Remote Sensing: New Challenges
1. Urban landscapes are composed of a diverse assemblage of materials (concrete, asphalt, metal, plastic, glass, water, etc.)
2. The goal of urban construction is to improve quality of life.
3. Urbanization is taking place at a dramatic rate, with or without planned development
4. Sustainable development (congestion, pollution, urban heat island, commuting time issue)
- Urban issues
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- Remote sensing
1. Urban is a heterogenous region, with different kinds of manmade materials (impervious surface), such as asphalt, concrete, glass, etc.
2. Urban objects are small comparing to natural objects (e.g. forests, agriculture, geological structure, etc.)
3. Remote sensing data are typically in a medium resolution (e.g. Landsat Thematic Mapper 30 meter)
2. Urban Remote Sensing: New Challenges
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3. Urban Land Use Classification
1) American Planning Association “Land-Based Classification standard” for urban/suburban land use.
2) U.S. Geological Survey “Land-Use/Land-Cover Classification System” was originally designed to be resource-oriented.
Developed by Anderson (1976) in U.S.G.S.Driven primarily by the interpretation of remote sensing dataMost land use classifications are based on this system (e.g. LULC data in 1990)
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Anderson Level II
1: Urban11: Residential12: Commercial and services13: Industrial14: Transportation, communications, and utilities15: Industrial and commercial complexes16: Mixed urban and built-up land17: Other urban and built-up land2: Agriculture3: Rangeland4: Forest land…
3. Urban Land Use Classification
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•Can be downloaded from United State Geological Survey (USGS) website (http://landcover.usgs.gov/ftpdownload.asp).
•Created from a satellite data, Thematic Mapper (TM), with 30 meter spatial resolution
Year 1992 - classification based on Anderson Level II (9 major classes with subclasses)
Year 2001- In addition to classification, impervious surface information and tree canopy coverage are available (not public available yet)
3. Urban Land Use Classification
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Land use (1992)
Commercial
High residential
Low residential
3. Urban Land Use Classification
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- Problems and current research
Problems:The average urban land use classification accuracy is about 80-85% (not adequate for urban growth monitoring and modeling)
Research:1) Spectral analysis (Sub-pixel classification)2) Spatial analysis (texture analysis, wavelet analysis, etc.)3) Con-textural analysis ( with localized knowledge)4) Knowledge based analysis (Neural network, fuzzy classification,
decision tree analysis)
3. Urban Land Use Classification
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4. The Vegetation-Impervious Surface-Soil Model
Assumptions
1) A remote sensing pixel includes more than one land cover types
2) Three basic compositions (vegetation, impervious surface, and soil) can represent the heterogeneous urban landscape.
3) The fractions of each composition can be calculated using mathematical techniques.
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4. The Vegetation-Impervious Surface-Soil Model
1. Important indicator of urbanization - a major component of urban infrastructure- an indicator of human activities
2. Essential environmental index- model run-off volume- monitor water quality
Impervious surface
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5. The Vegetation-Impervious Surface-Soil Model
-ANN Classification Artificial Neural Network (ANN?
Relatively crude electronic models based on the neural structure of the brain
Most widely used multi layer perceptron
Three layer perceptron with back propagation algorithm provide better alternatives than statistical
“Neuralnet back propagation classifier” tool in IDRISI are capable of creating activation level maps
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4. The Vegetation-Impervious Surface-Soil Model
Input neurons/ nodes Hidden neurons/ nodes Output neurons/ nodes
Three layer ANN structure
Band 0
Band 1
Band 2
Band 3
Soil
Vegetation
Impervious surface
ijw
Input layer nodes: the number of input bands is four for the base model
Output layer nodes: number of desired classes (3)
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5. Population Estimation- House Count
1. Count the number of houses
2. Survey the average persons per house
3. Population = #house * #person/house
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5. Population Estimation- House Count
The imagery must have sufficient spatial resolution to allow identification of individual structures
Some estimation of the average number of persons per dwelling unit must be available
Some estimation of the number of homeless, seasonal, and migratory workers required
It is assumed all dwellings are occupied
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6. Population Estimation- House Count
Advantages: Accurate
Disadvantages: Manual counts, labor intensive and time consuming Cannot be applied in large urban areas
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5. Population Estimation- Regression models
1. Regression with residential land use areas Census data available Residential land use classification
HL CCP 006.5006.20304.0ˆ
2. Regression with spectral reflectance and its transformations Census data available
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601.0565.358393.28142.33533.146525.83252.190
980.264261.388803.122633.273383.690371.13ˆ
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5. Population Estimation- Regression models
3. Regression with impervious surface in residential areas
UIIP HL 021 **ˆ
IL: impervious surface in low density residential areasIH: impervious surface in high density residential areas
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Population Estimation: Landscan Project
Input factors1) Road2) Slope3) Land cover4) Population places5) Nighttime Lights6) Urban density7) Coastlines
5. Population Estimation
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6. Urban Growth Monitoring- Methods
1. Classify remote sensing images of two dates, and compare the results
2. Image regression
3. Image differentiation
4. Normalized Vegetation Differential Index (NDVI) comparison
5. Impervious surface fraction comparison
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6. Urban Growth Monitoring- Problems
Image classification accuracy is not adequate for urban Growth monitoring study
Seasonality changes of urban spectra
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7. Urban Growth Modeling
From http://mcmcweb.er.usgs.gov/phil/modeling.html
1980 – 2025
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7. Urban Growth Modeling
1. Ecometric model (Land-bid theory)
Population
CBDSub-urban Sub-urban
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7. Urban Growth Modeling
2. Land use allocation model
Locate a land which minimizes certain criteria
1) Transportation costs (commuting time)2) Congestion3) Pollution
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7. Urban Growth Modeling
3. Cellular Automata (IDRISI software)
1. Bottom-up approach
2. Four components1) An action space2) a set of states3) the rules of neighborhood definition4) a set of state transition rules
3. Simulate and calibrate using existing data
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7. Urban Growth Modeling
4. Multi-agent models
1. Bottom-up approach
2. Multiple agents control urban growth process1) Urban planners2) Stakeholders3) Communities4) ……
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7. Urban Growth Modeling
Problems: Model calibrations
1) Although many models have been developed, few of them have been calibrated and compared (not conclusive).
2) Many models fail to explain the undergoing forces of urban growth or sprawl.