University of Wisconsin-Milwaukee Geography 403 Guest Lecture: Urban Remote Sensing Rama Prasada Mohapatra PhD Candidate Department of Geography Spring

University of Wisconsin-Milwaukee

Geography 403Guest Lecture: Urban Remote Sensing

Rama Prasada MohapatraPhD Candidate

Department of GeographySpring 2010


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


1. Traditional Remote Sensing Applications

Vegetation (bio-geography)

Vegetation indexBiomass estimationLeaf area index (LAI) estimationYield prediction

GeologyOil inventory

Soil scienceClimate studiesEtc.


Fieldspectrometry

Truck-mounted imaging radar

Vegetation (bio-geography)1. Traditional Remote Sensing Applications


Wildfires (ASTER data)San Bernardino Mountains, California, October 28, 2003



Wildfires (MODIS)Los Angeles, California,

October 27, 2003.



0

0.2

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


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


10 kilometer resolution ischaracteristic of someatmosphericmeasurementsfromgeosynchronousorbit.

10 km



1 kilometer resolution ischaracteristicof weathersatellite Earthimages fromgeosynchronousorbit.

1 km



30 m

30 meters is

the resolution

of a Landsat

image.



2. Scale Issues

10 m

- Scale issues


4 m



2 m



1 m

Some recentlow Earth orbitcommercial andEarth resourcesatellites haveresolutionsapproaching1 meter.



one-meter resolution

(sharpened 4 meter) satellite

image

11:46 a.m. EDT Sept.

12, 2001



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


- 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


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)


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…



•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)



Land use (1992)

Commercial

High residential

Low residential



- 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)



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.



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





-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



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)




5. Population Estimation- House Count

1. Count the number of houses

2. Survey the average persons per house

3. Population = #house * #person/house



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



Advantages: Accurate

Disadvantages: Manual counts, labor intensive and time consuming Cannot be applied in large urban areas


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

125354645361514

75431

601.0565.358393.28142.33533.146525.83252.190

980.264261.388803.122633.273383.690371.13ˆ

hdsrrrrr

RRRRRP


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


Population Estimation: Landscan Project

Input factors1) Road2) Slope3) Land cover4) Population places5) Nighttime Lights6) Urban density7) Coastlines

5. Population Estimation


5. Population Estimation

Defense Meteorological Satellite Program


6. Urban Growth Monitoring


City of Roses: Guadalajara, Mexico

6. Urban Growth Monitoring


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


6. Urban Growth Monitoring- Problems

Image classification accuracy is not adequate for urban Growth monitoring study

Seasonality changes of urban spectra


7. Urban Growth Modeling

From http://mcmcweb.er.usgs.gov/phil/modeling.html

1980 – 2025



1. Ecometric model (Land-bid theory)

Population

CBDSub-urban Sub-urban



2. Land use allocation model

Locate a land which minimizes certain criteria

1) Transportation costs (commuting time)2) Congestion3) Pollution



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



4. Multi-agent models

1. Bottom-up approach

2. Multiple agents control urban growth process1) Urban planners2) Stakeholders3) Communities4) ……



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.


END

Geography 750: Urban Remote Sensing

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University of Wisconsin-Milwaukee Geography 403 Guest Lecture: Urban Remote Sensing Rama Prasada Mohapatra PhD Candidate Department of Geography Spring