Chuphan Chompuchan (蘇潘) 8101042007

Dept. of Soil and Water Conservation

National Chung Hsing University

Seminar IVFall Semester

(02 Oct. 2015)

Introduction

What is Impervious Surface Area (ISA)?

Why ISA is important?

Review methods for extracting ISA

Linear spectral mixture analysis (LSMA)

Artificial neural networks (ANN)

Object based image analysis (OBIA)

Spectral indices approaches

Case study of Puli township (preliminary analysis)

Discussion and conclusion 2

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Impervious Surface Area (ISA)

Land surface which water cannot infiltrate

Artificial structures covered by impenetrable

materials such as asphalt, concrete, brick and stone

Mainly are building rooftops and pavement (roads,

highways, sidewalks and parking lots)

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ISA indicator of the degree of urbanization

and environmental quality

Hydrological impact (urban runoff, GW recharge)

Water quality (non-point source pollution)

Urban heat island effect (sensible and latent heat fluxes)

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Linear spectral mixture

analysis (LSMA)

Assumes that the

spectrum of each pixel is a

linear combination of the

“endmembers” spectra

Ridd (1995) proposed

Vegetation–Impervious

surface–Soil (V–I–S)

model for urban

ecosystem analysis

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Each endmember represents

a pure land cover type

Endmembers selection

Maximum Noise fraction (MNF)

Tasseled Cap (TC)

LSMA can be expressed as

C. Deng & C. Wu (2012)D. Lu, et.al. (2008)

Rj = the reflectance for each band j

N = the number of endmembers

fi = the fraction of endmember i

Rij = the reflectance of endmember i in band jej = the unmodeled residual

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M.C. Huang (2002)

Salt Lake City area, USA

LANDSAT image

LANDSAT ETM+ V-I-S model

Percentages of selected urban features in a V-I-S diagram

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Artificial neural networks

(ANN)

Multilayer perceptron (MLP)

network is structured with 3

types of layers: input,

hidden, and output layers.

The back-propagation (BP)

learning algorithm is a key

to the success of an ANN

model require fewer

training samples & yield the

best result3-layer neural-network structure

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Weng Q. and X. Hu (2008)

Indianapolis/Marion County, IN, USA

Terra’s ASTER and Landsat ETM+

Comparison between LSMA and ANN

Sample selection of

impervious surfaces

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ASTER LSMA

ASTER ANN ETM+ ANN

ETM+ LSMA

R2 = 0.6623

R2 = 0.6036R2 = 0.7707

R2 = 0.7713

Weng Q. and X. Hu (2008)

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Object based image analysis (OBIA)

From per-pixel image to object (segmentation) which

concerning color and shape homogeneity.

How to choose an optimal scale parameter and shape

factor is critical to the quality of classification

image object

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OBIA vs. Maximum likelihood classification

Minnesota State University, Mankato campus

Quickbird imagery acquired on October 6, 2003

F. Yuan (2006)

Object orientedMaximum likelihood

Overall accuracy = 87.1% Kappa = 0.804

Overall accuracy = 92.5% Kappa = 0.887

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Y. Deng et al. (2015)

0.42-0.52 0.53-0.61 0.63-0.69 0.78-0.90 1.55-1.75 2.09-2.35

Visible (BLUE)

Visible (GREEN)

Visible (RED)

Near-Infrared(NIR )

Short WaveInfrared (SWIR1)

Short WaveInfrared (SWIR2)

LANDSAT TM/ETM+ Spectral band specification

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R. C. Estoque and Y. Murayama(2015)

LANDSAT Spectral band specification

*Thermal Infrared (TIR)

at-satellite brightness temperature (K)

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NDBI: Normalized

Difference Built-up Index

NDBaI: Normalized

Difference Bareness Index

IBI: Index-based Built-up Index

EBBI: Enhanced Built-Up

and Bareness Index

Zha et al. (2003)

Zhao&Chen (2005)

Xu (2008)

As-Syakur et al. (2012)

NIR1SWIR

NIR1SWIRNDBI

TIR1SWIR

TIR1SWIRNDBaI

SWIR1)G/(G R) NIR/(NIR NIR) WIR12xSWIR1/(S

SWIR1)G/(G R) NIR/(NIR - NIR) WIR12xSWIR1/(SIBI

TIR1SWIR10

NIR1SWIREBBI

UI: Urban Index Kawamura et al. (1996) NIR2SWIR

NIR2SWIRUI

NBI: New Built-Up Index Jieli et al. (2010)NIR

R1SWIRNBI

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As-Syakur et al. (2012)

Denpasar (Bali, Indonesia)

Landsat ETM+

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As-Syakur et al. (2012)

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As-Syakur et al. (2012)

Varshney & RajeshAs-Syakur (2014)

Delhi northern capital region of India

Landsat-5 TM

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As-Syakur et al. (2012)

R.C. Estoque and Y. Murayama (2015)

The optimal threshold t* for image segmentation (the Otsu method)

Limitation of the index value for each type of index transformation

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Universal Soil Loss Equation (USLE)

A = R x K x LS x C x Prainfall

erodibility factorsoil erodibility

factortopographic

factor cover factor

supporting practice factor

C-factor will range

between 1 and almost 0

C=1 means no cover

effect and a soil loss

comparable to that from

a tilled bare fallow

C=0 means a very

strong cover effect

resulting in no erosionPuli township

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NDVI : Normalized Difference Vegetation Index

1

RNIR

RNIRNDVI

0

-1

Forest

Grassland/ Agriculture

Bare land/ ISA

Water/ Cloud

NDVI could not separate between

bare land and ISA

Bare land = high erosion (C=1)

ISA = no erosion (C=0)

= 2

= 1

Relationship between NDVI and C-Factor (Van der Knijff et al., 1999)

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C-Factor derived from NDVI C-Factor with ISA* & water body** masking

1

0

* ISA extraction from IBI (Index-based

Built-up Index)

** Water body extraction from MNDWI

(Modified Normalized Difference Water Index)

Average C-Factor

No masking = 0.041

Masking = 0.024

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Some coverages were classified as ISA

Jiu Fen Er Shan 九份二山

LSMA, ANN and OMIA required training samples

LSMA suitable for medium/low resolution imagery

OMIA suitable for high resolution imagery

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D. Liu & F. Xia (2010)

Limitation of spectral indices approach

Detect shadow on high resolution imagery

Seasonal sensitivity for estimating impervious surfaces

Optimum threshold value needed

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