Improving change vector analysis in multitemporal space to detect land cover changes by using cross-

correlogram spectral matching algorithm

Yuanyuan Zhao, Chunyang He, Yang yang

Beijing Normal University, Beijing, China, 100875

Email : zhyy@mail.bnu.edu.cn

2011 IEEE International Geoscience and Remote Sensing Symposium

Outline

Introduction

Methods

Case study

Effectiveness analysis of the new method

Accuracy assessment

Conclusions and discussion

Land cover change detection is of great significance

Land cover plays an important role in energy balance as well as biogeochemical and hydrological cycles in the Earth system (Avissar and Pielke, 1989; Lunetta et al., 2006).

Timely and accurate detection of land cover changes can a) provide essential information to

enhance our understanding of the mechanisms that drive the spatial-temporal processes of land cover change.

b) support the simulation and evaluation of the associated environmental impacts.

Traditional change vector analysis (TCVA)

TCVA has then been widely adopted in land cover change detection using VI data (Lambin and Strahler, 1994).

TCVA is sensitive to temporal fluctuations in VI values, which greatly limits the method’s accuracy. It may overestimate the actual changes. It is not able to determine whether the results represent land

cover conversion or simply VI variation of the same type of land cover.

Land cover conversion growth vigor changes

comparable change magnitude

?

The CCSM algorithm has demonstrated its merits in estimating the similarity of two VI profile curves

A cross-correlogram can be constructed for each pair of VI profiles and a goodness-of-match value can be calculated accordingly.

Advantage ： it is able to capture the shape similarity of two VI profiles even if there is a time lag between the two.

Objectives

Proposing a new approach that improves TCVA with an

adapted CCSM analysis.

The proposed approach was applied and validated

through a case study of land cover conversion detection

in the Beijing–Tianjin–Tangshan urban agglomeration

district (BTT-UAD), China, using the MODIS Enhanced

VI data (EVI) for 2000–2008.

Outline

Introduction

Methods

Case study

Effectiveness analysis of the new method

Accuracy assessment

Conclusions and discussion

Traditional change vector analysis

1 2T

nR r ,r , ,r

1 2T

nS s ,s , ,s

nn sr

sr

sr

SRV

22

11

The VI time series data in the period R ：

2222

211 )()()( nn srsrsrV

A greater M indicates a higher possibility of land cover change for pixel i.

A specific threshold is used to distinguish change pixels from no-change pixels （ Lambin and Strahler, 1994a ）。

The VI time series data in the period S ：

VI

0

0.2

0.4

0.6

0 5 10 15 20 25 30时间

As comparable magnitude

values of change vectors may

also result from phenological

variation of a vegetative type

of land cover or change in its

growth vigor, a threshold is

not always effective in

extracting land cover

conversions.

VI

0

0.2

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0.6

0 5 10 15 20 25 30时间

VI

0

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0 5 10 15 20 25 30时间

VI

0

0.2

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0 5 10 15 20 25 30时间

VI

0

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1

0 5 10 15 20 25 30时间

VI

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0 5 10 15 20 25 30时间

VI

0

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0 5 10 15 20 25 30时间

VI

0

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0 5 10 15 20 25 30时间

VI

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VI

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（ e ） No-change M =0.13

（ d ） Vegetation growth status change M =0.77

（ c ） Phenology change M =0.78

（ b ） From ‘winter wheat-summer maize’ to ‘spring maize’

（ a ） From cropland to built-up

M=0.78

M=0.82

Improved change vector analysis(ICVA)

Three step: • TCVA is employed to preliminarily detect land cover changes.• The CCSM approach is used to identify and eliminate areas in which the land cover type did not really change but only experienced some degree of VI variation. • The type of land cover conversion (e.g., from cropland to built-up area) is determined by further analyzing the change vectors for the remaining pixels of interest.

Preliminary detection of land cover change using traditional change vector analysis

Determination of land cover change types

Identification and elimination of land cover modifications using cross-correlogram spectral

matching analysis

Time series data in time r

Time series data in time s

Flow chart

Preliminary land cover change detection using TCVA

The change magnitude of VI time series was calculated using the

TCVA.

An optimal threshold was determined to extract the preliminary

change information.

A semi-automatic method called Double-windows Flexible Pace

Search method (DFPS) (Chen et al., 2003).

VI time series in time r

VI time series in time s

Change magnitude

Change information

TCVA DFPS

Identifying and eliminating pseudo-conversion by CCSM analysis

The correlation coefficients (Rm) of the two VI profile curves

between time r and s at different match positions (m) are calculated.

222 2

r s r sm

r r s s

nR

n n

where λs and λr are VI profile curve values for period r and s, respectively. m is the match position. n is the number of overlapping positions.

λs

λr

Rm

time

λr

time

λs

Eliminating land cover modification using the CCSM algorithm

The maximum correlation coefficient (Rmax) is chosen as the

shape similarity index of the two curves (Wang et al., 2009).

max 1 0 1max , , ,m mR R R R R R

where Rmax ranges from 0 to 1. The Rmax is equal to 1 when the shape of the VI profile curves between period r and s are completely the same. A larger Rmax indicates a smaller difference between the two shapes of the VI profile curves.

Time r Time sEVI

0

0. 2

0. 4

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0. 8

1

0 5 10 15 20 25 30时间

| V△ |= 0.78

Time

Rm

-0.4-0.2

00.20.40.60.8

1

-5 -4 -3 -2 -1 0 1 2 3 4 5

匹配位移（m）

Rmax=0.996

Match position (m)

The land cover modification is eliminated by an optimal threshold

for Rmax using a manual trial-and-error procedure .

Selecting sample areas with the help of the ancillary data.

Assessing the effectiveness of eliminating land cover modification for

different thresholds.

Assigning the optimal threshold for Rmax to the value at which the eliminating

effect is best.

Threshold

t1

…

tn

change 1

……

change n

reference

compare

compare

Kappa 1

……

Kappa n

Kappa kmax

tk

The optimal threshold

Change information

Eliminating land cover modification using the CCSM algorithm

Discriminating the land cover conversion type

Unsupervised clustering approach (Bruzzone and Prieto, 2000)。 Having no requirement for training data Partitioning remotely sensed data with multi-spectral or multi-

temporal information Transforming the partitioned classes into a thematic map of

interest by a posteriori

The unsupervised clustering method is adopted in this study

to the actual land cover conversion types with the support of

some ancillary data.

Change vector image

Class map

Unsupervised clustering

Change Typemap

Ancillary data

Outline

Introduction

Methods

Case study

Effectiveness analysis of the new method

Accuracy assessment

Conclusions and discussion

Study area

Latitude: 38°28′ N - 41°05′ N

Longitude: 115°25′ E -119°53′ E

Total area: 55774.5 km2

Climate: Sub-humid and temperate

monsoon climate

Main land cover type: cropland, built-up, forest

Over the past several decades, significant land cover changes have taken place in the BTT-UAD, mainly driven by rapid economic development and unprecedented urbanization (Tan et al., 2005).

Beijing–Tianjin–Tangshan urban agglomeration district

(BTT-UAD), China

Data

MODIS_EVI data (specifically MOD 13Q data

version 004)

The spatial resolution is 250m

The time spans from 2000 to 2008

They were downloaded from the Earth Resources

Observation Science Center of United States

Geological Survey (USGS EROS) Landsat ETM+ data：

123/32 20 August 2000 and 11 September 2008 122/33 10 June 2000 and 3 August 2008 They were downloaded from EROS data center

Other data ： The land use/cover data in 2000

Field survey data

Images obtained from Google Earth

2000

2008

MODIS_EVI

MODIS_EVI data preprocessing

Image mosaicing The four tiles (h26v04 、 h26v05 、 h27v04 、 h27v05)

covering the study area were mosaiced to a complete EVI image covering the study area.

Projection converting The mosaiced images were converted to the map

projection format commonly used in China, the Albers Conical Equal Area format.

Noise removing The Harmonic Analysis of Time Series (HANTS) was

performed on the image time series.

Image clipping

Image mosaicking

Projectionconverting

Noise removing

Image clipping

1 2 3 4

MODIS_EVI of the study area

Extracting preliminary pixels of land cover change

Change magnitude image of the study area, 2000-2008

Preliminary extraction of land cover change (2000-2008) in the study area

EVI time series in 2000

EVI time series in 2008

TCVACalculating

Change magnitude

Preliminary change

informationDFPS

Land cover conversion in the study area, 2000-2008

Rmax calculated by CCSM using the EVI profile curves in 2000 and 2008

Eliminating land cover modification in the study area using the CCSM algorithm

The preliminary

change information

EVI time series in 2000

EVI time series in 2008

Calculating the shape similarity

index Rmax

Manual trial-and-error procedure

land cover conversion

Obtaining the land cover conversion map

The land cover conversion map was obtained by classifying the change vector image of land cover using an unsupervised classification technique.

(a) from water to cropland ; (b) from cropland to built-up ; (c) from water to built-up

2000 ETM+ 2008 ETM+

Outline

Introduction

Methods

Case study

Effectiveness analysis of the new method

Accuracy assessment

Conclusions and discussion

Effectiveness analysis of the ICVA

The TCVA could not distinguish the land cover modification from land cover conversion accurately only by selecting the threshold for the change magnitude.

The ICVA can effectively eliminate partial land cover modification information by thoroughly use of the shape variation of the EVI profile curves and determining an

optimal threshold for Rmax.

1

EVI

0

0.2

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0.6

0 5 10 15 20 25 30Date order of 10-day

2000年2008年

R m

0

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1

-5 -4 -3 -2 -1 0 1 2 3 4 5

Match position (m ) 2

3

EVI

0

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0 5 10 15 20 25 30Date order of 10-day

R m

0

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0.8

1

-5 -4 -3 -2 -1 0 1 2 3 4 5

Match position (m ) 4

5

EVI

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30Date order of 10-day

R m

-0.4-0.2

00.20.40.60.8

1

-5 -4 -3 -2 -1 0 1 2 3 4 5

Match position (m ) 6

7

EVI

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30Date order of 10-day

R m

0

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1

-5 -4 -3 -2 -1 0 1 2 3 4 5

Match position (m ) 8

Rmax=0.991

M=0.77

Rmax=0.725

M=0.78

Rmax=0.569 M=0.82

Rmax=0.996

M=0.78

（a）From cropland to built-up

（b）From ‘winter wheat-summer maize’ to ‘spring maize’

（c）Advance of phenological period

（d）Vegetation growth status change

Outline

Introduction

Methods

Case study

Effectiveness analysis of the new method

Accuracy assessment

Conclusions and discussion

Visually comparing

(a) Differences in the case

of vegetation vigor change.

(b) Differences in the case

of phenological change.

The TCVA misinterpret

the vegetation vigor

change and phenological

change as land cover

conversion, while the

ICVA eliminate the two

types of changes.

The ICVA performed better than the TCVA in detecting the land cover conversion in the study area

The TCVA achieved a kappa coefficient of 0.29 and an overall accuracy of 60.40%, whereas the ICVA achieved a kappa coefficient of 0.42 and an overall accuracy of 71.20%.

Reference data (pixels) Reference data (pixels)

ICVAM Change No-change

Row

total

TCVAM Change No-change Row total

Change 118 132 250 Change 119 187 306

No-change 12 238 250 No-change 11 183 194 Results

(pixels) Column

total 130 370 500

Results

(pixels) Column

total 130 370 500

Total accuracy =71.20% Kappa coefficient = 0.42 Total accuracy = 60.40% Kappa coefficient = 0.29

Omission error Omission error

Change = 9.23% Change = 8.46%

No-change =35.68% No-change = 50.54%

Commission error Commission error

Change = 52.80% Change = 61.11%

No-change = 4.80% No-change = 5.67%

1

Outline

Introduction

Methods

Case study

Effectiveness analysis of the new method

Accuracy assessment

Conclusions and discussion

Conclusions

We have proposed a new approach, named ICVA, that improves TCVA with an adapted use of cross-correlogram spectral matching (CCSM) analysis.

ICVA was applied to detect land cover conversion in BTT-UAD, China from two time series of MODIS EVI data for 2000 and 2008. The results showed that ICVA is able to map land cover conversion with a significantly higher accuracy (71.20%, kappa = 0.42) than TCVA (60.40%, kappa = 0.29).

The higher accuracy has been achieved by analyzing the multi-temporal VI information with the consideration of not only change magnitude but also profile similarity.

Discussion

The application of ICVA has some limitations:

The approach is best used in distinguishing land cover modifications resulting from phenological and/or growth vigor changes.

More complex types of land cover changes, such as the cultivation pattern change of double cropping land to single cropping land, pose challenge to the proposed approach.

Thank you very much!