an autocorrelation analysis approach to detecting land cover change using hyper-temporal time-series...

An Autocorrelation Analysis Approach toDetecting Land Cover Change usingHyper-Temporal Time-Series Data

W. Kleynhans 1,2 B.P. Salmon 1,2 J.C. Olivier 1 K.J. Wessels 2

F. van den Bergh 2

1Electrical and Electronic Engineering, University of Pretoria, South Africa

2Remote Sensing Research Unit, Meraka Institute, CSIR, Pretoria, South Africa

July 25, 2011

Outline

Introduction

Study Area

Methodology

Results

Conclusion

Examples

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Introduction

I Remote sensing satellite data - effective way to monitor andevaluate land cover changes

I Bi-temporal change detection approach is not alwaysappropriate.

I The temporal frequency should be high enough to distinguishchange events from natural phenological cycles.

I Having a series of equally sampled acquisitions opens the doorfor the utilization basic signal processing methods.

I Temporal autocorrelation was effectively used as an inputfeature for a change detection alarm.

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Problem statement

I Settlement expansion is the most pervasive form of land coverchange in South Africa.

I Objective is to develop a change alarm that is able to detectthe formation of new settlement developments.

I These changes typically occur in areas that are naturallyvegetated.

I Change events should be distinguished from naturalphenological cycles.

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Outline

Introduction

Study Area

Methodology

Results

Conclusion

Examples

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Study Area

I The Gauteng province is located in northern South Africa andcovers a total area of approximately 17000 km2

I 592 natural vegetation and 372 settlement no-change MODISpixels were identified.

I 181 change MODIS pixels were identified.I Each pixel has a 7 year time-series (8 day composited:

2001-2007).www.csir.co.za

Change example: Quickbird image in 2002

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Change example: Quickbird image in 2007

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Hyper temporal MODIS band 4 Time Series : No change

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Hyper temporal MODIS band 4 Time Series : Change

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Outline

Introduction

Study Area

Methodology

Results

Conclusion

Examples

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Temporal ACF method

Assume time series in vector form:

X = Xn , n ∈ {1, 2, ...,N} (1)

The normalized ACF for time-series X:

RXX(τ) =E [(Xn −mean(X))(Xn+τ −mean(X))]

var(X)(2)

Stationarity assumption: mean(X) and var(X) should be constant!

Reflection time-series from nature very rarely adhere to thisassumption but some do more than others...

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Temporal ACF method

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Change metric formulation

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Use of simulated change to determine detection parameters

I Time-series of no change areas are relatively easy to obtainbut examples of real change is much more difficult.

I Solution: Obtain only examples of no-change time-series andsimulate the change time-series using time-series blending.

I Using simulated change the start and rate of change can becontrolled.

I Start of change date was random using a uniform distribution

I Duration of change was varied between 6 and 24 months.

I Using a no-change and simulated change dataset, the optimalband lag and threshold is determined (“off-line” optimizationphase)

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Use of simulated change to determine detection parameters

40 80 120 160 200 240 280 320 360

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

Time Lag (Days)

OA

Band 1

Band 2

Band 3

Band 4

Band 5

Band 6

Band 7

NDVI

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Opperational phase

A pixel is labeled as having changed by evaluating the following,

Change =

{true if Rb(τ) ≥ δb∗τfalse if Rb(τ) < δb∗τ ,

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Outline

Introduction

Study Area

Methodology

Results

Conclusion

Examples

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Results using simulated change

Confusion Matrix, overall accuracy (OA) and optimal threshold(δ∗) showing the best land cover change detection performanceduring the off-line optimization phase using MODIS band 4 550 nmwith a lag of 96 days

Simulatedchange(n=592)

No Change(n=482)

δ∗ OA

Change Detected 75.17% 14.73% 0.16 80.22%No Change Detected 24.83% 85.27%

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Results using real change

Confusion Matrix, overall accuracy (OA) and threshold (δ) for thecase of real change detection using the MODIS band 4 (550 nm)with a lag of 96 days as determined during the off-line optimizationphase

Real change(n=181)

No Change(n=482)

δ OA

Change Detected 92.27% 15.35% 0.16 88.46%No Change Detected 7.73% 84.65%

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Results : Timing of the change

Mean start of change OA

2001/06 70.67%2002/06 83.57%2003/06 85.33%2004/06 85.43%2005/06 84.92%2006/06 81.74%2007/06 76.66%

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Outline

Introduction

Study Area

Methodology

Results

Conclusion

Examples

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Concluding remarks

I A change metric can be formulated by selecting the lag thatshows the highest separability between the ACF of no-changeand simulated change time-series examples.

I The optimal band lag and threshold selection is determined inan “off-line” optimization phase.

I After the detection parameters are determined, the methodwas run blindly over the study area.

I The method shows a slight decrease in performance if thestart of change is in the first or last year.

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Outline

Introduction

Study Area

Methodology

Results

Conclusion

Examples

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Example 1 - Before

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Example 1 - After

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Example 2 - Before

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Example 2 - After

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Example 3 - Before

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Example 3 - After

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Example 4 - Before

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Example 4 - After

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Example 5 - Before

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Example 5 - After

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Example 6 - Feb 2005

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Example 6 - May 2006

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Example 6 - Sep 2006

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Example 6 - May 2007

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Example 7 - Apr 2002

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Example 7 - Mar 2004

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Example 7 - Apr 2005

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Example 7 - May 2005

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