Vor

lesu

ng A

llgem

eine

Fer

nerk

undu

ng, P

rof.

Dr.

C. S

chm

ulliu

s

Geoinformatik & Fernerkundung, Friedrich-Schiller-U niversität Jena

Change detection and time series analysis

Lecture by Martin HeroldWageningen University

Overview

1. Types of land dynamics

2. Methods for analyzing multi-temporal remote sensing data:

• Change detection

• Time series analysis

3. Selected examples (land/forest cover)

4. Coarse vs. fine scale satellite data for monitoring land dynamics

Land dynamics and

Change Detection

Why assess landscape change with remote sensing imagery?

– natural resource inventory/monitoring

– explain environmental phenomena

– develop/validate explanatory (process) models

– support integrated landuse/landcover change analysis and accounting applications

http://www.tiem.utk.edu/bioed/webmodules/circadianrhythm.html

Daily

Alan Belward Ispra 22 Feb,, 22 Apr., 22 Jul.22 Oct. 2005

Seasonally

http://www.ronet.com.br/marrocos/pv-antig/pv1-18.html

http://www.skyscrapercity.com/showthread.php?t=344422

Porto Velho, Brazil 2007Porto Velho, Brazil 1908

Annually

Shanghai, China 1987 Shanghai, China 2004

Michael Glantz NCAR

Types of change:

• short term change (synoptic weather events)

• cyclic change (seasonal phenology)

• directional change (urban development)

• multidirectional change (deforestation & regeneration)

• event change (catastrophic fires, disasters)

Change Detection

The accuracies of remote sensing based change detection depend on:

1.Spectral, spatial, and temporal characteristics of the multi-date

imagery,

2.Precise registration, calibration, or normalization between multi-

temporal images,

3.Complexity of investigated landscape and the related processes

affecting land change dynamics,

4.Change detection methods or schemes used,

5.Analyst’s experience and familiarity of the study area, and

availability of quality ground reference data, e.g. for accuracy

assessment,

6.Time and cost restrictions.

Change detection algorithms 1Change detection

algorithm

Approach Comments

Manual change

interpretation

Two images (or photographs)

are visually compared to

manually identify changes.

� This method is simple and sometimes the most feasible

method for change detection, especially when other techniques

fail to accurately identify changes

� Requires human experience � Results are subjective and strongly depend on interpretation

skills

� Time consuming interpretation processPost-classification

comparison

Independent spectral

classification results from each

image date and pixel-by-pixel

or segment-by-segment

comparison to detect changes

in land cover types.

� Minimizes radiometric differences between multi-date images

� Provides complete change matrix� The accuracy of the post-classification comparison is

dependent on the accuracy of the initial classification.

� Requires time and classification expertise

Composite

analysis

Multi-date images are analyzed

through joint classification

including change categories.

� Simple and time-saving in classification� Requires many classes (5 land cover classes=25 possible

change classes). � Demands prior knowledge of the logical interrelationships of

classesUnivariate image

algebra

(difference/ratio)

Subtraction/ratio of one date

original or transformed

imagery (e.g. vegetation index,

radiance, etc.) from a second

date image.

� Can only differentiate change or no-change, i.e. cannot

generate detailed change matrix information� Requires criterion/threshold of changes or no-changes (e.g.

Standard deviation method)

� Requires strict radiometric corrections

Change detection algorithms 2Change detection

algorithm

Approach Comments

Bi-temporal linear

data transformation

Use image transformation (e.g. Tasseled Cap, PCA) of multi-date composite image to identify changes (e.g. in brightness, greenness or wetness).

� Reduces data redundancy and emphasized differences

between images� Results might be hard to interpret� Requires threshold of change magnitude between change

and no change� Cannot generate detailed change matrix information

Change vector

analysis

Multivariate change detection technique that possesses the full dimensionality (spectral + temporal) of the image data and produces two outputs: change magnitude and change direction. A change has occurred if the vector surpasses a specific threshold value. The type of change that has occurred can be determined by the angle or direction of the change vector.

� Can produce detailed change detection information. Strict requirement for reliable image radiometry.

� Requires threshold of change magnitude between change and no change

� Difficult to identify change trajectories

Image Regression A mathematic model that describes the fit between two multi-date images. The dimension of the residuals is an indicator

of where change occurs.

� Can only differentiate change or no-change, i.e. cannot generate detailed change matrix information. Requires only initial radiometric image calibration/inter-

calibration

Multi-temporal

spectral mixture

analysis

Using spectral unmixing to detect changes in fraction images.

� Image fractions have biophysical meaning� Results are stable accurate and repeatable� Advanced image processing skills required� Requires threshold of change magnitude between change

and no change

Temporal Image Differencing (Band Subtraction)

The temporal image differencing procedure subtracts the DNs from one image date from another.

The differences in areas of little or no change will be small while areas of extreme change will be represented by large positive or negative changes in DNs (-255 / +255).

Temporal Image Differencing (Band Subtraction)

Pros:

• Band subtraction is simple to process

• The technique can be easily replicate across sites and time periods

• Continuous change values are created by the differencing

Temporal Image Differencing

Deviations from an index: (Image Differencing)

From Jensen http://www.cla.sc.edu/geog/rslab/rsccnew/rscc-frames.html

Temporal Image Differencing

Deviations from an index: (Image Differencing)

From Jensen http://www.cla.sc.edu/geog/rslab/rsccnew/rscc-frames.html

Forest cover 1973

Forest cover 1985

Change Vector Analysis (CVA)

Change vector analysis (CVA) is a multidimensional extension of the image differencing technique.

The difference between radiometric values (DNs) for multiple bands are calculated as change differences exhibiting vectors (change direction) and magnitudes (change intensity).

Change Vector Analysis (CVA)

One case (a) exceeds a change threshold is indicated with a vector arrow. The second case (b) depicts a pair of pixels whose radiometric values where determined to be within a threshold range and would be classified as no (insignificant) change.

Change Vector Analysis (CVA)

When multiple bands are used in the analysis, secto rs of change (quadrants) can be identified to depict different type of change (++, +-, --, -+).

Bands in the change vector analysis may represent preprocessed indices or data transformations. For example; The Tasselled Cap transformation yields an initial image with bands related to Greenness, Wetness, Brightness and Haziness.

When this type of transformation is applied before the CVA analysis, then change sectors may for example represent increases or decreases in any combination of bands.

Change Vector Analysis (CVA)

Four possible sectors of change derived by examining 2 bands of data.

Change Vector Analysis (CVA)

An interesting potential value of CVA which we are now exploring is the possibility of identifying trajectories of change over time.

For example: hypothetical trajectory of change depicting forest clearing, initial regeneration, ca nopy closure, and species change (conifer to deciduous).

The potential for assessing the “signature” for different types of ecological processes over time adds an interesting dimension to this type of analysis.

Change Vector Analysis (CVA)

An hypothetical trajectory of change depicting forest clearing, initial regeneration, canopy closure, and species change (conifer to deciduous).

Time Series Analysis

• Variation in images across different times:

– Cyclical variation (aka “seasonal” variation)

• LAI in deciduous ecosystems

• Temperature through the year, or throughout the day

– Trend variation (aka “long term change in mean”)

• LAI in human impacted ecosystems across years

• Temperature across years

– Other variation: random or non-random changes

across time

Cyclical Variation

• All ecosystems show a

degree of cycling, related

to, among other things,

evapotranspiration, day

length and temperature

differences throughout the

year.

• We want to characterize

period, amplitude and

offset of the cycles.

Examples/Techniques

Duration of growing

season

Principal Components Analysis (PCA)

• Rotation and scaling along orthogonal

directions of maximum variance

• Large correlation among multi-temporal

datasets – first component:

– state (Principal components)

– dynamics (seasonality, longer term trends) in

higher components

PC1

PC2

Consider multitemporal NDVI (1986-1990, monthly data):

Expect high degree of correlation

but also deviations from this

use PCT...

Monthly NDVI - Africa

96.68% of variance in PC1

Loadings very similar for all months

…average

Monthly NDVI - Africa

2% of variance in PC2

Dec-March minus

April-Nov

Seasonality - ITCZ movement

Observing change using coarse resolution data

1. Synergy of existing map products + ancillary data

– Challenging but some regional examples

2. Vegetation continuous change (VCC)

– Tree canopy cover/deforestation (20x20 km blocks)

3. Observation of active fire and burned areas

– Several operational products

4. Observing long-term trends

– AVHRR/NDVI times series data since 1981

– Night-time lights

5. Near-real time observations (i.e. deforestation/DETER)

Usefulness:

– Indicators and hot spots of change

– Guide more detailed analysis and true area estimations

– Understand inter-annual versus intra-annual dynamics

– Independent global measurements

Hot spots of forest cover change 1980-2000

Lepers et al., (2005). A synthesis of information on rapid land-cover change for the period 1981-2000. BioScience, 55 (2), 115-124

Longer term observations of vegetation

Relationship between positive temperature anomalies (left) and the trends in vegetation growth (right) for different

seasons periods 1998-2005 in Northern Eurasia based on SPOT Vegetation data

Huettich et al., 2006 IJRS

Global active fire observations

• Animated figure!

http://modis-fire.umd.edu/MCD45A1.asp

Contact: Luigi Boschetti <luigi@hermes.geog.umd.edu>

EXAMPLE APPLICATIONS

• 1 year of composite of MODIS

burned areas, superimposed on

surface reflectance to provide

geographic context.

• Burned area statistics for the

same period, for vegetation type

Africa

0.00E+00

1.00E+05

2.00E+05

3.00E+05

4.00E+05

5.00E+05

6.00E+05

Jul-0

1 BA

Aug-01 B

A

Sep-01 B

A

Oct-01 B

A

Nov-01 B

A

Dec-0

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Jan-0

2 BA

Feb-02

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Mar-02 B

A

Apr-0

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May-0

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Jun-0

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fire

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are

a [k

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0%

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10%

15%

20%

25%

30%

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d [%

]

croplands

barren_or_sparsely_vegetated

grasslands

savannas

woody_savannas

open_shrublands

closed_shrublands

mixed_forests

deciduous_broadleaf_forest

deciduous_needleleaf_forest

evergreen_broadleaf_forest

evergreen_needleleaf_forest

other

unmapped BA

DMSP nighttime light density: 1992 / 1998 / 2003

Night-time light data 1992-2003

Pan-humid tropics forest clearing, 2000-2005

Courtesy of M.Hansen, SDSU

Annual Gross Deforestation (Brazil)

Sinop – Mato Grosso, Brazil.

Source: INPE (2006)

• Annual rate: 11-29,000 km 2/ano• Total gross deforestation: 681.343 km 2

Ann

ualR

ate

-A

rea

(km

2 /an

o)

21050

17770

13730

29059

18161

13227

19400

27200

2375023266

18226

17259

17383

1816513786

11030

14896

0

5000

10000

15000

20000

25000

30000

35000

77/88

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94/95

95/96

96/97

97/98

98/99

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Summary and outlook

Observing types of change:• short term change (synoptic weather events)• cyclic change (seasonal phenology)• directional change (urban development)• multidirectional change (deforestation & regeneration)• event change (catastrophic fires, disasters)

Different types of earth observation data useful ba sed on spatial, temporal and thematic detail they provi de

Only specific changes are (usually) important for l and assessment and accounting purposes

Earth observation data is key source for studying spatio-temporal land processes (GIS, models)

Deforestation patterns and processes