Change Detection 

Prepared by:Prepared by:

Oluwafemi Opaleye

ObjectivesObjectives

• Introduction

• What is Change Detection?What is Change Detection?

• Pre‐processing / Requirement 

• Change Detection Techniques

• Application AreasApplication Areas

• Practical Example

• Further Readings

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Introduction

Remote Sensing (RS) methods try to answer four basic questions:f q

How much of What is Where? • What:  Type, Characteristic and Properties of Object.    e.g. Water, Vegetation, Land etc.g g

• How Much: determine by simple Counting, measuring Area covered or percentage of total areameasuring Area  covered or percentage of  total area coverage.

• Where: Relate locations and area covered to either a• Where: Relate locations and area covered to either a standard map or to the actual location on the ‘ground’ where the object occursground  where the object occurs.

Note: Where also refers to a moment in time04/07/2013 3

• What is the SHAPE and EXTENT of ... ?

(Area Boundaries Lineaments )(Area, Boundaries, Lineaments, ...)

• This extends the ‘WHERE’ to be a completely GEOMETRIC blGEOMETRIC problem.

– Identification and Delineation of Boundaries

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• What is the MIX of Objects?at s t e o Objects?

Th f f th E th i d b bj t likThe surface of the Earth is covered by objects like 

Soil, Water, Grass, Trees, Houses, Roads and so on. 

‐ Landuse/Landcover ‐ Classification

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• Has it CHANGED?

CHANGE   may occur with progress of TIME.

Change may be detected through comparison of observed states at different moments in time.

‐ CHANGE DETECTION04/07/2013 6

What is Change Detection?What is Change Detection?

• Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times. 

• It is the detection of class transition between a pair of co‐registered imagesof co‐registered images.

• The main goal is to use remote sensing to detect CHANGE on a landscape (landuse and landcover) over time.

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• Change detection algorithms analyze multiple images ofthe same scene – taken at different times – to identifyyregions of change.

• Changes on the earth surface could be directly caused bynatural forces, by the activities of animals and humani d dinduced.

Ti l d h d i f E h’ f• Timely and accurate change detection of Earth’s surfacefeatures provides the foundation for a betterunderstanding of the relationships and interactionsunderstanding of the relationships and interactionsbetween human and natural phenomena in order tobetter manage and use resources.

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• It can be performed with raw remote sensing bandsor thematic land cover maps classified from them.or thematic land cover maps classified from them.

G d Ch D t ti h h ld id th• Good Change Detection research should provide thefollowing:

• area changeg

• rate of change

• spatial distribution of changed types• spatial distribution of changed types

• accuracy assessment of change detection results

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Pre‐processing / Requirement

• Geometric Correction – Georeferencing ‐ precise coregistration between multitemporal imagescoregistration between multitemporal images

• Radiometric Correction ‐ precise radiometric and atmospheric calibration or normalization betweenatmospheric calibration or normalization between multitemporal images

/ f h l• Region/Area of Interest – same geographic location

• Remote sensing system consideration – spatial, spectral, radiometric and temporal– whenever possible, select images acquired from the same type of sensors, with the same spectral and spatial resolutions, and at the same seasonal timeframe in order to i i i t d iminimize unwanted variances.

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• Free of clouds in the area of analysis

• Select time periods – what is change detection period?period?

• Select Landcover scheme – they must be classified in accordance with the same classification schemeaccordance with the same classification scheme.– classes must also be defined identically

Cl ifi ti h l ifi ti l ith• Classification – choose classification algorithm

• Choose change detection method

• Change detection accuracy assessment 

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Major steps involved in a typical change analysis process change detection procedurep g p

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Change Detection TechniquesChange Detection Techniques

• Visual Analysis

• Image Differencing

• Image ratioing• Image ratioing

• Post Classification Comparison

• Statistical analysis

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Visual AnalysisVisual Analysis

• It is the first place to start

• Visually comparing multi‐images

• Manual digitizing changes in multi‐images is often g g g gused to both identify and classify change between imagesg

• Elements of image interpretation combined with the knowledge of the area of study are often usedknowledge of the area of study are often used.

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Drying up of Lake Faguibine ‐Mali

1974 2006

▪ It covered area of about 590km2▪ Water level have fluctuated widely since the beginning of 1980▪ An extended period of reduced precipitation led to a complete drying of the lakelake

Source: Africa: Atlas of Our Changing Environment , UNEP04/07/2013 15

Declining Water Levels in Lake Chad (1972‐2007)

1972 A (12 797 k )1972 1987Area (12,797sqkm)

Area (1,563sqkm)

2007

1987 Image show that lake Chad

Lake Chad, located at the junction of Niger, Nigeria, Chad and Cameroon, was once the sixth

greduced to about one-tenth of what it was in 1972 image.

2007 image show some improvement but the extent of the lake is stilllargest lake in the world.

Persistent drought and increased agriculture irrigation have reduced the lake’s extent

but the extent of the lake is still smaller to what it was 2-3 decades ago.

Area (1 753sqkm)Area (1,753sqkm)

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Image Differencingg g

• It requires selection of corresponding bands from twodates imageries of the same study area

• Uses software algorithm to identify and quantify thechanges between two temporal images

• The difference image is created by subtracting thebrightness values of one image from the other on a per‐g g ppixel basis.

• Unchanged areas will have values at or nearer zero; whileg ;areas with significant change will be progressivelypositive or negative.

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Example of image differencing  procedure

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Advantages

• It is relatively easy to understand and to implement.y y p

• This method of analysis involves only subtractionwith minimal human intervention.with minimal human intervention.

• So long as the two images have been sampled to thesame ground resolution and projected to the samesame ground resolution and projected to the samecoordinate system, the subtraction can be carriedout very quicklyout very quickly.

• The results of change detection are not subject

• to the inaccuracy inherent in classified land covermaps.

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LimitationsLimitations

• this method is limited in that it fails to reveal the• this method is limited in that it fails to reveal thenature of a detected change (e.g., the class fromwhich a land cover has changed).which a land cover has changed).

• identify threshold values of change and no change in• identify threshold values of change and no‐change in the resulting images.

• direct use of raw spectral data in change analysismakes the detected change highly susceptible tomakes the detected change highly susceptible toradiometric variations caused by illuminationconditions and seasonality.conditions and seasonality.

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Image Ratioing

• Similar to Image differencing conceptually and in its• Similar to Image differencing conceptually and in itssimplicity.

• This method uses one temporal image to divideimage of another date.

• Values near to 1.0 indicate – no change

• Values greater or less than 1.0 indicate changesg g

• Usually used for vegetation studies

• All other advantages and disadvantages of image• All other advantages and disadvantages of imagedifferencing apply to image ratioing.

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Example of image ratioing procedure

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Post Classification ComparisonPost Classification Comparison

• Most popular method of change detection

• In post classification comparison, each date of rectified imagery is independently classified to fit common landtype.

• Landcover maps are overlaid and compared  pixel by pixel basis.p e bas s

• The result is a map of landtype change

• The change map display acreage of each change• The change map display acreage of each change class

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AdvantagesAdvantages

• Many classification algorithms can be directly used. It can provide detailed matrix of change information and accuracy assessment is easy.

• Easy to quantify the area of change and rate of changec a ge

• It also attribute changes e.g.

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LimitationsLimitations

• Classification accuracy directly influences the accuracy of change detection.

• It is time‐consuming to create classification results and a professional operator is necessary.

• It is difficult and expensive to obtain appropriate multi‐temporal ground reference.u e po a g ou d e e e ce

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Sources of Error in Change DetectionSources of Error in Change Detection

• Errors in data – image quality

• Atmospheric errorAtmospheric error

• Mis‐registration between multiple image dates

• Seasonal variability

• Processing error 

• Radiometric error – due to sensor drift or age

• Error in ClassificationError in Classification

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Application AreasApplication Areas

l d /l d h• landcover/landuse changes• mapping urban growth• rate of deforestation• urban sprawlurban sprawl• desertificationdi t it i• disaster monitoring

• agriculture• coastal change• environmental impact assessmentp

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Practical Example:Practical xample:

Geospatial Assessment of Amanawa ForestGeospatial Assessment of Amanawa Forest Reserve, Sokoto State, Nigeria

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1996 Landcover Map of Amanawa Forest Reserve AreaArea

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2008 Landcover Map of Amanawa Forest Reserve Area

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Landcover Type 1996

A ( k )

1996

P t (%)

2008

A ( k )

2008

P t (%)Area (sqkm) Percentage (%) Area (sqkm) Percentage (%)

Farmland 30.627 74.71 30.772 75.07

Rock Outcrop 4.6449 11.33 4.0734 9.94

Bare Soil 3.537 8.63 4.1517 10.12

Forest Reserve 2.0133 4.91 1.89 4.61

Dam 0.171 0.42 0.1053 0.26

Total 40.9932 100 40.9932 100

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Change Detection Map showing transition of L d (1996 2008)Landcovers (1996‐2008)

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Landcover Area (sqkm) Difference  Increase/DeclineType

( q )(sqkm) (%)

1996 2008 1996 ‐ 2008 1996 ‐ 2008

Farmland30 627 30 772 0 145 0 47330.627 30.772 0.145 0.473

Rock Outcrop

4 645 4 073 0 572 12 304Outc op

4.645 4.073 ‐0.572 ‐12.304Bare Soil 3.537 4.152 0.615 17.379ForestForest Reserve

2.013 1.890 ‐0.123 ‐6.124Dam 0 171 0 105 0 066 38 421Dam 0.171 0.105 ‐0.066 ‐38.421

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• Prediction Analysisy

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Markov Probability of Change in Landcover (1996 –2008)

Bare Soil Dam Farmland Forest RockBare Soil   Dam  Farmland Forest Rock Outcrop

Bare Soil  0.7646  0.0382  0.1726  0.0233  0.0012 

Dam 0.2765  0.6137  0.1098  0.0000  0.0000 

Farmland 0.3212 0.0680  0.6097  0.0011  0.0000 

Forest 0.1849  0.0000  0.1475  0.6676  0.0000 

Rock Outcrop 0.3376  0.0000  0.0231  0.0000  0.6393

2018 Projected Landcover Map of Amanawa Forest AArea

Area and Percentage of 2018 Projected Landcover of Amanawa Forest Area

Landcover Type Area (sqkm) Percentage (%)

Farmland 27 5877 67 3Farmland 27.5877 67.3

Rock Outcrop 3.9555 9.65

Bare Soil 7.6527 18.67

Forest Reserve 1.71 4.17

Dam 0.0873 0.21Dam 0.0873 0.21

Total 40.9932 100

Reading for further informationg

• J.R. Jensen (2005)Introductory Digital Image J Je se ( 005) t oducto y g ta ageProcessing, A Remote sensing perspective. 467 492467‐492

• R. R. Jensen, J. D. Gatrell and D. McLean (2007) Geo‐Spatial Technologies in Urban Environments Policy, Practice, and Pixels. 145‐y, ,167

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Thank You for Listening

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