Change Detection

Prepared by:Oluwafemi Opaleye

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Objectives

• Introduction• What is Change Detection?• Pre-processing / Requirement • Change Detection Techniques• Application Areas• Practical Example• Further Readings

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

How much of What is Where? • What: Type, Characteristic and Properties of Object.

e.g. Water, Vegetation, Land etc.• How Much: determine by simple Counting,

measuring Area covered or percentage of total area coverage.

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

Note: Where also refers to a moment in time

Introduction

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

(Area, Boundaries, Lineaments, ...)• This extends the ‘WHERE’ to be a completely

GEOMETRIC problem.– Identification and Delineation of Boundaries

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• What is the MIX of Objects?

The 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 DETECTION

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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 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 of the same scene – taken at different times – to identify regions of change.

• Changes on the earth surface could be directly caused by natural forces, by the activities of animals and human induced.

• Timely and accurate change detection of Earth’s surface features provides the foundation for a better understanding of the relationships and interactions between human and natural phenomena in order to better manage and use resources.

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• area change• rate of change• spatial distribution of changed types• accuracy assessment of change detection results

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

• Good Change Detection research should provide the following:

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Pre-processing / Requirement

• Geometric Correction – Georeferencing - precise coregistration between multitemporal images

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

• 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 minimize unwanted variances.

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• Free of clouds in the area of analysis• Select time periods – what is change detection

period?• Select Landcover scheme – they must be classified in

accordance with the same classification scheme.– classes must also be defined identically

• 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 procedure

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Change Detection Techniques

• Visual Analysis• Image Differencing• Image ratioing • Post Classification Comparison• Statistical analysis

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Visual Analysis

• It is the first place to start• Visually comparing multi-images• Manual digitizing changes in multi-images is often

used to both identify and classify change between images

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

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Drying up of Lake Faguibine - Mali

▪ 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 lake

Source: Africa: Atlas of Our Changing Environment , UNEP

1974 2006

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Declining Water Levels in Lake Chad (1972-2007)1972 1987

2007

Lake Chad, located at the junction of Niger, Nigeria, Chad and Cameroon, was once the sixth largest lake in the world.

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

1987 Image show that lake Chad reduced to about one-tenth of what it was in 1972 image.

2007 image show some improvement but the extent of the lake is still smaller to what it was 2-3 decades ago.

Area (12,797sqkm)

Area (1,563sqkm)

Area (1,753sqkm)

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Image Differencing

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

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

• The difference image is created by subtracting the brightness values of one image from the other on a per-pixel basis.

• Unchanged areas will have values at or nearer zero; while areas with significant change will be progressively positive 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.• This method of analysis involves only subtraction

with minimal human intervention.• So long as the two images have been sampled to the

same ground resolution and projected to the same coordinate system, the subtraction can be carried out very quickly.

• The results of change detection are not subject• to the inaccuracy inherent in classified land cover

maps.

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Limitations

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

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

• direct use of raw spectral data in change analysis makes the detected change highly susceptible to radiometric variations caused by illumination conditions and seasonality.

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Image Ratioing

• Similar to Image differencing conceptually and in its simplicity.

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

• Values near to 1.0 indicate – no change• Values greater or less than 1.0 indicate changes• Usually used for vegetation studies• All other advantages and disadvantages of image

differencing apply to image ratioing.

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Example of image ratioing procedure

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Post 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.

• The result is a map of landtype change• The change map display acreage of each change

class

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Advantages

• 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 change

• It also attribute changes e.g.

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Limitations

• 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.

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Sources of Error in Change Detection

• Errors in data – image quality• Atmospheric error• Mis-registration between multiple image dates• Seasonal variability• Processing error • Radiometric error – due to sensor drift or age• Error in Classification

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Application Areas

• landcover/landuse changes• mapping urban growth• rate of deforestation• urban sprawl• desertification• disaster monitoring• agriculture• coastal change• environmental impact assessment

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Practical Example:

Geospatial Assessment of Amanawa Forest Reserve, Sokoto State, Nigeria

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1996 Landcover Map of Amanawa Forest Reserve Area

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2008 Landcover Map of Amanawa Forest Reserve Area

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Landcover Type 1996

Area (sqkm)

1996

Percentage (%)

2008

Area (sqkm)

2008

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 Landcovers (1996-2008)

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Landcover Type

Area (sqkm) Difference (sqkm) Increase/Decline(%)

1996 2008 1996 - 2008 1996 - 2008

Farmland30.627 30.772 0.145 0.473

Rock Outcrop

4.645 4.073 -0.572 -12.304Bare Soil 3.537 4.152 0.615 17.379Forest Reserve

2.013 1.890 -0.123 -6.124Dam 0.171 0.105 -0.066 -38.421

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• Prediction Analysis

Markov Probability of Change in Landcover (1996 – 2008)

Bare 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 Area

Landcover Type Area (sqkm) Percentage (%)

Farmland 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 0.21

Total 40.9932 100

Area and Percentage of 2018 Projected Landcover of Amanawa Forest Area

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Reading for further information

• J.R. Jensen (2005)Introductory Digital Image Processing, A Remote sensing perspective. 467-492

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

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Thank You for Listening